TITLE

The prevalence of gestational diabetes mellitus (GDM) among Aboriginal and Torres

Strait Islander women in Australia: a systematic review and meta-analysis

Short title: GDM prevalence among Indigenous women in Australia: meta-analysis

*Ms Catherine Chamberlain

Global Health and Society Unit

Department of Epidemiology and Preventive Medicine

Monash University

Melbourne. Vic. AUSTRALIA

Ph: +61 (0) 3 99030021

Email: Catherine.chamberlain@monash.edu

Dr Grace Joshy

National Centre for Epidemiology and Population Health

Australian National University

Canberra. ACT. AUSTRALIA.

Ph: +61 (0) 2 61250715

Email: grace.joshy@anu.edu.au

Dr Hang Li

Institute of Chronic Disease Control

Beijing Centers for Disease Control and Prevention

Beijing 100013. P.R. CHINA

Ph: +86 (10) 64407377

Email: Kuan_2001@hotmail.com

Professor Jeremy Oats

Melbourne School of Population and Global Health

University of Melbourne

Melbourne. Vic. AUSTRALIA.

Ph: +61 (0) 407 685532

Email: Jeremy.oats@thewomens.org.au

Professor Sandra Eades

Baker IDI Heart and Diabetes Institute

Level 2, 10 Quay St

Sydney. NSW. AUSTRALIA. 2000

Ph: +61 (0) 2 9514 5950

Email: sandra.eades@bakeridi.edu.au

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/dmrr.2570

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Professor Emily Banks

National Centre for Epidemiology and Population Health

Australian National University

Canberra. ACT. AUSTRALIA

Ph: +61 (0) 2 61250328.

Emily.Banks@anu.edu.au

*corresponding author

Key words: Indigenous, Aboriginal, gestational diabetes, pregnancy, diabetes, prevalence

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ABSTRACT

Introduction

Gestational diabetes mellitus (GDM) is an important and increasing health problem. This

study aims to investigate and explain the marked variation in reported GDM prevalence

among Australian Indigenous women.

Materials and methods

We searched five databases to August 2013 for studies of GDM prevalence; two people

independently assessed search results, extracted data, and appraised risk of bias. Meta-

analysis was conducted, and between-study heterogeneity examined using subgroup analyses.

Within-study findings were synthesized narratively.

Results

The pooled GDM prevalence from 23 of the 25 total studies (5.74%, 4.78-6.71) was similar

to that reported in national studies, but heterogeneity was substantial (I2=97%), making

conclusions from between study comparisons difficult. The greatest reductions in

heterogeneity were seen within subgroups using localized diagnostic criteria (I2=43%, 3

studies), universal screening (I2=58%) and some jurisdictions, probably reflecting proxy

measures of increased consistency in diagnostic and screening methods. Insufficient data

were available to assess the effect of factors such as rurality, diagnostic criteria, study design,

and datasources on prevalence. Synthesis of within-study findings showed: higher age-

adjusted prevalences of GDM in Indigenous versus non-Indigenous women; Indigenous

women have greater increases in prevalence with maternal age; and non-Indigenous appear to

have a steeper increase in GDM prevalence over time. Prevalence increased almost 4-fold in

two studies following introduction of universal screening when compared to selective risk-

based screening, although numbers were small.

Discussion/Conclusions

The published GDM prevalence among Indigenous women varies markedly, probably due to

variation in diagnostic and screening practices.

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*The term ‘Indigenous’ is used when referring to Indigenous populations internationally, and

where Aboriginal and Torres Strait Islander people are reported as a combined group. The

terms ‘Aboriginal’ and ‘Torres Strait Islander’ are used where the reports refer to these

distinct populations. This is for ease of reading in this paper only and we respectfully

acknowledge the diversity and autonomy of different communities included in the broad

terms.

INTRODUCTION

Gestational diabetes mellitus (GDM), broadly defined as diabetes diagnosed for the first time

during pregnancy [1], is increasing in prevalence [2, 3], with the highest prevalences reported

among Indigenous populations [4]. GDM causes serious complications in pregnancy and

birth [5,6], and identifies women at high risk of developing type 2 diabetes (T2DM) [7].

Importantly, exposure to diabetes in-utero increases the risk of diabetes among offspring,

compounding the diabetes epidemic Indigenous populations worldwide, due to higher

prevalences of diabetes disorders among women of childbearing age [ 8]. Within Australia,

Indigenous (Aboriginal and Torres Strait Islander) people experience much higher rates of

diabetes [9], including during pregnancy [10] , compared to non-Indigenous Australians.

GDM is strongly associated with obesity and lifestyle [11], but the root causes are related to

socio-economic disadvantage [12]. Growing evidence of the risks of GDM [6] has led to a

review of the existing international [13] and national [14] guidelines for GDM screening in

pregnancy . The main revisions include: offering screening in early pregnancy for women at high

risk of T2DM, in addition to 24-28 weeks as is currently recommended; identifying ‘probable’

undiagnosed T2DM; and changing GDM diagnostic thresholds [14]. These changes have

particular implications for Indigenous women who are categorised as ‘high risk’ of T2DM

[14]. While GDM screening guidelines have been available since 1991 [15], there has been

enormous variation in GDM screening practice and diagnostic criteria throughout Australia

[16].

While the early detection of glucose intolerance in pregnancy offers a unique ‘window of

opportunity’ [17] for public health interventions, there are essential criteria to be considered

when introducing population based screening [18,19], to ensure the intended benefits are

realised and outweigh identified risks. There is currently limited evidence demonstrating

these criteria are met for GDM screening [20], particularly for Indigenous women [21]. The

first of these criteria stipulates that the there is a ‘clear understanding of the prevalence and

natural history of the condition being screened for’ [18, 19].

There is marked variation in the prevalence of GDM in published reports with data on

Indigenous, and the reason for this is unclear. To our knowledge there is no quantitative

review and investigation of heterogeneity in published GDM prevalence rates among

Indigenous women in Australia, despite this lack of understanding, and the exploration of

heterogeneity being identified as a critical factor in reviews of observational studies [22-25].

The recent introduction of revised GDM screening guidelines in Australia [14] are likely to

significantly increase the current reported prevalence of GDM [26], and therefore a

systematic review of the current prevalence of GDM among Indigenous Australian women is

timely.

In this paper we aim to review the published evidence on the prevalence of GDM in

Indigenous women and explore any observed heterogeneity in the reported prevalence rates

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of GDM among Indigenous women, using different sources of data. We postulate that the

variability may be due to a number of factors, including: differences in identification of

Indigenous status [27]; risk of study bias (e.g. age-adjustment); the population location (state,

or remote/urban communities); whether the data source is hospital/clinic, community or

population-based [2, 28]; years of data collection; study designs (retrospective designs

generate lower rates than prospective designs) and data collection methods [2]; screening

practices (selective vs universal), and different definitions used for diagnosing diabetes and

identifying diabetes [16, 29].

MATERIALS AND METHODS

We used the ‘meta-analysis of observational studies in epidemiology (MOOSE)’ consensus

statement to guide this review [24].

Criteria for selecting studies for this review:

Types of studies

We included any original publication from which the number of Indigenous women in

Australia giving birth, and the number of women diagnosed with GDM, pre-existing T2DM,

or any unspecified type of diabetes in pregnancy (DIP) could be extracted. This includes

observational studies, intervention studies or primary data not reported elsewhere. We

excluded secondary publications which cited prevalence rates reported elsewhere.

Types of participants

Indigenous women, and where available, comparison data for non-Indigenous women who

gave birth in Australia, in rural/remote or urban settings

Types of outcome measures

Primary outcomes: The prevalence of GDM as a proportion of the number of births among

Indigenous women. This was extracted as crude numbers of women with GDM or DIP and

number of births, aggregated by Indigenous status. Crude numbers were converted from

ratios and reported percentage prevalence where possible.

Secondary outcomes: Potential confounding factors were extracted to investigate their

effects on reported GDM prevalence, including: Indigenous status; mean age or age-

adjustment; state; rurality of population (mixed, urban, rural/remote); data source

(population, community or clinic based); year of data collection (or publication year); study

design; GDM or DIP measurement (diagnostic and screening) criteria used; and risk of bias

of study results.

Search methods for identification of studies:

To identify studies reporting prevalence of GDM or DIP among Indigenous women in

Australia we used three strategies:

First, we conducted a search of the electronic literature from inception until 6 August 2013

using the electronic databases: Medline (1950-August 2013), Embase (1949-August 2013),

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CINAHL (1937-August 2013), PsychINFO (1905-August 2013), and Informit (1980 to

August 2013). We used multiple free text and MeSH headings to search the following

summary terms in any publication related to hyperglycaemia in pregnant women or infants

among Aboriginal and/or Torres Strait Islander women: Pregnan* or antenatal or prenatal,

or neonatal or newborn or infan* or fetal or fetus or foetal or foetus; AND Diabet*or

Hyperglyc?mi* or glucose intoleran* or obes* (or Gestational diabet*); AND Aborigin* or

Indigen* or native* or first nation or Torres Strait Island*. We extracted this search from a

previous systematic review search [21], where the full search strategy is detailed

(Supplementary material 1). We did not apply any language or date limitations.

Second, we searched a number of websites including Healthinfonet, the Australian Institute

of Health and Welfare, National Perinatal Data Collection Unit, and Lowitja, for any

publications related to diabetes in pregnancy.

Third, we reviewed the reference lists of reviews of both diabetes and pregnancy outcomes

for any studies which may include rates of DIP or GDM among Indigenous women as a

‘confounding factor’. Authors were contacted if the paper looked as though they had

collected data on DIP or GDM prevalence, but had not reported in a format which enabled

extraction of the above details.

Data collection and analysis

Selection of studies

Search results from all electronic databases were downloaded into Endnote X5© for de-

duplication, and all abstracts were reviewed by two people (CC/HL) to determine if

publications meet the criteria for inclusion in this review. Where there was any ambiguity

about whether the study might meet the inclusion criteria from the abstract, the reference was

included in the second stage for full text review. The full text of all publications potentially

meeting the inclusion criteria was reviewed by two authors (CC/HL/JO) to determine those

that were included in this review. A list of all publications not meeting the inclusion criteria

in full text review is available on request (Supplementary material 2).

Data extraction and management

To increase the accuracy of coding and data entry, critical appraisal and data extraction for

each study included in this review was coded independently by two authors (CC/HL) using a

standardized data extraction tool. Decisions about final coding were made by consensus or

discussion with a third author (GJ) where necessary.

Assessment of risk of bias

The risk for bias was critically appraised independently by two authors using pre-specified

criteria in a tool adapted from Strengthening the Reporting of Observational Studies in

Epidemiology (STROBE) [30], a systematic review including descriptive studies [31], and a

tool used by clinicians for rapid evidence appraisal [32]. The tool was piloted using a sample

of over 20 studies and modified (Supplementary material 3).

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The reasons for moderate and high risk of bias appraisal scoring for each study are

summarized in Table 1 (characteristics of included studies).

Unit of analysis issues

Where multiple reports potentially contained data from the same participants (e.g. used the

same data source at the same time), the most complete and detailed data were selected, as has

been used in other reviews [33]. Two national studies were reported in separate tables for

comparison and not combined with other studies to avoid risk of counting the same women

twice in state studies (table 1). Where data crossed more than three years but were reported

for individual years, the earliest, midpoint, and last year are reported in the characteristics of

included studies table and crude prevalence (figure 2), and the study periods were combined

into three periods for meta-analysis. Where studies reported prevalence rates at two or more

time-points, these were coded as separate sub-studies.

Dealing with missing data

Where studies did not report the study year, the submission date or year of publication was

used. Few studies reported screening or diagnostic criteria, therefore studies were coded as

(1) unclear (2) selective risk-based screening or <80% screening, and (3) universal screening

or >=80% screening rates.

Assessment of heterogeneity

I2

was used to assess statistical heterogeneity between studies. We anticipated considerable

heterogeneity, due to the variability of study designs, diagnostic criteria and identification of

GDM (I2>75%) [34]. Where I

2 was >75% the results were not pooled.

Data synthesis

The point prevalence of GDM was reported as a dichotomous outcome measure (%) for

Indigenous women, as the overall proportion of pregnancies, with binomial 95% confidence

intervals generated in STATA 11.0. Data were combined in subgroups, as generic inverse

variance (GIV) in Revman 5.2.5. A random effects model was used as this is the most

conservative measure.

Given the strong association of increased maternal age and GDM risk, and the significantly

younger mean maternal age among Indigenous women [35], only age-adjusted comparisons

between Indigenous and non-Indigenous women are appropriate. However only one study

reported age adjusted prevalence rates in this review, therefore we have reported crude

prevalence and omitted any comparisons between Indigenous and non-Indigenous women in

meta-analysis due to inability to account for the likely differences in age profiles between the

groups. Therefore, a narrative synthesis was used to describe the differences in GDM

prevalence by Indigenous status.

Subgroup analysis and heterogeneity

Standard subgroup analyses methods were employed to assess the possible source of

apparent heterogeneity in reported rates. It should be noted the comparisons between

subgroups are observational and no statistical inferences of difference can be made. The

variables were identified a priori [36], and included analyses of heterogeneity in GDM

prevalence according to:

Aboriginal, Torres Strait Islander, and Indigenous status;

State/territory;

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rurality of population (mixed, urban, rural/remote);

data source (population, community or clinic based);

midpoint year of data collection (1980-89; 1990-99; 2000-09).

Where there were insufficient studies to conduct meta-analysis, a narrative synthesis on the

characteristics was made, including:

mean age or age-adjustment;

study design (retrospective; prospective);

screening criteria used (selective or universal); and

diagnostic criteria.

Sensitivity analysis and assessment of risk of bias

The effect of risk of bias on prevalence was explored by conducting subgroup analyses with

trials at low risk of selection bias, outcome assessment bias, and confounding.

Ethics

This research has been approved by the Monash University Human Research Ethics

Committee (CF11/0554 – 2011000234).

RESULTS

Characteristics of included studies:

Abstracts of 565 studies were scanned and the full texts of 80 studies were examined.

Twenty-six studies meeting the inclusion criteria were identified. One study was excluded as

it reported only 12 births among Indigenous women, none of whom had diabetes in

pregnancy [37] (figure 1). Twenty-five included studies provided GDM prevalence data

among over 140,000 Indigenous women and over 3,800,000 non-Indigenous women giving

birth (see table 1).

Twenty-three studies provided data on the prevalence of GDM among both Aboriginal and

Torres Strait Islander (Indigenous) women combined, one study reported prevalence among

Torres Strait Islander women only [38] and one study reported prevalence separately among

Aboriginal and among Torres Strait Islander women [39]. Studies reported data collected

from as early as 1980 [40] until 2009 [41]. Included studies provided GDM data on women

giving birth in the Northern Territory (NT) (n=5), Queensland (Qld) (n=5), Western Australia

(WA) (n=4), South Australia (SA) (n=3), Victoria (Vic) (n=3), New South Wales (NSW)

(n=3) and nationally (n=2). The most common source of data was the local Midwives

Perinatal Data Collection (MPDC) (n=14) which was occasionally linked to other sources

[42-44] or validated by medical records [45, 46]; other studies used data from hospital

databases (n=5), medical records (n=4), or a combination of hospital databases and medical

records (n=1); and one national report used the National Hospital Morbidity Database

(NHMD) linked with the National Diabetes Surveillance System (NDSS) in addition to the

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MPDC [47]. Only one community-based study used a prospective study design [48] and the

remainder (n=24) used a retrospective study design.

GDM data from 14 studies (23 timepoints or ‘sub-studies’) were included in meta-analysis.

Two studies reporting national data and nine smaller overlapping studies were not pooled in

meta-analyses to avoid counting the same women twice. One study reported prevalence

rates, but not denominators, so could not be included in meta-analysis [49]. T2DM data

from eight studies (13 sub-studies); and ‘Any DIP’ data from two studies were also included.

Risk of bias:

Overall, one study met all the criteria for low risk of bias [50]; 20 studies had at least one

criterion assessed as moderate risk of bias; and four studies had at least one criterion assessed

as high risk of bias.

Most (23/25) studies were appraised as having a low risk of selection bias as all births were

included in the denominator, with one prospective study having a high risk of selection bias

with 45% participation [48], and one study was unclear [49].

There was a low risk of outcome assessment bias in seven studies (GDM diagnosis verified

by medical record review, diagnostic test results, or linked data); moderate risk of outcome

assessment bias for 17 studies which relied on recording in a single registry only; and a high

risk of outcome assessment bias in one study which did not report how prevalence was

obtained.

Age is a major confounder for GDM, therefore studies were assessed as ‘low risk’ of

confounding if prevalence rates were age adjusted (n=1) [51], age matched (n=1) [50], age-

stratified (n=1) [35], or reported comparative prevalence data (Incidence or Odds ratios)

which were age adjusted (n=4) [42, 43, 47, 52]; 15 studies where there was any references to

other confounders were assessed as moderate risk of bias; and three studies which did not

report any consideration of potential confounders were assessed as high risk of confounding.

Prevalence:

The pooled crude GDM prevalence among Indigenous women was 5.7% (4.8-6.7, I2=97%,

23 sub-studies), similar to 2005-8 national reported crude prevalence for GDM (5.1% among

Indigenous women vs 4.7 among non-Indigenous women in the MPDC [43]; and 4.8% vs

4.6% in the NHMD [47]). However, substantial heterogeneity precluded confidence that

these pooled results provide an appropriate summary. The marked variation in the reported

prevalence of GDM among Indigenous and non-Indigenous women ranged from as low as

1.3% (1.1-1.7) and 0.2% (0.18-0.23), respectively, from 1980-4 in WA [40]; to 18.5% (16.0-

21.2) and 12.0% (10.5-13.6) respectively in 1992-3 in Far North Qld [39] (figure 2).

Similarly high heterogeneity was seen in pooled prevalence of pre-existing T2DM in

pregnancy (1.0%, 0.95-1.1, I2=89%, 13 sub-studies), ranging from 0.5% (0.3-0.7) in WA in

1980-84 [40] to 4.6% (2.0-8.5) in Qld 2005-6 [38].

Substantial heterogeneity was also seen in pooled results of two studies in WA reporting ‘any

DIP’ (4.7%, 4.1-5.3, I2=99%), which ranged from 1.6% (1.2-2.1) in 1980-2 [53] to 12.2%

(10.5-14.1) in 1985-9 [54].

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Investigation of heterogeneity in studies in Indigenous women:

Indigenous status and Age:

Indigenous vs non-Indigenous women (narrative synthesis)

All studies which accounted for age reported significantly higher GDM prevalences among

Indigenous women, compared to non-Indigenous women. The single study reporting age-

adjusted prevalences [51] reported a crude GDM prevalence among Indigenous women in SA

(1988-99) of 4.3%, and an age-adjusted prevalence of 6.3%, compared to 2.4% among non-

Indigenous (not included in meta-analysis as Sharpe 2001 data covered longer time period).

A small Victorian study reported age-matched prevalences (1998-9) of 10.7% among

Indigenous women, compared to 4.5% among non-Indigenous women [50]. A third study

reported higher age-stratified prevalences among Indigenous women, compared to non-

Indigenous women in each age category (20-24 4.4vs 2.2%; 25-29 9.0 vs 3.2%; 30-34 12.1 vs

3.5%; 35-39 19.6 vs 6.2%; 40+ 23.08 vs 9.8%, respectively), except among women <20

years (2.7 vs 2.3%) [35]. National studies using data from 2005-8 reported age-standardised

Indigenous: non-Indigenous prevalence ratios of 1.5-1.6 for GDM [43, 47], and 3.2-10.4 for

T2DM [43]. Two studies reporting data from Victoria reported an age-adjusted incidence

ratio of 2.5 in 1996 [42] and an odds ratio of 1.1 in 2007 [52].

Increased risk with age

The single study in this review reporting age-stratified rates among Indigenous women in the

NT in 1992-5 [35] showed a much steeper increase in GDM prevalence with age among

Indigenous women, compared to non-Indigenous women.

Aboriginal vs Torres Strait Islander women:

Two studies in Qld reported GDM prevalence among Torres Strait Islander women

specifically [38, 39]. The heterogeneity within and between subgroups remained too high

(>90%) to pool rates for Aboriginal, Torres Strait Islander, and both Aboriginal and Torres

Strait Islander women, but this is unlikely to explain heterogeneity as the numbers of Torres

Strait Islander women in the overall analyses were small (supplementary material 4a). One

study [39] compared prevalence between Aboriginal and Torres Strait Islander women, but

there was no significant difference observed. Any non-significant differences are likely to be

accounted for by the particularly high screening rates (90-99.5% in 1999) reported among

Torres Strait Islander women [38], compared to lower rates (31% in 2006) reported among

Aboriginal women in the same region [55]. This study [39] also reported decreasing GDM

prevalence over time which was at odds with all other studies in this review.

Jurisdiction:

Sub-group analyses by jurisdiction demonstrated the greatest reduction in heterogeneity, but

these remained high. The highest prevalences were reported in the NT (4 sub-studies; 6.2%,

5.1-7.4, I2=64%), followed by SA (one study; 4.4%, 4.0-4.9), Vic (3 studies; 4.4%, 3.9-4.8,

I2=0%), and NSW (4 sub-studies; 3.1%, 2.7-3.6, I

2=71%). The heterogeneity was too high to

present pooled results from eight studies in Queensland (I2=95%)

and two studies in WA

(I2=100%) (figure 3).

Rurality of population (mixed, urban, rural, remote):

Heterogeneity was too high to pool results from ten studies in rural/remote communities

(I2=94%), and 11 larger studies using mixed population data (MPDC) (I

2=98%).

Heterogeneity was low in two small studies among women living in urban areas (I2=0%), but

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these included a total of only four Indigenous women, highlighting the paucity of data

specific to urban Indigenous women; the data from urban areas are unlikely to be a

significant source of heterogeneity overall (figure 4).

Data source (population, community or clinic based):

The heterogeneity was too high to report pooled data from 14 studies using population

sources, such as the MPDC (I2=97%), and nine studies using clinic or hospital sources

(I2=95%) (supplementary material 4b). The single study using data from a community-based

source [48) was not included in meta-analysis as data overlapped with another study [55], but

reported a prevalence of 6.8% among 220 women participating in a community-based

prospective cohort study, which was not significantly different from studies within that

region included in meta-analyses.

Time trends and decade of midpoint year:

Illustration of crude GDM prevalence by study publication year suggests an increase over

time, particularly among non-Indigenous women (figure 2). However, heterogeneity was too

high to report pooled results by decade of midpoint year from 1990-9 among 13 studies

(I2=95%), and from 2000-9 among nine studies (I

2=96%) (supplementary material 4c).

Study design (narrative synthesis):

All studies included in meta-analysis used a retrospective study design to ascertain GDM

prevalence, therefore subgroup analysis was not conducted. The single study using a

prospective design [48], excluded from meta-analysis as it overlapped with another larger

study, reported a GDM prevalence of 6.8% among 220 Indigenous community-based study

participants in Far North Queensland, and T2DM prevalence of 3.6%.

Screening rates:

Few studies reported details about screening rates. The pooled GDM prevalence from four

sub-studies reporting universal screening [55] or >80% screening [38, 50] was 5.0% (2.5-

7.35, I2=43%). The heterogeneity was too high to pool results among 13 sub-studies with

unclear screening rates/practice (I2=98%), so it is difficult to determine whether this is a

significant source of heterogeneity from the between study comparisons (supplementary

material 4d).

However, two studies comparing prevalence rates before and after the introduction of

universal screening reported an almost four-fold increase in prevalence rates, suggesting

screening practice may be a significant factor impacting on the reported prevalence rates [49,

55]. Patel [49] reported an increase in GDM from 3% in 1985-87 using selective screening,

to 12% in 1989 following introduction of universal screening for Aboriginal women in

Central Australia. Similarly, among Indigenous women in Far North Queensland, Davis [55]

reported GDM prevalence of 4.7% in 2006 using selective screening (screening rates 31%),

and 14.2% in 2008 following inclusion of Indigenous status as a risk factor for screening, and

screening rates had more than doubled (66%).

Diagnostic criteria:

Only eight studies reported the diagnostic criteria used [38, 39, 44, 50-52, 55, 56].

Heterogeneity was too high to pool results from five sub-studies reporting using the

Australian Diabetes in Pregnancy Society criteria [15] (50g Oral Glucose Challenge Test

(OGCT) with 1hr BSL ≥7.8 mmol/L; then 75g Oral Glucose Tolerance Test (OGTT),with

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Fasting Plasma Glucose (FPG) ≥5.5 mmol/L or 2hr ≥8.0 mmol/L) (I2=89%) (supplementary

material 4e); and 12 sub-studies with unclear diagnostic criteria (I2=97%). Three sub-studies

which described diagnostic criteria of Random Blood Glucose Level (RBGL) at first visit, 18,

24, 28 and 34 weeks, then a 50g OGCT if over 5.5mmol/L and 75g OGCT if ≥7.8mmol/L at

2hr, reported greater consistency in pooled results (5.0%, 2.5-7.4, I2=43%), although all these

studies were conducted in the same regional area at similar timepoints. One population-

based study in South Australia made general references to changes in the guideline

recommendations during the reported study times [44]. .

Risk of bias

Outcome assessment bias

Heterogeneity remained too high to pool results for seven studies categorized as

having low risk of outcome assessment bias (GDM diagnosis verified by direct

measurement or medical record review) (I2=96%); or 16 studies categorized as having

moderate risk of outcome assessment bias (database registration) (I2=97%)

(supplementary material 4f).

Confounding bias

Three studies categorized as low risk of confounding (adjusted ratios for age or age-

matched), all in Victoria, reported pooled crude GDM prevalence rates of 4.4% (3.9-

4.8, I2=0%). However heterogeneity was too high (I

2=97%) to pool results for 19

studies categorized at moderate risk of confounding bias (supplementary material 4g).

Other factors:

Only one prospective study [48] reported associations or predictors of GDM, and found

obesity was the most significant predictor (p<0.0001), with an age-adjusted risk of 4% for

diabetes in pregnancy (p<0.0001).

Sensitivity analysis was conducted using these same subgroups for pre-existing T2DM and

‘Any DIP’, and no differences in findings were seen.

DISCUSSION

We found 25 studies reporting GDM prevalence among Indigenous women. The pooled

GDM prevalence rates were similar to those reported in national studies [43, 47]. However,

there was substantial heterogeneity between studies, which suggests caution is needed when

considering pooled data, and this heterogeneity needs to be explained. We found no clear

explanation for this heterogeneity in our meta-analysis, but have made a number of

observations. First, age-adjusted comparisons between Indigenous and non-Indigenous

women clearly demonstrate that Indigenous women have a higher risk of GDM, and a single

study in the NT suggests Indigenous women have a more rapid increase in risk with maternal

age. However, the GDM prevalence over time appears to be increasing more steeply among

non-Indigenous than Indigenous women. Second, there was some increase in consistency

seen in subgroups coded as ‘universal screening’ (I2=58%) with prevalence reported around

5%; as well as one discrete set of diagnostic criteria used in a specific geographic locale

(I2=43%) and some jurisdictional subgroups, which probably reflects proxy measures of

increased consistency in screening, diagnosis and data collection methods. Third, universal

screening appeared to increase GDM prevalence markedly following the introduction of

universal screening.

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Our prevalence findings are similar to other general reviews of gestational diabetes [2, 3],

including among Indigenous women [57, 58]. This recent systematic review [57] did not

include meta-analysis and concluded that inconsistent study designs were interfering with

determining the accurate prevalence of GDM, while another reported significant variations in

reported GDM prevalence rates among Indigenous women in Australia, ranging from 3-15%

but noted that recent data are limited [58]. The Australian Institute of Health and Welfare

[43] has published a report on diabetes in pregnancy prevalence in Australia, however this

report covers a restricted time period (2005-2008), uses only two data sources (MPDC and

the NHMD) and does not investigate heterogeneity. The substantial heterogeneity of GDM

prevalence reported among Indigenous women is also similar to the variation in T2DM

prevalence reported among Indigenous Australian people [59]. We have added to the

evidence by conducting meta-analysis and exploring potential sources of heterogeneity, as

recommended by the MOOSE reporting guidelines for meta-analysis of observational studies

[24]. Our study supports previous recommendations from national reports not to make inter-

jurisdictional comparisons due to the differences in screening practice, diagnostic criteria,

and data collection methods [43]. The single prospective study which assessed risk factors

for GDM [48], reported that obesity was the strongest predictor of diabetes in pregnancy

among Indigenous women, similar to risk factors reported among other population groups

[11]. As such, obesity trends and ratios among Indigenous women parallel those

described in our review, with 67% Indigenous women in Australia measured as

overweight or obese in 2012-13 [60], up from 57% who self-reported being overweight

or obese in 2004-5 [61]; an age-adjusted risk 1.7 times that of non-Indigenous

Australian women [60]. There are several limitations to this review. Firstly, the studies were observational over a

wide time period and multiple communities; high heterogeneity would be likely under these

circumstances and study designs, limiting the utility of subgroup analysis to investigate

heterogeneity [24, 33]. Second, some of the factors under investigation were not well

reported and were difficult to assess, including diagnostic criteria for GDM [62] and

screening rates. GDM screening practice and diagnostic criteria during the study period

differed not only across states, but at individual health service level, with one survey

suggesting more than 170 criteria have been used nationally [16]. In general, the thresholds

have decreased and sensitivity has increased over time, which would contribute to an increase

in reported prevalence. However, increasing specification in MPDC diabetes in pregnancy

coding could potentially decrease observed trends in some years, with T2DM coded

separately from 1999-2009. . As well as changes over time, GDM screening practice may

differentially impact on Indigenous women due to Indigenous status being a ‘risk factor’ for

selective screening. Despite universal screening being recommended in Australia since 1998

[15], only three studies in this review reported comparatively high GDM screening rates of

99.5% [38], 85.7% [50], and >70% [45]. Studies elsewhere report GDM screening rates among Indigenous women lower than 50% [29, 63], where the expected prevalence could be

more than twice that reported, had all women been screened. Other factors could not be explored

due to the limited number of trials meeting those characteristics, such as studies using a

prospective design and community-based studies, and some studies suggesting under-

identification of GDM in MPDC data when compared to prospective community–based

studies [64]. All other studies in this review used clinic-based or hospital data, and recent data

suggests MPDC data has a reasonable level of accuracy when compared with other routinely

collected sources [65], so this is not expected to be a significant source of heterogeneity. We

were also unable to compare the prevalence among women in rural/remote areas with those

living in urban areas, as only four Indigenous women living in urban areas were identified in

This article is protected by copyright. All rights reserved.

this review, reflecting the paucity of data among urban Indigenous people more generally

[66].

Despite these limitations, this study provides an overview of the reported prevalence of GDM

among Aboriginal women from 1980-2013, and is the first to identify the sources of

heterogeneity inherent in the observational studies. This report is therefore timely given the

recent introduction of changes to GDM screening, which have particular implications for

Indigenous women. Studies in this review suggest screening practice and diagnostic criteria

appear to be important factors influencing prevalence rates, and studies reporting higher

screening rates are likely to more closely reflect the true prevalence (supplementary material

4d), and those with similar diagnostic practices are more appropriate for comparison

(supplementary material 4e).

The paucity of evidence to inform decision-making in relation to GDM screening among

Indigenous women [31], and the heterogeneity in reported GDM prevalence, suggests it is

important that changes are made in collaboration with affected communities and women, and

any new strategies include formative research and flexible evaluation plans with short

reflective cycles (such as participatory action research) so that unforeseen consequences can

be detected early and mitigating strategies can be employed. Due to the heterogeneity we

have seen in our study, and the limited reporting which hindered the capacity to assess the

impact of factors on the ‘true GDM prevalence’ we suggest that, where possible, future

prevalence studies report prevalence by the factors identified in this study, including; age,

indigenous status, state, data source, study design, screening rates and diagnostic criteria.

CONCLUSION

Indigenous women have higher GDM prevalence rates than non-Indigenous women, however

there is considerable variation in reported GDM prevalence among Indigenous women, which

cannot be clearly explained. Universal screening appears to significantly increase GDM

prevalence and greater consistency seen in jurisdictional subgroups and diagnostic criteria

specific to local areas suggests all these factors may influence the heterogeneity seen. The

level of uncertainty and heterogeneity in the prevalence of GDM should be considered when

introducing changes to GDM screening.

ACKNOWLEDGEMENTS

Catherine Chamberlain is supported by a National Health and Medical Research Council of

Australia PhD grant (607247). HL was supported by a training fellowship from the National

Health and Medical Research Council of Australia (606786). We are grateful to Dr Lina

Gubhaju for co-reviewing the titles/abstracts on a search update. EB is supported by a Senior

Research Fellowship from the National Medical Research Council of Australia.

AUTHOR CONTRIBUTIONS

CC coordinated the study, conducted analysis, and drafted the paper. GJ provided advice on

analysis and contributed to drafts. HL co-extracted data and contributed to drafts. JO

conceived the need for the study, provided PhD supervision, reviewed full texts, and

contributed to drafts. SE provided PhD supervision and contributed to drafts. EB provided

advice on analysis and contributed to drafts. All authors provided significant intellectual

input and agreed on the final manuscript to be submitted.

This article is protected by copyright. All rights reserved.

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GD

M

7.6

5

T2

DM

4.5

9

No

Mea

n a

ge:

1

999

:33.1

(DIP

),

24.6

(n

o D

IP);

2005

:32.6

(DIP

),

25.8

(no D

IP)

454

/0

Qu

een

slan

d,

rem

ote

com

mun

ity c

lin

ic

Med

ical

rec

ord

s G

DM

, T

2D

M

1999

: R

BG

L f

irst

vis

it, 1

8, 2

4, 2

8

an

d 3

4 w

eek

s +

50g

OG

CT

if

over 5

.5 a

nd

75

g

OG

CT

if

over

7.8

mm

ol.

A

fter

2000

, R

BG

L,

then

OG

TT

if

over 5

.5/

90

-99.5

%

scre

ened

Mod

erat

e

(con

fou

nd

ing)

Yes

(G

DM

, T

2D

M)

Har

t 1

985

Ind

igen

ou

s

/ n

on

-

Ind

igen

ou

s

1981

-2

DIP

2.0

0 0

.50

No

Mea

n a

ge:

3

0.7

%In

dig

eno

us

moth

ers<

20

;

7.4

% N

on

-In

dig

enou

s

moth

ers

<2

0

550

/37

400

Sou

th A

ust

rali

a,

mix

ed w

hole

popu

lati

on

Mid

wiv

es p

erin

atal

dat

a

An

y D

IP

No

t sp

eci

fied

/

unkn

ow

n

Mod

erat

e

(con

fou

nd

ing a

nd

ou

tcom

e as

sess

men

t)

No (

ov

erla

p w

ith

Sta

nle

y 1

985

An

y

DIP

)

Hu

nt

2003

Ind

igen

ou

s

/ n

on

-In

dig

enou

s

1999

GD

M

6.9

8 3

.33

T2

DM

2.7

1 0.0

0

No

Mea

n a

ge:

23.8

-25.6

(In

dig

enou

s);

28.3

(N

on

-

ind

igen

ou

s)

516

/15

0

Nort

her

n

Ter

rito

ry,

mix

ed

wh

ole

pop

ula

tion

Mid

wiv

es p

erin

atal

dat

a an

d m

edic

al

reco

rds

GD

M,

T2

DM

N

ot

speci

fied

/

>70

% s

cree

ned

Mod

erat

e

(con

fou

nd

ing)

On

ly T

2D

M

(over

lap

wit

h

Zh

ang 2

010

dat

a)

Ish

ak 2

003

Ind

igen

ou

s/

non

-

Ind

igen

ou

s

1988

-99

GD

M

4.2

7 2

.43

T2

DM

1.7

8 0

.33

yes

A

ge-

adju

sted

pre

val

ence

: G

DM

6.2

9%

An

y D

IP 9

.27

%

4843

/2251

68

Sou

th A

ust

rali

a,

mix

ed w

hole

popu

lati

on

Mid

wiv

es p

erin

atal

d

ata

GD

M ,

T2

DM

D

iag

no

sis

on

med

ica

l reco

rd

no

ted

on

mid

wiv

es

form

(AD

IPS

or W

HO

crit

eria

)/

unkn

ow

n

Mod

erat

e (o

utc

om

e

asse

ssm

ent)

No (

ov

erla

p w

ith

S

har

pe

20

05

)

This article is protected by copyright. All rights reserved.

Kim

1

999

a,

b &

c

Ab

ori

gin

al

and

Torr

es

Str

ait

Isla

nd

er

sep

arat

ely/

non

-

Ind

igen

ou

s

1992

-6

1992 T

S:2

1.1

; A

: 19.7

;

NI:

12.2

1994 T

S:1

6.4

; A

:12.9

NI:

9.3

8

1996 T

S:

14.3

9;

A 7

.0

NI:

3.6

No

2251

/5325

Far

Nort

h

Qu

een

slan

d,

rem

ote

com

mun

ity c

lin

ic

and

hosp

ital

Hosp

ital

cli

nic

al

dat

abas

e an

d m

edic

al

reco

rds

GD

M

5

0g

OG

CT

: 1

hr

BS

L>

7.8

mm

ol

then

75

g O

GT

T

FP

G >

5.5

mm

oL

or 2

hr

>8.0

mm

ol/

L/

unkn

ow

n

Mod

erat

e

(con

fou

nd

ing)

Yes

(G

DM

)

Mac

ker

ras

1998

a&b

Ind

igen

ou

s 1

990

-1

1994

-6

GD

M 5

.26

T2

DM

2.6

3

GD

M 7

.72

T2

DM

2.0

3

No

Mea

n a

ge:

1990

23.7

;

1994

22.7

474

/0

Nort

her

n

Ter

rito

ry,

rem

ote

wh

ole

pop

ula

tion

and

cli

nic

Mid

wiv

es p

erin

atal

d

ata

and

med

ical

reco

rds

GD

M,

PE

DM

, A

ny

DIP

N

ot

speci

fied

/ u

nkn

ow

n

Mod

erat

e (o

utc

om

e

asse

ssm

ent

and

con

foun

din

g)

On

ly T

2D

M a

nd

1

990

-91

dat

a in

clud

ed

(over

lap

wit

h Z

han

g

2010

)

Mar

key

19

96

Ind

igen

ou

s

/ n

on

-In

dig

enou

s

1992

-5

GD

M 6

.31

3

.04

Yes

Ag

e-gro

up

st

rati

fied

GD

M

<20

2

.7 2

.3

20

-24 4

.4

2.2

25

-29 9

.0

3.2

30

-34 1

2.1

3.5

3

5-3

9 1

9.6

6.2

40+

23

.1 9

.8

4937

/6006

Nort

her

n

Ter

rito

ry,

mix

ed

wh

ole

pop

ula

tion

Mid

wiv

es p

erin

atal

dat

a

GD

M b

y 5

-yea

r ag

e

gro

up

s

no

t sp

ecif

ied

/

unkn

ow

n

Mod

erat

e

(ou

tcom

e as

sess

men

t)

No (

ov

erla

p w

ith

Zh

ang 2

010

) but

age-

stra

tifi

ed r

ates

New

Sou

th

Wal

es P

ub

lic

Hea

lth

Unit

2009

a, b

&c

Ind

igen

ou

s

/ n

on

-

Ind

igen

ou

s

1996

-

2009

1996

GD

M 2

.69 2

.98

T

2D

M 0

.47

6.4

0

2002

GD

M 3

.11 4

.40

T

2D

M 0

.79

0.5

4

2008

GD

M 3

.63 4

.79

T

2D

M 1

.21

0.6

2

No

AT

SI

(21

.8%

<2

0,

73.3

% 2

0-3

4,

4.8

% 3

5+

);

All

(5

%<

20

,

80%

20

-34

, 1

4.9

%

35+

)

2944

4/1

03

265

7

New

Sou

th W

ales

,

mix

ed w

hole

popu

lati

on

Mid

wiv

es p

erin

atal

dat

a

GD

M ,

T2

DM

n

ot

spec

ifie

d/

unkn

ow

n

Mod

erat

e

(ou

tcom

e

asse

ssm

ent

and

con

foun

din

g)

Yes

(G

DM

, T

2D

M)

Pat

el 1

989

Ab

ori

gin

al

1985

-87

1989

GD

M 3

%

PE

DM

1.6

%

GD

M 1

2%

No

Un

cle

ar

Nort

her

n T

erri

tory

(Cen

tral

Au

stra

lia)

, p

opu

lati

on

uncl

ear

Un

clea

r G

DM

, P

ED

M

1985

-87

: R

isk

-

ba

sed

scre

en

ing

1989

: U

niv

ersa

l

scree

nin

g/

unkn

ow

n

Hig

h (

lim

ited

info

rmat

ion a

bou

t

dat

a so

urc

e re

port

ed)

No (

nu

mb

ers

not

incl

ud

ed)

Port

er 2

011

Ind

igen

ou

s

/ n

on

-

Ind

igen

ou

s

2000

-7

GD

M

8.4

2 6

.41

T2

DM

2.2

8 0.7

1

No

Mea

n a

ge:

In

d 2

7.9

8

Non

-In

d 3

1.7

4

4966

/7665

1

Wes

tern

Au

stra

lia,

mix

ed w

hole

popu

lati

on

Mid

wiv

es p

erin

atal

dat

a

GD

M,

PE

DM

n

ot

spec

ifie

d/

unkn

ow

n

Mod

erat

e

(ou

tcom

e

asse

ssm

ent

and

co

nfo

un

din

g)

Yes

(G

DM

,T2

DM

)

Po

wel

199

9

Ind

igen

ou

s

/ n

on

-In

dig

enou

s

1990

-2

GD

M 1

.52

2.1

9

No

146

/13

9

Qu

een

slan

d,

rura

l

com

mun

ity a

nd

hosp

ital

cli

nic

Com

mun

ity b

ased

reco

rds

and

hosp

ital

re

cord

s

GD

M

no

t sp

ecif

ied

/

unkn

ow

n

Hig

h

(con

fou

nd

ing)

Yes

(G

DM

) ?o

ver

lap

1992

This article is protected by copyright. All rights reserved.

Sh

arp

e 20

05

Ind

igen

ou

s

/ n

on

-In

dig

enou

s

1986

-

2000

GD

M

4.4

3 2

.20

T2

DM

1.3

0 0

.32

No

7542

/2641

21

Sou

th A

ust

rali

a,

mix

ed w

hole

p

opu

lati

on

Mid

wiv

es p

erin

atal

dat

a a

nd B

irth

Def

ects

R

egis

ter

GD

M,

PE

DM

D

ecrea

sed

thresh

old

s in

1999

fro

m >

7.8

to >

7.0

. P

rior t

o

1991

IG

T a

nd

GD

M w

ere

gro

up

ed

togeth

er, a

nd

rem

ain

ed

co

mb

ined

on

da

tab

ase

un

til

1997

/

unkn

ow

n

Mod

erat

e

(ou

tcom

e as

sess

men

t an

d

con

foun

din

g)

Yes

(G

DM

,T2

DM

)

Sim

mon

s 2

005

Ind

igen

ou

s

/ n

on

-

Ind

igen

ou

s

1998

-9

GD

M 1

0.7

1 4

.46

Yes

2

5.5

(ag

e

mat

ched

)

28/1

12

Vic

tori

a, r

ura

l co

mm

un

ity c

lin

ic

Med

ical

rec

ord

s G

DM

5

0g O

GC

T:

1h

r

BS

L>

7.8

mm

ol

then

75

g O

GT

T

FP

G >

5.5

mm

oL

or 2

hr

>8.0

mm

ol/

L /

85.7

0%

scr

een

ed

Lo

w

Yes

(G

DM

)

Sta

nle

y 1

985

Ind

igen

ou

s

/ n

on

-

Ind

igen

ou

s

1980

-2

DIP

1.6

1 0

.29

No

3220

/5904

5

Wes

tern

Au

stra

lia,

m

ixed

wh

ole

popu

lati

on

Mid

wiv

es p

erin

atal

d

ata

A

ny D

IP o

nly

n

ot

spec

ifie

d/

unkn

ow

n

Mod

erat

e (o

utc

om

e

asse

ssm

ent

and

con

foun

din

g)

Yes

(A

ny D

IP o

nly

)

Sto

ne

20

02

Ind

igen

ou

s

/ n

on

-In

dig

enou

s

1996

GD

M 4

.34

3.6

0

Yes

Cru

de

OR

1

.2

Ag

e ad

j. O

R 2

.5

438

/59

962

Vic

tori

a, m

ixed

wh

ole

pop

ula

tion

Mid

wiv

es p

erin

atal

dat

a an

d V

IMD

dat

abas

es (

link

ed t

o

asse

ss c

om

ple

ten

ess)

GD

M

no

t sp

ecif

ied

/

unkn

ow

n

Mod

erat

e

(ou

tcom

e

asse

ssm

ent)

Yes

(G

DM

)

Teh

201

1

Ind

igen

ou

s/

non

-

Ind

igen

ou

s

2007

GD

M 4

.8 5.4

Y

es

Cru

de

OR

0

.9

Ag

e ad

j. O

R 1

.1

21/1

21

3

Vic

tori

a, u

rban

cl

inic

bas

ed

popu

lati

on

Hosp

ital

dat

abas

e G

DM

7

5g

OG

CT

at

26

-

28 w

ks

>=

8.0

ha

d

an

OG

TT

, a

nd

GD

M i

f

FP

G>

=5.5

or 2

h

>=

8/

un

kn

ow

n

Mod

erat

e (o

utc

om

e

asse

ssm

ent)

Yes

(G

DM

)

Tem

ple

ton

2

008

Ind

igen

ou

s/

non

-

Ind

igen

ou

s

2001

-6

GD

M 4

.8 4.6

Y

es

SIR

1.5

9167

/2500

43

Nat

ion

al,

mix

ed

wh

ole

pop

ula

tion

Nat

Hosp

ital

M

orb

idit

y d

atab

ase

and

ND

SS

GD

M

no

t sp

ecif

ied

/ u

nkn

ow

n

Mod

erat

e (o

utc

om

e

asse

ssm

ent)

No (

ov

erla

p w

ith

A

IHW

-com

par

e as

dif

fere

nt

dat

a so

urc

es)

Yu

e 19

96

Ab

ori

gin

al/

An

glo

-

Cel

tic

Yea

r u

ncl

ear

GD

M 1

0.1

3.0

N

o

Mea

n a

ge

28

.8

89/2

41

2

New

Sou

th W

ales

, S

yd

ney

, u

rban

clin

ic b

ased

popu

lati

on

Hosp

ital

dat

abas

e G

DM

2

4-2

8w

k 5

0g

GC

T,

then

75g

OG

TT

(A

DIP

S

crit

eria

)/

unkn

ow

n

Mod

erat

e (o

utc

om

e

asse

ssm

ent

and

con

foun

din

g)

No )

ov

erla

p w

ith

N

SW

PH

U d

ata)

Zh

ang

2010

a, b

, c,

&d

Ind

igen

ou

s

/ n

on

-

Ind

igen

ou

s

1992

-5

1996

-9

2000

-3

2004

-6

GD

M 6

.35

3

.5

GD

M 5

.00

2

.9

GD

M 6

.30

4.0

G

DM

8.2

6.1

No

2423

2/4

30

79

Nort

her

n

Ter

rito

ry,

mix

ed

wh

ole

pop

ula

tion

Mid

wiv

es p

erin

atal

dat

a

GD

M

no

t sp

ecif

ied

/

unkn

ow

n

Mod

erat

e

(ou

tcom

e

asse

ssm

ent

and

co

nfo

un

din

g)

Yes

(G

DM

)

This article is protected by copyright. All rights reserved.

Figure 1. Flow chart of included studies

This article is protected by copyright. All rights reserved.

Figure 2. Crude GDM prevalence sorted by year of data collection

This article is protected by copyright. All rights reserved.

Figure 3. GDM Prevalence among Indigenous (Ind) and non-Indigenous (NI) Australian

women, by jurisdiction

This article is protected by copyright. All rights reserved.

Figure 4. Crude GDM prevalence among Aboriginal and/or Torres Strait Islander

women, subgrouped by rurality

This article is protected by copyright. All rights reserved.