Maryam Mozooni, MD - the UWA Profiles and Research ...

The Influence of Migration, Ethnicity and Acculturation

on the Risk of Stillbirth, Preterm Birth and Low

Birthweight in Western Australia

Maryam Mozooni, MD

This thesis is presented for the degree of Doctor of Philosophy

of The University of Western Australia

School of Population and Global Health

Faculty of Health and Medical Science

2020

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THESIS DECLARATION

I, Maryam Mozooni, certify that:

This thesis has been substantially accomplished during enrolment in this degree.

This thesis does not contain material which has been submitted for the award of any other degree

or diploma in my name, in any university or other tertiary institution.

In the future, no part of this thesis will be used in a submission in my name, for any other degree

or diploma in any university or other tertiary institution without the prior approval of The

University of Western Australia and where applicable, any partner institution responsible for the

joint-award of this degree.

This thesis does not contain any material previously published or written by another person,

except where due reference has been made in the text and, where relevant, in the Authorship

Declaration that follows.

This thesis does not violate or infringe any copyright, trademark, patent, or other rights

whatsoever of any person.

The research involving human data reported in this thesis was assessed and approved by the

Human Research Ethics Committee of the WA Department of Health (2015/23). The University of

Western Australia Human Research Ethics Committee recognised the existing approval of the non-

UWA ethics committee with no need for completion of its own ethics review process. Approval #:

RA/4/1/7602. Written patient consent was not required to conduct the study due to the use of

non-identifiable routinely collected linked administrative health data for the whole population in

this thesis.

Third party editorial assistance was provided in preparation of final version of this thesis by Tweak

Editing.

This thesis contains published work and/or work prepared for publication, some of which has been

co-authored.

Signature:

Date: 09/09/2020

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ABSTRACT

BACKGROUND

Adverse reproductive outcomes and racial disparities in the risk of pregnancy outcomes are still

observed in high-income countries despite the availability of high-standard pregnancy care and

perinatal interventions. Previous studies have indicated significant differences among migrants

and resettlement countries’ populations in terms of the utilisation of reproductive services and

reproductive outcomes. However, the findings are inconsistent across jurisdictions. Immigration is

the principal component of population growth in Australia; in 2016, 35% of residents had been

born overseas, and one-third of women who give birth annually were not born in Australia.

Western Australia (WA) recorded the highest population growth of all Australian states and

territories from 2006–2016 (24.8% increase), and most Western Australians have at least one

overseas-born parent. Yet, the influences of immigration and ethnicity on health service utilisation

and pregnancy outcomes of migrants are not well investigated or understood.

OBJECTIVES

This thesis investigated the influence of migration and ethnicity on the patterns of utilisation of

pregnancy-related healthcare services and selected pregnancy outcomes in WA from 2005 to

2013. The specific aims were:

Aim 1: To investigate prevalence proportion and the risk of antepartum and intrapartum

stillbirth in WA with respect to maternal country of birth and ethnic origin.

Aim 2: To investigate the pattern of healthcare utilisation among migrant women and its

relationship with the risk of stillbirth (antepartum and intrapartum) in WA.

Aim 3: To investigate the influence of acculturation on disparities observed in the risk of

stillbirth between migrant and Australian-born populations in WA.

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Aim 4: To investigate ethnic disparities in the risk of low birthweight (LBW) and preterm

birth (PTB), spontaneous and medically indicated, between migrant and Australian-born

populations from diverse ethnic backgrounds in WA.


PTB and LBW between migrant and Australian-born populations from diverse ethnic backgrounds

in WA.

METHODS AND RESULTS

A retrospective cohort analysis was undertaken of de-identified, linked, routinely collected

Midwives, Births, Deaths, Hospital and Birth Defects data for all births to non-Indigenous women

in WA from 2005–2013. A range of descriptive and analytical analyses was undertaken, including

Pearson X2 and Fisher exact tests, univariable and multivariable, binary and multinomial, logistic

regression modelling.

This thesis illustrated a broad, yet detailed, picture of health service utilisation and pregnancy

outcomes of women from migrant and diverse ethnic backgrounds who gave birth in WA from

2005–2013. Disparities in the risk of stillbirth (antepartum and intrapartum) and modifiable

individual- and system-related factors to prevent these deaths were identified. It also

demonstrated the ethnic disparities in the risk of LBW and PTB among the migrant and Australian-

born population in WA and the influence of acculturation on these outcomes. Key findings were as

follows:

No significant differences between Australian-born women with white and non-white

backgrounds for any type of stillbirth; however, non-white migrant women were more likely to

have stillbirth, both antepartum and intrapartum, than white migrants. Antepartum stillbirths

were more common among African, Indian and ‘other’ non-white migrants while intrapartum

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stillbirths were higher among African and ‘other’ non-white migrants than the Australian-born

population.

When migrant groups were stratified by timing of first antenatal visit, the odds of

antepartum stillbirth increased in those who commenced antenatal care later than 14 weeks

gestation in women from Indian, Māori and other non-white ethnicities. With midwife-only

intrapartum care, the odds of intrapartum stillbirth for viable births in African and other non-white

migrants (combined) were more than three times that of Australian-born women; however, with

multidisciplinary intrapartum care, the odds were similar to that of the Australian-born group.

Using interpreter services was associated with a lower risk of stillbirth in migrants than the

Australian-born population. Women from African, Indian and Asian backgrounds who gave birth in

the first two years after arrival in Australia experienced the highest risk of stillbirth. Except for

African women, this association attenuated with an increase in the length of residence in other

migrant groups. Having an Australian-born partner was associated with 20% lower odds of

stillbirth in migrants.

All non-Māori ethnic groups, regardless of their place of birth, had higher adjusted odds of

term-LBW than the white Australian-born population. Migrant Asian and Indian had higher and

white, Māori and African migrants had lower risk of specific types of PTB than white Australian-

born women. Australian-born women from ‘other’ ethnicity had a higher risk of medically

indicated PTB than their white Australian-born counterparts.

The least acculturated non-white non-Māori women had twice as high, and the most

acculturated had the same risk of term-LBW as the Australian-born women. The odds of PTB were

significantly lower in the least acculturated and significantly higher in the most acculturated

migrant women than their Australian-born counterparts.

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CONCLUSION

This project demonstrated that migrant status, ethnicity and acculturation are associated with

specific patterns of service utilisation and certain pregnancy outcomes. These variables are crucial

factors for consideration in policy development, on public health agenda, and in pregnancy and

obstetrics care provision to prevent adverse pregnancy outcomes, especially in populations from

migrant and culturally and linguistically diverse backgrounds, to improve health outcomes in

Australian society.

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ACKNOWLEDGEMENTS

“Yesterday I was clever, so I wanted to change the world. Today I am wise, so I am changing myself”

“What you seek is seeking you” Rumi

I started my PhD with so much passion for evidence-based practice, public health and medical

discovery after a few years of working as a physician. Little did I know that what I stepped into was

going to be my odyssey, a long journey of profound self-discovery and transformation. First and

foremost, I wish to express my immense gratitude for the given opportunity, strength and

knowledge to undertake this research and to persevere until its completion: I sought and I found.

My profound appreciation to Professor David Preen and Professor Craig Pennell, my PhD

supervisors, for their generous and patient guidance, encouragement, and advice through deep

waters of my entire journey. Working with you was a blessing. I cannot thank you enough for

sharing your wisdom and insight with me, and for your constructive criticism which made this

thesis possible.

My heartfelt gratitude to Dr Judy Straton, my mentor of nine years, for her time, support, and

patience. Thank you for being there for me all the way. I will always cherish your support.

My deepest gratitude to my family; to my wonderful husband Arash: thanks for your patience,

love and for accommodating my academic endeavour amidst building a new life after immigration.

To my amazing children Elya and Elina: giving birth to you, raising you and experiencing life with

you were the wind in my sails throughout this voyage, thanks for the joy you brought to my life

and for your understanding; I hope witnessing mummy’s determination and perseverance inspired

you to follow your dreams no matter what. To my lovely mum and dad, sister and brother: my

warmest thanks to you for always believing in me and for your unwavering support.

A special thanks to Associate Professor Gavin Pereira for his kind advice and comments in the final

year of this PhD research.

I also wish to thank the staff at the Western Australian Data Linkage Branch, the Data Custodians

at WA Department of Health, and the people of Western Australia for the data used in this study.

This research was supported by the Australian Government Research Training Program (RTP)

scholarship, a UWA Postgraduate Award and UWA top-up scholarship and from July 2016 to Dec

2016 by a Gordon King top-up scholarship.

This research was funded by a Red Nose (formerly SIDS and Kids) grant (0060/2017).

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AUTHORSHIP DECLARATION: CO-AUTHORED PUBLICATIONS

This thesis contains work that has been published and/or submitted for publication.

Details of the work: Stillbirth in Western Australia, 2005-2013: the influence of maternal migration and ethnic origin

Location in thesis: Chapter 4

Student contribution to work: 95%: Acquisition of ethics approval, feasibility letter and data from Department of Health, conceptualising the study, analysing data, interpreting results, drafting the manuscript, acquiring pre-submission approvals from the Data Linkage Branch (DLB) and data custodians, revising the paper according to the advice of DLB, data custodians, supervisors and journal’s reviewers and completing the process of submission to journals.

Co-author signatures and dates:

David Preen

Craig Pennel

Details of the work: Healthcare factors associated with the risk of antepartum and intrapartum stillbirth in migrants in Western Australia (2005–2013)


Student contribution to work: 95%: Acquisition of ethics approval, feasibility letter, and data from the Department of Health, conceptualising the study, analysing data, interpreting results, drafting the manuscript, acquiring pre-submission approvals from the Data Linkage Branch (DLB) and data custodians, revising the paper according to the advice of DLB, data custodians, supervisors and journal’s reviewers and completing the process of submission to journals.


Craig Penn

David Pree

PAGE | viii

Details of the work: The influence of acculturation on the risk of stillbirth in migrant women residing in Western Australia




David Pre

Craig Pen

Details of the work: Migration, ethnicity and the risk of low birthweight and preterm birth in Australia



Co-author si

David Preen

Gavin Pereir

Craig Penne

PAGE | ix

Details of the work: Acculturation, preterm birth and low birthweight in Western Australia




David Pree

Gavin Perei

Craig Penn

Student signatureDate:16/08/2020

I, David Preen, certify that the student’s statements regarding their contribution to each of the works listed above are correct.

Coordinating supervisor signatuDate: 17/08/2020

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OUTPUTS ARISING FROM THIS THESIS

PUBLISHED JOURNAL ARTICLES

1. Mozooni M, Preen DB, Pennell CE. Stillbirth in Western Australia, 2005–2013: the influence of

maternal migration and ethnic origin. Med J Aust. 2018;209(9):394–400. PubMed PMID:

30282563.

2. Mozooni M, Pennell CE, Preen DB. Healthcare factors associated with the risk of antepartum

and intrapartum stillbirth in migrants in Western Australia (2005–2013): A retrospective

cohort study. PLoS Med Journal Translated Name PLoS Medicine. 2020;17(3):e1003061. doi:

https://doi.org/10.1371/journal.pmed.1003061

3. Mozooni M, Preen DB, Pennell CE. The influence of acculturation on the risk of stillbirth in

migrant women residing in Western Australia. PLoS One. 2020;15(4):e0231106. doi:

10.1371/journal.pone.0231106. PubMed PMID: 32240255

SUBMITTED JOURNAL ARTICLES

4. Mozooni M, Pereira G, Preen DB, Pennell CE. Migration, ethnicity and the risk of low

birthweight and preterm birth in Australia: A linked health data study. International Journal of

Epidemiology. Under Review (submitted: May 2020)

5. Mozooni M, Pereira G, Preen DB, Pennell CE. The influence of acculturation on the risk of

preterm birth and low birthweight in Western Australia. PLoS One (Submitted: Sep 2020)

PUBLISHED JOURNAL ABSTRACTS

6. Mozooni, M., Preen, D., & Pennell, C. (2018). The ‘Healthy Migrant Phenomenon’: how long

does it last? European Journal of Public Health, 28(suppl_1), cky047.001.

https://doi.org/10.1093/eurpub/cky047.001

7. Mozooni, M., Preen, D., & Pennell, C. (2018). Modifiable factors for increased risk of

antepartum and intrapartum stillbirth in migrants and non‐Caucasian ethnicities. Journal of

Paediatrics and Child Health, 54(S1), 37–37.

8. Mozooni, M., Preen, D. & Pennell, C., 2018, Risk of preterm birth and low birthweight in first

and later generations of migrants. Journal of Paediatrics and Child Health, 54(S1), 95–95.

9. Mozooni, M., Pennell, C. & Preen, D. (2014). Ethnicity and intrapartum stillbirth in Western

Australia. Journal of Paediatrics and Child Health, 50(Suppl 1), 24.

https://doi.org/10.1371/journal.pmed.1003061

https://doi.org/10.1093/eurpub/cky047.001

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CONFERENCE PRESENTATIONS

10. WA Social Research Network, Insights to Action, Perth, 2019

11. Australasian Epidemiological Association Annual Meeting, Perth, 2018

12. 1st World Congress on Migration, Ethnicity, Race and Health, Edinburgh, 2018

13. Perinatal Society of Australia and New Zealand Congress, Auckland, 2018 (oral)

14. Perinatal Society of Australia and New Zealand Congress, Auckland, 2018 (Poster)

15. World Congress on Public Health, Melbourne, 2017

16. Perinatal Society of Australia and New Zealand Congress, Perth, 2014

PUBLISHED PODCAST

17. Swannell C. Stillbirth and ethnic origin, with Dr Maryam Mozooni. In: MJA Podcasts Episode

85, editor. Medical Journal of Australia. 2018.

INVITED GUEST SPEAKER PRESENTATIONS

18. UWA School of Biomedical Sciences: IMED2208 Issues in Women's Reproductive Health-Guest

Lecturer, Perth, 2020

19. UWA School of Social Sciences: Social Research and Health, Health Update Forum, Perth, 2019

20. Department of Nursing and Midwifery Education and Research- King Edward Memorial

Hospital, Perinatal Loss Study Day, Perth, 2019

21. Centre for Research Excellence in Stillbirth- National Stillbirth Forum, Brisbane, 2019

22. Department of Nursing and Midwifery Education and Research- King Edward Memorial

Hospital, Cultural Diversity Study Day, Perth, 2019

23. UWA School of Social Sciences: Migrant and Refugee Health and Mental Health:

Contemporary Social Issues, Contemporary Responses, Perth, 2019

24. Red Nose Inaugural Research Series, Melbourne, 2018

25. Preterm Birth International Collaborative Meeting, Florence Italy, 2016

26. Preterm Birth International Collaborative Meeting, Florence Italy, 2015

27. King Edward Memorial Hospital Community Advisory Council Meeting, Perth, 2013

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AWARDS/FUNDING RELATED TO THIS THESIS

28. Stillbirth Centre of Research Excellence Travel Funding: Guest speaker at National Stillbirth

Forum & Refugee and Migrant Advisory Group Meeting Aug 2019

29. Research Impact Grant, The University of Western Australia, Dec 2018

30. Red Nose Travel Funding: Guest speaker at Inaugural Red Nose Research Series Dec 2018

31. Australasian Epidemiology Association Student Award (AEA) Oct 2018

32. Group of Eight and Australian Council of Social Service (ACOSS) Bursary Oct 2017

33. Red Nose & Cure Kids research grant: Migration, acculturation and the risk of stillbirth in

Western Australia 2017-2019

34. Professor Gordon King travel funding for attending World Congress on Public Health 2017

35. UWA-Graduate Research School travel award for attending Preterm Birth International

Collaborative 2016

36. Professor Gordon King Scholarship, Women and Infants Research Foundation July 2016

ADVOCACY WORK RELATED TO THIS THESIS

37. Submission to the Australian Government National Stillbirth Action and Implementation Plan-

Public Consultation

38. Rapid Response: Leaving no one behind: Where are 2.6 million stillbirths? 08 Feb 2020 in BMJ.

Available from https://www.bmj.com/content/368/bmj.l6986/rr

39. Workshop organised for health professionals: Preventing Stillbirth in Migrant and CaLD

Population of Western Australia, Oct 2019

40. RED NOSE. Researcher spotlight: my vision for a future with zero stillbirth. Aug 2019.

Interview with Dr Maryam Mozooni. Available from: https://rednose.org.au/news/researcher-

spotlight-my-vision-for-a-future-with-zero-stillbirth.

41. Submission (No.40) to the Australian Senate Inquiry into stillbirth, Jul 2018. Available from

https://www.aph.gov.au/Parliamentary_Business/Committees/Senate/Stillbirth_Research_an

d_Education/Stillbirth/Submissions

42. Blog Post: Maryam Mozooni. Stillbirth, Migration and Ethnicity: The Bells Toll For Thee,

Healthy Newborn Network, Oct 2017. Available from

https://www.healthynewbornnetwork.org/blog/stillbirth-migration-ethnicity-bells-toll-thee/

https://www.bmj.com/content/368/bmj.l6986/rr

https://rednose.org.au/news/researcher-spotlight-my-vision-for-a-future-with-zero-stillbirth

https://rednose.org.au/news/researcher-spotlight-my-vision-for-a-future-with-zero-stillbirth

https://www.aph.gov.au/Parliamentary_Business/Committees/Senate/Stillbirth_Research_and_Education/Stillbirth/Submissions

https://www.aph.gov.au/Parliamentary_Business/Committees/Senate/Stillbirth_Research_and_Education/Stillbirth/Submissions

https://www.healthynewbornnetwork.org/blog/stillbirth-migration-ethnicity-bells-toll-thee/

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MEDIA COVERAGE RELATED TO THIS THESIS

43. Jess Reid, Migrant women at risk of stillbirth during first two years in Australia. University

News. 3 April 2020 http://www.news.uwa.edu.au/2020040311967/research/migrant-women-

risk-stillbirth-during-first-two-years-australia

44. Jess Reid, Early care key to preventing stillbirth in migrant women. Medical Xpress. 18 March

2018 https://medicalxpress.com/news/2020-03-early-key-stillbirth-migrant-women.html

45. Jess Reid, Study finds migrant women are more at risk of stillbirth. Medical Xpress. 8 October

2018 https://medicalxpress.com/news/2018-10-migrant-women-stillbirth.html

46. Cate Swannell, Stillbirth more common in non-white migrant women. The MJA, Oct 2018

https://www.mja.com.au/system/files/2018-10/FINAL%208%20OCT%20STILLBIRTHS%20.pdf

47. Charlotte Mitchell, Stillbirths high for migrant women, MJA InSight Plus. Issue 39 / October

2018 https://insightplus.mja.com.au/2018/39/stillbirths-high-for-migrant-women/

http://www.news.uwa.edu.au/2020040311967/research/migrant-women-risk-stillbirth-during-first-two-years-australia

http://www.news.uwa.edu.au/2020040311967/research/migrant-women-risk-stillbirth-during-first-two-years-australia

https://medicalxpress.com/news/2020-03-early-key-stillbirth-migrant-women.html

https://medicalxpress.com/news/2018-10-migrant-women-stillbirth.html

https://www.mja.com.au/system/files/2018-10/FINAL%208%20OCT%20STILLBIRTHS%20.pdf

https://insightplus.mja.com.au/2018/39/stillbirths-high-for-migrant-women/

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TABLE OF CONTENTS

THESIS DECLARATION I

ABSTRACT II

ACKNOWLEDGEMENTS VI

AUTHORSHIP DECLARATION: CO-AUTHORED PUBLICATIONS VII

OUTPUTS ARISING FROM THIS THESIS X

TABLE OF CONTENTS XIV

LIST OF TABLES XIX

LIST OF FIGURES XXI

LIST OF ABBREVIATIONS XXIII

CHAPTER 1. INTRODUCTION 1

OVERVIEW AND RATIONALE 1 1.1

SIGNIFICANCE OF THE STUDY 3 1.2

AIMS AND OBJECTIVE OF THIS THESIS 3 1.3

ROLE OF THE PHD CANDIDATE IN THE PROJECT 4 1.4

STRUCTURE OF THE THESIS 5 1.5

CHAPTER 2. REVIEW OF THE LITERATURE 6

EPIDEMIOLOGY OF PERINATAL MORTALITY, PRETERM BIRTH AND LOW BIRTHWEIGHT 6 2.1

Perinatal mortality 7 2.1.1

2.1.1.1 Stillbirth 9

2.1.1.2 Neonatal death 12

Preterm birth 13 2.1.2

Low birthweight 16 2.1.3

MIGRATION, ACCULTURATION AND ETHNICITY 19 2.2

Assimilation and acculturation 20 2.2.1

2.2.1.1 Language 21

2.2.1.2 Age on arrival 22

2.2.1.3 Length of residence 22

2.2.1.4 Intermarriage 22

Migrant populations in Australia and WA 23 2.2.2

Ethnic origin in WA 24 2.2.3

HEALTHCARE USE AND HEALTH OUTCOMES OF MIGRANT 25 2.3

Pregnancy and childbirth outcomes of migrant populations 27 2.3.1

Pattern of health service utilisation 31 2.3.2

Acculturation and pregnancy outcomes 38 2.3.3

SUMMARY 42 2.4

CHAPTER 3. GENERAL METHODS 45

DATA SOURCES 45 3.1

Routinely collected administrative health data 45 3.1.1

Data linkage 45 3.1.2

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WA DATA LINKAGE SYSTEM 46 3.2

History 47 3.2.1

Strength of the WA Data Linkage System 48 3.2.2

Record linkage, extraction and data release process 48 3.2.3

Privacy and security 51 3.2.4

Data collections 51 3.2.5

Geocoding 52 3.2.6

Genealogical linkage 54 3.2.7

DATA APPLICATION AND PROJECT MANAGEMENT 55 3.3

Feasibility assessment 56 3.3.1

Ethical review and approval for data release 56 3.3.2

Datasets used for this study 57 3.3.3

3.3.3.1 Midwives Notification System 58

3.3.3.2 Birth & death registrations and family connections 59

3.3.3.3 Hospital Morbidity Data System 59

3.3.3.4 WA Register of Developmental Anomalies 60

3.3.3.5 Indigenous status flag 60

Exposures ascertainment 61 3.3.4

Outcomes ascertainment 62 3.3.5

Sample size and power 63 3.3.6

Ethics approval 63 3.3.7

Data analysis 64 3.3.8

CHAPTER 4. STILLBIRTH, MIGRATION AND ETHNIC ORIGIN IN WESTERN AUSTRALIA 65

ABSTRACT 66 4.1

INTRODUCTION 66 4.2

METHODS 67 4.3

Study design and participants 67 4.3.1

Data sources and linkage 67 4.3.2

Exposures 68 4.3.3

Outcomes 68 4.3.4

Statistical analysis 69 4.3.5


RESULTS 70 4.4

DISCUSSION 79 4.5

Limitations 81 4.5.1

Conclusion 81 4.5.2

CHAPTER 5. HEALTHCARE USE AND STILLBIRTH IN MIGRANTS IN WESTERN AUSTRALIA 82

ABSTRACT 83 5.1

INTRODUCTION 84 5.2

METHODS 85 5.3

Study Design and Participants 85 5.3.1

Data Sources and Linkage 85 5.3.2

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Exposures 86 5.3.3

Outcomes 87 5.3.4

Other variables 87 5.3.5


Sensitivity analyses 89 5.3.7


RESULTS 90 5.4

Antepartum stillbirth 97 5.4.1

Intrapartum stillbirth 97 5.4.2

Antenatal care 103 5.4.3

Birth attendant and intrapartum care 104 5.4.4

Interpreter service 105 5.4.5

Controlling for the effect of LBW and PTB 106 5.4.6

Private health Insurance 106 5.4.7

Sensitivity analysis 106 5.4.8

DISCUSSION 106 5.5

Other findings 109 5.5.1

Generalisability and clinical relevance 110 5.5.2

Strength 110 5.5.3

Limitations 110 5.5.4

Conclusion 111 5.5.5

ACKNOWLEDGMENTS 112 5.6

CHAPTER 6. ACCULTURATION AND STILLBIRTH IN WESTERN AUSTRALIA 113

ABSTRACT 114 6.1

INTRODUCTION 115 6.2

METHODS 116 6.3

Study Design and Participants 116 6.3.1

Data Sources 116 6.3.2

Exposure and outcome variables 117 6.3.3


RESULTS 118 6.4

The overall level of acculturation 126 6.4.1

DISCUSSION 127 6.5

Strength and limitations 130 6.5.1

Implications and generalisability 130 6.5.2

CHAPTER 7. PRETERM BIRTH AND LOW BIRTHWEIGHT IN RELATION TO MIGRATION AND ETHNICITY IN WESTERN AUSTRALIA 132

ABSTRACT 133 7.1


METHODS 135 7.3

Study population and data sources 135 7.3.1

Variables 136 7.3.2

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Statistical methods 137 7.3.3

RESULTS 138 7.4

Sensitivity analysis 147 7.4.1

DISCUSSION 147 7.5



Strengths and limitations 149 7.5.3

CHAPTER 8. ACCULTURATION, PRETERM BIRTH AND LOW BIRTHWEIGHT IN WESTERN AUSTRALIA 151

ABSTRACT 152 8.1


METHODS 154 8.3

Study population and data sources 154 8.3.1

Variables 154 8.3.2

Statistical methods 156 8.3.3

RESULTS 156 8.4

Descriptive analysis 156 8.4.1

Acculturative factors 157 8.4.2

Level of acculturation 164 8.4.3

DISCUSSION 165 8.5



Strengths and limitations 169 8.5.3

CHAPTER 9. DISCUSSION AND FUTURE DIRECTION 171

FINDINGS 171 9.1

Stillbirth 171 9.1.1



STRENGTHS AND LIMITATIONS 177 9.2

IMPLICATIONS FOR POLICY AND PRACTICE 178 9.3

RESEARCH TRANSLATION, IMPACT AND DIRECTION FOR FUTURE RESEARCH 180 9.4

REFERENCES 183

APPENDICES 211

APPENDIX 1. PROJECT FEASIBILITY APPROVAL 212

APPENDIX 2. ETHICAL APPROVAL 214

APPENDIX 3. FINAL PROJECT APPROVAL 216

APPENDIX 4. UWA NOTIFICATION OF ETHICS APPROVAL FROM ANOTHER ETHICS COMMITTEE 217

APPENDIX 5. PUBLISHED PAPER 1 (CHAPTER 4) 218

APPENDIX 6. PAPER 1- ONLINE APPENDIX 225


APPENDIX 8. PAPER 2- SUPPORTING INFORMATION 252


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APPENDIX 10. MIDWIVES NOTIFICATIONS DATA APPLICATION VARIABLE LISTS 271

APPENDIX 11. BIRTH DATA APPLICATION VARIABLE LIST 277

APPENDIX 12. MORTALITY DATA APPLICATION VARIABLE LIST 280

APPENDIX 13. HOSPITAL MORBIDITY DATA APPLICATION VARIABLE LIST 285

APPENDIX 14. WARDA BIRTH DEFECTS DATA APPLICATION VARIABLE LIST 290

APPENDIX 15. FAMILY CONNECTIONS APPLICATION FORM 293

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LIST OF TABLES

TABLE 2.1 CRITERIA FOR REGISTRATION OF BIRTH AND PERINATAL DEATH INTERNATIONALLY 9

TABLE 2.2 PSYCHOSOCIAL IMPACTS IN EACH STAGE OF MIGRATION 20

TABLE 2.3 AUSTRALIA'S POPULATION BY COUNTRY OF BIRTH - 2019A 23

TABLE 3.1 DATASETS AND VARIABLES 58

TABLE 4.1 PREGNANCY OUTCOMES FOR 260 997 LIVE AND STILLBIRTHS IN WESTERN AUSTRALIA, 2005–

2013, BY MATERNAL ETHNIC ORIGIN 71

TABLE 4.2 CHARACTERISTICS OF MOTHERS FOR 260 997 LIVE AND STILLBIRTHS IN WESTERN AUSTRALIA,

2005–2013, BY MATERNAL ETHNIC ORIGIN 73

TABLE 4.3 PREVALENCE OF STILLBIRTHS IN WESTERN AUSTRALIA, 2005–2013, BY ETHNIC ORIGIN OF

MOTHER AND TYPE OF STILLBIRTH 76

TABLE 4.4 STILLBIRTH IN WESTERN AUSTRALIA, 2005–2013: COMPARISON OF MIGRANT WOMEN, BY

ETHNIC ORIGIN, WITH AUSTRALIAN-BORN WOMEN 77

TABLE 5.1 DEMOGRAPHIC CHARACTERISTICS OF THE STUDY POPULATION 91

TABLE 5.2 OBSTETRIC CHARACTERISTICS OF THE STUDY POPULATION 94

TABLE 5.3 LOGISTIC REGRESSION MODEL AND THE FACTORS ASSOCIATED WITH ANTESB (2005–2013) 98

TABLE 5.4 LOGISTIC REGRESSION MODEL AND THE FACTORS ASSOCIATED WITH INTRASB (2005–2013) 100

TABLE 5.5 COMPARISON OF ANTEPARTUM STILLBIRTH IN MIGRANT WOMEN, STRATIFIED BY ETHNICITY

AND TIMING OF 1ST ANTENATAL CARE VISIT, WITH AUSTRALIAN-BORN WOMEN (2010–2013) 104

TABLE 6.1 CHARACTERISTICS OF THE POPULATION OF THE STUDY 120

TABLE 6.2 ABSOLUTE NUMBERS, RATES, AND UNADJUSTED ODDS RATIOS OF STILLBIRTH FOR MIGRANTS,

STRATIFIED BY ACCULTURATIVE FACTORS, COMPARED WITH THE AUSTRALIAN-BORN

POPULATION 123

TABLE 6.3 LENGTH OF RESIDENCE AND THE ODDS OF STILLBIRTH IN MIGRANTS FROM SPECIFIC ETHNIC

BACKGROUNDS COMPARED TO THE AUSTRALIAN-BORN POPULATION 124

TABLE 7.1 DEMOGRAPHIC CHARACTERISTICS OF THE STUDY POPULATION 139

TABLE 7.2 CUMULATIVE INCIDENCE OF LBW AND PTB FOR BIRTHS TO AUSTRALIAN-BORN WOMEN AND

BIRTHS TO OVERSEAS-BORN WOMEN STRATIFIED BY ETHNIC BACKGROUND (2005–2013) 141

TABLE 7.3 ODDS OF PTB AND TERM-LBW IN AUSTRALIAN-BORN AND MIGRANT WOMEN FROM NON-WHITE

BACKGROUNDS COMPARED TO WHITE AUSTRALIAN-BORN WOMEN (2005–2013) 143

TABLE 7.4 RISK OF SPONTANEOUS AND MEDICALLY INDICATED PTB IN MIGRANTS FROM DIVERSE ETHNIC

BACKGROUNDS COMPARED TO THE WHITE AUSTRALIAN-BORN POPULATION (2005–2013) 144

TABLE 7.5 RISKS OF TERM-LBW AND PTB FOR MIGRANTS COMPARED WITH AUSTRALIAN-BORN

POPULATION FROM THE SAME ETHNIC GROUP 146

TABLE 8.1 CHARACTERISTICS OF THE STUDY POPULATION 159

TABLE 8.2 COMPARISON OF TERM-LBW AND PTB IN MIGRANTS, STRATIFIED BY ACCULTURATIVE FACTORS,

WITH AUSTRALIAN-BORN WOMEN 162

TABLE 8.3 COMPARISON OF TERM-LBW AND PTB IN MIGRANT AND AUSTRALIAN-BORN WOMEN

ACCORDING TO THE ACCULTURATION LEVEL OF MIGRANT WOMEN 164

PAGE | xx

PAGE | xxi

LIST OF FIGURES

FIGURE 2.1 DEFINITION OF THE PERINATAL PERIOD AND PERINATAL DEATH 6

FIGURE 2.2 INFANT MORTALITY RATES IN WESTERN AUSTRALIA 13

FIGURE 2.3 OBSTETRIC PRECURSORS OF PRETERM BIRTH 15

FIGURE 2.4 LOW BIRTH WEIGHT TREND IN WA 18

FIGURE 2.5 MAIN DETERMINANTS OF HEALTH 26

FIGURE 3.1 PERSON-BASED LINKAGES 47

FIGURE 3.2 DATA LINKAGE PROCESS 49

FIGURE 3.3 DATA EXTRACTION PROCESS 50

FIGURE 3.4 DATA COLLECTIONS (AS OF SEPTEMBER 2014) 52

FIGURE 3.5 GEOCODING AT THE WA DATA LINKAGE BRANCH 53

FIGURE 3.6 WA FAMILY CONNECTIONS SYSTEM 55

FIGURE 3.7 INDIGENOUS STATUS FLAG 61

FIGURE 4.1 PRETERM (20–36 WEEKS) STILLBIRTHS IN WESTERN AUSTRALIA, 2005–2013, BY ETHNIC ORIGIN

OF MOTHER 78

FIGURE 4.2 TERM AND POST-TERM (≥37 WEEKS) STILLBIRTHS IN WESTERN AUSTRALIA, 2005–2013, BY

ETHNIC ORIGIN OF MOTHER 79

FIGURE 5.1 GEOGRAPHICAL LOCATION OF THE PRIVATE AND PUBLIC HOSPITALS IN WESTERN AUSTRALIA. 86

FIGURE 5.2 GESTATIONAL AGE AT FIRST ANTENATAL CARE VISIT FOR SPECIFIED ETHNICITY GROUPS IN

COMPARISON TO THE AUSTRALIAN-BORN POPULATION (2010–2013) 103

FIGURE 5.3 CUMULATIVE INCIDENCE RATE OF INTRAPARTUM STILLBIRTH (POST 23 WEEKS GESTATION) BY

ACCOUCHEUR PROVIDING INTRAPARTUM CARE (2005–2013). 105

FIGURE 5.4 CUMULATIVE INCIDENCE RATE OF OVERALL STILLBIRTH ACCORDING TO INTERPRETER SERVICE

USE (2005–2013) 105

FIGURE 6.1 LENGTH OF RESIDENCE IN RELATION TO UTILISATION OF INTERPRETER, HAVING AN AUSTRALIAN-

BORN PARTNER, SMOKING IN PREGNANCY AND RATE OF STILLBIRTH IN THE MIGRANT

POPULATION OF WESTERN AUSTRALIA (2005–2013) 123

FIGURE 6.2 LENGTH OF RESIDENCE IN AUSTRALIA AND PERCENTAGE OF HAVING PRIVATE HEALTH

INSURANCE FOR MIGRANT WOMEN FROM SPECIFIC ETHNIC BACKGROUNDS DELIVERED IN WA

(2005–2013) 125

FIGURE 6.3 LENGTH OF RESIDENCE IN AUSTRALIA AND PERCENTAGE OF HAVING AN AUSTRALIAN-BORN

PARTNER FOR MIGRANT WOMEN FROM SPECIFIC ETHNIC BACKGROUNDS DELIVERED IN WA

(2005–2013) 126

FIGURE 6.4 LENGTH OF RESIDENCE IN AUSTRALIA AND THE PERCENTAGE OF UTILISATION OF INTERPRETER

FOR MIGRANT WOMEN FROM SPECIFIC ETHNIC BACKGROUNDS DELIVERED IN WA (2005–2013) 126

FIGURE 8.1 TERM-LBW RATE BY ETHNICITY OF MIGRANTS AND LENGTH OF RESIDENCE IN AUSTRALIA (2005–

2013) 163

FIGURE 8.2 ALL PTB RATE BY ETHNICITY OF MIGRANTS AND LENGTH OF RESIDENCE IN AUSTRALIA (2005–

2013) 163

PAGE | xxii

PAGE | xxiii

LIST OF ABBREVIATIONS

ANC antenatal care

AnteSB antepartum stillbirth

aOR adjusted odds ratio

ARIA Accessibility/Remoteness Index of Australia

BMI body mass index

CaLD Culturally and Linguistically Diverse

CS caesarean section

DLB data linkage branch

FGR fetal growth restriction

HIC high-income country

HMDC Hospital Morbidity Data Collection

IntraSB intrapartum stillbirth

IRSD Index of Relative Socioeconomic Disadvantage

LBW low birthweight

LMIC low- to middle-income country

MNS Midwives Notification System

MDG Millennium Development Goals

NPDC National Perinatal Data Collection

OR odds ratio

PPROM preterm pre-labour rupture of membranes

PTB preterm birth

RRR Relative Risk Ratio

SB stillbirth

SEIFA Socio-Economic Indexes For Areas

UK United Kingdom

US United States

WA Western Australia

WADLS WA Data Linkage System

WARDA WA Registry of Developmental Anomalies

WHO World Health Organization

CHAPTER 1. INTRODUCTION

PAGE | 1


OVERVIEW AND RATIONALE 1.1

Differences in access to reproductive health services across countries are well known.1-6 Low- and

middle-income countries often report suboptimal reproductive health indicators, such as maternal

and perinatal mortality, which have been attributed to a lack of access to quality obstetric care

and interventions.7,8 However, adverse reproductive outcomes and ethnic disparities in the risk of

such outcomes are still observed in high-income countries despite the availability of high-standard

pregnancy care and perinatal interventions.9-11

The United Nations (UN) International Migration Report 2013 indicated that there were 232

million international migrants in 2013, of whom almost 60% lived in developed countries.12

Previous research also revealed significant differences among migrants and resettlement

countries’ populations in terms of utilisation of reproductive services and reproductive outcomes;

however, findings are inconsistent across jurisdictions.11,13

The evidence suggests that service availability and access are not the only determinants of optimal

health outcomes and the utilisation of such services could be influenced by different

circumstances and decision making of both health professionals and patients.9,14-17 Such decision

making could be influenced by a range of sociocultural factors that are subsequently associated

with clinical outcomes.16,17

The level of health service engagement could affect the incidence of various reproductive

outcomes. For example, medically indicated induction of labour and caesarean section (CS) are

two medical procedures that have the potential to reduce mortality (maternal or infant) in some

situations; however, these procedures have been associated with an increased rate of preterm

birth.18 Anecdotal evidence and research from the United States (US) suggest that such obstetric


PAGE | 2

services are viewed differently by women from different ethnic backgrounds, which has the

potential to influence the risk of outcomes from provision (or lack) of such services.19

Reproductive health is vital to the sustainable development of societies worldwide. However,

disparities are conspicuous and achieving universal access has been aimed and repeatedly

emphasised by the World Health Organization (WHO).

The pregnancy period is especially an important window in an individual’s life, during which health

status, events and infant genetic programming may determine the later status of health and

disease experienced along the lifespan.20 Pregnancy-related health service utilisation and prenatal

care, as such, are pivotal for both mother’s and the offspring’s health and to ensure sustainable

development of the population, society, and economy.

Immigration is the principal component of population growth in Australia, and one-third of

Australian women who give birth are born overseas.21,22 Yet, ethnicity as a predictor of pregnancy

outcomes has received limited attention to date, and the status of reproductive health in migrants

in Australia has not been explored thoroughly.

According to figures released in December 2013 by the Australian Bureau of Statistics, one-third of

Western Australia (WA)’s population was born overseas—the highest proportion of any Australian

state or territory—and this population has increased by 39% since 2006.21

Most of the literature from WA, on ethnicity and pregnancy outcomes, pre-dated 200523-25 and

arguably do not represent the current composition of the Australian population given the changes

in demographic profile across time. Much of this work focused primarily on disparities among

Aboriginal and non-Aboriginal populations,25 and considered only a small number of ethnicities or

a small number of outcomes.26 Consequently, the association between major adverse pregnancy


PAGE | 3

outcomes such as preterm birth (PTB), low birthweight (LBW), and perinatal mortality and migrant

status or ethnicity is not very well known in Australia and completely unknown in WA settings.

SIGNIFICANCE OF THE STUDY 1.2

Using the WA Data Linkage System (WADLS)—which facilitates the capture of health service

contact, events and outcomes through multiple datasets across the WA—this project

comprehensively investigated the current utilisation of health services and pregnancy outcomes of

women in an Australian setting in relation to migrant status and ethnicity.

This study determined the current status of reproductive health, disparities in pregnancy

outcomes across ethnic groups, usage of pregnancy-related services and their impact on

pregnancy outcomes in different communities in the WA population. The availability of such

information will enhance targeting of modifiable factors, planning of appropriate policies and

implementation of required preventive measures or interventions to improve outcomes in these

populations and, consequently, the overall reproductive health of the population. Eventually, this

work will reduce the burden of adverse pregnancy outcomes and pregnancy-related healthcare

expenditure in the community.

AIMS AND OBJECTIVE OF THIS THESIS 1.3

This study explored the patterns of utilisation of pregnancy-related medical services, pregnancy

care, procedures and treatments in association with pregnancy and perinatal outcomes in WA

with a focus on migrants from 2005 onwards. The specific aims were:

Aim 1: To investigate prevalence rates and the risk of antepartum and intrapartum stillbirth

in WA with respect to maternal country of birth and ethnic origin.

Aim 2: To investigate the pattern of healthcare utilisation among migrant women and its

relationship with the risk of stillbirth (antepartum and intrapartum) in WA.


PAGE | 4


stillbirth between migrant and Australian-born populations in WA.

Aim 4: To investigate ethnic disparities in the risk of low birthweight (LBW) and preterm

birth (PTB), spontaneous and medically indicated, between migrant and Australian-born

populations from diverse ethnic backgrounds in WA.


PTB and LBW between migrant and Australian-born populations from diverse ethnic backgrounds

in WA.

The three hypotheses tested were that: (1) migrant women, especially those from non-English-

speaking countries, use available pregnancy-related health services in WA at lower rates than

Australian-born women; (2) migrant women have poorer perinatal outcomes than their

Australian-born counterparts; and (3) acculturation (i.e. age on arrival, length of residence in

Australia, language proficiency and inter-racial partnership) can mitigate these effects.

ROLE OF THE PHD CANDIDATE IN THE PROJECT 1.4

This project was entirely a PhD research project conceptualised by the PhD candidate and refined

according to the feedback and advice received from the supervisors. All tasks for developing the

study, acquiring the required approvals and funding, and completing the processes involved from

initial liaison with the DLB and data custodians, completion of application forms, provision of data,

data analyses, writing manuscripts to publishing manuscripts were done solely by the PhD

candidate with guidance and direction from the supervisors. The manuscripts were finalised

according to supervisor feedback, comments from data linkage staff and data custodians, and

eventually in response to the comments received from the journal’s editors and reviewers.


PAGE | 5

STRUCTURE OF THE THESIS 1.5

This thesis is written as a series of papers. The main analyses are presented in separate chapters

as published/submitted original research journal articles following the literature review and

general methods chapters. The findings are summarised and discussed at the end of the thesis.

The literature review (Chapter 2) explores the body of knowledge for the three main themes of

this study: (1) Epidemiology of adverse pregnancy outcomes including stillbirth, preterm birth and

low birthweight; (2) Migration, acculturation and ethnicity of the population, especially in high-

income countries and Australia; and (3) Healthcare use and health outcomes in migrants.

The General Methods (Chapter 3) outlines the sources of data and processes involved in data

acquisition. It details the WA Data Linkage System, its history, techniques used, and procedures

followed for extraction, linkage and provision of data and to maintain privacy.

Chapters 4–8 comprise five papers published or submitted to journals for publication. Each paper

explores one aim of this thesis and includes an Abstract, Introduction, Methods, Results and

Discussion sections.

Chapter 9 draws together the main findings of the thesis and synthesises the significance of the

results. It discusses findings, strengths and limitations, implications for policy and practice,

research translation, impact and direction for future research.

References and Appendices follow the chapters and include the resources used, a copy of each

published paper, the documents and application forms completed, and approvals acquired.

CHAPTER 2. LITERATURE REVIEW

PAGE | 6

CHAPTER 2. REVIEW OF THE LITERATURE

Literature relevant to the main themes of the thesis are explored and critiqued, including adverse

pregnancy outcomes, pregnancy-related medical services, patterns of care, migration and

acculturation, and relevant policies. This chapter also identifies knowledge gaps for further

research.

EPIDEMIOLOGY OF PERINATAL MORTALITY, PRETERM BIRTH AND LOW BIRTHWEIGHT 2.1

According to the Registration of Births, Deaths and Marriages Act 1963, the expulsion or extraction

of a child from its mother can be registered as a birth in Australia, if gestational age is at least 20

weeks or birthweight is at least 400 grams.27 The Australian Institute of Health and Welfare

collates data on all births in collaboration with state and territory health departments for the

National Perinatal Data Collection (NPDC).28 The NPDC covers both live births and stillbirths, where

gestational age is at least 20 weeks or birthweight is at least 400 grams (Figure 2.1). However, in

Victoria and Western Australia, births are included if gestational age is at least 20 weeks or, if it

cannot be reliably established whether the child’s period of gestation is more or less than 20

weeks, birthweight is at least 400 grams.28-30

FIGURE 2.1 DEFINITION OF THE PERINATAL PERIOD AND PERINATAL DEATH

Source: Australian Institute of Health and Welfare 2020. Stillbirths and neonatal deaths in Australia. Cat. no. PER 107.


PAGE | 7

PERINATAL MORTALITY 2.1.1

In Australia, 20 weeks gestation marks the commencement of the perinatal period in pregnancy

(Figure 2.1); any death that occurs during this time and within the first 28 days of life is counted as

perinatal mortality.30 Perinatal mortality is an important indicator for monitoring and improving

the health of pregnant women, unborn fetuses, new mothers and newborns in the population.30

This information helps decision-makers identify problems, disparities, and temporal and

geographical trends to implement necessary changes in practice and policy to improve public

health.31

The field of perinatal mortality has received considerable attention in the last two decades

worldwide. Neonatal deaths have been under scrutiny since early 2000, such that the 191 United

Nations (UN) members signed a declaration to reduce child mortality globally [Millennium

Development Goal 4 (MDG4)]. Stillbirth, on the other hand, was comparatively neglected in MDG

plans and global health agendas until the Lancet launched its first stillbirth article series in 2011

and focused global attention on stillbirths.32-34 Since then, efforts to end preventable stillbirth

have gained momentum and led to the recognition of stillbirth as an essential part of the

sustainable development agenda through the launch of Every Newborn Action Plan (ENAP) in

201435 and the Global Strategy for Women’s, Children’s and Adolescents’ Health (2016–2030) at

the UN General Assembly in 2015.36

The ENAP presented solutions to prevent newborn deaths and stillbirths based on the evidence

presented in The Lancet Every Newborn series35,37,38 with a vision of a world in which there are no

preventable deaths of newborns or stillbirths.35 ENAP’s Goal 1, ending preventable newborn

deaths, sets the target of “10 or fewer newborn deaths per 1000 live births and continue to

reduce death and disability, ensuring that no newborn is left behind”35(p7) by 2035; and Goal 2,


PAGE | 8

ending preventable stillbirths, sets the target as “all countries will reach the target of 10 or less

stillbirths per 1000 total births and continue to close equity gaps”35(p7) by 2035.

The second Lancet series on stillbirths was published in 2016 and further asserted the idea of

Ending Preventable Stillbirths by 2030 and provided a roadmap for achieving that goal.39-43

Fetal and neonatal mortality have been under observation for decades in Australia by the

Australian Institute of Health and Welfare and some state committees, such as Perinatal and

Infant Mortality Committee of Western Australia, which publish regular reports on the rates and

causes of perinatal mortality.44-47 Many major causes and risk factors for these outcomes have

been identified through research41,48 and efforts have been made by the Perinatal Society of

Australia and New Zealand to consolidate these in specific classification systems and clinical

practice guidelines.49,50

Nevertheless, there is substantial uncertainty around the potential causes of a significant

proportion of these deaths. Almost 3000 perinatal deaths occur each year with unexplained

antepartum death as the leading category of perinatal mortality for term singleton births (26.8%),

followed by congenital abnormality regardless of plurality (24.9%).51 Perinatal mortality rates have

fluctuated around 9–10 per 1000 births for the last 15 years, with stillbirths rates remaining static

despite the advanced knowledge of prenatal care52 and even greater proportions of unexplained

stillbirths reported in some regions, such as 41.5% of the cohort and 60% of term stillbirths as

described by Gordon and Jeffery for all births in New South Wales from 2002–2004.53

According to the Chairman’s Report of the 2017 Perinatal and Infant Mortality Committee of

Western Australia, many aspects of current obstetric care have reached a plateau of success,

indicating the need for high-quality research and thorough evaluation of the effectiveness of

current health care models.54


PAGE | 9

There is significant variation in the inclusion criteria for the registration of fetal death, birth and

the perinatal period among jurisdictions worldwide, which makes the comparison of relevant vital

statistics difficult to some extent (Table 2.1).

TABLE 2.1 CRITERIA FOR REGISTRATION OF BIRTH AND PERINATAL DEATH INTERNATIONALLY

Institution Perinatal death

Fetal death/Stillbirth Neonatal deaths Birthweight Gestational age

World Health Organisation55

International comparison 1000 g 28 weeks <7 days

National reporting 500 g 22 weeks <7 days

Australia and the United States of America52 400 g 20 weeks <28 days

Perinatal health monitoring in Europe56 <7 days

France, Finland and the Netherlands (civil reg.)56

500 g 22 weeks

Denmark56 – 22 Weeks

Germany56 500 g –

United Kingdom57 – 24 weeks

Italy and Spain56 – 25 weeks and 5 days

Sweden and Greece56 – 28 weeks

Norway56 – 12 weeks

Netherlands (Perinatal register)56 16 weeks

2.1.1.1 Stillbirth

The International Classification of Diseases, 10th revision (ICD-10) defines stillbirth as “death prior

to the complete expulsion or extraction from its mother of a product of conception, irrespective of

the duration of pregnancy; the death is indicated by the fact that after such separation the fetus

does not breathe or show any other evidence of life, such as beating of the heart, pulsation of the

umbilical cord, or definite movement of voluntary muscles”.58

According to the WHO, for international comparisons, a birthweight cut-off value of 1000 grams is

recommended; when birthweight is not known, a gestational age threshold of 28 weeks should be

considered. However, exclusion of births with a birthweight less than 1000 grams (1000 grams


PAGE | 10

threshold instead of 28 weeks gestation) can underestimate the health burden of stillbirth,

especially in high-income countries.59,60

Further, stillbirths are classified according to time of death and proximity to the commencement

of labour, being antepartum stillbirth for death of baby before commencement of labour, or

intrapartum stillbirth for death of baby after labour started.61

Global estimates showed that around 2.6 million stillbirths occurred in 2009, with a worldwide

rate of 18.9 stillbirths per 1000 births.34 The highest stillbirth rates were observed in South Asia

and Sub-Saharan Africa.34 India, Pakistan, Nigeria, China, Bangladesh, the Democratic Republic of

the Congo, Ethiopia, Indonesia, Tanzania and Afghanistan accounted for around 70% of the total

deaths, including almost 2 million stillbirths.34 Finland, Singapore, Denmark and Norway reported

the lowest rates of stillbirth in 2009, with about 2 per 1000 births.34

An estimated 2.6 million babies were stillborn in the third trimester of pregnancy in 2015,61 with

98% of all stillbirths occurring in low- and middle-income countries (77% in South Asia and Sub-

Saharan Africa). The trend showed a slow decline from previous observations with the slowest

progress reported in Sub-Saharan Africa.42 Variations in late-gestation stillbirth rates across high-

income countries ranged from 1.3 to 8.8 per1000 births, suggesting that rates can be further

reduced in countries such as Australia.62

Stillbirths account for more than 2000 deaths in Australia annually; the national fetal death rate of

7 per 1000 births has not decreased for decades.41,63,64 The fetal death rate in WA in 2013–2014

was 6.6 per 1000 births, which is slightly less than the reported national rates.63 However, this rate

also indicates a significant increase from the 4.91 per 1000 total births reported from 1980–1983

in WA.25 The evidence suggests that such an increase should be attributed to legislative and

clinical practice changes related to the diagnosis of congenital anomalies and pregnancy


PAGE | 11

terminations; thus the actual stillbirth rate has remained static.65 Further, some researchers have

recently indicated, upon exploration of the national published reports from 1994-2015, that the

trend in the rate of stillbirth has been different at different stages of pregnancy.66 Hence,

reporting the overall risk, rather than the stage-specific risk, masks the gains achieved in reduction

of stillbirth.66

According to Alessandri et al, almost 65% of stillbirths were antepartum, 25% were intrapartum

and 10% had an unknown time of death.25 No more recent study on timing of stillbirth is available

to compare Alessandri and colleagues’ findings in 1980 to. However, global estimates published in

the Lancet, using State Statistical Office data, reported that 14.0% of stillbirths in Australia were

intrapartum in 2015, with a rate of 0.4 per 1000 births.61

Risk factors widely suggested for stillbirth include demographic and obstetric characteristics, such

as advanced maternal age,6,48,67-71 not-married status,69,72,73 socioeconomic disadvantage,70,71,73-75

remoteness,25,46,67,75,76 smoking during pregnancy,69,70,74,77,78 nulliparity,6,67,68,70 pre-existing

medical conditions (such as overweight and obesity,70,71,74,79 diabetes41,77,80 and/or essential

hypertension73,74), history of previous stillbirth,70,74,80 multiple pregnancy6,70,73 and male sex of

baby.80,81

Major etiologic factors for stillbirth in low- and middle-income countries are not similar to the

etiology of stillbirth in high-income countries. Prolonged and obstructed labour, preeclampsia,

infection, lack of antenatal care and maternal socioeconomic disadvantage accounted for most

stillbirths in low- and middle-income countries.82,83Across high-income countries, maternal

overweight and obesity, advanced age and smoking were the most prevalent modifiable risk

factors.41,71 Small size for gestational age and abruption, which are indications of placental


PAGE | 12

pathology, and pre-existing diabetes and hypertension are the most important factors observed

among the disadvantaged populations in these countries.41

According to Alessandri et al., the cause of death for most (52%) stillbirths in WA was either

unknown or associated with lethal congenital malformations (13%), antepartum haemorrhage

(12%) or maternal hypertension (8%).25 Some ethnic minority groups,9,13,67,80,84-86 including women

born in South Asia and Africa, were suggested to be at higher risk for stillbirth; however, the only

study available on the influence of race and place of birth on stillbirth in WA is that of Alessandri

et al., which was published in 1988 and limited to a comparison of Aboriginal women and their

non-Aboriginal counterparts.25 Thus, the risk of antepartum and intrapartum stillbirth in non-

Indigenous ethnic minorities and migrant women remains unexplored.

2.1.1.2 Neonatal death

The neonatal period is defined as the first 28 complete days after birth and is subdivided into the

early neonatal period (0–6 days) and late neonatal period (7–27days). Death of an infant during

this period is defined accordingly as early or late neonatal death.87

In 2005, it was estimated that more than four million neonates die globally each year.88 More than

80% of these deaths were attributed to infection, complications of preterm birth, and intrapartum

problems. Despite an improvement in the outcomes over the last decade and the decline in the

number of deaths worldwide (2.9 million annual neonatal deaths),38 the least progress has been

made in controlling intrapartum and preterm birth related etiologies.89 Low coverage of skilled

birth attendance (<50%), significant lack of skilled human resources (<0.9 per 1000 population)

and low fund allocation (< 20 USD per capita per year) have been blamed for most intrapartum-

related deaths in high mortality regions.7 It is estimated that 71% of these deaths are preventable

by providing essential interventions.37


PAGE | 13

In Australia, neonatal mortality rates in 2013–2014 were highest in the Northern Territory (5.4 per

1000 live births) and lowest in northern Sydney, New South Wales (1.2 per 1000 live births) and

northern Perth, WA (1.4 per 1000 live births).46 In WA, the neonatal death rate significantly

decreased from 3.9 per 1000 live births in 1990–1992 to 1.7 per 1000 live births in 2013–2014,

which was the lowest rate among all states and territories.54

The most recent perinatal mortality statistics for WA are presented in Figure 2.2 and show that the

neonatal death rate in WA is below 1 per 1000 live births and most cases counted in perinatal

deaths are stillbirths.90

FIGURE 2.2 INFANT MORTALITY RATES IN WESTERN AUSTRALIA

Source: Midwives Notification System, Information and System Performance Directorate, Department of Health WA. Data extracted 06 July 2020

PRETERM BIRTH 2.1.2

The WHO defines preterm birth as birth before 37 completed weeks or 259 days of gestation

calculated from the first day of the last menstrual period of the pregnant woman.91 This may be

further sub-classified based on weeks of gestation into moderately preterm (33–36 completed


PAGE | 14

weeks of gestation), very preterm (<32 weeks) and extremely preterm (<28 weeks),92 or based on

etiology as spontaneous or medically indicated preterm birth.93,94

Spontaneous PTB consists of idiopathic PTB: when labour commences before term, or preterm

pre-labour rupture of membrane (PPROM), when the amniotic sac is ruptured without established

labour, and prior to 37 completed weeks of pregnancy.93,94

Medically indicated PTBs are those that occur due to induction of labour or caesarean delivery

without prior PROM or spontaneous onset of labour. These are also known as iatrogenic PTB and

are mainly due to maternal or fetal disorders or health conditions that indicate premature delivery

of baby to save the life of the mother, the baby, or both.93,94 Around 30–35% of PTBs are medically

indicated, 40–45% are idiopathic, and 25–30% follow PPROM (Figure 2.3).95

It is estimated that almost 15 million babies are born preterm globally each year, comprising more

than 11% of all live births worldwide96 and that more than one million deaths occur as a result of

complications of preterm birth among under-five children.96,97 More than 60% of preterm births

occur in South Asia and Sub-Saharan Africa, where 52% of the global live births occur.98 In 2014,

the preterm birth proportion from all live births was around 8.7% in Europe99,100and around 11% in

North America.99,101


PAGE | 15

FIGURE 2.3 OBSTETRIC PRECURSORS OF PRETERM BIRTH

Source: Goldenberg RL, Culhane JF, Iams JD, Romero R. Epidemiology and causes of preterm birth. Lancet. 2008;371(9606):75–84.

Slight variation in the normal length of the gestational period and probability of natural survival of

preterm-born neonates has been observed among different ethnicities.102,103 However, with

increasing availability and use of sophisticated intensive care equipment for preterm babies, the

survival gap between those who have and those who do not have access to such care is

widening.104

Various country-based reproductive health policies and medical practices, especially those related

to infertility treatment and indicated deliveries, multiple pregnancies, maternal age, body mass

index (BMI), smoking, and some ethnic backgrounds, such as non-Hispanic Black race in the US,

history of previous spontaneous preterm birth, short cervix, short interpregnancy interval, and

uterine anomalies are identified as risk factors for preterm birth.105,106

In Australia, about 9% of babies are born preterm, according to the latest Australia’s Mothers and

Babies reports, and preterm birth is more common in multiple births (66.0% of twins and 98.2% of


PAGE | 16

all other multiples compared with 7% of singleton births), babies of Indigenous mothers (5.7%

more than non-Indigenous), mothers who smoked during pregnancy (5.5% more than those who

did not smoke), women residing in very remote areas (5.1% more than those residing in major

cities) and babies of <20 and ≥40 years old mothers (2.8% and 4.6% more prevalent than in those

20–39 years old).30,63

Increase in the singleton PTB rates, driven by an increase in medically indicated PTB, has been

reported in South Australia from 1986 to 2014 with a reduction in preterm stillbirths and in

Victoria from 2007 to 2017 with no improvement in outcomes.107,108 In WA, from 1984–2006, the

prevalence of idiopathic PTB was reportedly 2.6%, with 1.5% and 2.0% from PPROM and medically

indicated PTB, respectively.109 According to Hammond et al., non-Caucasian ethnicity is a risk

factor for spontaneous PTM, but not PPROM or medically indicated PTB, among the non-

Aboriginal population of WA.109 However, this population has not been further stratified by

specific ethnic backgrounds such as Asian, Indian or African.

There has been substantial interest in safely lowering the rate of PTB in WA, which led to the

recent launch of the WA Preterm Birth Prevention Initiative.110,111 This program aims to lower the

rates through the implementation of a multifaceted state-wide program that includes introducing

evidence-based clinical guidelines, a dedicated Preterm Birth Prevention Clinic based at King

Edward Memorial Hospital, and raising public awareness by targeting women and families of

WA.110

LOW BIRTHWEIGHT 2.1.3

Birthweight has been a key indicator and an important determinant of health in infancy and adult

life for a long time.112 Low birthweight (LBW) is the first weight of a fetus or newborn—measured

during the first hour after birth—which is less than 2500 grams, regardless of gestational age, and

caused by a preterm birth or restriction of fetal growth in the uterus.113 It contributes to the


PAGE | 17

etiology of perinatal mortality and predisposes individuals to the development of several

morbidities and non-communicable diseases, including diabetes mellitus and cardiovascular

diseases, later in life and in adulthood.20,113

Small-for-gestational age (SGA), fetal growth restriction (FGR) or intrauterine growth retardation

(IUGR), sometimes used interchangeably in various contexts, pertain to birthweights less than the

10th percentile for gestational age, less than 2500 grams for gestational age greater than or equal

to 37 weeks, and less than two standard deviations below the mean value for gestational age.112

An estimated 20.5 million live births were LBW globally in 2015.114 More than 90% of LBW births

were reported in low- and middle-income countries, mainly from southern Asia (48%) and Sub-

Saharan Africa (24%).The estimated worldwide LBW prevalence was around 14.6% which showed

a reduction in comparison to the 17.5% estimates in 2000.113,114

According to the Australian Institute of Health and Welfare (AIHW), 1 in 15 live-born babies has

LBW in Australia.30 As illustrated in Figure 2.4, the proportion of LBW live-born babies in WA from

2006–2013 was consistently very close to that of Australia (around 6.1–6.3%).115 The proportion of

LBW babies in Australia did not changed much from 2007 to 2017, remaining between 6.1% and

6.7% and close to the OECD average (6.5%).30

Female babies, multiple pregnancies, babies of women who smoked in pregnancy, women living in

remote or disadvantaged areas, and those from Indigenous ethnicity had a higher proportion of

LBW in Australia. In comparison, women born overseas had a lower proportion of LBW (5.9%) than

Australian-born women (6.5%).30 The overseas-born population, however, were not stratified by

maternal country/region of birth or specific ethnic background.


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FIGURE 2.4 LOW BIRTH WEIGHT TREND IN WA

Source: Australian Institute of Health and Welfare. Children’s Headline Indicators [Internet]. Canberra: Australian Institute of Health and Welfare, 2018 [cited 2020

Sep. 7]. Available from: https://www.aihw.gov.au/reports/children-youth/childrens-headline-indicators

https://www.aihw.gov.au/reports/children-youth/childrens-headline-indicators

https://www.aihw.gov.au/reports/children-youth/childrens-headline-indicators


PAGE | 19

MIGRATION, ACCULTURATION AND ETHNICITY 2.2

Mobility is an inherent characteristic of all populations. The process of social change by which an

individual, either alone or accompanied by others, leaves one geographical area for a prolonged

stay or permanent settlement in another geographical region is called ‘migration’, regardless of

the reason behind such a movement—economic betterment, political upheaval, education or any

other purpose.116

Migration is a global phenomenon: today, there are more than one billion migrants globally,

representing one-seventh of the world’s population,117 and comprising more than 244 million

international migrants118 and 763 million internal migrants.119 To put these numbers into

perspective, the entire populations of the United Kingdom, France, Germany and Spain equate to

the number of international migrants, and that of Europe to the internal migrants. This level of

human mobility is unprecedented and foreseen to rise at a rapid rate due to ongoing conflicts and

climate change.120

From the 244 million international migrants, nearly half are female, women and girls mostly in

reproductive age,118 and their experience of migration and vulnerabilities can substantially differ

from those of male migrants.

Depending on the circumstances experienced in the country of origin (pre-migration phase), the

migratory route (migration phase), and the country of destination (post-migration phase), the

extent of the physical, psychosocial and environmental stressors that migrants are exposed to can

vary considerably (Table 2.2).121 Experiences of forced immigration due to war and political turmoil

or natural disasters, especially those of pre-migration and migration phases, often profoundly

impact the psychosocial wellbeing of migrants121; however, this pathway comprises a small

fraction of international migration worldwide, and nearly two-thirds of the population of


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international migrants are labour immigrants who also experience many stressors especially in

post-migration phase.118,122,123

Skill Stream, Family Stream, and Special Eligibility Stream constitute the majority of Australia's

permanent immigration program. According to Australian Bureau of Statistics (ABS), the Skill

Stream, Family Stream, and Special Eligibility Stream accounted for 50%, 26% and 0.3%,

respectively, of the total permanent residencies in Australia in mid-2011, with only 7% of the total

migrant intake through the Humanitarian Program in 2010–11.124 Thus, most of the migrant

population in Australia are from non-refugee backgrounds.

TABLE 2.2 PSYCHOSOCIAL IMPACTS IN EACH STAGE OF MIGRATION

Pre-migration Migration Post-migration

Discrimination Forced and stressful travel Loss of previous social capital

Feeling of insecurity Fear and anxiety Cultural diversity

Fear and anxiety Powerlessness Language barrier

Uncertain future Unknown future Feeling of not belonging

Social and professional regression

Discrimination

Financial instability

Environment or climate

ASSIMILATION AND ACCULTURATION 2.2.1

Immigration often entails a substantial change in community ties, the loss of support networks

and familiar bonds, and social and psychological challenges for settlement in an unfamiliar

environment to fit in a new culture and system of meaning, and to develop ties with the new

country. To describe the process of change to people’s attitudes, beliefs and practices as a result

of integration into another population over time, sociologists mostly use the word assimilation.

Anthropologists, on the other hand, prefer the term acculturation, given that their field of work is

more concerned with culture. This can be seen in the literature as early as the 1920s and 1930s—


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Park and Burgess, two American sociologists, initially described and then Redfield, Linton and

Herskovits, distinguished American anthropologists, addressed such phenomena.125,126

Assimilation is the process of sharing and acquiring attitude and history and experience with the

other interacting population, which results in integration in a common cultural life among

people.125 Acculturation encompasses those phenomena that result when populations from two

different cultures come into continuous first-hand interaction with each other, which

subsequently modifies the patterns of original culture of either or both groups involved.126

Milton Gordon presents the most comprehensive and methodical account of the assimilation

process in his book ‘Assimilation in American Life,’ published in 1964. He described seven

sequential steps in the process of assimilation: cultural, structural, marital, identity, prejudice,

discrimination, and civic. The first step, acculturation, involves the gradual adoption of the cultural

habits of the ‘core sub-society’ by the migrants.125,127 In this thesis, we use the term ‘acculturation’

to describe the consequential effects migrants experience, after settling in the new environment,

in relation to the possession of characteristics that are representative of their level of assimilation

and integration; these characteristics include language, age on arrival, length of residence and

intermarriage that will be used to determine the level of acculturation in migrants.

2.2.1.1 Language

Adoption of the English language is one of the most common indicators of acculturation used in

the literature.127 Proficiency in language and mother-tongue shift in the first and later generations

are phenomena commonly observed in the acculturation process.128,129 Adoption of the English

language by the first-generation followed by a strong preference for English in later generations

usually happens after embracing the cultural norms of the main society.127


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2.2.1.2 Age on arrival

It is believed that the inevitable exposure of children to the host society during learning ages by

attending school and attaining education leads to a greater degree of absorption of its cultural

patterns than adults.127,130 However, it is worth noting that English language proficiency and

socioeconomic status can in themselves be influential factors in this process.130 In other words,

those from higher socioeconomic backgrounds and those who speak English better have more

opportunities to intermingle with the new society and therefore a greater degree of acculturation

follows as a result.

2.2.1.3 Length of residence

Length of stay in the host country is commonly used as another measure of acculturation in the

literature.127,128 Longer lengths of stay provide potentially more opportunities for migrants to

interact with the destination society by obtaining accommodation, learning a language, attaining

education and employment, and establishing social relationships.129

2.2.1.4 Intermarriage

The amalgamation of the population also happens through intermarriage, resulting in marital

assimilation: the third step of assimilation described by Gordon, which occurs after acculturation

has taken place.131 Intermarriage is a strong predictor of integration in a multicultural society.132

Thus, there are two ways to measure assimilation in a population due to marriage, either through

exploring the ethnicity of ancestors of the individual in following generations, or by investigating

the ethnicity of their spouse among the first generation of migrants.131 In this thesis, the latter is

considered a proxy to acculturation.


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MIGRANT POPULATIONS IN AUSTRALIA AND WA 2.2.2

Australia has historically encouraged immigration and permanent settlement on a significant scale

for the country’s founding and development. Migrants, mostly born in the UK and New Zealand,

have constituted a substantial proportion of the Australian population; however, this picture is

changing. Statistics show that the proportion of residents born in the UK decreased from 5.6% in

2004 to 3.9% in 2019, while those born in China and India have more than doubled (1.0–2.7%) and

tripled (0.7–2.6%) over the last decade, respectively.21,133 The cultural and linguistic diversity of

Australian residents is consequently increasing; the most recent ABS publication indicated that the

proportion of the population born in China, India and Philippines (6.5%) exceeded the population

born in England and New Zealand (6.1%) in 2019 (Table 2.3).133 It is also worth noting that in 2011

there were 128,430 individuals living in Australia who identified as Māori, the Indigenous

population of New Zealand, with 17.1% of the New Zealand-born population residing in Australia,

and exceeding the proportion of this ethnic group in New Zealand.134

TABLE 2.3 AUSTRALIA'S POPULATION BY COUNTRY OF BIRTH - 2019A

Country of birthb '000 %c

England 986 3.9

China 677 2.7

India 660 2.6

New Zealand 570 2.2

Philippines 294 1.2

Vietnam 263 1.0

South Africa 194 0.8

Italy 183 0.7

Malaysia 176 0.7

Sri Lanka 140 0.6

All overseas-born 7 530 29.7

Australia-born 17 836 70.3 aEstimates are preliminary.

bAs at 30 June 2019.

cProportion of the total population of Australia.

Source: Australian Bureau of Statistics. Cat. No: 3412.0-Migration, Australia, 2018–19.


PAGE | 24

Western Australia accommodates a unique racial and ethnic composition of the Australian

population, with the highest proportion of overseas-born residents (33.4%) and the only state with

more migrant families (319 900) than non-migrant families (314 700) in 2013.21 There were also

twice as many Indian-born individuals than Chinese-born individuals, and WA had the highest

proportion of people born in South Africa (1.7%) and Malaysia (1.2%).135 This state also had

around 11% UK-born residents, the highest proportion among all states, and more than twice the

national figure for this country of origin (5.4%).21 According to the 2016 Census, 61.7% of the WA

population had at least one overseas-born parent, a 2.7% increase from 2011.136

Furthermore, between 2006 and 2011, the Māori population in WA increased by 87%, and it was

estimated that WA would become the second-most populous state for Māori after Queensland134,

probably due to the influx of Māori workers seeking employment in the WA mines.137

ETHNIC ORIGIN IN WA 2.2.3

Ethnicity is defined broadly based on a shared understanding of history, geographic origins of an

ethnic community and on specific cultural characteristics such as language and/or religion.

Therefore, the UN recommendations encourage acquiring information on ethnicity through self-

declaration with the option of indicating multiple ethnic affiliations. 138Self-reported ethnic origin

is recorded by WA midwives for all birth in WA and, since 1998, has been classified as Caucasian,

Aboriginal and/or Torres Strait Islander, Asian, Indian, African, Polynesian, Maori and Other.139

Surprisingly, this information has not been explored or used in published research yet despite the

availability of some evidence from New Zealand140 and other countries.102,141

However, it is important to note that cultural and language diversity is widely influenced by

immigration142 and there is no consensus on classification of ethnicity across nations and

worldwide. This is particularly imperative to be considered when making comparisons. For

example, the ethnic affiliation known as Asian in the UK includes individuals from Indian, Pakistani,


PAGE | 25

Bangladeshi, Chinese backgrounds143, but in Australia this ethnic origin includes who self-report

their ethnic origin as Asian including people of Asia, Japan and South East Asian origin i.e. Chinese,

Japanese, Vietnamese, Cambodian etc. However, there is another distinct ethnic origin as Indian

for those who self-report their ethnicity as Indian or descendants of people originating in the area

of the Indian subcontinent, Pakistan etc.

HEALTHCARE USE AND HEALTH OUTCOMES OF MIGRANT 2.3

Considering migrant status, ethnic origin and culture in a health context is imperative due to their

association with economic, social and environmental circumstances and health literacy among the

population—collectively known as the social determinants of health, and ‘causes of the causes’

(Figure 2.5).144,145This is evident and described in ‘healthy immigrant effect’ that suggests

migrants, on their arrival, have better health outcomes than the general population of the host

countries due to the selective process of immigration; however, they lose that advantage

gradually and their outcomes converge slowly to the host population levels over time .146-148

The circumstances in which one grows, lives and works are key drivers of one’s health according to

the WHO.149 Many aspects of lifestyle and health behaviours, such as diet, alcohol and drug use,

smoking, physical activity, and traditional medicine use, are determined by one’s upbringing,

culture, social class, education and religion, which can impact the likelihood of utilisation of

healthcare services.127,150,151 Migrant status and ethnic background often imply major

socioeconomic disadvantages, such as social exclusion, unsafe working conditions or

unemployment;144 hence, migrants’ lives that were shaped by social determinants in their country

of origin are confronted with new social, economic and political conditions beyond their control in

destination countries. This distinctive feature suggests that migration is a social determinant of

health.152


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FIGURE 2.5 MAIN DETERMINANTS OF HEALTH

Source: Dahlgren G, Whitehead M. Policies and strategies to promote social equity in health. Background document to WHO—strategy paper for Europe. Stockholm,

Sweden: Institute for Future Studies. 1991.

The healthcare system, with its regulated provision of services, also acts as a social determinant of

health that not just influences other determinants but is also influenced by them.153 For example,

in Australia, despite the availability of a universal health insurance scheme covering all Australian

citizens and permanent residents (Medicare), depending on the type of visa (Humanitarian visas

are excluded), a two- to four-year waiting period is applied before immigrants become eligible to

benefit from some health and social security services, including the Newstart/Jobseeker Allowance

(income support while looking for employment) and Healthcare Concession Card to get

medications and services at a cheaper rate.154 This is despite reports that recent migrants on a

permanent visa had an unemployment rate of 8.8% compared with 5.4% for the Australian-born

population, and 3.3% for migrants with Australian citizenship (those migrants who have lived in

Australia for at least four years) according to the 2016 survey.155


PAGE | 27

Given the above, investigating migration, the acculturation process and their interrelationship

with health services and health outcomes of individuals is pivotal for addressing any health issue

and identifying the policy/intervention needs of the multicultural population of Australia.

PREGNANCY AND CHILDBIRTH OUTCOMES OF MIGRANT POPULATIONS 2.3.1

Risk factors and causes of adverse pregnancy outcomes differ in low- and middle-income versus

high-income countries, due partially to an inadequate level of access to pregnancy and obstetric

care in low- and middle-income nations.7,92Notwithstanding, ethnic disparities in the risks of these

outcomes are also reported in many high-income countries, despite the availability and access to

high-standard prenatal care.84 Genetic predisposing factors, such as more prevalent congenital

anomalies due to consanguinity in some ethnicities, may play a role.84,156 However, the evidence

suggests that religious beliefs, cultural expectations and misconceptions about interventions, such

as caesarean delivery, may impact pregnant women's decision making, and result in delayed care-

seeking, non-compliance and underutilisation of health services in some ethnicities, even when

the procedure is clinically indicated.19,157,158

Small et al. investigated the pregnancy outcomes of Somali women after immigration in six

resettlement countries—Australia (NSW and Victoria), Belgium, Canada, Finland, Norway and

Sweden—between 1997 and 2004, and showed significantly higher odds of stillbirth for this ethnic

group (OR 1.86, 95% CI 1.38–2.51) than the native population of the resettlement countries in the

pooled analysis.13 Drysdale et al. assessed the rate of late-pregnancy antepartum stillbirth in

Australian women born in different regions of Asia, through a cross-sectional study of all singleton

births at 37–42 weeks gestation over ten years, and concluded that those born in South Asia were

almost 2.5-times more likely to have a late-pregnancy stillbirth than women born in Australia.159

Although these studies show disparities in the risk of stillbirth by ethnicity, they were limited to

specific population groups, Asian or Somali women, did not adjust the analysis for confounders


PAGE | 28

that can influence the risk of stillbirth, or used country of origin to determine the ethnic

background rather than self-reported ethnicity. More recently, Davies-Tuck, Davey and Wallace

investigated the maternal region of birth in association with the risk of stillbirth in a larger study;

they included all non-Indigenous singleton births at 24 or more weeks gestational age from 2000–

2011 in Victoria in their analysis and adjusted the analysis for many confounders.74 They

concluded that maternal region of birth is an independent risk factor for stillbirth.74 Despite these

investigations, the risk of stillbirth concerning time of death (antepartum or intrapartum) and

specific ethnic groups, such as African and Māori populations, remains unexplored due to the

limitations of the data registries used. Moreover, migration is a selective process and the

population of overseas-born residents across the states and territories of Australia can differ in

size, the composition of countries of origin, and their English language fluency and length of

residence. As such, there are considerable uncertainties and gaps in knowledge that need to be

investigated. As Flenady et al. stated in their “Stillbirth: recall to action in high-income countries”

in the Lancet series, the findings are not consistent in the literature and, where increased ethnic

vulnerability is evident, the challenge remains as to why we observe an excess risk of stillbirth in

women from some racial and ethnic backgrounds and how we address such risk within routine and

comprehensive antenatal care.62

In New Zealand, Craig and colleagues analysed Birth Registration data from 1 194 895 births

(including 1 189 120 singleton live births and 5775 stillbirths) from 1980–2001 and showed that

from 1980–1994, the rates of late fetal death declined by 49% for all three ethnic groups studied,

Māori, Pacific and European/other ethnicities.160 On the other hand, they observed a 30% increase

in the rate of preterm birth for European/other populations while the rates of preterm birth

slightly declined for the other two ethnic groups.160 It was not clear whether the increased PTB

rate among this ethnic group, which constitutes most residents in New Zealand, was in the


PAGE | 29

spontaneous PTB category or medically-induced births. An increase in medically induced PTB could

have been explained by a change in practice and to prevent stillbirth. The authors discussed the

potential effect of maternal age, obesity, or the growing population of Asian/Indian/other women

that were included in the European/other category but were not convinced that the observed

increase in PTB rate could be attributed to these factors.160

Compared with European-born women in New Zealand and despite adjusting for BMI, maternal

age, parity, smoking, social deprivation, diabetes, chronic hypertension and relevant pre-existing

medical conditions, Anderson et al. observed an independent reduced risk of preeclampsia in

Chinese women (adjusted OR 0.56, 95% CI 0.41–0.76) and 51% increased risk in Māori women

(adjusted OR 1.51, 95% CI 1.16–1.96).161

Dahlen et al. investigated the rates of obstetric intervention during birth and selected maternal

and perinatal outcomes for low-risk women born in Australia compared to those born overseas in

NSW. In their study population, 28% were non-Australian-born women mostly from New Zealand

(2.5%), England (2.2%), China (2.1%), Vietnam (2.0%), Lebanon (1.8%), Philippines (1.4%) and India

(1.2%).162 They showed that women born in the Philippines had the highest rate of preterm birth

(7.1%). Gestational diabetes was the highest among women born in China (13.8%) and Vietnam

(11.4%), being almost four times the rate of their Australian-born counterparts (3.1%). Lebanese

women had the highest rate of stillbirth (7.2/1000) followed by Indians, who also had the highest

rate of assisted vaginal delivery (16.3%). Women born in China had the highest rate of

instrumental birth (26.4%).162

In WA, Newnham et al. showed that the rate of preterm birth was significantly lower in women

born in China than in other Western Australians (4.4 vs 8.2%, P<0.0001) but higher than native

Chinese living in mainland China (Jiangsu province). In contrast, Australian-Asians and


PAGE | 30

India/Pakistan-born Indians had a higher prevalence of preterm birth than Caucasian

Australians.163 They concluded that the differences between traditional Chinese and contemporary

Western lifestyles, including sexual practices, may explain this finding.163

Higher rates of congenital abnormalities have been reported in the birth outcomes of migrant

populations in Europe.164-166 Findings from a previous study in Australia are not consistent with

those in Europe, and non-Caucasian non-Indigenous women in WA had a lower risk of major

malformations.167 This is probably due to the differential distribution of migrants among the

receiving countries and the variation in countries of birth of the immigrant women studied.

European studies particularly reported these adverse outcomes in Pakistani and Turkish

immigrants and blamed consanguinity, inadequate or late first prenatal care, and reluctance to

terminate after diagnosis of malformation due to religious or social challenges.165,166 In contrast,

the lower rates of congenital abnormalities in WA have been studied in immigrants from East Asia,

mostly the Vietnamese population.167

Thomas, Beckmann, and Gibbons investigated the risk of adverse pregnancy and neonatal

outcomes (stillbirth, preterm birth, CS, postpartum haemorrhage ≥1000 ml, eclampsia,

intrauterine growth restriction, LBW, admission to nursery, congenital abnormality and 3rd/4th

degree perineal trauma) related to immigrant characteristics, including country of birth, race,

primary language spoken, need for an interpreter and refugee status in Mater Mothers’ Hospital,

Queensland. They found no significant relationship between adverse outcomes and refugee status

(P=0.863) but concluded that using an interpreter (P=0.015) and/or being born outside

Australia/New Zealand (P<0.001) reduced the likelihood of an adverse outcome when at least one

adverse outcome had occurred.168 Their study population, however, comprised 4751 women

(including 461 women using an interpreter and 1046 non-Caucasian and 117 women identifying as


PAGE | 31

refugees) and did not have enough power to study the associations for less frequent outcomes,

such as stillbirth with a rate of 7 per 1000 total births.

PATTERN OF HEALTH SERVICE UTILISATION 2.3.2

The pregnancy-related health services discussed in the literature are mostly concerned with

prenatal/antenatal care visits and assisted birth, including induction of labour, instrumental birth,

or CS.

Reime et al. sought to understand the role of prenatal care on the risk of stillbirth among women

with migration background in Germany by studying singleton births in 1990, 1995 and 1999. They

reported inconsistent association patterns between stillbirth, region of origin and utilisation of

prenatal care.169 In their population, underutilisation of prenatal care among Mediterranean

women (OR 3.00, 95% CI 1.71–5.26) was associated with a higher risk for stillbirth; however,

women with adequate utilisation of prenatal care from Central and Eastern Europe (OR 1.74, 95%

CI 1.33–2.29) and the Middle East (OR 1.98, 95% CI 1.64–2.39) were also at higher risk for

stillbirths despite adjusting for age, parity, smoking, interpregnancy interval, employment status

and year of observation.169 The study used perinatal data for 182 444 births that were collected by

obstetricians and midwives during pregnancy and after birth and controlled the analyses for a

number of confounders, and thus provided good insight into the relationship between healthcare

utilisation and stillbirth among migrants in Germany. However, the study was limited in their

ability to adjust for socioeconomic status and to examine the influence of language barriers,

length of stay in Germany, and other indicators of acculturation, which may explain the

differences between these three groups of migrants and why central/Eastern European and

Middle Eastern women, despite adequate utilisation of prenatal care services, were still at higher

risk of stillbirth.169


PAGE | 32

It is well known that perceptions of health and disease differ among different populations, and

may determine or influence individuals’ help-seeking behaviour.162 Garcia-Subirats et al. alluded

that Chinese residing in Western countries access healthcare services only when their disease is

serious, since they usually try their own traditional medicine for treatment of their illnesses first,

and Moroccans and Pakistanis access healthcare services when their disease is in more advanced

stages because they prioritise their work and financial matters superior to their health, while

South Americans seem to use the channels established for accessing healthcare services more

effectively and timely.170

The utilisation of reproductive health services, as health behaviour, follows a similar pattern.

Sudanese women, for instance, tend to avoid instrumental and surgical assistance with labour as

they believe that giving birth is a natural process and should not be intervened.26 In some cultures,

the power of decision making for when to attend the clinic or birthing suites lies with someone

other than the pregnant women in the household, the husband or a co-resident mother-in-law,

according to Shafiei, Small and McLachlan.171 Further, having a male doctor, lack of an interpreter

when one is needed, religious dietary requirements (halal food for Muslim women) and

perceptions around hospital food guidelines not complying with those requirements may be

barriers to healthcare utilisation for some ethnic groups.171 Burmese refugees described the lack

of interpreting services in Australian hospitals and the absence of personal/communal care as they

expected, making their experience of childbirth confusing and distressing.172 The socioeconomic

status of the migrants, unfamiliarity with the healthcare system, and location of the facilities can

be other influential factors.162 Refugees are usually settled in outer city suburbs, while the

healthcare services are mostly located in central regions and inner suburbs. Thus, the need for

complex travel arrangements and transportation expenses can contribute to lower utilisation of

services.173,174


PAGE | 33

Language barriers, lack of culturally appropriate services, such as the preference of female

practitioner or interpreter in some migrant populations,174-177 and doctors’ preconceived attitudes

towards some cultural practices or traditions can negatively affect women’s experiences of their

interaction with the healthcare system.178-180 On the other hand, the staff’s compassionate

attitude,171 continuity of care and building trust between women and their interpreter, midwives,

and staff are positive factors reported by migrant women.173,176,177 The unfamiliarity of healthcare

practitioners with the conditions and/or endemic diseases that immigrants may have and the

consequent difficulty in diagnosis of the ailment, and the lack or cost of appropriate diagnostic

procedures or treatments resulting in lack of confidence in Australian-trained practitioners have

been expressed.174,179-181

More than 20% of the population of women who gave birth in Queensland (QLD) were born in

countries other than Australia. Hennegan, Redshaw and Miller investigated the labour and

childbirth experiences of overseas-born women who speak languages other than English at home

with those of English-speaking native-born women in this state.182 They reported that overseas-

born women giving birth in QLD were less likely to report care during labour as provided by

midwives, obstetricians or general practitioners and had 61% (95% CI 1.29–2.01) higher odds of

being cared for by nurses than their Australian-born counterparts after adjustment for

socioeconomic status, maternal age, remoteness and type of facility.182 Whether

misunderstanding of job titles, caregiver shortage, differential treatment by staff and women's

own choice in preferring a female nurse to a male obstetrician result in such disparities remain

unknown. However, the report of such disparities was not limited to QLD. Small et al. also showed

that only 27% of Vietnamese, 39% of Filipinos and 48% of Turkish rated their intrapartum care as

‘very good’ in Victoria in a study conducted in 2002.183 In another study undertaken by Shafei,

Small and McLachlan in 2012, 70% of Afghan women rated their intrapartum care as very good.171


PAGE | 34

Lack of professional caregivers and birthing facilities in Afghanistan and the organisation of care

and hospital environment in Australia with more resources, sympathy and provision of pain

management and medications were the reasons for such ratings among Afghan women.171

In the same study by Hennegan, Redshaw and Miller, overseas-born women, despite higher odds

of having one carer through labour and birth, were 32% more likely to report that they were not

looked after very well, were rushed or hurried by staff, and were twice as likely to report the need

for greater privacy in labour and birth environments than their native-born counterparts.182 While

this may imply discriminatory behaviour, it could also be an indication of the late attendance of

women to the birthing facility and genuine effort by staff to provide timely and medically indicated

care as soon as possible. Lack of trust in healthcare providers and the western health system, non-

compliance with the recommendations, and late booking or late arrival at the facility in an attempt

to avoid intervention have been expressed by healthcare providers who cared for women from

migrant backgrounds, especially for Somali women, both anecdotally in Australia and the

literature, including those from other jurisdictions internationally.179,180

Von Katterfeld et al. revealed that overseas-born women from culturally and linguistically diverse

(CaLD) backgrounds in WA were, on average, less likely to be induced and more likely to use fetal

monitoring during labour; women from Oceania, North Africa and the Middle East, in particular,

were less likely to have assisted delivery or episiotomy than their Australian-born counterparts.24

In contrast, Hennegan et al. showed that in QLD, where the East Asian population was the

predominant study population, showed that the odds of experiencing an episiotomy or perineal

trauma were significantly higher in overseas-born women (i.e. 2.43 and 1.69, respectively) and

they were less likely to be induced (adj. OR 0.72, 95% CI 0.55–0.96) despite 80% higher odds of

constant monitoring with belt or clip (95% CI 1.32–2.46) during labour.182 These may be

indications for being at higher risk of fetal distress due to attendance or admission to the birthing


PAGE | 35

facility at later stages of labour. In contrast, Dahlen et al., using NSW’s Midwives Data Collection,

showed a different picture with Indian women experiencing high rates of CS (31%), instrumental

birth (16%) and episiotomy (32%).162 In WA, 50% of Afghan participants delivered their babies by

CS.26 Indeed, the prevalence of CS is higher in certain migrant groups; this may, to some extent,

reflect a cooperative behaviour among some ethnic groups, or lack thereof, with healthcare

provider’s recommendations. For example, Chinese, Vietnamese and Burmese were happy to let

their doctors decide for them whether they need to undergo an intervention, but Sudanese

participants' consider childbirth a normal process that should occur naturally, without any medical

intervention; as such, they asserted their reluctance to undergo CS even if clinically indicated.26

The generalisability of these findings is, however, questionable given the very small size of the

sample—39 women from Afghanistan, Burma, China, Sudan and Vietnam—studied in WA.

Also, Carolan and Cassar investigated antenatal care perceptions of 18 pregnant African women

attending maternity services in Melbourne through in-depth qualitative interviews and reported

difficulties experienced by women attending antenatal care for the first time in Australia:

understanding the different approaches offered and confused/surprised by the attention while

childbearing is a ‘normal process’. Participants were reluctant to undergo induction of labour as

they viewed it as a disruption to a normal event that should be initiated by the ‘baby’ when he/she

is ‘ready to come’, and hence feared any interruption.184 Although the participants did not see the

value in regular antenatal visits early on, they later described the advice received on self-care,

diet, breastfeeding and contraception helpful and valuable. However, they struggled to comply if

the advice was contradictory to their cultural beliefs.184 Stapleton and colleagues also reported

similar challenges faced by refugee women who gave birth in a tertiary hospital setting and their

expectation to submit to a highly medicalised practice of childbirth that challenges their traditional

beliefs and customs.173 It is important to note that these findings are from a small qualitative study


PAGE | 36

and are prone to bias by reflecting the opinion of a small group of migrants; yet, the insight into

the diverse attitudes, challenges and needs of migrants from various backgrounds is of extreme

value to researchers, policymakers and practitioners and is consistent with other reports.179,180

Migrants born in New Zealand comprise a considerable population in Australia and those from

Māori background are an over-represented and fast-growing community within this

population.134,137 However, little research data on their reproductive health is available other than

those studies conducted in New Zealand. Harris et al. investigated the link between CS and

ethnicity in New Zealand from 1997–2001 and found that although high-risk pregnancy is more

prevalent among Māori women, emergency and elective CS rates were significantly higher among

non-Māori women (total CS, 21% vs 13%, P<0.0001).185 Anderson and colleagues compared a

broader range of ethnicities in New Zealand, distinguishing the non-Māori population as Pacific,

Chinese and Indians, with Europeans from 2006 to 2009; the authors showed that Pacific and

Chinese women had lower odds of elective CS (adjusted OR 0.42, 95% CI 0.24–0.73 and 0.68, 0.49–

0.94, respectively) and Indian women had higher odds of emergency CS (adjusted OR 1.54, 95% CI

1.26–1.88). Other ethnicities' rates of CS did not differ from those of Europeans.186

In Norway, where free maternal care is universally available, and all births occur within the public

health system, substantial variation in the utilisation of CS among different ethnicities has been

reported.187,188 Vangen et al. observed that the Vietnamese population had the lowest prevalence

of CS delivery (around 10%), while more than 20% of women born in Sri Lanka/India, Philippines,

Somalia/Eritrea/Ethiopia, and Chile/Brazil delivered through CS from 1986–1995.187 However,

Turks/Moroccans and Pakistanis had similar results to Norwegians (12%). Filipinos and

Chilean/Brazilians had the highest rates of elective CS, mostly due to feto-pelvic disproportion,

while Sri Lanka/India and Somalia/Eritrea/Ethiopia born women had higher rates of emergency CS

due to fetal distress and prolonged labour.187 Similarly, in Australia, von Katterfeld et al. studied


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the obstetric profile of migrants and showed that in WA from 1998–2006, the odds of elective

caesarean delivery was significantly higher in women from north-western Europe and Americas

and significantly lower in those from North Africa and the Middle East, Oceania and North-East

Asia compared to non-Indigenous Australian-born women; however, the proportion of emergency

CS was more common in most of the foreign-born populations including Asian, Sub-Saharan Africa

and American-born women than non-Indigenous Australians.24 High rates of elective CS may rule

out the possibility of a tendency to avoid CS or instrumental birth in a population while the high

rate of emergency CS in the other groups may be a consequence of reluctance to undergo assisted

procedures until it is too late.

With that said, there are some inconsistencies in the findings reported from Australia that are

worth discussing. Ma and Bauman studied the obstetric profile and pregnancy outcomes of

immigrant women in NSW from 1990–1992 and showed that African, Asian and American-born

women were 20% more likely, and Middle Eastern born women were 25% less likely to have CS

delivery than their Australian-born counterparts.189

In another study conducted later in NSW, Dahlen et al. stratified the migrant population by

country of birth and found that Indian women had the highest rates of CS delivery (30.7%) from

2000–2008.162 These discrepancies may be due to differences in the classification of ethnic origins

or the outcomes of interest in the literature, or changes in the composition of the population or

migrant intakes from specific origins. For instance, Ma and Bauman did not differentiate between

Indian and East Asian origin and categorised any maternal country of birth from these regions as

the ‘Asian’ population. They also did not distinguish the delivery method by type of CS, elective or

emergency, in their study.


PAGE | 38

ACCULTURATION AND PREGNANCY OUTCOMES 2.3.3

Proficiency in language and communication in English is often considered a proxy to acculturation

in studies concerning migrant populations.190-192 The relationship between the level of

acculturation and pregnancy outcomes has not been studied in Australia, but some aspects of that

have been explored, such as proficiency in English or use of interpreter indicating similar or even

favourable pregnancy outcomes among the less acculturated migrant women from low- and

middle-income countries compared with the Australian-born population. In QLD, non-Caucasian

women from non-English-speaking countries who used interpreter services experienced no

greater adverse pregnancy outcomes than their Caucasian English‐speaking Australian-born

women.168 Newnham et al. also showed that the rate of PTB in WA in Chinese-born women who

did not require an interpreter, and were considered more westernised, was almost double those

of Chinese-born women who required an interpreter and similar to Chinese-born women living in

China.163 The need for an interpreter may indicate a lower level of acculturation; however, it is not

clear whether the reported lower rates of adverse pregnancy outcomes were due to the

protective effects gained from using an interpreter or the inherent lower rates of those outcomes

among migrants with lower levels of acculturation. Further, it is not possible to conclude from this

study that less acculturated women were at lower risk of PTB because the rates were unadjusted,

and the less acculturated women could have had other characteristics, such as younger age, that

act as confounding factors explaining their lower rates.

A longer length of residence may also indicate a higher level of acculturation among the

immigrants.129,130 In Canada, Urquia and colleagues studied 83 233 singleton newborns of

immigrant mothers and 314 237 newborns of non-immigrant mothers from 2002–2007. The

investigation was a linkage study of a database of immigrants acquiring permanent residency in

Ontario, Canada, from 1985–2000 with mother-infant hospital records; and showed that recent


PAGE | 39

immigrants were at lower risk of PTB, but the risk was higher in those with >10 years length of

residence than their Canadian-born counterparts, showing that a 5-year increase in Canadian

residence was associated with 14% (95% CI 1.10–1.19) higher odds of PTB in migrants.193 This was

a retrospective population-based, data-linkage, cross-sectional study from a validated database,

adjusted for maternal age and parity at delivery; the sensitivity analyses examined the potential

effect of other variables, including infant sex, maternal morbidity during pregnancy, induction of

labour, CS, neighbourhood deprivation, year of arrival and year of birth of baby, and did not find

any substantial differences with the reported result. Thus, this study provides robust evidence that

longer length of residence is associated with a higher risk of PTB.

In Europe, Ekeus and colleagues conducted a population-based register study with data from the

Swedish Medical Birth Register and socioeconomic variables from national income and population

registers, including singleton births from 1992 to 2005, 219 832 births to foreign-born women and

1 094 146 births to Swedish-born women, and adjusted for confounding factors including parity,

disposable income in quintiles, urban/rural place of residence and maternal age, height and

obesity. The risk of stillbirth was higher among migrants with a length of residence <5 years than

those with longer residence periods (adjusted OR 1.21, 95% CI 1.05–1.40) in Sweden.67 In contrast,

Vik et al. conducted a nationwide population-based study including births to primiparous and

multiparous migrant women (N=198 520) and non-migrant women (N=1 156 444) in Norway from

1990–2013, and adjusted the analysis for year of birth, parity, maternal age, marital status,

mother’s income, level of education and consanguinity; the authors reported that stillbirth was

not associated with length of residence.194 The discrepancy in reports from these two Nordic

studies, with relatively comparable populations and health systems, is probably due to differences

in the definition of outcome in these countries or the confounders adjusted for in these two

studies. The Swedish study defined stillbirth as the death of a baby of at least 28 completed weeks


PAGE | 40

of gestation while the other study used a lower limit of 22 weeks gestation. The Norwegian study

did not adjust for overweight/obesity and smoking, although smoking is a well-established risk

factor for stillbirth and previous research suggested that the prevalence of smoking in pregnancy

among the migrant population increases with longer length of residence.195,196

Intermarriage could be an important indication of integration and acculturation for migrants.132

Lower risk of stillbirth for migrant women with a non-migrant partner has been reported in

Norway and the US as well as a lower risk of SGA in Chinese-American women with white or Black

partners in the US.194,197,198 In Canada, Bartsch et al. showed that, compared with couples with

both parents being Canadian-born, the adjusted odds of stillbirth was higher when parents were

both overseas-born, from the same country or different countries, and the risk was highest when

parents were from the same country with a high domestic stillbirth rate (aOR 1.60, 95% CI 1.30–

1.97), concluding that paternal country of birth matters.197

Lower age at the time of immigration could be an indicator of a higher likelihood of acculturation.

Martinson, Tienda and Teitler, using nationally representative samples and birth cohort surveys

from Australia, the UK, and the US from early 2000, sought to investigate whether, how and why

rates of low birthweight differ between natives and immigrants.199 They did not find any

association between LBW and mothers’ age at immigration in Australia. In their study, the odds of

LBW were the same for mothers who arrived as children and as adults (OR 0.99, P>0.05 and OR

1.08, P>0.05, respectively).199 In the UK group, age at arrival was not associated with the likelihood

of having an LBW baby while recent arrivals were less likely to have an LBW baby than the UK-born

population (OR 0.57, P<0.05); similarly, arrivals in the US also had lower odds of LBW than the US-

born population (OR 0.64, P<0.05).199 They adjusted their models for child sex, maternal age,

parity, marital status, maternal education, family income tercile, and smoking; however, the

sample size for the Australian survey, an analytic sample of only 3372 births, and the inability to


PAGE | 41

stratify this sample by race/ethnicity were important limitations of this investigation, affecting the

generalisability of the study and reducing its power to make reliable conclusions.

Furthermore, there are some reports of an increase in caesarean deliveries with length of

residence in the literature. In the US, Zlot, Jackson and Korenbrot defined acculturation based on

maternal country of birth and language spoken and studied its association with CS among 2102

low-risk Latinas in San Diego County, stratified by their parity, and showed that multiparous US-

born Latinas were more than twice as likely to have CS than multiparous Spanish-speaking women

born in Mexico, but the result was reversed for primiparous women.200 They concluded that in the

primiparous group (those who had never given birth to a live infant), acculturation appeared

protective while it was a risk factor in the multiparous group; the authors examined the effect

modification of prior CS delivery for the multiparous category and did not note any difference in

the results. The study population comprised low-income, low-risk Latinas and hence excluded

those with private/military payment plans, and adjusted for factors that could influence clinical

discretion in performing CS. It is important to note that they did not separate the method of

delivery by emergency or elective CS, did not consider the length of residence and other

acculturation factors in their analysis, and low-acculturated Latinas outnumbered high-

acculturated Latinas.

In Norway, Sørbye et al. studied the risk of CS delivery from 1990–2009 among immigrants

according to the length of residence.188 They used population-based birth registry data linked to

immigration data for first deliveries among 23 147 immigrants from 10 countries and 385 306 non-

immigrants from 1990–2009 and grouped countries of origin as having low or high CS levels. They

reported that planned CS was independently associated with longer length of residence in Norway

among the lower CS group. Immigrants with a history of 2–5 years of residence had a 70% higher

risk of elective CS than those who had less than one-year length of stay in the country, and more


PAGE | 42

than six years length of residence doubled the risk. Compared with non-immigrants, planned CS

was lower in those with <2 years residency and the risk converged by 2–5 years of residence in

Norway.188 This was a large study with the analysis adjusted for maternal age, year of delivery,

year of arrival, age at immigration, level of birth facility, multiple delivery, gestational age, pre-

gestational diabetes, preeclampsia, placenta previa, abruptio placentae (only for CS and

emergency CS), SGA (5 percentile) and large-for-gestational-age (95 percentile). The authors did

not have data on BMI or level of education to adjust their analyses for these factors; however, the

origins of the immigrants in the study population were closer to that of Australian society (Iraq,

Pakistan, Poland, Turkey, Yugoslavia, Vietnam, the Philippines, Somalia, Sri Lanka, Thailand) and

the study presented a reasonably robust analysis to conclude that acculturation is probably

associated with changes in the utilisation of elective CS as reported.

SUMMARY 2.4

Each year, millions of lives are lost worldwide due to mortality and morbidities attributed to

stillbirth, preterm birth and low birthweight. Most of these outcomes happen in low- and middle-

income countries due to the lack of access to prenatal and obstetric care and appropriate

interventions where indicated. However, some high-income countries, such as Australia, despite

having a universal health insurance scheme covering all Australian citizens and permanent

residents (Medicare), coupled with the provision of high-quality antenatal and obstetric care by

the public healthcare system, have reached a plateau of success; the prevalence of these adverse

outcomes has not decreased in the population in the last couple of decades.

Many maternal characteristics, such as age, pre-existing medical conditions, and smoking during

pregnancy, are now established risk factors for adverse pregnancy outcomes in the current body

of literature. There is some evidence that demographic characteristics, such as ethnicity, migrant

status, and even level of integration into the new society as social determinants of health, may be


PAGE | 43

associated with these adverse outcomes. However, findings are not consistent in all jurisdictions,

and the extent of the influence of these determinants depends not only on an individual’s

characteristics but a combination of those with the health service provider’s approach,

government policies and nationwide regulations within each jurisdiction.

In Australia, immigration is the main driver of population growth, and overseas-born residents

constitute almost one-third of the total population, with those born in China, India, and Philippine

exceeding those born in England and New Zealand. Despite the remarkable diversity and the influx

of immigrants from low- and middle-income countries, the effect of ethnicity, migrant status,

integration and acculturation of migrants on pregnancy outcomes of stillbirth, preterm birth and

low birthweight have not been investigated enough. This issue is even more relevant in WA where

migrant families outnumber non-migrants. Studies available are either old or limited to one or two

specific populations and, in general, lack the depth and breadth required for answering such

research questions. While qualitative studies can be insightful, they do not have the power and

completeness of data required to examine any association with adverse pregnancy outcomes and

to draw clinically and statistically meaningful conclusions. The most robust and reliable studies in

the literature, which have successfully addressed aspects of pregnancy outcomes in migrants in

relation to ethnicity and acculturation with sufficient statistical power, are population-based

studies conducted using administrative health registries and data linkage. Nordic countries have a

long tradition of using such nationwide and complete registries containing virtually all the

residents in those countries using unified identification.201 In Australia, the first resource of this

kind was formally established in WA in 1995 and has been a pioneer of innovation in data linkage

techniques and surrounding processes and protocols.202 Thus, the advanced data linkage system

available in WA, which links routinely collected administrative health data and a variety of non-


PAGE | 44

health datasets, provides the unique opportunity for a whole-population investigation with

enough power to investigate this thesis objective.

CHAPTER 3. GENERAL METHODS

PAGE | 45


This chapter explains the general methodology used to address the research questions covered by

this thesis, including the design of the project, data sources, study population and variables

considered. Furthermore, the role of the PhD candidate and the tasks undertaken to develop and

deliver the project are described.

DATA SOURCES 3.1

Linked routinely collected administrative health and registry data for the state of WA were used in

this thesis.

ROUTINELY COLLECTED ADMINISTRATIVE HEALTH DATA 3.1.1

Administrative health and medical records are rich resources of information that are routinely

collected in the health system for monitoring service management, provision, delivery and

outcomes, and have significant research potential.

DATA LINKAGE 3.1.2

Data linkage is defined as the process of bringing together records related to the same entity from

two or more different data sources.203,204 This process can be performed at a population level and

has numerous advantages for observational research. Figure 3.1 shows how data linkage can be

used to provide a picture of someone’s health history. Given the possibility of accessing health the

health records of the parents, the health picture can cover a period from before birth to death.

Prospective epidemiological studies are prone to selection bias. In other words, the population

selected for such studies may not represent the target population. This could happen during the

recruitment phase of the study, either as a result of the procedures considered for the selection of

the subjects in the design and/or implementation stage or arising from factors influencing the


PAGE | 46

participation of the individuals.205,206Population-based record linkage helps to reduce the

likelihood of such bias as the whole population is studied, and the selection phase is omitted.

It is believed that diseased participants tend to remember the possible exposures or risk factors

better than healthy counterparts, which is known as recall bias.206 The probability of such bias can

also be avoided as data are usually collected at the time of the event, before the outcome or

regardless of the outcome, and is not influenced by memory or choice of answer by study

participants.207

Cost-effectiveness is another advantage, as the data can address many hypotheses and/or public

health questions without conducting several cohort studies.204,207 Maintaining patient privacy is

also possible as researchers can use merged data without personal identifiers.204 Large sample

sizes, with increased power, facilitate the monitoring of marginal changes in the outcomes due to

changes in a policy or practice; generalisability of the results and rapid analysis are other

advantages of data linkage.208

Using routinely collected administrative health data can be challenging depending on the process

of generating data and factors influencing what items are and are not recorded.207,208 Such

influences could result from what the healthcare system; practitioner or even the patients

themselves choose and may lead to a poor data record or missing information on primary

exposure, which can confound or affect modification variables.209 Linkage may be helpful in such

instances by providing more resources for inter-dataset examination of information and

consequently alleviating concerns regarding the accuracy and reliability of the variables.

WA DATA LINKAGE SYSTEM 3.2

The WA Data Linkage System (WADLS) is one of the first few systematic health data linkages in the

world. It was established in 1995 to connect all available health and related information for the


PAGE | 47

WA population and includes a range of statutory and other data collections, some of which

contain records from as early as 1966.204 There has been growing interest within Australia over the

last few years to use the experience and leverage the expertise gained over decades of linkage in

WA and extend the infrastructure across the whole country. As such, WADLS has been used as a

successful model for the development of similar systems more recently, with support and funding

from the government and academic partners.210

FIGURE 3.1 PERSON-BASED LINKAGES

Source: WA Data Linkage website: https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-02-About-Data-Linkage.png

HISTORY 3.2.1

The need and potential for a systematic approach to record linkage in WA were first proposed by

Professor Michael Hobbs in 1970; however, the initial scheme that began in the 1970s remained

limited to specific purposes.204 The WA Maternal and Child Health Research Database was created

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-02-About-Data-Linkage.png

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-02-About-Data-Linkage.png


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by Professor Fiona Stanley and colleagues from the Telethon Kids Institute to link information

from midwives’ notifications, birth registrations, death certificates, hospital inpatient morbidity,

birth defects and cerebral palsy data for epidemiological perinatal and paediatric studies.207,208 The

development of full WA population-based data linkage occurred in 1995 as a result of an

infrastructure grant awarded by the WA Lotteries Commission to The University of Western

Australia for the establishment of a data linkage unit.211 The State Health Department became the

principal funder of the system in 1999 and further approvals by public, academic and clinical

forums reinforced WADLS by 2000.204

STRENGTH OF THE WA DATA LINKAGE SYSTEM 3.2.2

As described above, WADLS's strong roots have come from its long history of establishment,

researcher involvement in founding, collecting and organising the datasets from early on,

continuous research, evaluation of the variables, extensive ongoing publication outputs and

excellent collaboration of researchers with data providers, all of which have advanced the quality

of the data collections over the time. The linkage process by the Data Linkage Branch (DLB)

encompasses numerous automated and manual sub-processes and robust checking procedures,

including clerical review, chain sampling, data intimacy, duplicate checking and data quality

statements, to name a few, to reduce the likelihood of errors. As such, the DLB is confident that

the rate of errors in the WADLS is very low and prides itself on producing high-quality linkages.212

RECORD LINKAGE, EXTRACTION AND DATA RELEASE PROCESS 3.2.3

Data are provided by the DLB as part of the Department of Health WA. The DLB developed and

deployed a privacy protocol published as ‘a best-practice protocol’ by Kelman, Bass and Holman in

2002, which was reviewed without objection by the Australian Privacy Commissioner.213 This

protocol has since been adopted widely in linkage projects across Australia.213 Since unique health

care identification numbers are not available in Australia to identify individuals, master linkage


PAGE | 49

files are created using computerised probabilistic matching of identifiers, such as medical record

number (unique only to metropolitan public hospitals), surname, first given name, initial, date of

birth, sex and address as the principal matching fields.211 Soundex and NYSIIS name compression

algorithms and clerical checking of additional information are applied to the process.211 A

systematic review on the accuracy of probabilistic record linkage found the sensitivity of linkage

using such an approach ranged from 74–98% and specificity ranged from 99–100%.214

Duplication errors and other technical glitches in WA health records have been corrected by

application of systematic data linkage and resulted in greater accuracy of recording at an

administrative level; previous experience with the WA core datasets suggests a reasonably high

level of data accuracy.204,215The linkage processes are illustrated in Figures 3.2.

FIGURE 3.2 DATA LINKAGE PROCESS

Source: WA Data Linkage website available from https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-05-Data-Linkage-Process.png

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-05-Data-Linkage-Process.png

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DLB provides customised project specific linkage keys extracted by encrypting the “linkage key” for

each chain of records. Once the study population is defined, the Linkage Team extracts the

encrypted linkage keys for each requested dataset. The requested content data from each data

collection will be attached to the linkage keys. This process is arranged by Data Custodians. The

encrypted and password-protected data will then be released to the applicants via secure online

transfer by the DLB Client Services Team after quality assurance and checking that the data match

the request. Data extraction processes are illustrated in Figure 3.3.

FIGURE 3.3 DATA EXTRACTION PROCESS

Source: WA Data Linkage website available from https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-06-Linked-Data-Extraction-Process.png

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-06-Linked-Data-Extraction-Process.png

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-06-Linked-Data-Extraction-Process.png


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PRIVACY AND SECURITY 3.2.4

The Department of Health WA provides multiple layers of privacy protection to the original data.

The ‘best-practice protocol’ protects privacy by restricting access to personal identifying

information to a specialised Linkage Team that provide encrypted “linkage keys” to Data

Custodians who then can add them to content data for a specific project and release only de-

identified information to approved data applicants.213 The DLB has created a culture among its

staff that values the protection of individual privacy, which is maintained by employing Linkage

Officers under the Public Sector Management Act (1994) that is bound by privacy and

confidentiality provisions, undertaking Criminal Record Screening, signing confidentiality

acknowledgments, and providing staff with regular training about their obligations.216 The

approved applicants also sign data usage and confidentiality agreements whilst separate projects

receive data with a unique set of project identifier which make it impossible for researchers to join

together two linked data sets created for different projects.213

Physical and technological security layers are also applied by the Department of Health, including

locating servers in a secured room within a restricted area on a restricted access floor of the

building that is monitored and audited and requires separately authorised permission for DLB staff

to access. Furthermore, several layers of network security and monitoring, encrypting linkage

keys, performing data transfer through secure encrypted portals or by hand delivery, and

protecting the local intranet by an additional layer of monitored firewalling are some additional

strategies used to secure data.217

DATA COLLECTIONS 3.2.5

The WA Data Linkage System comprises many core datasets as well as satellite linkages to other

agencies' data collections in WA. Figure 3.4 shows the core datasets and some infrastructure

‘satellite’ linkages as of September 2014 when this project was being designed. Over the last few


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years, the linkage coverage has expanded and more data collections are now accessible through

the system.

FIGURE 3.4 DATA COLLECTIONS (AS OF SEPTEMBER 2014)

Source: WA Data Linkage website available from https://www.datalinkage-wa.org.au/

GEOCODING 3.2.6

Geocoding is the process of transforming a description of a location to a location on the earth's

surface. Figure 3.5 shows the process of Geocoding and its use at WADLS. Geocoding is performed

by converting an address into a latitude/longitude, using a set of reference data, and placing this

map point within spatial boundaries such as the Statistical Area Levels 1 and 2 (SA1 and SA2,

respectively) and Local Government Area (LGA). The Socio-Economic Indexes for Areas (SEIFA) and

Remoteness Area (RA) are derived by DLB from these boundaries by assigning the boundaries and

indices using mapping and concordance tables created by the Australian Bureau of Statistics

(ABS).218,219Remoteness Areas divide Australia into 5 classes of remoteness on the basis of a

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measure of relative access to services. Access to services are measured using the

Accessibility/Remoteness Index of Australia (ARIA)220 which is a geographical index defining

remoteness based on accessibility to goods, services and opportunities for social interaction across

Australia based on road distance from populated towns.220This index was produced by the Hugo

Centre for Migration and Population Research at the University of Adelaide, sponsored by the

Department of Health and Aged Care, and as a standard classification divides the whole country

into five categories of highly accessible, accessible, moderately accessible, remote and very

remote, that are used for making comparison and undertaking relevant statistical analysis.221,222

FIGURE 3.5 GEOCODING AT THE WA DATA LINKAGE BRANCH

Source: WA Data Linkage website available from https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-07-Geocoding.png

The four indices in SEIFA are (1) Index of Relative Socio-economic Disadvantage, derived from

Census variables related to disadvantages, such as low income, low educational attainment,

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-07-Geocoding.png

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-07-Geocoding.png


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unemployment, and dwellings without motor vehicles, (2) Index of Relative Socio-economic

Advantage and Disadvantage, a continuum of advantage to disadvantage derived from Census

variables related to both advantage and disadvantage, like households with low income and

people with a tertiary education, (3) Index of Economic Resources, derived from Census variables

like the income, housing expenditure and assets of households, and (4) Index of Education and

Occupation; including Census variables relating to the educational and occupational characteristics

of communities, like the proportion of people with a higher qualification or those employed in a

skilled occupation.219 In this project, the Index of Relative Socio-economic Disadvantage (IRSD) was

used.

GENEALOGICAL LINKAGE 3.2.7

The Family Connection Genealogical Register of the WADLS was initiated in 2002 to store family

links between genealogically-related individuals.204 It created additional systems of links that

represent genealogical relationships for the WA population, primarily using information from

births, deaths and marriage registrations. It allows identification of family relationships in multiple

generations and assesses the degree of relatedness.223 This was a substantial advancement for the

WADLS and brought about a unique opportunity to investigate the inheritance of human ailments,

the impact of familial factors in health and disease, and for risk assessment.204,223Figure 3.6

illustrates the processes involved in creating family connections and genealogical linkage at

WADLS.


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FIGURE 3.6 WA FAMILY CONNECTIONS SYSTEM

Source: WA Data Linkage website available from https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-08-Family-Connections.png

DATA APPLICATION AND PROJECT MANAGEMENT 3.3

Data application from WADLS involved many thorough assessment and review processes. A copy

of all the approvals and the data variable lists can be found in the Appendices.

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-08-Family-Connections.png

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FEASIBILITY ASSESSMENT 3.3.1

Before applying for Department of Health WA Human Research Ethics Committee approval, all

data applicants were required to submit a draft Application for Data with three modules:

Module 1: A completed Application for Data Form.

Module 2: Data Services, including completing and submitting application forms for Linkage,

Extraction, Geocoding, Family Connections, Matched Comparison Group Selection, and

Aboriginal and/or Torres Strait Islander Flag.

Module 3: Variable Lists, involving completing the variable list, with separate lists for mothers

and/or babies as appropriate, for each dataset required in the study. In the case of this

project, the variable list forms included those for Birth Registrations, Death Registrations,

Midwives Notification System, Hospital Morbidity Data Collections, and WA Register of

Developmental Anomalies – Birth Defects.

The feasibility assessment phase allowed data custodians and the DLB to familiarise themselves

with the project, provide advice on the request and identify any issues that need correction before

work begins. This process involved liaison with all parties involved, responding to queries received

or issues raised, including the need to take an alternative methodological approach to maintain

the standards required by the Department of Health. After receiving this preliminary approval,

ethics approval could be sought.

ETHICAL REVIEW AND APPROVAL FOR DATA RELEASE 3.3.2

The Human Research Ethics Committee of the WA Department of Health is responsible for

overseeing the use and disclosure of personal health information held in the data collections used

by the WADLS. The Committee is registered with the National Health and Medical Research

Council (NHMRC) and ensures compliance with the 2007 NHMRC National Statement on Ethical


PAGE | 57

Conduct in Human Research. If the project involves investigation of Aboriginal and Torres Strait

Islander people data, ethics approval must also be sought from the Western Australian Aboriginal

Health Ethics Committee (WAAHEC); however, as this project focused on migrants, data from this

population was not requested, and consequently, approval from WAAHEC was not sought.

All paperwork submitted for feasibility approval, including the research protocol and data

application forms, accompanied the completed Ethics Application form submitted to the Human

Research Ethics Committee of the WA Department of Health for review and approval.

DLB issued the final approval once the ethics approval was granted allowing the commencement

of linkage and extraction of data as specified in the data application.

DATASETS USED FOR THIS STUDY 3.3.3

The datasets used for this thesis included four core datasets—Midwives Notification System, Birth

Registrations, Hospital Morbidity Data Collection and Death Registrations. An additional WA

Health dataset, the WA Register of Developmental Anomalies, was linked to these datasets, and

Family Connections was used for the genealogical linkage to identify the children of the women in

the study. De-identified unit record level data was acquired from the DLB.

A brief overview of each dataset used for this doctoral research is provided below. A detailed list

of variables is presented in Table 3.1. A copy of the data application form for each dataset is

available in the Appendices.


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TABLE 3.1 DATASETS AND VARIABLES

Data collection Variables

Birth Registrations Child’s sex, date of birth, birthweight, born alive, plurality, gestation period, maternal age, mother and father’s place of birth, year mother and father arrived in Australia

Midwives Notification System (MNS)

Maternal age, height, marital status, ethnic origin, previous pregnancies and outcomes, previous caesarean, caesarean last delivery, previous multiple birth, smoking during pregnancy, complications of pregnancy, medical conditions, procedures/treatments, intended place of birth at onset of labour, labour details including onset of labour, augmentation, induction, analgesia (during labour), delivery details including, duration of labour 1st stage, duration of labour 2nd stage, anaesthesia (during delivery), complications of labour and delivery, perineal status, baby details including born before arrival, baby number, presentation, method of birth, accoucheur(s), gender, status of baby at birth, infant weight, length of baby (cm), head circumference, time to establish unassisted regular breathing, resuscitation, Apgar score at 1 minute, Apgar score at 5 minutes, estimated gestation. SEIFA

Hospital Morbidity Data Collection (HMDC)

Hospital category, length of stay, funding source, days of qualified newborn care, days of hospital-in-the-home care, mode of separation, all diagnosis and procedure codes

Mortality Register Age of person, country of birth, born overseas flag, time resident in Australia (years and/or months), cause of death, date of death

WA Register of Developmental Anomalies (Birth Defects)

Birth outcome (live birth, stillbirth, termination of pregnancy), date of death, gestational age, diagnosis code, diagnosis description, Is major, diagnosis time

3.3.3.1 Midwives Notification System

The WA Health Act 1911 requires that midwives report information on all births they attend in

WA.224 All births—where the infant is 20 weeks or more gestational age or 400 grams or more

birthweight if gestation is unknown have been routinely reported to the Midwives Notification

System (MNS) since 1974. The information that must be reported is specified on the Midwives

Notifications Form 2.139

Information about the mother is reported from conception to 24 hours following the birth or

death, discharge or transfer from the birth site, whichever is soonest. This includes the history of

previous pregnancy outcomes and medical conditions diagnosed before conception and present

during this pregnancy.135


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Information about the infant is reported from the time of birth to the time of discharge or transfer

from the birth site, or death, whichever is soonest. 135

3.3.3.2 Birth & death registrations and family connections

The Western Australian Registry of Births, Deaths and Marriages, which is a division of the WA

Department of the Attorney General, creates and preserves an accurate, permanent and

confidential record of births, deaths and marriages in WA in accordance with the Births, Deaths

and Marriages Registration Act 1998.225 This Act requires that a child's birth to be registered within

60 days of the birth and that both parents complete and sign a Birth Registration Form supplied by

the hospital or midwife who delivers the baby.

Death for all people in WA is registered within 14 days of the date of death by statutory

requirements.221 The date of death, as well as principal and contributing causes of death, is

available in this data collection.

3.3.3.3 Hospital Morbidity Data System

The Hospital Morbidity Data System (HMDS) contains information related to all inpatient discharge

summary data from all public and private hospitals in WA. The HMDS is a key information source

used throughout the Department of Health, public and private hospitals to meet the mandatory

and statutory reporting requirements. It is one of the largest data collections and comprises more

than 22 000 000 electronic inpatient records dating back to 1970.226 For each record there are

more than 200 data elements captured; most of these correspond with National Minimum Data

Set requirements and are based on the National Health Data Dictionary, as defined by the

Australian Institute of Health and Welfare.227 All admitting diagnoses (principal and secondary)

and procedures performed in a hospital are recorded in the HMDS.222


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3.3.3.4 WA Register of Developmental Anomalies

The WA Register of Developmental Anomalies (WARDA) covers two registers, the WA Birth

Defects Registry and the WA Cerebral Palsy Register, to record and monitor developmental

anomalies before six years of age in WA. This information has been collected in WA for more than

30 years. However, reporting of developmental anomalies became mandatory in 2011 by the State

Government.224The Chief Executive Officer of the hospital in which the diagnosis of such anomaly

is made and/or the doctor making the diagnosis or caring for the patient diagnosed are

responsible for notifying the Register. This is required within six months of the diagnosis. Further

details will be gathered by Register staff from medical records and/or doctors if required.228

3.3.3.5 Indigenous status flag

The Indigenous status flag was derived to systematically remove Indigenous births from the

dataset before the provision of data, and procedures performed in a hospital, because the project

focused on comparing migrant ethnic groups with the non-Indigenous Australian-born population,

as well as the need for an additional ethics approval procedure to be completed before working

with data inclusive of Indigenous data. As this study focused on migrants, data were excluded for

women of Aboriginal or Torres Strait Islander heritage, among whom the prevalence of stillbirth is

twice that of non-Indigenous Australians.229 This way, non-differential misclassification bias

towards the null hypothesis was avoided. The process of deriving Indigenous status is illustrated in

Figure 3.7.


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FIGURE 3.7 INDIGENOUS STATUS FLAG

Source: WA Data Linkage website available from https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-09-Derived-Indigenous-Status-Flag.png

EXPOSURES ASCERTAINMENT 3.3.4

The migrant status of the population was evaluated and ascertained based on available variables

from different datasets, including ethnic origin, mother's and father's country of birth, length of

stay in Australia and need for an interpreter. Ethnic origin was determined from the MNS and

recorded as 1=Caucasian, 2=Aboriginal/TSI, 3=Asian, 4=Indian, 5=African/Negroid, 6=Polynesian,

7=Māori, 8=Other; Mother's and father's place of birth was reported by parents on the Birth

Registration form as city, province/state and country. These data were double-checked through

country of birth information on the HMDC data. Typographical errors were identified and

corrected; old country names such as Yugoslavia were changed to Bosnia and

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/DLB-Public-Slides-2017-09-Derived-Indigenous-Status-Flag.png

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Herzegovina/Serbia/Montenegro/Kosovo, based on the city and province of place of birth, if

available. Missing countries of birth for mothers were also retrieved from the HMDC. Length of

stay in Australia was calculated by subtracting the year of birth of the baby (recorded on MNS)

from the year mother arrived in Australia (recorded on Birth Registration dataset), and need for an

interpreter was ascertained using the variable interpreter used (yes/no) that was available for all

births in hospital (99.0% of total births in WA) through the HMDC.

OUTCOMES ASCERTAINMENT 3.3.5

Stillbirth in Australia is defined as death of a baby of at least 20 completed weeks of gestation

before the complete expulsion or extraction from the mother.28 If death occurred before the

commencement of labour, it is recorded as antepartum stillbirth, and after labour started is

considered an intrapartum stillbirth.61 Type of stillbirth (antepartum/intrapartum), recorded on

the MNS from 2005 onwards, was available through the status of baby at birth. For stillbirths that

had not been reported by type, information on the presence or absence of the fetal heartbeat at

the commencement of labour was sought from death certificates. Terminations of pregnancies,

recorded as stillbirth, were identified through WARDA and death records, and were excluded.

Preterm birth was defined as birth before 37 completed weeks of gestation230 and was classified

as spontaneous (spontaneous onset) or medically indicated (premature birth after medical

intervention: induction of labour or elective CS). Spontaneous preterm birth was further

subdivided into idiopathic (spontaneous labour with intact fetal membranes) and preterm pre-

labour rupture of membranes (PPROM) where labour began spontaneously after

PPROM.93,94Estimated gestational age at time of birth (completed weeks), onset of labour

(spontaneous, induced, elective caesarean) and complications of pregnancy (data on pre-labour

rupture of membrane) from MNS were used to identify and classify these outcomes.


PAGE | 63

Low birthweight (LBW) was defined as birthweight less than 2500 grams at the time of birth28, and

the infant’s weight (in grams) information on MNS was used to identify this outcome. Term-LBW

cases were identified by further consideration of the estimated gestational age at time of birth (in

completed weeks).

SAMPLE SIZE AND POWER 3.3.6

The overall design of the project was a retrospective cohort study comprising all births to non-

Indigenous women from 2005–2013. We initially estimated the sample size required based on an

unexposed (Australian-born mothers) to exposed (Overseas-born mothers) ratio of 2:1 to ensure

that studying the nine-year population cohort had an appropriate statistical power to detect a

significant result, if present. The sample size required was based on one of the least prevalent

outcomes of interest (i.e. intrapartum stillbirth) to ensure that the study had enough power to

detect possible disparities in the risk of such an outcome. The odds of experiencing stillbirth in

overseas-born women from African and/or South Asian backgrounds have been estimated at

around twice that of Australian-born women.13,159 The expected annual incidence of intrapartum

stillbirth in the unexposed population in WA is 0.0016. Therefore, to detect a relative risk of 1.5

with 95% confidence and 80% power with a 1:2 exposed: non-exposed ratio required a study

sample of 146 862 participants or 48 954 exposed (i.e. migrant) and 97 908 non-exposed (i.e.

Australian-born) participants. Thus, a cohort of all births to non-Indigenous women from 2005–

2013, with a population study of more than 250 000 births, was deemed to have enough power

for this study.

ETHICS APPROVAL 3.3.7

This study was approved by the Human Research Ethics Committee of the WA Department of

Health (2015/23). Due to the use of non-identifiable linked routinely collected administrative

health data for the whole population, written consent was not required to conduct the study.


PAGE | 64

Further, The University of Western Australia Human Ethics Office was notified of this review and

approval, which recognised the existing approval of the non-UWA ethics committee with no need

for the completion of its ethics review process (RA/4/1/7602).

DATA ANALYSIS 3.3.8

Chapters 4-8 comprise five studies and the statistical analyses undertaken for this thesis and the

Methods section in each chapter contains the details of the specific analysis carried out to answer

the research questions in each study. This includes the efforts carried out to retrieve missing data

from various data sources and the sensitivity analyses undertaken.

CHAPTER 4. STILLBIRTH, MIGRATION AND ETHNIC ORIGIN IN WA

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CHAPTER 4. STILLBIRTH, MIGRATION AND ETHNIC ORIGIN IN WESTERN

AUSTRALIA

This chapter comprises a paper, published in the Medical Journal of Australia, which explores the

risk of antepartum and intrapartum stillbirth in migrants from white, Asian, Indian, African, Māori

and ‘other’ non-white ethnic backgrounds. The risk of stillbirth among the population groups was

explored in preterm (<37 weeks gestation) as well as term and post-term (≥37 weeks gestation)

periods separately. This study relates to the first objective of the thesis: To investigate prevalence

proportion and the risk of antepartum and intrapartum stillbirth in WA with respect to maternal

country of birth and ethnic origin.

The citation details for this paper are as follows and a copy of the paper is available in the

Appendices:

Mozooni M, Preen DB, Pennell CE. Stillbirth in Western Australia, 2005-2013: the influence of

maternal migration and ethnic origin. Med J Aust. 2018;209(9):394-400. PubMed PMID: 30282563.


PAGE | 66

ABSTRACT 4.1

Objective: To investigate prevalence rates and the risk of antepartum and intrapartum stillbirth in

Western Australia with respect to maternal country of birth and ethnic origin.

Design, setting and participants: Whole-population retrospective cohort analysis of de-identified,

linked routinely collected birth, perinatal and mortality data for all births to non-Indigenous

women in WA from 2005–2013.

Main outcome measures: Crude and adjusted odds ratios (aORs) with 95% confidence intervals

were estimated, using logistic regression controlling for confounding factors, for all stillbirths,

antepartum stillbirths and intrapartum stillbirths, stratified by migrant status and ethnic

background (white, Asian, Indian, African, Māori, other).

Results: Women born overseas were more likely to have a stillbirth than Australian-born women

(aOR 1.26, 95% CI 1.09–1.37). No significant differences occurred for any type of stillbirth between

Australian-born women of white and non-white backgrounds, but non-white migrant women were

more likely than white migrants to have a stillbirth (OR 1.42, 95% CI 1.19–1.70). Compared with

Australian-born women, migrants of Indian (aOR 1.71, 95% CI 1.17–2.47), African (aOR 2.12, 95%

CI 1.46–3.08), and ‘other’ ethnic origin (aOR 1.43, 95% CI 1.06–1.93) were more likely to have an

antepartum stillbirth; women of African (aOR 5.08, 95% CI 3.14–8.22) and ‘other’ (aOR 1.86, 95%

CI 1.15–3.00) origin were more likely to have an intrapartum stillbirth.

Conclusions: Immigrants of African or Indian background appear to be at greater risk of

antepartum and intrapartum stillbirth in WA. Specific strategies will reduce the prevalence of

stillbirth in these communities.

INTRODUCTION 4.2

More than 2.6 million stillbirths were reported around the world in 2015.42 Stillbirth challenges

families, societies, practitioners and health care systems,231,232 but its scientific investigation only

recently gained momentum, after the Lancet launched two stillbirth article series in 2011 and

2016.34,39 Almost 98% of stillbirths are in low- and middle-income countries, but they also occur in

high-income countries where socioeconomic factors influence the prevalence of stillbirth.61,62

International migration is at its highest level since the Second World War, and 64% of migrants live

in high-income countries.118 Migrants and women from non-white ethnic backgrounds generally

have more stillbirths than white migrants and non-migrant women.13,74,77,159,233


PAGE | 67

Immigration is the principal component of population growth in Australia; in 2016, 35% of

residents had been born overseas,124 and 33% of women who gave birth in 2015 had been born

overseas.28 Western Australia recorded the greatest population growth of all Australian states and

territories from 2006–2016 (24.8% increase),234 and 62% of Western Australians have at least one

overseas-born parent.136

About 50% of stillbirths worldwide are intrapartum stillbirths, deemed preventable by good

obstetric care. The prevalence of intrapartum stillbirths is lower in high-income countries, but

constitutes almost 20% of stillbirths in some of these countries; this is significant, as this

prevalence is related to quality of care.61 Women of non-white ethnic background, particularly

African and Asian women, are at greater risk of antepartum stillbirth than white women.13,159,233

However, the only published study of intrapartum stillbirth was a prospective study that found

higher crude rates and odds of intrapartum stillbirth for Black women in the United Kingdom

(1988–2000).9 Data on the timing of death is scarce, posing significant challenges for preventive

strategies in clinical practice.62

This study estimated ethnic group-specific prevalence rates to determine the association between

maternal country of birth and ethnic origin and the risk of antepartum and intrapartum stillbirth in

a whole-population sample of WA births (2005–2013).

METHODS 4.3

STUDY DESIGN AND PARTICIPANTS 4.3.1

A whole-population retrospective cohort analysis was undertaken using de-identified, linked

routinely collected data for all births to non-Indigenous women in WA from 1 January 2005 to 31

December 2013.

DATA SOURCES AND LINKAGE 4.3.2

The Western Australia Data Linkage System (WADLS) applies probabilistic matching based on full

name and address, phonetic compression algorithms, and other identifiers to link data from a

variety of health and other administrative datasets.211 Geocoding with address parsing software

assigns residential addresses to Census statistical areas to determine their Index of Relative Socio-

economic Disadvantage (IRSD), an Australian Bureau of Statistics-developed measure of

socioeconomic status that summarises a range of information about the economic and social

conditions of people and households in an area.219 The estimated frequency of invalid or missed


PAGE | 68

links based on evaluation of linked chains is very low (0.11%) and the linkage procedures are

widely accepted as best practice.204,213

The primary data source was the Midwives’ Notification System (MNS), a highly reliable statutory

data collection of demographic, pregnancy and delivery information for all births in WA.235 The

MNS data were supplemented by data from other WA statutory data collections: the Hospital

Morbidity Data Collection (information related to inpatient discharges from all hospitals), the WA

Registry of Developmental Anomalies (WARDA; developmental anomalies identified by six years of

age, including in fetuses of terminated pregnancies), and Birth and Death Registrations.

Genealogical linkage was also undertaken by the WADLS Family Connections Linkage Facility.

EXPOSURES 4.3.3

Maternal ethnic background (‘ethnic origin’: the ethnic background with which the woman

identifies) is recorded by the MNS as Caucasian (white), Aboriginal/Torres Strait Islander, Asian,

Indian, African, Māori, Polynesian, or other. As our study focused on migrants, data for women of

Aboriginal or Torres Strait Islander heritage, among whom the prevalence of stillbirth is twice that

of non-Indigenous Australians,44 were excluded to avoid non-differential misclassification bias

towards the null hypothesis. Women of Polynesian origin (289 women, 0.1%) were included in the

‘other’ group.

‘Migrant status’ (Australian- or overseas-born) was based on the mother’s place of birth (complete

for 99% of the births) in the Birth Registration data; this information was merged with MNS data.

‘Country of birth’ in the mother’s hospital record was also used for validation and to retrieve

missing values. The migrant group was stratified by ethnic origin, complete for 99.99% of the

population.

OUTCOMES 4.3.4

Stillbirth is defined as death of a baby of at least 20 completed weeks gestation before complete

expulsion or extraction from the mother.28 Death before labour commences is termed antepartum

stillbirth, and after labour commences, intrapartum stillbirth.61 ‘Status of baby at birth’ (MNS data)

was used to identify live births and stillbirths. Type of stillbirth was reported as antepartum or

intrapartum for 80% of stillbirths in MNS data; information on the presence or absence of the fetal

heartbeat at the start of labour was obtained from death certificates. Consequently, the type of


PAGE | 69

stillbirth was available for 99.98% of stillbirths. Terminations of pregnancy, identified in WARDA

and death records, were excluded (433 cases).

STATISTICAL ANALYSIS 4.3.5

All analyses were performed in Stata 13.1 (StataCorp). Demographic and obstetric characteristics

were tabulated by ethnic group and Pearson X2 or Fisher exact tests employed as appropriate in

descriptive analyses. Prevalence rates per 1000 total births of at least 20 weeks gestational age of

overall, antepartum and intrapartum stillbirths from 2005–2013 were calculated; specifically,

prevalence rates per 10 000 total births were calculated for term and post-term stillbirths. Migrant

populations of women—stratified into white, Asian, Indian, African, Māori, and ‘other’—were

compared with the Australian-born population. Crude odds ratios (ORs) with 95% confidence

intervals (CIs) were estimated by univariate logistic regression for overall, antepartum and

intrapartum stillbirths; P<0.05 was deemed statistically significant. Covariates—established and

clinically plausible risk factors significant in the univariate analysis—were selected a priori.

Multivariable logistic regression analysis was adjusted for previous stillbirth, year of birth,

maternal age group, sex of baby, marital status, pregnancy complications, medical conditions,

smoking during pregnancy, parity, plurality, and IRSD. IRSD data were missing for 3.3% of women,

who were categorised as a separate subgroup to retain all the cases in the analysis. Migrant

countries of birth were further assigned to United Nations geographic regions—South Asia (India,

Pakistan, Sri Lanka, Afghanistan, Bangladesh), South-East and East Asia (Vietnam, Malaysia,

Indonesia, China, Japan), Middle East (Iraq, Israel, Jordan, Turkey, Yemen, Cyprus), other Asia,

Oceania, Africa, Europe and the Americas—and data for these regions compared with the

combined data for Australia and New Zealand.

Two sensitivity analyses were undertaken: the first excluded women with more than one birth

record in the dataset to examine the effect of non-independence that can affect the analysis of

large perinatal datasets;74 the second excluded stillbirths of fetuses with major congenital

anomalies, as the mortality associated with congenital anomalies is higher for babies of women

from some ethnic backgrounds, perhaps due to restricted access or differing attitudes to screening

and termination of pregnancy.84


The Human Research Ethics Committee of the WA Department of Health approved this study

(reference, 2015/23).


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RESULTS 4.4

There were 260 997 live and stillbirths to non-Indigenous women in WA from 2005–2013,

including 172 571 births (66.1%) to Australian-born and 88 395 (33.9%) to migrant mothers (Table

4.1).

Most migrant mothers were slightly older than the Australian-born women; the proportion who

smoked during pregnancy greater among Australian-born mothers (14%) than most migrant

groups (1–9%; exception: Māori women, 39%). The proportion of women who were multiparous

was highest for African migrants (43.9%), as were the proportions of extremely preterm (20–27

weeks) births (1.3%) and post-term (≥42 weeks) pregnancies (2.1%). The proportions of women in

the least disadvantaged IRSD quintile were largest for migrant women with white and Asian

backgrounds (Table 4.2). About 24% of stillbirths were intrapartum stillbirths (Table 4.1).

Among Australian-born women there were 4.7 stillbirths (3.5 antepartum, 1.1 intrapartum) per

1000 births; among overseas-born women, there were 5.7 stillbirths (4.0 antepartum, 1.5

intrapartum) per 1000 births. The highest prevalence of stillbirth was among African migrant

women (12.3 stillbirths, 7.5 antepartum stillbirths, 4.8 intrapartum stillbirths per 1000 total births)

(Table 4.3).


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TABLE 4.1 PREGNANCY OUTCOMES FOR 260 997 LIVE AND STILLBIRTHS IN WESTERN AUSTRALIA, 2005–2013, BY MATERNAL ETHNIC ORIGIN

Australian-born women

Migrant women All women P

White Asian Indian African Māori Other

All births (proportion of all births)

172 571 (66.1%)

48 546 (18.6%)

18 212 (7.0%)

5503 (2.1%)

4155 (1.6%)

2941 (1.1%)

9038 (3.5%)

260 997

Pregnancy outcomes

Live birth 171 759 (99.5%)

48 315 (99.5%)

18 117 (99.5%)

5464 (99.3%)

4104 (98.8%)

2923 (99.4%)

8972 (99.3%)

259 684 (99.5%)

0.001

Stillbirth (total) 812 (0.5%)

231 (0.5%)

95 (0.5%)

39 (0.7%)

51 (1.2%)

18 (0.6%)

66 (0.7%)

1313 (0.5%)

0.001

Antepartum 605 (0.4%)

162 (0.3%)

69 (0.4%)

31 (0.6%)

31 (0.8%)

15 (0.5%)

47 (0.5%)

960 (0.4%)

0.002

Intrapartum 185 (0.1%)

57 (0.1%)

24 (0.1%)

<10 (<0.1%)

20 (0.5%)

<10 (<0.1%)

19 (0.2%)

317 (0.1%)

0.001

Undefined 22 (<0.1%)

12 (<0.1%)

<10 (<0.1%)

0 0 0 0 36 (<0.1%)

0.06

Sex of baby 0.98

Boy 88 307 (51.2%)

24 720 (50.9%)

9466 (52.0%)

2813 (51.1%)

2131 (51.3%)

1490 (50.7%)

4608 (51.0%)

133 549 (51.2%)

Girl 84 252 (48.8%)

23 821 (49.1%)

8745 (48.0%)

2690 (48.9%)

2023 (48.7%)

1451 (49.3%)

4429 (49.0%)

127 428 (48.8%)

Undetermined 12 (<0.1%)

<10 (<0.1%)

<10 (<0.1%)

0 <10 (<0.1%)

0 <10 (<0.1%)

20 (<0.1%)

Plurality 0.025

Singleton 167 481 (97.1%)

47 075 (97.0%)

17 822 (97.9%)

5389 (97.9%)

4031 (97.0%)

2883 (98.0%)

8725 (96.5%)

253 435 (97.1%)

Multiple 5090 (2.9%)

1471 (3.0%)

390 (2.1%)

114 (2.1%)

124 (3.0%)

58 (2.0%)

313 (3.5%)

7562 (2.9%)


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Gestational age (weeks) <0.001

20–27 1025 (0.6%)

261 (0.5%)

121 (0.7%)

45 (0.8%)

55 (1.3%)

25 (0.9%)

94 (1.0%)

1626 (0.6%)

28–31 1288 (0.8%)

328 (0.7%)

123 (0.7%)

47 (0.9%)

41 (1.0%)

19 (0.7%)

72 (0.8%)

1919 (0.7%)

32–36 11 893 (6.9%)

3172 (6.5%)

1260 (6.9%)

399 (7.3%)

235 (5.7%)

173 (5.9%)

622 (6.9%)

17 754 (6.8%)

37–41 157 498 (91.3%)

44 520 (91.7%)

16 665 (91.5%)

4997 (90.8%)

3737 (89.9%)

2705 (92.0%)

8187 (90.6%)

238 336 (91.3%)

≥42 867 (0.5%)

265 (0.6%)

43 (0.2%)

15 (0.3%)

87 (2.1%)

19 (0.7%)

63 (0.8%)

1362 (0.5%)

Complications of pregnancy <0.001

Gestational diabetes 7710 (4.5%)

2732 (5.6%)

2306 (12.7%)

868 (15.8%)

312 (7.5%)

117 (4.0%)

862 (9.5%)

14 907 (5.7%)

Preeclampsia 5114 (3.0%)

1194 (2.5%)

325 (1.8%)

124 (2.3%)

127 (3.1%)

77 (2.6%)

216 (2.4%)

7177 (2.8%)

Any complication 57 596 (33.4%)

15 330 (31.6%)

6489 (35.6%)

2162 (39.3%)

1339 (32.2%)

925 (31.5%)

3214 (35.6%)

87 059 (33.4%)


PAGE | 73

TABLE 4.2 CHARACTERISTICS OF MOTHERS FOR 260 997 LIVE AND STILLBIRTHS IN WESTERN AUSTRALIA, 2005–2013, BY MATERNAL ETHNIC ORIGIN



White Asian Indian African Māori Other

Total number 172 571 48 546 18 212 5503 4155 2941 9038 260 997

Marital status <0.001

Never married 18 016 (10.4%)

3026 (6.2%)

701 (3.9%)

99 (1.8%)

543 (13.1%)

570 (19.4%)

611 (6.8%)

23 568 (9.0%)

Divorced/separated 1554 (0.9%)

360 (0.7%)

160 (0.9%)

17 (0.3%)

109 (2.6%)

33 (1.1%)

132 (1.5%)

2366 (0.9%)

Married/de facto 151 831 (88.0%)

44 693 (92.1%)

17 107 (93.9%)

5327 (96.8%)

3449 (83.0%)

2268 (77.1%)

8214 (90.9%)

232 917 (89.2%)

Other 1170 (0.7%)

467 (1.0%)

244 (1.3%)

60 (1.1%)

54 (1.3%)

70 (2.4%)

81 (0.9%)

2146 (0.8%)

Parity <0.001

Nulliparous 73 456 (42.6%)

21 205 (43.7%)

8759 (48.1%)

3204 (58.2%)

1217 (29.3%)

955 (32.5%)

3532 (39.1%)

112 340 (43.0%)

Primiparous 60 403 (35.1%)

17 243 (35.5%)

6485 (35.6%)

1817 (33.0%)

1113 (26.8%)

792 (26.9%)

2695 (29.8%)

90 561 (34.7%)

Multiparous 38 712 (22.5%)

10 098 (20.8%)

2968 (16.3%)

482 (8.8%)

1825 (43.9%)

1194 (40.6%)

2811 (31.1%)

58 096 (22.3%)

Maternal age (years) <0.001

Mean (SD) 29.5 (5.6)

31.5 (5.3)

31.2 (4.9)

29.5 (4.4)

28.8 (5.7)

26.8 (6.0)

29.9 (5.6)

30.0 (5.6)

<20 7474 (4.3%)

724 (1.5%)

131 (0.7%)

17 (0.3%)

197 (4.7%)

300 (10.2%)

200 (2.2%)

9045 (3.5%)

20–24 27 516 (16.0%)

4364 (9.0%)

1401 (7.7%)

638 (11.6%)

826 (19.9%)

889 (30.2%)

1477 (16.3%)

37 115 (14.2%)


PAGE | 74

25–29 49 076 (28.4%)

11 423 (23.5%)

5208 (28.6%)

2288 (41.6%)

1254 (30.2%)

786 (26.7%)

2631 (29.1%)

72 673 (27.8%)

30–34 54 744 (31.7%)

17 464 (36.0%)

6937 (38.1%)

1869 (34.0%)

1169 (28.1%)

599 (20.4%)

2714 (30.0%)

85 510 (32.8%)

35–39 28 412 (16.5%)

11 716 (24.1%)

3713 (20.4%)

581 (10.6%)

586 (14.1%)

290 (9.9%)

1612 (17.8%)

46 914 (18.0%)

40–44 5159 (3.0%)

2703 (5.6%)

785 (4.3%)

103 (1.9%)

112 (2.7%)

77 (2.6%)

389 (4.3%)

9328 (3.6%)

>44 190 (0.1%)

152 (0.3%)

37 (0.2%)

<10(0.1%) 11 (0.3%)

0 15 (0.2%)

412 (0.2%)

Medical conditions <0.001

Diabetes 1040 (0.6%)

285 (0.6%)

97 (0.5%)

53 (1.0%)

30 (0.7%)

15 (0.5%)

63 (0.7%)

1583 (0.6%)

Asthma 21 728 (12.6%)

4208 (8.7%)

642 (3.5%)

154 (2.8%)

85 (2.1%)

356 (12.1%)

440 (4.9%)

27 615 (10.6%)

Hypertension 2134 (1.2%)

579 (1.2%)

109 (1.6%)

28 (0.5%)

33 (0.8%)

31 (1.1%)

70 (0.8%)

2984 (1.1%)

Smoked while pregnant <0.001

Yes 24 097 (14.0%)

4161 (8.6%)

317 (1.7%)

40 (0.7%)

78 (1.9%)

1152 (39.2%)

494 (5.5%)

30 342 (11.6%)

No 148 474 (86.0%)

44 385 (91.4%)

17 895 (98.3%)

5463 (99.3%)

4077 (98.1%)

1789 (60.8%)

8544 (94.5%)

230 655 (88.4%)

Index of Relative Socio-economic Disadvantage (quintiles) <0.001

1 (most disadvantaged) 40 521 (23.5%)

6547 (13.5%)

2181 (12.0%)

663 (12.1%)

513 (12.4%)

800 (27.2%)

1276 (14.1%)

52 504 (20.1%)

2 34 062 (19.7%)

8343 (17.2%)

3526 (19.4%)

1137 (20.7%)

781 (18.8%)

895 (30.4%)

1882 (20.8%)

50 631 (19.4%)

3 38 893 (22.5%)

14 192 (29.2%)

5380 (29.5%)

1863 (33.9%)

1998 (48.10%)

645 (21.9%)

3098 (34.3%)

66 081 (25.3%)


PAGE | 75

SD Standard deviation

4 22 519 (13.1%)

5666 (11.7%)

2939 (16.1%)

915 (16.6%)

441 (10.6%)

357 (12.1%)

1192 (13.2%)

34 031 (13.0%)

5 (least disadvantaged) 30 662 (17.8%)

12 337 (25.4%)

3615 (19.9%)

730 (13.3%)

295 (7.1%)

166 (5.6%)

1321 (14.6%)

49 132 (18.8%)


PAGE | 76

TABLE 4.3 PREVALENCE OF STILLBIRTHS IN WESTERN AUSTRALIA, 2005–2013, BY ETHNIC ORIGIN OF MOTHER

AND TYPE OF STILLBIRTH

Ethnic origin of mother Rate (per 1000 births)

All stillbirths Antepartum stillbirths

Intrapartum stillbirths

Australian-born 4.7 3.5 1.1

Overseas-born 5.7 4.0 1.5

White (from United Kingdom, 43%; New Zealand, 15%) 4.8 3.3 1.2

Asian (from China, 14%; Vietnam, 14%; Malaysia, 13%; Indonesia, 11%; Philippines, 11%)

5.2 3.8 1.3

Indian (from India, 70%) 7.1 5.6 1.5

African (from Sudan, 28%; Somalia, 13%) 12.3 7.5 4.8

Māori (from New Zealand, 97%) 6.1 5.1 1.0

Other* (from Iraq, 9%; Afghanistan, 6%; Sudan, 3.5%; Somalia 2%; Saudi Arabia, 2%)

7.3 5.2 2.1

All women 5.0 3.7 1.2

The rates of antepartum and intrapartum stillbirth may not sum to that of all stillbirths because of the small proportion of undefined stillbirths. *71% of women in this category were born in Asia or Africa but did not declare their ethnic origin as Asian, Indian or African.

Non-white migrant women were more likely than white migrants to have stillbirths (OR 1.42, 95%

CI 1.19–1.70), antepartum stillbirths (OR 1.45, 95% CI 1.18–1.79) and intrapartum stillbirths (OR

1.58, 95% CI 1.12–2.24). There were no significant differences between Australian-born women

with white and non-white backgrounds for any type of stillbirth (Appendix 6, Table 1). Women

born overseas were more likely to have a stillbirth (adjusted odds ratio [aOR] 1.26, 95% CI 1.09–

1.37), antepartum stillbirth (aOR 1.20, 95% CI 1.02–1.33), or intrapartum stillbirth (aOR 1.38, 95%

CI 1.09–1.72) than Australian-born women (Table 4.4).

Antepartum stillbirths were more frequent for migrant women of African (aOR 2.12, 95% CI 1.46–

3.08), Indian (aOR 1.71, 95% CI 1.17–2.47) and ‘other’ ethnic origins (aOR 1.43, 95% CI 1.06–1.93)

than for Australian-born women after adjusting for a range of covariates. Intrapartum stillbirth

was five times as frequent among African migrants (aOR 5.08, 95% CI 3.14–8.22) and almost twice

as frequent among migrants of ‘other’ backgrounds (aOR 1.86, 95% CI 1.15–3.00) than Australian-

born women after adjusting for a range of covariates (Table 4.4).

The influence of African or Indian ethnic origin on the likelihood of stillbirth was similar in

sensitivity analyses (data not shown).


PAGE | 77

TABLE 4.4 STILLBIRTH IN WESTERN AUSTRALIA, 2005–2013: COMPARISON OF MIGRANT WOMEN, BY ETHNIC ORIGIN, WITH AUSTRALIAN-BORN WOMEN

Ethnic origin All stillbirths Antepartum stillbirths Intrapartum stillbirths

OR (95%CI) aOR* (95%CI) OR (95%CI) aOR* (95%CI) OR (95%CI) aOR* (95%CI)

Australian-born 1 1 1 1 1 1

Overseas-born 1.20 (1.08–1.35) 1.26 (1.09–1.37) 1.15 (1.01–1.31) 1.20 (1.02–1.33) 1.38 (1.11–1.73) 1.38 (1.09–1.72)

White 1.01 (0.87–1.17) 1.07 (0.86–1.16) 0.95 (0.80–1.13) 1.01 (0.79–1.13) 1.10 (0.81–1.47) 1.11 (0.77–1.42)

Asian 1.11 (0.90–1.37) 1.17 (0.95–1.47) 1.08 (0.84–1.39) 1.15 (0.89–1.48) 1.23 (0.80–1.88) 1.20 (0.81–1.91)

Indian 1.51 (1.09–2.08) 1.58 (1.13–2.19) 1.61 (1.12–2.31) 1.71 (1.17–2.47) 1.36 (0.67–2.75) 1.25 (0.69–2.90)

African 2.63 (1.98–3.49) 2.74 (2.04–3.69) 2.14 (1.49–3.07) 2.12 (1.46–3.08) 4.51 (2.84–7.16) 5.08 (3.14–8.22)

Māori 1.30 (0.82–2.08) 1.27 (0.83–2.14) 1.46 (0.87–2.44) 1.39 (0.86–2.41) 0.95 (0.30–2.98) 1.00 (0.34–3.42)

Other 1.56 (1.21–2.00) 1.51 (1.17–1.96) 1.49 (1.10–2.00) 1.43 (1.06–1.93) 1.96 (1.22–3.15) 1.86 (1.15–3.00)

aOR=adjusted odds ratio; OR=odds ratio. *Adjusted for previous stillbirth, year of birth, maternal age group, sex of baby, marital status, pregnancy complication, medical

conditions, smoking during pregnancy, parity, plurality and Index of Relative Socio-economic Disadvantage.


PAGE | 78

It is notable that 71% of women in the ‘other’ ethnic background category (MNS data) were born

in Asia or Africa (Birth Registration data) but did not report their ethnic origin as Asian, Indian or

African. Analysed by maternal geographic region of birth, women from Africa (aOR 1.58, 95% CI

1.22–2.05) and South Asia (aOR 1.60, 95% CI 1.15–2.23) were more likely than women born in

Australia and New Zealand to have an antepartum stillbirth; women from Africa (aOR 2.57, 95% CI

1.76–3.76) were also more likely to have an intrapartum stillbirth (Appendix 6, Table 2).

The prevalence of antepartum stillbirth among migrant women with Indian (51 vs 31 per 1000

births; P=0.013) and Māori backgrounds (60 vs 31 per 1000 births; P=0.039) during the preterm

period was significantly higher than for Australian-born women (Figure 4.1) but similar to that of

Australian-born women during the term and post-term period (Indian women: 12 vs 10 per 10 000

births; P=0.66; Māori women: 7 vs 10 per 10 000 births; P=0.66) (Figure 4.2). Among migrant

women with African backgrounds, the prevalence rates of preterm antepartum (69 vs31 per 1000

births; P<0.001) and intrapartum stillbirth (48 vs 10 per 1000 births; P<0.001) were significantly

higher than for Australian-born women; the term and post-term antepartum stillbirth rate was

twice that of Australian-born women (21 vs 10 per 10 000 births; P=0.026), and the prevalence of

intrapartum stillbirth ten times as high (10 vs 1 per 10 000 births; P<0.001). Migrants of ‘other’

backgrounds had five times higher rates of intrapartum stillbirth (5 vs 1 per 10 000 births; P=0.024)

than Australian-born women (Figure 4.2). These differences remained significant in adjusted

analyses (data not shown).

FIGURE 4.1 PRETERM (20–36 WEEKS) STILLBIRTHS IN WESTERN AUSTRALIA, 2005–2013, BY ETHNIC

ORIGIN OF MOTHER


PAGE | 79

FIGURE 4.2 TERM AND POST-TERM (≥37 WEEKS) STILLBIRTHS IN WESTERN AUSTRALIA, 2005–2013, BY ETHNIC

ORIGIN OF MOTHER

DISCUSSION 4.5

In this whole-population study, the prevalence of stillbirth among migrant women in WA was

lower than in their countries of origin,42 but the prevalence of antepartum stillbirth was higher

among migrant women with Indian, African, or ‘other’ origins than Australian-born women, and

the prevalence of intrapartum stillbirth was higher among migrant women with African or ‘other’

origins than Australian-born women.

Our results are consistent with previous observations that migrant women are at higher risk of

stillbirth than locally born women. We also report, for the first time in Australia, ethnic group-

specific differences in the prevalence of both antepartum and intrapartum stillbirth. Particularly

notable was that the prevalence of term stillbirth was much higher among migrants of African

origin than Australian-born women. That the intrapartum stillbirth rate was twice as high among

African women is especially worrying, as intrapartum stillbirth is regarded as preventable and

indicative of inadequate quality of care.61 This finding shows the critical value of the time of death

(antepartum or intrapartum) when designing public health programs for averting stillbirth.

Why is the prevalence of term stillbirth in WA higher for migrants of African background? The

proportion of pregnancies lasting 42 or more weeks was higher for African migrants (2.1%) than

for Australian-born women (0.5%) or Asian migrants (0.2%) (Table 4.1). Post-term pregnancy is a

recognised risk factor for stillbirth.236 The high proportion of post-term pregnancies among African

migrants in WA contrasts with reports that the median gestational age at spontaneous labour

among Black women in London was 39 weeks,102 similar to Australian-born women in Victoria.74


PAGE | 80

Further, African Americans are less likely than white women to reach 40 weeks gestation (aOR

0.81, 95% CI 0.78–0.85).237 However, Black populations in overseas studies may not be directly

comparable with African migrants to Australia.

The greater proportion of post-term pregnancies among African women in WA may reflect their

lack of access to or uptake of obstetric interventions such as induction and caesarean delivery.

Many African women are reluctant to undergo obstetric interventions such as caesarean delivery

because they worry that multiple operations can lead to infertility or even death.19,238 It was

previously reported that Sub-Saharan African women in WA are significantly less likely than

Australian-born women to have induced labour (OR 0.74, 95% CI 0.69–0.79) and more likely to

have an emergency caesarean delivery (OR 1.22, 95% CI 1.12–1.32).24 In a qualitative study,

Sudanese women in WA emphasised their suspicions about assisted birth, believing that the

natural process should not be disturbed.26 These beliefs may inhibit their seeking routine

antenatal care during pregnancy, resulting in lost opportunities for medical interventions that

avert preterm birth and post-term pregnancy, each of which increases the risk of stillbirth. More

in-depth investigation of the patterns of health service use, pregnancy and labour care of migrant

women, particularly African migrants, is warranted. Culturally appropriate antenatal engagement

and educational programs about the risk of stillbirth and the indications for and safety of induction

and related interventions may be useful preventive strategies.

Consistent with previous reports, we found that women of Indian origin are more likely to have an

antepartum stillbirth than Australian-born women.74,159 These reports included data only on

antepartum stillbirth and maternal country of birth; by including information on the time of

stillbirth and ethnic background, we additionally reported that women from an Indian background

and those born in South Asia are not at increased risk of intrapartum stillbirth.

We also found that the rate of preterm (but not term) antepartum stillbirth among migrants of

Māori background was significantly higher than for Australian-born women even after adjusting

for several factors, including smoking. Māori women are a vulnerable population in New Zealand,

having greater difficulty with communication and access to maternity services than non-Māori

women, and with higher rates of adverse outcomes, including stillbirth.239 This migrant population

and their health service needs in Australia require further investigation.

The strength of our study was that type of stillbirth was available for 99.98% of stillbirths for a

complete population-based cohort. Further, the population of migrants was substantial (33.9%),


PAGE | 81

and we included several ethnic origin categories in our analyses. Our linked health data

methodology and whole-population design reduced the risk of selection, participation and recall

biases.

LIMITATIONS 4.5.1

We analysed routinely collected linked data, not data specifically collected for answering our

research questions, and this may have led to some misclassification of exposures and outcomes;

despite the cross-source ascertainment we undertook to optimise accuracy, a residual risk of

misclassification remains. Another limitation arising from the analysis of large datasets is that

women may have had more than one birth or stillbirth during the study period, resulting in non-

independence in the dataset, but limiting our analyses to women with only one birth record did

not affect the results. Further, while our rich dataset made it possible to control for many factors,

residual confounding by covariates not available in the dataset (e.g. maternal weight, which may

be associated with increased risk of stillbirth)70,74,159 is possible.

CONCLUSION 4.5.2

This whole-population study explored the relationships between ethnic background and migration

and the prevalence of antepartum and intrapartum stillbirth. We identified ethnic groups in which

the risk of one or both types is particularly high. Our findings suggest that, to reduce the number

of preventable stillbirths, it is imperative to know the country of origin and ethnic makeup of the

local population and to target preventive strategies accordingly.

CHAPTER 5. HEALTHCARE USE AND STILLBIRTH IN MIGRANTS IN WA

PAGE | 82

CHAPTER 5. HEALTHCARE USE AND STILLBIRTH IN MIGRANTS IN WESTERN

AUSTRALIA

This chapter comprises a paper, published in PLoS Medicine, that explored the underlying

mechanisms and modifiable factors associated with the increased risk of antepartum and

intrapartum stillbirths in migrants from diverse ethnic backgrounds. This chapter builds on the

results in Chapter 4, in relation to the second objective of the thesis, to investigate the pattern of

healthcare utilisation among migrant women and its relationship with the risk of stillbirth

(antepartum and intrapartum) in WA. The specific abbreviations used throughout this chapter are

below:


Appendices:

Mozooni M, Pennell CE, Preen DB. Healthcare factors associated with the risk of antepartum and

intrapartum stillbirth in migrants in Western Australia (2005-2013): A retrospective cohort study.

PLoS Med. 2020;17(3):e1003061. doi: https://doi.org/10.1371/journal.pmed.1003061.


PAGE | 83

ABSTRACT 5.1

Background: Migrant women, especially from Indian and African ethnicity, have a higher risk of

stillbirth than native-born populations in high-income countries. Differential access or timing of

antenatal care and the uptake of other services may play a role. We investigated the pattern of

healthcare utilisation among migrant women and its relationship with the risk of stillbirth (SB),

antepartum (AnteSB) and intrapartum (IntraSB) in Western Australia (WA).

Methods and Findings: A retrospective cohort study using de-identified linked data from perinatal,

birth, death, hospital and birth-defects registrations through the WA Data Linkage System was

undertaken. All (N=260 997) non-Indigenous births (2005–2013) were included. Logistic regression

analysis was used to estimate odds ratios and 95% CI for AnteSB and IntraSB, comparing migrant

women from white, Asian, Indian, African, Māori and ‘other’ ethnicities with Australian-born

women controlling for risk factors and potential healthcare-related covariates.

Of all the births, 66.1% were to Australian-born women and 33.9% to migrant women. The mean

age (years) was 29.5 among the Australian-born mothers and 30.5 among the migrant mothers.

Nulliparous women comprised 42.3% of Australian-born women, 58.2% of Indian women and

29.3% of African women. Only 5.3% of Māori and 9.2% of African migrants had private health

insurance in contrast to 43.1% of Australian-born women. Among Australian-born women, 14%

had smoked in pregnancy, compared with 0.7% and 1.9% of migrants from Indian and African

backgrounds, respectively.

The odds of AnteSB was elevated in African (OR 2.22, 95% CI 1.48–2.13, P<0.001), Indian (OR 1.64,

95% CI 1.13–2.44, P=0.013) and other women (OR 1.46, 95% CI 1.07–1.97, P=0.016) while IntraSB

was higher in African (OR 5.24, 95% CI 3.22–8.54, P<0.001) and ‘other’ women (OR 2.18, 95% CI

1.35–3.54, P=0.002) compared to Australian-born women.

When migrants were stratified by timing of first antenatal visit, the odds of AnteSB increased in

those who commenced antenatal care later than 14 weeks gestation in women from Indian (OR

2.16, 95% CI 1.18–3.95, P=0.013), Māori (OR 3.03, 95% CI 1.43–6.45, P=0.004) and ‘other’ (OR

2.19, 95% CI 1.34–3.58, P=0.002) ethnicities.

With midwife-only intrapartum care, African and ‘other’ migrants (combined) had three times the

odds of IntraSB for viable births than Australian-born women (OR 3.43, 95% CI 1.28–9.19,


PAGE | 84

P=0.014); however, with multidisciplinary intrapartum care, the odds were similar to that of

Australian-born group (OR 1.34, 95% CI 0.30–5.98, P=0.695).

Compared to Australian-born women, migrant women who used interpreter-services had a lower

risk of stillbirth (OR 0.51, 95% CI 0.27–0.96, P=0.035) and those who did not use interpreters had a

higher risk of stillbirth (OR 1.20, 95% CI 1.07–1.35, P<0.001).

Covariates that were partially available in the dataset comprised the main limitation of the study.

Conclusion: Late commencement of antenatal care, underutilisation of interpreter service and

midwife-only intrapartum care are associated with increased risk of stillbirth in migrant women.

Education to improve early engagement with antenatal care, better uptake of interpreter services,

and the provision of a multidisciplinary-team intrapartum care to women specifically from African

and ‘other’ backgrounds may reduce the risk of stillbirth in migrants.

INTRODUCTION 5.2

Despite the availability of quality antenatal and obstetric care in most developed nations,

disparities in rates of stillbirth within and between countries continue to be reported.61,62 Where

similar health systems exist, different ethnic compositions may explain some of this variation

between countries.240

In Australia, migrant women are at increased risk of stillbirth compared with Australian-born

women, despite having access to the same health resources.74,159,241 Specifically, we observed an

increased rate of antepartum stillbirth (AnteSB) in migrant women from African, Indian, and

‘other’ non-white ethnic backgrounds.241 Further, we reported an increased rate of intrapartum

stillbirth (IntraSB) in African and ‘other’ non-white migrant ethnicities despite adjusting for several

well-established risk factors for stillbirth.241 This warranted investigation for additional factors that

may explain the higher risk of stillbirth in migrant populations. Targeting such specific factors in

these at-risk populations is imperative for evidence-based practice and a precise public health plan

for reducing the risk of stillbirth in migrants. The evidence suggests that the risk profile of migrants

differs from that of the native-born population241,242 and strategies that are effective in their

native-born counterparts, such as lowering alcohol or tobacco consumption in pregnancy, may not

yield similar effects at a population level due to a considerably low prevalence of those habits

among them.241,242 In contrast, communication barriers,157 factors such as access to or timing of


PAGE | 85

antenatal-care157,243 and uptake of other services, due to unfamiliarity, socioeconomic or private

health insurance status, may play a greater role in these groups.242-244

Thus, we hypothesised that healthcare factors might explain the disparities observed in the risk of

stillbirth between migrant and Australian-born populations. We investigated whether the pattern

of healthcare utilisation among migrant women in WA differs from that of the Australian-born

population and whether the differences influence the risk of stillbirth. We specifically investigated

the relationship between stillbirth and timing of antenatal care, using interpreter service, health

insurance status, and type of intrapartum care.

METHODS 5.3


A retrospective cohort study using routinely collected administrative health data was undertaken.

We examined de-identified data for the entire non-Indigenous population of births occurred in WA

from 1 January 2005 to 31 December 2013 using the WA Data Linkage System (WADLS) of the WA

Department of Health. No separate protocol for this study is available other than the previous

study published from the same project.241

DATA SOURCES AND LINKAGE 5.3.2

WADLS was formally established in 1995 as a collaboration between the WA Department of Health

and researchers mainly for population health research purposes. It has a highly successful history

of linking data, dating back to the 1970s and only in its first ten years of operation has supported

more than 400 studies contributed to policy, practice and wellbeing of the population.204

WADLS applies probabilistic matching based on full name and address, phonetic compression

algorithms, and other identifiers to link data from a variety of health and other administrative

datasets.211 The frequency of invalid or missed links based on evaluation of linked chains is

estimated to be very low (0.11%), and the linkage procedures are widely known as best

practice.204,211,213

Data for this study were accessed from multiple data collections. We primarily used the Midwives

Notification System (MNS), a statutory highly reliable data collection of demographic, pregnancy

and delivery information for all births in WA. The MNS adheres to strict quality assurance

processes ensuring data completeness, validity and reporting compliance.215 To supplement the

MNS data, other WA statutory data collections were used: the Hospital Morbidity Data Collection


PAGE | 86

(HMDC) containing data related to inpatient discharges from all private and public hospitals,245 the

WA Registry of Developmental Anomalies (WARDA), a database of developmental anomalies

identified by six years of age, including fetuses of terminated pregnancies228 and Birth and Death

Registrations.29. These data collections are key information sources for meeting the mandatory

and statutory reporting requirements in WA. Further, genealogical linkage through the Family

Connections Linkage Facility of WADLS was used to link women with child outcomes.223

Figure 5.1 shows the geographical location, number, and distribution of hospitals across WA.246

Additional information can be found in the S1 WA Health System.

FIGURE 5.1 GEOGRAPHICAL LOCATION OF THE PRIVATE AND PUBLIC HOSPITALS IN WESTERN AUSTRALIA.

Base image by OpenClipart-Vectors from Pixabay

EXPOSURES 5.3.3

Migrant status for mothers, defined as country of birth other than Australia, was ascertained

through mother’s place of birth variable from Birth Registration data or country of birth from

mother’s HMDC records. Mother’s place of birth variable included city, province, and country of

birth as reported by parents on the Birth Registration Form. Using this variable, a new variable was

created to classify the population as Australian-born or migrant. Place of birth from Birth

Registration data was merged with MNS data, which was complete for 99.0% of the births.

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PAGE | 87

Country of birth from mother’s hospital record was used to ascertain mother’s place of birth and

to retrieve missing values. Thus, maternal migrant status achieved 99.99% completeness for the

population of study.

The migrant population was further stratified by self-reported ethnicity using the variable ethnic

origin, which was 100% complete (MNS data), as white (Caucasian), Asian, Indian, African, Māori

and ‘other’, and compared with Australian-born women from any ethnicity. For this comparison, a

categorical variable with values assigned as Australian-born, white, Asian, Indian, African, Māori

and ‘other’ was created. We did not stratify the Australian-born population by ethnicity as we had

previously reported that the proportion of non-white Australian-born women was very small

(3.3%); their risk of stillbirth was similar to that of white Australian-born women,241 with no

stillbirths in Australian-born women from Indian or African backgrounds.

Since this project focused on migrants, any data for women of Aboriginal or Torres Strait Islander

background were excluded by design. This also eliminates the risk of non-differential

misclassification bias towards the null hypothesis74,241 as the prevalence of stillbirth among this

population is twice that of non-Indigenous Australians.247

OUTCOMES 5.3.4

According to the Australian Institute of Health and Welfare and the National Perinatal Data

Collection, stillbirth is defined as the death of a baby of at least 20 completed weeks of gestation,

or 400 grams or more birthweight if the gestation is unknown, before the complete expulsion or

extraction from its mother.28 We further categorised stillbirth as antepartum (death before

commencement of labour) or intrapartum (death after labour started).61 Data on type of stillbirth

was available for 99.98% of stillbirths by cross-source checking the status of baby at birth (MNS)

and presence or absence of the fetal heart beat at the commencement of labour (death

certificates). Any termination of pregnancy, identified through WARDA and death records, were

excluded (N=433).

OTHER VARIABLES 5.3.5

Hospital type (tertiary, metro-public, metro-private and rural-private/public), as well as interpreter

use (yes/no), indicating if an official paid interpreter service was used, and private health

insurance-status (yes/no), indicating whether the patient had hospital insurance, were available

for all births in hospital (99.0%). The two variables, interpreter use and health insurance status,


PAGE | 88

were used to investigate the potential influences of access to healthcare services and

communication barriers on the risk of stillbirth in ethnic migrant groups.

Data on intrapartum-care provider were reported as accoucheur (birth-attendant/supervisor) on

MNS and was available for all births, coded as obstetrician, other medical practitioner, midwife,

student, self/no attendant and other; each case could contain single or multiple values. For

analyses, this variable was categorised as midwife, doctor, mix (team) and self/no-care. Also, a

dichotomous variable, midwife-only (yes/no), was created.

Gestational age at first antenatal care (ANC) visit, recorded in MNS since 2010, was used to stratify

the population (2010–2013) by timing of commencement of ANC (late booking: first visit after

week 14)244,248 in a subgroup analysis.

Low birthweight (LBW), defined as birthweight less than 2500 g114, and preterm birth (PTB),

defined as birth before 37 weeks gestation98, are considered intermediate variables and not

confounders; adjusting for them in perinatal mortality analyses can create bias.249 Therefore,

instead of adjusting for these two variables, adjusting for their risk is suggested.249,250 Hence, the

predicted probability of LBW or PTB above the 95th percentile,250 titled ‘high-risk-of-LBW’ and

‘high-risk-of-PTB’, respectively, were calculated and used in the analyses.

The Index of Relative Socio-economic Disadvantage (IRSD), summarising several disadvantage

measures including low income, low education, high unemployment and unskilled occupations,219

and the Accessibility/Remoteness Index of Australia (ARIA)220 were derived and provided by

geocoding using address parsing software by WADLS. ARIA is a geographical index defining

remoteness based on accessibility to goods, services and opportunities for social interaction across

Australia based on road distance from populated towns.220 A missing subgroup was created for

ARIA and IRSD for missing data (3.3%) to keep all the cases in the analysis.


Demographic and obstetric characteristics of study groups were tabulated. Pearson χ2 or Fisher

exact tests were used as appropriate for descriptive analyses. Independent sample t-tests were

used to compare means for continuous variables. Cumulative incidence rates of stillbirth (overall,

AnteSB, IntraSB), stratified by ethnicity, were calculated throughout the study, with denominators

determined by 10 000 total births (live and stillbirth).


PAGE | 89

Univariable logistic regression analysis, for the whole study population, was used to examine the

association between risk factors and each type of stillbirth and to calculate the crude odds ratio

(OR) and 95% confidence interval (CI). Multivariable logistic regression for all analyses included

established (ethnicity,9,13,67,80,84-86 year of birth,6,67,68,80,86,194 marital status,69,72,73 maternal age

group,6,48,67-69 parity,6,67,68 plurality,6,73,241 pre-existing diabetes,41,77,80 essential

hypertension,73,74,241 previous stillbirth,74,80,241 sex of baby,80,81,241 socioeconomic disadvantage,73-

75,241 accessibility/remoteness25,46,67,75,76 and smoking during pregnancy69,74,77,78) and potential

(health insurance251 and interpreter utilization157) factors associated with stillbirth to determine

the adjusted OR (aOR). An additional intrapartum-related co-variate, only-midwife accoucheur,

was specifically added to the IntraSB analysis. A P-value < 0.05 was considered significant;

variables with P-values > 0.1 were removed from the final models.252 In order to investigate the

relationship between stillbirth and timing of antenatal care, using interpreter service, health

insurance status and type of intrapartum care in more depth, the migrant population was further

stratified according to factors of interest as follows: interpreter utilisation (yes/no) for analysing all

stillbirths combined, timing of commencement of ANC (first visit before/after week 14) and private

health insurance status for AnteSB analysis, and by type of intrapartum care (midwife-only/team)

for IntraSB analysis in women from African and ‘other’ ethnic backgrounds combined, the only

population at-risk of IntraSB . We also limited the intrapartum care analysis to viable births;

gestational age at birth >23 completed weeks.

To explore whether the increased risk of AnteSB and/or IntraSB in migrant women is mediated by

LBW and PTB, the predicted probabilities of LBW and PTB were estimated from a logistic

regression model based on baseline covariates,249,250 including ethnicity, marital status, maternal

age group, parity, plurality, presence of a medical condition or pregnancy complication, smoking

during pregnancy and socioeconomic disadvantage; the new binary variables (yes/no), ‘high-risk-

of-LBW’ and ‘high-risk-of-PTB’, were defined as the predicted probability of LBW and PTB,

respectively, above the 95th percentile250 and used as additional covariates in the analyses.

SENSITIVITY ANALYSES 5.3.7

Analyses were undertaken by excluding stillbirths with major anomalies from the analysis, due to

potential restricted access or differing attitudes to screening or termination of pregnancy reported

for some ethnic backgrounds.84,156


PAGE | 90

Further, to examine the effect of non-independence, which can arise in the analysis of large

population data when women have more than one birth during the study, we limited the

population of women to those with just one birth record in the dataset. Also, in response to peer

review comments, the cluster effect was fitted using the ‘cluster’ option in the Stata package.

Analyses were performed using Stata (version 13·1; StataCorp LP, College Station, Texas).


This study was approved by the Human Research Ethics Committee of the WA Department of

Health (2015/23). Due to the use of non-identifiable routinely collected linked administrative

health data for the whole population, written consent was not required to conduct the study.

RESULTS 5.4

From 260 997 live and stillbirths in non-Indigenous WA women from 2005–2013, 99.0% were

delivered in hospital (258 296); 66.1% to Australian-born women and 33.9% to migrant women.

Non-white migrants predominantly used tertiary and public hospitals, whereas white migrants and

Australian-born women had more private hospital separations (Table 5.1).

Among migrant populations, women from African backgrounds had the lowest proportion of

nulliparous women, the highest proportion of interpreter service use (15.7%) at the hospital and

resided in very accessible areas in WA (92.2%). Māori women had the highest proportion of

socioeconomic disadvantage (27.2%), never married (19.4%) and smoking in pregnancy (39.2%).

Migrant women from Indian background had the highest proportion of nulliparous women (58.2%)

and experienced the highest prevalence of complications of pregnancy (39.3%) and labour (73.0%)

and emergency CS (24.4%) (Table 5.2).


PAGE | 91

TABLE 5.1 DEMOGRAPHIC CHARACTERISTICS OF THE STUDY POPULATION

Characteristics Australian-born women

Migrant women All women

P

White Asian Indian African Māori Other All migrants

Total number 172 571 48 546 18 212 5503 4155 2941 9038 88 395 260 997

Marital status <0.001


3026 (6.2%)

701 (3.9%)

99 (1.8%)

543 (13.1%)

570 (19.4%)

611 (6.8%)

5550 (6.3%)

23 568 (9.0%)


360 (0.7%)

160 (0.9%)

17 (0.3%)

109 (2.6%)

33 (1.1%)

132 (1.5%)

811 (0.9%)

2366 (0.9%)


44 693 (92.1%)

17 107 (93.9%)

5327 (96.8%)

3449 (83.0%)

2268 (77.1%)

8214 (90.9%)

81 058 (91.7%)

232 917 (89.2%)

Other 1170 (0.7%)

467 (1.0%)

244 (1.3%)

60 (1.1%)

54 (1.3%)

70 (2.4%)

81 (0.9%)

976 (1.1%)

2146 (0.8%)

Parity <0.001


21 205 (43.7%)

8759 (48.1%)

3204 (58.2%)

1217 (29.3%)

955 (32.5%)

3532 (39.1%)

38 872 (44.0%)

112 340 (43.0%)


17 243 (35.5%)

6485 (35.6%)

1817 (33.0%)

1113 (26.8%)

792 (26.9%)

2695 (29.8%)

30 145 (34.1%)

90 561 (34.7%)


10 098 (20.8%)

2968 (16.3%)

482 (8.8%)

1825 (43.9%)

1194 (40.6%)

2811 (31.1%)

19 378 (21.9%)

58 096 (22.3%)

Maternal age (years) <0.001

Mean (SD) 29.5 (5.6)

31.5 (5.3)

31.2 (4.9)

29.5 (4.4)

28.8 (5.7)

26.8 (6.0)

29.9 (5.6)

30.9 (5.4)

30.0 (5.6)

<20 7474 (4.3%)

724 (1.5%)

131 (0.7%)

17 (0.3%)

197 (4.7%)

300 (10.2%)

200 (2.2%)

1569 (1.8%)

9045 (3.5%)

20–24 27 516 (16.0%)

4364 (9.0%)

1401 (7.7%)

638 (11.6%)

826 (19.9%)

889 (30.2%)

1477 (16.3%)

9595 (10.9%)

37 115 (14.2%)


PAGE | 92

25–29 49 076 (28.4%)

11 423 (23.5%)

5208 (28.6%)

2288 (41.6%)

1254 (30.2%)

786 (26.7%)

2631 (29.1%)

23 590 (26.7%)

72 673 (27.8%)

30–34 54 744 (31.7%)

17 464 (36.0%)

6937 (38.1%)

1869 (34.0%)

1169 (28.1%)

599 (20.4%)

2714 (30.0%)

30 752 (34.8%)

85 510 (32.8%)

35–39 28 412 (16.5%)

11 716 (24.1%)

3713 (20.4%)

581 (10.6%)

586 (14.1%)

290 (9.9%)

1612 (17.8%)

18 498 (20.9%)

46 914 (18.0%)

40–44 5159 (3.0%)

2703 (5.6%)

785 (4.3%)

103 (1.9%)

112 (2.7%)

77 (2.6%)

389 (4.3%)

4169 (4.7%)

9328 (3.6%)

>44 190 (0.1%)

152 (0.3%)

37 (0.2%)

<10 (0.1%)

11 (0.3%)

0 (0.0%)

15 (0.2%)

222 (0.3%)

412 (0.2%)

Maternal height (cm) <0.001

Mean (SD) 165.8 (6.7)

165.1 (6.8)

158.6 (6.0)

159.3 (6.0)

164.5 (7.1)

165.8 (6.0)

161.7 (6.8)

163.0 (7.2)

164.9 (7.0)

Medical conditions <0.001

Pre-existing diabetes mellitus

1040 (0.6%)

285 (0.6%)

97 (0.5%)

53 (1.0%)

30 (0.7%)

15 (0.5%)

63 (0.7%)

543 (0.6%)

1583 (0.6%)

Hypertension 2134 (1.2%)

579 (1.2%)

109 (1.6%)

28 (0.5%)

33 (0.8%)

31 (1.1%)

70 (0.8%)

850 (1.0%)

2984 (1.1%)

Smoked in pregnancy

24 097 (14.0%)

4161 (8.6%)

317 (1.7%)

40 (0.7%)

78 (1.9%)

1152 (39.2%)

494 (5.5%)

6242 (7.1%)

30 342 (11.6%)

<0.001

Private health insurance

73 774 (43.1%)

19 247 (40.2%)

5495 (30.2%)

1379 (25.0%)

380 (9.2%)

153 (5.3%)

1471 (16.3%)

59 374 (32.1%)

101 902 (39.4%)

<0.001

Hospital category <0.001

Tertiary 26 337 (15.4%)

8081 (16.9%)

5425 (29.8%)

1956 (35.5%)

1682 (40.7%)

524 (18.1%)

4271 (47.6%)

21 796 (24.9%)

48 278 (18.7%)

Public metropolitan 39 285 (23.0%)

10 681 (22.3%)

6268 (34.5%)

2262 (41.1%)

2028 (49.0%)

1424 (49.1%)

2660 (29.6%)

25 320 (29.0%)

64 611 (25.0%)

Rural public/private 33 401 (19.5%)

4834 (10.1%)

1286 (7.1%)

224 (4.1%)

177 (4.3%)

718 (24.7%)

675 (7.5%)

7910 (9.0%)

41 317 (16.0%)


PAGE | 93

Private metropolitan 72 061 (42.1%)

24 326 (50.8%)

5201 (28.6%)

1067 (19.4%)

251 (6.1%)

237 (8.2%)

1367 (15.2%)

32 446 (37.1%)

104 513 (40.4%)

Interpreter service used

19 (0.0%)

306 (0.6%)

1779 (9.8%)

232 (4.2%)

651 (15.7%)

0 (0.0%)

931 (10.4%)

3896 (4.5%)

3918 (1.5%)

<0.001

Socioeconomic disadvantage <0.001

Most disadvantaged 40 521 (23.5%)

6547 (13.5%)

2181 (12.0%)

663 (12.1%)

513 (12.4%)

800 (27.2%)

1276 (14.1%)

11 980 (13.6%)

52 504 (20.1%)

Remaining population

124 940 (73.0%)

40 013 (83.5%)

15 433 (84.9%)

4652 (84.4%)

3496 (84.5%)

2035 (70.1%)

7436 (82.9%)

73 053 (83.4%)

198 013 (76.5%)

Accessibility/remoteness index of Australia <0.001

Highly accessible 124 890 (72.4%)

40 836 (84.1%)

16 160 (88.7%)

5035 (91.5%)

3830 (92.2%)

2060 (70.0%)

7948 (87.9%)

75 869 (85.8%)

200 778 (76.9%)

Accessible 15 736 (9.1%)

2390 (4.9%)

474 (2.6%)

78 (1.4%)

51 (1.2%)

224 (7.6%)

205 (2.3%)

3422 (3.9%)

19 162 (7.3%)

Moderately accessible

16 125 (9.34%)

2162 (4.5%)

514 (2.8%)

102 (1.9%)

85 (2.1%)

414 (14.1%)

293 (3.2%)

3570 (4.0%)

19 699 (7.6%)

Remote 7844 (4.6%)

1282 (2.6%)

396 (2.2%)

70 (1.3%)

39 (0.9%)

116 (3.9%)

231 (2.6%)

2134 (2.4%)

9979 (3.8%)

Very remote 2062 (1.2%)

415 (0.9%)

97 (0.5%)

23 (0.4%)

23 (0.6%)

49 (1.7%)

92 (1.1%)

699 (0.8%)

2761 (1.1%)


PAGE | 94

TABLE 5.2 OBSTETRIC CHARACTERISTICS OF THE STUDY POPULATION

Australian-born

women



All births 172 571 (66.1%)

48 546 (18.6%)

18 212 (7.0%)

5503 (2.1%)

4155 (1.6%)

2941 (1.1%)

9038 (3.5%)

88 395 (33.9%)

260 997 (100%)

1st ANC visit*

Median (IQRs)

Mean (SD)

10 (8)

12.6 (7.4)

12 (10)

13.5 (7.5)

11 (12)

13.8 (8.0)

11 (13)

13.4 (7.9)

15 (15)

16.2(8.9)

14 (16)

16.4 (9.7)

15 (14)

15.9 (8.8)

12 (12)

14.0 (8.0)

11 (10)

13.1(7.7)

Plurality 0.025

Singleton 167 481 (97.1%)

47 075 (97.0%)

17 822 (97.9%)

5389 (97.9%)

4031 (97.0%)

2883 (98.0%)

8725 (96.5%)

85 925 (97.2%)

253 435 (97.1%)


1471 (3.0%)

390 (2.1%)

114 (2.1%)

124 (3.0%)

58 (2.0%)

313 (3.5%)

2470 (2.8%)

7562 (2.9%)

Pregnancy complications <0.001

Gestational diabetes 7710 (4.5%)

2732 (5.6%)

2306 (12.7%)

868 (15.8%)

312 (7.5%)

117 (4.0%)

862 (9.5%)

7197 (8.1%)

14 907 (5.7%)


1194 (2.5%)

325 (1.8%)

124 (2.3%)

127 (3.1%)

77 (2.6%)

216 (2.4%)

2063 (2.3%)

7177 (2.8%)

Any complication 57 596 (33.4%)

15 330 (31.6%)

6489 (35.6%)

2162 (39.3%)

1339 (32.2%)

925 (31.5%)

3214 (35.6%)

29 459 (33.3%)

87 059 (33.4%)

Onset of labour <0.001

Spontaneous 83 237 (48.3%)

23 800 (49.0%)

10 804 (59.3%)

2921 (53.1%)

2585 (62.2%)

1975 (67.2%)

5084 (56.3%)

47 169 (53.4%)

130 428 (50.0%)

Induced 51 443 (29.8%)

13 356 (27.5%)

3824 (21.0%)

1527 (27.8%)

1037 (25.0%)

680 (23.1%)

2273 (25.2%)

22 697 (25.7%)

74 147 (28.4%)

Elective Caesarean 37 891 (22.0%)

11 390 (23.5%)

3584 (19.7%)

1055 (19.2%)

533 (12.8%)

286 (9.7%)

1681 (18.6%)

18 529 (21.0%)

56 422 (21.6%)


PAGE | 95

Complication of labour/delivery

106 318 (61.6%)

30 258 (62.3%)

12 249 (67.3%)

4016 (73.0%)

2833 (68.2%)

1700 (57.8%)

6301 (69.7%)

57 357 (64.9%)

163 691 (62.7%)

Mode of delivery <0.001

Spontaneous vaginal 88 492 (51.3%)

23 187 (47.8%)

8400 (46.1%)

2058 (37.4%)

2531 (60.9%)

2148 (73.0%)

4719 (52.2%)

43 043 (48.7%)

131 558 (50.4%)

Instrumental vaginal 24 796 (14.4%)

7452 (15.4%)

3307 (18.2%)

1252 (22.8%)

386 (9.3%)

237 (8.1%)

1225 (13.6%)

13 859 (15.7%)

38 660 (14.8%)

Caesarean 59 283 (34.4%)

17 907 (36.9%)

6505 (35.7%)

2193 (39.9%)

1238 (29.8%)

556 (18.9%)

3094 (34.2%)

31 493 (35.6%)

90 779 (34.8%)

Emergency Caesarean 26 144 (15.2%)

7954 (16.4%)

3484 (19.1%)

1340 (24.4%)

844 (20.3%)

331 (11.3%)

1756 (19.4%)

15 709 (17.8%)

41 855 (16.0%)

<0.001

Accoucheur <0.001

Obstetrician 58 780 (34.1%)

16 888 (34.8%)

5070 (27.8%)

1131 (20.6%)

370 (8.9%)

254 (8.6%)

1426 (15.8%)

25 139 (28.4%)

83 926 (32.2%)

Other medical practitioners

27 837 (16.1%)

9255 (19.1%)

3342 (18.4%)

1422 (25.8%)

1010 (24.3%)

362 (12.3%)

2011 (22.3%)

17 402 (19.7%)

45 243 (17.3%)

Midwife 49 508 (28.7%)

12 903 (26.6%)

4364 (24.0%)

1081 (19.6%)

1362 (32.8%)

1448 (49.2%)

2470 (27.3%)

23 628 (26.7%)

73 153 (28.0%)

Mix (team) 36 196 (21.0%)

9429 (19.4%)

5406 (29.7%)

1866 (33.9%)

1399 (33.7%)

864 (29.4%)

3112 (34.4%)

22 076 (25.0%)

58 274 (22.3%)

Self/No one 250 (0.1%)

71 (0.2%)

30 (0.2%)

<10 14 (0.3%)

13 (0.4%)

19 (0.2%)

150 (0.2%)

401 (0.2%)

Sex of baby 0.980

Boy 88 307 (51.2%)

24 720 (50.9%)

9466 (52.0%)

2813 (51.1%)

2131 (51.3%)

1490 (50.7%)

4608 (51.0%)

45 228 (51.2%)

133 549 (51.2%)

Gestational age (weeks) <0.001

20–27 1025 (0.6%)

261 (0.5%)

121 (0.7%)

45 (0.8%)

55 (1.3%)

25 (0.9%)

94 (1.0%)

601 (0.7%)

1626 (0.6%)

28–31 1288 328 123 47 41 19 72 630 1919


PAGE | 96

(0.8%) (0.7%) (0.7%) (0.9%) (1.0%) (0.7%) (0.8%) (0.7%) (0.7%)

32–36 11 893 (6.9%)

3172 (6.5%)

1260 (6.9%)

399 (7.3%)

235 (5.7%)

173 (5.9%)

622 (6.9%)

5861 (6.6%)

17 754 (6.8%)

37–41 157 498 (91.3%)

44 520 (91.7%)

16 665 (91.5%)

4997 (90.8%)

3737 (89.9%)

2705 (92.0%)

8187 (90.6%)

80 811 (91.4%)

238 336 (91.3%)

≥42 867 (0.5%)

265 (0.6%)

43 (0.2%)

15 (0.3%)

87 (2.1%)

19 (0.7%)

63 (0.8%)

492 (0.6%)

1362 (0.5%)

Birthweight Mean (SD) 3379.03 (585.0)

3371.9 (572.7)

3217.3 (549.3)

3114.7 (563.1)

3269.9 (630.3)

3403.8 (621.2)

3274.3 (620.7)

3310.3 (582.8)

3355.8 (585.2)

Pregnancy outcomes

Live birth 171 759 (99.5%)

48 315 (99.5%)

18 117 (99.5%)

5464 (99.3%)

4104 (98.8%)

2923 (99.4%)

8972 (99.3%)

87 895 (99.4%)

259 684 (99.5%)

0.001

Stillbirth (total) 812 (0.5%)

231 (0.5%)

95 (0.5%)

39 (0.7%)

51 (1.2%)

18 (0.6%)

66 (0.7%)

500 (0.6%)

1313 (0.5%)

0.001

Antepartum 605 (0.4%)

162 (0.3%)

69 (0.4%)

31 (0.6%)

31 (0.8%)

15 (0.5%)

47 (0.5%)

355 (0.2%)

960 (0.4%)

0.002

Intrapartum 185 (0.1%)

57 (0.1%)

24 (0.1%)

<10 (<0.1%)

20 (0.5%)

<10 (<0.1%)

19 (0.2%)

131 (0.2%)

317 (0.1%)

0.001

*Gestational age at first antenatal care visit. Available from January 2010 onwards for 123 655 births (47.4% of the total population). Note: Cells may not add up to 100% due to some variables having multiple values or some characteristics presented in more than one variable


PAGE | 97

ANTEPARTUM STILLBIRTH 5.4.1

In the multivariable analysis, primiparity, living in remote areas, private health insurance and using

interpreter services were associated with a lower risk of AnteSB (Table 5.3), while age>35 years,

multiple pregnancy, pre-existing diabetes and smoking were associated with increased risk of

AnteSB. However, controlling for these factors did not attenuate the odds of AnteSB in African,

Indian and ‘other’ migrant populations; if anything, the OR increased after adjusting for these

covariates (Table 5.3).

INTRAPARTUM STILLBIRTH 5.4.2

Female offspring, parity and socioeconomic disadvantage were associated with a lower risk of

IntraSB. Conversely, multiple pregnancy, pre-existing diabetes, age>35 years, living in very remote

areas, smoking, and midwife-only accoucheur were associated with a higher risk of IntraSB;

however, controlling for these factors did not explain and even increased the effect measure for

IntraSB in women from African and ‘other’ ethnic backgrounds (Table 5.4). When the analysis was

undertaken in viable birth (>23 weeks gestation), the higher odds of IntraSB remained significant

in women from African background (aOR 4.78, 95% CI 1.86–12.30 ) but lost significance in women

from ‘other’ ethnic backgrounds (aOR 1.96, 95% CI 0.78–4.97).


PAGE | 98

TABLE 5.3 LOGISTIC REGRESSION MODEL AND THE FACTORS ASSOCIATED WITH ANTESB (2005–2013)

Variables N Rates1 OR (Univariable) 95% CI aOR (Multivariable) 95% CI

Migrant status and ethnicity

Australian-born (Reference) 605 35 1.00 1.00

Overseas-born 355 40

White 162 33 0.95 0.80–1.13 0.94 0.78–1.12

Asian 69 38 1.08 0.84–1.39 1.21 0.93–1.56

Indian 31 56 1.61 1.12–2.31 1.64 1.13–2.44

African 31 75 2.14 1.49–2.07 2.22 1.48–2.13

Māori 15 51 1.46 0.87–2.44 1.24 0.74–2.08

Other 47 52 1.49 1.10–2.00 1.46 1.07–1.97

Previous SB 3.06 2.18–4.29 2.67 1.87–3.81

Maternal age group

20–24 (Reference) 1.00

<20 1.03 0.73–1.47 0.90 0.62–1.29

25–29 0.73 0.60–0.90 0.81 0.65–1.00

30–34 0.82 0.67–1.00 1.02 0.83–1.27

35–39 1.01 0.82–1.25 1.22 0.97–1.53

40–44 1.26 0.91–1.74 1.45 1.05–2.07

≥45 1.75 0.56–5.51 1.20 0.30–4.90

Parity

Nulliparous 1.00

Primiparous 0.78 0.68–0.90 0.76 0.65–0.88

Multiparous 1.10 0.94–1.29 0.89 0.75–1.06

Plurality


PAGE | 99

Singleton pregnancy 1.00

Multiple pregnancy 3.98 3.23–4.90 3.99 3.24–4.95

Medical conditions/pregnancy complications

Pre-existing diabetes mellitus 2.08 1.18–3.69 1.90 1.07–3.38

Essential hypertension 1.66 1.04–2.64 1.53 0.95–2.48

Smoked in pregnancy 1.40 1.17–1.68 1.30 1.07–1.56

Socioeconomically disadvantaged2 1.18 1.01–1.37 1.16 0.98–1.38

Accessibility/remoteness index of Australia

Highly accessible 1.00 1.00

Accessible 1.07 0.85–1.36 1.06 0.83–1.35

Moderately accessible 1.17 0.93–1.46 1.06 0.82–1.36

Remote 0.68 0.45–1.00 0.65 0.44–0.98

Very remote 0.88 0.46–1.70 0.90 0.46–1.74

Private health insurance 0.64 0.56–0.74 0.68 0.58–0.79

Interpreter service used 0.43 0.21–0.92 0.33 0.16–0.71 1Cumulative incidence rates are per 10 000 total births.

2The bottom 20% of the Index of Relative Socioeconomic Disadvantage was compared with the remaining population.

Bold values are P<0.05. Note: Factors in the table are those that have been included in the multivariable (adjusted model) analysis. These factors are not mutually exclusive and might coincide in the same woman. Abbreviations: AnteSB, antepartum stillbirth; aOR, adjusted odds ratio; OR, odds ratio; SB, stillbirth


PAGE | 100

TABLE 5.4 LOGISTIC REGRESSION MODEL AND THE FACTORS ASSOCIATED WITH INTRASB (2005–2013)

Variables N Rates1 OR (Univariable) 95% CI aOR (Multivariable) 95% CI


Australian-born (reference) 185 11 1.00 1.00

Overseas-born 131 15

White 57 12 1.10 0.81–1.47 1.00 0.74–1.36

Asian 24 13 1.23 0.80–1.88 1.36 0.93–2.24

Indian <10 15 1.36 0.67–2.75 1.60 0.78–3.30

African 20 48 4.51 2.84–7.16 5.24 3.35–8.91

Māori <10 10 0.95 0.30–2.98 0.75 0.24–2.36

Other 19 21 1.96 1.22–3.15 2.18 1.34–3.54

Previous SB 4.32 2.61–7.15 6.51 3.79–11.20

Year of birth

2005 (reference)

2006 1.58 0.93–2.68 1.62 0.96–2.76

2007 1.63 0.97–2.74 1.74 1.03–2.94

2008 1.31 0.76–2.25 1.34 0.78–2.30

2009 1.37 0.80–2.33 1.44 0.84–2.46

2010 1.09 0.62–1.91 1.22 0.70–2.14

2011 1.36 0.80–2.31 1.44 0.85–2.46

2012 1.64 0.99–2.72 1.69 1.01–2.82

2013 1.19 0.70–2.04 1.22 0.71–2.10

Baby sex

Female 0.77 0.61–0.96 0.74 0.59–0.93

Maternal age group


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20–24 (reference) 1.00

Less than 20 1.09 0.59–2.01 0.85 0.44–1.67

25–29 0.84 0.59–1.20 1.10 0.79–1.66

30–34 0.85 0.60–1.20 1.37 0.96–2.04

35–39 0.98 0.68–1.43 1.72 1.18–2.70

40–44 1.30 0.74–2.29 2.44 1.43–4.83

≥45 1.84 0.25–13.36 2.34 0.36–17.99

Parity

Nulliparous (reference) 1.00

Primiparous 0.81 0.63–1.04 0.56 0.44–0.73

Multiparous 0.69 0.50–0.93 0.31 0.23–0.50

Plurality

Singleton pregnancy (reference) 1.00

Multiple pregnancy 8.04 6.08–10.64 16.42 12.03–22.40

Medical conditions/Pregnancy complications

Pre-existing Diabetes Mellitus 2.24 0.83–6.00 3.13 1.15–8.55

Smoked in pregnancy 1.45 1.07–1.96 1.53 1.10–2.09

Socioeconomically disadvantaged2 0.68 0.50–0.93 0.67 0.48–0.94

Accessibility/remoteness index of Australia

Highly accessible (reference) 1.00

Accessible 0.69 0.42–1.13 0.82 0.50–1.33

Moderately accessible 0.63 0.38–1.05 0.71 0.41–1.20

Remote 0.86 0.47–1.57 0.84 0.44–1.59

Very remote 2.27 1.12–4.59 2.95 1.44–6.07

Birth attendant/supervisor3

Only midwife 5.05 4.00–6.38 9.00 6.85–11.82


PAGE | 102

1Cumulative incidence rates are per 10 000 total births.

2The bottom 20% of the Index of Relative Socioeconomic Disadvantage was compared with the remaining population.

Bold values are P<0.05. Note: Factors in the table are those that have been included in the multivariable (adjusted model) analysis. These factors are not mutually exclusive and might coincide in the same woman. Abbreviations: AnteSB, antepartum stillbirth; aOR, adjusted odds ratio; OR, odds ratio; SB, stillbirth


PAGE | 103

ANTENATAL CARE 5.4.3

Migrant women commenced antenatal-care visits (ANC visit) 1.5 weeks later (P<0.001) than

Australian-born women (Table 5.2). African and ‘other’ migrant women, specifically, commenced

ANC five weeks later than Australian-born women (P<0.001): more than 50% had their first visit

after week 14 of pregnancy (Table 5.2 & Figure 5.2).

FIGURE 5.4 GESTATIONAL AGE AT FIRST ANTENATAL CARE VISIT FOR SPECIFIED ETHNICITY GROUPS IN COMPARISON

TO THE AUSTRALIAN-BORN POPULATION (2010–2013)

When the population was stratified by late ANC-booking, the increased risk of AnteSB was

confined to those who booked late in Indian (OR 1.49, 95% CI 0.85–2.60 vs Late OR 2.16, 95% CI

1.18–3.95), Māori (OR 0.90, 95% CI 0.29–2.81 vs Late OR 3.03, 95% CI 1.43–6.44) and ‘other’

migrant women (OR 1.08, 95% CI 0.57–2.04 vs Late OR 2.19, 1.34–3.58). This finding was not

observed in migrant women from African backgrounds and remained the same in the adjusted

analysis (Table 5.5).


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TABLE 5.5 COMPARISON OF ANTEPARTUM STILLBIRTH IN MIGRANT WOMEN, STRATIFIED BY ETHNICITY AND TIMING

OF 1ST ANTENATAL CARE VISIT, WITH AUSTRALIAN-BORN WOMEN (2010–2013)

Characteristics AnteSB

Australian-born (Ref)1 (N=76 875) 1.00

First ANC visit in pregnancy

Migrant women (N) At any time (all)

aOR (95% CI)2

At/before week 14

aOR (95% CI)2

After week 14

aOR (95% CI)2

White (23 162) 1.06 (0.83–1.36) 1.05 (0.82–1.35) 0.89 (0.58–1.37)

Asian (10 514) 1.30 (0.93–1.83) 1.41 (0.96–2.10) 1.05 (0.58–1.91)

Indian (4069) 1.91 (1.25–2.94) 1.66 (0.94–2.92) 2.27 (1.23–4.21)

African (2303) 1.95 (1.16–3.26) 2.52 (1.36–4.67) 1.29 (0.56–2.16)

Māori (1681) 1.42 (0.75–2.69) 0.73 (0.23–2.30) 2.33 (1.09–5.01)

Other (5047) 1.53 (1.02–2.29) 1.03 (0.52–1.92) 2.09 (1.26–3.46) 1The reference group comprised all Australian-born women regardless of ethnicity and timing of first ANC visit.

2Adjusted for previous stillbirth, maternal age group, parity, plurality, socioeconomic status, remoteness/accessibility,

pre-existing diabetes mellitus, smoking in pregnancy, interpreter use and private health insurance status Bold values indicate P<0.05. Abbreviations: aOR, adjusted odds ratio; AnteSB, antepartum stillbirth

BIRTH ATTENDANT AND INTRAPARTUM CARE 5.4.4

When the birth attendant was a midwife, with no accompanying doctor, the odds of IntraSB in

African and ‘other’ women were more than three times that of Australian-born women (OR 3.43,

95% CI 1.28–9.19), which remained significant after adjusting for other factors. However, when

the intrapartum care was provided by mix attendants (team), the odds were the same (OR 1.34,

95% CI 0.30–5.98) with no difference after adjustment. For viable births (>23 weeks gestation),

intrapartum care with a midwife, mix (team) and doctor attendants showed similar IntraSB rates in

Australian-born women (Figure 5.3). In contrast, the IntraSB rate was more than 3-fold (P=0.009)

higher in African and ‘other’ migrants than Australian-born women with midwife-only attendants.

No difference in IntraSB rates of migrant and Australian-born women was observed with

intrapartum care by a doctor or by mix (team) attendants (Figure 5.3). No IntraSB was observed in

African or ‘other’ women when birth was attended by an obstetrician (N=1793).


PAGE | 105

FIGURE 5.3 CUMULATIVE INCIDENCE RATE OF INTRAPARTUM STILLBIRTH (POST 23 WEEKS GESTATION) BY

ACCOUCHEUR PROVIDING INTRAPARTUM CARE (2005–2013).

Note: medical birth attendant/supervisor indicates that birth was attended by an obstetrician and/or other medical practitioners whereas mix (team) attendant/supervisor refers to occasions when both medical and midwifery accoucheur were present at birth

INTERPRETER SERVICE 5.4.5

The rate of overall SB was lower (26 vs 58 per 10 000 births, P=0.011) in migrant women who used

interpreter services from those who did not, especially among African women (31 vs 141 per

10 000 births, P=0.034; Figure 5.4). Compared with Australian-born women, migrants who used

interpreter service had lower odds of SB (OR 0.51, 95% CI 0.27–0.96), whereas their counterparts

who did not have an interpreter had higher odds (OR 1.20, 95% CI 1.07–1.35).

FIGURE 5.4 CUMULATIVE INCIDENCE RATE OF OVERALL STILLBIRTH ACCORDING TO INTERPRETER SERVICE USE

(2005–2013)


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CONTROLLING FOR THE EFFECT OF LBW AND PTB 5.4.6

High-risk-for-LBW and high-risk-for-PTB were both strongly associated with higher odds of AnteSB

(OR 4.47, 95% CI 3.80–5.25 and OR 4.13, 95% CI 3.50–4.88, respectively) and IntraSB (OR 7.01,

95% CI 5.46–8.99 and OR 6.34, 95% CI 4.92–8.19, respectively). Adjusting for high-risk-for-LBW

attenuated the increased risk of AnteSB for Indian women (aOR 1.16, 95% CI 0.77–1.73) but not

for African (aOR 2.02, 95% CI 1.39–2.95) or ‘other’ (aOR 1.41, 95% CI 1.04–1.91) women. Adjusting

for high-risk-for-PTB did not alter the odds of AnteSB or IntraSB for any ethnicity.

PRIVATE HEALTH INSURANCE 5.4.7

Compared to Australian-born women, women from an Indian background who had private health

insurance did not experience increased odds of AnteSB (aOR 1.14, 95% CI 0.47–2.77), whereas

those who did not have private health insurance showed higher odds (aOR 2.04, 95% CI 1.35–

3.09).

SENSITIVITY ANALYSIS 5.4.8

Removing stillbirths with major anomalies or limiting the population of study to women with only

one birth record in the dataset did not result in any appreciable differences in the primary

findings.

DISCUSSION 5.5

In this linked data study, we showed that having private health insurance and using interpreter

services were associated with a lower risk of AnteSB and that midwife-only accoucheur was

associated with a higher risk of IntraSB. However, controlling for these factors, in addition to other

factors, did not wholly explain the increased risk of either AnteSB in Indian, African and ‘other’

ethnic migrants or IntraSB in African and ‘other’ migrants. In further stratified analyses, late

commencement of ANC visits and lack of access to or uptake of intrapartum care by doctors

emerged as underlying factors for the increased risk of SB in at-risk groups of migrants. Migrant

women from an Indian background who had private health insurance did not experience increased

odds of AnteSB, and those from African and ‘other’ backgrounds who had a doctor–midwife team

intrapartum care did not have a higher rate of IntraSB compared with Australian-born women.

Indian migrants, as a population, commenced ANC visits early, and 25% had private health

insurance, yet they had a high rate of AnteSB. Stratifying the population by the timing of ANC visit

in the analysis confirmed that the increased AnteSB was unique to Indian women who booked


PAGE | 107

late. Being high-risk-for-LBW regardless of cause, PTB or fetal growth restriction (FGR)73,77 had a

great impact on AnteSB in Indian migrants. This suggests that early engagement with the

healthcare system may reduce the number of SBs, perhaps through interventions that prevent PTB

and/or FGR, particularly in this group. Contrary to reports from the United States253, having private

health insurance was associated with a reduced risk of SB in this group in our study. Perinatal

mortality disparities between public and private care, though not stratified by ethnicity, have been

reported in Queensland, Australia.251

Detecting third-trimester FGR is challenging; clinical assessment misses approximately one-third of

cases254, ultrasound assessment is costly, and the most appropriate ultrasound biometry charts to

use, especially for non-white ethnic minorities and naturally short-stature, are controversial.255-257

It is of note that migrant women of Asian ethnicity, despite having similar height to Indian women,

did not have a high rate of AnteSB in our study. However, a larger proportion had private health

insurance, an obstetrician accoucheur, and delivered at a private hospital, which may indicate

better access to ultrasound during pregnancy. The finding that controlling for high-risk-for-LBW

status attenuated the increased odds of AnteSB in women from Indian background may also

indicate the difficulty with detecting FGR, especially in those who commence ANC visits late.

Universal third-trimester ultrasonography in the United Kingdom tripled detection of small-for-

gestational-age infants at risk of adverse perinatal outcomes.258 Thus, improving access to more

frequent ultrasound surveillance during pregnancy and third trimester for migrant women of

Indian ethnicity in public settings may afford a simple intervention to reduce the rate of AnteSB in

this high-risk group.

Stratifying by the timing of the first ANC visit also showed that, in women from ‘other’

backgrounds, the odds of AnteSB increased in those who commenced ANC later than 14 weeks.

Further, by removing pre-viable births, the increased odds of IntraSB in this group attenuated.

Timing of first ANC visit is important for ensuring optimal pregnancy outcomes and late booking

may result in loss of opportunity for potential comorbidity diagnosis and intervention.248,259

Moreover, interventions with demonstrated success in preventing PTB in some women (such as

aspirin, progesterone and cervical cerclage) are only effective when started early in pregnancy,

especially for pre-viable PTB prevention.260,261 Interventions can only be offered if women engage

early with ANC.


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Migrant women who used interpreter services had lower odds of SB than those who did not,

particularly in women of African ethnicity. Interpreter use among Chinese-born women residing in

WA was associated with lower rates of PTB and close to the rate of PTB reported in China;163 to

our knowledge, this is the first time that this factor has been examined in relation to the risk of SB.

We were surprised by the lower rates of SB in migrants who used an interpreter as most non-

white migrants in WA are from regions with high rates of SB.241 Variation in the rates of interpreter

use between migrant groups may reflect differences in English comprehension, willingness to use

interpreters and/or culture; evidence shows that in some ethnic groups, the husband acts as the

interpreter or insists on providing language support for women even when accredited interpreters

are available.157 The relationship between no-interpreter service used and higher rates of stillbirth

is concerning; it is not clear whether this group had enough English language proficiency or did not

use an interpreter due to the lack of/reluctance to access such services. The finding that migrant

women who used an interpreter had a lower rate of stillbirth may indicate the provision of a more

culturally responsive healthcare service; thus, this may be an opportunity for intervention to

reduce the rate of stillbirth.

Experts have expressed concern over the lack of access to interpreter services, often due to

unfamiliarity of migrants with the Australian health system and lack of awareness of their

entitlement to nationwide interpreter services free of charge.262-264 The Doctors Priority Line, run

through the Department of Immigration and Citizenship’s Translating and Interpreting Service,

links interpreters in over 160 languages, 24 h per day, seven days per week to doctors by

telephone within three minutes of their call, and can arrange on-site interpreters.264 Yet, the use

of family members instead of professional interpreters was reported in at least 49% of patients

during their inpatient stay in a study.263 Although it is well-established that language barriers are

linked to lower quality of care, misconceptions about the use of an interpreter being costly, time-

consuming or threatening confidentiality, or even uncertainty about the responsibility for

contacting interpreter service by healthcare providers, may also play a role in low uptake of this

national fee-free service which is unique in the Anglophone world.264-266

In this study, midwife-only accoucheur was associated with increased IntraSB even after removing

pre-viable births. To date, this area has not been thoroughly evaluated due to the large sample

size required for adequate statistical power. In New Zealand, where the majority of births are

delivered by midwives, an unexplained excess in adverse birth outcomes with midwife-led births


PAGE | 109

compared with medical-led births was reported.267 Despite 244 047 pregnancies, that study was

underpowered to evaluate stillbirth (321 stillbirths) and could not differentiate the type of

stillbirth.267 Further, the study did not control for confounders such as place of residence.268 We

had a substantially larger sample size (1313 stillbirths), adjusted for accessibility/remoteness and

socioeconomic disadvantage and knew the type of stillbirth; midwife-only intrapartum care was

associated with increased risk of IntraSB after 23 weeks gestation in migrants but not in

Australian-born population. The exact mechanism for this is unclear and warrants further research

as IntraSB rates in African and ‘other’ migrants with doctor–midwife (team) intrapartum care were

similar to Australian-born women. Of note, African women also had four-fold higher rates of post-

term pregnancy compared to Australian-born women. Whether this indicates a ‘preference’ for a

model of care for African and other ethnic women in choosing midwife-only care, reluctance to

undergo medical interventions (e.g. instrumental/caesarean delivery)19,26 or lack of access to

doctors due to logistics or timing of reaching facilities is not understood. Future investigation is

required to understand this difference and to develop strategies to reduce IntraSB in this at-risk

migrant group.

OTHER FINDINGS 5.5.1

Stillbirth rates are reportedly higher in remote and very remote areas in Australia;46 in this study,

we found that the rate of AnteSB in remote areas was lower than highly accessible areas. This

observation may be due to previous studies reporting all stillbirths, whereas we dichotomised into

AnteSB and IntraSB. Further, in WA, high-risk pregnancies are typically transferred to regional

centres or tertiary hospitals in the state capital; hence the population remaining in remote and

very remote areas are, by design, at low risk of AnteSB.25,269,270 In contrast, the increased rate of

IntraSB in very remote areas may indicate a lack of timely access to emergency intervention, due

to late arrival at the facility or long decision-delivery interval in obstetric emergencies for which

we did not have data.271,272 Additional information about the WA healthcare system and the

current model of care to improve rural perinatal outcomes is provided in the S1 WA Health. It

should be noted, however, that most migrant women lived in highly accessible areas and major

cities in our population. Thus, these findings cannot be used to draw inferences for specific ethnic

groups’ risk of stillbirth in relation to remoteness due to very small numbers. Thus, the observed

decreased and increased risk of AnteSB and IntraSB in remote and very remote regions,


PAGE | 110

respectively, effectively shows such risk in the non-Indigenous Australian-born population living in

those regions relative to those living in highly accessible regions.

The socioeconomic disadvantage being protective of IntraSB was an unexpected finding and may

be due to factors that we were unable to adjust for, such as (lower) Body Mass Index (BMI)79 or

healthcare options available to these women such as doctor–midwife (team) care. It was noted

that 23.5% of Australian-born versus 13.5% of the migrant population was in this socioeconomic

disadvantage category.

GENERALISABILITY AND CLINICAL RELEVANCE 5.5.2

The reported findings can be generalised to high-income settings serving migrant residents from

similar ethnic backgrounds, including other states of Australia, Europe and Canada. These findings

are of particular relevance to clinical practice. Primary healthcare services can be used to improve

health literacy and familiarity with the health system for migrant women, including information on

the necessity of early engagement with antenatal care program, crucial role of communication and

effective use of interpreter services, and value of doctor–midwife (team) intrapartum care.

Developing tools and guidelines for healthcare providers to assess their patient’s sufficient ability

to communicate in English is crucial for ensuring mutual understanding and effective transfer of

information.

STRENGTH 5.5.3

This is the most comprehensive stillbirth study on pregnancy outcomes of migrants from diverse

ethnic backgrounds in Australia. Access to a variety of databases and numerous variables made

cross-source ascertainment of exposure and outcomes possible and optimised accuracy and

completeness of data. The ability to differentiate type of stillbirth for 99.98% stillbirths to

investigate intrapartum factors, not previously undertaken, can guide appropriate policy and

practice to reduce IntraSBs in migrants and very remote areas.

LIMITATIONS 5.5.4

The main limitation of this study was that gestational age at first ANC visit was only recorded from

2010 onwards and was available for four years of our study period. Thus, the related analyses

reported were only possible for almost half of the whole study population.

We used whole-population linked data for this study. One limitation with analysis of large

population datasets can be the non-independence of data arising from occasions where women


PAGE | 111

have had more than one birth or stillbirth during the study period. In this study, accounting for

clustering or limiting the analyses to women with only one birth record did not affect the results.

Another limitation is the risk of misclassification due to the use of linked administrative health

data that have not been collected for this specific research. We conducted cross-source

ascertainment through multiple datasets; however, a residual risk of misclassification towards the

null hypothesis for the analyses used ethnicity (the variable only available from MNS) may remain.

For example, Indian women with a high risk of AnteSB have been misclassified as Asian. In such a

case, the actual risk of AnteSB could be higher than reported. However, such risk is very low as a

validation study of MNS confirmed the reliability of this database with a Proportion Records

Correct of 94.1% for the variable ethnicity.235

Also, the standard of care may change over time and create bias. We have adjusted the analysis

for year of birth to avoid such bias in our study.

Moreover, confounding due to covariates not available in the dataset (i.e. BMI given that obesity is

associated with increased risk of stillbirth) may also be present.77,79 However, we have adjusted

the analyses for many covariates, including pre-existing diabetes mellitus and essential

hypertension that are associated with high BMI.

Further, in the LBW/PTB analysis, the predictive power of LBW/PTB depends on the size of the

baseline covariates. Thus, a larger set of variables may change the estimates obtained to designate

‘high-risk’ status.250

Also, ARIA may not show intra-city difficulties that individuals may face for accessing services. The

accoucheur variable only records the birth attendant; neither the antenatal-care provider nor the

time from seeking intrapartum care to delivery was known.

Finally, despite the use of a large whole-population dataset, it should be noted that in the

interpretation of findings, multiple comparisons such as those in the adjusted intrapartum-related

analyses may have led to small numbers of samples in those analyses.

Considering the above, the results should be interpreted with caution.

CONCLUSION 5.5.5

This retrospective cohort study showed that the pattern of healthcare and service utilisation in

pregnant migrant women differs from that of Australian-born women and may contribute to the

increased risk of stillbirth in African, Indian, Māori and ‘other’ ethnic populations in WA. Thus, to


PAGE | 112

reduce stillbirth rates in the migrant population, modifying both women’s attitudes towards the

health system and certain aspects of health services are required.

Raising awareness of the importance of proactively seeking ANC and using/offering interpreter

services for migrant women of reproductive age is vital. For healthcare providers and

policymakers, this strategy has the potential to be used as an intervention to reduce the risk of

stillbirth. Culture-oriented educational programs/campaigns may help to address the concerns of

this at-risk group and facilitate greater engagement with the healthcare system early in pregnancy.

Improving access to doctor–midwife (team) intrapartum care for African and ‘other’ migrant

populations, as well as the provision of routine third-trimester ultrasound surveillance for migrant

women from an Indian background, to monitor fetal growth, may also reduce the rates of

stillbirth.

The length of residence in Australia, age on arrival and intermarriage can give an indication of

familiarity with the health system and along with competency in English language and ability to

communicate, as proxies to acculturation, influence health service utilisation in migrant women.

Given that findings of this paper highlight the influence of service utilisation on the risk of

stillbirth, an investigation of acculturation-related factors on the risk of stillbirth is warranted.

ACKNOWLEDGMENTS 5.6

We thank the staff at the Western Australian Data Linkage Branch, and the Data Collections staff

and Data Custodians of the Midwives Notification System, the Hospital Morbidity Data Collection

and the WA Registry of Births, Deaths, and Marriages, and the Western Australia Register of

Developmental Anomalies. We gratefully acknowledge the support of Red Nose as we work

together to prevent stillbirth.

CHAPTER 6. ACCULTURATION AND STILLBIRTH IN WA

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CHAPTER 6. ACCULTURATION AND STILLBIRTH IN WESTERN AUSTRALIA

This chapter comprises a paper, published in PLoS One, exploring the influence of acculturation on

the risk of stillbirth in migrants from diverse ethnic backgrounds in WA. This chapter advances the

understanding of factors associated with the increased risk of stillbirth beyond the current

established influential factors. It links the risk of stillbirth to emerging sociocultural characteristics

that are often overlooked when migrants are cared for during pregnancy and childbirth. This

investigation is related to the third objective of this thesis, to investigate the influence of

acculturation on disparities observed in the risk of stillbirth between migrant and Australian-born

populations in WA.


Appendices:

Mozooni M, Preen DB, Pennell CE. The influence of acculturation on the risk of stillbirth in migrant

women residing in Western Australia. PLoS One. 2020;15(4):e0231106. doi:

10.1371/journal.pone.0231106. PubMed PMID: 32240255.


PAGE | 114

ABSTRACT 6.1

Objective: To investigate the influence of acculturation, demonstrated by age on arrival, length of

residence, interpreter use and having an Australian-born partner, on disparities observed in the

risk of stillbirth between migrant and Australian-born populations in Western Australia (WA).

Methods: A retrospective cohort study using linked administrative health data for all non-

Indigenous births in WA from 2005–2013 was performed. Logistic regression analysis was used to

estimate odds ratios (OR) and 95% confidence intervals (CI). Adjusted odds ratios (aOR) for

stillbirth in migrants from six ethnicities of white, Asian, Indian, African, Māori and ‘other’ with

different levels of acculturation were compared with Australian-born women using multivariable

logistic regression analysis and marital status, maternal age group, socioeconomic status, parity,

plurality, previous stillbirth, any medical conditions, any pregnancy complications, sex of baby and

smoking during pregnancy as the covariates.

Results: From all births studied, 172 571 (66%) were to Australian-born women and 88 395 (34%)

to migrant women. Women from African, Indian and Asian backgrounds who gave birth in the first

two years after arrival in Australia experienced the highest risk of stillbirth (aOR 3.32, 95% CI 1.70–

6.47; aOR 2.71, 95% CI 1.58–4.65; aOR 1.93, 95% CI 1.21–3.05, respectively) compared with

Australian-born women. This association attenuated with an increase in the length of residence in

Asian and Indian women, but the risk of stillbirth remained elevated in African women after five

years of residence (aOR 1.96, 95% CI 1.10–3.49). Interpreter use and an Australian-born partner

were associated with 56% and 20% lower odds of stillbirth in migrants (P<0.05), respectively.

Conclusions: Acculturation is a multidimensional process and may lower the risk of stillbirth

through better communication and service utilisation and elevate such risk through an increase in

the prevalence of smoking in pregnancy; the outcome depends on how these factors are in play in

a population. It is noteworthy that in women of African background, risk of stillbirth remained

elevated for longer periods after immigrating to Australia extending beyond five years. For

migrants from Asian and Indian backgrounds, access to services, in the first two years of residence,

may be more relevant. Enhanced understanding of barriers to accessing health services and

factors influencing and influenced by acculturation may help developing interventions to reduce

the burden of stillbirth in identified at-risk groups.


PAGE | 115

INTRODUCTION 6.2

The majority of international migrants live in high-income countries (HICs).118 According to the

United Nations’ International Migration Report 2015, 28% of the total population in Australia were

migrants, representing a greater proportion than in countries such as the United States (US) (14%),

Canada (22%) and New Zealand (23%).118 Migrants are considered a vulnerable population given

the social, economic, environmental and occupational disadvantages to which they are often

exposed; yet, in some settings, they are healthier than the host country’s population.273 However,

research also shows that foreign-born women and ethnic minorities in the US,274 Europe84,275 and

Australia74,241 have a higher risk of stillbirth than their native-born counterparts. The fetal

mortality rate for non-Hispanic Black women in the US was more than twice the rate for non-

Hispanic white women,274 the risk of stillbirth among Turkish mothers in Europe was 1.6-times that

of the native-born Europeans275 and for African women more than twice that of Australian-born

women.241

Stillbirth profoundly impacts families, society and healthcare systems232 and efforts to reduce the

stillbirth rate have recently gained momentum globally.39,62,276 For migrant women, unfamiliarity

with new healthcare systems, language barriers, sociocultural factors and health habits may

contribute to the risk of stillbirth.169,241 Acculturation is the cultural, psychological and behavioural

changes experienced by migrants as a result of interactions with the host community over time.129

Variables such as country of birth, ethnic identification, proficiency in language and length of

residence in the new country have been used as proxies to measure acculturation in relation to

health outcomes among migrants in other settings.190,195,277 Evidence from the US and Europe

suggests that acculturation, perhaps through lifestyle modification,195,277 may improve birthweight

and gestational age in some migrant populations190 but can negatively impact these outcomes in

others.191 Further, a lower risk of stillbirth was reported in migrant women whose baby was

registered with a Norwegian-born father than those with foreign-born fathers, suggesting a

potential benefit from acculturation for stillbirth risk.194 Similar research is limited in Australia and

the impact of acculturation on stillbirth risk is poorly understood; we have previously shown that

the migrant population, compared with the Australian-born population, use healthcare services

differently. Late commencement of antenatal-care visits and lack of access to or uptake of doctor-

provided intrapartum care as well as absence of private health insurance emerged as underlying

factors for increased risk of stillbirth in specific ethnic groups in WA.278 However, it is not clear


PAGE | 116

whether the risk of stillbirth persists the longer migrants live and interact with the community in

Australia.

We investigated the influence of acculturation on disparities observed in the risk of stillbirth

between migrants from diverse ethnic backgrounds and Australian-born populations in WA from

2005 to 2013. Specifically, the effect of length of residence, age on arrival, interpreter use and

having an Australian-born partner were explored as proxies for acculturation.

METHODS 6.3


De-identified administrative health data for all non-Indigenous births in WA, from 1 January 2005

to 31 December 2013, linked through the WA Data Linkage System (WADLS) of WA Department of

Health,204,279 were used for this retrospective cohort study.

DATA SOURCES 6.3.2

WADLS was established in 1995, mainly for population health research purposes as a collaborative

work between the WA Department of Health and researchers. It has successfully linked data

dating back to the 1970s and has provided support for more than 400 studies in its first ten years

of operation.204 The linkage procedures used are widely known as ‘best practice’.204,213 Using

numerous automated and manual sub-processes specifically designed to reduce the likelihood of

errors, WADLS prides itself on the highest quality of the linkages it produces.212,215

For this study, a range of statutory data collections was used, including the Midwives’ Notification

System (MNS), the Hospital Morbidity Data Collection (HMDC), Birth and Death Registrations and

the WA Registry of Developmental Anomalies (WARDA). Genealogical linkage through the Family

Connections Linkage Facility of the WADLS223 was used to link mothers with child records. The

details of the datasets used were described elsewhere.241,278

Data from MNS, Birth Registration and HMDC records on the country of birth and ethnicity of

mothers and their migrant status (migrant=overseas-born) were available for 99.99% of women

through cross-source checking. The migrant population was further stratified by self-reported

maternal ethnicity (from MNS) as white (Caucasian), Asian, Indian, African, Māori and ‘other’,

which was available for the entire population of the study. It is worth noting that in Australia,

women born in India or those from an Indian background born elsewhere are categorised


PAGE | 117

separately and are not classified within the Asian group, of which the majority were born in China,

Vietnam, Malaysia or Indonesia.241

EXPOSURE AND OUTCOME VARIABLES 6.3.3

Stillbirth (defined as the death of a baby of at least 20 completed weeks of gestation, or 400 grams

or more birthweight, before the complete expulsion or extraction from the mother)28 was the

outcome of interest and was recorded in the status of the baby at birth (MNS data). Terminations

of pregnancy, identified and ascertained through WARDA and Death Registrations data, were

excluded (N=433).

We used length of residence, age on arrival, interpreter use and having an Australian-born partner

as proxies for acculturation.280-282 Interpreter used (yes/no) was available (from HMDC) for all

births in hospital (and 99.0% of total births in WA). Partner’s country of birth, through Birth

Registration, was available for 96.8% of women. Length of residence (years) was calculated by

subtracting year arrived in Australia (from Birth Registration) from year of birth of the baby (from

MNS) with 5.7% missing data (on mother’s arrival year). Age on arrival was calculated by

subtracting women’s length of residence from their age at time of birth (from MNS). Arriving as an

adult, using interpreter service, not having an Australian-born partner, and residing in Australia for

less than five years are considered indicators of being less acculturated.280-282

Private health insurance status (yes/no) was also available for all births in hospital (from HMDC).

Index of Relative Socioeconomic Disadvantage (IRSD), an area-based measure of socioeconomic

status developed by the Australian Bureau of Statistics summarising several disadvantage

measures including low income, low education, high unemployment and unskilled

occupations,219,283 was available for 96.7% of women through multiple datasets.

Missing data were categorised as a separate subgroup to retain all cases in the analysis.


Demographic and obstetric characteristics of the study groups were tabulated. The cumulative

incidence rate of stillbirth was calculated throughout the study, stratified by ethnicity with

denominators determined by 10 000 total birth (live and stillbirth). Univariate logistic regression

was initially used to examine the crude association of stillbirth with acculturative factors

(interpreter use, length of residence, age on arrival and Australian-born partner) at the P<0.05

level. To determine the adjusted odds ratios (aORs) and 95% CIs of stillbirth, multivariable logistic


PAGE | 118

regression analysis was performed for each specific ethnicity with Australian-born as the reference

group, adjusting for marital status, maternal age group, socioeconomic status, parity, plurality,

previous stillbirth, any medical conditions, any pregnancy complications, sex of baby and smoking

during pregnancy. Where stratifying the analysis by specific ethnic groups was not possible due to

small numbers, the whole population of migrants was compared with the Australian-born group,

but ethnicity was added to the multivariable regression model as a co-variable to control for the

effect of ethnicity.

An exploratory analysis was undertaken by combining the Asian and Indian populations and

adding private health insurance, as a measure of access to services, to the multivariable model.

Sensitivity analysis was performed by excluding stillbirths with major anomalies.

All analyses were performed using Stata (version 13·1; StataCorp LP, College Station, Texas).

Ethics approval for this study was granted by the Human Research Ethics Committee of the WA

Department of Health (reference, 2015/23). Written consent from participants was not required

to conduct the study due to the use of non-identifiable routinely collected linked administrative

health data for the whole population.

RESULTS 6.4

Demographic data for the study population are presented in Table 6.1. From 260 997 total non-

Indigenous births, 172 571 births (66%) were to Australian-born women and 88 395 births (34%)

were to migrant women. Migrant women were, on average, slightly older than Australian-born

women (mean age 30.9 years vs 29.5 years, respectively), and more likely to be married (91.7% vs

88.0%), non-smokers (7.1% vs 14.0%) and nulliparous (44.0% vs 42.6%), but less likely to have

private health insurance (32.1% vs 43.1%). In contrast, the proportion of migrant women

categorised as the most socioeconomically disadvantaged (using IRSD quintiles) was 12% less than

Australian-born women (P<0.001). Most migrants arrived in Australia as an adult, aged ≥18 years

old, and almost 12% of the whole population of migrant women gave birth before completing two

years of residence in Australia (Table 6.1). Among the migrant population, 30.0% had an

Australian-born partner, with African and Indian women having the lowest (4.5% and 4.8%,

respectively) and Māori and Asian women having the highest (23.4% and 19.7%, respectively)

percentage of Australian-born partners after white migrants (41.6%). African women had the


PAGE | 119

highest proportion of interpreter utilisation (15.7%), followed by ‘other’ (10.4%) and Asian (9.8%)

women, while no woman from Māori background used such services.

The ‘<2 years of residence’ had the highest odds of stillbirth in migrant women (OR 1.35, 95% CI

1.05–1.74) compared to the Australian-born women (Table 6.2). The odds were 14% higher among

migrant women who immigrated as an adult than Australian-born population, although it did not

reach statistical significance (OR 1.14, 95% CI 1.00–1.30). Compared to Australian-born women,

the non-white non-Māori migrant women who did not use an interpreter were at higher odds of

stillbirth (OR 1.55, 95% CI 1.34–1.79), while those who did have an interpreter had lower risk of

stillbirth (OR 0.47, 95% CI 0.24–0.95) (Table 6.2).

When stratified by ethnicity, the most significant association with lack of utilisation of interpreter

services was seen in African (aOR 3.16, 95% CI 2.34–4.26), followed by women from ‘other’ (aOR

1.65, 95% CI 1.27–2.14) and Indian (aOR 1.58, 95% CI 1.13–2.22) ethnic backgrounds.

The cumulative rate of stillbirth in migrant women steadily decreased, reaching the same rate as

Australian-born women when the length of residence was greater than 10 years (Figure 6.1).

Parallel to that, the proportion of smoking in pregnancy, having an Australian-born partner or

private health insurance increased among migrant women (Figure 6.1).


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TABLE 6.1 CHARACTERISTICS OF THE POPULATION OF THE STUDY

Characteristics Australian-born women

Migrant women All women


Total births 172 571 48 546 18 212 5503 4155 2941 9038 88 395 260 997

Stillbirths 812 (0.5%)

231 (0.5%)

95 (0.5%)

39 (0.7%)

51 (1.2%)

18 (0.6%)

66 (0.7%)

500 (0.6%)

1313 (0.5%)

Marital status


3026 (6.2%)

701 (3.9%)

99 (1.8%)

543 (13.1%)

570 (19.4%)

611 (6.8%)

5550 (6.3%)

23 568 (9.0%)


360 (0.7%)

160 (0.9%)

17 (0.3%)

109 (2.6%)

33 (1.1%)

132 (1.5%)

811 (0.9%)

2366 (0.9%)


44 693 (92.1%)

17 107 (93.9%)

5327 (96.8%)

3449 (83.0%)

2268 (77.1%)

8214 (90.9%)

81 058 (91.7%)

232 917 (89.2%)

Other 1170 (0.7%)

467 (1.0%)

244 (1.3%)

60 (1.1%)

54 (1.3%)

70 (2.4%)

81 (0.9%)

976 (1.1%)

2146 (0.8%)

Australian-born Partner 118 401 (71.8)

19 503 (41.6%)

3535 (19.7%)

264 (4.8%)

178 (4.5%)

633 (23.4%)

1540 (17.6%)

25 652 (30.0%)

144 053 (57.5%)

Age on arrival

<18 years old NA 20 273 (43.9%)

4233 (24.7%)

433 (8.4%)

604 (16.1%)

1016 (36.8%)

2068 (24.8%)

28 627 (34.4%)

NA

18 or older NA 25 909 (56.1%)

12 904 (75.3%)

4741 (91.6%)

3140 (83.9%)

1749 (63.3%)

6257 (75.2%)

54 700 (65.6%)

NA

Length of residence

0–1 year NA 4048 (8.3%)

2361 (13.0%)

1299 (23.6%)

729 (17.6%)

527 (17.9%)

1571 (17.4%)

10 535 (11.9%)

10537 (4.0%)

2–5 year NA 11 380 (23.4%)

5399 (29.7%)

2613 (47.5%)

1595 (38.4%)

857 (29.0%)

2787 (30.8%)

24 631 (27.9%)

24 634 (9.4%)

>5 NA 30 769 9384 1262 1419 1382 3968 48 184 48 188


PAGE | 121

(63.4%) (51.5%) (22.9%) (34.2%) (47.0%) (43.9%) (54.5%) (18.5%)

Unknown NA 2349 (4.8%)

1068 (5.9%)

329 (6.0%)

412 (9.9%)

175 (6.0%)

712 (7.9%)

5045 (5.7%)

5082 (2.0%)

Maternal age (years)

Mean (SD) 29.5 (5.6)

31.5 (5.3)

31.2 (4.9)

29.5 (4.4)

28.8 (5.7)

26.8 (6.0)

29.9 (5.6)

30.9 (5.4)

30.0 (5.6)

<20 7474 (4.3%)

724 (1.5%)

131 (0.7%)

17 (0.3%)

197 (4.7%)

300 (10.2%)

200 (2.2%)

1569 (1.8%)

9045 (3.5%)

20–24 27 516 (16.0%)

4364 (9.0%)

1401 (7.7%)

638 (11.6%)

826 (19.9%)

889 (30.2%)

1477 (16.3%)

9595 (10.9%)

37 115 (14.2%)

25–29 49 076 (28.4%)

11 423 (23.5%)

5208 (28.6%)

2288 (41.6%)

1254 (30.2%)

786 (26.7%)

2631 (29.1%)

23 590 (26.7%)

72 673 (27.8%)

30–34 54 744 (31.7%)

17 464 (36.0%)

6937 (38.1%)

1869 (34.0%)

1169 (28.1%)

599 (20.4%)

2714 (30.0%)

30 752 (34.8%)

85 510 (32.8%)

35–39 28 412 (16.5%)

11 716 (24.1%)

3713 (20.4%)

581 (10.6%)

586 (14.1%)

290 (9.9%)

1612 (17.8%)

18 498 (20.9%)

46 914 (18.0%)

40–44 5159 (3.0%)

2703 (5.6%)

785 (4.3%)

103 (1.9%)

112 (2.7%)

77 (2.6%)

389 (4.3%)

4169 (4.7%)

9328 (3.6%)

>44 190 (0.1%)

152 (0.3%)

37 (0.2%)

<10 (0.1%)

11 (0.3%)

0 (0.0%)

15 (0.2%)

222 (0.3%)

412 (0.2%)

Smoked in pregnancy 24 097 (14.0%)

4161 (8.6%)

317 (1.7%)

40 (0.7%)

78 (1.9%)

1152 (39.2%)

494 (5.5%)

6242 (7.1%)

30 342 (11.6%)

Interpreter service used 19 (0.0%)

306 (0.6%)

1779 (9.8%)

232 (4.2%)

651 (15.7%)

0 (0.0%)

931 (10·4%)

3896 (4.5%)

3918 (1.5%)

Socioeconomically* most disadvantaged

40 521 (23.5%)

6547 (13.5%)

2181 (12.0%)

663 (12.1%)

513 (12.4%)

800 (27.2%)

1276 (14.1%)

11 980 (13.6%)

52 504 (20.1%)

Private health insurance 73 774 (43.1%)

19 247 (40.2%)

5495 (30.2%)

1379 (25.0%)

380 (9.2%)

153 (5.3%)

1471 (16.3%)

59 374 (32.1%)

101 902 (39.4%)

Parity


PAGE | 122


21 205 (43.7%)

8759 (48.1%)

3204 (58.2%)

1217 (29.3%)

955 (32.5%)

3532 (39.1%)

38 872 (44.0%)

112 340 (43.0%)


17 243 (35.5%)

6485 (35.6%)

1817 (33.0%)

1113 (26.8%)

792 (26.9%)

2695 (29.8%)

30 145 (34.1%)

90 561 (34.7%)


10 098 (20.8%)

2968 (16.3%)

482 (8.8%)

1825 (43.9%)

1194 (40.6%)

2811 (31.1%)

19 378 (21.9%)

58 096 (22.3%)

Any medical condition 60 602 (35.4%)

14 748 (30.8%)

4889 (26.9%)

2095 (38.0%)

1567 (37.9%)

993 (34.2%)

3299 (36.8%)

27 590 (31.5%)

60 603 (35.3%)

Previous stillbirth 2011 (1.2%)

575 (1.2%)

179 (1.0%)

73 (1.3%)

149 (3.6%)

53 (1.8%)

182 (2.0%)

1211 (1.4%)

3222 (1.3%)

Any pregnancy complication 57596 (33.4%)

15 330 (31.6%)

6489 (35.6%)

2162 (39.3%)

1339 (32.2%)

925 (31.5%)

3214 (35.6%)

29 459 (33.3%)

87 059 (33.4%)

Plurality

Singleton 167 481 (97.1%)

47 075 (97.0%)

17 822 (97.9%)

5389 (97.9%)

4031 (97.0%)

2883 (98.0%)

8725 (96.5%)

85 925 (97.2%)

253 435 (97.1%)


1471 (3.0%)

390 (2.1%)

114 (2.1%)

124 (3.0%)

58 (2.0%)

313 (3.5%)

2470 (2.8%)

7544 (2.9%)

*The socioeconomically most disadvantaged group is comprised of the bottom 20% of IRSD.


PAGE | 123

TABLE 6.2 ABSOLUTE NUMBERS, RATES, AND UNADJUSTED ODDS RATIOS OF STILLBIRTH FOR MIGRANTS, STRATIFIED

BY ACCULTURATIVE FACTORS, COMPARED WITH THE AUSTRALIAN-BORN POPULATION

Acculturative factor Stillbirth

N Rate (per 10 000 births)

OR (95% CI)

Australian-born (Reference) 812 47 1.00

Overseas-born 500 57 1.20* (1.08–1.35)

Interpreter use

Non-white non-Māori migrant with interpreter <10 22 0.47* (0.24–0.95)

Non-white non-Māori migrant without interpreter

240 72 1.55* (1.34–1.79)

Length of residence

<2 years 66 63 1.35* (1.05–1.74)

2–5 years 134 55 1.17 (0.97–1.41)

>5 years 220 46 0.98 (0.85–1.14)

Age on arrival

<18 years old 133 46 0.98 (0.82–1.18)

≥18 years old 293 54 1.14 (1.00–1.30)

Australian-born partner

Migrant with Australian-born partner 118 46 0.97 (0.80–1.17)

Migrant with overseas-born partner 336 56 1.30* (1.15–1.47)

*P<0.05

FIGURE 6.1. LENGTH OF RESIDENCE IN RELATION TO UTILISATION OF INTERPRETER, HAVING AN AUSTRALIAN-BORN

PARTNER, SMOKING IN PREGNANCY AND RATE OF STILLBIRTH IN THE MIGRANT POPULATION OF WESTERN AUSTRALIA

(2005–2013)


PAGE | 124

When the migrant population was stratified by ethnicity, length of residence was not associated

with stillbirth in Māori or white migrant women compared to Australian-born women; however,

women from Asian and Indian backgrounds had significantly increased odds of stillbirth in their

first two years of residence in Australia (aOR 1.93, 95% CI 1.21–3.05; aOR 2.71, 95% CI 1.58–4.65;

respectively) which resolved with longer periods of residence. Women from African backgrounds

had more than three times higher odds of stillbirth than their Australian-born counterparts (aOR

3.32, 95% CI 1.70–6.47) in their first two years of residence. Although the risk of stillbirth

decreased with longer residence in these women, it remained significantly higher than Australian-

born women after five years of residing in Australia (aOR 1.96; 95% CI 1.10–3.49). This risk

persisted after adjusting for other stillbirth risk factors (Table 6.3).

TABLE 6.3 LENGTH OF RESIDENCE AND THE ODDS OF STILLBIRTH IN MIGRANTS FROM SPECIFIC ETHNIC

BACKGROUNDS COMPARED TO THE AUSTRALIAN-BORN POPULATION

Study population Length of residence

<2 years 2–5 years >5 years

Australian-born (Reference) 1.00 1.00 1.00

Migrant

White OR (95% CI) 0.73 (0.43–1.25) 0.99 (0.75–1.31) 0.94 (0.78–1.13)

aOR(95% CI) 0.88 (0.52–1.50) 1.20 (0.90–1.59) 1.02 (0.84–1.23)

Asian OR (95% CI) 1.71* (1.09–2.71) 0.90 (0.60–1.37) 1.00 (0.73–1.35)

aOR(95% CI) 1.93* (1.21–3.05) 1.01 (0.66–1.53) 1.06 (0.77–1.44)

Indian OR (95% CI) 2.30* (1.35–3.91) 1.22 (0.73–2.04) 0.67 (0.25–1.79)

aOR(95% CI) 2.71* (1.58–4.65) 1.38 (0.82–2.32) 0.67 (0.25–1.80)

African OR (95% CI) 2.64* (1.36–5.11) 2.41* (1.51–3.85) 1.80* (1.01–3.19)

aOR(95% CI) 3.32* (1.70–6.47) 2.77* (1.70–4.52) 1.96* (1.10–3.49)

Māori OR (95% CI) 0.80 (0.20–3.22) 1.74 (0.82–3.66) 1.07 (0.51–2.26)

aOR(95% CI) 0.75 (0.19–3.03) 1.71 (0.70–3.62) 1.07 (0.51–2.28)

Other OR (95% CI) 1.08 (0.54–2.17) 1.53 (0.98–2.38) 1.07 (0.69–1.67)

aOR(95% CI) 1.23 (0.61–2.49) 1.63* (1.04–2.56) 1.08 (0.74–1.80) aOR: Adjusted for marital status, maternal age group, socioeconomic status, parity, plurality, previous stillbirth, medical conditions, pregnancy complications, sex of baby and smoking during pregnancy. *P<0.05

The increase in the proportion of the migrant population with private health insurance was most

evident among women from white (15.8% in <2 years of residence to 34.4% in 2–5 years of

residence), Asian (12.2% in <2 years of residence to 21.6% in 2–5 years of residence) and Indian

(12.8% in <2 years of residence to 22.7% in 2–5 years of residence) backgrounds (Figure 6.2). It is


PAGE | 125

worth noting that migrant women from these ethnic backgrounds, who lived in Australia for >10

years, had a higher percentage of private health insurance than Australian-born women (46.2%,

45.1% and 53.8%, respectively).

FIGURE 6.2 LENGTH OF RESIDENCE IN AUSTRALIA AND PERCENTAGE OF HAVING PRIVATE HEALTH INSURANCE FOR

MIGRANT WOMEN FROM SPECIFIC ETHNIC BACKGROUNDS DELIVERED IN WA (2005–2013)

In a further exploratory analysis, including ‘having private health insurance’ in the multivariable

model resulted in a 33% reduction in the odds of stillbirth in Asian and Indian (combined) women

with less than two years length of residence (from aOR 2.18, 95% CI 1.52–3.10 to aOR 1.85, 95% CI

1.29–2.65).

The proportion of the population with an Australian-born partner is illustrated in Figure 6.3 for

each specific ethnic group. Overall, 71.8% of Australian-born and 30% of migrant women had an

Australian-born partner. The proportion of migrant women with an Australian-born partner

increased with a longer length of residence for all migrant groups; however, the rate varied for

each specific ethnicity with Indian women having the highest surge after ten years of residing in

Australia.

The proportion of interpreter utilisation was the highest in the first two years of residence in

Australia and gradually decreased with longer length of residence, albeit at various rates among

different ethnic groups of migrants (Figure 6.4).


PAGE | 126

FIGURE 6.3 LENGTH OF RESIDENCE IN AUSTRALIA AND PERCENTAGE OF HAVING AN AUSTRALIAN-BORN PARTNER FOR


FIGURE 6.4 LENGTH OF RESIDENCE IN AUSTRALIA AND THE PERCENTAGE OF UTILISATION OF INTERPRETER FOR


THE OVERALL LEVEL OF ACCULTURATION 6.4.1

Migrant women with an overseas-born partner who did not use an interpreter were the most at-

risk group among new migrants residing in Australia for less than five years. While their migrant

counterparts who used an interpreter had 62% lower odds of stillbirth (aOR 0.38, 95% CI 0.15–

0.95), these women had a 31% higher odds of stillbirth (aOR 1.31, 95% CI 1.03–1.65) than

Australian-born women. No significant difference was observed in those with a length of residence

of >5 years. Non-white non-Māori migrant women with an overseas-born partner who did not use

an interpreter were particularly at risk with 86% higher odds of stillbirth (aOR 1.86, 95% CI 1.50–

2.32) than Australian-born women.

Sensitivity analyses, excluding stillbirths with major congenital anomalies, did not affect the

findings.


PAGE | 127

DISCUSSION 6.5

We found that longer length of residence, using an interpreter, and having an Australian-born

partner were all associated with a lower risk of stillbirth in migrants. Further, we showed that

migrant women with an overseas-born partner, who did not use an interpreter, were the most at-

risk group among migrants residing in Australia for less than five years. Giving birth in the first two

years after arrival in Australia had the highest risk of stillbirth for migrant women from African,

Asian and Indian backgrounds. However, the increased risk disappeared beyond the first two years

of residence among women from Asian and Indian backgrounds.

A longer length of residence in Australia was associated with a lower risk of stillbirth in migrants in

this study, consistent with a previous nationwide study in Sweden where the risk of stillbirth was

higher in foreign-born women with <5 years of residence.67 In contrast, a recently published

nationwide population-based study from Norway did not find an association between length of

residence and stillbirth in immigrant women.194 The discrepancy in these two Nordic studies, with

seemingly comparable populations and health system, is probably due to differences in the

definition of outcome or lack of adjustments for potential confounders such as smoking and body

mass index in the Norwegian study. Sweden defines stillbirth as the death of a baby of at least 28

weeks gestation while Norway uses a lower limit of 22 weeks gestation. Smoking is a well-

established risk factor for stillbirth.78 In our study, the more acculturated women (as indicated by

having an Australian-born partner and longer length of residence) were more likely to smoke while

pregnant. Elevated rates of smoking in pregnant Hispanic women with a higher level of

acculturation have been reported in the US196 and among Turkish women residing in Europe.195

Given that smoking is a risk factor for stillbirth, the protective effect of longer length of residence

on stillbirth may have been counteracted and underestimated due to the simultaneous increase in

the prevalence of smoking in more acculturated women.

Of note, adjusting for private health insurance status reduced the odds of stillbirth by 33% in Asian

and Indian women residing in Australia for less than two years in our study. This may suggest that

rather than acculturative factors, unfamiliarity with the health system or other barriers to access

may influence the risk of stillbirth in newly arrived migrants from these ethnic backgrounds. In

Australia, despite the availability of a universal health insurance scheme covering all Australian

citizens and permanent residents (Medicare), depending on the type of visa (humanitarian visas

are excluded), a two- to four-year waiting period is applied before immigrants become eligible to


PAGE | 128

benefit from some health and social security services, including Newstart/Jobseeker Allowance

and Healthcare Concession Card.154 Whether this operates as a barrier to service utilisation and is

associated with the observed disparities in the rate of stillbirth among migrant women who gave

birth before completed two years of residence needs further investigation.

Effective communication is vital for navigating the health system and for optimal care. Language

discordance has been highlighted as a barrier to access health services for migrants and refugees

in Australian and to compromise the quality of care.174,262,263 In our study, migrant women who

used an interpreter had a significantly lower risk of stillbirth than those who did not. Given that

the majority of migrants who used an interpreter were from regions with higher rates of stillbirth

than Australia and had a residence length of <5 years in Australia, this may be evidence of a

‘healthy migrant paradox’.146 This paradox entails observing health outcomes better than the host

population among the newly arrived migrants, despite the disadvantages that migrants encounter,

that slowly converges to the host population levels over time.146 Not using an interpreter was a

particularly strong risk factor for non-white non-Māori migrant women who had an overseas-born

partner and delivered during the first five years of residing in Australia, suggesting lower levels of

acculturation. Anecdotal evidence and qualitative studies suggest that a reasonable proportion of

women who did not use an interpreter service was not competent in the language. Previous

reports from Australia and Europe indicated that despite the difficulty in communication, migrant

women from some ethnic backgrounds might not request an interpreter or their partners may

insist on acting as the translator which can compromise the care received.157,263,264 Despite the

availability of a unique fee-free rapid-access telephone interpreter service (Doctors Priority Line) in

Australia, it was estimated that this service was used for less than 1% of private general practice

consultations for patients with poor English proficiency.264 Further, qualitative studies have shown

that doctors tend to over-investigate and act on the results rather than organising an interpreter

and attending to patients’ symptoms.263 Thus, our findings may indicate a lack of communication

and mutual understanding between pregnant women and clinicians in those who did not use the

interpreter service, if the interpreter was required but not requested or offered. Using an

interpreter in our data may be an indicator that a culturally sensitive healthcare plan was in effect.

Mutual accommodation is required for healthy and successful integration of migrants; adopting

the basic values of the host country by migrants and adapting the healthcare and education

systems to meet the needs of all population groups appropriately.129 Hence, the lower rate of


PAGE | 129

stillbirth in migrants who used interpreter services may signify the success of a culturally sensitive

healthcare practice.

Our data suggest a slower acculturation rate in some ethnic groups, particularly those of African

descent. The rate of stillbirth in African migrant women decreased with longer duration of

residence but remained significantly higher than Australian-born women. Slower acculturation

may be a sign of marginalisation or segregation, rather than healthy integration, potentially due to

discrimination, which can be detrimental to the wellbeing of individuals and the community.129

This can be more prevalent in those whose physical features (e.g. skin colour or clothing) set them

apart from the majority population.129 A negative association between length of residence and

level of stress has been reported in African migrants in the US.284 Among Somali refugees residing

in the US, interpreter service use increased with longer length of residence while lower rates of

interpreter utilisation were associated with poorer birth outcomes.285 In our population, African

women had the highest utilisation of interpreters among all migrant ethnic groups. Despite this,

our findings suggest their ‘need’ for interpreters may be higher than currently being met and

captured by our data. Addressing the underutilisation of interpreters may be a simple strategy for

addressing the increased risk of stillbirth in this population. This finding also warrants further

investigation of whether racial discrimination, a risk factor for adverse birth outcomes 286, also

plays a role.

Consistent with the Norwegian study,194 having an Australian-born partner reduced the risk of

stillbirth in migrant women in WA. Intermarriage is considered one of the most powerful

indicators of integration.132 Having an Australian-born partner may result in more interaction

within the community, competency in English, and familiarity with the health system when

navigating pregnancy care.194,197

In conclusion, migrant populations are diverse, and the processes of immigration and

acculturation are complex. Leaving family and friends behind, lack of support in the new country,

isolation and experience of discrimination may impact the physical and mental health of

migrants284 and consequently be detrimental to the health of their babies. Thus, understanding

the effect of migration and acculturation on the health of immigrants and their babies is vital for

identifying disparities and targeting at-risk groups with a culturally responsive healthcare system

and ethnic-specific preventive strategies. Furthermore, investigating the social, cultural,


PAGE | 130

behavioural and other determinants of lower/slower acculturation is warranted to identify

opportunities for intervention and prevention of marginalisation.

STRENGTH AND LIMITATIONS 6.5.1

We used de-identified linked administrative health data in this study to investigate stillbirth in a

large population of migrant women. Such a method reduced the risk of selection, participation

and recall biases, enhanced accuracy through cross-source checking of data from multiple

databases, and consequently strengthened the reliability of findings; however, this method has

limitations due to the extent of variables available or classification of variables, and some

misclassification towards the null hypothesis may exist. Residual confounding due to the

covariates not recorded in the datasets (e.g. maternal body mass index)287, despite controlling for

a range of potential risk factors, may remain. Also, the duration of residence may result in

different levels of integration and acculturation whether the migrant arrived as an adult or as a

child.288 However, in our study, the majority of non-white non-Māori migrants (75.2–91.6%)

arrived in Australia as an adult, and the association of this factor with stillbirth was not statistically

significant. Further, we assessed acculturation in a multidimensional manner by considering

additional acculturative factors, such as interpreter use and partner’s country of birth. Given the

prevalence of stillbirth in Australia, a large population of participants is required to explore the

influence of migration, ethnicity and acculturation on the risk of stillbirth. We acknowledge that,

similar to other population studies, we have investigated the effect of acculturation using proxy

measures described in detail in this paper. An in-depth interview with participants may provide a

thorough and more accurate understanding of psychological, cultural and behavioural changes

they experience; given the sample size required for such a study and other considerations

necessary to interview bereaved parents from diverse backgrounds, this would not be practically

feasible. Thus, a population-based linked-data study, such as that undertaken here, is the only

feasible method for investigation and still provides useful insight into the influences of these

factors on stillbirth. Nevertheless, population data collections may be strengthened by linking

Census data with additional acculturation-related information, such as the language spoken at

home or sociocultural preferences or their shifts over time.

IMPLICATIONS AND GENERALISABILITY 6.5.2

Acculturation may elevate the risk of stillbirth by increasing the prevalence of unhealthy habits,

such as smoking, and decrease the risk by empowering migrants to better use health services and


PAGE | 131

effective communication. Thus, the resultant outcome will depend on how these underlying

factors interplay in populations over time. According to our findings and previous studies in other

settings, acculturation is emerging as an important factor to consider when providing health care

to immigrant populations. Therefore, the findings of this study have important implications for

policy and practice—a national educational program to familiarise migrants with the health

system, their risks, rights and entitlements to healthcare programs before, during and after

pregnancy, and a culturally responsive healthcare system to improve access to and utilisation of

interpreter and other services, especially in recently arrived migrants that are at greater risk, may

help to reduce the rate of stillbirth. It is particularly crucial to investigate and address the

underlying mechanisms for the increased risk of stillbirth in the first two years after the arrival in

Australia. Until then, pregnant women from non-white non-Māori ethnic backgrounds with a

length of residence <2 years may benefit from being treated as high risk for stillbirth.

Our findings can be generalised to other populations hosting migrants from similar ethnic

backgrounds in Australia and New Zealand, European countries and Canada; nevertheless,

interpretation should be with caution due to factors such as different immigration laws,

healthcare systems, cultures and lifestyle norms in host countries.

CHAPTER 7. PRETERM BIRTH AND LOW BIRTHWEIGHT IN RELATION TO MIGRATION AND ETHNICITY IN WA

PAGE | 132

CHAPTER 7. PRETERM BIRTH AND LOW BIRTHWEIGHT IN RELATION TO

MIGRATION AND ETHNICITY IN WESTERN AUSTRALIA

This chapter comprises a paper submitted as an article to the International Journal of

Epidemiology (May 2020) for publication and is currently under review. The research explored the

influence of migration and ethnicity on the risk of different types of preterm birth and term low

birthweight in the population of WA. It further investigated outcomes among the Australian-born

population from different ethnic backgrounds. The study is related to the fourth aim of the thesis:

“to investigate ethnic disparities in the risk of low birthweight (LBW) and preterm birth (PTB),

spontaneous and medically indicated, between migrant and Australian-born populations in WA.”


PAGE | 133

ABSTRACT 7.1

Background: Preterm birth (PTB) and low birthweight (LBW) are associated with short- and long-

term adverse health outcomes. The evidence suggests that migrant status and ethnicity are

influential factors in these conditions. We investigated the effect of migration and ethnicity on the

risk of PTB and term-LBW.

Methods: This retrospective cohort study examined the association between migrant status and

ethnicity with birth outcomes for all non-Indigenous singleton live births in Western Australia

(WA) from 2005–2013. Regression analyses determined the risk/odds of PTB [idiopathic,

premature pre-labour rupture of membrane (PPROM) and medically indicated] and term-LBW

comparing migrants and Australian-born women from white, Asian, African, Indian, Māori and

‘other’ ethnicities.

Results: All non-Māori ethnic groups—migrant and Australian-born—had higher odds of term-

LBW than the white Australian-born group, with aORs ranging from 1.12 (CI 1.01–1.23) to 4.10 (CI

3.51–4.80). Migrant Indians had a higher risk of idiopathic, PPROM and medically indicated PTB

(aRRR 1.26, 95% CI 1.06–1.50; aRRR 1.28, CI 1.03–1.60; aRRR 1.21, CI 1.03–1.42, respectively) and

migrant Asians had a higher risk of idiopathic PTB (aRRR 1.20, CI 1.09–1.31). Migrant white and

African women had a lower risk of idiopathic PTB (aRRR 0.87, CI 0.80–0.93; aRRR 0.75, CI 0.59–

0.95, respectively) and PPROM PTB (aRRR 0.90, CI 0.83–0.99; aRRR 0.70, CI 0.51–0.96,

respectively), while migrant Māori had lower risk of medically indicated PTB (aRRR 0.75, CI 0.56–

0.97).

Conclusion: While ethnicity and migrant status were associated with increased risk of term-LBW,

only specific ethnic groups had a higher risk of PTB than white Australian-born women. This

warrants further investigation of influential factors in specified ethnic groups to develop

appropriate policies and preventive strategies to reduce term-LBW and PTB in the population.


PAGE | 134

INTRODUCTION 7.2

Preterm birth (PTB), birth before 37 completed weeks of gestation230, and low birthweight (LBW),

birthweight less than 2500 grams regardless of the gestational age of the newborn28, are adverse

pregnancy outcomes associated with higher rates of mortality and morbidity not only in childhood

but throughout life.289 The short-term and long-term burden of these conditions and the resulting

financial costs imposed on families and healthcare systems are substantial.96,290

Recent reports indicate that more than 20 million babies were born alive with LBW worldwide in

2015; 91% from low- to middle-income (LMIC) countries, 48% from southern Asia and 24% from

Sub-Saharan Africa.114 Similar global reports have indicated that approximately 15 million live-born

babies were born premature in 2010 and PTB rates are increasing globally.96

High-income countries are home to more than 60% of international migrants.118 Australia, with

26% of its population born overseas, has a higher proportion of migrant residents than the United

States (US) (14%), Canada (22%), New Zealand (23%) and the United Kingdom (13%).291 Western

Australia has experienced the fastest population growth among all Australian states or territories

over the last decade, with the number of migrant families exceeded non-migrants,136,234 providing

an ideal population laboratory to study the impact of ethnicity and migration on adverse

pregnancy outcomes.

The evidence suggests that migrant status, region of birth and ethnicity are all influential factors

for birth outcomes.199,241,292 We previously reported ethnic disparities in the risk of antepartum

and intrapartum stillbirths between the Australian-born population and migrants from diverse

ethnic backgrounds.241,278 However, such disparities have not been investigated for PTB and LBW

between migrants and Australian-born populations.


PAGE | 135

The aim of this study was to investigate disparities in the risk of term-LBW and PTB between

migrant and Australian-born women.

METHODS 7.3

STUDY POPULATION AND DATA SOURCES 7.3.1

A retrospective cohort study was conducted using routinely collected administrative data from the

Department of Health WA, linked through the WA Data Linkage System (WADLS). We examined

de-identified data for the entire non-Indigenous population of singleton live births in WA from 1

January 2005 to 31 December 2013.

The WADLS, established in 1995, uses best-practice probabilistic matching, based on full name and

address, phonetic compression algorithms and other identifiers, to link data from a variety of

health and other administrative datasets.204,211,213 Additionally, numerous automated and manual

sub-processes have been designed specifically to reduce the likelihood of linkage error—the

details of which are reported in linkage quality statements elsewhere.212,215

We primarily sourced data from the Midwives Notification System (MNS), a statutory database of

pregnancy, birth and infant information collected by attending midwives for all births of at least 20

weeks gestation or at least 400 grams in weight (if unknown gestational age). The MNS includes

maternal, pregnancy and infant characteristics as well as infant outcomes. Data from the MNS

were supplemented with additional variables linked from the WA Birth Register, including

maternal, paternal and infant demographic characteristics for all registered births in WA, and the

Hospital Morbidity Data Collection (HMDC), which contains information related to all inpatient

discharges from all WA hospitals. The Family Connections Linkage Facility223 of the WADLS was

used to link migrant women with their child outcomes.


PAGE | 136

VARIABLES 7.3.2

PTB was defined as birth before 37 completed weeks of gestation230, and LBW as birthweight less

than 2500 grams at the time of birth.28

PTBs were classified as spontaneous PTB (spontaneous onset) and medically indicated PTB (PTB

after medical intervention: induction of labour or elective caesarean section). Spontaneous PTB

was further subdivided into idiopathic PTB (spontaneous labour with intact fetal membranes) and

preterm pre-labour rupture of membranes (PPROM) where labour began spontaneously after

PPROM.93,94,293,294

Birth status (live birth, stillbirth), plurality (number of babies in this birth), infant weight (grams),

estimated gestational age at time of birth (completed weeks), onset of labour (spontaneous,

induced, elective caesarean) and complications of pregnancy (data on pre-labour rupture of

membrane) from MNS were used to identify the population of study and classify the outcomes of

interest.

Migrant status (Australian- or overseas-born), using mother’s place/country of birth (from Birth

Registrations/HMDC) and ethnic origin (from MNS) were used to group the study population into

white, Asian, Indian, African, Māori or other ethnic backgrounds.241

Year of birth, maternal age (grouped as <20, 20–24, 25–29, 30–34, 35–39, 40–44, >44), marital

status (never married, divorced/separated, married/de facto, other), smoking in pregnancy (yes,

no), any medical condition (yes, no), gestational diabetes (yes, no), preeclampsia (yes, no), sex of

baby (female, male), fertility treatment (yes/no) and socioeconomic disadvantage (using Index of

Relative Socioeconomic Disadvantage: IRSD) were extracted from the MNS data. Private health

insurance (yes, no) was extracted from HMDC.


PAGE | 137

STATISTICAL METHODS 7.3.3

The demographic and obstetric characteristics of the study population were summarised and

tabulated. The cumulative incidence of the outcomes of interest was reported as the percentage

of births with LBW or PTB from all singleton live births recorded during the study period. Logistic

regression was used to calculate the odds ratios (OR) and 95% confidence intervals (CI) for

associations with term-LBW and PTB. Multinomial logistic regression was used to estimate the

Relative Risk Ratios (RRR) of spontaneous (idiopathic and PPROM) and medically indicated PTB

relative to term birth, among migrant women from white, Asian, Indian, African, Māori and ‘other’

ethnic backgrounds compared to Australian-born women from a white background. We also

compared migrants and Australian-born populations from the same ethnic groups. Analyses were

adjusted for year of birth, maternal age group, marital status, parity, socioeconomic disadvantage,

infertility treatment, smoking in pregnancy, any pre-existing medical condition and sex of the

baby.293,294

In the sensitivity analyses, gestational diabetes and preeclampsia were added to the adjusted

model to test the potential confounding effect of these pregnancy complications while avoiding

possible collider bias due to including intermediate variables in main analyses.249 We also

examined the effect of having private health insurance on the risk of medically indicated PTB by

adding this variable to the adjusted model. In a further exploratory analysis, we separated

Chinese-born women from other overseas-born Asian women to calculate their specific risk of PTB

compared with the white Australian-born population as previous research indicated that Chinese-

born women had lower rates of PTB than Australian-born women.163

Missing data were low (3.3% for IRSD) and were considered missing at random, and complete case

analysis was undertaken.

All analyses were performed using Stata (version 13·1; StataCorp LP, College Station, Texas).


PAGE | 138

Ethics approval for this study was granted by the Human Research Ethics Committee of the WA

Department of Health (2015/23).

RESULTS 7.4

Ethnic background and migrant status were complete for 99.99% of the mothers. The study

population comprised 252 256 singleton live births; 66.1% to Australian-born women (64.0% from

white and 2.1% from non-white ethnic backgrounds) and 33.9% to migrant women (18.6% from

white and 14.3% from non-white ethnic backgrounds).

Proportions of married (91.7% vs 88.0%), as well as nulliparous (44.0 vs 42.6%) women, were

higher among migrants than the Australian-born population. Migrants also had lower proportions

who smoked during pregnancy (7.1% vs 14.0%) and who were in the most disadvantaged group

(13.6% vs 23.5%) (Table 7.1).

Among all population groups, migrant women from Indian background had the lowest (0.3%), and

those from Māori background had the highest (10.4%) proportions of teenage pregnancies, while

white migrant women had the highest proportion of giving birth over 35 years of age (30.2%).


PAGE | 139

TABLE 7.1 DEMOGRAPHIC CHARACTERISTICS OF THE STUDY POPULATION

Characteristics Australian-born women Migrant women All women

White Asian Indian African Māori Other All White Asian Indian African Māori Other All

Live & singleton births

161 387 (64.0%)

1279 (0.5%)

296 (0.1%)

55 (0%)

239 (0.1%)

3519 (1.4%)

166 775 (66.1%)

46 878 (18.6%)

17 734 (7.0%)

5352 (2.1%)

3985 (1.6%)

2867 (1.1%)

8665 (3.4%)

85 481 (33.9%)

252 256 (100%)

PTB 10 115 (6.3%)

86 (6.7%)

18 (6.8%)

<10 (1.8%)

<10 (3.8%)

288 (8.2%)

10 517 (6.3%)

2699(5.8%) 1177 (6.6%)

387 (7.2%)

227 (5.7%)

169 (5.9%)

564 (6.5%)

5223 (6.1%)

15 741 (6.2%)

LBW 6539 (4.1%)

18 (6.1%)

79 (6.2%)

<10 (1.8%)

<10 (2.1%)

225 (6.4%)

6539 (4.1%)

1830 (3.9%)

962 (5.4%)

429 (8.0%)

221 (5.6%)

136 (4.7%)

477 (5.5%)

4055 (4.7%)

10 923 (4.3%)

Marital status


138 (10.8%)

23 (7.8%)

18 (32.7%)

54 (22.6%)

643 (18.3%)

17 381 (10.4%)

2933 (6.2%)

689 (3.9%)

99 (1.8%)

515 (13.1%)

554 (19.4%)

586 (6.8%)

5376 (6.3%)

22 759 (9.0%)

Divorced/

separated

1431 (0.9%)

<10 (0.6%)

<10 (0.7%)

<10 (1.8%)

<10 (1.3%)

48 (1.4%)

1492 (0.9%)

347 (0.7%)

154 (0.9%)

17 (0.3%)

102 (2.6%)

31 (1.1%)

122 (1.5%)

773 (0.9%)

2266 (0.9%)

Married/de facto

142 355 (88.2%)

1124 (87.9%)

266 (89.9%)

36 (65.5%)

179 (74.9%)

2805 (79.7%)

146 765 (88.0%)

43 144 (92.1%)

16 647 (93.4%)

5176 (96.7%)

3317 (83.2%)

2214 (77.2%)

7876 (90.1%)

78374 (91.7%)

225 164 (89.3%)

Other 1096 (0.7%)

10 (0.8%)

<10 (1.7%)

0 (0.0%)

<10 (1.3%)

23 (0.7%)

1137 (0.7%)

454 (1.0%)

244 (1.4%)

60 (1.1%)

51 (1.3%)

68 (2.4%)

81 (0.9%)

958 (1.1%)

2095 (0.8%)

Parity


655 (51.2%)

137 (46.3%)

28 (50.9%)

116 (48.5%)

1559 (44.3%)

70 996 (42.6%)

20 466 (43.7%)

8519 (48.0%)

3114 (58.2%)

1175 (29.5%)

932 (32.5%)

3360 (38.8%)

37 566 (44.0%)

108 571 (43.0%)


408 (31.9%)

106 (35.8%)

16 (29.1%)

60 (25.1%)

1174 (33.4%)

58 475 (35.1%)

16 658 (35.5%)

6326 (35.7%)

1768 (33.0%)

1069 (26.8%)

770 (26.9%)

2619 (29.8%)

29210 (34.2%)

87 691 (34.8%)


216 (16.9%)

53 (17.1%)

11 (20.0%)

63 (26.4%)

786 (22.3%)

37 304 (22.4%)

9754 (20.8%)

2889 (16.3%)

470 (8.8%)

1741 (43.7%)

1165 (40.6%)

2686 (31.0%)

18 705 (21.9%)

56 015 (22.2%)

Maternal age

Mean (SD) 29.5 (5.6)

28.2 (5.6)

29.4 (4.7)

27.5 (5.1)

23.9 (5.2)

28.2 (6.0)

29.5 (5.6)

31.5 (5.3)

31.2 (4.9)

29.5 (4.4)

28.8 (5.7)

26.8 (6.0)

29.9 (5.6)

30.9 (5.4)

30.0 (5.6)


PAGE | 140

<20 6918 (4.3%)

85 (6.7%)

<10 (3.0%)

<10 (5.5%)

45 (18.8%)

252 (7.2%)

7312 (4.4%)

703 (1.5%)

131 (0.7%)

17 (0.3%)

188 (4.7%)

297 (10.4%)

193 (2.2%)

1529 (1.8%)

8842 (3.5%)

20–24 25 642 (15.9%)

272 (21.3%)

38 (12.8%)

12 (21.8%)

107 (44.8%)

778 (22.1%)

26 849 (16.1%)

4254 (9.1%)

1383 (7.8%)

629 (11.8%)

793 (20.0%)

862 (30.1%)

1439 (16.6%)

9365 (11.0%)

36 216 (14.4%)

25–29 46 061 (28.5%)

371 (29.0%)

95 (32.1%)

22 (40.0%)

50 (20.9%)

1003 (28.5%)

47 602 (28.5%)

11 150 (23.8%)

5099 (28.8%)

2228 (41.6%)

1209 (30.3%)

766 (26.7%)

2546 (29.4%)

22 998 (26.9%)

70 607 (28.0%)

30–34 51 274 (31.7%)

385 (30.1%)

116 (39.2%)

14 (25.4%)

24 (10.0%)

927 (26.3%)

52 740 (31.6%)

16874 (36.0%)

6751 (38.1%)

1816 (33.4%)

1116 (28.0%)

591 (20.6%)

2590 (29.9%)

29 738 (34.8%)

82 492 (32.7%)

35–39 26 536 (16.4%)

144 (11.3%)

34 (11.5%)

<10 (5.5%)

11 (4.6%)

444 (12.6%)

27 172 (16.3%)

11184 (23.9%)

3587 (20.2%)

557 (10.4%)

556 (14.0%)

277 (9.6%)

1522 (17.6%)

17 683 (20.7%)

44 859 (17.8%)

40–44 4790 (3.0%)

20 (1.6%)

<10 (1.4%)

<10 (1.8%)

<10 (0.8%)

112 (3.2%)

4929 (2.3%)

2590 (5.5%)

754 (4.3%)

101 (1.9%)

107 (2.7%)

74 (2.6%)

360 (4.2%)

3986 (4.7%)

8915 (3.5%)

>44 166 (0.1%)

<10 (0.2%)

0 (0.0%)

0 (0.0%)

0 (0.0%)

<10 (0.1%)

171 (0.1%)

123 (0.3%)

29 (0.2%)

<10 (0.1%)

11 (0.3%)

0 (0.0%)

15 (0.2%)

182 (0.2%)

353 (0.1%)

Any medical condition

56 106 (34.8%)

413 (32.3%)

107 (36.2%)

19 (3.6%)

74 (31.0%)

1666 (47.3%)

58 385 (35.0%)

14 255 (30.4%)

4744 (26.8%)

2038 (38.1%)

1489 (37.4%)

974 (34.0%)

3163 (36.5%)

26 663 (31.2%)

85 054 (33.7%)

Infertility treatment

4987 (3.1%)

28 (2.2%)

<10 (3.0%)

0 (0.0%)

<10 (0.8%)

82 (2.3%)

5108 (3.1%)

1608 (3.4%)

395 (2.2%)

120 (2.2%)

33 (0.8%)

15 (0.5%)

153 (1.8%)

2324 (2.7%)

7432 (3.0%)

Smoked 22 502 (13.9%)

82 (6.4%)

21 (7.0%)

<10 (10.9%)

85 (35.6%)

614 (17.5%)

23 310 (14.0%)

4010 (8.6%)

313 (1.8%)

40 (0.8%)

70 (1.8%)

1125 (39.2%)

465 (5.4%)

6023 (7.1%)

29 336 (11.6%)

Most disadvantaged*

38 120 (23.6%)

215 (16.8%)

37 (12.5%)

12 (2.8%)

90 (37.7%)

768 (21.8%)

39 242 (23·5%)

6326 (13·5%)

2140 (12·1%)

642 (12·0%)

491 (12·3%)

777 (27·1%)

1237 (14·3%)

11 613 (13.6%)

50 858 (20·2%)

Gestational diabetes

6984 (4.3%)

88 (6.9%)

17 (5.7%)

<10 (12.7%)

<10 (2.5%)

214 (6.1%)

7316 (4.4%)

2612 (5.6%)

2243 (12.7%)

834 (15.6%)

297 (7.5%)

115 (4.0%)

822 (9.5%)

6931 (8.1%)

14 239 (5.6%)


37 (2.9%)

<10 (2.7%)

<10 (1.8%)

<10 (2.5%)

116 (3.3%)

4650 (2.8%)

1053 (2.3%)

298 (1.7%)

119 (2.2%)

116 (2.9%)

73 (2.6%)

198 (2.3%)

1857 (2.2%)

6507 (2.6%)

Sex of baby

Male 82 479 (51.1%)

677 (52.9%)

144 (48.7%)

25 (45.5%)

127 (53.1%)

1833 (52.1%)

85 285 (51.1%)

23 882 (51.0%)

9207 (51.9%)

2729 (51.0%)

2055 (51.6%)

1450 (50.6%)

4435 (51.2%)

43 758 (51.2%)

129 057 (51.2%)

*The bottom 20% of IRSD


PAGE | 141

Cumulative incidence of LBW during the period of study was higher for migrants (4.7%) than the

Australian-born population (4.1%) whereas, for PTB, migrants had slightly (0.2%) lower cumulative

incidence (Table 7.2). When the migrant population was stratified by ethnicity, the rate of LBW

was diverse with women from an Indian background having the highest proportion (3.9%) of term-

LBW, three times that in Australian-born women (Table 7.2).

TABLE 7.2 CUMULATIVE INCIDENCE OF LBW AND PTB FOR BIRTHS TO AUSTRALIAN-BORN WOMEN AND BIRTHS TO

OVERSEAS-BORN WOMEN STRATIFIED BY ETHNIC BACKGROUND (2005–2013)


Cumulative incidence

Low birthweight Preterm birth

All LBW

(%)

Term-LBW

(%)

All PTB

(%)

Idiopathic PTB (%)

PPROM

(%)

Medically indicated PTB (%)

Australian-born 4.1 1.3 6.3 2.4 1.4 2.5

White 4.1 1.3 6.3 2.4 1.4 2.5

Asian 6.2 2.2 6.7 3.1 1.1 1.5

Indian 6.1 2.9 6.1 2.0 1.4 2.7

African 1.8 0 1.8 0 1.8 0

Māori 2.1 0.9 3.8 1.7 1.3 0.8

Other 6.4 2.3 8.2 2.8 1.6 3.8

Migrant 4.7 1.8 6.1 2.3 1.3 2.5

White 3.9 1.3 5.8 2.1 1.3 2.4

Asian 5.4 2.1 6.6 2.9 1.4 2.3

Indian 8.0 3.9 7.2 2.6 1.6 3.0

African 5.6 2.2 5.7 1.8 1.0 2.9

Māori 4.7 2.0 5.9 3.0 1.1 1.9

Other 5.5 1.9 6.5 2.4 1.4 2.8

All 4.3 1.5 6.2 2.4 1.4 2.5

Apart from white migrants, whose unadjusted odds of term-LBW were similar to that of the white

Australian-born population, all other migrant ethnic groups showed higher odds of term-LBW than

the white Australian-born group (Table 7.3). Migrant women from white and Māori backgrounds

had lower adjusted odds of PTB (aOR 0.93, 95% CI 0.89–0.98; aOR 0.85, 95% CI 0.73–1.00,

respectively, P=0.049), while women from Asian and Indian backgrounds had higher adjusted odds

than white Australian-born women (aOR 1.16, 95% CI 1.09–1.24; aOR 1.24, 95% CI 1.12–1.38,

respectively) (Table 7.3).


PAGE | 142

Australian-born women from Indian (OR 2.69, 95% CI 1.32–5.46), ‘other’ (OR 1.70, 95% CI 1.34–

2.15) and Asian (OR 1.98, 95% CI 1.34–2.93) backgrounds had higher odds of LBW than their

counterparts from a white background. Australian-born women from ‘other’ ethnic backgrounds,

when compared to white Australian-born women, had 33% higher odds of PTB (OR 1.33, 95% CI

1.18–1.51), which attenuated after adjusting for covariates (aOR 1.22, 95% CI 1.08–1.39) (Table

7.3).

Among Australian-born women, non-white women had 34% higher risk (RRR 1.34, 95% CI1.15–

1.56) of medically indicated PTB. This effect was limited to Australian-born women from ‘other’

ethnic backgrounds (RRR 1.57, 95% CI 1.32–1.88) and remained the same after adjusting for

covariates (Table 7.4).

Among migrant women, the higher risk of PTB in Asian women was confined to idiopathic PTB

(aRRR 1.39, 95% CI 1.26–1.59), whereas in Indian migrant women, higher risks of PTB were seen

for idiopathic PTB, PPROM and medically indicated PTB (Table 7.4). Migrant women from African

and white backgrounds had a lower risk of idiopathic PTB than the white Australian-born

population; only in the African group, the observed association remained the same after

adjustment (aRRR 0.79, 95% CI 0.62–1.00, Table 7.4). Similarly, migrant women of Māori and

white backgrounds had a lower risk of medically indicated PTB than white Australian-born women

(aRRR 0.74, 95% CI 0.56–0.98; aRRR 0.93, 95% CI 0.87–1.00, respectively). The lower risk of

PPROM in migrant women of African and white backgrounds did not remain the same after

adjusting for covariates (Table 7.4).

In stratified analyses, migrant women from non-white backgrounds had lower odds of PTB (OR

0.87, 95% CI 0.78–0.97, P=0.011) than their non-white Australian-born counterparts; this finding

did not hold after adjusting for confounders (Table 7.5). The differential lower odds of PTB was

restricted to migrant women of ‘other’ ethnic background (OR 0.78, 95% CI 0.67–0.91, P=0.001)

and was due to lower risk of medically indicated PTB (aRRR 0.74, 95% CI 0.59–0.93).


PAGE | 143

TABLE 7.3 ODDS OF PTB AND TERM-LBW IN AUSTRALIAN-BORN AND MIGRANT WOMEN FROM NON-WHITE BACKGROUNDS COMPARED TO WHITE AUSTRALIAN-BORN WOMEN

(2005–2013)

Ethnicity and migrant status

N (%) Term-LBW PTB

OR (95% CI) P aOR (95% CI)** P OR (95% CI) P aOR(95% CI)** P

White Australian-born (Reference)

161 387 (64.0%)

1.00 – 1.00 – 1.00 – 1.00 –

All non-white Australian-born

5388 (2.1%) 1.76 (1.45–2.14) 0.000 1.71 (1.43–2.12) 0.000 1.21 (1.09–1.34) 0.000 1.15 (1.04–1.28) 0.011

Asian 1279 (0.5%) 1.74 (1.18–2.57) 0.006 1.98 (1.36–2.99) 0.000 1.08 (0.87–1.34) 0.503 1.13 (0.89–1.40) 0.316

Indian 296 (0.1%) 2.31 (1.14–4.68) 0.020 2.69 (1.32–2.45) 0.006 0.97 (0.60–1.56) 0.895 1.01 (0.63–1.66) 0.959

African 55 (0.0%) – – – – 0.28 (0.04–2.00) 0.204 0.28 (0.04–2.00) 0.199

Māori 239 (0.1%) 0.69 (0.17–2.76) 0.594 0.48 (0.12–1.97) 0.312 0.59 (0.30–1.14) 0.115 0.56 (0.28–1.06) 0.066

Other 3519 (1.4%) 1.83 (1.45–2.31) 0.000 1.70 (1.34–2.15) 0.000 1.33 (1.18–1.51) 0.000 1.22 (1.07–1.37) 0.001

All migrant 85 481 (33.9%) 1.35 (1.27–1.45) 0.000 1.56 (1.45–1.68) 0.000 0.98 (0.95–1.01) 0.123 1.01 (0.99–1.06) 0.873

White 46 878 (18.6%) 1.02 (0.92–1.12) 0.736 1.12 (1.01–1.23) 0.025 0.91 (0.87–0.95) 0.000 0.93 (0.90–0.99) 0.002

Asian 17 734 (7.0%) 1.70 (1.51–1.90) 0.000 2.16 (1.91–2.43) 0.000 1.06 (1.00–1.13) 0.055 1.16 (1.09–1.24) 0.000

Indian 5352 (2.1%) 3.17 (2.73–3.69) 0.000 4.10 (3.51–4.80) 0.000 1.17 (1.05–1.30) 0.004 1.24 (1.08–1.34) 0.000

African 3985 (1.6%) 1.74 (1.39–2.18) 0.000 2.34 (1.87–2.94) 0.000 0.90 (0.79–1.03) 0.141 0.92 (0.82–1.08) 0.293

Māori 2867 (1.1%) 1.59 (1.21–2.09) 0.001 1.15 (0.86–1.50) 0.356 0.94 (0.80–1.10) 0.414 0.85 (0.71–1.00) 0.044

Other 8665 (3.4%) 1.49 (1.26–1.76) 0.000 1.80 (1.53–2.15) 0.000 1.04 (0.95–1.14) 0.367 1.06 (0.96–1.15) 0.200

**Adjusted for the year of birth, maternal age group, marital status, parity, socioeconomic disadvantage, smoking, any medical condition, infertility treatment, and sex of the baby


PAGE | 144

TABLE 7.4 RISK OF SPONTANEOUS AND MEDICALLY INDICATED PTB IN MIGRANTS FROM DIVERSE ETHNIC BACKGROUNDS COMPARED TO THE WHITE AUSTRALIAN-BORN

POPULATION (2005–2013)

Population N (%) Spontaneous PTB Medically indicated

Idiopathic PPROM

RRR (95% CI)

P aRRR (95% CI)*

P RRR (95% CI)

P aRRR (95% CI)*

P RRR (95% CI)

P aRRR (95% CI)*

P

White Australian-born (Reference)

161,387 (64.0)

1.00 1.00 1.00 1.00 1.00 1.00

Non-white Australian-born

5388 (2.1)

1.17 (0.95–1.29)

0.774 1.11 (0.94–1.31)

0.211 1.03 (0.86–1.30)

0.066 1.00 (0.80–1.27)

0.974 1.34 (1.15–1.56)

0.000 1.27 (1.09–1.48)

0.002

Asian 1279 (0.5)

1.30 (0.99–1.38)

0.103 1.36 (0.98–1.86)

0.063 0.79 (0.46–1.33)

0.371 0.83 (0.47–1.37)

0.490 1.02 (0.85–1.05)

0.891 1.07 (0.78–1.52)

0.712

Indian 296 (0.1)

0.84 (0.37–1.88)

0.667 0.89 (0.40–2.03)

0.801 0.96 (0.36–2.59)

0.941 1.01 (0.38–2.71)

0.986 1.10 (0.54–2.22)

0.791 1.12 (0.55–2.26)

0.757

African 55 (0.0)

– – – 1.24 (0.18–8.97)

0.831 1.16 (0.16–8.57)

0.869 – – – –

Māori 239 (0.1)

0.68 (0.25–1.82)

0.436 0.53 (0.20–1.43)

0.210 0.87 (0.28–2.73)

0.816 0.80 (0.26–2.51)

0.702 0.33 (0.08–1.34)

0.121 0.36 (0.09–1.44)

0.149

Other 3519 (1.4)

1.20 (0.98–1.47)

0.074 1.11 (0.91–1.36)

0.297 1.14 (0.87–1.49)

0.341 1.07 (0.82–1.41)

0.611 1.57 (1.32–1.88)

0.000 1.42 (1.19–1.69)

0.000

Migrant 85 481 (33.9)

0.96 (0.91–1.01)

0.240 1.04 (0.98–1.10)

0.107 0.94 (0.88–1.02)

0.094 0.98 (0.91–1.05)

0.748 0.99 (0.93–1.09)

0.924 0.98 (0.93–1.04)

0.370

White 46 878 (18.6)

0.87 (0.80–0.93)

0.000 0.93 (0.87–1.00)

0.053 0.90 (0.83–0.99)

0.028 0.94 (0.89–1.08)

0.151 0.97 (0.90–1.03)

0.327 0.93 (0.87–1.00)

0.049

Asian 17 734 (7.0)

1.20 (1.09–1.31)

0.000 1.39 (1.26–1.59)

0.000 1.03 (0.90–1.17)

0.686 1.14 (1.00–1.30)

0.051 0.95 (0.85–1.05)

0.292 0.97 (0.88–1.08)

0.673

Indian 5352 (2.1)

1.10 (0.92–1.30)

0.300 1.26 (1.06–1.50)

0.008 1.19 (0.95–1.46)

0.118 1.28 (1.03–1.60)

0.020 1.22 (1.04–1.44)

0.014 1.21 (1.03–1.42)

0.018

African 3985 0.75 0.018 0.79 0.052 0.70 0.025 0.78 0.149 1.17 0.103 1.10 0.205


PAGE | 145

(1.6) (0.59–0.95) (0.62–1.00) (0.51–0.96) (0.57–1.07) (0.97–1.41) (0.96–1.40)

Māori 2867 (1.1)

1.22 (0.98–1.52)

0.071 1.01 (0.82–1.27)

0.872 0.77 (0.54–1.10)

0.150 0.72 (0.50–1.01)

0.069 0.75 (0.56–0.97)

0.040 0.74 (0.56–0.98)

0.033

Other 8665 (3.4)

0.98 (0.85–1.13)

0.806 1.05 (0.91–1.21)

0.516 0.98 (0.82–1.18)

0.862 1.06 (0.88–1.25)

0.532 1.13 (0.99–1.29)

0.066 1.10 (0.96–1.25)

0.205

*Adjusted for: year of birth, maternal age, marital status, parity, socioeconomic disadvantage, infertility treatment, smoking, any medical condition, sex of the baby.


PAGE | 146

TABLE 7.5 RISKS OF TERM-LBW AND PTB FOR MIGRANTS COMPARED WITH AUSTRALIAN-BORN POPULATION FROM THE SAME ETHNIC GROUP

Ethnicity Term-LBW PTB

OR (95% CI) P aOR (95% CI)* P OR (95% CI) P aOR (95% CI)* P

Australian-born ethnicity** (Reference)

1.00 1.00 1.00 1.00

All non-white migrants 1.05 (0.86–1.27) 0.642 1.11 (0.90–1.36) 0.283 0.87 (0.78–0.97) 0.011 0.92 (0.83–1.03) 0.178

Asian 0.97 (0.65–1.46) 0.902 1.04 (0.68–1.56) 0.852 0.99 (0.79–1.24) 0.904 1.07 (0.85–1.35) 0.579

Indian 1.37 (0.67–2.81) 0.387 1.30 (0.62–2.71) 0.451 1.20 (0.74–1.96) 0.456 1.21 (0.73–2.00) 0.468

African 0 – 0 – 3.26 (0.45–23.68) 0.242 3.55 (0.46–24.90) 0.216

Māori 2.33 (0.56–9.61) 0.243 2.26 (0.54–2.53) 0.265 1.60 (0.81–3.17) 0.178 1.37 (0.68–2.74) 0.364

Other 0.82 (0.62–1.08) 0.155 1.10 (0.75–1.37) 0.963 0.78 (0.67–0.91) 0.001 0.86 (0.74–1.02) 0.071

*Adjusted for the year of birth, maternal age group, marital status, parity, SES, smoking, any medical condition, sex of the baby **Migrant population from each ethnic group was compared to the Australian-born population from the same ethnic group


PAGE | 147

SENSITIVITY ANALYSIS 7.4.1

For Chinese-born women (N=2487), unadjusted odds of PTB were lower than the Australian-born

population (OR 0.78; 95% CI 0.65-0.94; P=0.009), however, adjusting for covariates attenuated the

OR to 0.87 (95% CI 0.73-1.05; P=0.133). In multinomial regression analysis, only the risk of

medically-indicated PTB was lower among Chinese-born women (aRRR 0.61; 95% CI 0.44-0.85;

P=0.003) compared to white Australian-born women.

Adding private health insurance to the adjusted analysis made the association of medically indicated

PTB with Australian-born from ‘other’ and migrant-born from Indian ethnic backgrounds stronger.

However, adding gestational diabetes and preeclampsia to the multivariable regression analyses, did

not result in any appreciable difference.

DISCUSSION 7.5

To our knowledge, this is the first study reporting the effect of ethnicity and migration status on

the risk of term-LBW, spontaneous and medically indicated PTB in Australia to explore differences

between non-white migrants from diverse ethnic backgrounds to their corresponding Australian-

born counterparts.

In this population-based linked data study, we showed that migrants from diverse ethnic

backgrounds had higher odds of term-LBW than the Australian-born white population. We also

reported that Australian-born women from Asian, Indian and ‘other’ backgrounds were at higher

risk of term-LBW than their white Australian-born counterparts. Further, migrant women from

Asian and Indian backgrounds had higher odds of PTB and migrant women with white ethnicity

had lower odds of PTB than Australian-born white women. Migrant women from African

backgrounds were at lower risk of idiopathic PTB, and those from Māori and Chinese-born

backgrounds were at lower risk of medically indicated PTB than their white Australian-born

counterparts. Among non-white Australian-born women, those from ‘other’ backgrounds had

higher odds of medically indicated PTB than white Australian-born women.


Consistent with national reports,28 the cumulative incidence of LBW was 4.3% in our study

population comprising of all singleton non-Indigenous live births. Both ethnicity and migration

influenced the risk of term-LBW in our study. The risk was higher in migrants and the non-white


PAGE | 148

Australian-born population from Asian, Indian and ‘other’ ethnic descent than their white

Australian-born counterparts.

Racial and ethnic disparities in the rate of LBW have been well documented in the US,295-297 where

the odds of LBW for US- and foreign-born blacks were 2.5-times that of US-born whites.295 African

American women had a greater prevalence of LBW, even at the highest educational levels,

indicating disparities influenced by ethnicity and race.297 For migrant women, however, research

findings have been conflicting. Juarez and Revuelta-Eugercios found a 13–35% lower risk of

delivering LBW for most migrant groups compared to the non-migrant population in Spain.298

Similar to our study, they also investigated term-LBW separately but reported higher odds of term-

LBW only for women from Sub-Saharan Africa. Martinson, Tienda, and Teitler found that migrants

from LMICs in Australia, and those from Asian countries in the United Kingdom (UK) and the US

had higher odds of LBW than those born in the destination countries.13 Further, the second

generation of migrants, especially those of South Asian descent, had notably higher odds of LBW

in the UK and the US than their first-generation counterparts.299

Taken together, this evidence suggests that the rate of LBW among immigrants and ethnic

minorities is country-specific. The rate may be influenced by country of origin, socioeconomic

status and access to health services in destination countries, or immigration-related policies,

processes or circumstances in both countries of origin and destination.199,299,300 Our results suggest

that migrant status and ethnicity may be independent predictors of LBW or proxies for other

influential factors, such as diet or sociocultural experiences, not recorded in the dataset. These

findings may also show the intergenerational effect of disadvantage experienced by the first

generation of migrants300 and emphasise the need for investigation, intervention and prevention

to ameliorate short- and long-term impacts of LBW on individuals, populations and the health

system.

PRETERM BIRTH 7.5.2

Incidence of PTB in migrant women was slightly lower than the Australian-born white population

in our study; however, stratified analysis by ethnic background revealed a higher risk of PTB for

Asian and Indian migrants and lower rates for migrants from white, Māori and Chinese

backgrounds than the Australian-born group. Our findings were consistent with previous studies

that reported lower rates of PTB for women from migrant backgrounds than the non-migrant

population in Australia, Canada and the US.13,163,301 Almeida et al. reported a lower risk of PTB for


PAGE | 149

migrants from white backgrounds than the non-migrants in the US.295 Women born in China and

Somalia had lower rates of PTB than Australian-born women.13,163 Chinese-born women also

showed lower rates of PTB than their US-born counterparts.198 On the other hand, Gagnon et al.

reported that Asian and Sub-Saharan African migrants were at greater risk of PTB in a systematic

review.11 Furthermore, we studied the influence of ethnicity on the risk of PTB among the

Australian-born population and showed a higher risk for non-white Australian-born women from

the ‘other’ ethnic background. These data highlight the heterogeneity of the population and the

importance of considering both country of birth and ethnicity in the analysis to identify at-risk

groups.

We note that in some of our results, the adjusted effect estimates are larger, and the relationship

stronger than the crude ratios. This may be explained by the ‘healthy migrant effect’,302 given that

migrants in our population had collectively lower prevalence of risk factors, such as smoking and

socioeconomic disadvantage, than the Australian-born group.303 Where a study group had a higher

prevalence of those risk factors, such as the case with Māori women, adjusting resulted in smaller

effect measures.

STRENGTHS AND LIMITATIONS 7.5.3

Our study was a population-based cohort with a substantial proportion of migrants (33.9%), which

allowed stratification by both country of birth and ethnicity. We differentiated the type of PTB by

spontaneous and medically indicated birth and identified population groups affected by each type

of PTB. Moreover, we reported term-LBW, which is an outcome comparable globally. The design

and linked health data methodology reduced the risk of selection, participation and recall biases.

Cross-source ascertainment optimised the reliability of data and accuracy of findings.

Our findings are generalisable to other countries hosting migrants from similar ethnic

backgrounds; however, interpretation should be made with caution due to differences in health

services, the proportion of countries of origin and immigration policies as well as some limitations.

We used administrative health data that were not primarily collected to answer the research

questions of our study; although cross-source ascertainment reduced such risk, a small risk of

misclassification bias towards the null hypothesis may exist. We also had small population sizes for

some ethnic groups of the Australian-born women that may have reduced the statistical power

and the ability to detect any appreciable difference in the odds for sub-types of PTB in the

Australian-born ethnic groups.


PAGE | 150

In conclusion, there is evidence of lower risk of PTB in migrant women of some ethnicities; in

others, migrant women had increased rates of PTB. Ethnicity impacts the rates of term-LBW with

migrant status, further increasing the risk in women of some ethnic backgrounds. Our findings

warrant further investigation of risk as well as protective factors in specified population groups to

develop appropriate policies and preventive strategies to reduce these adverse pregnancy

outcomes. Investigating the role of acculturation, for example, through age arrived in Australia

and/or length of residence may provide a clearer picture of the pathways influencing these

outcomes.304

CHAPTER 8. ACCULTURATION, PRETERM BIRTH AND LOW BIRTHWEIGHT IN WA

PAGE | 151

CHAPTER 8. ACCULTURATION, PRETERM BIRTH AND LOW BIRTHWEIGHT IN

WESTERN AUSTRALIA

This chapter comprises a paper submitted to PLoS One (September 2020) for publication. This

chapter builds on the findings of the studies presented in previous chapters and explores the risk

of preterm birth and low birthweight in relation to the emerging sociocultural factors specific to

migrant populations, including but not limited to communication barriers, length of residence and

potential opportunity for integration through marriage/partnership or young age on arrival. This

investigation is related to the fifth objective of this thesis, to investigate the influence of

acculturation on disparities observed in the risk of preterm birth and low birthweight between

migrant and Australian-born populations from diverse ethnic backgrounds in WA.


PAGE | 152

ABSTRACT 8.1

Background: The risk of preterm birth (PTB) and low birthweight (LBW) may change over time

after immigrants have settled in high-income countries. We studied the influence of acculturation

through age on arrival, length of residence, interpreter use and having an Australian-born partner

on the risk of these outcomes in Australia.

Methods: A retrospective cohort study was undertaken using linked health data for all non-

Indigenous births from 2005–2013 in Western Australia. Adjusted odds ratios (aOR) for PTB and

term-LBW in migrants from six ethnicities of white, Asian, Indian, African, Māori and ‘other’ with

different levels of acculturation, compared with Australian-born population, were calculated using

multivariable logistic regression analysis.

Results: Migrant women had 49% higher odds of term-LBW (aOR 1.49, 95% CI 1.39–1.60) than

Australian-born women. The least acculturated non-white non-Māori women had twice the risk of

term-LBW, and the most acculturated had the same risk as Australian-born women. The odds of

PTB was 42% lower (aOR 0.58, 95% CI 0.46–0.73) in the least acculturated and 40% higher (aOR

1.40, 95% CI 1.18–1.66) in the most acculturated women than their Australian-born counterparts.

Conclusion: Acculturation emerged as an important factor when considering appropriate care for

the prevention of PTB and LBW in migrants. Overall, acculturation may improve the risk of term-

LBW and worsen the risk of PTB in migrant women residing in Western Australia; however, the

effect can vary in different ethnic groups and warrants further investigation to understand the

processes involved.


PAGE | 153

INTRODUCTION 8.2

Preterm birth (PTB), birth before 37 completed weeks of pregnancy,230 and low birthweight (LBW),

birthweight less than 2500 grams regardless of the gestational age of the newborn28, are more

common among some migrant groups than the non-migrant population in high-income countries

(HICs), such as Australia, the United Kingdom (UK) and the United States (US).11,199 These adverse

pregnancy outcomes are associated with considerable financial costs imposed on families and

healthcare systems96,290 and result in higher rates of mortality and morbidity in childhood and

throughout life.289

Culture can affect the quality of diet,305 smoking in pregnancy,195,196 and physical activity277 that

are associated with birthweight and other adverse pregnancy outcomes,306-308 while one’s culture,

and these factors as a result, can be influenced by the environment and change due to encounters

with other cultures after immigration; this is known as ‘acculturation’.129 Mounting evidence from

the US and Europe suggests that acculturation may influence the risk of adverse pregnancy

outcomes.190,191,196,309-312 We previously investigated the influence of acculturation on the risk of

stillbirth;304 however, the effect of acculturation on the risk of LBW and PTB in migrant

populations is not well understood in Australia.

We studied the influence of age on arrival, length of residence, using an interpreter and having an

Australian-born partner, as the proxies to integration and acculturation, on disparities observed in

the risk of PTB and term-LBW between Australian- and overseas-born populations from diverse

ethnic backgrounds.


PAGE | 154

METHODS 8.3

STUDY POPULATION AND DATA SOURCES 8.3.1

Routinely collected administrative health and Registry data from the Department of Health WA

linked through the WA Data Linkage System (WADLS) was used to undertake a retrospective

cohort study. Non-identifiable data for the entire non-Indigenous population of singleton live

births in WA from 1 January 2005 to 31 December 2013 were used.

The WADLS links data from a variety of health and other administrative datasets using best-

practice probabilistic matching based on full name and address, phonetic compression algorithms

and other identifiers.204,211,213 Further, it has designed and uses numerous automated and manual

sub-processes specifically to reduce the likelihood of linkage error—the details of which are

reported in linkage quality statements published elsewhere.212,215

Primarily, data were sourced from the Midwives Notification System (MNS). The MNS is a

statutory database, including maternal, pregnancy, birth and newborn information collected by

attending midwives for all births of at least 20 weeks gestation or at least 400 grams in weight (if

gestational age was unknown). We supplemented the data from the MNS with additional variables

linked from the WA Birth Register, including maternal, paternal and infant demographic

characteristics for all registered births in WA, and also from the Hospital Morbidity Data Collection

(HMDC) which contains information related to inpatient separations from all WA private and

public hospitals.245 We used the Family Connections Linkage Facility223 of the WADLS to link

migrant women with their child outcomes.

VARIABLES 8.3.2

PTB was defined as birth before 37 completed weeks of gestation,230 and LBW as birthweight less

than 2500 grams at the time of birth.28


PAGE | 155

Birth status (live birth, stillbirth), plurality (number of babies in this birth), infant weight (grams)

and estimated gestational age at time of birth (completed weeks), all extracted from MNS data,

were used to identify live singleton births and LBW and PTB outcomes.

Migrant status (Australian- or overseas-born), using mother’s place/country of birth (99.0% from

Birth Registrations and 1.0% from HMDC), and ethnic origin (100% from MNS) were used to group

the study population into Australian-born and migrant from white, Asian, Indian, African, Māori or

other ethnic backgrounds.241

Year of birth, maternal age (<20, 20–24, 25–29, 30–34, 35–39, 40–44, >44), marital status (never

married, divorced/separated, married/de facto, other), smoking in pregnancy (yes/no), history of

pre-existing medical conditions (yes/no), gestational diabetes, preeclampsia, fertility treatment

(yes/no), sex of baby and socioeconomic disadvantage (using IRSD quintiles) were extracted from

the MNS. Length of residence and age on arrival were calculated using mother’s year of arrival in

Australia (with 4.5% missing values) and baby’s year of birth, and status of having an Australian-

born partner (yes/no) from father’s place of birth from Birth Registration data (with 3.3% missing

values). Interpreter utilisation (yes/no) and private health insurance (yes/no) were retrieved from

HMDC records that were available for 99.0% of the whole population of births in WA.

Acculturation level was first examined by creating a new variable that had the following five

values: 0=Australian-born women, 1=migrant women with overseas-born partner and interpreter,

2=migrant women with overseas-born partner but no interpreter, 3=migrant women with

Australian-born partner and 4=others. The ‘least acculturated’ migrant women were defined as

non-white non-Māori women with a length of residence of <5 years who had an overseas-born

partner, immigrated as an adult and used an interpreter when navigating the healthcare system.

The non-white non-Māori women who lived in Australia >10 years, immigrated as a child, had an

Australian-born partner and did not have an interpreter in hospital were considered ‘the most


PAGE | 156

acculturated’ migrant women. This was examined in a separate analysis by creating a new

variable, acculturated, with the following values: 0=Australian-born, 1=the least acculturated,

2=the most acculturated and 3=other migrants.

STATISTICAL METHODS 8.3.3

The demographic and obstetric characteristics of the study populations were summarised and

reported by percentage. Logistic regression was used to calculate the odds ratios (OR) and 95%

confidence intervals (CI) for associations with term-LBW and PTB. Analyses were adjusted for year

of birth, maternal age group (<20, 20–24, 25–29, 30–34, 35–39, 40–44, >44), marital status (never

married, divorced/separated, married/de facto, other), parity (nulliparous, primiparous,

multiparous), socioeconomic disadvantage (using IRSD quintiles), infertility treatment (yes/no),

pre-existing medical condition (yes/no), private health insurance (yes/no), sex of the baby, and

smoking in pregnancy (yes/no).293,294 P<0.05 was considered significant and variables with P>0.1

were removed from the final adjusted model. When specific ethnic groups were investigated, due

to the small numbers, the analyses were only adjusted for age and smoking.

RESULTS 8.4

DESCRIPTIVE ANALYSIS 8.4.1

From the 252 256 live singleton births studied, 33.9% belonged to migrant mothers and 66.1% to

women born in Australia (Table 8.1). Migrant women were slightly older than the Australian-born

group (mean age in years: 30.9 vs 29.5) and had a higher proportion of married women than the

Australian-born population (91.7% vs 88.0%). Migrant women were less likely to smoke in

pregnancy (7.1% vs 14.0%), fall in the most socioeconomically disadvantaged category (13.6% vs

23.5%) or have pre-existing medical conditions (31.2% vs 35.0%), but were more likely to

experience gestational diabetes (8.1% vs 4.4%) than their Australian-born counterparts. Among

the migrant women, 30% had an Australian-born partner, 55.0% arrived in Australia as an adult,


PAGE | 157

and 40.3% gave birth in their first five years of residence, including 12.1% who gave birth in the

first two years after immigrating to Australia.

ACCULTURATIVE FACTORS 8.4.2

Less than five years length of residence in Australia, immigrating as an adult and having an

Australian-born partner were slightly associated with lower odds of PTB and using the interpreter

service was strongly associated with lower odds of this outcome. However, the only factor that

remained significant after adjusting for covariates was using the interpreter service (aOR 0.65,

95% CI 0.55–0.77). Using an interpreter had the most protective effect among women from

African background (OR 0.31, 95% CI 0.18–0.54).

Migrant women had 49% higher odds of term-LBW (aOR 1.49, 95% CI 1.39–1.60) than Australian-

born women. The odds of term-LBW in migrant women with an Australian-born partner was

similar to that of the Australian-born women and the odds of term-LBW reduced in migrants with

longer length of residence (Table 8.2).

While the rate of term-LBW decreased with longer length of residence in women from African and

other ethnic backgrounds, it followed a U-shape pattern in births to Asian and Indian women and

increased among Māori women residing in Australia for more than five years (Figure 8.1).

Compared to Australian-born women, migrant women from Indian (OR 3.13, 95% CI 2.61–3.76),

African (OR 2.07, 95% CI 1.55–2.76), Asian (OR 1.74, 95% CI 1.46–2.08) and ‘other’ (OR 1.49, 95%

CI 1.17–1.91) backgrounds had significantly higher odds of term-LBW in the first five years of

residence. Among those with 5–10 years residence, women from Indian and Māori backgrounds

had higher odds of term-LBW (OR 2.64; 95% CI1.85–3.76, OR 1.78; 95% CI 1.03–3.09 respectively)

than Australian-born women. For those with >10 years residence, women from Indian (OR 3.57;

95% CI 1.85–3.76), Māori (OR 1.79; 95% CI 1.15–2.80) and Asian (OR 1.70; 95% CI 1.42–2.04)

backgrounds had higher odds of term-LBW outcome than their Australian-born counterparts.


PAGE | 158

Adjusting for age and smoking significantly attenuated the odds of term-LBW solely in women

from Māori background (aOR 1.20, 95% CI 0.76–1.87) but not in other groups.


PAGE | 159

TABLE 8.1 CHARACTERISTICS OF THE STUDY POPULATION

Characteristics


Migrant women

All women

All White Asian Indian African Māori Other All

Live & singleton births 166 775 (66.1%)

46 878 (18.6%)

17 734 (7.0%)

5352 (2.1%)

3985 (1.6%)

2867 (1.1%)

8665 (3.4%)

85 481 (33.9%)

252 256 (100%)

PTB 10 517 (6.3%)

2699 (5.8%)

1177 (6.6%)

387 (7.2%)

227 (5.7%)

169 (5.9%)

564 (6.5%)

5223 (6.1%)

15 741 (6.2%)

LBW 6539

(4.1%) 1830

(3.9%) 962

(5.4%) 429

(8.0%) 221

(5.6%) 136

(4.7%) 477

(5.5%) 4055

(4.7%) 10 923 (4.3%)

Marital status


2933 (6.2%)

689 (3.9%)

99 (1.8%)

515 (13.1%)

554 (19.4%)

586 (6.8%)

5376 (6.3%)

22 759 (9.0%)

Divorced/separated 1492

(0.9%) 347

(0.7%) 154

(0.9%) 17

(0.3%) 102

(2.6%) 31

(1.1%) 122

(1.5%) 773

(0.9%) 2266

(0.9%)


43 144 (92.1%)

16 647 (93.4%)

5176 (96.7%)

3317 (83.2%)

2214 (77.2%)

7876 (90.1%)

78 374 (91.7%)

22 5164 (89.3%)

Other 1137

(0.7%) 454

(1.0%) 244

(1.4%) 60

(1.1%) 51

(1.3%) 68

(2.4%) 81

(0.9%) 958

(1.1%) 2095

(0.8%)

Parity


20 466 (43.7%)

8519 (48.0%)

3114 (58.2%)

1175 (29.5%)

932 (32.5%)

3360 (38.8%)

37 566 (44.0%)

108 571 (43.0%)


16 658 (35.5%)

6326 (35.7%)

1768 (33.0%)

1069 (26.8%)

770 (26.9%)

2619 (29.8%)

29 210 (34.2%)

87 691 (34.8%)


9754 (20.8%)

2889 (16.3%)

470 (8.8%)

1741 (43.7%)

1165 (40.6%)

2686 (31.0%)

18 705 (21.9%)

56 015 (22.2%)

Maternal age


PAGE | 160

Mean (SD) 29.5 (5.6)

31.5 (5.3)

31.2 (4.9)

29.5 (4.4)

28.8 (5.7)

26.8 (6.0)

29.9 (5.6)

30.9 (5.4)

30.0 (5.6)

<20 7312

(4.4%) 703

(1.5%) 131

(0.7%) 17

(0.3%) 188

(4.7%) 297

(10.4%) 193

(2.2%) 1529

(1.8%) 8842

(3.5%)

20–24 26 849 (16.1%)

4254 (9.1%)

1383 (7.8%)

629 (11.8%)

793 (20.0%)

862 (30.1%)

1439 (16.6%)

9365 (11.0%)

36 216 (14.4%)

25–29 47 602 (28.5%)

11 150 (23.8%)

5099 (28.8%)

2228 (41.6%)

1209 (30.3%)

766 (26.7%)

2546 (29.4%)

22 998 (26.9%)

70 607 (28.0%)

30–34 52 740 (31.6%)

16 874 (36.0%)

6751 (38.1%)

1816 (33.4%)

1116 (28.0%)

591 (20.6%)

2590 (29.9%)

29 738 (34.8%)

82 492 (32.7%)

35–39 27 172 (16.3%)

11 184 (23.9%)

3587 (20.2%)

557 (10.4%)

556 (14.0%)

277 (9.6%)

1522 (17.6%)

17 683 (20.7%)

44 859 (17.8%)

40–44 4929

(2.3%) 2590

(5.5%) 754

(4.3%) 101

(1.9%) 107

(2.7%) 74

(2.6%) 360

(4.2%) 3986

(4.7%) 8915

(3.5%)

>44 171

(0.1%) 123

(0.3%) 29

(0.2%) <10

(0.1%) 11

(0.3%) 0

(0.0%) 15

(0.2%) 182

(0.2%) 353

(0.1%)

Australian-born Partner 115 463 (71.8%)

19 130 (41.7%)

3454 (19.7%)

257 (4.8%)

168 (4.4%)

627 (23.5%)

1482 (17.6%)

25 118 (30.0%)

140 581 (57.5%)

Age on arrival

<18 years old NA 22 516

(48.0) 6021

(34.0%) 675

(12.6%) 1087

(27.3%) 1499

(52.3%) 2850

(32.9%) 34 648 (40.5%)

NA

18 or older NA 22 744

(48.5%) 10 793 (60.9%)

4420 (82.6%)

2569 (64.5%)

1226 (42.8%)

5277 (60.9)

47 029 (55.0%)

NA

Length of residence

<2years NA 3961

(8.5%) 2316

(13.1%) 1282

(24.0%) 712

(17.9%) 519

(18.1%) 1543

(17.8%) 10 333 (12.1%)

NA

2–5 years NA 11 162

(23.8%) 5302

(29.9%) 2570

(48.0%) 1556

(39.1%) 839

(29.3%) 2710

(31.3%) 24 139 (28.2%)

NA

>5 years NA 30 123 9192 1243 1383 1367 3872 47 180 NA


PAGE | 161

(64.3%) (51.8%) (23.2%) (34.7%) (47.7%) (44.7%) (55.2%)

Unknown NA 1632

(3.5%) 924

(5.2%) 257

(4.8%) 334

(8.4%) 142

(5.0%) 540

(6.2%) 3829

(4.5%) NA

Any medical condition 58 385 (35.0%)

14 255 (30.4%)

4744 (26.8%)

2038 (38.1%)

1489 (37.4%)

974 (34.0%)

3163 (36.5%)

26 663 (31.2%)

85 054 (33.7%)

Infertility treatment 5108

(3.1%) 1608

(3.4%) 395

(2.2%) 120

(2.2%) 33

(0.8%) 15

(0.5%) 153

(1.8%) 2324

(2.7%) 7432

(3.0%)

Smoked 23 310 (14.0%)

4010 (8.6%)

313 (1.8%)

40 (0.8%)

70 (1.8%)

1125 (39.2%)

465 (5.4%)

6023 (7.1%)

29 336 (11.6%)

Most disadvantaged* 39 242 (23·5%)

6326 (13·5%)

2140 (12·1%)

642 (12·0%)

491 (12·3%)

777 (27·1%)

1237 (14·3%)

11 613 (13.6%)

50 858 (20·2%)

Gestational diabetes 7316

(4.4%) 2612

(5.6%) 2243

(12.7%) 834

(15.6%) 297

(7.5%) 115

(4.0%) 822

(9.5%) 6931

(8.1%) 14 239 (5.6%)

Preeclampsia 4650

(2.8%) 1053

(2.3%) 298

(1.7%) 119

(2.2%) 116

(2.9%) 73

(2.6%) 198

(2.3%) 1857

(2.2%) 6507

(2.6%)

Sex of baby

Male 85 285 (51.1%)

23 882 (51.0%)

9207 (51.9%)

2729 (51.0%)

2055 (51.6%)

1450 (50.6%)

4435 (51.2%)

43 758 (51.2%)

129 057 (51.2%)

*The bottom 20% of IRSD


PAGE | 162

TABLE 8.2 COMPARISON OF TERM-LBW AND PTB IN MIGRANTS, STRATIFIED BY ACCULTURATIVE FACTORS, WITH AUSTRALIAN-BORN WOMEN

Acculturative factor N (%) Term-LBW All PTB

OR (95% CI) aOR (95% CI)** OR (95% CI) aOR (95% CI)**

Australian-born (Reference) 1.00 1.00 1.00 1.00

Overseas-born 1.35*(1.27–1.45) 1.49*(1.39–1.60) 0.98 (0.95–1.01) 0.98 (0.95–1.02)

Interpreter use

Non-white non-Māori migrant with interpreter

3493 (9.8) 1.58*(1.24–2.01) 2.03*(1.58–2.60) 0.64*(0.55–0.76) 0.65*(0.55–0.77)

Non-white non-Māori migrant without interpreter

32 066 (90.2) 1.85*(1.70–2.02) 2.24*(2.05–2.46) 1.09*(1.04–1.14) 1.13*(1.07–1.19)

Length of residence

<5 years 29 392 (34.4) 1.51*(1.37–1.66) 1.67*(1.51–1.85) 0.93*(0.88–0.98) 0.95 (0.91–1.01)

5–10 years 17 844 (20.9) 1.22*(1.07–1.39) 1.39*(1.22–1.58) 0.95 (0.89–1.01) 0.97 (0.91–1.04)

>10 years 34 416 (40.3) 1.22*(1.11–1.35) 1.32*(1.20–1.46) 1.00 (0.95–1.05) 1.00 (0.95–1.05)

Age on arrival

≥18 years old 53 555 (65.6) 1.38*(1.27–1.49) 1.30*(1.17–1.45) 0.94*(0.90–0.98) 1.00 (0.95–1.06)

<18 years old 28 074 (34.4) 1.25*(1.12–1.39) 1.61*(1.48–1.75) 1.00(0.95–1.05) 0.97 (0.93–1.01)

Australian-born partner

Migrant with overseas-born partner 60 363 (70.6) 1.49*(1.39–1.61) 1.69*(1.57–1.83) 0.96*(0.92–1.00) 0.98 (0.94–1.02)

Migrant with Australian-born partner 25 118 (29.4) 1.03 (0.91–1.16) 1.07 (0.95–1.21) 0.99 (0.93–1.05) 1.00 (0.94–1.05)

*P<0.05; **Adjusted for insurance status, age group, sex of baby, pre-existing medical conditions, infertility treatment, socioeconomic disadvantage, parity and smoking in pregnancy.


PAGE | 163

FIGURE 8.1 TERM-LBW RATE BY ETHNICITY OF MIGRANTS AND LENGTH OF RESIDENCE IN AUSTRALIA (2005–2013)

Rate of PTB increased with longer length of residence in Australia for all migrants, except for those

from Māori and African backgrounds (Figure 8.2). Among migrant women who had resided in

Australia for more than ten years, the age and smoking adjusted odds of PTB in Māori women

were 32% lower (aOR 0.68, 95% CI 0.50–0.92) while for women from Asian, other and Indian

backgrounds the odds were 15%, 26% and 50% higher (aOR 1.15, 95% CI 1.04–1.2; aOR 1.26, 95%

CI 1.083–1.46; aOR 1.50, 95% CI 1.137–1.98, respectively) than the Australian-born population.

FIGURE 8.2 ALL PTB RATE BY ETHNICITY OF MIGRANTS AND LENGTH OF RESIDENCE IN AUSTRALIA (2005–2013)


PAGE | 164

LEVEL OF ACCULTURATION 8.4.3

When acculturation was examined in a multidimensional manner considering many factors

simultaneously, migrant women with an overseas-born partner who used an interpreter had

significantly higher odds of term-LBW and lower odds of PTB than Australian-born women. In

contrast, for migrant women with an Australian-born partner, the odds of term-LBW and PTB were

similar to those of Australian-born women (Table 8.3). For the least acculturated women, the odds

of term-LBW was twice as high as Australian-born women (aOR 2.00, 95% CI 1.45–2.74) while the

odds of PTB was 42% lower (aOR 0.58, 95% CI 0.46–0.73). In contrast, the most acculturated

women had 40% increased odds of PTB and similar odds of term-LBW compared with their

Australian-born counterparts (Table 8.3).

TABLE 8.3 COMPARISON OF TERM-LBW AND PTB IN MIGRANT AND AUSTRALIAN-BORN WOMEN ACCORDING TO

THE ACCULTURATION LEVEL OF MIGRANT WOMEN

*P<0.05; **Adjusted for insurance status, age group, sex of baby, pre-existing medical conditions, infertility

treatment, socioeconomic status, parity and smoking in pregnancy; ***Least acculturated: non-white non-Māori

women lived in Australia <5 years, had an overseas-born partner, immigrated as an adult and, used interpreter

service; ****Most acculturated: non-white non-Māori women, lived in Australian >10 years, immigrated as a child,

had an Australian-born partner and did not use interpreter service.

Population Term-LBW PTB

OR (95% CI) aOR (95% CI)** OR (95% CI) aOR (95% CI)**

Australian-born 1.00 1.00 1.00 1.00

Migrant women with overseas-born partner and interpreter

1.60* (1.26–2.02)

2.03* (1.59–2.58)

0.62* (0.52–0.73)

0.62* (0.53–0.74)

Migrant women with overseas-born partner but no interpreter

1.49* (1.38–1.61)

1.68* (1.55–1.81)

0.98 (0.94–1.02)

1.00 (0.96–1.04)

Migrant women with Australian-born partner

1.04 (0.92–1.17)

1.07 (0.95–1.21)

0.98 (0.93–1.04)

0.99 (0.94–1.09)

Least acculturated*** 1.54*

(1.13–2.11) 2.00*

(1.45–2.74) 0.56*

(0.44–0.70) 0.58*

(0.46–0.73)

Most acculturated**** 1.16

(0.77–1.74) 1.29

(0.86–1.93) 1.37*

(1.15–1.62) 1.40*

(1.18–1.66)


PAGE | 165

DISCUSSION 8.5

In this whole-population linked data study, we showed that disparities in the risk of PTB and term-

LBW between migrant and Australian-born women differed according to the acculturative

characteristics of the population. Migrant women who used the interpreter service, did not have

an Australian-born partner, and had been in Australia <5 years (less acculturated) had lower odds

of PTB than the Australian-born population. All migrants had a higher risk of term-LBW than the

Australian-born women except for those with an Australian-born partner. Acculturation was

associated with an increased risk of PTB but decreased risk of term-LBW in non-white non-Māori

migrant women.


We found that immigrants, regardless of their length of residence in Australia or whether they

arrived as a child or adult, had a higher risk of LBW than the Australian-born group; however, the

difference was higher in those who had been in Australia for <5 years and/or immigrated as adults.

This finding is similar to previous observations in Australia, but findings from other nations are to

some extent, equivocal.199,313 Hyman and Dussault in 1996 reported increased odds of term-LBW

for Asian women and decreased odds for Italian and Greek migrant women living in Canada,

implying that higher odds were associated with a higher level of acculturation.313,314 Martinson,

Tienda and Teitler in 2017 reported that foreign-born status (collectively) protected against LBW

among migrants residing for ≤5 years in the US and the UK but observed an increased odds of LBW

(OR=1.29), although non-significant (P>0.1), among migrants residing in Australia for ≤5 years.199

They reported that the the three countries studied had a similar overall pattern of LBW by

duration of residence and in their final multivariable analysis, the risk was highest during the first

two years following resettlement. Notwithstanding, the results differed for migrants from

different ethnic backgrounds; African and Asian migrant women resettled in the UK had 3.9–4.3


PAGE | 166

times higher odds of having an LBW baby than immigrant mothers from English-speaking countries

(P<0.05), while in Australia, women from Asian and other low-income countries had 4.5–7.2 times

higher odds of LBW babies than their counterparts from Anglophone nations (P<0.05). It is worth

noting that this study was based on a sample of 3732 participants from the Australian population,

without knowing the ethnicity of the population; unlike our study population (N=252 256) with

100% completeness for ethnicity. Thus, the small sample size may be a reason for the low

statistical power to detect an effect similar to what we detected. The higher risk of LBW in the first

few years after immigration may be a result of stressors related to immigration, acculturative and

financial instabilities experienced by migrants.296

With a longer length of residence, the rate of term-LBW decreased among women from African

and other ethnic backgrounds in our study, increased among Māori women residing in Australia

for more than five years, and followed a U-shape pattern in births to Asian and Indian women.

Similarly, a U-shaped pattern for associations of LBW with length of residence for immigrant

groups in Denmark315 and with time since naturalisation for immigrants residing in Belgium316 was

reported in Europe. Teitler, Hutto and Reichman have also reported a curvilinear association

pattern in infants’ birthweight by maternal duration of residence in the US for both immigrants

overall, and for Hispanic immigrants in particular.317 This finding suggests that the ‘healthy migrant

paradox’, which implies better initial health outcomes for migrants than the host population that

deteriorate with time302,191 may not be applicable for LBW risk to all migrant groups, and the

change in the risk of LBW over time may not be linear in either direction (improves or declines).

The initial decrease observed in the risk may be explained by an improvement in employment and

financial status in the short term. We previously reported a considerable rise in the proportion of

those with private health insurance in migrant women from white, Asian and Indian ethnic

backgrounds in our population.304 On the other hand, deterioration in the long run may be due to


PAGE | 167

the accumulation of stressors related to discrimination, acculturation and unhealthy lifestyle

choices, such as smoking, among migrants.304,316

We also showed that having an Australian-born partner was significantly associated with a lower

risk of LBW in migrants. A lower risk of stillbirth for migrant women with a non-migrant partner

than those with overseas-born partners has been reported in Australia, Norway and the US, as

well as a lower risk of small-for-gestational-age in Chinese-American women with white or Black

partners in the US, compared to those with Chinese-American partners.194,197,198,304 Paternal

genetic impact,318,319 fathers’ lifelong socioeconomic position320 and/or factors determining

maternal environmental and lifestyle circumstances, including stress or social support and dietary

or smoking habits,309,314 influenced by integration and acculturation, all may play a role. Given that

intermarriage is a strong predictor of integration in a multicultural society,132 migrant women with

an Australian-born partner may experience enhanced social support and, thus, a favourable

environment compared to their migrant counterparts who have an overseas-born partner.

Previous studies have highlighted the impact of acculturative stress, lack of support and the

increased likelihood of experiencing marriage conflict and even domestic violence for migrant

women with migrant partners.321-323

PRETERM BIRTH 8.5.2

Migrant women who immigrated as a child, women who had ≥5-year length of residence, and

women with an Australian-born partner did not differ from the Australian-born population in the

risk of PTB. When the migrant population were stratified by ethnicity among Asian, Indian and

‘other’ migrants, the risk of PTB was significantly higher in those with longer length of residence.

Further, when level of acculturation, among the non-white non-Māori migrant women collectively,

was determined by considering all proxies simultaneously, acculturation was associated with a

higher risk of PTB. Similarly, in Canada, recent immigrants were at lower risk of PTB, but the risk


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was higher in those with >10 years of stay than their Canadian-born counterparts.193 A higher level

of acculturation was significantly associated with higher odds of preterm birth in the US.280,311 The

influence of acculturation on the risk of PTB among migrant groups may be explained by the

stressors experienced and/or the change of health behaviours in migrant populations. The

evidence suggests that women with higher levels of cortisol are at higher risk of experiencing

PTB.324Cortisol secretion is influenced by stress, and researchers have investigated the relationship

between acculturative stress and cortisol levels in migrants.325,326 Ruiz et al. showed that the more

acculturated women were more likely than the less acculturated women to exhibit higher stress

responses measured by cortisol in blood samples.325 Nicholson et al. showed, through diurnal

cortisol response measured by AUC analysis and using salivary samples, a higher level of

proficiency in English in former Soviet immigrants residing in the US produced an increased

cortisol response.326 Higher levels of acculturation were also associated with attenuation of the

cortisol awakening response in Mexican-American adults.327 A flattened or blunted cortisol

awakening response has been reported in Indigenous participants and is believed to be associated

with chronic experience of stress.328 Furthermore, progesterone is an important hormone for

maintaining pregnancy and decreased levels have been linked to preterm birth.95 On the other

hand, greater English proficiency in Hispanic women, hence more acculturation, has been linked to

a decrease in progesterone levels and predicted lower gestational age.311 Thus, we believe that

hormonal alterations in response to acculturative stress may explain the findings we observed in

this study.

In conclusion, our data provided evidence that recent migrants had a higher risk of term-LBW and

lower risk of PTB than the Australian-born population in their first few years after immigration. We

also showed that acculturation had positive effects on the risk of term-LBW but was associated

with a higher risk of PTB in migrant women in Australia collectively. This pattern, however, may


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differ in specific ethnic groups. Nevertheless, our findings suggest that acculturation level and

related characteristics are emerging as important factors that should be considered in pregnancy

care of migrant women. This emphasises the need for understanding acculturation-related factors

to plan the interventions required for the prevention of PTB and LBW in migrants and ethnic

minorities at risk.

STRENGTHS AND LIMITATIONS 8.5.3

Our study is the first and the most comprehensive investigation to explore the influence of

acculturation on the risk of PTB and term-LBW in migrants from diverse ethnic backgrounds in

Australia. The large size of the study—including all migrants and non-Indigenous Australian-born

women giving birth—having access to numerous variables from many linked databases and

registries, and the methodology of a whole-population retrospective cohort design, which

eliminates the risk of selection, participation and recall biases, strengthened the robustness of the

study and the reliability of our findings.

We acknowledge, however, that our study has some limitations. We used routinely collected

administrative health data that had not been collected specifically to answer the research

questions of this specific study. Nevertheless, WADLS was originally established as a result of a

collaboration between the WA Department of Health and researchers, mainly for population

health research.204 WADLS prides itself on the quality of the linkages and very low frequency of

errors.212,215 High reliability of MNS data and the variables we used have been validated before.235

Further, acculturation is a dynamic process, and its influence on health behaviour and outcomes is

multifactorial. While we examined acculturation effect using proxies and not through interview or

survey, similar to other population studies, efforts were made to undertake the investigation in a

multidimensional manner using multiple variables, self-reported ethnicity, language proficiency,

age on arrival, intermarriage and length of residence. This approach helped to provide useful


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information for a large and diverse population of migrants and can be used to inform policy and

practice and further investigation.

Our findings can be generalised to other populations hosting migrants from similar ethnic

backgrounds; however, care should be taken with interpretation of the results due to factors such

as different immigration and citizenship laws, healthcare systems, cultures and lifestyle norms in

host countries.

CHAPTER 9. DISCUSSION AND FUTURE DIRECTION

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This thesis was proposed at a time when stillbirth was still in the shadows. There was a lack of

awareness about its prevalence and disparities in risk among different ethnic population groups. In

WA, no study on this topic had been undertaken since 1988, when only Aboriginal and non-

Aboriginal ethnicities were compared with regard to the risk of stillbirth.25 More recently, in other

Australian states, studies examining the relationship between ethnicity and stillbirth were based

on country of birth and limited to specific population groups.13,159

Over the last few years, while this project was ongoing, the topic of stillbirth has received

substantial attention. Public awareness, government consideration and advocate efforts for

collaboration and planning have created a momentum for change to end preventable stillbirth.

This PhD thesis and research project, with numerous presentations and three in-depth analytic

studies on stillbirth, has enhanced the knowledge on the influence of migration, ethnicity,

healthcare utilisation and acculturation through both scholarly contributions and advocacy efforts.

This work has been cited by many other research studies and informed guidelines and policies to

manage and prevent stillbirth more effectively in migrants, specific ethnic groups and specific

regional areas. Further, it has expanded the knowledge and evidence-based understanding of

associations between migration and ethnicity with LBW and PTB. These pregnancy outcomes are

precursors to non-communicable diseases, such as obesity, diabetes and cardiovascular disease,

the leading causes of premature death worldwide.329-331

This chapter summarises and discusses the findings of the research presented in Chapters 4–8, the

strength and limitations of the investigation, the implications for policy and practice, and

suggestions for future research based on this body of work.

FINDINGS 9.1

STILLBIRTH 9.1.1

Chapters 4–6 investigated the pregnancy outcomes of stillbirth among diverse migrant

populations in WA. This research showed that the prevalence of stillbirth among migrant women

in WA was lower than reported in their countries of origin; however, consistent with previous

reports, migrant women were at higher risk of stillbirth than locally born women.241 Further, this

project demonstrated the ethnic group-specific differences in the prevalence of both antepartum

and intrapartum stillbirth for the first time in Australia. The prevalence of antepartum stillbirth


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was higher among migrant women with Indian, African or ‘other’ origins than Australian-born

women. Further, the prevalence of intrapartum stillbirth was higher among migrant women with

African or ‘other’ origins than Australian-born women. Moreover, we noted that the prevalence of

term stillbirth among the migrants of African origin was 2.6 times that of Australian-born women.

The rate of intrapartum stillbirth was twice as high among African women, potentially indicative of

poor quality of care at the time of birth.

The prevalence of post-term pregnancy, a recognised risk factor for stillbirth,236 was significantly

higher among African migrants (2.1%) than Australian-born women (0.5%) and Asian migrants

(0.2%). This was despite previous reports that the median gestational age of spontaneous labour

among women of African origin was less than 40 weeks in populations from London, Victoria

(Australia) and the US.74,102,237 Further investigation of the WA data demonstrated an association

between midwife-only accoucheur and increased rate of intrapartum stillbirth, even after

restricting the study to viable births after 23 completed weeks of gestation.278 This risk of

intrapartum stillbirth in African women who had a mixed model of care (i.e. both medical and

midwifery teams) was similar to Australian-born women. The Australian-born group had the same

risk of stillbirth, whether they had midwife-only accoucheur or mixed-model team intrapartum

care providers. Our quantitative population-based research design did not allow us to investigate

this further to determine whether this observation indicated a patient-driven ‘choice’ or

preference for midwife-only care or a system-driven lack or shortage of staff. Preference of a

model of care for African women choosing midwife-only rather than the mixed model of care is

suspected due to the frequent reluctance of African women to undergo medical interventions (e.g.

induction of labour and instrumental or caesarean delivery).19,26 This was demonstrated in the

African population in WA who had four-fold higher rates of post-term pregnancy than Australian-

born women. However, lack of access to medical care due to logistics or timing of reaching the

birthing facilities or other reasons cannot be ruled out based on our data and warrants further

investigation.

Moreover, this project demonstrated a late commencement of antenatal care visits as an

underlying factor for the increased risk of antepartum stillbirth observed in migrant women from

Indian, Māori and ‘other’ non-white ethnic backgrounds. Migrant women from an Indian

background had the same risk of antepartum stillbirth as Australian-born women if they had

private health insurance, while those who did not have this privilege experienced a two-fold


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increase in the risk of stillbirth. It was noted that being ‘high-risk-for-LBW’, regardless of cause,

PTB or fetal growth restriction, was associated with antepartum stillbirth in Indian migrants. This

suggests that early engagement with the healthcare system may reduce the number of stillbirths,

perhaps by implementing interventions that prevent PTB and/or growth restriction, particularly in

this group. Māori women also had a significantly higher rate of preterm (but not term) antepartum

stillbirth than Australian-born women, even after adjusting for several factors including

smoking;241 further investigation revealed that it could be due to their late commencement of

antenatal-care visits.278

This thesis further investigated the effect of level of acculturation—determined through the length

of residence, age arrived in Australia, English language competency, and having an Australian-born

partner—on the risk of stillbirth.304 A longer length of residence, history of using an interpreter

when navigating the health system, and having an Australian-born partner were all associated

with a lower risk of stillbirth in migrants. Migrant women with an overseas-born partner, who did

not use an interpreter, were the most at-risk group among migrants residing in Australia for <5

years. Giving birth in the first two years after arrival was associated with the highest risk of

stillbirth for migrant women from African, Asian and Indian backgrounds compared with

Australian-born women. Adjusting for private health insurance status in further stratified analysis

reduced the odds of stillbirth by 33% in Asian and Indian women who resided in Australia for <2

years. To our surprise, the increased risk of stillbirth disappeared beyond the first two years of

residence among women from Asian and Indian backgrounds. These findings suggest that

unfamiliarity with the healthcare system or other barriers to accessing it, rather than the

acculturative factors per se, may influence the risk of stillbirth in newly arrived migrants from

these two ethnic backgrounds. In Australia, despite the availability of a universal health insurance

scheme covering all Australian citizens and permanent residents (Medicare), depending on the

type of visa (Humanitarian visas are excluded), a two- to four-year waiting period is applied before

immigrants become eligible to benefit from some health and social security services including

Newstart/Jobseeker Allowance and Healthcare Concession Card.154

Surprisingly, women with an overseas-born partner who gave birth in the first two years after

arrival had 62% lower odds of stillbirth if they used an interpreter service and 31% increased odds

of this outcome if they did not use an interpreter than Australian-born women. Language

discordance has been highlighted as a barrier to accessing health services for migrants and


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refugees in Australia and to compromising the quality of care by over-investigation and acting on

the results rather than on patient’s symptoms.174,262,263 Given that most migrants who used an

interpreter were from regions with higher rates of stillbirth than Australia and had a residence

length of <5 years in Australia, this may be evidence of a ‘healthy migrant paradox’.146,303 This

implies newly arrived migrants have initially better health outcomes than the host population,

despite the disadvantages migrants may encounter, which slowly converges on host population

levels over time.146

Although one may argue that women who did not use the interpreter service could have been

competent in English, anecdotal evidence and qualitative studies suggest otherwise. Previous

reports from Australia and Europe indicated that despite the difficulty in communication, migrant

women from some ethnic backgrounds might not request an interpreter or their partners may

insist on acting as the translator, which can compromise the care received.157,158,263,264 Phillips and

Travaglia estimated that less than 1% of private general practice consultations for patients with

poor English proficiency used the Doctors Priority Line, a unique fee-free rapid-access telephone

interpreter service in Australia.264 Thus, our findings indicate a lack of communication and mutual

understanding between pregnant women and clinicians in those who did not use the interpreter

service, if the interpreter was required but not requested or offered. Using an interpreter in our

data may be an indicator that a culturally sensitive healthcare plan was in effect.

The finding that women who had an Australian-born partner had a lower risk of stillbirth may be

another clue that these women had more interactions within the wider community, were more

competent in English, and more familiar with the healthcare system when navigating their

pregnancy care.

The findings of this thesis demonstrate the critical value of determining the time of stillbirth

(antepartum or intrapartum) given the underlying factors for stillbirth may vary according to the

type of stillbirth. Further, the findings suggest that specific demographics, ethnic and acculturative

status of pregnant women can influence their risk of stillbirth. Thus, to end preventable stillbirths,

it is imperative to consider the identified factors in public health planning, policy design and

provision of obstetrics and pregnancy care, especially for recently immigrated women.

PRETERM BIRTH 9.1.2

Chapters 7 and 8 were dedicated to investigating the influence of ethnicity, migration and

acculturation on the risk of PTB in the WA population. This work identified that migrant women


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from Asian and Indian backgrounds had higher adjusted odds of PTB (aOR 1.16, 95% CI 1.09–1.24;

aOR 1.24, 95% CI 1.12–1.38, respectively) whereas migrant women with white and Māori

backgrounds had lower adjusted odds of PTB (aOR 0.93, 95% CI 0.89–0.98; aOR 0.85, 95% CI 0.71–

1.00, P=0.044) than Australian-born white women. Migrant women from African backgrounds

were at lower risk of idiopathic PTB, and those from Māori and Chinese-born backgrounds were at

lower risk of medically indicated PTB than their white Australian-born counterparts. Among non-

white Australian-born women, those from ‘other’ backgrounds had higher odds of medically

indicated PTB than white Australian-born women.

Previous studies have similarly observed a lower rate of PTB for migrant women301 from white,295

Chinese163,332 and Somali backgrounds13 than the non-migrant populations in Australia, Canada

and the US, while a greater risk of PTB in migrants from Asian and Sub-Saharan African

backgrounds has been reported in a systematic review.11 We have presented a comprehensive

analysis of the risk in several migrant population groups and further shown a higher risk for non-

white Australian-born women from ‘other’ ethnic backgrounds by studying the influence of

ethnicity on the risk of PTB among the Australian-born population from different ethnic

backgrounds.

Moreover, this research showed that migrant women who immigrated as a child, women who had

a ≥5 year length of residence, and women with an Australian-born partner at the time of giving

birth had a similar risk of PTB to the Australian-born population. When the migrant population was

stratified by ethnicity, among Asian, Indian and ‘other’ migrants, the risk of PTB was significantly

higher in those with longer length of residence. Thus, among the non-white non-Māori migrant

women assessed collectively, acculturation was associated with a higher risk of PTB than the

Australian-born population. This finding is consistent with previous research from Canada and the

US reporting that recent immigrants were at lower risk of PTB, but the risk was higher in those

with >10 years of residence than their Canadian-born counterparts193 and that higher levels of

acculturation were associated with higher odds of preterm birth in the US.280,311

The stressors experienced and/or the change of health behaviours in migrant populations may

explain why the more acculturated women are at higher risk of PTB than less acculturated women.

Altered patterns of hormone secretion, especially cortisol and progesterone, which have an

important role in maintaining pregnancy, have been linked to acculturative stress in the US.324-327


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These data and the generated evidence highlight the heterogeneity of the population and the

importance of considering both country of birth and ethnicity, as well as the level of acculturation

in the analysis, obstetric practice, and in public health planning to identify and target at-risk

groups.


Chapters 7 and 8 in this thesis explored the influence of ethnicity, migration and acculturation on

the risk of LBW. For the first time in Australia, this research showed that migrants from six

different ethnic backgrounds had higher odds of term-LBW than the Australian-born white

population. The cumulative incidence of LBW was 4.3% among all singleton non-Indigenous live

births, and both ethnicity and migration influenced the risk of term-LBW. The risk was higher in

migrants and the non-white Australian-born population from Asian, Indian and ‘other’ ethnic

descents than their white Australian-born counterparts. Also, the Australian-born women from

Asian, Indian and ‘other’ backgrounds were at higher risk of term-LBW than their white Australian-

born counterparts.

Martinson, Tienda and Teitler reported in 2017 that foreign-born status was protective against

LBW among migrants residing in the US and the UK for ≤5 years but had non-significant increased

odds of LBW (OR=1.29) among migrants residing in Australia for ≤5 years.199 However, they

reported that the three countries studied had a similar overall pattern of LBW by duration of

residence and, in their final multivariable analysis, the risk was highest during the first two years

following resettlement. Notwithstanding, in the same study, African and Asian migrant women

resettled in the UK had 3.9–4.3-times higher odds of having a LBW baby than immigrant mothers

from English-speaking countries, while in Australia women from Asian and other low-income

countries had 4.5–7.2 times higher odds of LBW than their counterparts from Anglophone nations.

This thesis demonstrated that immigrants, regardless of their length of residence in Australia or

whether they arrived as a child or adult, had a higher risk of LBW than the Australian-born group;

however, the difference was higher in those who had been in Australia for <5 years and/or

immigrated as adults. The stressors related to immigration, acculturative and financial instabilities

experienced by migrants may explain the higher risk of LBW in the first few years after

immigration.296

These results suggest that migrant status and ethnicity may be independent predictors of LBW or

proxies for other influential factors, such as diet or sociocultural experiences, not recorded in the


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dataset. The intergenerational effect of disadvantage experienced by the first generation of

migrants may explain some of these findings and emphasise the need for investigation,

intervention and prevention to ameliorate the short- and long-term impacts of LBW on individuals,

populations and the health system.

STRENGTHS AND LIMITATIONS 9.2

The research submitted in this thesis represents the most comprehensive quantitative study on

the effect of migration, ethnicity and acculturation on pregnancy outcomes of stillbirth, PTB and

LBW in Australia. The migrant population in this study was substantial (88 395 births, 33.9% of the

total study population), and multiple ethnic origins were included in the analyses. The linked

health data methodology and whole-population design reduced the risk of selection, participation,

reporting and recall biases and enabled greater precision and the ability to detect even marginal

shifts in practice or outcomes. The variety of variables available made it possible to link

sociocultural concepts, such as immigration and acculturation, with health outcomes through an

innovative approach.

An additional strength of the study was the ability to examine the specific type of stillbirth

(antepartum or intrapartum) with data available for 99.98% of stillbirths for a complete population

cohort. PTB was also investigated as an overall outcome and by sub-types, spontaneous or

medically indicated. Further, separating preterm births from cases of LBW and studying only term-

LBW made the interpretation of findings more accurate and valid as the impact of gestational age

on birthweight was ruled out systematically.

Analysing routinely collected linked data, rather than data mainly collected for answering specific

research questions, has its limitations and may have led to some misclassification of exposures.

We conducted extensive cross-source ascertainment through multiple datasets to minimise this

potential issue. For example, maternal country of birth was accessible through Birth Registrations

and hospital discharge records. Stillbirth was recorded on MNS, HMDC, Birth and Death

Registrations. However, despite all these efforts, a small risk of misclassification may have biased

the results, towards the null hypothesis, for the analyses that used the ethnicity variable, given

that the self-reported ethnic origin data was only available from MNS. For example, Indian women

with a high risk of antepartum stillbirth may have been misclassified as Asian (population with a

lower risk of stillbirth) given that India is located in Asia and the UK, Indian and Pakistani women

are classified under Asian ethnicity. In such cases, the actual risk of antepartum stillbirth in Indian


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women can be even higher than what we reported. However, such risk is very low, as a validation

study of MNS confirmed the reliability of this database with a Proportion Records Correct of 94.1%

for the variable ethnicity.235

Another potential limitation arising from the analysis of large datasets is that women may have

had more than one birth or stillbirth during the study period, resulting in non-independence of

data. In this thesis, limiting analyses to women with only one birth record or accounting for

clustering in the analyses did not affect the results. Further, the nine-year observation period used

in this thesis can act as both a strength and limitation; if the standard of care changed over time,

this may create bias. Thus, we adjusted the analyses for year of birth to limit the potential impact

of this issue.

While our rich dataset made it possible to control for many factors, residual confounding is still

possible due to covariates not being available—data on maternal weight70,74,159 or interpregnancy

interval105,106 for example—in the dataset. It is noteworthy, however, that we adjusted the

analyses for pre-existing diabetes mellitus and essential hypertension that are associated with

high BMI. Further, recent evidence suggests that short interpregnancy interval, although

associated with PTB, may not explain the risk disparities observed according to Lonhart et al. who

studied the risk of PTB between non-Hispanic White and non-Hispanic Black women in the US.333

Also, Swaminathan and colleagues, who studied the reproductive histories of women from 2002–

18 and identified live births and stillbirths in the preceding five years using Demographic and

Health Surveys from 58 LMICs, reported that associations observed in the first interpregnancy

intervals of less than 12 months with increased risk of stillbirth might not be causal because these

effects attenuated when second and third intervals were considered.

Finally, the gestational age at first ANC visit was not recorded in WADLS until 2010; hence, this

information was only available for four years of the study period. Thus, the related analyses

reported were limited to almost half of the whole study population.

IMPLICATIONS FOR POLICY AND PRACTICE 9.3

It is estimated that 272 million migrants (3.5% of the world’s population) live globally, and nearly

two-thirds of them live in high-income countries.122 International migration is at its highest level

since the Second World War,118 and the proportion of international migrants is already ahead of

some projections made for the year 2050 (2.6% or 230 million).122


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The findings reported in this thesis may be of use to other high-income communities serving

migrant residents from similar ethnic backgrounds and countries of origin. These include other

states of Australia, European and American countries such as the UK’s and Canadian population

given some similarity in their healthcare systems and being in the top ten destinations for

international migration.122

The findings of this thesis are of particular relevance to clinical practice. Engaging women with

ANC early in pregnancy, offering interpreter service proactively, providing more frequent

ultrasound surveillance, and involving a team (both doctor and midwife) for care during birth for

specific at-risk groups may reduce the risk of stillbirth. A culturally responsive health system can

meet the educational and healthcare needs of at-risk ethnic groups, and enhanced data records on

obesity, antenatal and intrapartum care information can strengthen future studies to further

inform policy and practice. Primary healthcare services can be used to educate and improve health

literacy and familiarity with the healthcare system, including the promotion of early engagement

with antenatal care, use of interpreter services and the value of medical-midwifery (team)

intrapartum care among the migrant population.

This investigation has further shown that acculturation may elevate the risk of stillbirth by

increasing the prevalence of unhealthy behaviours, such as smoking during pregnancy, and

decrease the risk through better utilisation of health services and effective communication. Thus,

the resultant outcome would depend on how these underlying factors interplay in populations

over time. According to the current findings, and studies in other jurisdictions,190,280 acculturation

status has presented as an important factor to consider when providing health care to immigrant

populations. Mutual accommodation is required for healthy and successful integration of all

migrant groups, by adopting the basic values of the host country by migrants as well as adapting

the healthcare and education systems to meet the needs of all population groups appropriately.129

Therefore, these findings emphasise the value of national educational programs to familiarise

migrants with the health system and inform them of their risks, rights and entitlements to

healthcare programs, before, during and after pregnancy. The need for a culturally responsive

healthcare system to improve access to and utilisation of interpreter and other services is

highlighted to help reduce the rates of stillbirth, preterm birth and low birthweight. This is

especially necessary for recently arrived migrants who are at greater risk of stillbirth. It is

particularly crucial to investigate and address the underlying mechanisms for the increased risk of


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stillbirth among non-white non-Māori migrant women in the first two years after their arrival. In

the absence of such additional data, pregnant women from these ethnic backgrounds with a

length of residence less than two years may benefit from being treated as high risk for stillbirth

and LBW. For those with longer length of residence, raising awareness of the slightly increased risk

of PTB and preventive strategies is strongly recommended.

RESEARCH TRANSLATION, IMPACT AND DIRECTION FOR FUTURE RESEARCH 9.4

The work in this thesis has resulted in more than 40 outputs, including publications and

local/national/international presentations (please refer to pages x–xiii). This project has

substantially advanced knowledge in this important area of research and communicated

disparities in the risk of adverse pregnancy outcomes in the WA population. Further, it has

identified many underlying risk factors involved in experiencing these outcomes among

community members and proposed potential strategies for improving pregnancy outcomes in the

population. For example, these findings have been considered and cited in a position statement of

the Perinatal Society of Australia and New Zealand and Centre of Research Excellence in Stillbirth

on Improving decision-making about the timing of birth for women with risk factors for

stillbirth;334 in clinical practice guidelines for perinatal bereavement care, models of maternity and

postpartum care;335 and in risk prediction models for stillbirths336 by prominent professional

societies and leading peak organisations. The findings have also been submitted to The Australian

Government, Department of Health, through the “National Stillbirth Action and Implementation

Plan - Public Consultation” to further contribute to informing policy and practice for prevention of

stillbirth.

Through the distinguished and instrumental platforms of the Red Nose Foundation

(www.rednose.org.au) and National Centre for Research Excellence in Stillbirth, this project has

distributed its message to far-reaching audiences and widely advocated for the health of migrants

and ethnic minorities. Given the strong political interest and bipartisan support that helped raise

the profile of stillbirth in Australia, the evidence provided by this project on stillbirth in migrant

and non-white ethnic groups has been particularly timely and relevant in its contribution to

improving population health in that area.

This investigation identified certain aspects of the health behaviours and the healthcare system

that are not currently routinely captured in Australian data registries but are likely to influence the

risk of adverse pregnancy outcomes in the population. One of these aspects was the pattern of


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health service utilisation that differed between migrant and non-migrant Australian women.278 It

was not clear whether an individual’s perceptions, knowledge and beliefs were the reason for this

difference and/or the healthcare providers’ perspectives and approach were the underlying basis.

It is believed that migrants, especially those from African backgrounds, have a different view

towards care and intervention in pregnancy and this influences their underutilisation of healthcare

services, which is linked to a higher risk of stillbirth.19Qualitative investigation on this issue may

help to enhance knowledge and guide the development of culturally appropriate preventive

strategies or interventions. This thesis also denoted that African women had a slower pace of

acculturation and, while other ethnic groups’ risk of stillbirth decreased over time, the risk in this

specific population remained high for an extended length of time beyond five years after arrival in

Australia.304 On the other hand, factors such as racism and discrimination have been linked to

adverse pregnancy outcomes in other nations.337

According to the Scanlon Foundation’s Mapping Social Cohesion 2019 report,338 among all

Australian states and territories, WA had the highest percentage (22%) of reported experience of

discrimination and the highest level of agreement (30%) with discrimination on the basis of race or

ethnicity in 2018–19. It is unknown, however, what implications this may have for the CaLD

population’s experience of pregnancy care and childbirth. For instance, anecdotal evidence

suggests that each stillbirth experienced by a member of some ethnic communities has an

immense ‘ripple effect’ on the attitude of other community members towards lower utilisation of

that specific healthcare provider or facility, which continues to remain in effect for a relatively long

time after the incident. Racism may also be associated with health service use outcomes such as

delaying/not getting healthcare and lack of adherence to treatment uptake.339 In this thesis, late

commencement of ANC was an underlying factor for higher risk of stillbirth in Indian, Māori and

‘other’ non-white migrant groups and those who started at or before week 14 of their pregnancy

had similar risks to Australian-born women.278 Although this did not hold for the African group and

they were at increased risk of stillbirth, regardless of the time of commencement of ANC, it is

worth noting that in 91% of cases, the African group did not have an obstetrician birth-

attendant.278 Thus, further in-depth investigation is recommended to uncover the mechanisms for

all these findings that this project did not have enough data to explore.

To conclude, this project demonstrated that migrant status, ethnicity and acculturation can be

associated with specific patterns of service utilisation and with certain pregnancy outcomes. These


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data were highlighted as crucial factors for consideration in policy development, on public health

agendas, and in pregnancy and obstetric care provision to prevent adverse pregnancy outcomes,

especially in populations from migrant and CaLD backgrounds and to improve health outcomes in

Australian society.

Specific recommendations:

1. Provision of a culturally responsive healthcare service and interpreter as an intervention to

reduce the rate of stillbirth should be considered by healthcare providers.

2. For migrant women of Indian ethnicity, improving access to more frequent ultrasound

surveillance during pregnancy and third trimester in public settings as an intervention to

reduce the rate of antepartum stillbirth

3. A doctor–midwife (team) model of intrapartum care for women from African and ‘other’

ethnic backgrounds to reduce the rate of intrapartum stillbirth in this at-risk population.

4. Education of migrant population to commence antenatal care visits early, before week 14,

in pregnancy and to avoid post-term, beyond week 41, pregnancy through culturally

appropriate means.

5. Less than two years’ time since arrival in Australia should be considered as a risk factor for

stillbirth and LBW for migrants from culturally and linguistically diverse backgrounds.

6. Migrant women should be exempted from waiting period for benefiting from health and

social security services such as Newstart/Jobseeker Allowance and Healthcare Concession

Card that may restrict their access to health services during pregnancy.

7. For migrant women of Māori background strategies to assist with quitting smoking in

pregnancy may reduce the risk of term LBW. However, determinants of term LBW in

migrants from different ethnic backgrounds need more in-depth investigation.

REFERENCES

PAGE | 183

REFERENCES

1. Darmstadt GL, Yakoob M, Haws RA, Menezes EV, Soomro T, Bhutta ZA. Reducing stillbirths:

Interventions during labour. BMC Pregnancy Childbirth 2009; 9(SUPPL. 1).

2. Haws RA, Yakoob M, Soomro T, Menezes EV, Darmstadt GL, Bhutta ZA. Reducing stillbirths:

Screening and monitoring during pregnancy and labour. BMC Pregnancy Childbirth 2009; 9(SUPPL.

1).

3. Johnston T, Coory M. Reducing perinatal mortality among Indigenous babies in Queensland: Should

the first priority be better primary health care or better access to hospital care during confinement?

Aust New Zealand Health Policy URL http://wwwanzhealthpolicycom/content/2/1/11 2005; 2(1).

4. Kumar GA, Dandona R, Chaman P, Singh P, Dandona L. A population-based study of neonatal

mortality and maternal care utilization in the Indian state of Bihar. BMC Pregnancy Childbirth 2014;

14: 357.

5. Mathis A, Barnes PA, Moonesinghe R. Racial and Ethnic Disparities in Post-neonatal Mortality in

Florida. Florida Public Health Review 2012.

6. Naimy Z, Grytten J, Monkerud L, Eskild A. Perinatal mortality in non-western migrants in Norway as

compared to their countries of birth and to Norwegian women. BMC Public Health 2013; 13: 37.

7. Lawn JE, Kinney M, Lee AC, et al. Reducing intrapartum-related deaths and disability: can the health

system deliver? Int J Gynaecol Obstet 2009; 107 Suppl 1: S123-40, S40-42.

8. Bhutta ZA, Darmstadt GL, Haws RA, Yakoob MY, Lawn JE. Delivering interventions to reduce the

global burden of stillbirths: Improving service supply and community demand. BMC Pregnancy

Childbirth 2009; 9(SUPPL. 1).

9. Balchin I, Whittaker JC, Patel R, Lamont RF, Steer PJ. Racial variation in the association between

gestational age and perinatal mortality: Prospective study. Br Med J 2007; 334(7598): 833-5.

10. Cacciani L, Asole S, Polo A, et al. Perinatal outcomes among immigrant mothers over two periods in

a region of central Italy. BMC Public Health 2011; 11: 294.

11. Gagnon AJ, Zimbeck M, Zeitlin J, et al. Migration to western industrialised countries and perinatal

health: a systematic review. Soc Sci Med 2009; 69(6): 934-46.

12. United Nations, Department of Economic and Social Affairs, Population Division. The UN

International Migration Report 2013. available from

http://wwwanzhealthpolicycom/content/2/1/11

REFERENCES

PAGE | 184

http://www.un.org/en/development/desa/population/publications/pdf/migration/migrationreport2

013/Full_Document_final.pdf.

13. Small R, Gagnon A, Gissler M, et al. Somali women and their pregnancy outcomes postmigration:

Data from six receiving countries. BJOG Int J Obstet Gynaecol 2008; 115(13): 1630-40.

14. Bottoms SF, Paul RH, Iams JD, et al. Obstetric determinants of neonatal survival: Influence of

willingness to perform cesarean delivery on survival of extremely low-birth-weight infants. Am J

Obstet Gynecol 1997; 176(5): 960-6.

15. McNellis D, Medearis AL, Fowler S, et al. A clinical trial of induction of labor versus expectant

management in postterm pregnancy: The National Institute of Child Health and Human

Development Network of Maternal-Fetal Medicine Units. Am J Obstet Gynecol 1994; 170(3): 716-23.

16. Okonkwo NS, Ojengbede OA, Morhason-Bello IO, Adedokun BO. Maternal demand for cesarean

section: Perception and willingness to request by Nigerian antenatal clients. Int J Womens Health

2012; 4(1): 141-8.

17. Kornelsen J, Hutton E, Munro S. Influences on decision making among primiparous women choosing

elective caesarean section in the absence of medical indications: findings from a qualitative

investigation. J Obstet Gynaecol Can 2010; 32(10): 962-9.

18. Bettegowda VR, Dias T, Davidoff MJ, Damus K, Callaghan WM, Petrini JR. The Relationship Between

Cesarean Delivery and Gestational Age Among US Singleton Births. Clin Perinatol 2008; 35(2): 309-

23.

19. Brown E, Carroll J, Fogarty C, Holt C. "They get a C-section...they gonna die": Somali women's fears

of obstetrical interventions in the United States. J Transcult Nurs 2010; 21(3): 220-7.

20. Barker DJ. The fetal and infant origins of adult disease. BMJ 1990; 301(6761): 1111.

21. Australian Bureau of Statistics. Migration. Cat No:34120. Australia; 2013-14.

22. Australian Bureau of Statistics. Perspectives on Migrants. Cat No: 34160. Australia; March 2013.

23. von Katterfeld B, Li J, McNamara B, Langridge AT. Perinatal complications and cesarean delivery

among foreign-born and Australian-born women in Western Australia, 1998-2006. Int J Gynaecol

Obstet 2012; 116(2): 153-7.

24. von Katterfeld B, Li J, McNamara B, Langridge AT. Obstetric profiles of foreign-born women in

Western Australia using data linkage, 1998-2006. Aust N Z J Obstet Gynaecol 2011; 51(3): 225-32.

25. Alessandri LM, Stanley FJ, Waddell VP, Newnham J. Stillbirths in Western Australia 1980-1983:

influence of race, residence and place of birth. Aust N Z J Obstet Gynaecol 1988; 28(4): 284-92.

http://www.un.org/en/development/desa/population/publications/pdf/migration/migrationreport2013/Full_Document_final.pdf

http://www.un.org/en/development/desa/population/publications/pdf/migration/migrationreport2013/Full_Document_final.pdf

REFERENCES

PAGE | 185

26. von Katterfeld B. Motherhood after migration: perinatal health and wellbeing among culturally and

linguistically diverse foreign-born women in Western Australia. 2011. https://research-

repository.uwa.edu.au/en/publications/motherhood-after-migration-perinatal-health-and-

wellbeing-among-c.

27. REGISTRATION OF BIRTHS, DEATHS AND MARRIAGES ACT 1963.

https://www.legislation.gov.au/Details/C2015Q00223.

28. Australian Institute of Health and Welfare. Australia’s mothers and babies 2015—in brief.Perinatal

statistics series no. 33. 2017. https://www.aihw.gov.au/getmedia/728e7dc2-ced6-47b7-addd-

befc9d95af2d/aihw-per-91-inbrief.pdf.aspx?inline=true.

29. Government of Western Australia. Western Australia Births, Deaths and Marriages Registratio Act

1998.

https://www.slp.wa.gov.au/statutes/swans.nsf/%28DownloadFiles%29/Births+Deaths+and+Marriag

es+Registration+Act+1998.pdf/$file/Births+Deaths+and+Marriages+Registration+Act+1998.pdf.

30. Australian Institute of Health and Welfare 2019. Australia’s mothers and babies 2017—in brief.

Perinatal statistics series no. 35. Cat. no. PER 100. Canberra: AIHW.

https://www.aihw.gov.au/getmedia/2a0c22a2-ba27-4ba0-ad47-ebbe51854cd6/aihw-per-100-in-

brief.pdf.aspx?inline=true.

31. Ahman E, Zupan J, World Health Organization. Dept. of Making Pregnancy Safer. Neonatal and

perinatal mortality : country, regional and global estimates. Geneva: World Health Organization;

2007.

32. Goldenberg RL, McClure EM, Bhutta ZA, et al. Stillbirths: the vision for 2020. Lancet 2011; 377(9779):

1798-805.

33. Lawn JE, Blencowe H, Pattinson R, etal. Stillbirths: Where? When? Why? How to make the data

count? Lancet URL http://wwwjournalselseviercom/the-lancet/ 2011; 377(9775): 1448-63.

34. Cousens S, Blencowe H, Stanton C, et al. National, regional, and worldwide estimates of stillbirth

rates in 2009 with trends since 1995: A systematic analysis. Lancet URL

http://wwwjournalselseviercom/the-lancet/ 2011; 377(9774): 1319-30.

35. WHO, UNICEF. 2014. Every Newborn: an action plan to end preventable deaths. Geneva: World

Health Organization. 2014. https://www.who.int/docs/default-source/mca-documents/advisory-

groups/quality-of-care/every-new-born-action-plan-(enap).pdf?sfvrsn=4d7b389_2.

36. Every Woman Every Child. Global Strategy for Women’s, Children’s and Adolescents’ Health (2016–

2030). Every Woman Every Child; New York, 2015.

https://research-repository.uwa.edu.au/en/publications/motherhood-after-migration-perinatal-health-and-wellbeing-among-c



https://www.legislation.gov.au/Details/C2015Q00223

https://www.aihw.gov.au/getmedia/728e7dc2-ced6-47b7-addd-befc9d95af2d/aihw-per-91-inbrief.pdf.aspx?inline=true

https://www.aihw.gov.au/getmedia/728e7dc2-ced6-47b7-addd-befc9d95af2d/aihw-per-91-inbrief.pdf.aspx?inline=true

https://www.slp.wa.gov.au/statutes/swans.nsf/%28DownloadFiles%29/Births+Deaths+and+Marriages+Registration+Act+1998.pdf/$file/Births+Deaths+and+Marriages+Registration+Act+1998.pdf

https://www.slp.wa.gov.au/statutes/swans.nsf/%28DownloadFiles%29/Births+Deaths+and+Marriages+Registration+Act+1998.pdf/$file/Births+Deaths+and+Marriages+Registration+Act+1998.pdf

https://www.aihw.gov.au/getmedia/2a0c22a2-ba27-4ba0-ad47-ebbe51854cd6/aihw-per-100-in-brief.pdf.aspx?inline=true

https://www.aihw.gov.au/getmedia/2a0c22a2-ba27-4ba0-ad47-ebbe51854cd6/aihw-per-100-in-brief.pdf.aspx?inline=true

http://wwwjournalselseviercom/the-lancet/


https://www.who.int/docs/default-source/mca-documents/advisory-groups/quality-of-care/every-new-born-action-plan-(enap).pdf?sfvrsn=4d7b389_2

https://www.who.int/docs/default-source/mca-documents/advisory-groups/quality-of-care/every-new-born-action-plan-(enap).pdf?sfvrsn=4d7b389_2

REFERENCES

PAGE | 186

37. Dickson KE, Simen-Kapeu A, Kinney MV, et al. Every Newborn: Health-systems bottlenecks and

strategies to accelerate scale-up in countries. Lancet URL http://wwwjournalselseviercom/the-

lancet/ 2014; 384(9941): 438-54.

38. Lawn JE, Blencowe H, Oza S, et al. Every Newborn: Progress, priorities, and potential beyond

Survival. Lancet URL http://wwwjournalselseviercom/the-lancet/ 2014; 384(9938): 189-205.

39. de Bernis L, Kinney MV, Stones W, et al. Stillbirths: ending preventable deaths by 2030. Lancet 2016;

387(10019): 703-16.

40. Easterly W, Freschi L. National, regional, and worldwide estimates of stillbirth rates. Lancet 2011;

378(9794): 873; author reply -4.

41. Flenady V, Koopmans L, Middleton P, et al. Major risk factors for stillbirth in high-income countries:

a systematic review and meta-analysis. Lancet 2011; 377(9774): 1331-40.

42. Blencowe H, Cousens S, Jassir FB, et al. National, regional, and worldwide estimates of stillbirth rates

in 2015, with trends from 2000: a systematic analysis. Lancet Glob Health 2016; 4(2): e98-e108.

43. Chou D, Daelmans B, Jolivet RR, Kinney M, Say L. Ending preventable maternal and newborn

mortality and stillbirths. BMJ 2015; 351: h4255.

44. Ballestas T. on behalf of the Perinatal and InfantMortality Committee of Western Australia. The 14th

report of the Perinatal and Infant Mortality Committeeof Western Australia for deaths in the

triennium2008-2010. Perth: Department of Health, WesternAustralia,. 2014.

https://ww2.health.wa.gov.au/%7E/media/Files/Corporate/Reports%20and%20publications/Perinat

al%20infant%20and%20maternal/12781_the_14th_perinatal_report.pdf.

45. Australian Institute of Health and Welfare. Australia’s mothers and babies 2013—in brief. Perinatal

statistics series no. 31. . Cat no PER 72 Canberra: AIHW; 2015.

46. Australian Institute of Health and Welfare 2018. Perinatal deaths in Australia: 2013–2014. Cat. no.

PER 94.Canberra: AIHW.; 2018.

47. Lancaster P, Huang J, Pedisich E. Australia’s mothers and babies 1991 (Perinatal statistics series

no.1). Canberra: Australian Institute of Health and Welfare. 1994.

48. O'Leary CM, Bower C, Knuiman M, Stanley FJ. Changing risks of stillbirth and neonatal mortality

associated with maternal age in Western Australia 1984-2003. Paediatr Perinat Epidemiol 2007;

21(6): 541-9.

49. Chan A, King JF, Flenady V, Haslam RH, Tudehope DI. Classification of perinatal deaths: development

of the Australian and New Zealand classifications. J Paediatr Child Health 2004; 40(7): 340-7.




https://ww2.health.wa.gov.au/~/media/Files/Corporate/Reports%20and%20publications/Perinatal%20infant%20and%20maternal/12781_the_14th_perinatal_report.pdf


REFERENCES

PAGE | 187

50. Perinatal Society of Australia and New Zealand Clinical Practice Guideline for Perinatal Mortality;

Second Edition, Version 2.2. Section 7: Perinatal Mortality Classifications; Appendix 1. 2009.

51. Laws PJ, Li Z, Sullivan EA. Australia’s mothers and babies 2008. Perinatal statistics series no 24 Cat

no PER 50 Canberra: AIHW; 2010.

52. Australian Bureau of Statistics 2009. Perinatal Deaths. Canberra: ABS; 2011.

53. Gordon A, Jeffery HE. Classification and description of stillbirths in New South Wales, 2002-2004.

Med J Aust 2008; 188(11): 645-8.

54. Ballestas T. on behalf of the Perinatal and Infant Mortality Committee of Western Australia (2017).

The 15th Report of the Perinatal and Infant Mortality Committee of Western Australia, 2011-2013.

Perth: Department of Health WA. https://ww2.health.wa.gov.au/-/media/Files/Corporate/Reports-

and-publications/Perinatal-infant-and-maternal/PIMC_Report_2011-2013.pdf.

55. World Health Organization. The WHO application of ICD-10 to deaths during pregnancy, childbirth

and puerperium: IDC MM (in IRIS). Geneva: World Health Organization; 2012.

56. Gissler M, Mohangoo AD, Blondel B, et al. Perinatal health monitoring in Europe: results from the

EURO-PERISTAT project. Inform Health Soc Ca 2010; 35(2): 64-79.

57. Office for National Statistics. Gestation-specific Infant Mortality in England and Wale, 2011, 2013.

58. International Statistical Classification of Diseases and Related Health Problems 10th revision. In: ICD

version:2016 [website]. Geneva: World Health Organization; 2016.

http://apps.who.int/classifications/icd10/browse/2016/en

59. Mohangoo AD, Blondel B, Gissler M, et al. International Comparisons of Fetal and Neonatal Mortality

Rates in High- Income Countries: Should Exclusion Thresholds Be Based on Birth Weight or

Gestational Age? Plos One 2013; 8(6).

60. Smith LK, Hindori-Mohangoo AD, Delnord M, et al. Quantifying the burden of stillbirths before 28

weeks of completed gestational age in high-income countries: a population-based study of 19

European countries. Lancet 2018; 392(10158): 1639-46.

61. Lawn JE, Blencowe H, Waiswa P, et al. Stillbirths: rates, risk factors, and acceleration towards 2030.

Lancet 2016; 387(10018): 587-603.

62. Flenady V, Wojcieszek AM, Middleton P, et al. Stillbirths: recall to action in high-income countries.

Lancet 2016; 387(10019): 691-702.

https://ww2.health.wa.gov.au/-/media/Files/Corporate/Reports-and-publications/Perinatal-infant-and-maternal/PIMC_Report_2011-2013.pdf

https://ww2.health.wa.gov.au/-/media/Files/Corporate/Reports-and-publications/Perinatal-infant-and-maternal/PIMC_Report_2011-2013.pdf

http://apps.who.int/classifications/icd10/browse/2016/en

REFERENCES

PAGE | 188

63. Hilder L, Zhichao Z, Parker M, Jahan S, Chambers GM. Australia’s mothers and babies 2012. 2014.

http://www.aihw.gov.au/publication-detail/?id=60129550033.

64. Australian Institute of Health and Welfare 2020. Stillbirths and neonatal deaths in Australia . Cat. no.

PER 107. Canberra: AIHW. . 2020. https://www.aihw.gov.au/reports/mothers-babies/stillbirths-and-

neonatal-deaths-in-australia.

65. Farrant BM, Stanley FJ, Hardelid P, Shepherd CC. Stillbirth and neonatal death rates across time: the

influence of pregnancy terminations and birth defects in a Western Australian population-based

cohort study. BMC Pregnancy Childbirth 2016; 16: 112.

66. Hilder L, Flenady V, Ellwood D, Donnolley N, Chambers GM. Improving, but could do better: Trends

in gestation-specific stillbirth in Australia, 1994-2015. Paediatr Perinat Epidemiol 2018; 32(6): 487-

94.

67. Ekeus C, Cnattingius S, Essen B, Hjern A. Stillbirth among foreign-born women in Sweden. European

journal of public health 2011; 21(6): 788-92.

68. Sørbye IK, Vangen S, Juarez SP, et al. Birthweight of babies born to migrant mothers - What role do

integration policies play? SSM Popul Health 2019; 9: 100503.

69. Bjornholt SM, Leite M, Albieri V, Kjaer SK, Jensen A. Maternal smoking during pregnancy and risk of

stillbirth: results from a nationwide Danish register-based cohort study. Acta Obstet Gynecol Scand

2016; 95(11): 1305-12.

70. Cnattingius S, Stephansson O. The epidemiology of stillbirth. Semin Perinatol 2002; 26(1): 25-30.

71. Fretts RC. Etiology and prevention of stillbirth. Am J Obstet Gynecol 2005; 193(6): 1923-35.

72. Balayla J, Azoulay L, Abenhaim HA. Maternal marital status and the risk of stillbirth and infant death:

a population-based cohort study on 40 million births in the United States. Womens Health Issues

2011; 21(5): 361-5.

73. Hirst JE, Villar J, Victora CG, et al. The antepartum stillbirth syndrome: risk factors and pregnancy

conditions identified from the INTERGROWTH-21st Project. BJOG 2018; 125(9): 1145–53.

74. Davies-Tuck ML, Davey MA, Wallace EM. Maternal region of birth and stillbirth in Victoria, Australia

2000-2011: A retrospective cohort study of Victorian perinatal data. PLoS One 2017; 12(6):

e0178727.

75. Australian Institute of Health and Welfare 2019. Stillbirths and neonatal deaths in Australia 2015 and

2016: in brief. Perinatal statistics series no. 36. Cat. no. PER 102. Canberra: AIHW. 2019.

http://www.aihw.gov.au/publication-detail/?id=60129550033

https://www.aihw.gov.au/reports/mothers-babies/stillbirths-and-neonatal-deaths-in-australia

https://www.aihw.gov.au/reports/mothers-babies/stillbirths-and-neonatal-deaths-in-australia

REFERENCES

PAGE | 189

https://www.aihw.gov.au/getmedia/12d0156d-b343-403f-ab62-8700861edeca/aihw-per-

102.pdf.aspx?inline=true.

76. Abdel-Latif ME, Bajuk B, Oei J, et al. Does rural or urban residence make a difference to neonatal

outcome in premature birth? A regional study in Australia. Arch Dis Child Fetal Neonatal Ed 2006;

91(4): F251-6.

77. Gardosi J, Madurasinghe V, Williams M, Malik A, Francis A. Maternal and fetal risk factors for

stillbirth: population based study. BMJ 2013; 346: f108.

78. Marufu TC, Ahankari A, Coleman T, Lewis S. Maternal smoking and the risk of still birth: systematic

review and meta-analysis. BMC Public Health 2015; 15: 239.

79. Cnattingius S, Villamor E. Weight change between successive pregnancies and risks of stillbirth and

infant mortality: A nationwide cohort study. Lancet URL http://wwwjournalselseviercom/the-lancet/

2016; 387(10018): 558-65.

80. Engel PJ, Smith R, Brinsmead MW, Bowe SJ, Clifton VL. Male sex and pre-existing diabetes are

independent risk factors for stillbirth. Aust N Z J Obstet Gynaecol 2008; 48(4): 375-83.

81. Mondal D, Galloway TS, Bailey TC, Mathews F. Elevated risk of stillbirth in males: systematic review

and meta-analysis of more than 30 million births. BMC Med 2014; 12: 220.

82. McClure EM, Saleem S, Pasha O, Goldenberg RL. Stillbirth in developing countries: a review of

causes, risk factors and prevention strategies. J Matern Fetal Neonatal Med 2009; 22(3): 183-90.

83. Di Mario S, Say L, Lincetto O. Risk factors for stillbirth in developing countries: a systematic review of

the literature. Sex Transm Dis 2007; 34(7 Suppl): S11-21.

84. Gissler M, Alexander S, MacFarlane A, et al. Stillbirths and infant deaths among migrants in

industrialized countries. Acta Obstet Gynecol Scand 2009; 88(2): 134-48.

85. Willinger M, Ko CW, Reddy UM. Racial disparities in stillbirth risk across gestation in the United

States. Am J Obstet Gynecol 2009; 201(5): 469 e1-8.

86. Sørbye IK, Stoltenberg C, Sundby J, Daltveit AK, Vangen S. Stillbirth and infant death among

generations of Pakistani immigrant descent: A population-based study. Acta Obstet Gynecol Scand

2014; 93(2): 168-74.

87. World Health Organization. Neonatal and perinatal mortality : country, regional and global

estimates. Geneva: World Health Organization; 2006.

88. Lawn JE, Cousens S, Zupan J. 4 Million neonatal deaths: When? Where? Why? Lancet 2005;

365(9462): 891-900.

https://www.aihw.gov.au/getmedia/12d0156d-b343-403f-ab62-8700861edeca/aihw-per-102.pdf.aspx?inline=true

https://www.aihw.gov.au/getmedia/12d0156d-b343-403f-ab62-8700861edeca/aihw-per-102.pdf.aspx?inline=true


REFERENCES

PAGE | 190

89. Lawn JE, Kerber K, Enweronu-Laryea C, Cousens S. 3.6 Million Neonatal Deaths-What Is Progressing

and What Is Not? Semin Perinatol 2010; 34(6): 371-86.

90. Maternal and Child Health Unit, Department of Health Western Australia. WA Midwives Notification

System. Western Australias Mothers and Babies summary information. 2019.

https://ww2.health.wa.gov.au/Reports-and-publications/Western-Australias-Mothers-and-Babies-

summary-information/data?report=mns_mort_y.

91. Steer P. The epidemiology of preterm labour. BJOG 2005; 112 Suppl 1: 1-3.

92. Lawn JE, Gravett MG, Nunes TM, Rubens CE, Stanton C, Grp GR. Global report on preterm birth and

stillbirth (1 of 7): definitions, description of the burden and opportunities to improve data. BMC

Pregnancy Childbirth 2010; 10.

93. Menon R. Spontaneous preterm birth, a clinical dilemma: etiologic, pathophysiologic and genetic

heterogeneities and racial disparity. Acta Obstet Gynecol Scand 2008; 87(6): 590-600.

94. National Institute for Health and Care Excellence (NICE). Preterm labour and birth (NICE Guideline

25). 2015. https://www.nice.org.uk/guidance/ng25/resources/preterm-labour-and-birth-pdf-

1837333576645 (accessed Nov 2019).

95. Goldenberg RL, Culhane JF, Iams JD, Romero R. Epidemiology and causes of preterm birth. Lancet

2008; 371(9606): 75-84.

96. Blencowe H, Cousens S, Chou D, et al. Born too soon: the global epidemiology of 15 million preterm

births. Reprod Health 2013; 10 Suppl 1: S2.

97. Liu L, Oza S, Hogan D, et al. Global, regional, and national causes of under-5 mortality in 2000-15: an

updated systematic analysis with implications for the Sustainable Development Goals. Lancet 2016;

388(10063): 3027-35.

98. Blencowe H, Cousens S, Oestergaard MZ, et al. National, regional, and worldwide estimates of

preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic

analysis and implications. Lancet 2012; 379(9832): 2162-72.

99. Chawanpaiboon S, Vogel JP, Moller AB, et al. Global, regional, and national estimates of levels of

preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Health 2019; 7(1):

e37-e46.

100. Zeitlin J, Szamotulska K, Drewniak N, et al. Preterm birth time trends in Europe: a study of 19

countries. BJOG 2013; 120(11): 1356-65.

https://ww2.health.wa.gov.au/Reports-and-publications/Western-Australias-Mothers-and-Babies-summary-information/data?report=mns_mort_y

https://ww2.health.wa.gov.au/Reports-and-publications/Western-Australias-Mothers-and-Babies-summary-information/data?report=mns_mort_y

https://www.nice.org.uk/guidance/ng25/resources/preterm-labour-and-birth-pdf-1837333576645

https://www.nice.org.uk/guidance/ng25/resources/preterm-labour-and-birth-pdf-1837333576645

REFERENCES

PAGE | 191

101. Witt WP, Cheng ER, Wisk LE, et al. Preterm birth in the United States: the impact of stressful life

events prior to conception and maternal age. Am J Public Health 2014; 104 Suppl 1: S73-80.

102. Patel R, Steer P, Doyle P, Little M, Elliott P. Does gestation vary by ethnic group? A London-based

study of over 122 000 pregnancies with spontaneous onset of labour. Int J Epidemiol 2004; 33(1):

107-13.

103. Allen MC, Alexander GR, Tompkins ME, Hulsey TC. Racial differences in temporal changes in

newborn viability and survival by gestational age. Paediatr Perinat Epidemiol 2000; 14(2): 152-8.

104. Lawn JE, Davidge R, Paul VK, et al. Born too soon: care for the preterm baby. Reprod Health 2013; 10

Suppl 1: S5.

105. Delnord M, Blondel B, Zeitlin J. What contributes to disparities in the preterm birth rate in European

countries? Curr Opin Obstet Gyn 2015; 27(2): 133-42.

106. Purisch SE, Gyamfi-Bannerman C. Epidemiology of preterm birth. Semin Perinatol 2017; 41(7): 387-

91.

107. Burger RJ, Temmink JD, Wertaschnigg D, et al. Trends in singleton preterm birth in Victoria, 2007 to

2017: A consecutive cross-sectional study. Acta Obstet Gynecol Scand 2020.

108. Verburg PE, Dekker GA, Venugopal K, et al. Long-term Trends in Singleton Preterm Birth in South

Australia From 1986 to 2014. Obstet Gynecol 2018; 131(1): 79-89.

109. Hammond G, Langridge A, Leonard H, et al. Changes in risk factors for preterm birth in Western

Australia 1984-2006. BJOG 2013; 120(9): 1051-60.

110. The Western Australian Preterm Birth Prevention Initiative. The Whole Nine Months-Lasts a Lifetime

2014. http://www.thewholeninemonths.com.au/wp-content/uploads/2014/11/Health-Professional-

Booklet-final-Nov14.pdf.

111. Newnham JP, White SW, Meharry S, et al. Reducing preterm birth by a statewide multifaceted

program: an implementation study. Am J Obstet Gynecol 2017; 216(5): 434-42.

112. Kramer MS. Determinants of low birth weight : methodological assessment and meta-analysis; 1987.

113. World Health Organization., UNICEF. Low birthweight : country, regional and global estimates.

Geneva: World Health Organization; 2004.

114. Blencowe H, Krasevec J, de Onis M, et al. National, regional, and worldwide estimates of low

birthweight in 2015, with trends from 2000: a systematic analysis. Lancet Glob Health 2019; 7(7):

e849-e60.

http://www.thewholeninemonths.com.au/wp-content/uploads/2014/11/Health-Professional-Booklet-final-Nov14.pdf

http://www.thewholeninemonths.com.au/wp-content/uploads/2014/11/Health-Professional-Booklet-final-Nov14.pdf

REFERENCES

PAGE | 192

115. Australian institute of Health and Welfare. Children’s Headline Indicators- 3. Low Birthweight. 2018.

https://www.aihw.gov.au/reports/children-youth/childrens-headline-indicators/contents/3-low-

birthweight.

116. Bhugra D. Migration, distress and cultural identity. Br Med Bull 2004; 69: 129-41.

117. World Economic Forum. Migration and Its Impact on Cities. Oct 2017.

http://www3.weforum.org/docs/Migration_Impact_Cities_report_2017_HR.pdf (accessed 30 Oct

2017.

118. United Nations, Department of Economic and Social Affairs, Population Division (2017). International

Migration Report 2017: Highlights (ST/ESA/SER.A/404).

http://www.un.org/en/development/desa/population/migration/publications/migrationreport/docs

/MigrationReport2015_Highlights.pdf).

119. United Nations Department of Economic and Social Affairs. Cross-national comparisons of internal

migration: An update on global patterns and trends. 2013.

http://www.un.org/en/development/desa/population/publications/pdf/technical/TP2013-1.pdf.

120. Watts N, Amann M, Ayeb-Karlsson S, et al. The Lancet Countdown on health and climate change:

from 25 years of inaction to a global transformation for public health. Lancet 2017.

121. Nabi F. The impact of the migration on psychosocial well-being: A study of Kurdish refugees in

resettlement country. J Community Med Health Educ 2014; 4: 273.

122. International Organization for Migration. World Migration Report 2020. 2019. https://www.un-

ilibrary.org/content/publication/b1710e30-en.

123. Bhugra D. Migration and mental health. Acta Psychiatr Scand 2004; 109(4): 243-58.

124. Australian Bureau of Statistics. Characteristics of Recent Migrants. Cat No: 62500. Australia; Nov

2010.

125. Gordon M. The Nature of Assimilation. Assimilation in American Life. New York: Oxford University

Press; 1964: 60-83.

126. Redfield R, Linton R, Herskov MJ. Memorandum for the study of acculturation. American

Anthropologist 1936; 38: 149-52.

127. Greenman E, Xie Y. Is Assimilation Theory Dead? The Effect of Assimilation on Adolescent Well-

Being. Soc Sci Res 2008; 37(1): 109-37.

128. Wallace PM, Pomery EA, Latimer AE, Martinez JL, Salovey P. A Review of Acculturation Measures

and Their Utility in Studies Promoting Latino Health. Hisp J Behav Sci 2010; 32(1): 37-54.

https://www.aihw.gov.au/reports/children-youth/childrens-headline-indicators/contents/3-low-birthweight

https://www.aihw.gov.au/reports/children-youth/childrens-headline-indicators/contents/3-low-birthweight

http://www3.weforum.org/docs/Migration_Impact_Cities_report_2017_HR.pdf

http://www.un.org/en/development/desa/population/migration/publications/migrationreport/docs/MigrationReport2015_Highlights.pdf

http://www.un.org/en/development/desa/population/migration/publications/migrationreport/docs/MigrationReport2015_Highlights.pdf

http://www.un.org/en/development/desa/population/publications/pdf/technical/TP2013-1.pdf

https://www.un-ilibrary.org/content/publication/b1710e30-en

https://www.un-ilibrary.org/content/publication/b1710e30-en

REFERENCES

PAGE | 193

129. Berry JW. Immigration, acculturation, and adaptation. Appl Psychol-Int Rev 1997; 46(1): 5-34.

130. Kuo BC, Roysircar G. Predictors of acculturation for Chinese adolescents in Canada: Age of arrival,

length of stay, social class, and English reading ability. Journal of Multicultural Counselling and

Development 2004; 32: 143-54.

131. Williams JA, Ortega ST. Dimensions of Ethnic-Assimilation - an Empirical Appraisal of Gordon

Typology. Soc Sci Quart 1990; 71(4): 697-710.

132. Siew-Ean K. Chapter 6: Intermarriage, Integration and Multiculturalism: A Demographic Perspective.

In: Clyne M, ed. Multiculturalism and Integration: A Harmonious Relationship

Canberra 2011.

133. Australian Bureau of Statistics. Cat. No: 3412.0-Migration, Australia, 2018-19. . 2020.

https://www.abs.gov.au/ausstats/[email protected]/Latestproducts/3412.0Main%20Features32018-

19?opendocument&tabname=Summary&prodno=3412.0&issue=2018-19&num=&view=.

134. Kukutai T, MPawar S. A Socio-demographic Profile of Maori in Australia, NIDEA Working PapersNo.3,

University of Waikato, National Institute of Demographic and Economic Analysis. 2013.

https://poseidon01.ssrn.com/delivery.php?ID=6771190210860980011110061141161270881270080

49065074002106110009027029109077123094073029003016045000030051091114080023125112

08405704209403507206500700412107308712403108903208602502210611907508708200509408

1093102088105096027004090080079102073107106106013&EXT=pdf.

135. Office of Multicultural Interests. Government of Western Australia. Cultural Diversity in Western

Australia: A Demographic Profile. 2013.

https://www.omi.wa.gov.au/Resources/Publications/Documents/Diversity/Cultural_Diversity_2013.

pdf.

136. Australian Bureau of Statistics. 2016 Census QuickStats: Western Australia. Updated July 2018.

http://www.censusdata.abs.gov.au/census_services/getproduct/census/2016/quickstat/5?opendoc

ument.

137. Hamer P. One in Six? The Rapid Growth of the Māori Population in Australia. New Zealand

Population Review 2008; 33/34: . 153-76.

138. Farkas L. Analysis and comparative review of equality data collection practices in the European

Union- Data collection in the field of ethnicity. 2017.

https://ec.europa.eu/newsroom/just/document.cfm?action=display&doc_id=45791.

139. Government of Western Australia. Guidelines for Midwives-Notification of Case Attended.How to

complete and submit Form 2 – Health (Notification by Midwives) Regulations 1994. 2015.

https://www.abs.gov.au/ausstats/[email protected]/Latestproducts/3412.0Main%20Features32018-19?opendocument&tabname=Summary&prodno=3412.0&issue=2018-19&num=&view=

https://www.abs.gov.au/ausstats/[email protected]/Latestproducts/3412.0Main%20Features32018-19?opendocument&tabname=Summary&prodno=3412.0&issue=2018-19&num=&view=

https://poseidon01.ssrn.com/delivery.php?ID=677119021086098001111006114116127088127008049065074002106110009027029109077123094073029003016045000030051091114080023125112084057042094035072065007004121073087124031089032086025022106119075087082005094081093102088105096027004090080079102073107106106013&EXT=pdf




https://www.omi.wa.gov.au/Resources/Publications/Documents/Diversity/Cultural_Diversity_2013.pdf

https://www.omi.wa.gov.au/Resources/Publications/Documents/Diversity/Cultural_Diversity_2013.pdf

http://www.censusdata.abs.gov.au/census_services/getproduct/census/2016/quickstat/5?opendocument

http://www.censusdata.abs.gov.au/census_services/getproduct/census/2016/quickstat/5?opendocument

https://ec.europa.eu/newsroom/just/document.cfm?action=display&doc_id=45791

REFERENCES

PAGE | 194

https://ww2.health.wa.gov.au/~/media/Files/Corporate/general%20documents/Data%20collection/

PDF/Guidelines_Completion_of_NOCA_201607.pdf.

140. Mantell CD, Craig ED, Stewart AW, Ekeroma AJ, Mitchell EA. Ethnicity and birth outcome: New

Zealand trends 1980-2001: Part 2. Pregnancy outcomes for Maori women. Aust N Z J Obstet

Gynaecol 2004; 44(6): 537-40.

141. Schulpen TW, van Wieringen JC, van Brummen PJ, et al. Infant mortality, ethnicity, and genetically

determined disorders in The Netherlands. Eur J Public Health 2006; 16(3): 291-4.

142. Australian Bureau of Statistics. Cat. No:1200.0.55.009-Ancestry Standard 2014-Version 2.1.

https://www.abs.gov.au/ausstats/[email protected]/PrimaryMainFeatures/1200.0.55.009?OpenDocument.

143. The UK Government Statistical Service. GSS Harmonised Principle-Ethnic Group. 2017.

https://gss.civilservice.gov.uk/wp-content/uploads/2019/04/Ethnic-Group-June-17.pdf.

144. Australian Institute of Health and Welfare 2016. Australia’s health 2016. Australia’s health series no.

15. Cat. no. AUS 199. Canberra: AIHW. 2016. https://www.aihw.gov.au/getmedia/9844cefb-7745-

4dd8-9ee2-f4d1c3d6a727/19787-AH16.pdf.aspx?inline=true.

145. Braveman P, Gottlieb L. The social determinants of health: it's time to consider the causes of the

causes. Public Health Rep 2014; 129 Suppl 2: 19-31.

146. Juarez SP, Revuelta-Eugercios BA. Exploring the 'Healthy Migrant Paradox' in Sweden. A Cross

Sectional Study Focused on Perinatal Outcomes. J Immigr Minor Health 2016; 18(1): 42-50.

147. McDonald JT, Kennedy S. Insights into the 'healthy immigrant effect': health status and health

service use of immigrants to Canada. Soc Sci Med 2004; 59(8): 1613-27.

148. Miller LS, Robinson JA, Cibula DA. Healthy Immigrant Effect: Preterm Births Among Immigrants and

Refugees in Syracuse, NY. Maternal & Child Health Journal 2016; 20(2): 484-93.

149. Marmot M. Review of social determinants and the health divide in the WHO European Region: final

report. 2014.

150. Bhopal RS. MIGRATION, ETHNICITY, RACE AND HEALTH. MIGRATION, ETHNICITY, RACE AND HEALTH

IN MULTICULTURAL SOCIETIES, OXFORD UNIVERSITY Public Health Panorama; 2016. p. 548-59.

151. Stringhini S, Sabia S, Shipley M, et al. Association of socioeconomic position with health behaviors

and mortality. JAMA 2010; 303(12): 1159-66.

152. Castaneda H, Holmes SM, Madrigal DS, Young ME, Beyeler N, Quesada J. Immigration as a social

determinant of health. Annu Rev Public Health 2015; 36: 375-92.

https://ww2.health.wa.gov.au/~/media/Files/Corporate/general%20documents/Data%20collection/PDF/Guidelines_Completion_of_NOCA_201607.pdf

https://ww2.health.wa.gov.au/~/media/Files/Corporate/general%20documents/Data%20collection/PDF/Guidelines_Completion_of_NOCA_201607.pdf

https://www.abs.gov.au/ausstats/[email protected]/PrimaryMainFeatures/1200.0.55.009?OpenDocument

https://gss.civilservice.gov.uk/wp-content/uploads/2019/04/Ethnic-Group-June-17.pdf

https://www.aihw.gov.au/getmedia/9844cefb-7745-4dd8-9ee2-f4d1c3d6a727/19787-AH16.pdf.aspx?inline=true

https://www.aihw.gov.au/getmedia/9844cefb-7745-4dd8-9ee2-f4d1c3d6a727/19787-AH16.pdf.aspx?inline=true

REFERENCES

PAGE | 195

153. CSDH (2008). Closing the gap in a generation: health equity through action on the social

determinants of health. Final Report of the Commission on Social Determinants of Health. Geneva,

World Health Organization.

154. Australian Government Department of Human Services. Newly arrived resident's waiting period.

https://www.humanservices.gov.au/individuals/topics/newly-arrived-residents-waiting-

period/30726 (accessed 14 September 2019).

155. Australian Government Department of Employment. Australian Labour Market Update—July 2017.

https://docs.employment.gov.au/system/files/doc/other/july_2017_almu.pdf.

156. Nybo Andersen AM, Gundlund A, Villadsen SF. Stillbirth and congenital anomalies in migrants in

Europe. Best Pract Res Clin Obstet Gynaecol 2016; 32: 50-9.

157. Yelland J, Riggs E, Szwarc J, et al. Compromised communication: a qualitative study exploring Afghan

families and health professionals’ experience of interpreting support in Australian maternity care.

BMJ Quality & Safety 2016; 25(4): e1-e.

158. Esscher A, Binder-Finnema P, Bodker B, Hogberg U, Mulic-Lutvica A, Essen B. Suboptimal care and

maternal mortality among foreign-born women in Sweden: maternal death audit with application of

the 'migration three delays' model. BMC Pregnancy Childbirth 2014; 14: 141.

159. Drysdale H, Ranasinha S, Kendall A, Knight M, Wallace EM. Ethnicity and the risk of late-pregnancy

stillbirth. Med J Aust 2012; 197(5): 278-81.

160. Craig ED, Mitchell EA, Stewart AW, Mantell CD, Ekeroma AJ. Ethnicity and birth outcome: New

Zealand trends 1980-2001: Part 4. Pregnancy outcomes for European/other women. Aust N Z J

Obstet Gynaecol 2004; 44(6): 545-8.

161. Anderson NH, Sadler LC, Stewart AW, Fyfe EM, McCowan LM. Ethnicity, body mass index and risk of

pre-eclampsia in a multiethnic New Zealand population. Aust N Z J Obstet Gynaecol 2012; 52(6):

552-8.

162. Dahlen HG, Schmied V, Dennis CL, Thornton C. Rates of obstetric intervention during birth and

selected maternal and perinatal outcomes for low risk women born in Australia compared to those

born overseas. BMC Pregnancy Childbirth 2013; 13: 100.

163. Newnham JP, Sahota DS, Zhang CY, et al. Preterm birth rates in Chinese women in China, Hong Kong

and Australia - The price of Westernisation. Aust New Zealand J Obstet Gynaecol 2011; 51(5): 426-

31.

https://www.humanservices.gov.au/individuals/topics/newly-arrived-residents-waiting-period/30726

https://www.humanservices.gov.au/individuals/topics/newly-arrived-residents-waiting-period/30726

https://docs.employment.gov.au/system/files/doc/other/july_2017_almu.pdf

REFERENCES

PAGE | 196

164. Bollini P, Pampallona S, Wanner P, Kupelnick B. Pregnancy outcome of migrant women and

integration policy: a systematic review of the international literature. Soc Sci Med 2009; 68(3): 452-

61.

165. David M, Pachaly J, Vetter K. Perinatal outcome in Berlin (Germany) among immigrants from Turkey.

Arch Gynecol Obstet 2006; 274(5): 271-8.

166. Cornel M, Houwink E, Houwink P. Information should be given on consanguinity as a risk factor for

congenital malformations. Ned Tijdschr Geneeskd 2014; 158(5).

167. Kwon S, Bower C, English D. Birth defects in the offspring of non-Caucasian, non-Indigenous women

in Western Australia. Birth Defects Res A Clin Mol Teratol 2003; 67(7): 515-21.

168. Thomas PE, Beckmann M, Gibbons K. The effect of cultural and linguistic diversity on pregnancy

outcome. Aust N Z J Obstet Gynaecol 2010; 50(5): 419-22.

169. Reime B, Lindwedel U, Ertl KM, Jacob C, Schucking B, Wenzlaff P. Does underutilization of prenatal

care explain the excess risk for stillbirth among women with migration background in Germany?

Acta Obstet Gynecol Scand 2009; 88(11): 1276-83.

170. Garcia-Subirats I, Perez G, Rodriguez-Sanz M, Salvador J, Jane M. Recent immigration and adverse

pregnancy outcomes in an urban setting in Spain. Matern Child Health J 2011; 15(5): 561-9.

171. Shafiei T, Small R, McLachlan H. Women's views and experiences of maternity care: a study of

immigrant Afghan women in Melbourne, Australia. Midwifery 2012; 28(2): 198-203.

172. Niner S, Kokanovic R, Cuthbert D. Displaced mothers: birth and resettlement, gratitude and

complaint. Med Anthropol 2013; 32(6): 535-51.

173. Stapleton H, Murphy R, Correa-Velez I, Steel M, Kildea S. Women from refugee backgrounds and

their experiences of attending a specialist antenatal clinic. Narratives from an Australian setting.

Women Birth 2013; 26(4): 260-6.

174. Fozdar F, Hartley L. Refugees in Western Australia: Settlement and Integration. 2010.

http://library.bsl.org.au/jspui/bitstream/1/5051/1/FozdarF_Refugees-in-Western-Australia-

settlement-and-integration_MMRCI-2012.pdf.

175. Riggs E, Davis E, Gibbs L, et al. Accessing maternal and child health services in Melbourne, Australia:

reflections from refugee families and service providers. BMC Health Serv Res 2012; 12: 117.

176. Gibson-Helm M, Boyle J, Cheng IH, East C, Knight M, Teede H. Maternal health and pregnancy

outcomes among women of refugee background from Asian countries. Int J Gynecol Obstet 2015;

129(2): 146-51.

http://library.bsl.org.au/jspui/bitstream/1/5051/1/FozdarF_Refugees-in-Western-Australia-settlement-and-integration_MMRCI-2012.pdf

http://library.bsl.org.au/jspui/bitstream/1/5051/1/FozdarF_Refugees-in-Western-Australia-settlement-and-integration_MMRCI-2012.pdf

REFERENCES

PAGE | 197

177. Murray L, Windsor C, Parker E, Tewfik O. The experiences of African women giving birth in Brisbane,

Australia. Health Care Women Int 2010; 31(5): 458-72.

178. Yuan B, Qian X, Thomsen S. Disadvantaged populations in maternal health in China who and why?

Glob Health Action 2013; 6: 19542.

179. Binder P, Johnsdotter S, Essen B. Conceptualising the prevention of adverse obstetric outcomes

among immigrants using the 'three delays' framework in a high-income context. Soc Sci Med 2012;

75(11): 2028-36.

180. Suphanchaimat R, Kantamaturapoj K, Putthasri W, Prakongsai P. Challenges in the provision of

healthcare services for migrants: a systematic review through providers' lens. BMC Health Serv Res

2015; 15: 390.

181. Son J. Assimilation and health service utilization of Korean immigrant women. Qual Health Res 2013;

23(11): 1528-40.

182. Hennegan J, Redshaw M, Miller Y. Born in another country: Women's experience of labour and birth

in Queensland, Australia. Women Birth 2014; 27(2): 91-7.

183. Small RYJLJBSLP. Immigrant women's views about care during labor and birth: An Australian study of

Vietnamese, Turkish, and Filipino women. Birth 2002; 29(4): 266-77.

184. Carolan M, Cassar L. Antenatal care perceptions of pregnant African women attending maternity

services in Melbourne, Australia. Midwifery 2010; 26(2): 189-201.

185. Harris R, Robson B, Curtis E, Purdie G, Cormack D, Reid P. Maori and non-Maori differences in

caesarean section rates: a national review. N Z Med J 2007; 120(1250): U2444.

186. Anderson NH, Sadler LC, Stewart AW, Fyfe EM, McCowan LM. Ethnicity and risk of caesarean section

in a term, nulliparous New Zealand obstetric cohort. Aust N Z J Obstet Gynaecol 2013; 53(3): 258-64.

187. Vangen S, Stoltenberg C, Skrondal A, Magnus P, Stray-Pedersen B. Cesarean section among

immigrants in Norway. Acta Obstet Gynecol Scand 2000; 79(7): 553-8.

188. Sorbye IK, Daltveit AK, Sundby J, Stoltenberg C, Vangen S. Caesarean section by immigrants' length

of residence in Norway: a population-based study. European Journal of Public Health 2015; 25(1):

78-84.

189. Ma J, Bauman A. Obstetric profiles and pregnancy outcomes of immigrant women in New South

Wales, 1990-1992. Aust N Z J Obstet Gynaecol 1996; 36(2): 119-25.

REFERENCES

PAGE | 198

190. Barcelona de Mendoza V, Harville E, Theall K, Buekens P, Chasan-Taber L. Acculturation and Adverse

Birth Outcomes in a Predominantly Puerto Rican Population. Matern Child Health J 2016; 20(6):

1151-60.

191. Callister LC, Birkhead A. Acculturation and perinatal outcomes in Mexican immigrant childbearing

women: an integrative review. J Perinat Neonatal Nurs 2002; 16(3): 22-38.

192. English PB, Kharrazi M, Guendelman S. Pregnancy outcomes and risk factors in Mexican Americans:

the effect of language use and mother's birthplace. Ethn Dis 1997; 7(3): 229-40.

193. Urquia ML, Frank JW, Moineddin R, Glazier RH. Immigrants' duration of residence and adverse birth

outcomes: a population-based study. BJOG 2010; 117(5): 591-601.

194. Vik ES, Aasheim V, Schytt E, Small R, Moster D, Nilsen RM. Stillbirth in relation to maternal country

of birth and other migration related factors: a population-based study in Norway. BMC Pregnancy

Childbirth 2019; 19(1): 5.

195. Reiss K, Breckenkamp J, Borde T, Brenne S, David M, Razum O. Smoking during pregnancy among

Turkish immigrants in Germany-are there associations with acculturation? Nicotine Tob Res 2015;

17(6): 643-52.

196. Detjen MG, Nieto FJ, Trentham-Dietz A, Fleming M, Chasan-Taber L. Acculturation and cigarette

smoking among pregnant hispanic women residing in the United States. Am J Public Health 2007;

97(11): 2040-7.

197. Bartsch E, Park AL, Pulver AJ, Urquia ML, Ray JG. Maternal and paternal birthplace and risk of

stillbirth. J Obstet Gynaecol Can 2015; 37(4): 314-23.

198. Liu Y, Zhang J, Li Z. Perinatal outcomes in native Chinese and Chinese-American women. Paediatr

Perinat Epidemiol 2011; 25(3): 202-9.

199. Martinson ML, Tienda M, Teitler JO. Low birthweight among immigrants in Australia, the United

Kingdom, and the United States. Soc Sci Med 2017; 194: 168-76.

200. Zlot AI, Jackson DJ, Korenbrot C. Association of acculturation with cesarean section among Latinas.

Matern Child Health J 2005; 9(1): 11-20.

201. United Nations Economic Commission for Europe. Register-vi based statistics in the nordic countries,

review of best practice with focus on population and social statistics. 2007.

http://www.unece.org/fileadmin/DAM/stats/publications/Register_based_statistics_in_Nordic_cou

ntries.pdf.

http://www.unece.org/fileadmin/DAM/stats/publications/Register_based_statistics_in_Nordic_countries.pdf

http://www.unece.org/fileadmin/DAM/stats/publications/Register_based_statistics_in_Nordic_countries.pdf

REFERENCES

PAGE | 199

202. Data Linkage Branch- WA Department of Health. WA Data Linkage System. 2020.

https://www.datalinkage-wa.org.au/about/.

203. Bohensky MA, Jolley D, Sundararajan V, et al. Data linkage: a powerful research tool with potential

problems. BMC Health Serv Res 2010; 10: 346.

204. Holman CD, Bass AJ, Rosman DL, et al. A decade of data linkage in Western Australia: strategic

design, applications and benefits of the WA data linkage system. Aust Health Rev 2008; 32(4): 766-

77.

205. Delgado-Rodriguez M, Llorca J. Bias. J Epidemiol Community Health 2004; 58(8): 635-41.

206. Hammer GP, du Prel JB, Blettner M. Avoiding bias in observational studies: part 8 in a series of

articles on evaluation of scientific publications. Dtsch Arztebl Int 2009; 106(41): 664-8.

207. Stanley F, Croft M, Gibbins J, Read A. A population database for maternal and child health research

in Western Australia using record linkage. Paediatr Perinat Epidemiol 1994; 8(4): 433-47.

208. Stanley F, Read A, Kurinczuk J, Croft M, Bower C. A population maternal and child health research

database for research and policy evaluation in Western Australia. Semin Neonatol 1997; 2(3): 195-

201.

209. Deeny S, Steventon A. Making sense of the shadows: Priorities for creating a learning healthcare

system based on routinely collected data. BMJ Qual Saf 2015; 24(8): 505-15.

210. Boyd JH, Ferrante AM, O'Keefe CM, Bass AJ, Randall SM, Semmens JB. Data linkage infrastructure for

cross-jurisdictional health-related research in Australia. BMC Health Serv Res 2012; 12: 480.

211. Holman CD, Bass AJ, Rouse IL, Hobbs MS. Population-based linkage of health records in Western

Australia: development of a health services research linked database. Aust N Z J Public Health 1999;

23(5): 453-9.

212. WA Data Linkage Branch. Data linkage– making the right connections 2016.

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/Data-Linkage-Branch-Linkage-

Quality.pdf.

213. Kelman C, Bass A, Holman C. Research use of linked health data - A best practice protocol. Aust New

Zealand J Public Health 2002; 26(3): 251-5.

214. Silveira DP, Artmann E. Accuracy of probabilistic record linkage applied to health databases:

systematic review. Rev Saude Publica 2009; 43(5): 875-82.

215. WA Data Linkage Branch. Midwives notifications: linkage quality statement. 2017.

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/06/mns_quality.pdf.

https://www.datalinkage-wa.org.au/about/

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/Data-Linkage-Branch-Linkage-Quality.pdf

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/02/Data-Linkage-Branch-Linkage-Quality.pdf

https://www.datalinkage-wa.org.au/wp-content/uploads/2019/06/mns_quality.pdf

REFERENCES

PAGE | 200

216. data Linkage Branch- WA Department of Health. Privacy. https://www.datalinkage-

wa.org.au/privacy-and-ethics/privacy.

217. Data Linkage Branch- WA Department of Health. Security. https://www.datalinkage-

wa.org.au/privacy-and-ethics/security.

218. data Linkage Branch- WA Department of Health. Geocoding. https://www.datalinkage-

wa.org.au/dlb-services/geocoding.

219. Pink B. An introduction to Socio-Economic Indexes for Areas (SEIFA)- Technical paper 2006. 2008.

https://www.ausstats.abs.gov.au/ausstats/subscriber.nsf/0/D729075E079F9FDECA2574170011B08

8/$File/20390_2006.pdf.

220. Department of Health and Aged Care. Occasional Papers: New Series No 14. Measuring Remoteness:

Accessibility/Remoteness Index of Australia (ARIA). October 2001.

http://www.health.gov.au/internet/main/publishing.nsf/Content/E2EE19FE831F26BFCA257BF0001F

3DFA/$File/ocpanew14.pdf

221. Department of Health and Aged Care. Measuring Remoteness: Accessibility/Remoteness Index of

Australia (ARIA). 2001.

https://www1.health.gov.au/internet/main/publishing.nsf/Content/E2EE19FE831F26BFCA257BF000

1F3DFA/$File/ocpanew14.pdf.

222. Australian bureau of Statistics. Australian Statistical Geography Standard (ASGS): Volume 5 -

Remoteness Structure (cat. no. 1270.0.55.005). 2016.

https://www.abs.gov.au/websitedbs/D3310114.nsf/home/remoteness+structure.

223. Glasson EJ, de Klerk NH, Bass AJ, Rosman DL, Palmer LJ, Holman CD. Cohort profile: The Western

Australian Family Connections Genealogical Project. Int J Epidemiol 2008; 37(1): 30-5.

224. Government of Western Australia. WA Health Act 1911-Reprint 16. 2013.

https://www.legislation.wa.gov.au/legislation/prod/filestore.nsf/FileURL/mrdoc_25565.pdf/$FILE/H

ealth%20Act%201911%20-%20%5B16-a0-01%5D.pdf?OpenElement.

225. Mahase E. An estimated one in seven babies is born with a low birthweight. BMJ 2019; 365: l2210.

226. Government of Western Australia Department of Health. Hospital Morbidity Data System

REFERENCE MANUAL PART A: Contacts, Hospital Responsibilities, Data Element Definitions.JULY

2017 V1.0

https://ww2.health.wa.gov.au/~/media/Files/Corporate/general%20documents/Clinical%20coding/

Guides%20and%20summaries/HMDS-REF-Manual-PartA.pdf.

https://www.datalinkage-wa.org.au/privacy-and-ethics/privacy

https://www.datalinkage-wa.org.au/privacy-and-ethics/privacy

https://www.datalinkage-wa.org.au/privacy-and-ethics/security

https://www.datalinkage-wa.org.au/privacy-and-ethics/security

https://www.datalinkage-wa.org.au/dlb-services/geocoding

https://www.datalinkage-wa.org.au/dlb-services/geocoding

https://www.ausstats.abs.gov.au/ausstats/subscriber.nsf/0/D729075E079F9FDECA2574170011B088/$File/20390_2006.pdf

https://www.ausstats.abs.gov.au/ausstats/subscriber.nsf/0/D729075E079F9FDECA2574170011B088/$File/20390_2006.pdf

http://www.health.gov.au/internet/main/publishing.nsf/Content/E2EE19FE831F26BFCA257BF0001F3DFA/$File/ocpanew14.pdf

http://www.health.gov.au/internet/main/publishing.nsf/Content/E2EE19FE831F26BFCA257BF0001F3DFA/$File/ocpanew14.pdf

https://www1.health.gov.au/internet/main/publishing.nsf/Content/E2EE19FE831F26BFCA257BF0001F3DFA/$File/ocpanew14.pdf

https://www1.health.gov.au/internet/main/publishing.nsf/Content/E2EE19FE831F26BFCA257BF0001F3DFA/$File/ocpanew14.pdf

https://www.abs.gov.au/websitedbs/D3310114.nsf/home/remoteness+structure

https://www.legislation.wa.gov.au/legislation/prod/filestore.nsf/FileURL/mrdoc_25565.pdf/$FILE/Health%20Act%201911%20-%20%5B16-a0-01%5D.pdf?OpenElement

https://www.legislation.wa.gov.au/legislation/prod/filestore.nsf/FileURL/mrdoc_25565.pdf/$FILE/Health%20Act%201911%20-%20%5B16-a0-01%5D.pdf?OpenElement

https://ww2.health.wa.gov.au/~/media/Files/Corporate/general%20documents/Clinical%20coding/Guides%20and%20summaries/HMDS-REF-Manual-PartA.pdf

https://ww2.health.wa.gov.au/~/media/Files/Corporate/general%20documents/Clinical%20coding/Guides%20and%20summaries/HMDS-REF-Manual-PartA.pdf

REFERENCES

PAGE | 201

227. Australian Institute of Health and Welfare 2015. National Health Data Dictionary: version 16.2.

National Health Data Dictionary series. Cat. no.HWI 131. Canberra: AIHW.

228. Bower C, Rudy E, Quick J, Rowley A, Watson L, Cosgrove P. Report of the Western Australian Register

of Developmental Anomalies 1980-2012. ISSN 2200-8837: King Edward Memorial Hospital, 2014.

229. Ballestas T. ,on behalf of the Perinatal and Infant Mortality Committee of Western Australia.The

14th Report of the Perinatal and Infant Mortality Committee of Western Australia for deaths in the

triennium 2008-2010. Perth: Department of Health WA 2014.

https://ww2.health.wa.gov.au/~/media/Files/Corporate/Reports%20and%20publications/Perinatal

%20infant%20and%20maternal/12781_the_14th_perinatal_report.pdf.

230. WHO: recommended definitions, terminology and format for statistical tables related to the

perinatal period and use of a new certificate for cause of perinatal deaths. Modifications

recommended by FIGO as amended October 14, 1976. Acta Obstet Gynecol Scand 1977; 56(3): 247-

53.

231. Ogwulu CB, Jackson LJ, Heazell AEP, Roberts TE. Exploring the intangible economic costs of stillbirth.

BMC Pregnancy Childbirth 2015; 15(1): 188.

232. Heazell AE, Siassakos D, Blencowe H, et al. Stillbirths: economic and psychosocial consequences.

Lancet 2016; 387(10018): 604-16.

233. Khalil A, Rezende J, Akolekar R, Syngelaki A, Nicolaides KH. Maternal racial origin and adverse

pregnancy outcome: a cohort study. Ultrasound Obstet Gynecol 2013; 41(3): 278-85.

234. Australian Bureau of Statistics. Population Density, Cat. 3218.0 Regional Population Growth,

Australia 2016.

https://www.abs.gov.au/ausstats/[email protected]/Previousproducts/3218.0Main%20Features752016

(accessed December 2019).

235. Downey F. A validation study of the Western Australian Midwives’ Notification System. 2005 data.

Perth: Department of Health, Western Australia.;. 2007.

mhttps://ww2.health.wa.gov.au/~/media/Files/Corporate/general%20documents/Data%20collectio

n/PDF/Midwives_Validation_Study_2007.pdf (accessed June 2018.

236. Gulmezoglu AM, Crowther CA, Middleton P, Heatley E. Induction of labour for improving birth

outcomes for women at or beyond term. Cochrane Database Syst Rev 2012; (6): CD004945.

237. Caughey AB, Stotland NE, Washington AE, Escobar GJ. Who is at risk for prolonged and postterm

pregnancy? Am J Obstet Gynecol 2009; 200(6): 683 e1-5.



https://www.abs.gov.au/ausstats/[email protected]/Previousproducts/3218.0Main%20Features752016

https://ww2.health.wa.gov.au/~/media/Files/Corporate/general%20documents/Data%20collection/PDF/Midwives_Validation_Study_2007.pdf

https://ww2.health.wa.gov.au/~/media/Files/Corporate/general%20documents/Data%20collection/PDF/Midwives_Validation_Study_2007.pdf

REFERENCES

PAGE | 202

238. Deyo NS. "Cultural Traditions and the Reproductive Health of Somali Refugees and Immigrants"

[Master's]: University of San Francisco; 2012.

239. Ratima M, Crengle S. Antenatal, Labour, and Delivery Care for Maori: Experiences, Location within a

Lifecourse Approach, and Knowledge Gaps*. 2013; 2013.

240. Pol LG, Thomas RK. The Demography of Health and Healthcare. 3rd ed. Dordrecht: Springer

Netherlands

2013.

241. Mozooni M, Preen DB, Pennell CE. Stillbirth in Western Australia, 2005-2013: the influence of

maternal migration and ethnic origin. Med J Aust 2018; 209(9): 394-400.

242. Jayaweera H, Quigley MA. Health status, health behaviour and healthcare use among migrants in the

UK: evidence from mothers in the Millennium Cohort Study. Soc Sci Med 2010; 71(5): 1002-10.

243. Chote AA, Koopmans GT, Redekop WK, et al. Explaining ethnic differences in late antenatal care

entry by predisposing, enabling and need factors in The Netherlands. The Generation R Study.

Matern Child Health J 2011; 15(6): 689-99.

244. Guevarra MV, Stubbs JM, Assareh H, Achat HM. Risk factors associated with late entry to antenatal

care visits in NSW in 2014. Aust N Z J Public Health 2017; 41(5): 543-4.

245. Government of Western Australia. Hospital Morbidity Data System. REFERENCE MANUAL PART A:

Contacts, Hospital Responsibilities,Data Element Definitions.V1.0. 2017.

246. Australian Institute of Health and Welfare. My Local Area Hospitals. 2019.

https://www.aihw.gov.au/reports-data/myhospitals/my-local-area/hospitals.

247. Gee V. Perinatal, Infant and Maternal Mortality in Western Australia, 2006-2010. 2013.

https://ww2.health.wa.gov.au/~/media/Files/Corporate/Reports%20and%20publications/Perinatal

%20infant%20and%20maternal/Perinatal_Infant_Maternal_Mortality_WA_2006-2010.pdf.

248. Moller AB, Petzold M, Chou D, Say L. Early antenatal care visit: a systematic analysis of regional and

global levels and trends of coverage from 1990 to 2013. Lancet Glob Health 2017; 5(10): e977-e83.

249. Ananth CV, Schisterman EF. Confounding, causality, and confusion: the role of intermediate

variables in interpreting observational studies in obstetrics. Am J Obstet Gynecol 2017; 217(2): 167-

75.

250. VanderWeele TJ, Mumford SL, Schisterman EF. Conditioning on intermediates in perinatal

epidemiology. Epidemiology 2012; 23(1): 1-9.

https://www.aihw.gov.au/reports-data/myhospitals/my-local-area/hospitals

https://ww2.health.wa.gov.au/~/media/Files/Corporate/Reports%20and%20publications/Perinatal%20infant%20and%20maternal/Perinatal_Infant_Maternal_Mortality_WA_2006-2010.pdf

https://ww2.health.wa.gov.au/~/media/Files/Corporate/Reports%20and%20publications/Perinatal%20infant%20and%20maternal/Perinatal_Infant_Maternal_Mortality_WA_2006-2010.pdf

REFERENCES

PAGE | 203

251. Adams N, Tudehope D, Gibbons KS, Flenady V. Perinatal mortality disparities between public care

and private obstetrician-led care: a propensity score analysis. BJOG 2018; 125(2): 149-58.

252. Bursac Z, Gauss CH, Williams DK, Hosmer DW. Purposeful selection of variables in logistic regression.

Source Code Biol Med 2008; 3: 17.

253. Gould JB, Madan A, Qin C, Chavez G. Perinatal outcomes in two dissimilar immigrant populations in

the United States: a dual epidemiologic paradox. Pediatrics 2003; 111(6 Pt 1): e676-82.

254. Sparks TN, Cheng YW, McLaughlin B, Esakoff TF, Caughey AB. Fundal height: a useful screening tool

for fetal growth? J Matern Fetal Neonatal Med 2011; 24(5): 708-12.

255. Buck Louis GM, Grewal J, Albert PS, et al. Racial/ethnic standards for fetal growth: the NICHD Fetal

Growth Studies. Am J Obstet Gynecol 2015; 213(4): 449 e1- e41.

256. Anderson NH, Sadler LC, McKinlay CJD, McCowan LME. INTERGROWTH-21st vs customized

birthweight standards for identification of perinatal mortality and morbidity. Am J Obstet Gynecol

2016; 214(4): 509 e1- e7.

257. Villar J, Papageorghiou AT, Pang R, et al. The likeness of fetal growth and newborn size across non-

isolated populations in the INTERGROWTH-21st Project: the Fetal Growth Longitudinal Study and

Newborn Cross-Sectional Study. The lancet Diabetes & endocrinology 2014; 2(10): 781-92.

258. Sovio U, White IR, Dacey A, Pasupathy D, Smith GCS. Screening for fetal growth restriction with

universal third trimester ultrasonography in nulliparous women in the Pregnancy Outcome

Prediction (POP) study: a prospective cohort study. Lancet 2015; 386(10008): 2089-97.

259. Cresswell JA, Yu G, Hatherall B, et al. Predictors of the timing of initiation of antenatal care in an

ethnically diverse urban cohort in the UK. BMC Pregnancy Childbirth 2013; 13: 103.

260. Romero R, Nicolaides KH, Conde-Agudelo A, et al. Vaginal progesterone decreases preterm birth </=

34 weeks of gestation in women with a singleton pregnancy and a short cervix: an updated meta-

analysis including data from the OPPTIMUM study. Ultrasound Obstet Gynecol 2016; 48(3): 308-17.

261. Rolnik DL, Wright D, Poon LC, et al. Aspirin versus Placebo in Pregnancies at High Risk for Preterm

Preeclampsia. N Engl J Med 2017; 377(7): 613-22.

262. Reid C. Interpreter services essential for safe medical practice. Medicus 2017; 57(5): 34.

263. White J, Plompen T, Osadnik C, Tao L, Micallef E, Haines T. The experience of interpreter access and

language discordant clinical encounters in Australian health care: a mixed methods exploration. Int J

Equity Health 2018; 17(1): 151.

REFERENCES

PAGE | 204

264. Phillips CB, Travaglia J. Low levels of uptake of free interpreters by Australian doctors in private

practice: secondary analysis of national data. Aust Health Rev 2011; 35(4): 475-9.

265. Huang Y-T, Phillips C. Telephone interpreters in general practice: bridging the barriers to their use.

Australian Family Physician 2009; 38: 443–6.

266. Phillips C. Using interpreters - a guide for GPs. Australian Family Physician 2010; 39(4): 188-95.

267. Wernham E, Gurney J, Stanley J, Ellison-Loschmann L, Sarfati D. A Comparison of Midwife-Led and

Medical-Led Models of Care and Their Relationship to Adverse Fetal and Neonatal Outcomes: A

Retrospective Cohort Study in New Zealand. PLoS Med Journal Translated Name PLoS Medicine

2016; 13(9): e1002134.

268. Tracy SK. Study claims midwifery care harms babies: A critique of flawed research. Women Birth

2016; 29(6): 471-2.

269. Akl N, Coghlan EA, Nathan EA, Langford SA, Newnham JP. Aeromedical transfer of women at risk of

preterm delivery in remote and rural Western Australia: why are there no births in flight? Aust N Z J

Obstet Gynaecol 2012; 52(4): 327-33.

270. Department of Health Western Australia. Framework for the care of neonates in Western Australia.

2009.

https://ww2.health.wa.gov.au/~/media/Files/Corporate/general%20documents/Health%20Network

s/Womens%20and%20Newborns/Framework-for-the-Care-of-Neonates-in-WA.pdf (accessed

December 2019).

271. Neogi SB, Sharma J, Negandhi P, Chauhan M, Reddy S, Sethy G. Risk factors for stillbirths: how much

can a responsive health system prevent? BMC Pregnancy Childbirth 2018; 18(1): 33.

272. Heller G, Bauer E, Schill S, et al. Decision-to-Delivery Time and Perinatal Complications in Emergency

Cesarean Section. Dtsch Arztebl Int 2017; 114(35-36): 589-96.

273. Abubakar I, Aldridge RW, Devakumar D, et al. The UCL-Lancet Commission on Migration and Health:

the health of a world on the move. Lancet 2018; 392(10164): 2606-54.

274. MacDorman MF, Gregory EC. Fetal and Perinatal Mortality: United States, 2013. Natl Vital Stat Rep

2015; 64(8): 1-24.

275. Villadsen SF, Sievers E, Andersen AM, et al. Cross-country variation in stillbirth and neonatal

mortality in offspring of Turkish migrants in northern Europe. Eur J Public Health 2010; 20(5): 530-5.

276. Horton R, Samarasekera U. Stillbirths: ending an epidemic of grief. Lancet 2016; 387(10018): 515-6.

https://ww2.health.wa.gov.au/~/media/Files/Corporate/general%20documents/Health%20Networks/Womens%20and%20Newborns/Framework-for-the-Care-of-Neonates-in-WA.pdf

https://ww2.health.wa.gov.au/~/media/Files/Corporate/general%20documents/Health%20Networks/Womens%20and%20Newborns/Framework-for-the-Care-of-Neonates-in-WA.pdf

REFERENCES

PAGE | 205

277. Chang SJ, Im EO. Testing a Theoretical Model of Immigration Transition and Physical Activity. Res

Theory Nurs Pract 2015; 29(3): 177-88.

278. Mozooni M, Pennell CE, Preen DB. Healthcare factors associated with the risk of antepartum and

intrapartum stillbirth in migrants in Western Australia (2005-2013): A retrospective cohort study.

PLoS Med Journal Translated Name PLoS Medicine 2020; 17(3): e1003061.

279. Eitelhuber T, Davis G, Rosman D, Glauert R. Western Australia unveils advances in linked data

delivery systems. Aust N Z J Public Health 2014; 38(4): 397-8.

280. Premkumar A, Debbink MP, Silver RM, et al. Association of Acculturation With Adverse Pregnancy

Outcomes. Obstet Gynecol 2020; 135(2): 301-9.

281. Hoyt AT, Shumate CJ, Canfield MA, et al. Selected acculturation factors and birth defects in the

National Birth Defects Prevention Study, 1997-2011. Birth Defects Res 2019; 111(10): 598-612.

282. Chen L, Shi L, Zhang D, Chao SM. Influence of Acculturation on Risk for Gestational Diabetes Among

Asian Women. Prev Chronic Dis 2019; 16: E158.

283. Australian Bureau of Statistics. Socio-Economic Indexes for Areas (SEIFA) Technical Paper 2016.

https://www.ausstats.abs.gov.au/ausstats/subscriber.nsf/0/756EE3DBEFA869EFCA258259000BA74

6/$File/SEIFA%202016%20Technical%20Paper.pdf.

284. Ukiru JM. Acculturation experience of Africa Immigrants in the United States of America. 2002.

https://scholarworks.lib.csusb.edu/cgi/viewcontent.cgi?referer=https://www.google.com.au/&https

redir=1&article=3128&context=etd-project.

285. Flynn PM, Foster EM, Brost BC. Indicators of acculturation related to Somali refugee women's birth

outcomes in Minnesota. J Immigr Minor Health 2011; 13(2): 224-31.

286. Alhusen JL, Bower KM, Epstein E, Sharps P. Racial Discrimination and Adverse Birth Outcomes: An

Integrative Review. J Midwifery Womens Health 2016; 61(6): 707-20.

287. Delavari M, Sonderlund AL, Swinburn B, Mellor D, Renzaho A. Acculturation and obesity among

migrant populations in high income countries--a systematic review. BMC Public Health 2013; 13:

458.

288. Salant T, Lauderdale DS. Measuring culture: a critical review of acculturation and health in Asian

immigrant populations. Soc Sci Med 2003; 57(1): 71-90.

289. Rogers LK, Velten M. Maternal inflammation, growth retardation, and preterm birth: insights into

adult cardiovascular disease. Life Sci 2011; 89(13-14): 417-21.

https://www.ausstats.abs.gov.au/ausstats/subscriber.nsf/0/756EE3DBEFA869EFCA258259000BA746/$File/SEIFA%202016%20Technical%20Paper.pdf

https://www.ausstats.abs.gov.au/ausstats/subscriber.nsf/0/756EE3DBEFA869EFCA258259000BA746/$File/SEIFA%202016%20Technical%20Paper.pdf

https://scholarworks.lib.csusb.edu/cgi/viewcontent.cgi?referer=https://www.google.com.au/&httpsredir=1&article=3128&context=etd-project

https://scholarworks.lib.csusb.edu/cgi/viewcontent.cgi?referer=https://www.google.com.au/&httpsredir=1&article=3128&context=etd-project

REFERENCES

PAGE | 206

290. Murray CJ, Vos T, Lozano R, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries

in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet

2012; 380(9859): 2197-223.

291. Australian Bureau of Statistics. Census of Population and Housing: Australia Revealed, 2016. Cat No:

2024.0. ABS. 2017. https://www.abs.gov.au/ausstats/[email protected]/mf/2024.0.

292. Garcia R, Ali N, Guppy A, Griffiths M, Randhawa G. Differences in the pregnancy gestation period and

mean birth weights in infants born to Indian, Pakistani, Bangladeshi and white British mothers in

Luton, UK: a retrospective analysis of routinely collected data. Bmj Open 2017; 7(8): e017139.

293. Pennell CE, Jacobsson B, Williams SM, et al. Genetic epidemiologic studies of preterm birth:

guidelines for research. Am J Obstet Gynecol 2007; 196(2): 107-18.

294. Henderson JJ, McWilliam OA, Newnham JP, Pennell CE. Preterm birth aetiology 2004-2008. Maternal

factors associated with three phenotypes: spontaneous preterm labour, preterm pre-labour rupture

of membranes and medically indicated preterm birth. J Matern Fetal Neonatal Med 2012; 25(6):

642-7.

295. Almeida J, Mulready-Ward C, Bettegowda VR, Ahluwalia IB. Racial/Ethnic and nativity differences in

birth outcomes among mothers in New York City: the role of social ties and social support. Matern

Child Health J 2014; 18(1): 90-100.

296. Almeida J, Becares L, Erbetta K, Bettegowda VR, Ahluwalia IB. Racial/Ethnic Inequities in Low Birth

Weight and Preterm Birth: The Role of Multiple Forms of Stress. Matern Child Health J 2018; 22(8):

1154-63.

297. Ratnasiri AWG, Parry SS, Arief VN, et al. Recent trends, risk factors, and disparities in low birth

weight in California, 2005-2014: a retrospective study. Matern Health Neonatol Perinatol 2018; 4:

15.

298. Juarez SP, Revuelta-Eugercios BA. Too heavy, too late: investigating perinatal health outcomes in

immigrants residing in Spain. A cross-sectional study (2009-2011). J Epidemiol Community Health

2014; 68(9): 863-8.

299. Ramraj C, Pulver A, Siddiqi A. Intergenerational transmission of the healthy immigrant effect (HIE)

through birth weight: A systematic review and meta-analysis. Soc Sci Med 2015; 146: 29-40.

300. Racape J, Schoenborn C, Sow M, Alexander S, De Spiegelaere M. Are all immigrant mothers really at

risk of low birth weight and perinatal mortality? The crucial role of socio-economic status. BMC

Pregnancy Childbirth 2016; 16: 75.

https://www.abs.gov.au/ausstats/[email protected]/mf/2024.0

REFERENCES

PAGE | 207

301. Shah RR, Ray JG, Taback N, Meffe F, Glazier RH. Adverse pregnancy outcomes among foreign-born

Canadians. J Obstet Gynaecol Can 2011; 33(3): 207-15.

302. Wingate MS, Alexander GR. The healthy migrant theory: variations in pregnancy outcomes among

US-born migrants. Soc Sci Med 2006; 62(2): 491-8.

303. Mozooni M, Preen D, Pennell C. 1.1-O1The ‘Healthy Migrant Phenomenon’: how long does it last?

European Journal of Public Health 2018; 28(suppl_1): cky047.01-cky.01.

304. Mozooni M, Preen DB, Pennell CE. The influence of acculturation on the risk of stillbirth in migrant

women residing in Western Australia. PLoS One 2020; 15(4): e0231106.

305. Pestoni G, Krieger JP, Sych JM, Faeh D, Rohrmann S. Cultural Differences in Diet and Determinants of

Diet Quality in Switzerland: Results from the National Nutrition Survey menuCH. Nutrients 2019;

11(1).

306. Englund-Ogge L, Brantsaeter AL, Juodakis J, et al. Associations between maternal dietary patterns

and infant birth weight, small and large for gestational age in the Norwegian Mother and Child

Cohort Study. Eur J Clin Nutr 2019; 73(9): 1270-82.

307. Garay SM, Savory KA, Sumption L, Penketh R, Janssen AB, John RM. The Grown in Wales Study:

Examining dietary patterns, custom birthweight centiles and the risk of delivering a small-for-

gestational age (SGA) infant. PLoS One 2019; 14(3): e0213412.

308. El-Sayed AM, Galea S. Interethnic mating and risk for preterm birth among Arab-American mothers:

Evidence from the Arab-American Birth Outcomes Study. J Immigr Minor Healt 2011; 13(3): 445-52.

309. Cheng ER, Taveras EM, Hawkins SS. Paternal Acculturation and Maternal Health Behaviors: Influence

of Father's Ethnicity and Place of Birth. J Womens Health (Larchmt) 2018; 27(5): 724-32.

310. David M, Borde T, Brenne S, et al. Obstetric care quality indicators and outcomes based on the

degree of acculturation of immigrants-results from a cross-sectional study in Berlin. Arch Gynecol

Obstet 2018; 297(2): 313-22.

311. Ruiz RJ, Saade GR, Brown CEL, et al. The effect of acculturation on progesterone/estriol ratios and

preterm birth in Hispanics. Obstet Gynecol 2008; 111(2 Pt 1): 309-16.

312. Ruiz RJ, Trzeciakowski J, Moore T, Ayers KS, Pickler RH. Acculturation Predicts Negative Affect and

Shortened Telomere Length. Biol Res Nurs 2017; 19(1): 28-35.

313. Hyman I, Dussault G. The effect of acculturation on low birthweight in immigrant women. Can J

Public Health 1996; 87(3): 158-62.

REFERENCES

PAGE | 208

314. Hyman I, Dussault G. Negative consequences of acculturation on health behaviour, social support

and stress among pregnant Southeast Asian immigrant women in Montreal: an exploratory study.

Can J Public Health 2000; 91(5): 357-60.

315. Pedersen GS, Mortensen LH, Gerster M, Rich-Edwards J, Andersen AM. Preterm birth and

birthweight-for-gestational age among immigrant women in Denmark 1978-2007: a nationwide

registry study. Paediatr Perinat Epidemiol 2012; 26(6): 534-42.

316. Sow M, Schoenborn C, De Spiegelaere M, Racape J. Influence of time since naturalisation on

socioeconomic status and low birth weight among immigrants in Belgium. A population-based study.

PLoS One 2019; 14(8): e0220856.

317. Teitler JO, Hutto N, Reichman NE. Birthweight of children of immigrants by maternal duration of

residence in the United States. Soc Sci Med 2012; 75(3): 459-68.

318. Wells JC, Sharp G, Steer PJ, Leon DA. Paternal and maternal influences on differences in birth weight

between Europeans and Indians born in the UK. PLoS One 2013; 8(5): e61116.

319. Demetriou C, Abu-Amero S, Thomas AC, et al. Paternally expressed, imprinted insulin-like growth

factor-2 in chorionic villi correlates significantly with birth weight. PLoS One 2014; 9(1): e85454.

320. Collins JW, Jr., Rankin KM, David RJ. Paternal Lifelong Socioeconomic Position and Low Birth Weight

Rates: Relevance to the African-American Women's Birth Outcome Disadvantage. Matern Child

Health J 2016; 20(8): 1759-66.

321. Nilsson JE, Brown C, Russell EB, Khamphakdy-Brown S. Acculturation, partner violence, and

psychological distress in refugee women from Somalia. J Interpers Violence 2008; 23(11): 1654-63.

322. El-Murr A. Intimate partner violence in Australian refugee communities. 2018.

https://aifs.gov.au/cfca/sites/default/files/publication-

documents/50_intimate_partner_violence_in_australian_refugee_communities.pdf.

323. Khawaja NG, Milner K. Acculturation stress in South Sudanese refugees: Impact on marital

relationships. International Journal of Intercultural Relations 2012; (36): 624– 36.

324. Giurgescu C. Are maternal cortisol levels related to preterm birth? J Obstet Gynecol Neonatal Nurs

2009; 38(4): 377-90.

325. Ruiz RJ, Pickler RH, Marti CN, Jallo N. Family cohesion, acculturation, maternal cortisol, and preterm

birth in Mexican-American women. Int J Womens Health 2013; 5: 243-52.

https://aifs.gov.au/cfca/sites/default/files/publication-documents/50_intimate_partner_violence_in_australian_refugee_communities.pdf

https://aifs.gov.au/cfca/sites/default/files/publication-documents/50_intimate_partner_violence_in_australian_refugee_communities.pdf

REFERENCES

PAGE | 209

326. Nicholson LM, Miller AM, Schwertz D, Sorokin O. Gender differences in acculturation, stress, and

salivary cortisol response among former Soviet immigrants. J Immigr Minor Health 2013; 15(3): 540-

52.

327. Mangold D, Wand G, Javors M, Mintz J. Acculturation, childhood trauma and the cortisol awakening

response in Mexican-American adults. Horm Behav 2010; 58(4): 637-46.

328. Berger M, Leicht A, Slatcher A, et al. Cortisol Awakening Response and Acute Stress Reactivity in First

Nations People. Sci Rep 2017; 7: 41760.

329. Hjort R, Alfredsson L, Carlsson PO, et al. Low birthweight is associated with an increased risk of LADA

and type 2 diabetes: results from a Swedish case-control study. Diabetologia 2015; 58(11): 2525-32.

330. Markopoulou P, Papanikolaou E, Analytis A, Zoumakis E, Siahanidou T. Preterm Birth as a Risk Factor

for Metabolic Syndrome and Cardiovascular Disease in Adult Life: A Systematic Review and Meta-

Analysis. J Pediatr 2019; 210: 69-80 e5.

331. Tanz LJ, Stuart JJ, Williams PL, et al. Preterm Delivery and Maternal Cardiovascular Disease in Young

and Middle-Aged Adult Women. Circulation 2017; 135(6): 578-89.

332. Liu YH, Zhang J, Li Z. Perinatal outcomes in native Chinese and Chinese-American women. Paediatr

Perinat Epidemiol 2011; 25(3): 202-9.

333. Lonhart JA, Mayo JA, Padula AM, Wise PH, Stevenson DK, Shaw GM. Short interpregnancy interval as

a risk factor for preterm birth in non-Hispanic Black and White women in California. Journal of

perinatology : official journal of the California Perinatal Association 2019; 39(9): 1175-81.

334. Perinatal Society of Australia and New Zealand and Centre of Research Excellence Stillbirth. Position

statement: Improving decision-making about the timing of birth for women with risk factors for

stillbirth. . 2019 (accessed September 2019.

335. Rogers HJ, Hogan L, Coates D, Homer CSE, Henry A. Responding to the health needs of women from

migrant and refugee backgrounds-Models of maternity and postpartum care in high-income

countries: A systematic scoping review. Health Soc Care Community 2020.

336. Sexton J, Coory M, Kumar S, et al. Protocol for the development and validation of a risk prediction

model for stillbirths from 35 weeks gestation in Australia (Preprint). 2020.

https://www.researchsquare.com/article/rs-16494/latest.pdf.

337. Dominguez TP, Dunkel-Schetter C, Glynn LM, Hobel C, Sandman CA. Racial differences in birth

outcomes: the role of general, pregnancy, and racism stress. Health Psychol 2008; 27(2): 194-203.

https://www.researchsquare.com/article/rs-16494/latest.pdf

REFERENCES

PAGE | 210

338. Markus A. Mapping Social Cohesion-THE SCANLON FOUNDATION SURVEYS. 2019.

https://scanlonfoundation.org.au/wp-content/uploads/2019/11/Mapping-Social-Cohesion-2019-

FINAL-3.pdf.

339. Ben J, Cormack D, Harris R, Paradies Y. Racism and health service utilisation: A systematic review and

meta-analysis. PLoS One 2017; 12(12): e0189900.

340. Australian Government Department of Human Services. About Medicare.

https://www.humanservices.gov.au/individuals/subjects/whats-covered-medicare/about-medicare.

341. Government of Western Australia. Overview of the WA Health System. 2019.

https://healthywa.health.wa.gov.au/Articles/N_R/Overview-of-the-WA-health-system.

342. Government of Western Australia. Department of Health. Hospital Information. 2019.

https://ww2.health.wa.gov.au/About-us/Hospital-Information.

https://scanlonfoundation.org.au/wp-content/uploads/2019/11/Mapping-Social-Cohesion-2019-FINAL-3.pdf

https://scanlonfoundation.org.au/wp-content/uploads/2019/11/Mapping-Social-Cohesion-2019-FINAL-3.pdf

https://www.humanservices.gov.au/individuals/subjects/whats-covered-medicare/about-medicare

https://healthywa.health.wa.gov.au/Articles/N_R/Overview-of-the-WA-health-system

https://ww2.health.wa.gov.au/About-us/Hospital-Information

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APPENDIX 1. PROJECT FEASIBILITY APPROVAL

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APPENDIX 2. ETHICAL APPROVAL

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APPENDIX 3. FINAL PROJECT APPROVAL

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APPENDIX 4. UWA NOTIFICATION OF ETHICS APPROVAL FROM ANOTHER ETHICS

COMMITTEE

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APPENDIX 5. PUBLISHED PAPER 1 (CHAPTER 4)

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APPENDIX 6. PAPER 1- ONLINE APPENDIX

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APPENDIX 8. PAPER 2- SUPPORTING INFORMATION

Health system in Western Australia

Australia’s universal health care system is called Medicare. The Australian government pays for

Medicare through the Medicare levy, which is paid by working Australians as part of their income

tax.340 Western Australia’s health system (WA Health) is a mix of services provided by the

Australian and State Governments and private healthcare providers. There are three types of

hospitals: public hospitals managed by WA Health, private hospitals managed by private

organisations, and public hospitals run in partnership with private organisations. An individual can

be admitted as either a public or private patient to a public hospital. Private patients in public

hospitals may choose their treating doctor where possible, while a private patient in a private

hospital can choose their doctor, hospital and when to be treated.341

WA Health has more than 80 hospitals spread across an area of 2.5 million square kilometres,

providing service to approximately 2.5 million people. There are six tertiary hospitals including the

sole tertiary perinatal centre with state-of-the-art medical facilities, four general and two

specialised non-teaching hospitals, nine large rural and numerous country hospitals, and three

Private Public Partnership hospitals delivering free public healthcare to the community.342

The HMDC is one of the largest data collections managed by WA Health and contains information

related to all inpatient discharge summary data from all public and private hospitals in WA.245 The

HMDC is a key information source for meeting mandatory and statutory reporting requirements. It

comprises more than 22 000 000 electronic inpatient records dating back to 1970 and increases

every year in line with population growth.245 Figure 1 shows the number and the distribution of

hospitals across WA.246

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Figure 1 The distribution of hospitals (public and private) in WA.


Remoteness in Western Australia

The Australian Statistical Geography Standard defines Remoteness Areas into five classes of

remoteness: Major Cities of Australia, Inner Regional Australia, Outer Regional Australia, Remote

Australia, and Very Remote Australia.222 This classification is intended to allow users to make

comparisons and undertake statistical analysis to inform research and policy development. The

Accessibility and Remoteness Index of Australia (ARIA), produced by the Hugo Centre for

Migration and Population Research at the University of Adelaide, is used to measure access to

services.222 This index is derived by measuring the road distance from a point to the nearest urban

centres and localities in five separate population ranges of highly accessible, accessible,

moderately accessible, remote and very remote. Some of the most remote areas in Australia,

albeit with low population density,234 are situated in WA.

Aeromedical transfer of high-risk pregnancies in WA

Complicated births to rural mothers are at a higher risk of stillbirth and neonatal mortality than

urban infants in Australia.25,76 To improve perinatal outcomes of WA rural population, women with

high-risk pregnancies, such as those at risk of preterm labour, have been transferred by Royal

Flying Doctor Service from rural areas to the sole tertiary perinatal centre, King Edward Memorial



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Hospital (KEMH), for decades.25,269,270 Figure 2 shows WA geographic regions and the numbers of

transfers from each region from September 2007 to 31 December 2009 based on a published

article.269 Please note the distance from Perth in which KEMH is located.

Figure 2 Number of transfers from each geographic region of Western Australia (September

2007 to 31 December 2009).




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APPENDIX 10. MIDWIVES NOTIFICATIONS DATA APPLICATION VARIABLE LISTS

Midwives Notifications Data

Every request for Midwives Notifications will be evaluated separately on its merit by the Data

Custodian. To prevent potential delays, it is strongly recommended applicants spend time

discussing their needs with the MNS Data Custodian before submitting an application for data. See

the contacts at:

http://www.health.wa.gov.au/healthdata/contact/index.cfm

Further information can be found in the Guidelines for Completion of the Notification of Case

Attended Health Act (Notification by Midwife) Regulations form No.2 at

http://www.health.wa.gov.au/publications/subject_index/p/Perinatal_infant_maternal.cfm

________________________________________________________________________

Request the variables you require below by clicking on the box on the left.

Request Variable Description

Mother’s Details

Subset date of birth MMYYYY

Subset date of birth YYYY

Maternal age

State

Height Mothers height in centimeters

Weight Available January 2012 onwards

Marital status


http://www.health.wa.gov.au/publications/subject_index/p/Perinatal_infant_maternal.cfm

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Ethnic origin

The quality of this variable is improved by also

sourcing this from the inpatient birth record from

HMDC where available

Pregnancy Details

Previous pregnancies

Previous pregnancy outcomes Each baby recorded separately in multiple births.

Therefore this ≠ total previous pregnancies

Previous caesarean section

Number previous caesarean

sections Available January 2012 onwards

Caesarean last delivery

Previous multiple birth

Is the LMP date certain?

Basis of expected due date

Gestational Age at First AN Care

Visit

Available January 2010 onwards. Use with caution

as antenatal care models in WA make accurate

determination difficult.

Number of AN Visits

Available from July 2012 onwards. Use with

caution as antenatal care models in WA make

accurate determination difficult.

Smoking during pregnancy Yes/no

Number tobacco cigarettes

smoked each day in the first 20

weeks of pregnancy

Available January 2010 onwards

Number tobacco cigarettes

smoked each day after the first 20

weeks of pregnancy

Available January 2010 onwards

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Complications of pregnancy Tick box value supplied, not ICD code

Medical conditions Tick box value supplied, not ICD code

Procedures/treatments

Intended place of birth at onset of

labour

Labour Details

Onset of labour Method (e.g. induced)

Augmentation

Induction

Analgesia (during labour)

Delivery Details

Duration of labour 1st stage

Duration of labour 2nd stage

Anaesthesia (during delivery)

Complications of labour and

delivery Tick box value supplied, not ICD code

Perineal status

Baby Details

Indigenous Status Available January 2011 onwards

Born before arrival Yes/No



Plurality Number of babies in this birth

Baby number Order in delivery

Presentation Position (e.g. breech)

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Method of birth

Accoucheur(s) Person who delivered the baby

Gender

Status of baby at birth Alive/stillborn

Infant weight

Length of baby (cms)

Head circumference

Time to establish unassisted

regular breathing Recorded in minutes

Resuscitation Method used

Apgar score at 1 minute

Apgar score at 5 minutes

Estimated gestation Clinical estimation in weeks, available 1986

onwards

Baby separation date MMYYYY only

Baby length of stay in days Derived variable

Mode of separation E.g. transferred, went home

Number of Days in Special Care

Nursery at birth site

Other variables

The following variables are derived using the algorithms developed by Dr Eve Blair et al.

References:

(1) Blair, E.M., Liu, Y., de Klerk, N.H. & Lawrence, D.M. (2005) Optimal fetal growth for the Caucasian

singleton and assessment of appropriateness of fetal growth: an analysis of a total population

perinatal database. BMC Pediatrics, 5, 13-25.

(2) Blair, E.M., Liu, Y. & Cosgrove, P. (2004) Choosing the best estimate of gestational age from

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routinely collected population-based perinatal data. Paediatric and Perinatal Epidemiology, 18,

270-276.

POBW Percentage Optimal Birth Weight

POHC Percentage Optimal Head Circumference

POL Percentage Optimal Length

Estimate of Gestational Age Algorithmic Estimate of Gestational Age

Based on LMP, EDD, baby date of birth

Geocoding

Postcode

SEIFA Socioeconomic status

ARIA Accessibility/Remoteness Index

Local Government Area (ABS)

Statistical Local Area (ABS)

Radius (ABS) Should be requested with SEIFA & ARIA

Geocoding Information:

Collectors District is not usually supplied on data extracts. However the Data Linkage Branch offers

the service of assigning 1996, 2001 and 2006 Census SEIFA and/or ARIA codes to post-1993

records at CD and SLA level. Should you require CD on your extract for another purpose please

request this and provide written justification in the Sensitive Variables section of this document.

________________________________________________________________________

Sensitive Variables

All of the variables below have been determined as sensitive by the MNS Data Custodian and

therefore require written justification. Please provide this in the space in the table below. Items in

bold require DOHWA HREC approval.

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Full date of birth of mother DDMMYYYY

Requires DOHWA HREC approval

Enter justification here

Full date of birth of baby DDMMYYYY


Expected due date MMYYYY only


Collectors District (ABS) Refer to geocoding information above


Date of last menstrual period DDMMYYYY

Be careful with the reliability of this variable


Baby transferred to

May require approvals from the Chief Executives of

the Area Health Services and/or private hospitals.

If requesting this variable, also request ‘mode of

separation’.


Comments:

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APPENDIX 11. BIRTH DATA APPLICATION VARIABLE LIST

Birth Data

Every request for Birth data will be evaluated separately on its merit by the Data Custodian. To

prevent potential delays it is strongly recommended applicants spend time discussing their needs

with the Data Custodian, Diana Rosman ([email protected]) before submitting an

application for data.



Child’s details

Birth registration year Year event registered not necessarily same as year

of birth

Sex



ATI status 2007 onwards

Birth weight

Born alive

Plurality

Gestation period

Born in hospital Flag derived by DLU which indicates if the child was

born in hospital

Place of birth state

mailto:[email protected]

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Place of birth country

Mother’s details

Occupation

ATI status Not available on records prior to 1992

Place of birth

Age 1992 onwards

Year mother arrived in Australia 2002 onwards

Father’s details

Occupation

ATI status Not available on records prior to 1992

Place of birth

Age 1992 onwards

Year father arrived in Australia 2002 onwards

Other

Date of marriage Year only

Sensitive Variables

All of the variables below have been determined as sensitive by the Data Custodian and therefore

require written justification. Please provide this in the space in the table below.

Items in bold require DOHWA HREC approval.


Baby’s Full date of birth DDMMYYYY


Place of birth postcode Requires DOHWA HREC approval

May require approvals from Area Health Services.

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279

Please note that this is most often the postcode of

a hospital, not the residential postcode.


Place of birth hospital


May require approvals from Area Health Services.

Numeric field


Informant’s postcode


Please note that the parent is not always the

informant.


Comments:

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APPENDIX 12. MORTALITY DATA APPLICATION VARIABLE LIST

Mortality Data

Every request for Mortality data will be evaluated separately on its merit by the Data Custodian.

To prevent potential delays it is strongly recommended applicants spend time discussing their

needs with the Mortality Data Custodian, Diana Rosman ([email protected]) before

submitting an application for data.

It is imperative applicants have referred to the online summary of Mortality data fields before

requesting data. See this document online at:

http://www.datalinkage-wa.org.au/downloads/data-collections



Registration year Year of the record

Sex

Died in hospital flag Flag derived by DLU



Age of the person

When requesting Age of the person, also request

Age text (below)

Age text Context in which the age can be quantified

ATI status Aboriginal or Torres Strait Islander descent

ATSI status (doctor) Aboriginal or Torres Strait Islander descent as

indicated by doctor



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Post mortem Whether a post mortem was/was not/is yet to be

carried out

Marital status

Date of death 1

YYYYMMDD

When requesting Date of death 1, also request

Date of death 2 and Date of death code (below)

Date of death 2 1983 onwards

Date of death code

Occupation

Occupation text 1984 onwards

Main task 2002 onwards

Occupation of the father of the

deceased

Occupation of the mother of the

deceased

Country of birth 2002 onwards

Born overseas flag 2002 onwards

Time resident in Australia (years

and/or months) 2002 onwards

Total time residency in

Australian states 1984 - 2001

Time of occupancy in Western

Australia 1984 - 2001

State unknown State of residence unknown

State of residence Up to 8 fields

1984 - 2001 Period of occupancy in state

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Deceased pregnant within 6

weeks of death These variables can be unreliable.

Deceased pregnant between 6

weeks and 12 months of death

ABS Variables

Unless otherwise stated, ABS coded variables are available from 1969 – 2011.

Aboriginal flag When requesting Aboriginal flag, also request

Registration year (top)

Post mortem code 1999 - 2006

Occupation code

Country of Birth code

Cause of death code

Multiple cause of death codes

Entity Axis data ICD codes as they appear on medical certificate

1997 to 2011.

Record Axis data

ICD codes as they appear on medical certificate-

cleaned data 1997 to 2011.

When requesting these also required is Cause of

Death code (ABS).

Multi cause of death format

type

Added by DLB. Indicates which format the multiple

cases of death data is in.

Place of occurrence code 1999-2002. Set for deaths due to or involving

external causes W00-Y34.

Activity code 1999-2002. Set for deaths due to external causes

V01-Y34.

Type of firearm used 1999-2002

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Geocoding

Postcode



Radius (ABS) Should always be requested when SEIFA, ARIA, LGA

&/or SLA requested.








Sensitive Variables


require written justification. Please provide this in the space in the table below.

All Items require DOHWA HREC approval.


Full date of birth DDMMYYYY


Cause of Death text

May be provided where coded COD is not

available. Contains multiple fields- see data

dictionary for details.

COD text is required where ICD coding for cause is not available.

Place of death suburb 1984 on

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Not necessarily a residential address - may be the

suburb of a hospital.


Place of death postcode Not necessarily a residential address - may be the

postcode of a hospital.


Place of death hospital

This variable can be unreliable and will require

approvals from the CEOs of the Area Health

Services.




Place of birth text

This variable can be unreliable and will require

approvals from the CEOs of the Area Health

Services.


Comments:

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APPENDIX 13. HOSPITAL MORBIDITY DATA APPLICATION VARIABLE LIST

Hospital Morbidity Data

Every request for inpatient data will be evaluated separately on its merit by the HMDC Data

Custodian. To prevent potential delays it is strongly recommended applicants spend time

discussing their needs with the HMDC Data Custodian before submitting an application for data.

See the contacts at:


It is imperative applicants have referred to the online summary of HMDC data fields of the HMDC

reference manual before requesting data. These documents record the changes in HMDC data

collection practices/protocols over time and detail the HMDC variables. See these documents

online at:


For all requests, unless requested otherwise, typical exclusions include unqualified (healthy)

newborns, boarders, organ procurements, aged care residents, funding (duplicate) hospital cases

and residential aged care facilities.

Please note: Certain combinations of variables are potentially identifying and release of these

combinations will be at the discretion of the HMDC Data Custodian.



Patient Information

Admission age Age in years





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Sex

Indigenous status

Marital status

Be cautious about the reliability of these variables.

When selecting Interpreter Service, also select

Language

Employment status

Interpreter service

Language

Country/State of birth

Health Region of Hospital

OR (request only one)

Hospital Category

The 9 WA health regions (North & South Metro,

Goldfields, South-West, Great Southern,

Wheatbelt, Midwest, Pilbara, Kimberley).

Tertiary, public metro, private metro and rural

Subset admission date MMYYYY

Subset separation date MMYYYY

Length of stay Excludes days on leave

≠ Separation date – admission date.

Source of referral- location

Only available on data after July 2000 Source of referral- professional

Source of referral- transport

Source of referral Available on data prior to July 2000

Admission status Waitlist, non-waitlist or emergency. Variable is

unreliable from 1986-1989 and 1996-1998.

Infant weight/neonate

Funding source AHCA, private insurance, self funded, worker’s

compensation etc

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Insurance status

Care type Acute, rehab, palliative etc

Total leave days

Number of leave periods

Days of psychiatric care

Days of qualified newborn care

Days of Hospital in the Home

care

Days in ICU

Hours CVS

Mode of separation Transferred to another hospital, deceased,

discharged etc

Diagnosis Codes

Principal diagnosis

ICD code Co-diagnosis

Additional diagnoses

Procedure Codes

Principal procedure ICD code

Additional procedures

E-Codes

External cause of injury ICD code

Activity code

Place of occurrence

Geocoding

Postcode

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Radius (ABS) Request this if you need SEIFA






Please note: Geocoding for HMDC data commenced in 1993. Prior to 1993 full address details

were not recorded in HMDC records.

Other Variables

All of the variables below require written justification. Please provide this in the space in the table

below. Items in bold require DOHWA HREC approval.

Hospital Establishment codes are considered identifying information, especially if they involve

private establishments. Provision of these codes requires DOHWA HREC approval and/or hospital

ethics approval. Approval from the Chief Executive of the area health service may also be required.


Full date of birth DDMMYYYY



Age in days at admission




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Admission date DDMMYYYY

Separation date DDMMYYYY

Principal procedure date DDMMYYYY


Additional procedure dates DDMMYYYY

Can be incomplete


Mental health legal status


DRG Diagnostic Related Group

Available July 1993 onwards

MDC Major Diagnostic Category

Available July 1993 onwards


Patient Identifier (UMRN) Requires DOHWA HREC and AHS CE approval


Hospital Identifier Requires DOHWA HREC and AHS CE approval


Comments:

Enter any extra comments here

APPENDICES

PAGE | 290

APPENDIX 14. WARDA BIRTH DEFECTS DATA APPLICATION VARIABLE LIST

WARDA Birth Defects Data

Every request for data from the WA Register of Developmental Anomalies will be evaluated

separately on its merits by the WARDA Data Custodian.

Before approval to obtain WARDA data will be given:

Researchers must discuss their project and their data needs with the WARDA Data

Custodian; and

Researchers must also provide a separate lay summary (~200 words) of their project to the

Data Custodian for consideration by the WARDA Consumer Reference Group.

The WARDA Data Custodians are:

Professor Carol Bower; phone: (08) 9340 2721; email: [email protected]

Dr Hugh Dawkins, phone (08) 9222 6888; email [email protected]

*Two of the variables below relate to tables in the Report of the Birth Defects Registry of Western

Australia 1980-2007. This document and other information may be found at:

http://kemh.health.wa.gov.au/services/register_developmental_anomalies/

Information on British Paediatric Association Coding used for WARDA can be found at

http://kemh.health.wa.gov.au/services/register_developmental_anomalies/diagnostic_codes_birt

h_defects.htm

Please note that all requests for WARDA data require DOHWA HREC approval.




http://kemh.health.wa.gov.au/services/register_developmental_anomalies/

http://kemh.health.wa.gov.au/services/register_developmental_anomalies/diagnostic_codes_birth_defects.htm

http://kemh.health.wa.gov.au/services/register_developmental_anomalies/diagnostic_codes_birth_defects.htm

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PAGE | 291


Child’s details



Sex

Weight

Race

Plurality

Birth Outcome Livebirth, stillbirth, termination of pregnancy

Date of Death Days at death

Multcode Whether birth defect is isolated or multiple

Gestational age

Mother’s details



Father’s details



Diagnosis Codes (1-10)

Diagnosis code ICD-9 coding. Britsh Paediatric Association.

ARDiagnosis Code ICD diagnosis coded to BDR Categories (Table 4*).

Diagnosis Description May contain minor variations within Diagnosis

code

Is Major Major or minor congenital malformation

Diagnosis time Coded time of diagnosis

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Notification codes (1-10)

ARNotification Code Coded source of notification. Coded to Annual

Report (Table 6*) categories.

Sensitive Variables


require written justification. Please provide this in the space in the table below. All items require

DOHWA HREC approval.


Baby’s Full date of birth DDMMYYYY

Mother’s Full date of birth DDMMYYYY


Father’s Full date of birth DDMMYYYY


Birth hospital


Postcode Postcode of mother at time of birth/first

notification


Comments:

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PAGE | 293

APPENDIX 15. FAMILY CONNECTIONS APPLICATION FORM

Family Connections

The WA Family Connections System contains links between individuals who are related, i.e.

biologically (by birth) or legally (such as by marriage). The links are created using information

recorded on original birth, death and marriage registrations as well as other sources. No

information is known about adoptions, including step, local or overseas adoptions.

Please note: Currently, the genealogy held by the Data Linkage Branch is primarily limited to

parents and siblings of people born in WA since 1974. However, extended family members

(including grandparents and other second-degree relatives) have been identified in many cases.

Genealogical data received from this application may differ from actual data collected from

families via questionnaires or other methods. Family structures may include individuals who have

been adopted (in or out), step-relatives, or recent deaths. It is important that any such

discrepancies should be treated with respect and sensitivity. If, though fieldwork, you learn that a

subject has been adopted, you will not be able to obtain additional linked data for that individual’s

biological relatives.

Family Relationships

Please provide a description of the family relationships you require.

E.g. Mother, father, grandparents OR first-degree relatives only

Do you have any restrictions on the information?

E.g. only full siblings needed, not half siblings

For Office Use Only

EOI# 2014.38

DL#

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Comments

Service data extraction

Please specify the datasets you require, the time period and any restrictions on which records you

need.

WA Cancer Registry: Please refer to the variable list for record scope. If you require specific

exclusions please list them in the comments section of that form.

Midwives Notification System: Please specify below whether you need records for the birth of a

person, records where they are the parent or both.

*Attach variable lists (Module 3) and specify which family group they relate to.

Restrictions

E.g. I am only interested in

records for colorectal cancer;

see attached ICD codes.

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