TYPE 2 DIABETES AND ITS ASSOCIATED RISK FACTORS

1

TYPE 2 DIABETES AND ITS ASSOCIATED RISK FACTORS:

MAGNITUDE AMONG ADULT OBESE PATIENTS AT GOPC,

FETH ABAKALIKI, EBONYI STATE, NIGERIA.

A DISSERTATION SUBMITTED TO THE NATIONAL

POSTGRADUATE MEDICAL COLLEGE OF NIGERIA IN

PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE

AWARD OF THE FELLOWSHIP IN FAMILY MEDICINE.

BY

DR. AMAZUE HENRY IKECHUKWU (MBBS, NIG.)

DEPARTMENT OF FAMILY MEDICINE

FEDERAL TEACHING HOSPITAL ABAKALIKI

EBONYI STATE, NIGERIA

NOVEMBER, 2016.

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TABLE OF CONTENTS

Table of contents i

Declaration ii

Certification iii

Dedication iv

Preface and Acknowledgement v

List of tables vi

List of figures vii

List of abbreviations viii

Appendices x

Summary 1

Chapter one: Introduction 3

Chapter two: Review of the Literature 10

Chapter three: Materials and Method 23

Chapter four: Results 32

Chapter five: Discussion 64

Conclusion 78

Limitation of study 79

Recommendations 80

References 81

Appendices

3

DECLARATION

I, Dr. Amazue Henry Ikechukwu of the Department of Family Medicine, Federal Teaching

Hospital, Abakaliki, declare that the contents of this dissertation are my original work and

that none of its contents has ever been submitted to any professional body or Institution.

Signature/Date ………………………………………………………

Name: ………………………………………………………

Department: ………………………………………………………..

………………………………………………………………………

………………………………………………………………………

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CERTIFICATION

This is to certify that the contents of this dissertation were conceptualized and developed

under our supervision. We undertook the supervision of the write up of the dissertation.

Trainer/Supervisor

Signature/Date: ……………………………………………………………………….

Dr. (Mrs.) Nwazor F.O.O

(FMCGP)

Consultant Adviser

Signature/Date: ……………………………………………………………………….

Dr. Imediegwu O.O

(FMCGP)

Head of Department

Signature/Date: ………………………………………………………………………

Dr, Oguejiofor N.C

FWACP (FM)

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DEDICATION

This book is dedicated first to God the owner of my life; then to my adorable and beloved

wife Dr. (Mrs.) Chinelo Amazue who has been very supportive and taking very good care of

the home while I was busy with my residency training programme. Also to my children:

Ikechukwu, Chibuzo, and Chinedum who were always there to lighten my days and to my

parents Sir and Lady Lawrence Amazue who ensured that I took the path of academics early

in life.

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PREFACE/ACKNOWLEDGEMENT

The epidemics of type 2 diabetes and obesity are increasing in the sub-Saharan Africa.

Nigeria and Ebonyi State are not an exception. This study was done with a view to assessing

the magnitude of type 2 diabetes and its associated risk factors in a hospital population in

Abakaliki, Ebonyi state. I wish to acknowledge the priceless efforts of my supervisor Dr.

(Mrs.) Nwazor F.O.O and My Consultant Adviser, Dr. Imediegwu O.O, in nudging me on to

complete this work and their painstaking efforts in supervising and proof reading this work

over and over to ensure good work. I will not fail to acknowledge my Head of Department

Dr. Oguejiofor N.C and many consultant Family Physicians in my institution who helped in

no small measure to see that this work was well done. I will not forget the uncommon

contribution of those who helped in typing this work.

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LIST OF TABLES

Table 1: Sociodemographic / baseline characteristics of the subjects

Table 2: Prevalence of blood glucose categories in the non-obese and the obese

Table 3: Prevalence of known diabetes in the non-obese and the obese groups

Table 4: Assessment of blood glucose by age categories

Table 5: Family history of diabetes and blood glucose categories in the respondents

Table 6: Stratification of respondents’ family history of diabetes and the blood glucose

categories

Table 7: Sex and blood glucose categories in the respondents

Table 8: Blood glucose and waist circumference categories

Table 9: Blood glucose and BMI categories of the respondents

Table 10: Blood glucose categories and cigarette smoking in the respondents

Table 11: Alcohol consumption and blood glucose categories

Table 12: Blood glucose and units of alcohol consumed categories of the respondents

Table 13: Blood glucose categories and physical activity

Table 14: Blood glucose and blood pressure categories of the respondents

Table 15: Correlation of whole carpillary blood glucose and continuous variable risk factors

Table 16: Logistic regression of risk factors of diabetes

Table 17: Awareness of lifestyle modification and blood glucose categories

Table 18: Occupational status and blood glucose categories of the respondents

Table 19: Blood glucose categories and locality of the respondents

Table 20: Blood glucose and marital status categories of the respondents

Table 21: Blood glucose and educational status categories of the respondents

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LIST OF FIGURES

Figure 1: Study flow chart

Figure 2: Scatter plot of whole capillary blood glucose (mg/dl) and age (years)

Figure 3: Scatter plot of whole capillary blood glucose (mg/dl) and waist circumference (cm)

Figure 4: Scatter plot of whole capillary blood glucose (mg/dl) and systolic blood pressure

(mm/Hg)

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LIST OF ABBREVIATIONS

ABV: Alcohol by volume

ADA: America Diabetic Association

BMI: Body mass index

BP: Blood pressure

CAM: Complimentary and Alternative Medicine

DAWN: Diabetes Attitudes, Wishes and Needs

DBP: Diastolic blood pressure

EBSUTH: Ebonyi State University Teaching Hospital

FBG: Fasting blood glucose

FETHA: Federal Teaching Hospital Abakaliki

FMCA: Federal Medical Centre Abakaliki

GABA: Gamma amino butyric acid

GOPC: General Out-Patient Clinic

HDL: High density lipoprotien

IBM: International Business Machines

IDA: International Diabetic Association

ISO: International Standard Organization

JNC: Joint National Committee on Prevention, Detection, Evaluation and Treatment

of high blood pressure

MKS: System of units in physics based on measuring Length in metres, mass in

kilograms and time in seconds.

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NCD: Non-communicable disease

NHANES: National Health and National Examination Surveys

NIH: National Institute of Health

RBG: Random blood glucose

REC: Research and Ethics Committee

RGZ: Residential Growth Zone

ROC: Receiver Operator Characteristic

SBP: Systolic blood pressure

SD: Standard deviation

SIGN: Scottish Intercollegiate Guideline Network

SPSS: Statistical Package for Social Sciences

USA: United States of America

WHO: World Health Organization

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APPENDICES

Appendix I: Alcohol by volume of common alcoholic beverages in Abakaliki

Appendix 2: Ethical approval

Appendix 3: Letter of consent

Appendix 4: Research questionnaire

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SUMMARY

Background: The epidemics of obesity and type 2 diabetes (diabetes) in sub-Saharan Africa

are increasing. Studies on the association of obesity and that of other risk factors with type 2

diabetes in hospital population study in this region of the country is not much. This study

evaluated the magnitude of type 2 diabetes and its associated risk factors in adults in a tertiary

hospital, primary care clinic in Abakaliki, Ebonyi State, Nigeria.

Objectives: To determine the prevalence of diabetes and of known diabetics in the obese and

non-obese groups. To assess the modifiable risk factors (waist circumference, tobacco use,

alcohol consumption, physical activity) and the non-modifiable risk factors (age, sex and

family history of diabetes) among study population. To correlate continuous variable risk

factors with the blood glucose.

Methods: This was a hospital-based cross-sectional comparative study of 280 adults (140

obese and 140 non-obese) selected through systematic random sampling. The association

between obesity, other risk factors and type 2 diabetes were also examined. Chi-square test,

Fisher’s exact test, t-test, correlation and logistic regression analysis were used for analysis.

Results: The crude prevalence of diabetes in the study population was 10.0% (28). The

prevalence of diabetes in the obese was 12.1% (17) compared to 7.9% (11) in the non-obese

group. The prevalence of known diabetics was slightly higher among the non-obese group

7.9% (11) when compared to the obese group 7.1% (10). When Spearman’s rho correlation

was run to determine the relationship between 280 subjects’ whole carpillary blood glucose

(RBG) and continuous risk factors; only age, waist circumference and average systolic blood

pressure had weak positive correlation and had statistically significant association with

diabetes (rs = .271, p = 0.0001), (rs = .175, p = 0.003) and (rs = .161, p = 0.007) respectively.

Family history of diabetes and age showed independent association with diabetes in logistic

13

regression (p = 0.007) and (p = 0.001) respectively. Age seemed to have a protective effect

in logistic regression (OR = 0.940)[CI = 0.906-0.976]. The lower the age the less chance a

subject has of having diabetes. This deduction is strong. Family history of diabetes had

higher odds for diabetes (OR = 3.718)[CI = 1.438-9.613]. Subjects with family history of

diabetes had 3.7 times chances of having diabetes than those without family history of

diabetes. Sex, smoking, alcohol consumption, BMI, physical activity had no association or

independent association with diabetes in the respondents.

Conclusion: The prevalence of diabetes was high and the prevalence of known diabetes was

relatively low. The findings from this study suggest that non-modifiable risk factors: age and

family history of diabetes and modifiable risk factors namely: WC and systolic blood

pressure had an association with types 2 diabetes in the respondents.

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CHAPTER ONE

1.1 INTRODUCTION

Obesity and type 2 diabetes (diabetes) are important non-communicable health challenges

whose magnitude is increasing worldwide. Non–communicable disease (NCD) is a medical

condition or disease that is non-transmissible among people1. When compared with the year

2000, it is projected that there will be about 157% increase in prevalence of diabetes by the

year 2030 in developing countries, namely: Middle Eastern crescent, sub-Saharan Africa and

India 2.

The number of people with diabetes is increasing in every country3. The highest numbers

of undiagnosed cases of diabetics are found in Africans, about 80%. Some of the reasons

fuelling this crisis include: most are asymptomatic or with mild symptoms, poverty and poor

health insurance coverage which is mainly for government workers 3.

Type 2 diabetes is associated with many preventable risks and causative factors, such as

obesity, hypertension 4, 5, 6, dyslipidaemia, poor diet, physical inactivity 4,7,8,9 or irregular

exercise 3, increasing age 3 and family history of diabetes 10. Risk factors are factors whose

presence is associated with an increased probability that the disease will develop later. Some

are amenable to change (modifiable), while others are immutable (non-modifiable) 1.

Globally, diabetes was the cause of 1.5 million deaths in 2012 11.

1.2 DEFINITION OF TERMS

1. Diabetes: Subjects were diagnosed based on random blood glucose (RBG) of

200mg/dl(11.1mmol/L) or more who had symptoms of diabetes. Diagnosis was also

based on being a known diabetic and/or current use of anti-diabetic medication(s).

15

Subjects were grouped into normoglycaemia, impaired glucose and diabetes based on

result of RBG.

Normoglycaemia - < 140mg/dl (7.7mmol/l)

Impaired - 140-199mg/dl (7.7-11.0mmol/l)

Diabetes - ≥ 200mg/dl (≥11.1mmol/l)

2. Hypertension: Hypertension was defined as blood pressure ≥ 140mmHg systolic

(SBP) and/or ≥ 90mmHg diastolic (DBP). It was also defined as history of a known

hypertensive and/or current use of antihypertensive medication(s). The blood pressure

was classified using Joint National Committee on Prevention, Detection, Evaluation

and Treatment of high blood pressure JNC 7 report guideline 21. For easy analysis,

subjects were grouped into normotensive, SBP < 140mmHg and DBP < 90mmHg;

and hypertensive, SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg or history of being a

known hypertensive, and/or a current use of antihypertensive medication(s).

3. Known diabetes: Known diabetes was referred to as having a previous diagnosis of

diabetes before the study. This implies that the subject has been previously informed

by a medical professional of his or her diabetic status.

4. Obesity: Based on body mass index (BMI): BMI ≥ 30.0 kg/m2. It was further

categorized into

Class I (mild) = 30.0- 34.9kg / m2

Class II (moderate) = 35.0 – 39.9kg / m2

Class III (Severe) = ≥ 40.0kg / m2

5. Abdominal obesity: Normal abdominal waist circumference (WC) for women. WC <

80cm, while abnormal WC (abdominal obesity) for women WC ≥ 80cm. Normal WC

for men was WC < 94cm, while abnormal WC for men WC ≥ 94cm18.

16

6. The family history of risk factor of diabetes was captured as Yes or No for presence

or absence of type 2 diabetes in the subject’s family and was assessed in terms of

degree of relationship.

First degree: Affecting subject’s siblings

Second degree: Affecting subject’s parents, uncles and aunties

Third degree: Affecting subject’s grandparents and cousins.

7. Alcohol consumption: Was assessed on whether subject had ever consumed alcohol

or not. Those who had ever consumed alcohol were further graded as moderate or

heavy drinkers based on units of alcohol consumed in a week.

One unit of alcohol equals 10mls or 8g of pure alcohol. This is about the amount of

alcohol the average adult can process in one hour 22. Units of alcohol consumed were

calculated by multiplying the alcohol by volume (ABV), also known as percentage of

alcohol of the brand (Appendix I) consumed by the volume in milliliter (ml) of the

amount of alcohol consumed and divided by one thousand.

Units of alcohol = ABV × Volume (ml)

1000

Moderate intake < 21 units/week for men

< 15 units/week for women

Heavy intake ≥ 21 units/week for men

≥ 15 units/week for women

8. Tobacco use: It was assessed based on whether subjects had ever smoked cigarette or

not. Cigarette smoking was assessed using smoking index = number of sticks

smoked/day x number of years smoked. Where by >300 was significant.

17

9. Physical activity: It was assessed and coded based on the subject’s type of occupation

and involvement in sporting or recreational activity regularly 23.

Physically active: Manual labourer, artisans, farmers, and apprentice.

Moderate active: Traders, house workers or nanny, students, corpers,

seamstress/tailor.

Physically inactive: Office workers, unemployed, drivers and retirees.

Physically inactive + recreational activity or exercise regularly = moderately active

Moderately active + recreational activity or exercise regularly = physically active.

For analysis, subjects were grouped into physically active and physically inactive (this

include moderately active and physically inactive).

1.3 STATEMENT OF PROBLEM `

There has been tremendous increase in the number of people who are obese suffering from

such obesity-related morbidity as diabetes worldwide3. The situation in Abakaliki, Ebonyi

State seems not to be different as the number seen in General out-Patient Clinic (GOPC) of

Federal Teaching Hospital Abakaliki (FETHA), seems to be on the increase. This may be due

to adoption of western lifestyle such as eating more of high energy rich foods, saturated fats

following the proliferation of fast food shops and sedentary lifestyle. Obesity increases the

risks of developing a number of health conditions, whose consequences occur with greater

magnitude among obese subjects. The most important of these health conditions include type

2 diabetes, hypertension and dyslipidaemia12.

Surprisingly due to lack of education, most of these obese subjects do not understand their

condition and the risk they are exposed to. Unfortunately, most of these obese groups seem to

indulge in lifestyle and behaviours that endanger their lives4. These include sedentary

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lifestyle, eating more high energy foods and saturated fats, smoking and heavy alcohol

consumption.

There are many complications associated with diabetes that are difficult to manage and

sometimes result in significant number of deaths and impairment of quality of life, both for

the patient and the care giver 13.

1.4 JUSTIFICATION OF THE STUDY

Obesity is associated with increased risk of cardiometabolic diseases such as diabetes.

When obesity and diabetes co-exist, the risk of cardiovascular disease is increased 14.

Most of the studies on this subject were community based study. Hospital based studies

were scarce to the best knowledge of the author. Since type 2 diabetes plays a major role in

the development of cardiovascular morbidity and mortality in obese subjects, it is essential

that this group should be screened for diabetes and other co-morbidities such as hypertension.

Lifestyle modification should be instituted, so as to prolong or prevent development of

diabetes for those who are at impaired glucose tolerance stage and complications in those

who are diabetic 15, 16, 17,18,19,20.

This study established the prevalence of diabetes in Abakaliki, the prevalence of known

type 2 diabetes and association of risk factors of diabetes with diabetes, among the study

population, hence has provided information on the burden of obesity related diabetes. It

highlighted the importance of family physicians using every encounter with the obese

patients as an opportunity to counsel, screen and control diabetes among them.

This study bridges the gap of studies and data on this subject within Abakaliki and Ebonyi

State by providing needed information.

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The information provided by this study, will help family physicians, health professionals

and healthcare managers in the formulation and implementation of strategies for health

preventive and health promotive care.

1.5 RESEARCH QUESTION: 1) Is there significant difference between the magnitude of

type 2 diabetes in the obese and the non-obese?

2) Is there significant association between diabetes and its associated risk factors in the study

population?

1.6 AIM AND OBJECTIVES

1.6.1 AIM: To determine the magnitude of type 2 diabetes and its associated risk factors

among obese adult patients attending GOPC, FETH Abakaliki, Ebonyi State, with a view to

reducing the burden of the disease through advocating for lifestyle modification and

screening of the obese.

1.6.2 OBJECTIVES

1. To determine the prevalence of type 2 diabetes among the obese and the non-

obese subjects.

2. To determine the prevalence of known type 2 diabetics among the obese and the

non-obese groups.

3. To assess the modifiable (waist circumference, cigarette smoking, alcohol

consumption, physical activity) and non-modifiable (age, sex, and family history

of diabetes) risk factors among the study population.

4. To correlate the body mass index, waist circumference, age, units of alcohol

consumed and smoking index with random blood glucose.

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1.7 RESEARCH HYPOTHESIS:

The null: There is no significant difference between magnitude of type 2 diabetes

and its associated risk factors in the obese and in the non-obese.

The alternative: There is significant difference between magnitude of type 2

diabetes and its associated risk factors in the obese and in the non-obese.

Test statistics such as Fisher’s exact test, Chi-square test and t-test will be

used to test for significance. P-value less than 0.05 will result in rejecting the null

hypotheses.

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CHAPTER TWO

2.0 REVIEW OF LITERATURE

2.1 TYPE 2 DIABETES: PATHOGENESIS AND ITS PREVALENCE

Diabetes mellitus according to World Health Organisation (WHO) summary of technical

report recommendation is defined in part as fasting blood glucose(FBG) of more than or

equal to 126 mg/dl (7.0 mmol/l) or random blood glucose (RBG) of more than or equal to

200mg/dl (11.1 mmol/l) with symptoms of diabetes 24.

Diabetes is about the 4th largest contributor to mortality in the majority of low– and

middle–income countries. Presently, nearly two thirds of people with diabetes live in low–

and middle-income countries 25.

Non–communicable diseases are increasing tremendously in Africa. This has been

attributed to a number of factors such as demographic transition (increase in life expectancy,

resulting in greater number attaining old age), epidemiological transition and life style related

to urbanization and westernization 26, 27. Natural history of diabetes is a continuum from

normal glycaemia to impaired fasting glucose or impaired glucose tolerance and finally to

frank diabetes 15, 28, 29. Pathogenesis of diabetes has been largely attributed to factors which

include hyperplasia and hypertrophy of adiposities with release of proinflammatory cells

which result in metabolic derangement (insulin resistance). All these with the interplay of

genes, feeding habit and activity level determine the degree of abnormality manifested by an

individual 4, 28. Type 2 diabetes accounts for about 90% to 95% of those with diabetes

mellitus, while 5% or 10% are type 1 diabetes 10.

Prevalence of diabetes worldwide is between 1.2% and 15.1%. There are about 371 million

people with diabetes worldwide30. Africa has 4.3% prevalence of diabetes and has the highest

22

mortality rate due to diabetes. In Africa there was an estimated 10.8 million diabetics in the

year 200630. Nigeria has about 3.2 million people with diabetes and 2.5 million people are

undiagnosed 12.

In United States of America (USA), Cowie CC and colleagues in a community survey

reported highest prevalence of diabetes in non–Hispanic blacks (10.0%), and lowest

prevalence in non-Hispanic whites (5.6%) 31. The prevalence of diabetes varies in different

countries of the world. Aksu H and colleagues reported prevalence of 6.1% in an urban

population study in Nilufer district, Bursa, Turkey 32. In a rural population in Bangladesh

with 3.6% of overweight and obese subjects, a prevalence of 2.8% was reported 14. In an

urban population study in Chennai, India, a 12.0% prevalence of diabetes was reported 33.

WHO expert panel reported a prevalence of type 2 diabetes 20.8% in South Asians, 16.3% in

blacks, 9.5% in whites and 9.3% in Chinese 34.

In Africa, prevalence of diabetes varies from country to country. In an urban

population cohort study in South Africa, a prevalence of 14.3% was reported 35, while a rural

population in South Africa gave a prevalence of 4.6% 36. An urban population study in Futa

Jallon Guinea reported a prevalence of 10.3% 37. Most of the studies were community based,

while hospital based studies were scarce to the best knowledge of the author.

In Nigeria, a prevalence of 4.6% was reported3, though it varies from state to state. In

Port Harcourt, Rivers state, in an urban population study, the prevalence of diabetes was

6.8% 23. In Uyo, Akwa-Ibom state, in urban population study, a prevalence of 12.7% was

reported 26. In Ibadan, Oyo state, in an urban population study, a prevalence of 4.7 % was

reported38. In Naze, Owerri , Imo state, in a study of “August meeting women ” a prevalence

of 6.7% was reported among women 5. A study in a tertiary hospital in Owerri, Imo state

reported 15.1% prevalence of diabetes in an obese population39.

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2.2 DIABETES SCREENING/SOCIOECONOMIC BURDEN/STRATEGY TO

REDUCE THE EPIDEMIC

America Diabetic Association (ADA) has recommended screening for abnormal glucose

tolerance using fasting blood glucose 16. Screening is the process of identifying those

individuals who are at risk of a specific disorder to warrant further investigation or direct

action. It is usually initiated by medical authorities 40.

Venous plasma glucose estimation is the best but Hemocue 201+ which uses glucose

dehydrogenase method, produced whole blood capillary blood glucose result which compares

well with venous plasma glucose 41. Accu check glucometer with compatible strip has been

shown to meet International Standard Organisation (ISO 15197) minimum criteria. It also

out-performed others when tighter limits were applied. Hemocue 201+ was defined as

reference method 42.

The cost of diabetes management which includes direct healthcare cost (sick leave,

early retirement and premature death) is quite enormous for low– and middle–income

countries of which Nigeria occupies a significant population13.

Complications of type 2 diabetes includes both microvascular complications such as

retinopathy, neuropathy and foot ulcers and macrovascular complications like heart attack,

stroke and amputations 13.

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Diabetes to some extent and obesity are preventable health conditions since most of the risk

factors are modifiable. Studies have suggested that with lifestyle modification subjects with

impaired glucose can be prevented from developing type 2 diabetes 15, 16, 17, 18, 19.

In the sixty–second World Health Assembly in 2008, the Family Physician was recognized as

an important member of the primary healthcare team. Family Physician offers comprehensive

care and are most suited to lead the way in the strategy to preventing diabetes epidemic 43,

others that should be involved include government and its health department, food industry,

national and international diabetes organizations et cetera18. Culture in some parts of Africa

like eating from same food bowl, discourages dietary restrictions and will pose a setback in

the strategy of preventing diabetes hence regular education and involvement of family

members are vital in surmounting these challenges 18,44. Family Physicians practice

comprehensive and holistic care of patients and their family43. Biopsychosocial model is a

model of care advocated for by family physicians. The Diabetes Attitudes, wishes and Needs

(DAWN) study which involved thirteen (13) countries brought this to the fore 45. This study

looked at the psychological aspect of diabetes. Among those whose diabetes self-care was

affected, about 41.0 % of them had poor psychological well-being. Only ten percent received

psychological care. The study also observed that when diabetics have good relationship with

their family members, colleagues at place of work or group of friends that this improves

patient’s sense of wellbeing. Similarly, Chinenye S. and colleagues in Nigeria advocated for

incorporation of beneficial accepted socio-cultural practices such as traditional medicine,

25

religion and lifestyle practices to the care of diabetics46. This is due to the fact that a large

number of patients and their family members believe in and patronize them46. This fact

cannot be wished away.

Moreover, Family Physicians seem to be advocating for the formal recognition and regulation

of complementary and alternative medicine (CAM) and the practice of integrative medicine.

2.3 OBESITY PREVALENCE, PATHOGENESIS AND ASSOCIATION WITH

DIABETES

Overweight and obesity pose major risk factors for chronic diseases such as type 2

diabetes, cardiovascular disease, hypertension, stroke and certain cancers 12. There are about

1 billion overweight adults globally, out of which 300 million are obese. A 2005 WHO report

shows obesity was the fifth leading cause of mortality globally 47. Obesity is excess adipose

tissue in the body and has been identified as one of the most important risk factors of

diabetes.

In a survey of adult population in USA, in 2003, obesity was found to be significantly

associated with diabetes. Prevalence of obesity increased to 20.9 % in 2001; blacks had the

highest rate of obesity 31.1% 48.

Data from African studies show that obesity is most prevalent in urban middle aged

women 35. Prevalence of obesity in sub-Saharan Africa ranges from 0.4% to 43.0% 30. In a 20

year cohort study in South Africa , obesity prevalence of 50.1% and abdominal obesity

26

prevalence of 69.3% where reported 49. Obesity prevalence of 8.4% was reported in Futa

Jallon, Guinea and was found to be more in diabetics than non-diabetics 37. Obesity

prevalence of 22.0% in women and 7.0% in men and abdominal obesity prevalence of 67.0%

in women and 7.0% in men was reported among urban population in Cameroon 27. Ziraba AK

and colleagues in a ten-year systematic review of seven African countries reported increase in

urban prevalence of obesity of about 35.0% 50.

In Nigeria prevalence of obesity varies from state to state. Prevalence of 25.0% was reported

in general population study in Uyo, Akwa-Ibom state (South Southern Nigeria) 26. Obesity

prevalence of 23.3% and abdominal obesity prevalence of 20.1% were reported in Abuja.

Obesity was more in urban area (40.7%), than rural area (14.3%) 51. Obesity prevalence

22.2% was reported in Lagos 52, 8.1% in Maiduguri (North Eastern), Nigeria 53. In a rural

mission hospital in Imo state 6.0% was reported, 54 while in an urban tertiary hospital in

Owerri, Imo state (South Eastern), Nigeria, prevalence of 8.6% was reported 39.

Obesity is generally assessed using body mass index (BMI) or Quenelle’s index.

Body mass index is calculated using weight in kilogram (kg) divided by height in meters

squared (m2). Body mass index is a good predictor of general obesity while waist

circumference (WC) measured in centimetres (cm) is a good predictor of abdominal

obesity57. Waist circumference is not measured routinely in clinics55. Some of the reasons

include time, extra work load and inexperience 55.

27

The National Institute of Health (NIH), defined underweight as body mass index

(BMI) of less than 18.5kg/m2, normal weight as body mass index (BMI) of 18.5-24. 9kg/m2,

overweight as body mass index (BM1) of 25.0-29.9kg/m2 and obesity as (BMI) of more than

or equal to 30.0kg/m2 90. Obesity is further categorized as follows:

Class I (mild) obesity as (BMI: 30.0-34.9kg/m2).

Class II (moderate) obesity as (BMI: 35.0 – 39.9 kg/m2).

Class III (severe / extreme) obesity as (BMI: ≥ 40.0 kg/m2)90.

International Diabetes Association (IDA) recommended WC that is greater than or equal to

94 cm for men and greater than or equal to 80 cm for women as a measure of abdominal

obesity. These values should be used for black African subjects though derived using

Caucasian subjects56. On the other hand, a cohort study among black women in South Africa,

using Receiver Operator Characteristic (ROC) curve analysis suggested waist circumference

cut–off point of 91.5 cm as appropriate for black African women 35.Reciever operator curve

is the graph of sensitivity against false positive rate (1-specificity). The area under the curve

is used to calculate the diagnostic accuracy of the test.

The National Institute of Health and Scottish Intercollegiate Guideline Network (SIGN),

recommended use of BMI cut–off point for initial assessment of obesity and WC cut-off

point as alternative40. World Health Organization recommends concurrent use of BMI and

28

WC57. In a study that looked at various measures of heights and weights, body mass index

was reported as the most suitable assessment of obesity in Nigerian adult population 58.

The association between diabetes and body weight has been a subject of study especially in

developed countries and there is a strong positive correlation between obesity and diabetes.

It has been noted that glucose impairment (diabetes and impaired glucose tolerance) was

more in obese than normal BM1 subjects14. Hospital based studies on these are rare to the

best of the author’s knowledge.

Though many studies have found association between BMI and diabetes, some

observed graded association between different classes of obesity and diabetes. These findings

included studies outside and within sub-Saharan Africa 31,32,33,23. Some, other studies did not

find any association between BMI and diabetes 14,37,59,60. There are questions as to why these

discrepancies and what could have being responsible. World Health Organization had

recommended lower BMI cut-off point for Chinese and Asians of ≥ 23.0kg/m2 (22kg/m2 to

25kg/m2) as increased risk and ≥ 27.5kg/m2 (26kg/m2 to 31kg/m2) as high risk61. This is

consequent upon the fact that Asians generally have higher percentage of body fat than

whites. Again, obesity in this group does not directly correspond with diabetes rates61.

Most of the measures of obesity (BMI and WC) used for blacks were derived using

Caucasian subjects. Could this be responsible for the variations in studies in blacks?

Crowther CJ and colleagues in South Africa tend to suggest a possible reason35. They

29

observed that using Receiver Operator Curve (ROC), that women of black African descent,

have significant metabolic syndrome occurring in them at a higher WC of 91.5 cm and hence

advocated that in black women higher WC of ≥ 91.5cm will be appropriate as cut-off point

for diagnosing metabolic syndrome, instead of ≥ 80.0cm recommended by International

Diabetic Association (IDA)35.

The National Health and National Examination Surveys (NHANES) have shown that

BM1 increase is usually associated with increase in prevalence of diabetes and other

cardiovascular risk factors 62.

Chui M and colleagues reported significant increase in the risk of diabetes with

increasing body mass index in obese subjects when compared with subjects with normal body

mass index. The rate varied in different ethnicities, black 4.5, white 6.2, south Asia 6.3 and

Chinese 11.7 34. Iloh GUP and colleagues in a study in a tertiary hospital reported prevalence

of diabetes among obese subjects of 15.0%. Diabetes was the third most common morbidity

in these obese subjects 39.

The relationship between obesity and diabetes was for a long time perceived to be

strong. A general finding from majority of earlier studies suggested that obesity was central

to a common pathway linking non-communicable and other cardiovascular morbidities 4.

Again, obesity seems to play a causal role in the pathogeneses of type 2 diabetes 49.

30

2.4 PREVALENCE OF KNOWN DIABETES

This is the percentage of diabetic subjects that knew about their diabetic status prior to a

study. This knowledge may be attributed to a number of factors such as the level of health

education on diabetes a person has and also the manner of health seeking behaviour a person

has. Literacy level which may give insight to the socio-economic status of an individual has

been found to play a role in the development of diabetes. High socio-economic status has

been found to be associated with diabetes especially in the developing countries 27,63. There is

also high prevalence of poor education (no formal education and primary education) and poor

health seeking behavior. This may account for low knowledge of diabetic status among

diabetics63. Unlike in the developed countries, where high literacy level (secondary education

and above) may result in good knowledge and attitude of risk factors of diabetes. In

developed countries like Turkey with high literacy level, there is high prevalence of known

diabetes, when compared with low prevalence of known diabetes in developing countries like

Bangladesh 7.0% and 2.5% respectively14,32.

In USA, Cowie CC and colleague, reported prevalence of known diabetes of about

6.5%31. In Chennai, India, Mohan V and colleagues reported known diabetes prevalence of

7.2% 33. In Futa Jallon, Guinea 3.4% of known diabetes was reported among urban subjects,

while among rural subjects, none of them knew about their diabetic status prior to the study37.

In an urban population study in Port Harcourt, Rivers state about 5.5% of the subjects knew

of their diabetic status23.

31

2.5 NON-MODIFIABLE RISK FACTORS

A study in USA observed that type 2 diabetes was significantly high in men and non-

Hispanic blacks in the last decades 31. Also higher prevalence was seen in men until ≥ 65

years among blacks and Mexicans. Standardized prevalence was not significantly different

between men and women 31. Valdez R and colleagues reported that prevalence of diabetes

increased with age, body mass index and hypertension 64. Diabetes prevalence increased with

increasing age for males and females 14. In Africa, life expectancy seems to have increased

relatively and this may be attributed to relatively better healthcare, feeding habit and

economy which has made it possible for more people to attain old age27. Chronic diseases,

like diabetes has been shown to increase with age64 and the reason could be that mentioned

above. In South Africa, Motola AA and colleagues observed that diabetes increased with

age60. Balde NM and colleagues in Guinea found independent association between age and

diabetes, just like Oyegbade OO and colleagues in Nigeria, found independent association

between age and diabetes 37,65.

Family history of diabetes has been identified as a strong risk factor for developing

diabetes. Lysenko V and colleagues in prospective cohort study in Sweden showed that first

degree family history of diabetes is associated with two-fold increase in risk of developing

type 2 diabetes66. A cohort study of middle aged men and women observed that family

history of diabetes was independently associated with risk of incident diabetes67. They also

observed that risk was highest in subjects with maternal history of diabetes 67. When those

32

with family history of diabetes were compared, a graded increase in the risk of diabetes was

observed. The risk increased from those with no family history of diabetes to those with third

degree family history of diabetes then to those with second degree and finally, to those with

first degree family history of diabetes 64.

2.6 MODIFIABLE RISK FACTORS

Alcohol consumption is an old practice among black Africans. Alcohol is prepared in

different forms locally and consumed for different purposes. Some forms of locally made

alcoholic beverages include palm wine, ‘ogogoro’, ‘burukutu’, ‘kai kai’ etc. Anecdotal report

have cited some reasons for consuming alcohol to include: as medicine, to cure low mood,

enhance performance, to increase breast milk flow, to be able to sleep well among other

reasons. Unfortunately, alcohol has many disadvantages when compared with its advantages.

Alcohol acts in the pleasure area of the limbic system by altering the GABA and the

dopamine neurotransmitter levels88. One gets enough pleasure or feeling of being in a high

mood with a small quantity of alcohol, at the initial period of experimenting with alcohol88.

As time goes on, one would require larger amount of alcohol to achieve the same level of

pleasure, hence leading to alcohol abuse. At the point when one finds it difficult to cope

unless one has taken alcohol, this is known as alcohol dependence88. Alcohol abuse has

ruined many lives, jobs, families, caused accidents and chronic diseases, law breaking and

encounter with law enforcement agents among others88. Some tools may be used in clinics to

assess the level of alcohol misuse or abuse includes the CAGE questionnaire89. Where C

33

stands for: Have you ever felt like cutting down on the quantity of alcohol you consumed? A

stands for: Have you ever felt angry when people criticized your drinking? G stands for:

Have you ever felt guilty about your habit of drinking? While E stands for: Have you ever

gotten up to take alcohol as first thing in the morning “eye opening” so as to have your nerves

steadied? Any answer to two of the questions means positive response, hence needs further

assessment 89. Moderate alcohol consumption improves insulin sensitivity, increase HDL

cholesterol and adinopectin. While heavy alcohol on the other hand, result in excess calorie

intake and obesity, increased triglyceride and pancreatitis 61. Some studies have found a U-

shaped relationship between new cases of diabetes and alcohol consumption 68, 69, 61. This

implies that non-drinkers, light drinkers and heavy drinkers have increased risk of diabetes

unlike moderate drinkers that have decreased risk of diabetes68,69. Ekpenyong CE and

colleagues in Nigeria and Aksu H in Turkey did not find any association between alcohol

consumption and diabetes 26,32. Motola AA and colleagues in South Africa found independent

association between alcohol consumption and type 2 diabetes60. In a meta–analysis of 15

cohort studies on alcohol consumption and development of type 2 diabetes by Koppes LLJ

and colleagues, showed that thirty percent reduction in the risk of type 2 diabetes was

observed in moderate alcohol consumers when compared with heavy alcohol consumers and

abstainers. No risk reduction was observed in low and high BM1 categories 69. In New

Zealand, a study showed a U-shaped relationship between alcohol consumption and incident

cases of type 2 diabetes, impaired glucose tolerance and hypertension68. Moderate alcohol

34

drinkers had lower risk of diabetes in normal and overweight subjects but higher risks of

diabetes in obese individuals, even after adjusting for confounders 68. When one has multiple

lifestyle behaviours (factors) that reduce development of type 2 diabetes, moderate alcohol

consumption will further reduce the risk of developing diabetes by 40% when compared to

abstention 70.

Many people in sub-Saharan Africa seem to be adopting western lifestyle which

encourages sedentary lifestyle, at the same time not involving in regular exercise. This has

helped in no small measure in fuelling diabetes epidemic. Some of the factors encouraging

this type of life style include modernization of means of transportation, driving and time

spent watching television among others61. Physical activity has been shown to reduce risk of

diabetes 8,9, while sedentary lifestyle has an important association with diabetes through the

mechanism of encouraging weight gain. Regular exercise may delay or prevent the

progression of diabetes9. Weight reduction of 5-7% and moderate physical activity for at

least 30 minutes each day (150 minutes per week) was found to lower the risk of developing

diabetes by 58% in overweight people with pre-diabetes (impaired glucose) 71. The standard

method of assessing physical activity is through assessing energy expenditure or metabolic

equivalents (METs)8. Metabolic equivalent is the ratio of metabolic rate during a specific

physical activity to a reference metabolic rate8. Physical activity has inverse association with

type 2 diabetes 36, 72, 73. Cardio-respiratory fitness which is health–related component of

physical fitness: moderate to high level cardiorespiratory fitness is associated with a lower

35

risk of mortality from all–causes and cardiovascular diseases 8. It also seems to reduce the

higher risk of associated with obesity, even though it does not seem to reduce it completely 8.

Cigarette smoking seems to be common among blacks especially in the younger

population. Some of the reason people give for indulging in such lifestyle include peer group

influence, to prevent cold, to reduce weight74. It is cheap and readily available. Smoking is

not without its attendant health problems not only to the smokers but also to those around the

smoker through the influence of secondary smoke 74. Studies have suggested that nicotine

which is one of the active components of cigarette decreases insulin sensitivity causes

pancreatitis, causes disorder of glucose and lipid metabolism75. It worsens diabetes by the

above mechanism. Current smokers were observed to have 45% increased risk of diabetes

compared with the non-smokers 61. These effects seemed to have a dose response

relationship. Smoking more than 20 sticks of cigarette per day and a smoking duration of ≥

40 years were associated with increased risk of diabetes76.

Cigarette smoking was associated with increased risk of type 2 diabetes. The risk increased

with the number of sticks smoked per day 72, 73, 76, 77. This association was when smokers

were compared with people who never smoked. There was a little difference in risk between

light smokers and heavy smokers among the current smokers.

36

CHAPTER THREE

3.0 MATERIALS AND METHOD

3.1 STUDY AREA

This study was carried out at the General Out-Patient Clinic (GOPC) of Federal Teaching

Hospital Abakaliki, Ebonyi State between 10:00am and 4:00pm every working day. Ebonyi

State is located in South Eastern Nigeria. It has a population of about 2.2 million. Abakaliki

which is the state capital has an estimated population of 141,428 from 2006 census figure78.

Abakaliki hosts many farms, quarry factories, rice mills, luxurious hotels and higher

Institutions, federal and state government ministries, departments and agencies. Abakaliki is

inhabited by mainly farmers, professionals such as civil servants; skilled, semi-skilled,

unskilled workers and students. The economic activity in Abakaliki is low when compared to

industrial and commercial cities like Onitsha, Lagos, Abuja and Port-Harcourt.

Majority of the people in the city have igbo culture, in that most dwellers are Igbo,

reflecting the region of the country. The staple foods of inhabitants consist of carbohydrate

meals (derived from yam, rice, cassava, maize e.t.c).

3.2 HEALTH FACILITY

Federal Teaching Hospital Abakaliki (FETHA), is located in the state capital of Ebonyi

State. It is the only tertiary hospital in Abakaliki and Ebonyi State at present. In December

2011, the Federal Government of Nigeria acquired former Ebonyi State University Teaching

Hospital (EBSUTH), upgraded former Federal Medical Centre Abakaliki (FMCA) and

merged both to form the Federal Teaching Hospital Abakaliki (FETHA). This hospital serves

Ebonyi State and sizeable population from the four (4) surrounding states namely: Abia,

Enugu, Cross-River and Benue States.

37

The General Out-Patient Clinic (GOPC), of the hospital is manned by Family

Medicine Department. It is the gateway of undifferentiated patients into the hospital. From

the hospital records, about one hundred and twenty (120) adult patients are seen every

working day at GOPC.

3.3 STUDY POPULATION

This comprised of all adult obese patients aged 18 years and above that attended GOPC

of FETHA who gave consents to participate in the study and a non-obese comparison group.

3.4 STUDY DESIGN

This is a cross-sectional comparative study.

3.5 SAMPLE SIZE

Sample size formula for comparative study with quantitative outcome79

n = 2

2

221

d

ZZ

SDxr

r

where

n = Minimum sample size

r = Ratio of control to cases, 1 for equal case and control

SD = Standard deviation of Random blood sugar, taken as 3.439.

d = Expected mean difference of random blood sugar of cases and control23,39

(9.1mmol/l – 7.9mmol/l)

Zβ = Standard normal variate for power taken at 80%, which is 0.84

38

Zα = Standard normal variate of confidence level of 95% which is 1.96

n =

2

2

2

2.1

96.184.04.3

1

11 xx

= 2 x 44.1

84.756.11 x

= 128

Thirteen (13) which is 10% of minimum sample size was added as non-respondents

rate, hence; total sample size of 141 each for the obese and non-obese groups. The total

sample size expected was 282. The confidence level chosen was 95% and p-value less than

0.05.

3.6 INCLUSION CRITERIA

1. Patients aged 18 years and above presenting to GOPC of FETHA.

2. Obese patients; with BMI ≥ 30kg/m2 and non-obese with BMI < 30kg/m2 (<

18.5kg/m2 and 18.5-24.9kg/m2 and 25.0-29.9kg/m2).

3. Patients who gave consent to participate in the study.

3.7 EXCLUSION CRITERIA

1. Pregnant women

2. Patients with demonstrable ascites and other forms of oedema.

3. Patients who have physical deformities affecting the spine and/or the limbs.

4. Critically ill patients

39

3.8 SAMPLING METHOD

The sample size of 140 registered obese patients aged 18 years and above was recruited by

systematic random sampling based on inclusion and exclusion criteria. One (1) declined from

participating; hence response rate was 99.3%. First, balloting was done between odd

numbers and even numbers. Even numbers was chosen. Then every even number starting

with two were selected. Those, who refused to participate in the study, were exempted and

next other even number selected. Those who declined from participating or those who did not

meet the selection criteria were exempted and next even number sample chosen. Those who

refused to participate were accounted for by the 10% non-responders’ rate. GOPC had an

average of 4 new obese patients’ visit every working day (from one month census of all obese

patients without repetition by the researcher and average for one day of the five working days

taken). Height and weight was introduced as part of routine assessment of patients attending

GOPC, FETHA during the time of writing of the proposal, hence there was no record of such

prior to writing of the researcher’s proposal. This was the reason why one month census of all

obese patients by the author was used to estimate the number of obese seen in the GOPC

every working day. The study population will consist of 4 obese patients’ × 5 day/week x 4

weeks/ month x 3.5 months = 280. A sample size of 140 of non-obese comparison group was

chosen using systematic random sampling. Since an average of 116 non-obese patients’ visit

every working day and 8120 non-obese patients in 3.5 months. Balloting first was done

between odd numbers and even numbers. Even numbers was chosen. Every other 58th even

number was chosen starting with 2, since there are about 116 non-obese visits every working

day based on hospital records. Any sample which did not meet the selection criteria was

exempted and the next even sample that met selection criteria selected.

40

3.9 DURATION OF DATA COLLECTION

Data was collected for 3 months and two weeks between January 15th and April 30,

2015. The researcher attended to the obese patients and recruited subjects from them.

Comparison group was recruited from the non-obese patients by the researcher.

3.10 ETHICAL CONSIDERATION

This study was approved by the Research and Ethical Committee of Federal Teaching

Hospital Abakaliki (Appendix II). It complied with Helsinki Declaration 1975 (revised 1983,

1989 and 1996) on human experimentation. Permission to conduct the study was obtained

from the Head of Department of Family Medicine. Informed consent (appendix III) was

obtained from all participants and confidentiality was maintained in accordance with standard

practice.

3.11 METHOD OF DATA COLLECTION

A combination of structured interview and clinical and biochemical measurements were used

in data collection.

The structured interview of participants was done using researcher administered

questionnaire based on the modified WHO STEPwise80 (appendix IV), instrument approach

to surveillance of chronic non-communicable disease risk factors. Following the interview,

the clinical and biochemical measurement were done.

1. Blood pressure: Each patient was rested for 5 minutes. Thereafter; the blood pressure

was measured in sitting position with the right arm resting on the table. An adequate

cuff-size on the right arm, using a mercury sphygmomanometer (Dekamet Accoson,

made in England MKS) and a 3m Littman Stethoscope were used to measure blood

pressure. The first Korotkoff sound heard over the brachial artery on the cubital fossa,

41

was recorded as systolic blood pressure (SBP) and the fifth Korotkoff (the

disappearing) recorded as the diastolic blood pressure (DBP). Two measurements

were taken 5 minutes apart and the average calculated to the nearest 1mmHg was

reported as the blood pressure. Blood pressure criteria were based on Joint National

Committee on Prevention, Detection, Evaluation and Treatment of high blood

pressure JNC721.

2. Height (Ht) and weight (Wt): The height (Ht) and weight (Wt) of each subject were

measured using a stadiometre, that had a weighing scale (RGZ-160 health scale)

attached to it. This instrument was made by Lincoln Mark Medical England. Height

was recorded while subject was standing erect on stadiometre with the back and the

buttocks on stadiometre steel and subject looking straight ahead (an imaginary line

drawn from the angle of the eyes to the upper point of the attachment of the ear and a

vertical line through the vertex were perpendicular). Measurement was to the nearest

centimetres (cm) and then converted to metres (m) by dividing with one hundred.

Weight was measured with subject wearing light cloths and with bare feet.

Measurement was reported to the nearest 0.1kg. Zero mark calibration was ensured

before each measurement and standard weight of 10kg was used each day to ensure

accuracy of measurements. The body mass index (BMI) of each subject was

calculated by using

Body mass index categories were

< 18.5kg/m2 underweight

18.5-24.9kg/m2 normal weight

42

25-29.9kg/m2 overweight

kg/m2 obese

3. Waist circumference (WC): Was determined using dressmaker’s measuring tape,

while in standing position and breathing normally. Measurement was taken mid-way

between lowest ribs and iliac crest with the tape horizontal. Measurement was taken

to the nearest centimetre. For women subjects: normal WC was < 80cm, while

abnormal (abdominal obesity) was WC 80cm. Men subjects: normal WC was <

94cm, while abnormal was WC 94cm.

4. Blood glucose estimation: Subjects were diagnosed of diabetes based on RBG

200mg/dl (11.1mmol/l) with symptoms of diabetes. Also current history of using

anti diabetic medication and/or past medical history of being diabetic. Random blood

glucose was used due to the fact that it was by 10:00am that GOPC takes off actively

and after this time the hospital protocol would not accept blood glucose estimation as

fasting blood glucose. This is an epidemiologic survey; hence one abnormal RBG

was used to make diagnosis of diabetes40. Subjects were seated, tip of the middle

finger cleaned with spirit swab and dried. A prick was made with a fine lancet, a drop

of blood placed into the curvet of Accu chek Active Roche Laboratory glucometre

equipment with compatible strip and reading taken and recorded. The glucometre

was validated by ensuring that each compatible strip pack used the corresponding

code, since there was no control serum in the pack.

3.12 FUNDING OF THE STUDY

The cost of the relevant investigations and the logistics was borne by the researcher

43

3.13 STUDY PROTOCOL

1. Conceptualization of the study topic between February 10th, and May 5, 2013.

2. Literature review/writing of the proposal/ethical consideration between June 2nd, 2013

and March 5, 2014.

3. Proposal approval by the Faculty and the College between August 17th and December

10, 2014 respectively.

4. Printing of the questionnaire between December 12th and December 14, 2014.

5. Purchase of glucometre and other materials for the study on January 5th, 2015.

6. Collection of data between January 15th, and April 30, 2015.

7. Analysis of data and reporting between May 5th, and July 20, 2015.

3.14 DATA ANALYSIS

The data was analysed using IBM SPSS statistics 20 software version. Subjects’

characteristics were described by percentages, means and standard deviations (SD).

Comparison for continuous variable was done using Student’s t test. Chi-square test and

Fisher’s exact test were used when any of the expected in any of the cells is less than 5, for

discrete and categorical variables. These were used to assess association between Type 2

diabetes and the risk factors. Spearman’s rho correlation analysis was used to assess the

strength of association between continuous risk factor variables and the blood glucose.

Logistic regression was used to ascertain which risk factor had independent association with

type 2 diabetes. The statistical tests were considered significant at a probability level of less

than 5% (p-value < 0.05). Data was presented in tables, charts and graphs.

44

3.1

5 S

TUD

Y F

LOW

CH

AR

T

Figu

re 1

: St

ud

y Fl

ow

Ch

art

122

1

1

28

2

8

7

9

0

45

CHAPTER FOUR

4.1 RESULT

Two hundred and eighty (280) out of the two hundred and eighty-two (282) subjects

selected for this study participated giving a response rate of 99.3%. See figure 1. This is made

up of one hundred and forty subjects of the non-obese group and one hundred and forty

subjects of the obese group. Subjects with diabetes, impaired glucose and normal glucose

were compared in the two groups. A total of 120 subjects in the non-obese group had normal

glucose levels and 112 subjects in the obese group had normal glucose levels. Those with

impaired glucose were 9 among the non-obese and 11 among the obese. There were a total of

11 diabetic subjects in non-obese group and 17 diabetic subjects in the obese group. Fisher’s

exact test was used when any of the expected in any of the cells was less than 5. Below are

the data of the subjects.

4.2 SOCIODEMOGRAPHIC CHARACTERISTICS

There were more non-obese participants that lived in the rural area (55.0%), unlike in the

obese, where 27.9% lived in the rural area. There was statistically significant difference

observed in the locality of the participants in the non-obese and the obese groups (p = 0.001).

See table 1.

The proportion of females to males in both the non-obese and obese group was about 6:1

each. There was no significant difference between the proportion of females to males in both

groups (p = 0.59).

Age group forty to forty- nine (40-49) years had the highest proportion of participants in the

non-obese 37(28.2%) while age group 30-39 years had the highest proportion in the obese

46

40(31.0%). There was no significant difference between the proportions of different age

groups in the non-obese and the obese (Fisher’s exact = 1.71, p = 0.85). See table 1.

The occupation with highest proportion of participants in the non-obese 43(30.7%) and obese

group 57(40.7%) was trading. There was significant difference between the occupational

status of the non-obese and the obese groups (Χ2 = 17.12, p = 0.007). See table 1.

There were more married participants in both the non-obese and the obese groups 98(70.0%)

and 111(79.3%) respectively. There was no statistically significant difference between

marital status of the obese and the non-obese (Χ2 = 5.38, p = 0.06). See table 1.

47

Table 1: Sociodemographic characteristics of the respondents

Characteristics Non-obese Obese Test statistics P-value

Locality

Rural

Urban

77(55.0%)

63(45.0%)

39(27.9%)

101(72.1%)

X2 = 21.25

0.001

Sex

Male

Female

20(14.3%)

120(85.7%)

17(12.9%)

123(87.1%)

X2 = 0.28

0.591

Age Group (years)

< 20

20 – 29

30 – 39

40 – 49

50 – 59

≥ 60

1(0.8%)

20(15.3%)

34(26.0%)

37(28.2%)

24(18.3%)

15(11.4%)

0(0.0%)

15(11.6%)

40(31.0%)

33(25.6%)

22(17.1%)

19(14.7%)

Fisher’s Exact =

1.71

0.85

Marital Status

Single

Married

Widowed

19(13.6%)

98(70.0%)

23(16.4%)

8(5.7%)

111(79.3%)

21(15.0%)

X2 = 5.38

0.068

Occupational Status

Employed

Student

7(5.0%)

9(6.5%)

8(5.7%)

6(3.6%)

Business Executive

Civil Servant

Farming

Trading

House help

Manual labourers

Artisan

Retiree

Others

0(0.0%)

34(24.3%)

29(20.7%)

43(30.7%)

2(1.4%)

0(0.0%)

9(6.4%)

1(0.7%)

6(4.3%)

1(0.7%)

41(29.3%)

16(11.4%)

57(40.7%)

0(0.0%)

1(0.7%)

1(0.7%)

6(4.3%)

4(2.9%)

Χ2 = 17.123

0.007

48

4.3 PREVALENCE OF TYPE 2 DIABETES AMONG THE OBESE AND NON-

OBESE

Out of the 280 people screened for diabetes, 11(7.9%) subjects that were diagnosed with

diabetes in the non-obese while 17(12.1%) had diabetes in the obese. Hence the crude

prevalence of diabetes in the two groups was 28(10.0%). Nine (6.4%) of the non-obese group

had impaired blood glucose when compared with 11(7.8%) of the obese which had impaired

blood glucose. There was no statistically significant difference between the magnitude of

diabetes in the obese and the non-obese (Χ2 = 1.762, p = 0.414). See table 2.

Table 2: Comparison of blood glucose in the obese and non-obese group

Group

Non-obese Obese Total

Normoglycaemia 120(85.7%) 112(80.0%) 232(82.9%)

Blood glucose

categories

Impaired glucose 9(6.4%) 11(7.9%) 20(7.1%)

Diabetes 11(7.9%) 17(12.1%) 28(10.0%)

Total 140(100%) 140(100%) 280(100%)

X2 1.762

P-value 0.414

49

4.4 PREVALENCE OF KNOWN DIABETICS IN THE OBESE AND THE NON-

OBESE GROUPS

Out of the 280 participants screened for diabetes, 11(7.9%) were known to have diabetes

among the non-obese prior to the study, while 10(7.1%) were known diabetics in the obese

group. The crude prevalence of known diabetes in the study population was 7.5%, average of

prevalence of known diabetics in obese and the non-obese. There was no statistically

significant difference between the magnitude of known diabetics in the obese and the non-

obese (Χ2 = 0.510, p = 0.821). See table 3.

Table 3: Prevalence of known diabetics in the non-obese and the obese group

Group

Non-obese Obese Total

Yes 11(7.9%) 10(7.1%) 21(7.5%)

Known diabetics No 129(92.1%) 130(92.9%) 259(92.5%)

Total 140(100%) 140(100%) 280(100%)

X2 0.510

P-value 0.821

50

4.5 AGE DISTRIBUTION

The subjects that participated in this study were aged between 18 years to 77 years. The mean

age of the study population was 43.4±11.9. The mean age of the non-obese group was 43.1±

12.2 years. The mean age of the obese group was 43.8 ± 11.7 years and there was no

statistically significant difference in the mean age of the obese group and the non-obese group

(t= 0.47, p = 0.63). The male to female ratio in the non-obese group was 1 : 6, while that of the

obese group was 1: 7. The male to female ratio in the obese and non-obese was about the same.

There was progressive increase in the proportion of those who have diabetes from age group

20-29 years 0.0%(0) to 60 years and above 23.9%(9). Diabetes started occurring from among

those who were 30 years and above. Impaired glucose increased gradually from age group 20-

29 years 2.8%(1) till age group 40-49 years 11.4%(9), and the declined progressively till age

group 60 years and above 5.7%(2). There was statistically significant association between age

group and diabetes (Χ2 = 26.572, p = 0.008). See table 4

72.7% 27.3%

%

52.9% 47.1%

51

Table 4: Assessment of blood glucose by age category

Blood Glucose categories A

ge

gro

up

(yea

rs)

Normoglycaemia Impaired Diabetes Total

< 20

20 – 29

30 – 39

40 – 49

50 – 59

≥ 60

1(100%)

35(97.2%)

69(89.6%)

62(78.5%)

40(78.4%)

25(70.4%)

0(0.0%)

1(2.8%)

3(3.9%)

9(11.4%)

5(9.8%)

2(5.7%)

0(0.0%)

0(0.0%)

5(6.5%)

8(10.1%)

6(11.8%)

9(23.9%)

1(100%)

36(100%)

77(100%)

79(100%)

51(100%)

36(100%)

Χ2 26.572

P – Value 0.008

52

4,6 FAMILY HISTORY OF DIABETES

Family history of diabetes was obtained from 56(20.0%) out of 280 subjects. Out of this

number, 11(19.6%) of those with family history of diabetes, had diabetes when compared

with 17(7.6%) in those who did not have family history of diabetes. There were more

subjects with impaired glucose who had no family history of diabetes 17(7.6%) when

compared with 3(5.4%) of those who had family history of diabetes. There was statistically

significant association between family history of diabetes and diabetes (Χ2 = 7.343, p =

0.025). See table 5.

Table 5: Family history of diabetes and Blood glucose categories in the respondents

Family history of diabetes

Yes No

B

lood g

luco

se c

ateg

ori

es Normoglycaemia 42(75.0%) 190(84.8%)

Impaired glucose 3(5.4%) 17(7.6%)

Diabetes 11(19.6%) 17(7.6%)

Total 56(100%) 224(100%)

Χ2 7.343

P – Value 0.025

53

When family history of diabetes was stratified based on degree of relative affected, diabetes

was more in subjects who had first degree relatives that had diabetes 4(30.8%), then followed

by those who had second degree relatives with family history of diabetes 7(18.9%). Impaired

glucose was found more in subjects that had family members of first and second degree

relatives that had diabetes 1(25.0%). There was no statistically significant association

between diabetes and the degree of relatives with history of diabetes (Fisher’s exact = 5.675,

p = 0.402). See table 6.

Table 6: Stratification of respondents’ family history of diabetes and the blood glucose

categories

Blood glucose categories

Normoglycaemia Impaired Diabetes

Str

ati

fica

tion

First degree relatives 9(69.2%) 0(0.0%) 4(30.8%)

Second degree

relatives

28(75.7%) 2(5.4%) 7(18.9%)

Third degree relatives 2(100%) 0(0.0%) 0(0.0%)

Both first and second

degree relatives

3(75.0%) 1(25.0%) 0(0.0%)

Total 42(75.0%) 3(5.4%) 11(19.6%)

Fisher’s exact 5.675

P – value 0.402

54

4.7 SEX

Diabetes was more in males 18.9%(7) than in females 8.6%(21), unlike in the impaired

glucose which was commoner in females 7.4%(17) compared to the males 5.4%(2). There

was no statistically significant association between diabetes and sex. (Fisher’s exact = 4.045,

p = 0.117). See table 7.

Table 7: Sex and blood glucose categories in the respondents

Sex

Male Female Total

B

lood g

luco

se c

ateg

ori

es Normoglycaemia 28(75.7%) 204(84.0%) 232(82.9%)

Impaired glucose 2(5.4%) 18(7.4%) 20(7.1%)

Diabetes 7(18.9%) 21(8.6%) 28(10.0%)

Total 37(100%) 243(100%) 280(100%)

Fisher’s Exact 4.045

P – Value 0.117

55

4.8 Waist Circumference

Diabetes and impaired blood glucose were commoner in those with abnormal waist

circumference 11.4%(23) and 7.9%(16) respectively. There was no statistically significant

association between diabetes and waist circumference. (Χ2 = 2.417, p = 0.299). See table 8.

Table 8: Blood glucose and waist circumference categories

Blo

od g

luco

se c

ateg

ori

es

Waist circumference

Normal WC Abnormal WC

Normoglycaemia 69(88.5%) 163(80.7%)


Diabetes 5(6.4%) 23(11.4%)

Total 78(100%) 202(100%)

X2 = 2.417

p – value = 0.299

56

4.9 BODY MASS INDEX

Diabetes and impaired blood glucose was commoner in the obese subjects 12.1%(17) and

7.9%(11) respectively. There was progressive increase in the proportion of diabetics from

normal weight to overweight to obese 5.9%(4), 9.7%(7), and 12.1%(17) respectively. There

was no statistically significant association between diabetes and BMI categories (X2 = 3.342,

p = 0.502). See table 9.

Table 9: Blood glucose and BMI categories of the respondents

BMI groups

Blo

od

glu

cose

cate

gori

es

Normal Overweight Obese

Normoglycaemia 58(85.3%) 62(86.1%) 112(80.0%)

Impaired 6(8.8%) 3(4.2%) 11(7.9%)

Diabetes 4(5.9%) 7(9.7%) 17(12.1%)

Total 68(100%) 72(100%) 140(100%)

Χ2

P – value

3.342

0.502

57

4.10 CIGARETTE SMOKING

Diabetes was commoner among those who had ever smoked cigarette 12.0%(3), while

impaired blood glucose was commoner among those who had never smoked cigarette

7.5%(19) respectively. There was no statistically significant association between cigarette

smoking and diabetes (Fisher’s exact, p = 0.781). See table 10.

Table 10: Blood glucose categories and cigarette smoking

Cigarette smoking

B

lood

glu

cose

cate

gori

es Yes No



Diabetes

Total

3(12.0%)

25(100%)

25(9.8%)

255(100%)

Fisher’s exact

P – value

0.781

58

4.11 ALCOHOL CONSUMPTION

Diabetes was commoner among those who had ever consumed alcohol 12.4%(19), while

impaired glucose was commoner in those who had never consumed alcohol 8.7%(11). There

was no statistically significant association between alcohol consumption and diabetes (Χ2 =

2.474, p = 0.263). See table 11.

Table11: Alcohol Consumption and blood glucose categories

Alcohol consumption

B

lood

glu

cose

cate

gori

es Yes No

Normoglycaemia 125(81.7%) 107(84.3%)


Diabetes

Total

19(12.4%)

153 (100%)

9(7.0%)

127 (100%)

X2 = 2.474

P = 0.263

59

When those who had ever consumed alcohol were grouped into moderate and heavy

consumers, diabetes and impaired blood glucose were commoner among those who were

heavy alcohol consumers 20.0%(1) and 20.0%(1) respectively. There was no statistically

significant association between diabetes and the quantity of alcohol consumed (Fisher’s exact

= 2.221, p = 0.284). See table 12.

Table 12: Blood glucose and units of alcohol consumed categories of the respondents

Alcohol Intake

B

lood

glu

cose

cate

gori

es Moderate Heavy



Diabetes

Total

18(12.2%)

148 (100%)

1(20.0%)

5 (100%)

Fisher’s Exact

P – value

2.221

0.284

60

4.12 PHYSICAL ACTIVITY:

Diabetes was commoner among those who were physically active 14.0%(8) and impaired

glucose which was commoner among those who were physically inactive 7.6%(17). There was

no statistically significant association between physical activity and diabetes (Χ2 = 1.559, p =

0.459) See table 13.

Table 13: Blood glucose categories and physical activity

Physical activity

Inactive Active

B

lood g

luco

se c

ateg

ori

es



Diabetes

Total

20(9.0%)

233 (100%)

8(14.0%)

57 (100%)

Χ2

P – value

1.559

0.459

61

4.13 BLOOD PRESSURE:

Diabetes and impaired blood glucose was commoner among the hypertensives 12.4%(14) and

9.8%(11) respectively. There was no statistically significant association between blood

pressure groups and diabetes (Χ2 = 3.431. p = 0.193). See table 14.

Table 14: Blood glucose and blood pressure (BP) categories of the respondents

BP groups

B

lood g

luco

se c

ateg

ori

es

Normotensives Hypertensives



Diabetes 14(8.4%) 14(12.4%)

Total 167(100%) 113(100%)

X2

P – value

3.431

0.193

62

4.14 BIVARIATE CORRELATION OF WHOLE CAPILLARY BLOOD GLUCOSE

(RBG) WITH QUANTITATIVE RISK FACTORS

There was a Spearman’s rho correlation used to determine the relationship between

capillary blood glucose (RBG) and the quantitative variables risk factors at significant

level of 1% which was lower than the significant level of 5% that was set for this

study. The findings were as follows:- There was a weak positive correlation between

RBG and age, which was statistically significant (rs = .271, p = 0.0001, n = 280).

There was a weak positive correlation between RBG and average systolic blood

pressure which was statistically significant (rs = .161, p = 0.007, n = 280). There was

a weak positive correlation between RBG and BMI, which was not statistically

significant (rs = .106, p = 0.076, n = 280). There was a very weak positive correlation

between RBG and average diastolic blood pressure which was not statistically

significant (rs = .093, p = 0.120, n = 280). There was a weak positive correlation

between RBG and WC, which was statistically significant (rs = .175, p = 0.003, n =

280). There was a weak positive correlation between RBG and smoking index, which

was not statistically significant (rs = .325, p = 0.113, n = 25). There was weak positive

correlation between RBG and units of alcohol consumed, which was not statistically

significant (rs = .115, p = 0.157, n = 153). See table 15.

63

Table 15: CORRELATIONS OF WHOLE CAPILLARY BLOOD GLUCOSE WITH CONTINUOUS VARIABLE RISK

FACTORS

Correlations

Whole capillary blood glucose

(mg/dl) Random (RBG) value

Age (yrs) SBP Average DBP Average Weight (kg) BMI (kg/m2) Waist circumference (WC)

Alcohol intake

(unit/week

Smoking index

Spearman's rho

Whole capillary blood glucose (mg/dl) Random (RBG) value

Correlation Coefficient 1.000 .271** .161** .093 .105 .106 .175** .115 .325

Sig. (2-tailed) . .000 .007 .120 .079 .076 .003 .157 .113

No of subjects 280 280 280 279 280 280 153 25

Age (yrs)

Correlation Coefficient 1.000 .378** .240** -.019 .069 .194** .114 -.098

Sig. (2-tailed) . .000 .000 .749 .247 .001 .159 .642

No of subjects 280 280 279 280 280 153 25

SBP Average

Correlation Coefficient 1.000 .779** .189** .215** .291** .015 -.113

Sig. (2-tailed) . .000 .002 .000 .000 .852 .591

No of subjects 280 279 280 280 153 25

DBP Average

Correlation Coefficient 1.000 .213** .237** .315** -.037 -.059

Sig. (2-tailed) . .000 .000 .000 .652 .780

No of subjects 279 280 280 153 25

Weight (kg)

Correlation Coefficient 1.000 .846** .791** .042 .019

Sig. (2-tailed) . .000 .000 .612 .928

No of subjects 279 279 152 25

BMI (kg/m2)

Correlation Coefficient 1.000 .865** -.051 -.140

Sig. (2-tailed) . .000 .531 .504

No of subjects 280 153 25

Waist circumference (WC)

Correlation Coefficient 1.000 .098 -.053

Sig. (2-tailed) . .228 .802

No of subjects 153 25

Alcohol intake (unit/week

Correlation Coefficient 1.000 .411

Sig. (2-tailed) . .051

No of subjects 23

Smoking index

Correlation Coefficient 1.000

Sig. (2-tailed) . No of sujects

**. Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

64

4.15 CORRELATION SCATTER PLOTS OF CONTINUOUS RISK FACTORS WITH

RANDOM BLOOD GLUCOSE THAT WERE STATISTICALLY SIGNIFICANT

In further analysis of capillary blood glucose and the continuous variable risk factors that were

significant namely capillary blood glucose and age; capillary blood glucose and waist

circumference; and capillary blood glucose and systolic blood pressure. Scatter plot graphical

method was used to explain further the association between capillary blood glucose and these

risk factors.

65

Scatter plot of whole capillary blood glucose and age

Using the slope of the line of best fit, it was calculated using ruler that for every 20 years

increase in age the whole capillary blood glucose increases by about 30mg/dl. The dots that were

clustered below were far more than those that were above the line of best fit, showing that the

strength of the relationship between age and whole capillary blood glucose was weak. The

direction of line best fit is positive, showing that as age increases the whole capillary blood

glucose increases. See figure 2.

Figure 2: Scatter plot of whole capillary blood glucose and age

rs = .271

n = 280

p =0.0001

00000.000

1

66

Scatter plot of whole capillary blood glucose and waist circumference

Using the slope of the line of best fit, it was calculated using ruler that for every 20cm increase in

waist circumference, there was about 15mg/dl increase in whole capillary blood glucose. The

dots that were clustered below were far more than those above the line of best fit showing that

the strength of association between waist circumference and whole capillary blood glucose is

weak. The direction of the line of best fit was positive showing that as the waist circumference

increases, the whole capillary blood glucose increases as well. See figure 3.

Figure 3: Scatter plot of whole capillary blood glucose and waist circumference

rs = .175

n = 280

p = 0.003

67

Scatter plot of whole capillary blood glucose and systolic blood pressure

Using the slope of the line of best fit, it was calculated using ruler that for every 50mmHg

increase in the systolic blood pressure, there is about 40mg/dl increase in whole capillary blood

glucose. The dots that were clustered below were far more than those above the line of best fit,

between systolic blood pressure 80mmHg and 160mmHg, showing that the strength of

association between systolic blood pressure and whole capillary blood glucose was weak. The

direction of the line of best fit was positive, showing that as the systolic blood pressure increases

the whole blood capillary blood glucose increases too. See figure 4.

Figure 4: Scatter plot of whole capillary blood glucose and systolic blood pressure

rs = .093

n = 280

p = 0.007

68

4.16 LOGISTIC REGRESSION ANALYSIS OF RISK FACTORS OF DIABETES

The probability of not having diabetes is lower in those who live in urban areas than those who

live in rural areas by 44.3% (OR = 0.557) [CI = 0.210-1.475]. This deduction is not statistically

significant (p = 0.239).

The probability of having diabetes is higher in males than in females by 3.9 times (OR =

3.905)[CI = 1.001-15.244]. This deduction is not statistically significant (p = 0.05) because the

p-value set for this study was p < 0.05.

For every unit increase in age, the probability of not having diabetes will be lower by 0.6%(OR =

0.940)[CI = 0.906-0.976]. This deduction is statistically significant (p = 0.001).

The probability of having diabetes in those who have family history of diabetes is 3.7 times

higher than in those without family history of diabetes (OR = 3.718)[CI = 1.438-9.613]. This

deduction is statistically significant (p = 0.007).

The probability of not having diabetes among smokers is lower by about 68.9% than in the non-

smokers (OR = 0.311)[CI = 0.054-1.797]. This deduction is not statistically significant (p =

0.192).

The probability of having diabetes among alcohol consumers is higher by 1.6 times than in the

non-alcohol consumers (OR = 1.592)[CI = 0.614-4.123]. This deduction is not statistically


The probability of not having diabetes among those with high blood pressure is lower by 21.3%

than in those without high blood pressure (OR = 0.787)[CI = 0.885-1.206]. This deduction is not

statistically significant (p = 0.616).

69

The probability of having diabetes among those with abnormal BMI (obese) is higher by 3.3%

than in the non-obese (OR = 1.033)[CI = 0.885-1.206] . This deduction is not statistically


The probability of not having diabetes among those with abnormal WC is lower by 1.4% than

those with normal WC (OR = 0.986)[CI = 0.915-1.063]. This deduction is not statistically


The probability of not having diabetes among the physically inactive is lower by 13.6% than in

those who are physically active (OR = 0.864)[CI = 0.519-1.437]. This deduction is not

statistically significant (p = 0.573).

Table 16: Logistic Regression of risk factors of diabetes

B S.E. Wald df p-value Exp(B)

or

OR

95% C.I.for

EXP(B)

Lower Upper

Locality -.586 .497 1.389 1 .239 .557 .210 1.475

Sex 1.362 .695 3.844 1 .050 3.905 1.001 15.244

Age -.061 .019 10.380 1 .001 .940 .906 .976

Family history 1.313 .485 7.344 1 .007 3.718 1.438 9.613

Smoking -1.168 .895 1.703 1 .192 .311 .054 1.797

Alcohol consumption .465 .486 .916 1 .339 1.592 .614 4.123

Blood pressure -.239 .478 .252 1 .616 .787 .309 2.007

BMI .032 .079 .169 1 .681 1.033 .885 1.206

WC -.014 .038 .129 1 .719 .986 .915 1.063

Physical activity -.146 .260 .318 1 .573 .864 .519 1.437

Constant 3.629 2.955 1.508 1 .219 37.675

70

4.17 AWARENESS OF LIFESTYLE MODIFICATION

Diabetes and impaired blood glucose was commoner among those who were not aware of at least

one lifestyle modification activity 14.7%(10) and 8.8%(6) respectively. There was no statistically

significant association between awareness of lifestyle modification and diabetes (Χ2 = 2.786, p =

0.248). See table 17.

Table 17: Awareness of lifestyle modification and blo1` od glucose categories

Awareness of lifestyle modification

Yes No

B

lood g

luco

se c

ateg

ori



Diabetes 18(8.5%) 10(14.7%)

Total 212(100%) 68(100%)

Χ2 2.786

P – value 0.248

71

4.18 OCCUPATIONAL STATUS

Diabetes was commoner in traders 42.9%(12), then followed by in farmers 17.9%(5). Impaired

blood glucose was commoner also in the traders 30.0%(6), then followed by in the civil servants

25.0%(5). There was no statistically significant association between occupational status and

diabetes (Fisher’s exact = 24.814, p = 0.176). See table 18.

Table 18: Occupational status and blood glucose categories in the respondents

Occupation Normoglycaemia Impaired

glucose

Diabetes Fisher’s

exact

P - value

Unemployed

Business executive

Students

Civil servant

Farming

Trading

Nanny

Manual labourers

Artisan

Retired

Others

Total

11(4.7%)

1(0.4%)

13(5.6%)

67(28.9%)

38(16.4%)

82(35.3%)

2(0.9%)

1(0.4%)

6(2.6%)

3(1.3%)

8(3.4%)

232(100%)

1(5.0%)

0(0.0%)

1(5.0%)

5(25.0%)

2(10.0%)

6(30.0%)

0(0.0%)

0(0.0%)

1(5.0%)

2(10.0%)

2(10.0%)

20(100%)

3(10.7%)

0(0.0%)

0(0.0%)

3(10.7%)

5(17.9%)

12(42.9%)

0(0.0%)

0(0.0%)

3(10.7%)

2(7.1%)

0(0.0%)

28(100%)

24.814

0.176

72

4.19 LOCALITY

Diabetes and impaired blood glucose was commoner in those who lived in the urban areas

11.0%(18) and 8.5%(14) respectively. There was no statistically significant association between

locality and diabetes (Χ2 = 1.722, p = 0.432). See table 19.

Table 19: Blood glucose categories and locality of the respondents

Locality

Rural Urban

B

lood g

luco

se c

ateg

ori



Diabetes 10(8.6%) 18(11.0%)

Total 116(100%) 164(100%)

Χ2

P – value

1.722

0.423

73

4.20 MARITAL STATUS

Diabetes was commoner among those who were widowed 15.6%(7), while impaired blood

glucose was commoner among the married 7.6%(16). There was no statistically significant

association between marital status and diabetes (Fisher’s exact = 2.950, p = 0.558). See table 20.

Table 20: Marital status and blood glucose categories



M

ari

tal

Sta

tus Single

Married

Widowed

24(88.9%)

173(82.8%)

36(80.0%)

2(7.4%)

16(7.6%)

2(4.4%)

1(3.7%)

19(9.6%)

7(15.6%)

27 (100%)

208 (100%)

45 (100%)

Fisher’s exact

P – value

2.950

0.558

74

4.21 EDUCATIONAL STATUS

Diabetes was commoner among those with primary education 15.4%(12), then followed by those

with secondary education and tertiary education 6.3%(4) and 5.9%(6) respectively. Impaired

blood glucose was also commoner in those with primary education 11.5%(9) then followed by

those with tertiary education 6.9%(7). There was no statistically significant association between

educational status and diabetes (Fisher’s exact = 10.827, p = 0.080). See table 21.

Table 21: Blood glucose and educational status categories of the respondents



Ed

uca

tion

al

Sta

tus No formal education

Primary

Secondary

Tertiary

29(93.6%)

59(73.1%)

59(90.5%)

91(87.2%)

1(3.2%)

9(11.5%)

2(3.2%)

7(6.9%)

1(3.2%)

12(15.4%)

4(6.3%)

6(5.9%)

31 (100%)

80 (100%)

65 (100%)

104 (100%)

Fisher’s exact

P – value

10.827

0.080

75

In this study, diabetes was higher in the obese (BMI ≥ 30kg/m2), urban dwellers, and males.

Diabetes was more in those with abnormal waist circumference, with family history of diabetes,

those who had ever smoked cigarette, those who had ever consumed alcohol and heavy alcohol

drinkers. Diabetes was commoner in the hypertensives. Diabetes was more in physically active,

and those with less literacy level. Diabetes was higher in the widowed, trader and among those

who were not aware of at least one lifestyle modification activity.

76

CHAPTER FIVE

5.0 DISCUSSION

5.1 PREVALENCE OF DIABETES

The crude prevalence of diabetes in this study population (obese and non-obese) was

10.0%. This was similar to 10.3% observed in an urban community population study in Futa

Jallon, Guinea37 and lower than that observed in an urban community population study in Uyo,

Akwa Ibom State, Nigeria of 12.7%26. However, the crude prevalence of diabetes in Nulifer

district Turkey was 6.1%34. The high prevalence of diabetes in Uyo community population study

may be attributed to urbanization and westernization of the lifestyle in the capital city, of an oil

rich state. The high prevalence in this study could be partly ascribed to the hospital population

studied. The low crude prevalence in Nulifer Turkey, an urban population community study,

could be partly attributed to ethnicity because blacks are more prone to diabetes compared with

the whites31. Again, better lifestyle modification attitudes like exercise and recreation activities

in whites could explain the low prevalence of diabetes in Turkey.

Prevalence of diabetes in the obese in this study was 12.1% which was lower than that observed

by Iloh GUP and colleagues in a study carried out among obese in a tertiary hospital in Owerri,

Imo State, of 15.1% 39. The prevalence of diabetes among obese in a rural mission hospital in

Imo State was 3.9% 54. The similarity of the Owerri study with this study could be due to

77

homogeneity of the study population, both being a hospital population study and from the same

ethnicity and in the same region of the country. The difference in prevalence between that of this

study and that of a mission hospital in Imo State could be largely due to difference in locality.

While the mission hospital study was among rural hospital population, this study was an urban

hospital population. Prevalence of diabetes in the rural population is usually lower than that of

the urban population, probably due to rural population where work is more physically demanding

unlike the urban population that may be involved with white collar jobs that are usually

sedentary in nature and may likely be feeding on refined foods some times.

5.2 PREVALENCE OF KNOWN DIABETES

The general prevalence of known diabetes in this study population was 7.5% (21). The

prevalence of known diabetes was more in the non-obese group 7.9% (11) than the obese group

7.1% (10) . In USA, Cowie CC and colleagues reported prevalence of known diabetes of about

6.5% in a community population study31. The similarity could be due to the fact that this is a

hospital-based study hence screening for diabetes as part of care of some patients could have

resulted in high hospital prevalence of known diabetes. Also, Mohan V and colleagues reported

prevalence of known diabetes in an urban community population study of 7.2% in Chennai,

India33. Similar reason as above could explain this similarity.

78

In Futa Jallon, Guinea, 3.4% prevalence of known diabetes was reported among urban

community population and none among rural community population knew about their diabetic

status prior to the study37. The difference could be due to possible poor health seeking behavior

in most low- and middle-income countries which could be a fall-out of low socio-economic

status and educational status of most populace. The situation was worse in the rural population.

In an urban community population study in Port Harcourt, Rivers state, Nigeria, prevalence of

known diabetes of 5.5% was reported23. The difference could be due to the fact that it was a

community population study while this is a hospital population study. The similarity with the

high prevalence of known diabetes in the Turkey study 7.0%32 could be due to their better health

seeking behavior. However, in the rural community populations study in Futa Jallon Guinea

none of the subjects knew their diabetic status prior to the study37.

5.3 NON-MODIFIABLE RISK FACTORS

Diabetes was more in those aged 60 years and above in this study. This could likely be

attributed to the fact that diabetes increases with age due to insulin resistance. On the other

hand, Oyegbade OO and colleagues in a community study in Ile Ife, Osun State65, observed that

diabetes was commoner in those aged 45 years and above and that sex had significant association

with type 2 diabetes. The difference in age at which diabetes prevalence was more could be due

79

to the difference in the age group cut-off point in both studies. While this study was a hospital

population study, that of Ile Ife was an urban community population study.

The age range of subjects in this study was 18 years to 77 years which was similar to that

of tertiary hospital in Owerri of 19 years to 76 years. The male/female ratio of the tertiary

hospital study in Owerri of 1:5.239 was similar to that of this study of 1:6.5. There was similarity

in the age range and male to female ratio of both study population.

In this study, age had an independent association with type 2 diabetes in the respondents,

in logistic regression analysis of the risk factors of diabetes. In Turkey, in a community

population study, an independent association was reported between type 2 diabetes and age32.

Hussain A. and colleagues, in a community population study in Bangladesh, reported significant

association between age, body mass index and diabetes30. In Uyo, Nigeria, Ekpenyong CE and

colleagues in a community population study, observed that age was significantly associated with

non-communicable diseases such as type 2 diabetes26. The similarity with these studies could be

due to the fact that insulin resistance increases with age and hence, diabetes is more likely to

occur with aging.

In this study, type 2 diabetes was commoner in male respondents. There was no

significant association between sex and type 2 diabetes in the respondents. Similarly, Valdez and

colleagues in USA in a community population study did not find significant association between

sex and type 2 diabetes64. In a meta-analysis study by Cowie CC and colleagues in USA31in a

80

community population study, the prevalence of diabetes was reported to be significantly higher

in males in the general population similar to the findings of this study though not statistically

significant. In the non-Hispanic blacks, prevalence was higher in females, unlike the findings in

this study. Hussain A and colleagues in Bangladesh in a community population study, observed

higher prevalence of type 2 diabetes in females though not statistically significant14, and when

other risk factors were adjusted for, sex showed statistically significant association with diabetes

in females, unlike in this study, where males had higher prevalence of diabetes and after

adjusting for other risk factors, sex showed no statistically significant association with diabetes

in the respondents. The similarity in the sex not been significant could be due to the low number

of male subjects in this study compared to that of the Bangladeshi study. Ekpenyong in Uyo,

Nigeria, reported slightly higher prevalence of diabetes in males26. This is similar to the findings

of this study. Possible reason could be similarity of race and region of the country of the

subjects.

In this study, diabetes was commoner in subjects with family history of diabetes in the

respondents. There was an independent significant association between family history and

diabetes in the respondents. The odds of having diabetes in those with family history of diabetes

was high in the respondents (OR = 3.718)[CI = 1.438-9.613]. Similarly, Mumu SJ and

colleagues in a community population study, found independent association between family

history of diabetes and diabetes in a Bangladesh population63. This was equally similar to that

81

observed by Oyegbade OO and colleagues in a community population study in Ile Ife, Osun

State, Nigeria65. The similarity between these studies and this study could likely be due to

similarity in race. Also, Motala AA and colleagues in a community population study in South

Africa; and Aksu H and colleagues in a community population study in Turkey had similar

observation in their studies32,60. Sukurai M and colleagues in a community population cohort

study in Japan observed that those with family history of diabetes had 80% greater risk of

incident diabetes compared with those without family history of diabetes72. In this study,

diabetes was commonest among respondents with first degree family relatives that had diabetes

30.8%(4). Most community population based studies found significant association between

family history of diabetes and diabetes bringing to the fore the strong part played by gene in

having diabetes. In USA in a community population study, when family history was graded into

high risk (subjects with two first degree relatives with diabetes); moderate risk (subjects with one

first degree relatives and one second degree relatives) and average risk (subjects with no family

history of diabetes), graded risk of type 2 diabetes was observed in decreasing order from high

risk to average risk64. This was similar to the finding in this study. First degree relative family

history of diabetes had highest prevalence 30.8%(4), followed by those with second degree

family relatives 718.9%(7). This study did not grade the risk of diabetes in similar fashion as that

of the American study. This brings to the fore the need to screen adult hospital clients with

family history of diabetes.

82

5.4 MODIFIABLE RISK FACTORS

In this study, locality had no association with diabetes in the respondents. Rural

respondents had less odds for diabetes (OR = 0.557)[CI = 0.210-1.475], though it is not

statistically significant. About 55.0% of the non-obese lived in the rural areas and they may

likely be doing manual jobs and eating natural food, unlike 72.1% of obese that lived in the

urban area with its attendant western lifestyle32. There was no independent statistically

significant association between locality and diabetes in this study. Balde and colleagues in a

community population study in Guinea, similarly, observed significant association between

diabetes and urban location37. The similarity between the finding in this study and that of the

above study could be largely attributed to similarity in race and socio-demographic factors. They

all belong to low- and middle-income countries. Also, Motola AA and colleagues in South

Africa, found significant association between the history of urban living and diabetes in males60,

though this effect was lost after adjusting for other risk factors.

In this study, diabetes was more in those with low literacy level (primary school education)

12(15.4%) then followed by those with secondary education 4(6.3%) in the respondents. There

was no association between educational status and diabetes. Mumu SJ and colleagues in a

community population cross-sectional analytical study in Bangladesh observed that high literacy

level was associated with good knowledge and attitude of risk factors of diabetes63. Another

83

study in Bangladesh observed that diabetes was more prevalent in those with high literacy level

and that high literacy level had independent association with diabetes14. This study on the

contrary found that diabetes was more in those with low literacy level. High socioeconomic

status and high literacy have been associated with increased risk of diabetes in developing

countries27. This could be partly attributed to belief that when one eats and puts on weight that it

is an evidence of wealth and good living unlike in developed countries, where high

socioeconomic status or high literacy result in more discretion in feeding habit and lifestyle32.

Again difference in locality of both populations could be a contributing factor. A rural

population study in Imo State observed that low literacy level was more in the obese54 just as

was observed in the Inter Act Consortium study72.

Diabetes was more prevalent among traders and civil servants and there was no

significant association between occupational status and diabetes in the responders. In this

environment, most traders sit down waiting for customers (sedentary lifestyle) most of the time,

eat a lot of junk food like snacks, soft drinks and when they want to eat food may likely

patronize the food vendors. The food vendors often spice their food with monosodium glutamate

and other spices to make it tasty. On the other hand, Azimi-Nezhad M and colleagues in a

community population study in Iran did observe significant association between occupation and

diabetes, which was highest among the retirees, followed by the unemployed and the

84

housewives81. This was similar to the finding by Peykari N and colleagues in a community

population study in Iran too82.

Diabetes was more prevalent in the widowed and the married in the responders. Marital

status had no significant association with diabetes in the responders. Similarly, Hwang J and

colleagues in a community population study in Korea observed higher prevalence of diabetes

among the married and the partners than in the singles83.The similarity could be due to the high

proportion of the married in both populations. Azimi-Nezhad M and colleagues in a community

population study in Iran observed significant association in the prevalence of diabetes among

married, singles, widowed and divorced subjects81, unlike this study which did not show any

statistically significant association between marital status and diabetes.

In this study, there was significant weak positive correlation between random blood

sugar and waist circumference in the respondents. Waist circumference is expected to correlate

positively with blood glucose. There was no significant association between BMI and type 2

diabetes in this study. Surprisingly, Frank LK and colleagues in a case–control tertiary hospital

population study in Ghana observed that neither BMI nor WC had any association with type 2

diabetes, even when those with poor glucose control among the cases were excluded 84. In this

study however, those with poor blood glucose control were not excluded. Park SW and

colleagues had suggested that in diabetics, poor glucose control was the underlying mechanism

85

for reduced BMI and mean body cell mass 85. Many studies within and outside Africa had found

significant association between high body mass index, increased waist circumference and type 2

diabetes 23,31,32,33 . In different regions of Africa, studies with different study designs, that used

different obesity measurements, different diabetes measurements, and different sample sizes had

observed different types of association between BMI and diabetes. In some, BMI was

consistently associated with diabetes both in males and females. In others, association differed in

males and females, while in other there was no association 14, 37, 59, 60. Extraneous confounders

may be possible explanation for the masking of the association between BMI and diabetes in this

study. On the other hand, it seems illogical for it to explain why family history of diabetes, age,

waist circumference, average systolic pressure had significant association with diabetes. The

likely explanation could be racial inappropriate cut-off point of BMI for the blacks. A different

body mass index cut-off point had been advocated for the Chinese and Asians. This was

consequent upon the fact that obesity does not directly correspond with diabetes in this group.

Chinese and Asians have higher waist circumference when compared with the whites61. The

recommended WHO body mass index cut-off point of ≥ 23 kg/m2 for public health action for

Asian represents increased risk and ≥ 27.5kg/m2 as high risk86. Crowther NJ and colleagues in a

prospective cohort study in South Africa suggested waist circumference of ≥ 91.5cm as

appropriate cut-off point for black women35, instead of ≥ 80cm advocated by International

Diabetes Association (IDA).

86

In this study, it was observed that blood glucose showed weak positive correlation with body

mass index in the respondents which was not significant. Body mass index is expected to

correlate with blood glucose positively. Bakari AG and colleagues in Zaria, Nigeria, observed

positive correlation between BMI and random blood glucose among the females, but no

correlation in the males91.

This study showed high degree of awareness of lifestyle modification with an average of

about 70.6% of all subjects having knowledge of at least one lifestyle modification. A similar

study in a rural population showed average awareness of 38.1% of subjects having knowledge of

at least one lifestyle modification54. The difference could be attributed to difference in locality of

the two populations. Generally rural population have low literacy level, Mumu SJ and colleagues

observed that high literacy level (secondary School and above education) is independently

associated with good knowledge and attitude of risk factors of diabetes63. The proportion of

diabetics who were not aware of at least one lifestyle modification activity was higher

14.5%(10), unlike those who were aware of at least one lifestyle modification activity 8.5%(18).

Diabetes was more in the respondents who had ever smoked 12.0%(3). The odds of not

having diabetes in smokers was low (OR = 0.311)[CI = 0.054-1.797). This effect was not

significant (p = 0.192). Similarly in a community population study in Turkey, no significant

association was found between cigarette smoking and diabetes 32. A community population study

87

in South Africa did not find any significant association between diabetes and smoking 60. This

was similar to finding in Inter Act consortium study82. The similarity with these studies could be

similarity in study design, since they were all observational cross-sectional or case-control

studies. However, a community population cohort prospective study in China observed that

smoking more than 20 sticks of cigarette per day and a smoking duration of 40 years or more

was associated with increased risk of diabetes69. The difference observed could be due to the

prospective study design. Nicotine which is one of the active components of cigarette decreases

insulin sensitivity, and causes disorder of glucose and lipid metabolism75. In a community

population meta-analysis study, it was observed that current smokers had 45% increased risk of

diabetes than non-smokers69. There seems to be a dose response relationship between risk of

diabetes and the number of sticks of cigarette smoked69. In this study, the lack of association

between cigarette smoking and diabetes may be due to the small number of the respondents that

smoked (25). Again, none of the subjects in this study smoked 20 sticks of cigarette per day and

none of the smoking index was significant.

In this study, diabetes was more prevalent in those that have ever consumed alcohol, in

the responders 12.4%(19). There was no significant association between alcohol consumption

and diabetes. Those who were heavy consumers had more diabetes 20.0%(1). The effect of

alcohol consumption seems to increase the odds for diabetes in the responders (OR = 1.592)[CI

= 0.614-4.123] though the deduction is not strong that it is not significant because the confidence

88

interval crossed one. Moderate alcohol consumption improves insulin sensitivity, increases HDL

cholesterol and adinopectin, while heavy alcohol intake results in excess calorie intake and

obesity, increased triglyceride and pancreatitis61. Some community population studies found

significant association between alcohol consumption and diabetes. Light and heavy alcohol

consumption increase risk of diabetes 68, 69. Similar to this study, Ekpenyong CE and colleagues

in a community population study in Uyo, Nigeria and Aksu H and colleagues in a community

population study in Bursa, Turkey did not find any association between alcohol consumption and

diabetes 26,32. The similarity between Uyo study and this study could be due to similarity in the

race of respondents.

Diabetes was commoner among those who were physically active. Physical activity did

not have significant association with diabetes. Physical activity seemed to lower the risk of

diabetes in the respondents (OR = 0.864)[CI = 0.915-1.063], this deduction is not strong because

the confidence interval crossed one. This effect was not statistically significant (p = 0.573).

Using occupation to assess physical activity in this study was not the best. Energy expenditure or

metabolic equivalents (METs) still remain the standard indices for assessing physical activity.

Using METs was not feasible in this study because very few subjects engaged in regular

exercise. The non-association observed could be due to non-standard method used for assessing

physical activity. However, physical activities have been shown to reduce the risk of diabetes by

58.0% in overweight prediabetic people. It improves glycaemic control and prevents

89

cardiovascular diseases73. Inactivity encourages weight gain. Hu HB and colleagues in a

community population study, observed that activities that reduce physical activity like

modernization of mode of transportation, driving and time spent watching television have been

associated with diabetes61. These are common practices in our environment. Aksu and colleagues

in Turkey did not observe any association between physical activity and diabetes32.

There was weak positive correlation between 280 subjects’ RBG and average systolic blood

pressure, which was statistically significant (rs = .161, p = 0.007). Diabetes was commoner in

the hypertensive subjects of the respondents 12.4%(14). There was no significant association

between blood pressure grouping and diabetes. Hypertension increased the risk of metabolic

syndrome and insulin resistance. Moreover, some antihypertensive drugs are diabetogenic6.

Giday A and colleagues in Ethiopia observed significant association between hypertension and

diabetes59. Motola AA and colleagues in a community study in South Africa equally observed

significant association between hypertension and diabetes60. However, Sukurai M and colleagues

in a community population study in Japan did not observe any association between hypertension

and other chronic diseases and diabetes67. This was similar to the findings of this study. Possibly

the no association observed in this study could be due to use of Random blood glucose to screen

for diabetes which might have resulted in fewer subjects being identified as newly diagnosed

diabetics.

90

5.5 CONCLUSION

The prevalence of diabetes was high in this population both in the obese and non-obese though

higher in the obese. There was no statistically significant difference between the prevalence of

diabetes in the obese and the non-obese. The prevalence of known diabetics was relatively low in

both groups, despite being a hospital population. The two independent associated risk factors in

the respondents namely: family history of diabetes and age were both non-modifiable risk

factors. Obesity (BMI) surprisingly did not show any association with diabetes in this study.

There was weak positive but statistically significant correlation of 280 subjects’ random blood

glucose and WC, age, and average systolic blood pressure in the respondents. There is need to

screen all adult hospital clients especially those who are 60 years and above, who have family

history of diabetes for diabetes since these risk factors were statistically significantly associated

with diabetes.

91

5.6 LIMITATIONS

1) This is a hospital-based study. Its findings may not be applied to the general population.

2) Cross-sectional study design has weakness of not being able to ascertain temporal

relationship and the strength of recommendation is low

3) Use of random blood glucose for screening for abnormal blood glucose is not ideal as

many with abnormal glucose could have been missed, unlike fasting glucose or glucose

tolerance tests.

4) All potential confounders may not have been completely detected and accounted for and

hence cannot be ruled out.

5) There was delay between the time of printing the questionnaire and collecting data due to

financial constraint.

5.7 STRENGTH OF THIS STUDY

There was homogeneity of the sources of the obese and the non-obese groups. They were all

from hospital-based subjects.

92

5.8 RECOMMENDATIONS

1. There is low prevalence of known type 2 diabetes among this hospital-based population, hence

the need to educate adults attending primary care clinics on diabetes and the need of screening

for type 2 diabetes.

2. Traders in this study had the highest number of diabetics. There is need to reach out to this

population with a view to finding out factors that predispose them to diabetes, educating them

and screening them for diabetes.

3. Hospital clients that are 60 years old and above, having family history of diabetes with

abnormal WC and high systolic blood pressure should be routinely screened for diabetes, since

these increase the risk of type 2 diabetes in them.

4. Diabetes and impaired blood glucose was commoner among those who are not aware of at

least one lifestyle modification activity. Emphasis should therefore be placed on counseling

patients on lifestyle modification activity during consultations so as to help increase awareness

of lifestyle modification, since it may help reduce prevalence of diabetes.

93

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APPENDICES

Appendix I

Common alcoholic beverages in Abakaliki and their alcohol by volume (ABV)

(1) Legend stout 33cl or 60cl 7.5%

(2) Harp larger 60cl 5.15%

(3) Star larger 60cl 5.1%

(4) “33” Export larger 60cl 5.0%

(5) Turbo king stout 60cl 6.5%

(6) Guinness stout 32.5cl or 60cl 7.5%

(7) Vodka lemon flavour (Redd’s) 37.5cl 5.0%

(8) Hero 60cl 5.2%

(9) Eagle larger 60cl 5.0%

(10) Snap 30cl 5.0%

(11) Origin spirit 60cl 6.0%

(12) Smirnoff premium 30cl 5.5%

(13) Gulder 60cl 5.2%

(14) Stazenbrau 45cl 5.0%

(15) Squard 5 Dark rum 50cl 45.0%

(16) Whisky 180ml 42.8%

(17) Baron Snap 50cl 40.0%

(18) Palm wine 4.0%87

A shot is about 30ml

A glass is about 150ml

107

APPENDIX II

108

APPENDIX III

INFORMED CONSENT FORM

TYPE 2 DIABETES AND ITS ASSOCIATED RISK FACTORS: MAGNITUDE AMONG

ADULT OBESE PATIENTS ATTENDING GOPC, FETHA, EBONYI STATE, NIGERIA.

Dear sir/Madam,

I am currently a part II candidate of the National Postgraduate Medical College of Nigeria in the

faculty of Family Medicine. I am conducting a research on the above mentioned topic. This is

aimed at improving our service delivery, adding to knowledge and inspiring others into more

research on surrounding issues.

The questionnaire will take about 20mins, and some measurements will be taken (height,

weight, blood pressure and waist circumference). Then fasting blood glucose or random blood

glucose, will be determined by making a fine lancet prick on your middle finger and using a drop

of blood on a glucometer strip. Lancet prick is done after cleaning the middle finger with a spirit

swab and drying. Lancet prick with a fine lancet is mildly painful. Pain usually stops after few

minutes.

Participation is voluntary and you are at liberty to decline anytime without offering any

reason. Withdrawal from research work will not in any way affect your being attended to as a

patient of this hospital at any time. All information given will be treated confidentially and will

not specifically be linked with you.

The study has been ethically approved by the research and Ethical Committee of the

Federal Teaching Hospital Abakaliki (FETHA) and they can be contacted via e-mail at

[email protected] or by phone +2348039558074 or +2783409650.

Should you have any enquiries please feel free to contact me at any time via e-mail at

[email protected], or by phone +2347030547313.

Your assistance would be much appreciated.

Yours sincerely,

DR. AMAZUE HENRY

By signing or thumb printing here, you consent to participate in this study

……………………. …………………….. ……………………..

Initials Sign/ Right thumb print Date

mailto:[email protected]

mailto:[email protected]

109

Appendix IV

RESEARCH QUESTIONNAIRE

INTRODUCTION

1. This is a research questionnaire on the dissertation topic; Type 2 diabetes and its

associated risk factors: magnitude among obese adult patients attending GOPC of

FETHA, Ebonyi State, Nigeria.

The dissertation is a requirement for the award of fellowship in the Faculty of Family

Medicine of the National Postgraduate Medical College of Nigeria.

To achieve this goal, your participation and cooperation is highly needed. You are please

required to provide information required in the consent form indicating your willingness

to participate in the study. The information you give will be treated with the highest level

of confidentiality and it will take you a few minutes to answer.

2. Kindly give only one response for each question or information required except where

otherwise instructed. Where a blank space exist, kindly give the appropriate answer(s)

3. Thank you for your cooperation

Serial number -----------------------------------------

Hospital number --------------------------------------

Address------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------------------------------------

---------------------------------------------------

Rural Urban

SECTION A

Socio-Demographic Data

1. Sex

(a) Male (b) Female

2. Marital status

110

(a) Single (b) married (c) separated (d) divorced

(e) Widowed

3. Occupation

(a) Unemployed (b) student (c) business executive

(d) Civil servant (e) farming (f) trading

(g) Nanny (h) manual laborer (i) artisan (specify)--------------------

(j) Retiree (k) other (specify) -----------------------

4. Educational status

(a) No formal education (b) primary (c) secondary

(d) Tertiary

5. Date of Birth Age(years)

Age Group (years)

< 20

20-29

30-39

40-49

50-59

≥ 60

SECTION B

6. Have you been told previously by a doctor or other health worker that you have diabetes?

(a) Yes (b) No

If No, skip question 7, 8 and 9 and go to 10

7. How long have you had it?

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(a) 1< year (b) 1-5years (c) 6-10years (d) 11-15year (e)

15-20 years (f) > 20yrs

8. Are you currently taking medication(s) for diabetes? (a) Yes (b) No

9. Name the drug (s) -----------------------------------------------------------------

10. Do any member(s) of your family have diabetes?

(a) Yes (b) No

If No, skip question 11 and go to 12

11. Which member(s) of your family? (specify)-----------------------------------

(a) Father (b) mother (c) brother (d) sister (e) uncle

(f) Aunty (g) cousins (h) grandfather (i) grand mother

Stratification

(a) first degree relative

(b) Second degree relative

(c) Third degree relative

(d) Both first and second degree relative

12. Do you have hypertension? (high blood pressure) (a)Yes (b) No

If No, skip question 13, 14 and 15 and go to 16

13. How long have you had it? (a) < 1 year (b) 1-5 years (c) 6-10years

(d) 11-15 years (e) 15-20 years (f) > 20yeras

14. Are you currently taking medication(s) for high blood pressure?

(a) Yes (b) No

15. Name the drug(s) (specify) -----------------------------------------------

SECTION C: HEALTH BEHAVIOURS

16. Have you ever smoked cigarette in any form? (a) Yes (b) No

If No skip question 17, 18 19, 20 and go to 21

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17. When did you quit cigarette (a) < 1 year (b) 1-5 years (c) 6-10years (d)

11-15 years (e) 15-20 years (f) > 20years

18. How many sticks/packs of cigarette do you smoke per day? ------------------

19. Have you ever consumed alcohol? (a) Yes (b) No

If No, skip questions 22, 23, 24, 25 and go to 26.

20. What type of alcohol drink do you consume? (you can tick more than one) (a) Beer

(b) stout (c) palm wine (d) brandy (e) whiskey (f) Local gin

(g) Red wine

21. How much of the alcohol do you take at one episode? (specify) -------------

22. How frequently especially in the last 30days have you had at least one alcoholic drink?

(a) daily (b) 2days per week (c) 3days per week

(d) 4 days per week (e) 5days per week (f) 6 days per week

23. How long have you been drinking? (specify) (a) < 1 year (b) 1-5 years (c) 6-

10yeras (d) 11-15 years (e) 15-20 years (f) > 20yeras

24. Are you aware of lifestyle modification? (a) Yes (b) No

If No, skip No 27 and go to No 28.

25. What are the lifestyle modifications for preventing disease that you know?

(a) avoidance of excessive carbohydrate and/or fat intake

(b) Adequate physical activities/exercise

(c) Quitting alcohol

(d) Quitting smoking

(e) Others specify

26. Do you do any form of sports, fitness or recreational (Leisure) activities regularly?

Yes No

27. Which type? (specify).---------------------------------------------------------------

SECTION D: CLINICAL/BIOCHEMICAL MEASUREMENTS

28. Whole capillary blood glucose mg/dl(mmol/l)

Fasting (FBG) value ------------------------

Random (RBG) value ----------------------

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Ask for symptoms of diabetes, if ≥ 200mg/dl(11.1mmol/l)

Blood glucose grouping

FBG RBG Log value

Normoglycaemia

Impaired glucose tolerance

Diabetes

29. Blood pressure reading SBP DBP

1 ------------ -------------

2 ------------ -------------

Average ------------ -------------

Blood press. Group (a) Normotensive (b) Hypertensive

SBP < 140 mmHg SBP ≥ 140 mmHg

DBP < 90 mmHg DBP ≥ 90 mmHg

30. Height (m) ----------------------- m2 ----------------------------

31. Weight (kg) -----------------------

32. BMI (Kg/m2)-----------------------

Normal

Overweight

OBESITY CLASSIFICATIUON

Class I

Class II

Class III

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33. Waist Circumference (WC) -------------------------- cm

WC group Normal WC Abnormal WC

Women < 80cm ≥ 80cm

Men < 94cm ≥ 94cm

TYPE 2 DIABETES AND ITS ASSOCIATED RISK FACTORS

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