TYPE 2 DIABETES MELLITUS IN THE ARUMERU DISTRICT OF NORTHERN

TANZANIA: EVALUATION OF THE PREVALENCE AND ASSOCIATED

RISK FACTORS IN RURAL COMMUNITIES

By

BENJAMIN JOHN MILLER

A dissertation submitted in partial fulfillment of

the requirements for the degree of

DOCTOR OF PHILOSOPHY

WASHINGTON STATE UNIVERSITY

College of Nursing

May 2013

© Copyright by Benjamin J Miller, 2013

All Rights Reserved

© Copyright by Benjamin J Miller, 2013

All Rights Reserved

ii

To the Faculty of Washington State University:

The members of the Committee appointed to examine the dissertation of BENJAMIN

JOHN MILLER find it satisfactory and recommend it be accepted.

________________________________________

Lorna L Schumann, Ph.D., Chair

________________________________________

John Roll, Ph.D.

________________________________________

Robert Short Ph.D.

________________________________________

Cynthia Corbett, Ph.D.

iii

Acknowledgement

I would like to thank my family for their continued support. Spending summers in Tanzania,

learning about the community and culture has required patience and understanding. To my wife,

thank you for everything. To my children, the times I was not home, the sacrifices you have

made, helped this dream to come true.

I wish to thank my committee members for the endless time spent reading

and offering insight and wisdom into this dissertation.

Dr. Leonard Mboera, with the National Institute for Medical Research in Dar es Salaam. Thank

you for working with me these past several years. Your role as my local collaborator on this

research project was invaluable. Your patience in helping me navigate the

research regulations cannot really be acknowledged by words alone.

During the summer of 2012, I could not have tested 709 people in Tanzania without the help of

Tyler Ellis, Summer Carney, and Sarah Berg. Taking time out of your life helped me accomplish

this goal. Because of your commitment and the work of the other members of our group, this

project came to life. The information we collected will make a difference in the

lives in this region. This could not have occurred without your help.

Asante Sana!

Askofu Eliud Issangya: Asante sana kwa urafiki na mchango wenu mkubwa. Ushirikiano

mlionipa umewezesha kukamilisha utafiti huu katika muda uliokusudiwa. Maneno yangu

hayawezi kuonesha hisia za shukrani nilizonazo kwa watu wote wa Sakila na Arumeru

kwa ujumla. Shukrani zangu za kipekee ni kwa International Evangelism Centre na

wafanyakazi wake wote ambao wamenisaidia kufanikisha utafiti huu.

iv

TYPE 2 DIABETES MELLITUS IN THE ARUMERU DISTRICT OF NORTHERN

TANZANIA: EVALUATION OF THE PREVALENCE AND ASSOCIATED

RISK FACTORS IN RURAL COMMUNITIES

Abstract

by Benjamin John Miller, Ph.D.

Washington State University

May 2013

Chair: Lorna L. Schumann

Purpose: Describe the prevalence of diabetes in rural northern Tanzania and the association

between biometric markers and lifestyle indicators with diabetes, hypertension, and obesity.

Background: Diabetes in sub-Sahara Africa is expected to increase by 161% in the next 15

years. Estimates suggest the prevalence of diabetes is 4.8% in east Africa and 1.4% in rural

Tanzania. The cost of health care is high when compared to average household income.

Understanding prevalence rates as well as increased risk factors will help develop preventative

interventions.

Methods: Cross-sectional observational study was used to estimate the indirect the age-adjusted

prevalence rates of pre-diabetes and diabetes in rural Tanzania. Data regarding socioeconomic

status (SES), past medical history, behavioral lifestyle factors, and anthropometric measurements

described the association and odds ratio for the development of impaired glucose metabolism

(IGM), hypertension (HTN) and excessive adiposity.

Findings: The age adjusted rates for pre-diabetes and T2DM are 2.55% (95% CI [0.06; 0.1]) and

2.81% (95% CI [0.07; 0.12]), respectively. Impaired glucose metabolism (IGM) was associated

with excessive adiposity (p=.003) and hypertension (p=.001). Advancing age was significantly

associated with IGM (p=.004), HTN (p=.001) and excess adiposity (p<.001). Higher glucose

v

levels were associated with an increased risk of developing hypertension (p=.001) and excessive

adiposity (p=.006). Factors associated with excess adiposity included advancing age, female

gender (p<.001) and wooden or concrete household flooring (p=.001). When regressed, higher

frequency of sweet drink consumption was associated with higher fasting plasma glucose levels

(p=.012).

Significance: The prevalence of pre-diabetes and diabetes has been established in the rural

AruMeru district Tanzania. Socioeconomic development increased the risk of developing

hypertension, diabetes, and adiposity. Understanding the prevalence rates for diabetes and factors

with IGM will guide in the planning intervention strategies and health policy.

vi

Table of Contents

Acknowledgement ......................................................................................................................... iii

Abstract .......................................................................................................................................... iv

Table of Contents ........................................................................................................................... vi

List of Tables .................................................................................................................................. x

List of Figures ............................................................................................................................... xii

Chapter 1 ......................................................................................................................................... 1

Background ................................................................................................................................. 2

Globalization and urbanization. .............................................................................................. 2

Type 2 diabetes mellitus. ........................................................................................................ 3

Economics. .............................................................................................................................. 5

Complications. ........................................................................................................................ 7

Tanzania ...................................................................................................................................... 7

Research Questions ................................................................................................................... 10

Specific aims. ........................................................................................................................ 10

Theoretical Model ..................................................................................................................... 10

Conclusions ............................................................................................................................... 11

Chapter 2 ....................................................................................................................................... 12

Diagnosis of Type 2 Diabetes Mellitus ..................................................................................... 14

Type 2 Diabetes in Sub-Sahara Africa and Tanzania ............................................................... 16

Diabetes in Tanzania. ............................................................................................................ 22

Type I diabetes in sub-Sahara Africa. ................................................................................... 24

Tropical diabetes. .................................................................................................................. 25

vii

Risk Factors for Diabetes in Tanzania ...................................................................................... 25

Wealth. .................................................................................................................................. 27

Body Mass Index. ................................................................................................................. 28

Obesity in Sub-Sahara Africa. .............................................................................................. 29

Conclusions ............................................................................................................................... 30

Chapter 3 ....................................................................................................................................... 31

Research Design........................................................................................................................ 31

Participants ................................................................................................................................ 33

Participant recruitment. ......................................................................................................... 33

Inclusion criteria. .................................................................................................................. 34

Exclusion criteria. ................................................................................................................. 35

Human Subjects Protection. .................................................................................................. 35

Data Collection ......................................................................................................................... 36

Variables ................................................................................................................................... 37

Demographic variables. ........................................................................................................ 37

Socioeconomic variables. ..................................................................................................... 37

Lifestyle variables. ................................................................................................................ 38

Glucose. ................................................................................................................................ 38

Blood Pressure. ..................................................................................................................... 40

Body Mass Index. ................................................................................................................. 40

Waist-to-Hip Ratio. ............................................................................................................... 41

Medical follow-up ..................................................................................................................... 41

Analysis Plan ............................................................................................................................ 42

viii

Aim 1. ................................................................................................................................... 42

Aim 2. ................................................................................................................................... 43

Aim 3. ................................................................................................................................... 44

Conclusions ............................................................................................................................... 44

Chapter 4 ....................................................................................................................................... 45

Descriptive analysis .................................................................................................................. 45

Prevalence ................................................................................................................................. 47

Anthropometric findings ........................................................................................................... 48

Impaired glucose metabolism and demographic/biometric indicators. .................................... 48

Hypertension and demographic/biometric indicators. .......................................................... 50

Adiposity and demographic/biometric indicators. ................................................................ 51

Impaired glucose metabolism and globalization................................................................... 53

Hypertension and globalization. ........................................................................................... 55

Adiposity and globalization. ................................................................................................. 56

Conclusions ............................................................................................................................... 57

Chapter 5 ....................................................................................................................................... 59

Prevalence of diabetes............................................................................................................... 59

Biometric indicators of health ................................................................................................... 61

Globalization and Wealth ......................................................................................................... 63

Habits. ................................................................................................................................... 63

Lifestyle/wealth..................................................................................................................... 64

Limitations of the study ............................................................................................................ 67

Conclusions ............................................................................................................................... 70

ix

References ..................................................................................................................................... 72

Appendix A ................................................................................................................................... 92

Human subject’s protection certificates .................................................................................... 92

Washington State University Institutional Review Board. ................................................... 92

National Institute for Medical Research, Ethical Clearance Certificate. .............................. 93

Appendix B ................................................................................................................................... 94

Research Protocol Forms .......................................................................................................... 94

IRB approved consent: English version. ............................................................................... 96

IRB approved consent. ........................................................................................................ 100

Data collection form: English version. ............................................................................... 104

Data collection form: Swahili with English subtitles. ........................................................ 106

Results sheet provided to participant. ................................................................................. 108

Appendix C ................................................................................................................................. 110

Individual village results ......................................................................................................... 110

Meru Central. ...................................................................................................................... 110

Leguruki. ............................................................................................................................. 111

Mareu. ................................................................................................................................. 112

Maga Ya Chai. .................................................................................................................... 113

Ngurdoto. ............................................................................................................................ 114

Kikititi. ................................................................................................................................ 115

Kingori. ............................................................................................................................... 116

x

List of Tables

TABLE 1 DISTRIBUTION OF DIABETES AND IMPAIRED GLUCOSE TOLERANCE PREVALENCE .......... 117

TABLE 2 GLOBAL HEALTHCARE EXPENDITURE FOR DIABETES IN 2010 ........................................ 118

TABLE 3 HISTORICAL DIAGNOSTIC CRITERIA OF TYPE 2 DIAEBTES MELLITUS .............................. 119

TABLE 4 SUMMARY OF EPIDEMIOLOGY STUDIES IN SUB-SAHARA AFRICA ................................... 120

TABLE 5 SELECTED VILLAGES FOR RESEARCH LOCATIONS ........................................................... 123

TABLE 6 INCLUSION AND EXCLUSION CRITERIA .......................................................................... 124

TABLE 7 RECODING OF DEMOGAPHIC AND BIOMETRIC VARIABLES .............................................. 125

TABLE 8 RECODING OF SOCIOECONOMIC VARIABLES ................................................................... 126

TABLE 9 RECODING OF LIFESTYLE VARIABLES ............................................................................. 127

TABLE 10 DESCRIPTION OF VILLAGE STATISTICS ......................................................................... 128

TABLE 11 CRUDE AND AGE-ADJUSTED PREVELENCE RATES OF PRE-DIABETES AND DIABETES ..... 129

TABLE 12 EXAMINING THE ASSOCIATION BETWEEN IGM, HTN, AND ADIPOSITY ........................ 130

TABLE 13 STRENGTH OF ASSOCIATION OF BIOMETRIC INDICES ON FPG, SBP, AND BMI ............. 131

TABLE 14 ODDS ASSESSMENT OF BIOMETRIC VARIABLES ASSOCIATED WITH THE DEVELOPMENT

OF IMPAIRED GLUCOSE TOLERANCE ............................................................................ 132

TABLE 15 ODDS ASSESSMENT OF BIOMETRIC VARIABLES ASSOCIATED WITH THE DEVELOPMENT

OF HYPERTENSION ....................................................................................................... 133

TABLE 16 ODDS ASSESSMENT OF BIOMETRIC VARIABLES ASSOCIATED WITH THE DEVELOPMENT

OF EXCESSIVE ADIPOSITY ............................................................................................. 134

TABLE 17 ASSOCIATION BETWEEN LIFESTULE INDICATORS AND IGM, HTN, AND ADIPOSITY ..... 135

TABLE 18 ASSOCIATION OF LIFESTYLE BEHAVIORS N FPG, SBP, AND BMI ................................ 136

xi

TABLE 19 ASSOCIATED SOCIOECONOMIC FACTORS AND THE DEVELOPMENT OF ELEVATED

FPG, SBP, AND BMI ................................................................................................... 137

TABLE 20 ODDS ASSESSMENT OF LIFESTYLE AND ECONOMIC VARIABLES AND THE DEVELOPMENT

OF IMPAIRED GLUCOSE METABOLISM ........................................................................... 138

TABLE 21 ODDS ASSESSMENT ON LIFESTULE AND ECONOMIC VARIABLES AND THE DEVELOPMENT

OF HYPERTENSION ....................................................................................................... 139

TABLE 22 ODDS ASSESSMENT OF LIFESTYLE AND ECONOMIC VARIABLES AND THE DEVELOPMENT

OF EXCESSIVE ADIPOSITY ............................................................................................. 140

xii

List of Figures

FIGURE 1 GLOBAL LIFE EXPCTANCE BY INCOME STATUS ............................................................. 141

FIGURE 2 FACTORS CONTRIBUTING TO THE DEVELOPMENT OF CHRONIC DISEASE ....................... 142

FIGURE 2 FACTORS CONTRIBUTING TO THE DEVELOPMENT OF CHRONIC DISEASE ....................... 142

FIGURE 3 GLOBAL PERSPECTIVE OF THE AFRICIAN CONTINENT ................................................... 143

FIGURE 4 MAP OF TANZANIA ...................................................................................................... 144

FIGURE 5 MAP OF ARUSHA REGION IN TANZANIA ....................................................................... 145

FIGURE 6 CAPILLARY BLOOD SAMPLE SIZE ................................................................................. 146

FIGURE 7 PARTICIPANT SCREENING RESULTS .............................................................................. 147

FIGURE 8 AVERAGE NUMBER OF SWEET DRINKS CONSUMED PER WEEK ...................................... 148

1

Chapter 1

Diabetes is a chronic health condition that is becoming a global epidemic. In developing

countries, traditional tribal societies are adopting a modern lifestyle, while developing chronic

health conditions typically associated with developed nations (Assah, Ekelund, Brage, Mbanya,

& Wareham, 2011). The direct and indirect disease burden exceeds the financial and human

resources of the healthcare system in sub-Sahara Africa (SSA) (Kirigia, Sambo, Sambo, &

Barry, 2009). Currently, hypertension, diabetes, and coronary artery disease are the leading

chronic health conditions observed in sub-Sahara Africa (Dalal et al., 2011; Habib & Saha, 2010;

Kapiga, 2011). Infectious diseases such as human immunodeficiency virus (HIV), tuberculosis

(TB), and malaria are the leading cause of death in sub-Sahara Africa; however, with

international attention to these conditions, treatment options are improving and the mortality

rates are decreasing (Dalal et al., 2011; Joint United Nations Programme on HIV/AIDS WHO,

2006). Treatment of infectious disease has led to increased life expectancy, as well as an

increased prevalence of non-communicable diseases (Levitt, Steyn, Dave, & Bradshaw, 2011).

The combination of communicable and non-communicable diseases, referred to as double

disease burden, has increased (de-Graft Aikins et al., 2010; Levitt et al., 2011). According to

Unwin (1999), the prevalence of non-infectious diseases in developing countries will soon

outpace infectious diseases. The magnitude of these predictions were echoed by others (Dalal et

al., 2011; Habib & Saha, 2010; Lopez, Mathers, Ezzati, Jamison, & Murray, 2006), suggesting

chronic health conditions are becoming a significant concern. Currently, mortality from

communicable diseases accounts for 69% of the overall mortality in SSA, but the age specific

chronic disease mortality is sevenfold higher in low income versus high income countries (de-

Graft Aikins et al., 2010) (see Figure 1). The reason for this change is not entirely clear;

2

however, migratory patterns from rural to urban communities, adoption of a western lifestyle,

and longer life expectancy seem to contribute to the prevalence of chronic disease morbidity and

mortality (Assah et al., 2011). Products and services once available in developed countries, such

as cellular phones, motorized vehicles, and soda beverages are now easily accessible in low-

income countries. Access to western products is part of globalization and a significant

contributor to the adoption of a western lifestyle.

Background

Globalization and urbanization.

Globalization is a process where villages, regions, countries, and continents are becoming

interconnected through the movement of people, products, capital, and ideas (Maher, Smeeth, &

Sekajugo, 2010). Advancements in transportation, telecommunications, economic development,

and global awareness are contributing to development and urbanization around the world. The

United Nations Populations Division estimates that more than 50% of the world’s population

resides in urban settings. The population in Tanzania is currently 75-80% rural dwellers,

however this number is expected to change significantly by 2045; estimates predict more than

50% of the population will reside in urban communities (United Nations, 2007). The forecasted

urban growth in Tanzania will be in part of natural population growth--estimated at 60%--while

migration and spatial expansion will account for the remainder (Montgomery, 2008).

Globalization and urbanization present significant changes to dietary and lifestyle

behaviors not only in the urban setting, but in the neighboring cities and villages with urban

expansion (Montgomery, 2008; Seto, Fragkias, Güneralp, & Reilly, 2011). Access to processed

foods, sweetened drinks, refined sugars, animal products, changes in edible cooking oils, and a

decrease in daily activity has resulted in increasing rates of obesity, cardiovascular disease, and

3

diabetes (Assah et al., 2011; Maher et al., 2010; Maruapula et al., 2011; Nesto, Nelinson, &

Pagotto, 2009) (see Figures 1 and 2). Studies have identified a higher prevalence rate of T2DM

in the urban communities compared to rural dwellers in Tanzania, Mozambique, Cameroon, and

Kenya (Aspray et al., 2000; Christensen et al., 2009; Silva-Matos et al., 2011; Sobngwi et al.,

2004).

Type 2 diabetes mellitus.

T2DM is a significant global problem around the world and has health authorities

concerned (Danaei et al., 2011; Hall, Thomsen, Henriksen, & Lohse, 2011). According to the

International Diabetes Federation Atlas (2011), there are more than 366 million people

worldwide with diabetes and this number is expected to exceed 500 million people by the year

2030 (Whiting, Guariguata, Weil, & Shaw, 2011). The Middle East and Northern Africa

(MENA) region have the highest prevalence of diabetes (11.0%) followed by North America and

Caribbean (NAC) region (10.7%) and South and Central America region (9.2%).

The WHO Africa region, which consist of all of sub-Sahara Africa, currently has the

lowest prevalence of diabetes at 4.5% (Whiting et al., 2011) (see Table 1). The highest change in

T2DM prevalence rates over the next 25 years will involve the Arab crescent countries (83-166%

increase) and sub-Sahara Africa (90-161% increase) (Whiting et al., 2011; Wild, Roglic, Green,

Sicree, & King, 2004). These predictions were made from regional estimates using data collected

from the 1990 and 2000 global burden of disease study. When data was not available for a

specific country, prevalence estimates from neighboring countries provided regional estimates

for the country. A follow-up study by Whiting et al. (2011) predicted that the prevalence of

diabetes in sub-Sahara Africa will increase by 90% by the year 2030 (Whiting et al., 2011).

Whiting’s data compared the regional increases in diabetes prevalence from the International

4

Diabetes Federation’s 2011 Atlas and suggested these data were a conservative estimate of the

diabetes prevalence, noting that more than 80% of people with diabetes are undiagnosed

(Whiting et al., 2011).

An accurate description of diabetes prevalence and associated risk factors can lead to

behavior modification and other preventative interventions to decrease the burden of diabetes, as

well as associated chronic conditions, such as coronary artery disease, cerebrovascular disease,

chronic kidney disease, retinopathy, and tropic diabetic limb (Abbas, Lutale, Game, & Jeffcoate,

2008; Huffman et al., 2011). Prevention is an essential component in disease management in

economically constrained low-income countries. Available evidence suggests that in sub-Sahara

Africa, T2DM is primarily related to obesity resulting from dietary and lifestyle changes,

suggesting it can be a preventable condition (Idemyor, 2010; Travers & McCarthy, 2011). A

dietary change from high fiber diet with complex carbohydrates and fruits to a diet that includes

edible oils, processed goods, refined sugars, and non-alcoholic ready to drink beverages (NRTD)

has resulted in a pandemic of obesity in urban dwellers (Maruapula et al., 2011; Popkin, Adair,

& Ng, 2012).

T2DM is one aspect of glucose metabolic disorders, which has numerous etiologic

origins including genetic, epigenetic, and lifestyle (Bonnefond, Froguel, & Vaxillaire, 2010;

Cruickshank et al., 2001; Travers & McCarthy, 2011). Recent advances demonstrated that

several loci associated with obesity, pancreatic β-cell dysfunction, decrease in β-cell mass, and

environmental mutations are also associated with an increased risk of developing T2DM

(Malecki, 2005; McCarthy, 2010; Stitzel et al., 2010; Travers & McCarthy, 2011). Many

different genes are implicated in the pathogenesis of T2DM. Interestingly, in the genome wide

scans, the genes associated with diabetes in northern European populations did not have the same

5

association in west African populations (McCarthy, 2010). There is evidence that maternal and

childhood epigenetic exposure may increase the risk of T2DM in later life (Chen et al., 2007;

Prokopenko, McCarthy, & Lindgren, 2008) (see Figure 2). While more knowledge about genetic

factors associated with T2DM continue to be discovered, the presence of obesity and sedentary

lifestyle continue to overshadow genetic causes (Cruickshank et al., 2001; Malecki, 2005; Osei,

Schuster, Amoah, & Owusu, 2003; Travers & McCarthy, 2011).

Obesity has positive connotations in low-income countries representing wealth and

health. Residents in rural communities engage in activities to promote obesity by consuming

sweet drinks and increasing fat consumption to have a visual appearance of wealth (Selembo,

2009). The desire to become overweight has a strong association with the development of

diabetes; however, undernourishment is representative of disease and illness (Renzaho, 2004).

The pathophysiology of T2DM is complex, but closely associated with obesity. Adipose cells

function as endocrine cells releasing resistin and leptin, which suppress adiopenectin, an insulin

synthesizer, resulting in insulin resistance. Chronic hyperglycemia down regulates the GLUT3

transport molecules resulting in apoptosis of the pancreatic β-cells and decreasing insulin

production (Gallagher, Leroith, & Karnieli, 2011; Leroith, 2012; Miller, 2013). Often times these

pathophysiologic changes with obesity remain unrecognized until diabetes has progressed to end

organ damage. In an attempt to provide the visual appearance of health, some people in

developing countries unknowingly contribute to health risks (Renzaho, 2004; Selembo, 2009).

Economics.

According to the World Bank, most of the 47 countries in SSA are considered low

income countries with a Gross National Income (GNI) per capita of less than $1,005 per year

(n=26). Lower middle income and upper middle-income countries have GNI per capita with a

6

range of $1,006 - $3,975 (n=14) and $3,976-$12,275 (n=7), respectively. According to the

United Nations, life expectancy increases as the country’s economic status improves (United

Nations, 2010) (See Figure 1). Low and Middle income countries are collectively referred to as

“developing counties” (The World Bank, 2011). In 1990, the Tanzania GNI per capita was

$190.00 per year increasing to $290.00 per year (53% increase) in the year 2000 and $490.00 per

year (69% increase) in the year 2010. The average GNI per capita for the other SSA countries is

$1,130 per capita per year. These data indicate modest growth despite mean population growth

of 2.8% (range 2.5-3.2%) since 1990 (Mungi, 2011). Tanzania spends 5.1% of its Gross

Domestic Product (GDP)(CIA, 2009) on healthcare compared to the United States spending

17.4% (Centers for Medicare and Medicaid Services, 2009).

The cost to diagnose and treat T2DM is significant and failure to recognize and treat has

a considerable effect on morbidity and mortality. In conservative estimates, the global

expenditure for management of diabetes exceeded $370 billion in 2010 corresponding to 12% of

all healthcare spending. These numbers were based on prevalence studies, total population, and

total healthcare spending (Narayan, Echouffo-Tcheugui, Mohan, & Ali, 2012; Zhang et al.,

2010). In the WHO Africa region and Tanzania, the expenditures for diabetes account for 7%

and 5% or an average of $112 or $30.73 per year, respectively (Zhang et al., 2010) (see Table 2).

In low-income countries, management of diabetes can exceed more than 50% of the monthly

household income limiting access to proper treatment (Justin-Temu, Nondo, Wiedenmayer,

Ramaiya, & Teuscher, 2009; Khan, Hotchkiss, Berruti, & Hutchinson, 2006; Kolling, Winkley,

& von Deden, 2010). In a study comparing economic status, geographic location, and health care

services, the poor rural communities had the least access to medical care and services

7

emphasizing the need to understand the prevalence of chronic health conditions in developing

countries (Khan et al., 2006).

Complications.

Inadequate treatment of diabetes can have profound effects on morbidity and mortality.

Chronic hyperglycemia from untreated diabetes is a well-known risk factor for cardiovascular

disease, cerebrovascular disease, retinopathy, cataracts, chronic kidney disease, neuropathy, and

opportunistic infections, such as Tuberculosis. (Abbas & Archibald, 2007; Ikem & Sumpio,

2011; Lutale, Thordarson, Abbas, & Vetvik, 2007; Neuhann, Warter-Neuhann, Lyaruu, &

Msuya, 2002; Tesfaye & Gill, 2011; Unwin et al., 2010; Viswanathan et al., 2010).

Global age adjusted mortality from diabetes is 6.8%, which was derived from five large

cohort studies and applied to WHO region populations (Roglic & Unwin, 2010). In the WHO

Africa region, mortality from diabetes was 5%, with more than 300,000 deaths attributed to

diabetes in the age group of 20-79 (Roglic & Unwin, 2010). Coronary heart disease accounts for

29.2% of the worldwide mortality, with 80% of these deaths occurring in residents of low and

lower-middle income countries (Ikem & Sumpio, 2011). Infected diabetic foot ulcers, also

known as tropic diabetic limb has a corresponding mortality rate greater than 50% (Abbas &

Archibald, 2007).

Tanzania

The Tanzanian health care system has changed from socialized medicine to a private free

enterprise (Benson, 2001). There is a tier system with villages having access to inconsistent

health services through a community health aid at a village dispensary (Masalu, Kikwilu,

Kahabuka, Senkoro, & Kida, 2009; Munga, Songstad, Blystad, & Maestad, 2009). Within each

region, there are district clinics and hospitals, however depending on the size of the region; these

8

services may be 10-15 kilometers from a village (Benson, 2001; Whole Village Project, 2011).

There are four tertiary medical centers within Tanzania (National Bureau of Statistics, 2011).

Most clinical services are located in the densely populated urban cities with understaffed

dispensaries located in the rural communities (Khan et al., 2006; Munga et al., 2009). For rural

dwellers, the travel distance to seek healthcare services creates a geographical/financial barrier.

In a study of care seeking patterns of rural Tanzanian women with pregnancy, more than

51% of the cost to receive care was spent in transportation (Kruk, Mbaruku, Rockers, & Galea,

2008). According to the World Diabetes Foundation, there are six diabetologists and twenty

diabetic clinics in Tanzania, all located in urban settings, to provide care for the more than two

million people with T2DM. In contrast, Kenya has 490 specialty trained doctors to manage the

estimated 1.3 million people with diabetes (Chege, 2009; Lugongo, 2010; World Diabetes

Foundation, 2008).

Understanding the true prevalence of diabetes in SSA continues to be a significant

challenge. Epidemiologic studies in Africa report the prevalence rates to be between 3% and

8%, with the most significant prevalence occurring in the urban settings (Amoah, Owusu, &

Adjei, 2002; Aspray et al., 2000; Baldé et al., 2007; Christensen et al., 2009; Mbanya, Ngogang,

Salah, Minkoulou, & Balkau, 1997; Silva-Matos et al., 2011). In Tanzania, the prevalence of

T2DM is about 6% representing more than 2.4 million people and doubling by 2020 (Lugongo,

2010).

Treatment of T2DM remains inconsistent depending on the economic status and urban

versus rural residency within the country (Neuhann et al., 2002). For the affluent, who seek care

at private clinics, the availability of recognized treatment options including metformin

($0.10/tablet), glipizide ($0.10/tablet), and humulin insulin ($36.00/vial) are consistently

9

available at a premium price (Justin-Temu et al., 2009; Kolling et al., 2010). For the urban poor

and those in rural communities, limited access to anti-hyperglycemic agents increases the

challenge of management (Justin-Temu et al., 2009). The cost for anti-hyperglycemic agents

from public health facilities can be as much as a quarter of household monthly income adding to

the financial hardship and poor adherence in taking the recommended medications (Justin-Temu

et al., 2009; Kolling et al., 2010; Lugongo, 2010).

In parts of SSA, recent epidemiologic studies have described an increasing prevalence of

T2DM in the rural settings necessitating a decentralization of diabetes services to rural

communities (Hightower, Hightower, Vázquez, & Intaglietta, 2011; Lugongo, 2010; World

Diabetes Foundation, 2008). It is important to have an accurate understanding of the prevalence

of T2DM in the rural communities and be able to identify at-risk populations, so that resources

directed at the prevention and treatment of diabetes are developed. It is unclear if diabetes is

predominately an urban phenomena from obesity and increased wealth or if the prevalence of

diabetes is increasing in the poor, rural populations in Tanzania. It is important to understand the

prevalence of T2DM in rural communities and to ascertain whether diabetes is associated with

wealth or changing lifestyle. This study described the current prevalence of T2DM in a rural

community of northern Tanzania, which may inform healthcare workers and policy makers about

the allocation of resources to rural communities.

Arusha is an urban city in Tanzania with a population of 250,000 people (National

Bureau of Statistics, 2011). The city of Arusha is juxtaposed by the AruMeru district, a rural

district within close proximity of urban sprawl. The AruMeru district was selected to describe

the prevalence of T2DM in Tanzania, given its rural status and proximity to a populated area.

10

The investigator has been a volunteer in this region and is familiar with local customs and

culture.

Research Questions

What is the rural prevalence of T2DM in the AruMeru district of Tanzania?

Is there an association between environmental factors, lifestyle behaviors, and the

development of T2DM?

Specific aims.

1) Describe the prevalence of T2DM in seven cluster-randomized rural villages in the

AruMeru district of Tanzania

2) Describe the association between demographic and anthropometric data in rural

Tanzanians with T2DM, hypertension, and obesity.

3) Describe the association between lifestyle behaviors and the presence of T2DM,

hypertension, and obesity in a rural Tanzanian population.

Theoretical Model

Epidemiology is the study of disease occurrence in human populations (Friedman, 2004).

Once considered atheoretical in nature, epidemiology has developed a variety of theoretical

constructs including biomedical, social epidemiology, and life course epidemiology (Friedman,

2004; Krieger, 2001; Lynch & Smith, 2005). Understanding disease prevalence and etiology

originally focused the biomedical model’s “germ theory” in that a single vector caused a specific

disease (Weed, 2001); however, as epidemiologists studied diseases with multiple causation, the

theoretical framework developed (Morris, 2007; Weed, 2001).

Lifestyle factors became recognized as a mode of transmission, resulting in a new

framework of biomedical and lifestyle which was termed “web of causation” (Friedman, 2004;

11

Krieger, 2011). The biomedical and the biomedical-lifestyle framework reduced the number of

confounding variables in an attempt to isolate causative risk factors of disease. Elimination of

potential factors narrows the application to various populations and is considered reductionist

(Hartge, 2001; Krieger, 1994).

Disease conditions can be related to single bacteria, a lifestyle behavior, or an

environmental factor. Social epidemiology seeks to understand how social factors lead to

lifestyle changes resulting in risk factors and disease (Krieger, 2011). T2DM is related to

obesity, but through the lens of social epidemiology, this study described factors leading to

obesity and how the relationship between western lifestyle, socioeconomic status, and obesity

contributes to diabetes (sees Figure 2).

Conclusions

Using the framework of social epidemiology, the prevalence of T2DM in rural Tanzania

was described. The prevalence of obesity and T2DM are increasing in Tanzania and other sub-

Saharan countries. Limited access to healthcare, quality of healthcare services, changing patterns

of wealth in rural communities, and adoption of western lifestyles may all contribute to the

development of T2DM. The inter-relationships of these potential contributory factors have not

been previously reported for residents in rural Tanzania.

12

Chapter 2

The continent of Africa is the second largest continent in the world. It measures 30.2

million square kilometers and encompasses 20% of the world land mass and almost 15% of the

population (CIA, 2009). The entire United States, Western Europe, India, China, and Argentina

can be combined to approximate the equivalent land mass of Africa (see Figure 3). Given the

significant size and diversity of cultures of the African continent, clinical studies conducted in

one region of Africa may not be generalizable to other regions. Indigenous African people

originate from five historical language groups and comprise more than 410 tribes with a variety

of cultural beliefs (Campbell & Tishkoff, 2008).

Tanzania is 945,087 square kilometers (about twice the size of California) and located in

eastern Africa with more than 116 different tribal groups originating from the Bantu language

tribes (Campbell & Tishkoff, 2008; Douglas, 1964). Tanzania is subdivided into 26 regions, with

the Arusha region being located along the northern area, sharing the northern border with Kenya

(see Figure 4 and 5). The population of the Arusha region is 1.2 million residents, with more than

75% of the population living in a suburban or rural community (National Bureau of Statistics,

2011). The AruMeru district is one of five districts with an estimated population of 514,651 in

133 villages, with a population density of 177 people per square kilometer. In comparison, the

Arusha district adjoins the AruMeru district and has a population of 281,608 persons with a

density of 3,028 people per square kilometer (The city of Seattle has a population density of

2,596 people per square kilometer). According to the most recent census data, more than 80% of

the poor reside in the rural villages (National Bureau of Statistics, 2009).

13

The AruMeru district primarily consists of the Meru tribal members who own property

and have a stationary lifestyle compared to nomadic tribes like the Massai who also live in this

district (Aspray et al., 2000; National Bureau of Statistics, 2011; Whole Village Project, 2011).

Historically, Meru people depend on agricultural sustenance compared to the Massai, which are

considered hunters and gatherers. Most residents of the AruMeru district are farmers and grow a

variety of crops including coffee, bananas, corn, rice, and an assortment of vegetables (Hillbom,

2010). This region has been classified as an optimal environment for agriculture with high

humidity and fertile soil, providing moderate economic benefit compared to other regions of

Tanzania (Hillbom, 2010).

Globalization has transformed the cultural landscape of the region with access to cellular

phones, non-traditional diets, processed foods and sweetened beverages (Popkin, 1999; Zimmet,

2000). Across SSA and in Tanzania, there has been a migratory pattern from a traditional

lifestyle to an urban lifestyle with a resultant increase in chronic diseases, because of changes in

excessive caloric intake and a decrease in energy expenditure (Maruapula et al., 2011;

Montgomery, 2008; Popkin et al., 2012). National and international attention towards the

diagnosis and treatment infectious diseases such as HIV, malaria, and TB have decreased the

mortality rates, while allowing people to age and develop chronic diseases. The changing

migratory patterns and increasing life expectancy contribute to the difficulty in chronic disease

surveillance (Assah et al., 2011).

Understanding the prevalence rates of Type 2 diabetes mellitus (T2DM) in sub-Sahara

Africa (SSA) and Tanzania is important because of the significant financial burden associated

with the diagnoses and treatment of diabetic complications, which include retinopathy,

neuropathy, nephropathy, coronary artery disease, and cerebrovascular disease. End organ

14

damage and complications associated with untreated diabetes has a high mortality rate resulting

in increased financial burden on families from lost financial productivity (Ikem & Sumpio, 2011;

Neuhann et al., 2002; Sobngwi et al., 2012). Epidemiologic studies have been conducted over the

past 20 years, during which time there has been a rural to urban migratory pattern of residents,

several changes in the diagnostic criteria for T2DM, and industrialization of low-income

countries, which has promoted a western lifestyle. Consequently, it has been difficult to track the

incidence or prevalence of T2DM.

Diagnosis of Type 2 Diabetes Mellitus

Investigating the prevalence and the change in prevalence of diabetes requires

comparison of historic data. The definition of T2DM has changed multiple times between 1979

and 2012. There are two dominate consensus groups, which have developed diagnostic criteria

for diabetes. The American Diabetes Association (ADA) and the World Health Organization

(WHO) definitions are predominate; however, most of the African epidemiology studies have

used the 1985 WHO screening criteria. A PubMed literature search from 1979 to 2012 using the

terms type-2 diabetes, diabetes classification, diagnosis, and diagnostic criteria identified 162

articles. There were ten published consensus reports from four different organizations describing

the diagnostic criteria for diabetes. The National Diabetes Data Group (NDDG) established the

original diagnostic criteria for T2DM, setting the diagnostic threshold as a fasting plasma

glucose (FPG) greater than 140 mg/dl or a 2-hour oral glucose tolerance test (OGTT) glucose

level greater than 200 mg/dl (National Diabetes Data Group, 1979). This criteria was adopted by

the World Health Organization (WHO) in 1980 and then revised in 1985 to advocate for the 2-

hour oral glucose tolerance test (2-h OGTT) to be the primary diagnostic assessment for T2DM,

15

because of increased accuracy with minimal venipuncture’s (Harbuwono, 2011; Harris, Hadden,

Knowler, & Bennett, 1985).

Few changes were made to these criteria until 1997, when the American Diabetes

Association (ADA) advocated to lower the fasting plasma level cut point from 140 mg/dl to 126

mg/dl. The changes in diagnostic criteria were based on three landmark epidemiologic studies

using the presence of common macro- and micro-vascular complications to establish the cut

point for the diagnosis of diabetes (Harbuwono, 2011). Despite the pathologic changes related to

chronic hyperglycemia, there was a group of people with elevated glucose levels and did not

have diabetes. This group is at high risk for the development of diabetes; therefore, a new

classification labeled “pre-diabetes” was developed for people with elevated glucose levels (110

and 125 mg/dl) who did not meet the diagnostic threshold for diabetes. The Expert Committee on

the Diagnosis and Classification of Diabetes Mellitus developed the terms “Impaired Fasting

Glucose” (IFG) and “Impaired Glucose Tolerance” (IGT) in 1997. IFG and IGT were classified

as a glucose level between fasting serum glucose of 110-125 mg/dl and post prandial 2-h OGTT

serum glucose level of 140-199 mg/dl, respectively (Gavin, Davidson, & DeFronzo, 1997;

Harbuwono, 2011). People with pre-diabetes have either impaired fasting glucose tolerance or an

aberrant metabolism of post-prandial glucose. In the spectrum of glucose metabolism disorders,

people with pre-diabetes are at a substantial risk for developing diabetes, but have not developed

target organ damage, which is associated with T2DM. People with pre-diabetes are a target

population to prevent diabetes through lifestyle modification (Miller, 2013).

In 2007, the ADA lowered the cut point for the diagnosis of IFG to a fasting glucose level

between 100-126 mg/dl because of observed micro-vascular complications (American Diabetes

Association, 2007), while the WHO disagreed with the ADA and maintained that the fasting

16

glucose level between 110-126 mg/dl would be considered diagnostic for IFG (WHO, 2003).

Finally in 2012, the diagnostic criteria for T2DM was redefined as a fasting plasma glucose

greater than 125 mg/dl, a 2-hour OGTT equal to or greater than 200 mg/dl, a random glucose

level equal to or greater than 200 mg/dl, or a glycated A1c of 6.5% or higher (American Diabetes

Association, 2012). The addition of glycated hemoglobin for the diagnosis of T2DM was a

significant change in criteria. In previous recommendations, confirmation of the diagnosis

required repeat testing on two separate days, however the use of glycated hemoglobin provided

diagnostic confirmation at the time of screening (American Diabetes Association, 2012).

Despite the changes in diagnostic criteria from the ADA, the WHO and the International

Diabetes Federation (IDF) maintained the screening recommendations of a 2-hour OGTT to

screen and diagnose diabetes. In 2003 the joint WHO/IDF consensus guidelines changed

allowing fasting whole blood or capillary blood sample to screen for pre-diabetes and diabetes

with a confirmatory 2-hour OGTT to confirm the diagnosis of these conditions (WHO, 2003).

The most recent changes to the classification of diabetes have been glycated hemoglobin levels.

Currently, the ADA and the WHO recommend a fasting blood glucose level for screening of

T2DM, but urge the use of a 2-hour OGTT or glycated hemoglobin for confirmation (American

Diabetes Association, 2012; WHO, 2003, 2011). The single difference between the 2003 and the

2011 WHO guidelines is recognition that a glycated hemoglobin greater than 6.5% is diagnostic

for T2DM (WHO, 2011) (See Table 3).

Type 2 Diabetes in Sub-Sahara Africa and Tanzania

Epidemiologic studies in SSA have used different diagnostic criteria between studies,

with a number using the 1985 WHO criteria, the 1999 WHO criteria, and one study using the

1997 ADA criteria. In a retrospective review by Levitt et al. (2000), the 1997 ADA criteria were

17

applied to African studies using the older WHO criteria. The results suggested a slightly higher

prevalence of T2DM. This study used the 2003 WHO guidelines to screen for people with

T2DM and pre-diabetic conditions in the AruMeru district of northern Tanzania. The 2003 WHO

guidelines were selected because capillary blood glucose screening provided easy access to

screen large numbers of people, while performing a confirmatory 2-hour OGTT for people with

abnormal fasting glucose values. There are few African studies using the WHO 2003 criteria for

the classification of diabetes. Access to glycated hemoglobin analysis is limited in rural Tanzania

and point of care A1c monitors are controversial because of inaccurate results for people with

hemoglobinopathies and thalassemia’s (WHO, 2011). Consequently, this study is significant

because it used the 2003 WHO guidelines to classify people with pre-diabetes and diabetes in a

region of Tanzania that had not been previously examined. As will be reported, this study

provided baseline a prevalence rate of T2DM and pre-diabetes in the AruMeru district and, when

compared to other prevalence studies in different parts of Tanzania, suggested an increase in age-

adjusted prevalence rate.

A literature search of PubMed using the key words: Africa, Diabetes, Type 2 Diabetes,

Prevalence, and Epidemiology between the years of 1979 and 2012, resulted in 402 citations.

After screening the abstracts, four meta-analyses regarding prevalence of T2DM in Africa, 16

epidemiologic studies describing the prevalence of T2DM in Africa, and 4 Tanzania specific

epidemiologic studies were identified as pertinent to the study and critically reviewed.

Impaired glucose metabolism, hypertension, and other chronic diseases are increasing at

alarming rates around the world and across the continent of Africa (Kapiga, 2011). Once

considered rare in Africa, T2DM is expected to increase by 161% in the next 15 years (Hall et

al., 2011; Wild et al., 2004). Several studies describing the prevalence of T2DM in SSA have

18

mixed findings. There have been four meta-analyses conducted with T2DM prevalence ranging

between 1% in rural Uganda to 12% in urban Kenya (Hall et al., 2011; Levitt et al., 2000). In a

review by Levitt et al. (2000), a retrospective analysis was conducted of SSA prevalence studies

using the 1985 WHO diagnostic criteria and compared the original data to the new 1997 ADA

criteria. The change in diagnosis of T2DM and pre-diabetes (IGT or IFG) was slightly higher

with the 1997 ADA criteria by 1-2%. These differences may have been related to the age, with

older adults having a greater degree of glucose intolerance (Levitt et al., 2000).

In an analysis by Danaei et al. (2011), the authors compared studies of global prevalence

of diabetes to forecast changes in diabetes. The authors standardized fasting plasma glucose

levels, fasting capillary glucose levels and glycated hemoglobin levels to determine global mean

fasting glucose level. These data were used to estimate prevalence changes per decade per year

on a global and regional level. A limitation of these analyses was the exclusion of studies using a

2-hour OGTT as the screening method (1985 WHO criteria). The prevalence of T2DM was

lowest in SSA, as most of the prevalence studies have used the 1985 WHO criteria (Danaei et al.,

2011). However, differing diagnostic criteria were used to diagnose T2DM and IGT in these

meta-analyses, making the prevalence rates difficult to compare across studies.

The meta-analysis conducted by Whiting and colleagues (2011) reviewed all diabetes

prevalence studies regardless of the diagnostic criteria. The focus of the analysis was to assess

global and regional trends in diabetes prevalence. A logistic regression analyses model

controlled for age and economic status by country. Predictions were forecasted based on

prevalence change and estimated regional population growth. The model’s estimates were

similar to Danaei et al.’s findings (2011). Whiting reported that SSA would have the greatest

proportional increase in diabetes by the year 2030, compared to all other IDF regions. In

19

Tanzania, the number of adults with diabetes will increase annually by 33,000 per year whereas,

Kenya will increase by 48,000, Malawi will increase by 21,000, and the Democratic Republic of

Congo will increase by 36,000 (Whiting et al., 2011). The findings provide the best available

data of T2DM prevalence in SSA and suggest that the age-adjusted prevalence of T2DM in SSA

is currently 5% and will increase to 5.9% by the year 2030 (Whiting et al., 2011). The limitations

of this analysis in SSA were the lack of recent prevalence studies and the absence of national

diabetes registries to obtain a true prevalence of diabetes.

A systematic review by Hall et al. (2011) examined published reports between 1999 and

2010, which described the incidence, prevalence, morbidity, and mortality of T2DM in SSA. In

determining prevalence, the authors considered 16 studies from nine countries using multiple

diagnostic criteria including a 2-hour OGTT, fasting plasma glucose (FPG), or random plasma

glucose level (RPG). The primary aim of the review was to examine the impact of diabetes in the

past 12 years. The authors were unable to generalize the prevalence rate between regions or even

in countries given the wide prevalence variation. The prevalence rates were higher in urban

dwellers (2-10%) compared to rural dwellers (0.8-5.3%). The wide variance may be attributed, at

least in part, to the differing diagnostic criterion, different geographic locations, access to

saturated cooking oils, and high fructose, non-alcohol ready to drink beverages (soda). In

contrast to Danaei’s and Whiting’s review, there was not an age adjustment for the prevalence.

Authors described diabetes complications with a prevalence of neuropathy ranging from 27-66%,

retinopathy 7-63%, nephropathy 9.8-83% (Hall et al., 2011).

The 1985 WHO criteria for the diagnosis of diabetes are dependent on a 75-gm, 2-hour

OGTT glucose level equal to or greater than 140 mg/dl. In SSA there were eight published

studies between 1989 and 2010 using the 1985 WHO criteria to diagnose diabetes (Ceesay,

20

Morgan, Kamanda, Willoughby, & Lisk, 1997; Mathenge, Foster, & Kuper, 2010; Mbanya et al.,

1999; Mbanya et al., 1997; McLarty et al., 1989; Swai, Lutale, & McLarty, 1990; Van Der Sande

et al., 1997). The prevalence of diabetes in these eight studies demonstrated a higher rate of

diabetes and impaired glucose tolerance in the urban dwellers (1.1-2.1%; 1.4-7.5%) compared to

rural dwellers (0.0-7.6%; 2.6-7.7%). These studies were conducted in Tanzania, Cameroon,

Sierra Leone, The Gambia, and Kenya. The varying rates of diabetes and IGT can be related to

the age distribution of the study population, three studies enrolled participants starting at 15

years of age (Ceesay et al., 1997; McLarty et al., 1989; Van Der Sande et al., 1997), while 2

studies examined diabetes in people between 26 and 74 years of age (Mbanya et al., 1999;

Mbanya et al., 1997), and one study limited enrollees to 50 years of age or older (Mathenge et

al., 2010). Although most of these studies used the 2-hour OGTT, one study limited data

collection to a single random glucose level for the diagnosis of diabetes. Using a random glucose

level, there were no reported cases of diabetes in the rural population (Ceesay et al., 1997) (see

Table 4).

The 1997 ADA criteria and the 1998 WHO criteria are similar, using a FPG level equal

to or greater than 126 mg/dl, a 2-hour OGTT equal to or greater than 200 mg/dl, or random

plasma glucose (RPG) level equal to or greater than 200 mg/dl, as the criteria for diabetes. The

addition of pre-diabetic classification with having IFG or IGT allows for risk stratification of

high-risk groups. Between the year 2000 and 2011, the 1997 ADA and the 1998 WHO criteria

were used in nine SSA diabetes epidemiology studies (Amoah et al., 2002; Aspray et al., 2000;

Baldé et al., 2007; Christensen et al., 2009; Motala, Esterhuizen, Gouws, Pirie, & Omar, 2008;

Nyenwe, Odia, Ihekwaba, Ojule, & Babatunde, 2003; Silva-Matos et al., 2011; Sobngwi et al.,

2004; Sobngwi, Mbanya, et al., 2002). These studies examined the prevalence of diabetes and

21

pre-diabetes in East Africa (Tanzania, Kenya, & Mozambique), West Africa (Cameroon,

Nigeria, Ghana, & Guinea), and South Africa. The prevalence of diabetes and pre-diabetes in

urban dwellers compared to rural dwellers is higher, although there was some variability. Some

studies reported crude prevalence rates, while others reported age adjusted prevalence rates

(Amoah et al., 2002; Nyenwe et al., 2003). Some studies combined the presence of diabetes and

pre-diabetes into a single value increasing the difficulty in determining prevalence (Nyenwe et

al., 2003; Sobngwi et al., 2004). These studies identified a higher rate of diabetes and pre-

diabetes in the urban participants with an increasing trend in prevalence rates. This trend appears

to be related to chronicity. The study by Aspray et al. (2000) identified the rural age-adjusted

prevalence of diabetes/IGT to be 1.1 and 6.5%, respectively, whereas a study by Christensen et

al. (2009) identified the age-adjusted prevalence of diabetes/IGT to be 4.2 and 12%, respectively.

Both of these studies were conducted in rural east Africa, used the same diagnostic criteria, used

the world population to standardize the sample for age adjustments, and represent a marked

increase in diabetes and pre-diabetes over the span of a decade (Aspray et al., 2000; Christensen

et al., 2009). A study by Nyenwe et al. (2003) investigated the prevalence T2DM in Nigerian

residents over the age of 40 years, reporting a combined age-adjusted rate of diabetes and IFG of

7.9% (Nyenwe et al., 2003).

The 2007 ADA decreased the lower diagnostic limit of IFG to 100-126 mg/dl. The other

criteria remained consistent with the 2003 ADA and 2003 WHO classification. The reduction of

IFG threshold increases the probability to diagnosis pre-diabetes (see Table 3). The WHO did

not adopt the lower threshold level of IFG, maintaining the 2003 guidelines. Between 2010 and

2011, there were four epidemiology studies which used the 2007 ADA diagnostic criteria

(Evaristo-Neto, Foss-Freitas, & Foss, 2010; Hightower et al., 2011; Oladapo et al., 2010; Solet et

22

al., 2011). Three of the studies investigated rural populations, while the study by Hightower et al.

(2011) investigated the crude prevalence of combined diabetes/IFG in traditional, transitional,

and modern communities. Africa is globalizing with telecommunication advancements,

development of electrical grids, and modern amenities. Traditional communities are rural

communities who have little exposure to telecommunications, and modern advancements, the

transitional communities are rural communities in close proximity to urban centers. They have

access to public transportation, some households are attached to an electrical grid, and many

people have access to cellular phones. People living in urban centers are classified as modern

communities (Hightower et al., 2011). The results of the study demonstrated a high crude rate of

combined diabetes/IFG of 47%, 88%, and 91% for the traditional, transitional, and modern

community, respectively. The participants in all three groups were older with a mean age of 36,

43, and 44 years, respectively. These crude prevalence rates are high and most likely represent

the combination of older age group, the 2007 ADA’s lower threshold level of IFG, and the

effects of globalization.

Diabetes in Tanzania.

The true prevalence of T2DM and IGT in Tanzania is unknown. There have been four

studies published since 1984 describing an increasing prevalence of diabetes in Tanzania. When

Aherns and Corrigan studied the prevalence of T2DM in 1984, using the 1979 National Diabetes

Data Group (NDDG) criteria, they reported rates of 0.5 and 2.5% among rural villages in the

same region. In the urban area of Mwanza, the estimated prevalence was 1.9%. These data

suggest prevalence rates of diabetes vary depending on the geographic distribution of the

population (Ahren & Corrigan, 1984). The study was limited by the age of the population, more

23

than 60% of the participants were under the age of 20 years, and the authors did not adjust the

prevalence rate to age.

In 1989, McLarty et al. examined the prevalence of T2DM in six rural villages from the

Morogoro and Kilimanjaro regions using the 1985 WHO criteria. These regions are similar

agricultural communities from the northern and southern part of the Tanzania. They estimated an

age adjusted prevalence of diabetes and IGT to be 0.9 and 7.7%, respectively. The authors also

identified a significant correlation between T2DM and both severely undernourished people and

those who were over nourished suggesting that either could be risk factors for diabetes. The

findings of severely undernourished people were observed in all six villages (McLarty et al.,

1989). The Kilimanjaro region of Tanzania is approximately 35 kilometers from the AruMeru

district, sharing some similar characteristics.

In 1992, Swai expanded the work of McLarty and examined characteristics of diabetes in

a prevalence of T2DM in eight villages in the Morogoro and Kilimanjaro regions using the 1985

WHO criteria. These villages were part of a national surveillance program and included some of

the villages reported by McLarty (1989). Swai estimated the crude prevalence of diabetes and

IGT to be 1.2 and 6.7%, respectively for males and 0.7 and 7.4%, respectively for females. In

people who were over the age of 50, the prevalence of diabetes and IGT were similar to people

with a BMI >25 and people with a BMI < 20 (Swai et al., 1992). When examining the

association of obesity and severe undernourishment with diabetes, Swai (1992) did not find

obesity to have a strong positive predictive correlation to diabetes. Based on Swai’s results, it is

unclear if obesity has an association to diabetes in the African population or if an African person

with diabetes have different presenting characteristics.

24

Using the 1999 WHO criteria Aspray et al. compared the prevalence of T2DM between

Dar es Salaam, an urban city, and Shari, a rural village in the Kilimanjaro region of Tanzania.

The T2DM/IGT urban age adjusted prevalence rates were 4.5% and 4.8%, respectively and the

rural age adjusted T2DM/IGT prevalence rates were 1.1% and 1.3%, respectively. The age

adjusted, rates IFG/DM for men were 0.8/1.7 and for women 1.6/1.1, respectively (Aspray et al.,

2000). The authors used the world population figures to control for age variations.

Diabetes and pre-diabetes are increasing in prevalence in SSA, The mean prevalence

rates for diabetes and IGT have increased from 1.74 and 5.44 in the 1990s with the 1985 WHO

criteria to 4.91 and 8.08 in the 2000s with the 1997 ADA/1999 WHO criteria, to 4.08 and 9.16

with the 2007 ADA criteria. These studies used differing criteria and some studies are age-

adjusted while others published crude rates. Never-the-less, all studies indicate that there has

been an increase in diabetes and pre-diabetes in rural east Africa in the last 10 years. The most

recent published epidemiologic study of T2DM in Tanzania was in 2000. Further studies are

needed using an age-adjusted prevalence with standardized diagnostic criteria.

Type I diabetes in sub-Sahara Africa.

Type 1 diabetes is an autoimmune form of diabetes with an onset in childhood or early

adolescence. The body develops an absolute deficiency of insulin, requiring exogenous

administration of insulin. The prevalence for Type 1 diabetes is not entirely clear, but has been

estimated between 0.01 and 0.012% in SSA (Hall et al., 2011; Motala, 2002). The mortality rate

for type 1 diabetes is high and related primarily to metabolic emergencies. Some studies estimate

the 1-year mortality rate is between 60% and 80% (Levitt, 2008; McLarty, Kinabo, & Swai,

1990). Given the high mortality rate and low prevalence of Type 1 diabetes, as well as other

25

forms of diabetes, adults presenting with hyperglycemia were presumed to have T2DM unless

they had a pre-existing medical history of Type 1 diabetes

Tropical diabetes.

Tropical diabetes or malnutrition related diabetes mellitus (MRDM) has been proposed as

a possible cause of diabetes in SSA. Studies have identified people in SSA who have non-ketotic

hyperglycemia with evidence of severe under nutrition (BMI < 20) (Sobngwi, Mauvais-Jarvis,

Vexiau, Mbanya, & Gautier, 2002; Swai et al., 1990). These reports describe a positive response

to insulin, but those affected have periods of remission and are able to stop insulin and other anti-

hyperglycemic agents for extended periods of time (Akanji, 1990). In theory, periods of famine

result in pancreatic β-cell damage with resultant hyperglycemia (Ekow & Shipp, 2001). The

presentations are similar to Type 1 diabetes mellitus (T1DM), with the exception of non-ketone

producing hyperglycemia and intermittent return of insulin production allowing the individual to

discontinue insulin therapy. Additional studies have been unable to identify autoimmune

antibodies in this population (Ducorps et al., 2002). The low body weight and the associated

occurrence in developing countries has resulted in the controversial terminology of “tropical

diabetes” or “malnutrition related diabetes mellitus.” Evidence has demonstrated this subgroup

of diabetes is best classified as idiopathic type 1B diabetes (Ekow & Shipp, 2001; Sobngwi,

Mauvais-Jarvis, et al., 2002). The studies by McLarty (1990), Swai (1992), and Christensen

(2010) suggest obesity and severe undernourishment may be an independent risk factors for

diabetes (Christensen et al., 2009; McLarty et al., 1989; Swai et al., 1992; Swai et al., 1990).

Risk Factors for Diabetes in Tanzania

A literature search of PubMed from 1980 to 2012 using the key words: Risk factors,

diabetes, Type 2 diabetes, Africa, and Tanzania resulted in identification of 211 articles. After

26

reviewing the abstracts, 22 articles were deemed relevant to the study and were critically

reviewed.

Risk factors for T2DM in developed countries have been well established and include

obesity, diet, physical inactivity, and genetic predisposition. In developing countries, the inter-

relationship between the risk factors of T2DM are complicated and include lifestyle changes,

decrease in energy expenditure, changes in types of food and patterns of consumption resulting

in obesity and sedentary lifestyles. Tanzania has undergone moderate infrastructure development

in the last 10 years resulting in a migratory pattern of rural dwellers relocating to urban locations

for employment and globalization of urban services to rural communities (Ngowi, 2009; Unwin

et al., 2010). Development indices include: the distribution of electricity, cellular phones,

protected water sources, and access to public transportation (Popkin, 2002). Residents in the

rural and urban settings have changed lifestyle patterns to mirror diet and exercise patterns of

developed countries, a process called “westernization” (Delisle, Ntandou-Bouzitou, Agueh,

Sodjinou, & Fauomi, 2011; Maletnlema, 2002; Popkin et al., 2012). Adoption of a western

lifestyle which includes changes in diet and exercise patterns leads to a greater prevalence of

obesity, but it is not clear if the western lifestyle leads to the development of T2DM (Jones-

Smith, Gordon-Larsen, Siddiqi, & Popkin, 2011). T2DM has been characterized as an affluent

disease despite the rising prevalence in the rural and poor populations in SSA (Agardh, Allebeck,

Hallqvist, Moradi, & Sidorchuk, 2011). People in SSA use obesity as a surrogate indicator of

wealth, even in poor communities. Knowledge of the association between wealth, obesity, and

diabetes will provide a deeper understanding for planning prevention and treatment

interventions.

27

Wealth.

Wealth in rural Tanzania can be measured by annual income, asset ownership, and body

mass index (BMI). According to the 2007 Household Budget Survey in Tanzania, the average

household income in urban locations is 58,722 to 78,680 Tanzanian Shillings (Tsh) per month

(37.62 to 50.43 US dollars [USD]), while average monthly income in rural Tanzania is about

27,279 Tsh per month (17.48 USD) representing a significant income difference between urban

and rural communities (National Bureau of Statistics, 2009). However, these statistics represent a

93% increase in annual household income in six years (National Bureau of Statistics, 2003,

2009). In the AruMeru district, most residents are dependent on agricultural sales and have a

lower annual income, as compared to other districts (Aspray et al., 2000; National Bureau of

Statistics, 2009; Whole Village Project, 2011). Assessment of wealth is usually conducted by

survey analysis; however, obtaining accurate household income indicators is difficult because of

cultural barriers. Wealth is determined through surrogate indicators, which include education

level and asset ownership such as transportation, cellular phones, and housing construction. In

the 2007 household budget survey, rural communities experienced a 7% increase in bicycle

ownership ( 38.4 - 45.4%), a 16% increase in radio ownership (45.7- 62.2%) and 14% owned

cell phones (this was a new category, so no comparison data available) (National Bureau of

Statistics, 2009). Several surveys have queried indirect economic indicators which are attributed

to the type of home flooring (compacted dirt, wood slats, concrete, tile), type of home building

construction (mud/wood, earthen brick, earthen brick with concrete facing), and household water

source (piped water supply, protected well, unprotected well, river or creek) (Hargreaves et al.,

2007; Khan et al., 2006; Kusumayati & Gross, 1998; National Bureau of Statistics, 2009; Whole

Village Project, 2011).

28

Body Mass Index (BMI) has been suggested as an indicator of wealth. Studies have

suggested higher BMIs relate to a higher socio-economic status (Neuman, Finlay, Davey Smith,

& Subramanian, 2011; Subramanian, Perkins, Özaltin, & Davey Smith, 2011). Using data

extrapolated from the 1996 Tanzania Demographic Health Survey, Kahn et al. (2006) developed

a wealth index for Tanzania demonstrating a statistically significant correlation between higher

household incomes and higher BMI ratios (Khan et al., 2006).

Subramanian et al. (2011) conducted a large cross sectional review of data from 54

demographic and health surveys that had been conducted between 1994 and 2008 in low and

lower middle income countries. Responses of 538,140 women were pooled and after accounting

for national gross domestic product and individual household income, the authors were able to

correlate a 0.54 increase in BMI for every quartile increase in wealth. Overall, those in the

highest quartile of wealth were 33% more likely to be obese, than those in the lowest quartile

(Subramanian et al., 2011).

Body Mass Index.

Body Mass Index (BMI) is an anthropometric indicator used to categorize levels of

adiposity. Higher BMI levels can be used to assess risk for development of T2DM and other

chronic health conditions. According to the World Health Organization, BMI is an effective

indicator of obesity (WHO, 2000). As previously discussed, epidemiology studies conducted in

SSA suggest that low BMI, (<20) is an independent risk factor for development of diabetes

(McLarty et al., 1989; Swai et al., 1990). Several studies have examined the relationship of BMI

as a predictor of T2DM (Barrett-Connor, 1989; Huxley, Mendis, Zheleznyakov, Reddy, & Chan,

2009; Nyamdorj, 2010; Sluik et al., 2011), while others have suggested the waist circumference

(Schulze et al., 2006), waist to hip ratio (Petursson, Sigurdsson, Bengtsson, Nilsen, & Getz,

29

2011), and waist to height ratio (Sluik et al., 2011) may be better predictors of T2DM and

mortality. These studies have limited data in various ethnic populations and have not been

examined in rural sub-Saharan residents. The Sympathetic Activity and Ambulatory Blood

Pressure in Africans (SABPA) study examined a cut point of waist circumference to predict

metabolic syndrome. These data suggest a waist circumference 94 cm (37 inches) as a predictor

of metabolic syndrome (Prinsloo, Malan, de Ridder, Potgieter, & Steyn, 2011); however, these

results are limited to South Africa and have not been repeated.

Historically BMI measures nutritional status, however evidence has suggested other

measures may have greater validity in assessing nutritional status, while predicting risk factors

for T2DM and mortality. In epidemiology studies, in SSA and Tanzania, BMI has been the only

anthropometric measure used. This dissertation examined the relationship between BMI and

waist to hip ratio (WHR) to T2DM and pre-diabetes. Information from the study reported here

will contribute to understanding how body habitus interacts with T2DM in rural SSA.

Obesity in Sub-Sahara Africa.

The increasing prevalence of obesity is complex, with obesity having a different meaning

in developing countries versus developed countries. Residents in developing counties associate

obesity as healthy and opposite of ill. Conditions like tuberculosis (TB) and Acquired Immune

Deficiency Syndrome (AIDS) are associated with cachexia and weight loss (Popkin et al., 2012).

Obesity in SSA has profound cultural implications representing beauty, health, and wealth.

People will strive to achieve a degree of obesity as marker of prosperity within their village

(Renzaho, 2004; Selembo, 2009). Women will eat lard to increase their weight and demonstrate

to the village that their husband is able to take care of them (Selembo, 2009). A phrase used by

men to express wealth in Tanzania is “Chakula ya wazungu” or “food of white people”

30

(Renzaho, 2004). Increasing rates of obesity are being reported across socioeconomic divisions

and are no longer restricted to wealthy (Agardh et al., 2011; Delisle et al., 2011; Jones-Smith et

al., 2011; Nube, Asenso-Okyere, & van den Bloom, 1998; Renzaho, 2004). Access to cheap

cooking oils, processed sugars, and sweetened drinks, such as soda are accessible to all social

classes contributing to the obesity epidemic (Jones-Smith et al., 2011; Popkin et al., 2012;

Renzaho, 2004).

Obesity is a risk factor for T2DM, but obesity has positive perceptions for people in low-

income countries and rural Tanzania. Obesity is associated with wealth, however using it as a

marker may not be a reliable method of determining wealth in rural communities. This study

examined proxy markers of wealth, in addition to assessing the relationship between BMI and

T2DM. In doing so, this study provided current evidence on factors associated with T2DM in the

rural community of northern Tanzania.

Conclusions

Type 2 diabetes mellitus is increasing and will become a significant burden on health

status globally. In developing countries with limited resources, understanding the prevalence and

associated risk factors are needed to prepare and develop preventative strategies. Rural Tanzania

is experiencing many of the global implications of obesity without the resources to address the

consequences. Describing the interaction between BMI, lifestyle behaviors, and the presence of

T2DM or pre-diabetes will help identify high-risk populations. This study adds to the literature

by describing the prevalence of T2DM in rural Tanzania, determining which anthropometric

measurements are most predictive of T2DM in rural Tanzania, and exploring the relationship

between socio-economic factors and obesity and T2DM.

31

Chapter 3

The AruMeru district in northern Tanzania is considered a rural region, however the close

proximity to a large urban city may account for urbanization and globalization factors in the rural

community and becoming a transitional community. There have been four epidemiologic studies

conducted in Tanzania since 1984, with no published epidemiologic reports of diabetes or pre-

diabetes from the AruMeru district. Investigating the prevalence of T2DM in a rural Tanzanian

district located in proximity to an urban center provided an increased understanding of diabetes

in this region. The effects of urban sprawl and western lifestyles may extend into the surrounding

rural communities contributing to the prevalence of diabetes. Chapter three describes the

methods used to address the research questions and specific aims of the study.

Research Design

This study is an observational, cross sectional examination to estimate the prevalence of

type 2 diabetes mellitus in rural communities of the AruMeru district of Tanzania. Prevalence is

the number of cases in the population compared to incidence which describes the number of new

cases per given unit of time. Data were collected at each village on a single occasion to estimate

crude prevalence rates of T2DM.

The population of the participating villages was estimated by village leaders and was

collected at the time of data collection. According to the national census, the population of the

region is estimated to be 514,651 and 55.7% of the population are 15 years of age or older

(National Bureau of Statistics, 2009). The study examined people aged 18 years or older and

reported crude and indirect age-adjusted prevalence rates of T2DM and pre-diabetes. In

32

determining an appropriate sample size, a confidence interval of 95% was used from

standardized tables with a z score of 1.962 multiplied by the probability (p) and multiplied by 1-

probability (1-p) divided by the error rate (c) squared. Based on studies by Apsray (2000) and

Christensen (2009), the prevalence rate of T2DM in east Africa ranges between 4 and 10%.

Using known prevalence rates, the sample size required to estimate the prevalence of T2DM

with a confidence interval of 95% with a 3% margin of error would be 384 participants.

( ) ( )

( ) ( )

(3% margin of error)

Population estimates have been used in survey research and have validity in

understanding the trends of the population of interest. Using standardized Z scores of 1.96

provides a 3% margin of error in the sample size. These estimates are dependent on two

assumptions: randomization and appropriate questions. There was a moderate degree of

variability, as randomization was based on village clusters, while villagers self-selected to

participate resulting in a convenience sample. This method of sampling has some selection bias

based on the number of participants; however, the pragmatic use of this method was appropriate

for limited resource allocation.

Age standardization.

Rates of diabetes were collected from a homogeneous population of rural northern

Tanzania. The crude prevalence rates were reported as a baseline description regarding the

significance of pre-diabetes and diabetes in the AruMeru region. Age adjustment was performed

using the indirect method. Estimates from the 2010 Demographic and Health survey were used

to calculate the national proportion of people for each age group in this study. The population

33

percentage of each age group was multiplied by the crude prevalence rate for the corresponding

age group to calculate the age-adjusted prevalence rate for each age group. The sum of all age-

adjusted prevalence rates was used to determine the total age-adjusted prevalence rate for pre-

diabetes and diabetes. Data regarding the distribution of ages in each of the villages or from the

AruMeru district were not available, limiting a direct age adjustment.

Participants

The target population was adults, 18 years of age or older who resided in one of the

selected villages. Data were collected from self-selected volunteers at seven cluster-randomized

rural villages, located in the northeast corner of the AruMeru district in northern Tanzania. There

are 133 villages in the AruMeru district encompassing three distinct ethnic groups including

Meru, Chagga, and Massai. Arusha is the largest city located in close proximity to the AruMeru

district, 46 villages were excluded from randomization because of the close proximity to the city

of Arusha. The remaining 87 villages were randomized with a random number generator. The

pool of villages was evaluated and the first seven villages considered rural, maintaining Meru

tribal homogeneity, and separated by at least ten kilometers from each other were selected (see

Appendix C). Although randomization through clustering and not through simple randomization

increases selection bias, using it in this study allowed for a pragmatic approach in terms of data

acquisition and resources.

Participant recruitment.

Recruiting research participants in the selected villages was performed through bulletins

and announcements within a network of churches (see Appendix B). Placing flyers at community

gathering places such as churches, community markets, and water sources disseminated

34

information to a large number of each community. Announcements during church services

communicated the pending screening survey to the largest group of potential participants.

The flyers contained information in Swahili inviting all members of the village who were

at least 18 years to participate in the screening examination. Potential participants were asked to

experience an eight hour calorie-free fast prior to screening. Print media in developing countries

has been effective in participant recruitment (Burgess & Sulzer, 2010); however, access to print

media in rural Tanzania is limited. Posting the flyer at local gathering spots, such as water wells,

public markets, and churches, increased community awareness of the research opportunity

(Yancey, Ortega, & Kumanyika, 2006). The risk of community resentment is a concern in low

socio-economic status (SES) communities. In low-income countries, participants who do not

meet the inclusion criteria or who are excluded may feel resentment if those who do participate

are given any form of compensation. Community retaliation against members of the society for

receiving compensation has negative effects on participation and recruitment. Compensation to

the study participants was avoided to prevent resentment or retaliation (Emanuel, Wendler,

Killen, & Grady, 2004; Molyneux, Kamuya, & Marsh, 2010). All potential participants received

screening; however, data with exclusionary criteria were removed from analysis.

Inclusion criteria.

The prevalence of T2DM is dependent on an adequate representation of the population in

the village. Type 2 diabetes is an age progressive disorder and becomes increasingly prevalent in

adults and older adults, therefore participants were 18 years or older. Participants were fasting

for eight hours prior to screening. Potential participants who indicated that they had not fasted

were offered a random capillary glucose level, if this was equal to or greater than 200 mg/dl,

they were considered positive for diabetes. If their level was between 110 and 199, they were

35

given the opportunity to fast during the day and return in the late afternoon for screening

provided they were able to fast for eight hours (see Table 6).

Exclusion criteria.

Certain medical conditions may artificially elevate serum glucose levels, such as active

infections, use of corticosteroids or pregnancy/lactation (Kauh et al., 2012; Mazze, Yogev, &

Langer; Polito et al., 2011). Participants that had a temperature greater than 101.4 degrees

Fahrenheit or reported that they were currently taking antibiotics, antimalarial, or antivirals

medications were considered to have an active infection, women who were known to be pregnant

or currently lactating, and people taking glucocorticoid steroids were offered screening, but their

data were excluded from analysis (see Table 6).

Human Subjects Protection.

Institutional Review Board (IRB) approval from Washington State University and ethical

clearance from Tanzania’s National Institute for Medical Research (NIMR) was obtained (see

Appendix A). All participants were self-selected and could withdraw at any time. All participants

provided informed consent in Swahili, with both a consent written in Swahili and by having a

Swahili/English interpreter explain the research consent to the participants. Each participant was

assigned a unique identification number which was affixed to their consent and the data

collection form. The consent forms and data collection forms were separated at the collection

site, then each form was scanned into separate password protected Portable Document File

(PDF) files. De-identified data and consents were stored on a password protected external hard

drive and secured in a locked safe inside a locked room. Data were transcribed into a computer-

based data set stored on an external hard drive.

36

Data Collection

Announcements and flyers with the times and location of data collection were

disseminated at least 3 days prior to the date of collection. All adult members of the village were

invited to participate in the study. Data collection commenced at 7:00 a.m. each morning and

concluded between 3:00 and 5:00 p.m. each day.

After written or verbal informed consent was obtained, informants were given a unique

study identification number to de-identify the informants at the point of collection. Participants

completed a brief survey form (see Appendix B) with questions regarding past health history,

socioeconomic status, and western lifestyle behaviors. Part one obtained basic demographic

information including village, age, and gender. Part two collected data associated with SES and

included questions regarding level of education, the construction of their household flooring,

source of cooking water supply, and mode of transportation. Part three described the presence or

absence of health conditions associated with T2DM including prior diagnosis of diabetes,

hypertension, heart disease, and cerebrovascular disease. Part four collected data to describe

lifestyle behaviors, which are surrogate indicators of urbanization and a western lifestyle. These

factors included tobacco use, alcohol use, and consumption of sweet drinks like sweet coffee or

soda. Part five collected data pertaining to biometric and laboratory indicators of health.

Participants who had been fasting overnight were able to complete the screening exam with

minimal discomfort. Non-fasting participants received a random capillary glucose sample

collection and if the capillary blood glucose level was between 110 and 199 mg/dl, they had the

option of returning provided they had been fasting for eight hours prior to having their glucose

level reassessed (see Appendix B).

37

Variables

Demographic variables.

Data were collected regarding the participant’s residential location, gender, and age.

Participants’ village and gender were categorical variables; however, the continuous variable age

was transformed into a categorical variable “age groups” to represent groupings. The age group

variable was derived assigning participants to 18-29 year age group, 30-39 year age group, 40-49

year age group, 50-59 year age group, and 60 years and older age group (see Table 7).

Socioeconomic variables.

Several studies have investigated markers of wealth in developing counties. Factors

associated with ranking of wealth were selected from three studies, because they applied to the

geographic area of interest (Hargreaves et al., 2007; Khan et al., 2006; Kusumayati & Gross,

1998). Level of education, mode of transportation, type of household flooring construction, and

source of cooking water were selected as markers of wealth and were used to compute an income

score.

The income score was derived by assigning a numeric value to each level of sub group,

which included level of education, mode of transportation, source of cooking water, and

household flooring construction. The sum of the sub group scores was used to determine the total

income score. The composite income score was divided into tertiles to represent low, middle,

and high-income groups to represent the socioeconomic status (SES). Once the income score was

calculated, the sub group variables education, household flooring, and source of cooking water

were recoded to create an even distribution for each of the sub groups (see Table 8).

38

Lifestyle variables.

Data were collected regarding the frequency of tobacco use, alcohol use, and sweet

beverage consumption. Tobacco use was categorized as life-long non-tobacco, former tobacco

use, and current tobacco use. The use of alcohol and sweet drinks was assessed from a memory

recall and estimates of how many times a week the participants used these products. Categorical

variables were used to quantify the frequency of alcohol and sweet drink consumption.

Initially, there were six frequency intervals for alcohol; however, to develop an even

distribution, the variable alcohol consumption was recoded to create three categories of alcohol

consumption representing non-drinkers, rare alcohol use, and regular weekly consumers of

alcohol. The number of sweet drinks consumed was assessed by categorical variables, which had

a range of number of sweet drinks. The analyses of this variable identified a bimodal peak with

4-10 sweet drinks and 21-25 sweet drinks per week (see Figure 8). These data were transformed

from six categories to four categories resulting in an even distribution of sweet drink

consumption (see Table 9).

Glucose.

Capillary blood glucose was obtained using the Righttest GM300 TM

series glucose

monitoring system. A 27-gauge solid core lancet was used to access capillary whole blood from

the participant’s finger. According to the manufacture recommendation, the first drop of blood

was discarded and a second drop, approximately 1.4 μl, of blood was used for analysis, which is

the size of the sample well on the test strip (see Figure 6). A single level control calibration of

the Bionine GM 300 TM

was performed daily, whenever a new vial of test strips was being used,

and whenever the meter was dropped (Bionime, 2012).

39

Using the 2003 World Health Organization guidelines, a fasting capillary glucose less

than 110 mg/dl is considered normal. A fasting capillary glucose level greater than 125 mg/dl is

considered positive for diabetes. A fasting capillary glucose level between 110mg/dl and 125

mg/dl is suggestive of impaired fasting glucose and resulted in additional testing with a 75 gram,

2-hour Oral Glucose Tolerance Test (2-h OGTT). A 2-hour OGTT capillary glucose level equal

to or greater than 200 mg/dl was positive for diabetes and a 2-hr OGTT capillary glucose level

between 140 mg/dl and 199 mg/dl was considered diagnostic for impaired glucose tolerance.

Final analysis considers participants as having normal glucose levels, pre-diabetes (the

combination of IGT or IFG) and diabetes (WHO, 2003).

The prevalence of T2DM was determined by counting the number of people who had a

previous diagnosis of diabetes, who were taking anti-hyperglycemic medications, had a fasting

plasma glucose level greater than 125 mg/dl, or random plasma glucose greater than 199 mg/dl.

The prevalence of pre-diabetes was determined by counting the number of people who did not

have a previous history of diabetes, but had a fasting plasma glucose level between 110 and 125

mg/dl, or a 2-hour OGTT capillary glucose level between 140 mg/dl and 199 mg/dl. All other

participants were considered to have a normal glucose metabolism (NGM). To answer the first

aim of this study, the proportion of people with NGM, pre-diabetes, and diabetes were

determined. To answer the remaining aims of the study, the variable “diabetes” was recoded into

a dichotomous variable of impaired glucose metabolism (IGM), which included people with the

criteria of pre-diabetes and diabetes. The other category, normal glucose metabolism (NGM) was

derived from people without evidence of IGM.

40

Blood Pressure.

After resting for five minutes, blood pressure was assessed, using an appropriate sized

blood pressure cuff and aneroid sphygmomanometer, on two occasions separated by 15 minutes.

The two systolic blood pressure (SBP) readings were used to obtain a mean SBP. The

sphygmomanometer was calibrated prior to commencement of the study and as needed according

to manufacture recommendation that the sphygmomanometer be recalibrated whenever the

indicator fell outside the oval/square indicator when zero pressure was applied (Welch Allyn,

2001).

Systolic blood pressure was recoded from a continuous variable into a dichotomous

variable called “hypertension (HTN)” using the Joint National Committee (JNC) cut point of a

SBP of 140 mm/Hg or higher to signify hypertension and a SBP of less than 140 mm/Hg to

blood pressure to represent absence of hypertension (National High Blood Pressure Education

Program, 2004).

Body Mass Index.

Height and weight were assessed to calculate the body mass index (BMI). Using a

balance beam scale with attached height rod, participants’ height, and weight were assessed

twice and recorded to the nearest 0.5 cm and 0.5 kg, respectively. The average of the two

assessments was used to determine height and weight and to calculate BMI using weight (kg)

divided by height (m) 2

. To ensure accuracy, the scale was calibrated daily following the

manufacturer’s guidelines.

BMI is a measurement of body habitus used to represent adiposity. According to the

World Health Organization, a BMI of less than 18.5 is underweight, a BMI between 18.5-24.9 is

normal or healthy, a BMI between 25.0 and 29.9 is considered overweight, and a BMI of 30.0 or

41

greater is considered obese (WHO, 2000). The continuous variable of BMI was recoded into a

dichotomous variable labeled “adiposity” using the cut point of a BMI less than 25 to represent

people with healthy levels of adiposity and a BMI of 25 or greater to represent people with

unhealthy levels of adiposity and referred to as having “excess adiposity” (see Table 7).

Waist-to-Hip ratio.

The waist-to-hip ratio (WHR) is an alternative method of assessing excess adiposity by

dividing the waist circumference by the hip circumference. Waist and hip circumference was

obtained using a stretch resistant tape measure, with the circumference measured to the nearest

0.5 cm. According to the WHO criteria, waist circumference was obtained half way between the

12th

rib and the iliac crest. Waist circumference was measured parallel to the floor with the tape

measure being snug. Participants were allowed to wear light clothing (T-shirt/pants or dress).

The hip circumference was obtained from the widest portion of the buttocks with the tape

measure being snug and parallel to the floor (World Health Organization, 2008). The waist and

hip measurements were repeated and the average was used to determine the circumference. The

WHR is a mathematical calculation dividing the waist circumference by the hip circumference.

The WHR reference ranges for men are ≤ 0.95, 0.96-1.0, and ≥ 1.1 for low risk, moderate risk,

and high risk, respectively. The WHR reference ranges for women are ≤ 0.80, 0.81-0.85, and ≥

0.86 for low risk, moderate risk, and high risk, respectively. The continuous values for WHR

ratio were recoded into discrete variables of low, medium, and high-risk groups.

Medical follow-up

Participants were given documentation of their results (See appendix B). If the

participants had abnormal findings, they were advised to seek confirmation with their primary

provider or seek follow up at the Sakila clinic. These data were not shared with the clinic. The

42

director of the clinic had agreed to see all research participants who wished to have further

evaluation and/or management of their condition according to local treatment protocols.

Diagnosis and treatment were separate from the research protocol and patients were subject to

usual clinic fees.

Analysis Plan

Data were collected and categorized into five groups of data, which included glycemic,

socioeconomic, anthropometric, blood pressure, and lifestyle indicators. These data were

analyzed to identify associations between the glycemic status and each of the variables to

identify risk groups and risk factors for the development of diabetes. The data were screened for

missing data, outliers, and normality. Descriptive analysis was conducted to describe the

frequency distribution of age, gender, SES, tobacco use, alcohol consumption, obesity, and

hypertension.

Aim 1.

The primary aim of this study was to describe the prevalence of type 2 diabetes and pre-

diabetes in the AruMeru district of Tanzania. To accomplish this aim, the estimated T2DM

prevalence was calculated by taking the number of cases of T2DM and pre-diabetes for all

participants and dividing them by the number of participants sampled to determine the crude

prevalence rate by 5-year incremental groups. Second, the indirect age-adjusted prevalence rate

was calculated by determining national population percentages for each the five-year incremental

age groups then multiplying the crude prevalence rate of pre-diabetes and diabetes by the

national percentage of people in each age group to determine the age specific prevalence rate.

The age specific prevalence rates were summed to provide the overall indirect age-adjusted

43

prevalence rate for people in these seven villages. Based on the power analysis, these data have a

3% margin of error.

Aim 2.

The second aim was to describe the association between demographic and

anthropometric data in rural Tanzanians and the presence of impaired glucose metabolism,

hypertension, and obesity. Body mass indices and waist-to-hip ratios were independently

regressed to determine which measurement of obesity had the highest predictive correlation to

the disease states of diabetes and hypertension. The existing literature contains conflicting data

about whether BMI or WHR is a better indicator of obesity in the African population (Barrett-

Connor, 1989; Huxley et al., 2009; Nyamdorj, 2010; Petursson et al., 2011; Sluik et al., 2011).

The chi-square statistic examined the association between people with glucose

metabolism disorders, hypertension, and adiposity with independent demographic and biometric

variables. Analysis of Variance (ANOVA) was used to examine the group and main effects of

the categorical independent variables: age groups, gender, HTN, and adiposity on the continuous

dependent fasting plasma glucose, mean systolic blood pressure, and body mass index.

A binary logistic regression with a forward conditional method was performed to assess

which independent variables (age group, gender, obesity, hypertension, and metabolic group)

could predict the development of IGM, HTN, and excessive adiposity. This study was a cross

sectional observational study and did not test a theory, but rather explored a phenomena. Little

data is available reflecting the predictive characteristics of chronic diseases in sub-Sahara Africa,

a forward conditional method for logistic regression analyses was appropriate for this study to

identify variables which may predict the presence of chronic disease conditions in rural northern

Tanzania (Field, 2009). The forward conditional method enters each predictor variable to the

44

model one at a time and then removes the variable to assess the observed interaction. If a

significant change is observed, the variable is retained in the model as a predictor of the

dependent variable. To avoid multicollinearity, similar variables were not analyzed together.

Aim 3.

The third aim of this study was to describe the association between socioeconomic

indicators and lifestyle behaviors and the presence of impaired glucose metabolism,

hypertension, and obesity in rural Tanzanians. Khan et al. (2006) suggested diabetes is a disease

of the wealthy; hence, these data were analyzed using a chi-square statistic to determine if the

SES is associated with the development of IGM, HTN, and excessive adiposity.

Analysis of Variance (ANOVA) was used to examine the main and individual effects of

the categorical independent variables of lifestyle and socioeconomic status on the continuous

dependent variables of fasting plasma glucose, mean systolic blood pressure, and body mass

index. A binary logistic regression using a forward conditional method was performed to assess

which independent variables could predict the development of IGM, HTN, and excessive

adiposity.

Conclusions

An observational study describing the prevalence of T2DM in Tanzania was completed

as outlined in this chapter. The last published prevalence study in Tanzania was conducted more

than 10 years ago. There has been a global increase in prevalence of obesity and T2DM.

Describing the current rate of diabetes and the relationship between T2DM and SES, lifestyle,

and anthropometric levels will inform healthcare workers of the significance of diabetes while

recognizing the risk factors of diabetes. These data may allow for the development of culturally

appropriate interventions to prevent or reduce diabetic disease burden.

45

Chapter 4

Diabetes is increasing at alarming rates worldwide. The aims of this study were to

describe the prevalence of diabetes in rural Tanzania, as well as explore factors associated with

the increasing prevalence including, biometric indicators of diabetes and effects of globalization.

Data were collected during June and July of 2012 to address the study aims.

After recruitment, 709 people were screened as potential participants in this prevalence

study, 64 of whom were excluded from data analysis because of predefined criteria (see Table 6)

leaving 645 participants for analysis (see Figure 7). One participant was able to provide a fasting

plasma glucose sample; however, she had previously had a traumatic injury with fractured

pelvis, hip, and bilateral femur fractures with rotational mal-union. She was confined to a

wheelbarrow preventing the ability to obtain her weight, height, waist circumference and her hip

circumference with accuracy. Two participants had missing height and weight data limiting the

ability to obtain a BMI and one participant was unable to recall her age. Fasting plasma glucose

levels were obtained in 635 participants, random plasma glucose levels were obtained in 41

participants with nine participants receiving a 2-hour oral glucose tolerance test (2hr OGTT).

The 2hr OGTT procedure was abandoned after nine tests because of inconsistency of the glucose

solution. In all nine tests, the 2-hour glucose level was less than 100 mg/dl indicating they did

not have impaired glucose tolerance. In subsequent analyses, missing data were handled using a

pairwise deletion approach.

Descriptive analysis

The majority of the participants were cash crop farmers, who had a primary school

education (70%), while a moderate number of people had no formal education (20%). The most

46

common type of flooring in participants’ homes was a concrete slab for a floor (49%), with 31%

of participants having homes with dirt floors. Most of the participants walked or used some form

of public transportation (87%) and obtained their water from a protected water source (73%).

The nearest hospital was located in the township of Tengeru, with a distance of 35-55 kilometers

depending on the village location. Two villages were regional centers for trade and transportation

(village 6 and 7) and both had local access to formal health clinic services (see Table 10).

The participants without diabetes ranged in age from 18 to 103 (n = 540, Mean = 49.9, sd

= 17.3). The participants with pre-diabetes ranged in age from 23 to 92 (n = 46, Mean 53.5, sd =

16.5) and the participants with diabetes ranged in age from 23 to 90 (n = 58, Mean 57.8, sd =

14.8). Participants with diabetes were statistically older than those without diabetes (F (2,641) =

6.37, p =.002); however, using Bonferroni contrasts, no statistically significant age difference

between those with normal glucose metabolism and those with impaired glucose metabolism was

observed. The participation of women compared to men was not statistically significant, with

64% of the participants being female. The proportion of participants with pre-diabetes and

diabetes was higher in males than females, 26.1% v. 8.3% and 12.6% v. 7.8%, respectively;

however, these differences were not statistically significant (χ2 (2) = 5.33, p = .07). Hypertension

and excessive adiposity was observed in 25% of the participants while there was an increased

association between people who had higher income scores and excessive adiposity, (χ2 (2) =

10.95, p = .004).

The preexisting prevalence of participants with diabetes was 3.1% (n = 20) resulting in

66% of participants having met the diagnostic criteria of diabetes and therefore having a new

diagnoses. A previous history of hypertension was self-reported in 5.3% of the participants (n =

47

34), a history of cardiovascular heart disease was reported in 2.9% of the participants (n = 19),

and cerebrovascular disease was reported in 0.5% of the participants (n = 3).

Prevalence

The first aim of this study was to estimate the prevalence of type 2 diabetes and pre-

diabetes in the rural communities of the AruMeru district. The overall mean fasting plasma

glucose was 100.8 (sd = 23.6), with a mean range of 94.1-117.3 across the seven villages. There

were 46 (7.1%) people who had fasting plasma glucose levels consistent with pre-diabetes and

58 (9.0%) people who fulfilled the diagnostic criteria for having diabetes (see Table 11). Using

the rural Tanzanian national population estimates, the indirect age-adjusted prevalence rate for

pre-diabetes and diabetes was 2.54% (95% CI [0.06; 0.1]) and 2.84% (95% CI [0.07; 0.12]),

respectively. When standardizing the crude rates of diabetes and pre-diabetes to the world

population estimates, the indirect age-adjusted prevalence rates for pre-diabetes and diabetes

increased to 4.71% (95% CI [0.06; 0.1]) and 5.13% (95% CI [0.07; 0.12]), respectively. The

increase in prevalence using the world population as a standard measure, is related to the older

world population compared to the Tanzanians, thus a higher statistical weight. The mean age of

people living in Tanzania is 19 years and the life expectancy is 53 years of age (CIA, 2009),

using the Tanzanian rural national statistics provides a more accurate estimation of the

prevalence rates. More than 50% of the participants of this study were older than 50 years

(n=339) and the proportion of people with pre-diabetes and diabetes increased significantly in

people with advancing age (χ2 (8) = 21.19, p = .007). Univariate ANOVA was performed

demonstrating a statistically significant difference in mean fasting plasma glucose between

villages (F (6,628) = 8.94, p <.001). A post hoc Bonferroni correction confirmed that village

48

seven had a higher mean fasting plasma glucose as well as higher counts of pre-diabetes and

diabetes compared to villages one, three, four, and five (see Appendix C).

Anthropometric findings

The second aim of the study was to determine which anthropometric and demographic

variables were associated with health status with respect to impaired glucose metabolism,

hypertension, and excess adiposity.

Measurements of adiposity were collected to calculate body mass index (BMI) and waist-

to-hip ratio (WHR). Each measure was regressed separately on fasting plasma glucose (FPG) and

mean systolic blood pressure (SBP) to examine the relative strength of association. BMI (F

(1,630) = 7.96, p = .005; R = 0.11) had a stronger association with fasting plasma glucose than

WHR (F (1,632) = 4.85, p = .028; R = 0.09), suggesting a stronger association between BMI and

glucose levels. BMI (F (1,640) = 30.31, p <.001; R = 0.21) and WHR (F (1,642) = 25.64, p

<.001; R = 0.2) were both significantly associated with systolic blood pressure; however, BMI

had a stronger association and accounted for more variance in SBP than did WHR. Because in

both the case of fasting plasma glucose level and systolic blood pressure a stronger association

was noted with BMI than with WHR, BMI was chosen to represent adiposity in subsequent

analyses.

Impaired glucose metabolism and demographic/biometric indicators.

An exploratory analysis using the chi-square statistic was conducted to describe the

association between IGM, hypertension, excess adiposity, age groups, and gender. There was a

statistically significant association between IGM and hypertension (χ2

= 10.86, p = .001) and

between IGM and adiposity (χ2

= 8.67, p = .003). There was a significant association between

IGM and age groups (χ 2(4) = 15.5, p = .004), HTN and age groups (χ

2(4) = 43.43, p <.001), and

49

adiposity and age groups (χ 2(4) = 24.5, p <.001). There was not a significant association

between HTN and adiposity or gender and IGM, HTN, or adiposity (see Table 12).

Univariate ANOVA was conducted to determine which anthropometric and demographic

variables were associated with a higher fasting plasma glucose level. The dependent variable

fasting plasma glucose (FPG) was analyzed as a continuous variable, while the independent

variables were age groups, HTN, adiposity, and gender. The generated model was statistically

significant (F (35,596) = 2.06, p < .001, ή2 = .12) and the main effect of adiposity had

statistically significant association with FPG (F (1,596) = 11.36, p = .001, ή2 = .02). The other

main effects and interactions were not statistically significant (see Table 13).

A forward binary logistic regression was conducted to determine which biometric

indicators (gender, age groups, HTN, and adiposity) were predictors of IGM. The model

included age groups, HTN and adiposity and was statistically significant in predicting IGM (χ 2

(6) = 28.71, p < .001). The variables of adiposity (p =.006, OR 1.9, 95% CI [1.2, 3.02]), HTN (p

=.037, OR 1.64, 95% CI [1.03, 2.62]) and age groups (p = 0.03) with the greatest risk being in

the older age groups were risk factors for the development of IGM. Participants’ in the age group

50-59 (p =.025, OR=3.21, 95% CI [1.16, 8.86]) and those over the age of 60 (p = .044, OR 2.76,

95% CI [1.03, 2.62]) had a significant risk for the development of IGM, while gender was not

significant and was removed from the model. The odds of developing impaired glucose

metabolism (IGM) increased by 90% for people with excessive adiposity, by 64% if their SBP

was greater than 140mm/Hg, by 221% if they were between the age of 50-59, and by176% if

they were over the age of 60 (see Table 14).

50

Hypertension and demographic/biometric indicators.

In this study, hypertension was observed in 24.4% of the participants. The proportion of

people with hypertension was higher in older participants (χ2 (12) = 68.53, p < .001), and in

those with IGM (χ2

(6) = 20.84, p =.002) (see Table 12).

Univariate ANOVA was conducted to determine which anthropometric and demographic

variables were associated with hypertension. The dependent variable “systolic blood pressure”

was analyzed as a continuous variable, while the independent variables were age groups, IGM,

adiposity, and gender. The overall model was statistically significant (F (35,606) = 3.86, p <

.001, ή2

= .182) and the main effects of age groups (F (4,606) = 3.03, p = .017, ή2 = .02), IGM (F

(1,606) = 10.63, p = .001, ή2 = .017), and adiposity (F (1,606) = 10.47, p = .001, ή

2 = .017) were

statistically significant. There was a significant two-way interaction between IGM and excess

adiposity on systolic blood pressure (F (1,606) = 8.84, p = .003, ή2 = .014). Gender had no

statistically significant association with elevated systolic blood pressure (see Table 13).

A forward binary logistic regression was conducted to determine which biometric

indicators (gender, age groups, IGM, and adiposity) were predictors of HTN. The model

included age groups and IGM and was statistically significant in predicting HTN (χ2

(5) = 52.25,

p < .001). The variables of IGM (p = .026; OR 1.69, 95% CI [1.06, 2.69]) and age groups (p <

.001) were associated with an increased risk of developing hypertension. The greatest age risk of

developing hypertension occurred in people who were between the ages 40-49 (p=.01, OR 4.25,

95% CI [1.42, 12.78]), ages 50-59 (p <.001, OR=8.32, 95% CI [2.84, 24.42]), and being over the

age of 60 (p <.001, OR 8.50, 95% CI [2.95, 24.26]). Gender and adiposity were not statistically

significant in the development of HTN and removed from the model. The odds of developing

hypertension increased 69% for people who had IGM and by 325% if they were between the

51

ages of 40-49. The odds of developing HTN increased by 732% if they were between the ages of

50-59, and by 750% if they were over the age of 60 (see Table 15).

Adiposity and demographic/biometric indicators.

Excess adiposity affected 27% of the population sampled with 18% (n=115) having a

BMI between 25.0 and 29.9, while 9% had a BMI equal to or greater than 30.0. Using the chi-

square statistic, there was a statistically significant higher rate of IGM in people with excessive

adiposity (χ2 (1) = 8.67, p = .003, ή

2 = .12) (see Table 12).

Univariate ANOVA was conducted to determine which anthropometric and demographic

variables were associated with excess adiposity. The dependent variable “BMI” was analyzed as

a continuous variable, while the independent variables were age groups, IGM, HTN, and gender.

The overall model was statistically significant (F (37,603) = 4.14, p < .001, ή2

= .202) and the

main effects of age groups (F (4,603) = 5.84, p < .001, ή2

= .037), HTN (F (1,603) = 12.86, p <

.001, ή2

= .021), and gender (F (1,603) = 10.44, p = .001, ή2

= .017), were statistically significant.

There was a significant two-way interaction between age group and HTN on adiposity (F (4,603)

= 2.68, p = .03, ή2

= .017) as well as HTN and IGM on adiposity (F (1,603) = 6.58, p = .011, ή2

=

.011) (see Table 13).

A forward binary logistic regression was conducted to determine which biometric

indicators (gender, age groups, HTN, and IGM) were predictors of excess adiposity. The model

included gender, IGM, and age groups, and was statistically significant in assessing risk of

developing adiposity (χ 2

(6) = 68.61, p <.001). There was a statistically significant risk for the

development of excessive adiposity for females (p <.001; OR 3.56, 95% CI [2.27, 5.59]), people

with IGM (p =.004, OR 2.02, 95% CI [1.25, 3.25]) and people with advancing age (p < .001).

With respect to age, the greatest risk of developing excessive adiposity were for people between

52

the ages of 30-39 (p=.003, OR 3.73, 95% CI [1.58, 8.8]), 40-49 (p<.001, OR 4.77, 95% CI [2.07,

10.99] and 50-59 (p<.001, OR 4.81, 95% CI [2.07, 11.19]). The variable HTN was not

significant and removed from the model. The odds of developing excessive adiposity increased

102% for people who had IGM and by 256% for female participants. Participants had a 273%

increased risk if they were between the age of 30-39, a 377%, increase if they were between the

age of 40-49, and a 381%, increase if they were between 50-59 years old (see Table 16).

Globalization and Lifestyle

Globalization is the advancement of outside lifestyle behaviors or lifestyle changes

within a culture. The third aim of this study was to investigate the association of lifestyle and

globalization on the selected health status indicators of glucose metabolism, hypertension, and

adiposity. To investigate the association between lifestyle changes and the development of

chronic health conditions, data regarding lifestyle habits, and indicators of wealth were examined

to determine if these factors influenced the development of IGM, HTN, and Adiposity. Surrogate

markers of wealth were measured by two domains, which included acquired wealth (mode of

transportation and education level) and domestic wealth (source of cooking water and type of

household flooring).

An exploratory analysis was conducted with the chi-square statistic to identify

associations between lifestyle indicators and the presence of IGM, HTN, and adiposity. There

was a statistically significant association between IGM and those with no formal education (χ2

(4) = 7.84, p = .02). Income score, water source, household flooring construction, mode of

transportation, sweet drink consumption, tobacco use, and alcohol use had no significant

association to the development of IGM. The association between people with hypertension and

their water source was statistically significant suggesting primitive water sources had an

53

increased association with the development of hypertension (χ2

(3) = 8.11, p = .044). Tobacco

use was associated with a higher rate of hypertension, as compared to non-tobacco users (χ2

(2) =

13.63, p= .001); however, there was an inverse relationship between tobacco use and obesity

with a statistically significant number of non-tobacco users having excess adiposity (χ2

(4) =

16.4, p < .001). The type of household flooring construction and mode of transportation had a

statistically significant association to excess adiposity. Participants having concrete or tile floors

were more likely to have excess adiposity compared to those with dirt and wooden household

floors (χ2

(4) =15.99, p < .001). Participants with motorized transportation were more likely to

have excess adiposity compared to those who walk or ride bicycles (χ2

(4) = 10.44, p = .034). In

terms of education, those with secondary school education and beyond had a higher rate of

excess adiposity compared to those that had no education or primary school education (χ2(4) =

9.28, p = .01). Participants with higher composite income scores were more likely to have excess

adiposity (χ2

(4) =10.95, p = .004) (see Table 17).

Impaired glucose metabolism and globalization.

Univariate ANOVA was conducted to determine which lifestyle variables were

associated with elevated FPG levels. The dependent variable FPG was analyzed as a continuous

variable, while the independent variables were tobacco use, alcohol use, and sweet drink

consumption. The overall model was not statistically significant and there were no significant

main effects observed. A Bonferroni correction was performed demonstrating a significant

association between people who consumed more than four sweet drinks per week having a

higher FPG level (p = .007) (see Table 18).

Univariate ANOVA was conducted to determine which socioeconomic variables were

associated with elevated glucose levels. The dependent variable FPG was analyzed as a

54

continuous variable, while the independent variables were level of education, type of household

flooring, source of cooking water, and mode of transportation. The overall model was

statistically significant (F (54,580) = 5.82, p < .001, ή2 = 0.352) and there were significant main

effects between FPG and level of education (F (2,580) = 3.43, p = .033, ή2 = .012), type of

household flooring (F (2,580) = 17.23, p < .001, ή2 = .056), source of cooking water (F (3,580) =

33.36, p < .001, ή2 =.147), and mode of transportation (F (2,580) = 23.21, p < .001, ή

2 = .074).

There was a two-way interaction noted between education level and source of cooking water, (F

(2,580) = 23.21, p < .001, ή2 = .02) and mode of transportation and source of cooking water (F

(3,580) = 67.72, p < .001, ή2 = .26) (see Table 19).

A forward binary logistic regression was conducted to determine which lifestyle variables

(tobacco, alcohol, and sweet drink) and socioeconomic indicators (education, flooring

construction, water source, and mode of transportation) were predictors of IGM. The model

included level of education and was statistically significant in predicting IGM (χ 2

(2) = 7.26, p =

.027). The variables tobacco use, sweet drink consumption, alcohol use, household flooring

construction, water source, and mode of transportation were not statistically significant and were

removed from the model. The higher level of education appeared to be protective for the

development of IGM compared to people with no formal education. For participants who

completed primary school, there was a 60% risk reduction of developing IGM (p = .009; β -

0.641, OR 0.58, 95% CI [0.33, 0.85]) and for people who completed secondary school or higher

had a 44% risk reduction of developing IGM (p = .058, β -0.82, OR 0.44, 95% CI [0.19, 1.03])

(see Table 20).

55

Hypertension and globalization.

Univariate ANOVA was conducted to determine which lifestyle variables were

associated with HTN. The dependent variable systolic blood pressure was analyzed as a

continuous variable, while the independent variables were tobacco use, alcohol use, and sweet

drink consumption. The overall model was statistically significant, (F (33,611) = 1.83, p = .004,

ή2= .09) and a significant main effect was observed with tobacco use (F (2,611) = 6.99, p = .001,

ή2= .022). A Bonferroni correction was performed identifying people who were former tobacco

users having higher systolic blood pressure readings compared to life-long non-tobacco users (p

< .001) (see Table 18).

Univariate ANOVA was conducted to determine which socioeconomic variables were

associated with HTN. The dependent variable systolic blood pressure was analyzed as a

continuous variable, while the independent variables were level of education, type of household

flooring, source of cooking water, and mode of transportation. The overall model was not

statistically significant (p = .06); however, there was a significant main effect between education

level and elevated systolic blood pressure (F (2,590) = 7.35, p = .001, ή2 =.024). A post hoc

Bonferroni correction was performed demonstrating a significant association between levels of

education and SBP, (p = .021), indicating those without formal education were more likely to

develop hypertension compared to those who completed primary or secondary school (see Table

19).

A forward binary logistic regression was conducted to determine which lifestyle variables

(tobacco, alcohol, and sweet drink) and socioeconomic indicators (education, flooring

construction, water source, and mode of transportation) were predictors of HTN. The model

included tobacco use and source of cooking water and was statistically significant in predicting

56

the development of HTN, (χ2 (5) = 20.75, p =.047). The variables sweet drink consumption,

alcohol use, flooring construction, education, and mode of transportation were not statistically

significant and removed from the model. Being a former smoker had a significant increase risk

of developing hypertension, (p < .001, OR 2.26, 95% CI [1.46, 3.50]). For participants who were

former smokers, there was a 126% increased risk of developing HTN (see Table 21).

Adiposity and globalization.

Univariate ANOVA was conducted to determine which lifestyle variables were

associated with excess adiposity. The dependent variable body mass index was analyzed as a

continuous variable, while the independent variables were tobacco use, alcohol use, and sweet

drink consumption. The overall model was statistically significant, (F (33,608) = 1.93, p = .002,

ή2= .095) and there was a significant main effect observed with tobacco use (F (2,611) = 7.65, p

= .001, ή2= .025). A Bonferroni correction was performed demonstrating that people who were

current and former tobacco users had a significantly lower BMI compared to non- tobacco users

(p < .001) (see Table 18).

Univariate ANOVA was conducted to determine which socioeconomic variables were

associated with excessive adiposity. The dependent variable body mass index was analyzed as a

continuous variable, while the independent variables were level of education, type of household

flooring, source of cooking water, and mode of transportation. The overall model was

statistically significant (F (54,587) = 2.05, p < .001, ή2= .158) and there was a significant two-

way interaction between level of education and mode of transportation to higher BMI levels (F

(2,587) = 3.78, p = .023, ή2= .013). A post hoc Bonferroni correction was performed

demonstrating a significant association on flooring type suggesting those with concrete

household floors were more likely to develop excess adiposity compared to those with dirt floors

57

(p < .001) and wooden household floors (p = .02). Those who used a motorized means of

transportation were more likely to develop excess adiposity compared to those who used bicycles

(p = .008) but not those who walked. Participants who completed primary school (p < .001) or

higher levels of education (secondary school or higher) (p = .006) were more likely to develop

excess adiposity compared to those without formal education (see Table 19).

A forward binary logistic regression was conducted to determine which lifestyle variables

(tobacco, alcohol, and sweet drink) and socioeconomic indicators (education, flooring

construction, water source, and mode of transportation) were predictors of developing excessive

adiposity. The model included tobacco use and household flooring construction and was

statistically significant in predicting the development of excessive adiposity, (χ2 (5) = 32.77, p <

.001). The variables sweet drink consumption, alcohol use, education level, mode of

transportation, and education level were not statistically significant and removed from the model.

Being a former tobacco user (p = .002, β -0.873, OR 0.42, 95% CI [0.24, 0.73]) and a current

tobacco user (p = .032, β -1.32, OR 0.27, 95% CI [0.08, 0.89]) appeared to have a significant risk

reduction for the development of excessive adiposity. The type of household flooring

construction has a significant effect on the development of excessive adiposity: compared to

people with earthen floors, people who had wooden plank floors have a 98% increase risk of

developing excessive adiposity (p = .015, OR 1.98, 95% CI [1.14, 3.44]) and people who had

concrete floors have a 131% increased risk of developing excessive adiposity, (p < .001, OR

2.32, 95% CI [1.46-3.66]) (see Table 22).

Conclusions

The first aim of this study was to describe the prevalence of pre-diabetes and diabetes in

rural AruMeru district of Tanzania. The crude prevalence rate for pre-diabetes and diabetes is

58

7.1% and 9% respectively, while the age-adjusted prevalence rates for pre-diabetes and diabetes

are 2.52% and 2.84% respectively. This is the first prevalence study of diabetes in the AruMeru

district and will provide a baseline prevalence rate for diabetes and pre-diabetes among people

who live in the AruMeru district.

The second aim of this study examined demographic and biometric indicators on the

development of impaired glucose metabolism, hypertension, and excessive adiposity. Systolic

blood pressure, age, and body mass index were identified as being significantly associated with

the development of IGM. The third aim of the study examined identified lifestyle factors that

contributed to the development of IGM, HTN, and excess adiposity. IGM was associated with all

wealth indicators suggesting people with higher levels of education, better household flooring,

indoor plumbing and owners of automobiles were more likely to develop IGM. Hypertension

was associated with improved water sources and the use of tobacco products while the

development of excessive adiposity was associated with motorized means of transportation,

higher levels of education, improvement of household flooring, and the presence of indoor

plumbing.

The effects of urbanization may result in improved quality of life for people in rural

Tanzania; however, the changes warrant consideration of two factors. Asset acquisition such as

improved flooring, vehicular ownership, education level, and indoor plumbing may represent

increasing wealth and are all associated with the development of IGM and excessive adiposity.

Second, these variables may be individually associated with the development of IGM, HTN and

excessive adiposity and concomitant changes in lifestyle patterns, which by themselves may alter

the balance between caloric consumption and metabolic energy expenditure.

59

Chapter 5

Diabetes and other chronic diseases are present with increasing prevalence in developing

counties and specifically in sub-Sahara Africa. Once thought of as a rare occurrence in sub-

Sahara Africa, diabetes will soon become a significant health challenge. Recent studies have

estimated the prevalence of type 2 diabetes in sub-Sahara Africa to range from 4.5% in Kenya to

47% in the Democratic Republic of Congo (Christensen et al., 2009; Hightower et al., 2011).

According to the International Diabetes Federation, the prevalence of diabetes is close to 4% on

the African continent compared to 10.2% in North America (Whiting et al., 2011). The primary

aim of this study was to describe the prevalence of type 2 diabetes mellitus for residents in rural

northern Tanzania.

Prevalence of diabetes

Crude prevalence estimates for pre-diabetes and diabetes in this study were 7.1% and

9.0%, respectively, while the indirect age-adjusted prevalence rates were 2.79% and 2.84%

respectively, in AruMeru district of northern Tanzania. This was the first prevalence study

reporting diabetes prevalence rates for the AruMeru district; however, changes in prevalence are

determined by comparing regional estimates. Aspray et al. (2000) examined the prevalence of

pre-diabetes and diabetes in a rural village in the Kilimanjaro region of northern Tanzania, which

is about 50 kilometers from where this study site. Aspray and colleagues reported the prevalence

of diabetes to be 1.1%, while in 2009 Christenson reported the estimated prevalence of diabetes

in southern Kenya to be 4.2%. Both Aspray and Christenson used the world population, rather

than national or district level population, to adjust their findings for the age standardization.

When standardizing the results from this study to the world population, the estimated prevalence

rates for pre-diabetes and diabetes increase from 2.52/2.84% to 4.71/5.13% respectively because

60

of the older age of the participants. Comparing the results of this study to Aspray’s estimates, it

appears there may be modest increase in the prevalence of pre-diabetes and diabetes in northern

Tanzania. The AruMeru district and the Kilimanjaro region consist of different tribal groups;

however, both areas are located in high mountainous, fertile regions with economic advantages

from agriculture.

The WHO/IDF criteria were used in this study to assess both pre-diabetes and diabetes;

however, the American Diabetes Association (ADA) 2012 criteria have a lower diagnostic

threshold for impaired fasting glucose. Applying the 2012 criteria to these data, the age-adjusted

rate of pre-diabetes would have increased from 2.52% to 11.89% in the AruMeru district.

Alarming concerns from this study are the advanced age of participants and the

prevalence of pre-diabetes and diabetes in the aged. The Tanzanian government estimates life

expectancy to be 53 years of age (Masalu et al., 2009); however, The mean age of this study was

50.1 years of age, with more than 212 (33%) people over the age of 60 years including one

person reporting being more than 100 years old. There is a statistically significant association

between advancing age and the development of diabetes, the number of people who are at

significant risk for developing diabetes. Considering previous reports by Whiting (Whiting et al.,

2011) and Christenson (Christensen et al., 2009) evidence suggests between 60-85% of new

cases diabetes are identified during prevalence studies in SSA, corresponding to the 66% of

people in this study had unrecognized diabetes. Understanding that significant numbers of people

may indeed have unrecognized diabetes, the burden of diabetes and diabetic related

complications may increase significantly in the future.

The reported prevalence rates of pre-diabetes and diabetes are higher than expected,

based on previous reports. Despite promoting, the study in Swahili and requesting an 8-hour

61

caloric free fast, it is possible some of the participants will not have been fasting, thus skewing

the results. However, based on the preexisting prevalence of people known to have diabetes and

the ratio of known and unknown rate, the prevalence of diabetes is consistent with previous

reports.

These data suggest a moderate burden of diabetes in this region and poses serious

financial implications for people with diabetes who wish to seek healthcare. The diabetes clinics

and specialty diabetes providers are limited to urban centers (National Bureau of Statistics,

2011). Previous reports have described people in Tanzania spending as much as 50% of their

household income on anti-hyperglycemic agents and transportation to receive medical care

(Justin-Temu et al., 2009; Kolling et al., 2010; Lugongo, 2010).

Diabetes is a well-known risk factor for the development of coronary artery disease

(Wamala, Merlo, & Bostrom, 2006); however, a history of heart disease was reported with low

frequency. Participants that reported a previous history of diabetes were 20 (3.1%) while those

participants reporting a history of coronary artery disease were similar (n=19, 2.9%) with an

association between people with a history of diabetes and coronary disease. It is possible with the

limited number of healthcare facilities; people with coronary disease with or without diabetes

could succumb to their health condition prior to receiving care.

Biometric indicators of health

The second aim of the study examined the association between anthropometric and

demographic indicators and the presence of selected health conditions. An association between

IGM, hypertension, and excessive adiposity was detected. Examining the interaction between

biometric variables and the presence of IGM, HTN, and adiposity provided insight regarding the

interrelationship between these variables in residents in this rural community. Advancing age has

62

a significant association with the development with each of these chronic conditions. BMI and

WHR have been used to categorize obesity and some reports have suggested that WHR or waist

circumference is better for people in developing countries (Petursson et al., 2011; Schulze et al.,

2006). This study demonstrated that BMI is a better measurement of adiposity and is more

sensitive for detecting associations between adiposity, hypertension, and IGM for people living

in the AruMeru district. The combination of HTN, IGM and adiposity are inter-connected and

can be predicted based on body habitus. It is not clear if obesity is the sentinel event or whether

the combination of the characteristics, which could be classified as metabolic syndrome, has an

underlying pathophysiologic implication.

According to previous studies by Swai et al. (1992), there was no association with obesity

and diabetes, but rather with malnourishment. The results from the present study clearly showed

an increased risk of developing diabetes for overweight and obese participants. Studies by

McLarty, Swai, and Christenson suggested that being underweight might be a predictor of

diabetes, which is contradictory to the results of this study. There was a 1.6 fold reduction in pre-

diabetes/diabetes for people with a BMI less than 18.5, compared to people with a normal BMI.

This study excluded people with evidence of an active infection or who were taking antiviral

medications. There is evidence to suggest that antiviral medications can increase the risk for

diabetes leading to a relative increase in diabetes for those who are malnourished from AIDS

(Field, 2009; Masalu et al., 2009). The results of this study suggested that the risk of developing

diabetes is associated with being overweight (6.8 fold increase) and obese (15.9 fold increase)

compared to people with normal body mass indices.

Advancing age was common predictor variable for the development of IGM, HTN, and

excessive adiposity. Females were more likely to become obese compared to males; however,

63

there was no effect of gender and the development of diabetes or hypertension. The paradigm of

obesity is complex in developing countries, as health and wealth can be associated with excess

adiposity (Neuman et al., 2011; Subramanian et al., 2011). People with disease conditions such

as HIV, tuberculosis, and severe malnutrition often have emaciated and cachectic physical

appearance owing to the physical observation of disease (Popkin et al., 2012). The visual

appearances of obesity demonstrates to community members that people with excess adiposity

can afford to purchase food and are free of serious disease conditions.

Globalization and Wealth

The third aim of the study examined selective lifestyle factors and implications of

globalization and the presence of selected health conditions including glucose metabolism,

hypertension, and excess adiposity.

Habits.

Lifestyle habits are reflective of western influence in terms of tobacco use and soda

beverages. The public display of participation in these behaviors may offer a demonstration of

pseudo-wealth as soda and tobacco products are inexpensive in rural Tanzania. Consumption of

these products may not be representative of a higher SES, but rather habitual or a public display.

These data suggest that tobacco use is low in rural Tanzania and current smokers account for

5.4% of the sample (n=35) with the majority of current smokers being over the age of 60.

Tobacco use had implications on health as smokers had lower BMI compared to non-smokers,

but were more prone to having hypertension. The number of participants who admitted to using

tobacco was low and it is hard to make inferences based on 6% of the participants. This study

examined tobacco use as current, former, and non-smokers. Future studies should quantify the

amount of tobacco use by smokers.

64

The consumption of sweet drinks, which included sweet coffee, sweet tea, and soda, was

not associated with IGM and excess adiposity, but it was associated with higher fasting plasma

glucose levels. The distribution of sweet drink consumption was multi-modal and may have

skewed the results with peak levels of consumption being recorded at 4-10 sweet drinks per

week and more than 21 sweet drinks per week. The bimodal distribution of sweet drinks was not

associated with wealth or village location and the factors associated with this phenomenon were

not well understood. One explanation for this variance could be the translation of this question to

the participant. There is a wide categorical interval in determining how many sweet drinks a day

were consumed. Perhaps a two-week dietary log would provide additional data to answer these

questions. Although these numbers are limited, there was a significant association between FPG

and participants who consumed three or less sweet drinks per week compared to those who

consumed four or more sweet drinks per week.

The reported frequency of alcohol consumption was low, which is contrary to other

reports (Cubbins, Kasprzyk, Montano, Jordan, & Woelk, 2012; Masalu et al., 2009; Selembo,

2009). Most people reported a status of non-drinker, which may be associated with the

community stigma associated with alcohol use. The screening locations were inside community

churches and people may not have felt comfortable admitting to alcohol use.

Lifestyle/wealth.

Globalization is transference of goods and technology from developed countries to

developing countries. Some aspects of globalization become wealth indicators, while others

become status symbols. Four surrogate indicators of wealth were examined, as part of this study.

These could be described as domestic wealth, which included the type of household flooring

construction and source of water for cooking, or acquired wealth, which included the mode of

65

transportation and level of education. All four of these indicators had some influence on the

development of IGM, hypertension, and excess adiposity. The results are dynamic, as higher

levels of education resulted in a higher proportion of obesity, but a lower rate of diabetes. The

more rudimentary source of water had an association with hypertension. Study participants who

owned motorcycles or automobiles had higher rates of obesity and diabetes; however, those who

primarily walked had similar rates of diabetes and obesity as car owners compared to those with

bicycles. It is not clear, if participants with bicycles traveled farther from home and expended

more energy compared to the ambulatory group. The ambulatory group was similar to motorist

in terms of obesity and diabetes. What is not known about the participants in the ambulatory

group is the distance they would walk in the course of daily activity and whether the resulting

expenditure similar to participants who had automobiles.

The effects of wealth and globalization extend into the development of chronic diseases

and examination of those relationships was a novel aspect of this study. Future studies should

examine factors regarding caloric expenditure, including the use of a pedometer to measure daily

step counts to compare the activity level of motorists, those who use bicycles, and those who rely

on walking and public transportation. Maintaining a daily activity log with an analogous scale of

workload perception would allow duration and quantification of workload energy expenditure.

The appearance of wealth can be assumed by some external indicators such as excess

adiposity (Renzaho, 2004; Selembo, 2009; Subramanian et al., 2011); however, factors such as

indoor plumbing and household flooring construction represent prosperity, which is not readily

observed by members of the community. The type of household flooring appeared to have an

association to excessive adiposity in this study and may be considered an indicator of tangible

wealth whereas obesity can represent pseudo-wealth (Khan et al., 2006; Popkin et al., 2012).

66

Findings suggest that improved water sources have a protective effect on the

development of hypertension. Improving water quality and access seems to decrease the

prevalence of hypertension; however, there is an association between having immediate access to

water with indoor plumbing and the development of IGM and excessive adiposity. It is not clear

if indoor plumbing decreases energy expenditure resulting in IGM and adiposity or if having

indoor water is a marker of generalized wealth and resultant IGM. The sample of the population

with indoor water was low, with only 3% (n=21) having indoor plumbing. Previous studies have

not described how improvements of water sources influence chronic diseases or if globalization

has increased the number of homes with indoor plumbing and domestic wealth. Future studies

should examine source of cooking water and chronic disease to identify changes.

Study Strengths

A cross sectional research design, powered to an estimated margin of error of 2.2%,

examined the prevalence of pre-diabetes and diabetes in the rural communities of the AruMeru

district in northern Tanzania, providing baseline rates in this rural community. Biometric

indicators were examined to determine the strength of the relationships between biometric

characteristics and the presence of IGM, HTN, and adiposity. Further, this study examined the

association between socioeconomic status and proxy markers, as well as lifestyle and behavioral

issues and the impact on diabetes, obesity, and hypertension in the rural Tanzanian population.

Development of wealth has an association with adiposity and diabetes both as a proxy marker of

wealth and as independent factor representing lifestyle patterns that increase the risk of chronic

conditions.

Social epidemiology and the “web of causation” suggest many factors are associated with

disease conditions or health status. Examining how socioeconomic factors and behavioral

67

lifestyle variables, which are associated with chronic health conditions, is a complex. Improving

the living conditions for people in developing regions may tilt caloric intake-energy expenditure

balance resulting in the development of excessive adiposity and chronic disease. Through social

epidemiology, this study examines how community improvements are implicated with adiposity,

hypertension, and impaired glucose metabolism. For example, access to indoor plumbing is

associated with the development of excessive adiposity and diabetes representing increasing

wealth as well as a decrease in energy expenditure to obtain water. The approach to examining

social variables and the implications of chronic disease provides a new lens on emerging health

implications.

Limitations of the study

The prevalence of pre-diabetes and diabetes were examined as an exploratory study and

incorporated a significant assumption that participants presented in a fasting state or presented

factual data regarding their fasting state. A capillary blood glucose sample was collected from

participants and the results were classified as normal, pre-diabetes, and diabetes based on this

sample. Although this method of sampling would not be adequate for the formal diagnosis and

treatment of diabetes, it does provide valuable information to estimate the prevalence. A

confirmatory sample using a point of care HgbA1c monitor, a follow up fasting capillary blood

glucose sample, or a 2- hour oral glucose tolerance test would have provided a greater degree of

assurance regarding the prevalence rates. Conducting a study using glycated hemoglobin as a

primary method of data collection would eliminate the need for an 8-hour caloric fasting prior to

sampling increasing reliability in the data. There are some limitations with glycated hemoglobin

and results may not be accurate for people with thalassemia’s and hemoglobinopathies (WHO,

2011). Estimate suggest that 80% of all cases of thalassemia occur in low and middle income

68

countries in the and the genetic predisposition increase the risk of these conditions for people

who live in Mediterranean and sub-Sahara Africa countries (Weatherall, 2012). Experts

recommend glycated hemoglobin analysis should not be conducted with point of care monitors

for the diagnosis of diabetes; however, screening with point-of-care monitors may provide a

reliable method of screening with laboratory confirmation. The commercial cost for a Bayer

A1CNow self-check system is about $20.00 per test, which would increase the operational

expense.

Another limitation of the study was failure to perform a 2-hour oral glucose tolerance

test. Glucose solution was obtained from a local pharmacist in Arusha for 75-grams of powder

glucose that was diluted in 250 cc of drinking water. During the study, participants with elevated

fasting plasma glucose were administered a 2-hour oral glucose tolerance test glucose with

resultant glucose levels less than 100 mg/dl. It is difficult to ascertain if the glucose powder had

75-grams of glucose or if the glucose solution was easily metabolized lending to inconsistent

results. Future studies may use a standardized premade glucose solution or an alternative would

be to have participants eat 35 “gummy bears” to create a 75-gram glucose load.

Conducting an epidemiology study with a random sample would increase the reliability

of estimating the prevalence of diabetes in the general population. Randomization occurred at the

village level, and then a convenience sample of participants from the village were screened.

Through self-selection, a convenience sample has some inherent bias and in this study, the

participants tended to be older. Published data from the 2010 Demographic and Health Survey

were used to estimate the rural age-adjusted prevalence as this was the best data available. In the

absence of direct village age distributions, having district level census data would have provided

the next best means to age-adjust for this region.

69

Data regarding wealth indicators were based on limited reports from previously published

papers in developing countries. From the time, the research protocol was developed to data

collection, the use of cellular phones, internet services, and television access had increased

significantly in the AruMeru district of northern Tanzania. The factors examined in this study

accounted for a small to moderate amount of the variance for people who have impaired glucose

metabolism, hypertension, and excess adiposity suggesting other factors may be involved with

the progression of these conditions. The increased prevalence in diabetes may be related to

factors not examined in this study.

The possibility of having both type I and type II errors in these analyses is present.

Despite having a moderate number of participants, many participants did not own vehicles, have

indoor plumbing, or attend higher levels of education. These variables may indeed have more

significance than detected and should be examined in future studies. A significant number of

participants obtained their cooking water from a protected water source and may contribute to a

type II error because of the large number of participants in the category and smaller numbers

who obtain their cooking water from a river or unprotected well.

Future studies

Future studies to confirm the prevalence as well as the incidence of pre-diabetes and

diabetes in the AruMeru district as well as other rural regions of Tanzania should be conducted.

These studies should consider comparing point of care glycated hemoglobin to standard capillary

blood glucose levels to determine the efficacy of this modality for screening purposes.

Examining the association between biometric indices added to the current literature;

however, investigating the effects of globalization and culture changes and the development of

IGM, HTN, and adiposity was a novel exploration and should be repeated.

70

Access to clinical services are limited in rural Tanzania and people who have pre-

diabetes and diabetes need both medical and nursing care in order to manage their health

condition while preventing complications. Additional studies should examine healthcare seeking

patterns for people who are at risk for diabetes, as well as people who have diabetes.

Understanding the patterns and barriers to seeking care may help the Ministry of Health as well

as local non-governmental organizations (NGO) develop treatment protocols, which are

appropriate for both individuals with diabetes who have varying levels of education and health

literacy.

Coronary disease has an association to diabetes but was reported with low frequency in

these data. Future studies should examine the association between coronary artery disease and

diabetes in the rural community to confirm this association in the AruMeru district and gain an

increased understanding of prevalence and implications of coronary disease in the AruMeru

district.

Conclusions

The findings of this study provide initial data on the prevalence of diabetes in the

AruMeru district and suggest the prevalence of diabetes may be increasing in northern Tanzania.

In a society where access to healthcare is limited and resources to pay for healthcare are scarce,

these conditions have devastating effects. The study findings indicate a significant association

between IGM and excess adiposity suggesting that additional studies investigating these chronic

diseases would be beneficial. Additional studies are needed to evaluate the prevalence of

diabetes in other parts of Tanzania as well as prevalence of hypertension and obesity.

Globalization and technology are apparent in the urban areas in Tanzania and these

technologies are increasing in availability for people living in rural communities. Access to safe

71

water, improvements in household construction, and access to modern transportation are

interacting with the lifestyle and health of people in the AruMeru district. Advancements to

improve quality of life are potentially decreasing energy expenditures resulting in excessive

adiposity and potentially contributing to the prevalence of diabetes. Understanding the health

implications of these advancements is paramount to prevent unnecessary morbidity and

mortality.

The results of this study provide some information about the association between lifestyle

changes and the development of diabetes, hypertension, and excessive adiposity for residents

living in rural northern Tanzania. Future studies should investigate other factors of globalization

including the use of the internet, cellular phones, and the impact of food preparation including

the use of cooking oils and the interaction with chronic health conditions

72

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Appendix A

Human subject’s protection certificates

Washington State University Institutional Review Board.

93

National Institute for Medical Research, Ethical Clearance Certificate.

94

Appendix B

Research Protocol Forms

95

96

IRB approved consent: English version.

97

98

99

100

IRB approved consent: Swahili version.

: Swahili version

101

102

103

104

Data collection tool: English version.

105

106

Data collection tool: Swahili with English subtitles.

107

108

Results sheet provided to participant.

109

110

Appendix C

Individual village results

Meru Central.

Population: 500 (estimated)

Sample Size: 92

Village health services: None

Hospital: Tengeru district hospital – 40 kilometers

Age Gender Hx Diabetes Hx CVA Hx CAD Hx HTN

Range: 18-90

Mean: 54.6

sd = 18.3

Male: 29

(32%)

Female: 63

(68%)

Yes: 3 (3%) Yes: 2 (2%) Yes: 7(8%) Yes: 5 (5%)

CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension

Age groups Crude rates of

Pre-diabetes

Crude rates

of Diabetes

Crude rates of

HTN

Crude rates of

Excessive

Adiposity

Age 18-29 (n=8) 0 0 % 0 0 % 0 0 % 1 12.5 %

Age 30-39 (n=15) 2 13.3 % 0 0 % 2 13.3 % 7 46.7 %

Age 40-49 (n=13) 1 7.7 % 1 7.7 % 1 7.7 % 2 15.4 %

Age 50-59 (n=19) 4 21.1 % 1 5.3 % 11 57.9 % 9 47.4 %

Age 60 and older (n=37) 0 0 % 5 13.5 % 17 45.9 % 8 21.6 %

Total (N=92) 7 7.6 % 7 7.6 % 31 33.7 % 27 29.3 %

Group variable Count per variable Percentage

Tobacco Use

Lifelong non tobacco use 71 77 % Former tobacco use 16 17 % Current tobacco use 5 5 %

Alcohol use

No alcohol use 88 96 % Rare alcohol use (<3 drinks per month) 1 1 % Regular alcohol use (drinks weekly) 3 3 %

Sweet drink

consumption

Less than 3 sweet drinks per week 4 4 % 4-10 sweet drinks per week 24 26 % 11-20 sweet drinks per week 44 48 % 21 or more sweet drinks per week 20 22 %

Group variable Count per variable

Income group

Low 43 46.7 %

Medium 40 43.5 %

High: 9 9.8 %

Education level:

No school: 10 10.9 % Primary school: 74 80.4 % Secondary school or higher: 8 8.7 %

Mode of

transportation

Ambulatory: 88 95.7 % Bicycle: 1 1.1 % Motorized (motorcycle/automobile): 3 3.3 %

Source of cooking

water

River/stream: 45 48.9 % Unprotected well: 32 34.8 % Protected well: 15 16.3 % Indoor plumbing: 0 0 %

Household flooring

construction

Earthen/dirt flooring: 22 23.9 % Wooden plank flooring: 52 56.5 % Concrete/Tile flooring: 18 19.6 %

111

Leguruki.

Population: 1,500 (estimated)

Sample Size: 53

Village health services: Dispensary

Hospital: Tengeru district hospital – 55 kilometers

Age Gender Hx Diabetes Hx CVA Hx CAD Hx HTN

Range: 10-103

Mean: 50.62

sd = 18.63

Male: 23

(43%)

Female: 30

(57%)

Yes: 1 (2%) Yes: 0 Yes: 2 (4%) Yes: 3 (6%)

CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension

Age groups Crude rates of

Pre-diabetes

Crude rates

of Diabetes

Crude rates of

HTN

Crude rates of

Excessive

Adiposity

Age 18-29 (n=6) 0 0 % 0 0 % 2 33.3 % 0 0 %

Age 30-39 (n=11) 0 0 % 0 0 % 1 9.1 % 1 9.1 %

Age 40-49 (n=9) 0 0 % 0 0 % 1 11.1 % 3 33.3 %

Age 50-59 (n=12) 2 16.7 % 1 8.3 % 5 41.7 % 6 50.0 %

Age 60 and older (n=15) 1 6.7 % 1 6.7 % 4 26.7 % 4 26.7 %

Total (N=53) 3 5.7 % 2 3.8 % 13 24.5 % 14 26.4 %

Group variable Count per variable Percentage

Tobacco Use

Lifelong non tobacco use 42 80 % Former tobacco use 8 15 % Current tobacco use 3 5 %

Alcohol use

No alcohol use 44 83 % Rare alcohol use (<3 drinks per month) 3 6 % Regular alcohol use (drinks weekly) 6 11 %

Sweet drink

consumption

Less than 3 sweet drinks per week 3 6 % 4-10 sweet drinks per week 14 26 % 11-20 sweet drinks per week 15 28 % 21 or more sweet drinks per week 21 40 %

Group variable Count per variable Percentage

Income group

Low 8 15.1 %

Medium 15 28.3 %

High: 30 56.6 %

Education level:

No school: 11 20.8 % Primary school: 33 62.3 % Secondary school or higher: 9 17 %

Mode of

transportation

Ambulatory: 45 84.9 % Bicycle: 5 9.4 % Motorized (motorcycle/automobile): 3 5.7 %

Source of cooking

water

River/stream: 1 1.9 % Unprotected well: 3 5.7 % Protected well: 45 84.9 % Indoor plumbing: 4 7.5 %

Household flooring

construction

Earthen/dirt flooring: 16 30.2 % Wooden plank flooring: 4 7.5 % Concrete/Tile flooring: 33 62.3 %

112

Mareu.

Population: 850 (estimated)

Sample Size: 64

Village health services: None

Hospital: Tengeru district hospital – 50 kilometers

Age Gender Hx Diabetes Hx CVA Hx CAD Hx HTN

Range: 18-80

Mean: 44.0

sd = 13.09

Male: 28

(44%)

Female: 36

(56%)

Yes: 1

(2%)

Yes: 0 Yes: 0 Yes: 3

(5%)

CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension

Age groups Crude rates

of Pre-

diabetes

Crude rates

of Diabetes

Crude rates of

HTN

Crude rates of

Excessive

Adiposity

Age 18-29 (n=8) 0 0 % 0 0 % 1 16.7 % 0 0 %

Age 30-39 (n=15) 1 4.5 % 0 0 % 4 18.2 % 7 31.8 %

Age 40-49 (n=13) 1 6.3 % 1 6.3 % 5 31.3 % 6 37.5 %

Age 50-59 (n=19) 0 0 % 0 0 % 1 11.1 % 1 11.1 %

Age 60 and older (n=37) 0 0 % 2 18.2 % 4 36.4 % 1 9.1 %

Total (N=92) 2 3.1 % 3 4.7 % 15 23.4 % 15 23.4 %

Group variable Count per variable Percentage

Tobacco Use

Lifelong non tobacco use 46 71.9 % Former tobacco use 14 21.9 % Current tobacco use 4 6.3 %

Alcohol use

No alcohol use 61 95.3 % Rare alcohol use (<3 drinks per month) 0 0 % Regular alcohol use (drinks weekly) 3 4.7 %

Sweet drink

consumption

Less than 3 sweet drinks per week 3 4.7 % 4-10 sweet drinks per week 17 26.6 % 11-20 sweet drinks per week 18 28.1 % 21 or more sweet drinks per week 26 40.6 %

Group variable Count per variable Percentage

Income group

Low 6 9.4 %

Medium 21 32.8 %

High: 37 57.8 %

Education level:

No school: 10 15.6 % Primary school: 47 73.4 % Secondary school or higher: 7 10.9 %

Mode of

transportation

Ambulatory: 51 79.7 % Bicycle: 3 4.7 % Motorized (motorcycle/automobile): 10 15.6 %

Source of cooking

water

River/stream: 2 3.1 % Unprotected well: 0 0 % Protected well: 57 89.1 % Indoor plumbing: 6 7.8 %

Household flooring

construction

Earthen/dirt flooring: 24 37.5 % Wooden plank flooring: 0 0 % Concrete/Tile flooring: 40 62.5 %

113

Maga Ya Chai.

Population: 1,500 (estimated)

Sample Size: 120

Village health services: village dispensary

Hospital: Tengeru district hospital – 55 kilometers

Age Gender Hx Diabetes Hx CVA Hx CAD Hx HTN

Range: 18-87

Mean: 50.6

sd = 15.54

Male: 30

(25%)

Female: 90

(75%)

Yes: 5 (5%) Yes: 0 Yes: 1 (1%) Yes: 10

(8%)

CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension

Age groups Crude rates of

Pre-diabetes

Crude rates

of Diabetes

Crude rates of

HTN

Crude rates of

Excessive

Adiposity

Age 18-29 (n=13) 0 0 % 0 0 % 0 0 % 1 7.7 %

Age 30-39 (n=16) 0 0 % 1 6.3 % 1 6.3 % 8 50 %

Age 40-49 (n=24) 2 8.3 % 0 0 % 3 12.5 % 12 50 %

Age 50-59 (n=24) 2 8.3 % 2 8.3 % 7 25.9 % 6 25 %

Age 60 and older (n=43) 0 0 % 6 14 % 16 37.2 % 10 23.3 %

Total (N=120) 4 3.3 % 9 7.5 % 27 22.5 % 37 30.8 %

Group variable Count per variable Percentage

Tobacco Use

Lifelong non tobacco use 93 77.5 % Former tobacco use 19 15.8 % Current tobacco use 8 6.7 %

Alcohol use

No alcohol use 106 88.3 % Rare alcohol use (<3 drinks per month) 6 5 % Regular alcohol use (drinks weekly) 8 6.7 %

Sweet drink

consumption

Less than 3 sweet drinks per week 6 5 % 4-10 sweet drinks per week 33 27.5 % 11-20 sweet drinks per week 41 34.2 % 21 or more sweet drinks per week 40 33.3 %

Group variable Count per variable Percentage

Income group

Low 9 7.5 %

Medium 36 30 75

High: 75 62.5 %

Education level:

No school: 21 17.5 % Primary school: 82 68.3 % Secondary school or higher: 17 14.2 %

Mode of

transportation

Ambulatory: 110 91.7 % Bicycle: 2 1.7 % Motorized (motorcycle/automobile): 8 6.7 %

Source of cooking

water

River/stream: 1 0.8 % Unprotected well: 2 1.7 % Protected well: 112 93.3 % Indoor plumbing: 5 4.2 %

Household

flooring

construction

Earthen/dirt flooring: 26 21.7 % Wooden plank flooring: 12 10 % Concrete/Tile flooring: 82 68.3 %

114

Ngurdoto.

Population: 1,200(estimated)

Sample Size: 100

Village health services: None

Hospital: Tengeru district hospital – 40 kilometers

Age Gender Hx Diabetes Hx CVA Hx CAD Hx HTN

Range: 18-90

Mean: 52.04

sd = 18.55

Male: 36

(36%)

Female: 64

(64%)

Yes: 2 (2%) Yes: 0 Yes: 0 Yes: 1

(1%)

CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension

Age groups Crude rates of

Pre-diabetes

Crude rates

of Diabetes

Crude rates of

HTN

Crude rates of

Excessive Adiposity

Age 18-29 (n=15) 0 0 % 0 0 % 0 0 % 4 26.7 %

Age 30-39 (n=13) 0 0 % 0 0 % 0 0 % 2 15.4 %

Age 40-49 (n=19) 1 5.3 % 1 5.3 % 5 26.3 % 5 26.3 %

Age 50-59 (n=17) 0 0 % 0 0 % 6 35.3 % 2 11.8 %

Age 60 and older (n=36) 3 8.3 % 1 2.8 % 11 30.6 % 2 5.7 %

Total (N=100) 4 4.0 % 2 2.0 % 22 22 % 15 15.2 %

Group variable Count per variable Percentage

Tobacco Use

Lifelong non tobacco use 60 60 % Former tobacco use 28 28 % Current tobacco use 12 12 %

Alcohol use

No alcohol use 82 82 % Rare alcohol use (<3 drinks per month) 5 5 % Regular alcohol use (drinks weekly) 13 13 %

Sweet drink

consumption

Less than 3 sweet drinks per week 12 12 % 4-10 sweet drinks per week 26 26 % 11-20 sweet drinks per week 20 20 % 21 or more sweet drinks per week 42 42 %

Group variable Count per variable Percentage

Income group

Low 14 14 %

Medium 63 63 %

High: 23 23 %

Education level:

No school: 27 27 % Primary school: 67 67 % Secondary school or higher: 6 6 %

Mode of

transportation

Ambulatory: 96 96 % Bicycle: 2 2 % Motorized (motorcycle/automobile): 2 2 %

Source of

cooking water

River/stream: 4 4 % Unprotected well: 8 8 % Protected well: 88 88 % Indoor plumbing: 0 0 %

Household

flooring

construction

Earthen/dirt flooring: 38 38 % Wooden plank flooring: 33 33 % Concrete/Tile flooring: 29 29 %

115

Kikititi.

Population: 5,000 (estimated)

Sample Size: 122

Village health services: Government clinic part 3 days a week

Hospital: Tengeru district hospital – 35 kilometers

Age Gender Hx Diabetes Hx CVA Hx CAD Hx HTN

Range: 19-88

Mean: 51.2

sd = 17.63

Male: 46

(38%)

Female: 76

(62%)

Yes: 6 (5%) Yes: 1 (1%) Yes: 6 (5%) Yes: 6 (5%)

CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension

Age groups Crude rates of

Pre-diabetes

Crude rates

of Diabetes

Crude rates of

HTN

Crude rates of

Excessive

Adiposity

Age 18-29 (n=15) 1 6.7 % 0 0 % 0 0 % 1 6.7 %

Age 30-39 (n=21) 2 9.5 % 2 9.5 % 3 14.3 % 5 23.8 %

Age 40-49 (n=20) 1 5 % 1 5 % 5 25 % 9 45 %

Age 50-59 (n=25) 4 16 % 5 20 % 6 24 % 14 56 %

Age 60 and older (n=41) 5 12.2 % 7 17.1 % 12 29.3 % 11 26.8 %

Total (N=122) 13 10.7 % 15 12.3 % 26 21.3 % 40 32.8 %

Group variable Count per variable Percentage

Tobacco Use

Lifelong non tobacco use 101 82.8 % Former tobacco use 20 16.4 % Current tobacco use 1 0.8 %

Alcohol use

No alcohol use 117 95.9 % Rare alcohol use (<3 drinks per month) 3 2.5 % Regular alcohol use (drinks weekly) 2 1.6 %

Sweet drink

consumption

Less than 3 sweet drinks per week 13 10.7 % 4-10 sweet drinks per week 51 41.8 % 11-20 sweet drinks per week 36 29.5 % 21 or more sweet drinks per week 22 18.0 %

Group variable Count per variable Percentage

Income group

Low 12 9.8 %

Medium 45 36.9 %

High: 65 53.3 %

Education level:

No school: 25 20.5 % Primary school: 81 66.4 % Secondary school or higher: 16 13.1 %

Mode of

transportation

Ambulatory: 106 86.9 % Bicycle: 5 4.1 % Motorized (motorcycle/automobile): 11 9.0 %

Source of cooking

water

River/stream: 12 9.8 % Unprotected well: 10 8.2 % Protected well: 96 78.7 % Indoor plumbing: 4 3.3 %

Household flooring

construction

Earthen/dirt flooring: 25 20.5 % Wooden plank flooring: 18 14.8 % Concrete/Tile flooring: 79 64.8 %

116

Kingori.

Population: 6,500 (estimated)

Sample size: 93

Village health services: Government district clinic, and Private religious clinic

Hospital: Tengeru district hospital – 55 kilometers

Age Gender Hx Diabetes Hx CVA Hx CAD Hx HTN

Range: 18-85

Mean: 50.35

sd = 16.40

Male: 41

(44%)

Female: 53

(56%)

Yes: 4 (4%) Yes: 0 Yes: 3 (3%) Yes: 6 (6%)

CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension

Age groups Crude rates of

Pre-diabetes

Crude rates

of Diabetes

Crude rates of

HTN

Crude rates of

Excessive

Adiposity

Age 18-29 (n=12) 1 8.3 % 3 25 % 1 8.3 % 1 8.3 %

Age 30-39 (n=7) 1 14.3 % 1 14.3 % 1 14.3 % 1 14.3 %

Age 40-49 (n=24) 5 20.8 % 3 12.5 % 5 20.8 % 7 29.2 %

Age 50-59 (n=21) 2 9.5 % 7 33.3 % 7 33.3 % 4 19.0 %

Age 60 and older (n=29) 4 13.8 % 6 20.7 % 9 31.0 % 4 14.3 %

Total (N=93) 13 21.5 % 20 21.5 % 23 24.7 % 17 18.5 %

Group variable Count per variable Percentage

Tobacco Use

Lifelong non tobacco use 80 85.1 % Former tobacco use 12 12.8 % Current tobacco use 2 2.1 %

Alcohol use

No alcohol use 88 93.6 % Rare alcohol use (<3 drinks per month) 2 2.1 % Regular alcohol use (drinks weekly) 4 4.3 %

Sweet drink

consumption

Less than 3 sweet drinks per week 5 5.3 % 4-10 sweet drinks per week 24 25.5 % 11-20 sweet drinks per week 36 39.3 % 21 or more sweet drinks per week 29 30.9 %

Group variable Count per variable Percentage

Income group

Low 26 27.7 %

Medium 38 40.4 %

High: 30 31.9 %

Education level:

No school: 23 24.5 % Primary school: 70 74.5 % Secondary school or higher: 1 1.1 %

Mode of

transportation

Ambulatory: 66 70.2 % Bicycle: 21 22.3 % Motorized (motorcycle/automobile): 7 7.4 %

Source of

cooking water

River/stream: 3 3.2 % Unprotected well: 32 34 % Protected well: 56 59.6 % Indoor plumbing: 3 3.2 %

Household

flooring

construction

Earthen/dirt flooring: 49 52.1 % Wooden plank flooring: 11 11.7 % Concrete/Tile flooring: 34 36.2 %

117

Table 1

Distribution of diabetes and impaired glucose tolerance prevalence

2011 2030 Increase

in the

no. of

people

with

diabetes

Population

No of

people

with

diabetes

Comparative

diabetes

prevalence

Population

No of

people

with

diabetes

Comparative

diabetes

prevalence

Region Millions Millions % Millions Millions % %

AFR 387 14.7 4.5 658 28.0 4.9 90

EUR 653 52.8 6.7 673 64.2 6.9 22

MENA 356 32.6 11.0 539 59.7 11.3 83

NAC 322 37.7 10.7 386 51.2 11.2 36

SACA 289 25.1 9.2 376 39.9 9.4 59

SEA 856 71.4 9.2 1188 120.9 10.0 69

WP 1544 131.9 8.3 1766 187.9 8.5 42

World 4407 366.2 8.5 5586 551.8 8.9 51

AFR= Africa region, EUR= European region, MENA= Middle East and North Africa, NAC= North

American and Caribbean, SACA= South and Central America, SEA= South-East Asia, WP = Western

Pacific

International Diabetes Federation. IDF Diabetes Atlas, 5th ed. Brussels, Belgium: International

Diabetes Federation, 2011. http://www.idf.org/diabetesatlas

118

Table 2

Global healthcare expenditure for diabetes in 2010

Region

Health expenditure for diabetes in 2010

Spending

on diabetes

as a % of

total health

expenditure

in 2010

R=2

Mean

health

expenditure

per person

with

diabetes in

2010 R=2

US Dollars (USD) International Dollars (ID) US

D ID

R=2 R=3 R=2 R=3 AFR 1,360,001 2,428,829 2,760,601 4,933,394 7 112 227

EMME 5,575,419 9,254,580 11,255,720 19,019,468 14 210 424

EUR 105,466,358 196,048,243 106,347,710 197,115,798 10 1911 1927

NA 214,225,151 373,276,922 216,859,501 377,783,710 14 5751 5822

SACA 8,051,822 14,384,661 17,273,767 30,924,764 9 458 982

SEA 3,099,199 5,413,277 8,955,615 15,639,475 11 53 153

WP 38,205,994 71,428,989 54,365,057 100,288,354 8 508 723

Global 375,983,944 672,235,502 417,817,971 745,704,963 12 1330 1478

R is the ratio of healthcare spending based on age and gender. In countries where this data is available, the R

factor was between 2 and 3. For estimates, healthcare spending was calculated with R factor of 2 and 3.

AFR= Africa region, EMME= Easter Mediterranean and Middle East. EUR= European region, NA = North

American region, SACA= South and Central America, SEA= South-East Asia, WP = Western Pacific

Zhang, P., Zhang, X., Brown, J., Vistisen, D., Sicree, R., Shaw, J., & Nichols, G. (2010). Global healthcare

expenditure on diabetes for 2010 and 2030. Diabetes Research and Clinical Practice, 87(3), 293-301. doi:

10.1016/j.diabres.2010.01.026

119

Table 3

Historical diagnostic criteria of type 2 diabetes mellitus

120

Table 4

Summary of epidemiology studies in sub-Sahara Africa

121

122

123

Table 5

Selected villages for research locations

Meru Central, Est. population 500

Leguruki, Est. population 1500

Mareu, Est. population 850

Maga Ya Chai, Est. Population 1500

Ngurdoto, Est. population 1200

Kikititi, Est. population 5000

Kingori, Est. population 6500

124

Table 6

Inclusion and exclusion criteria for prevalence study

Inclusion Criteria Exclusion Criteria

18 years of age or older Temperature greater than 101.4 degrees

Fahrenheit

Able to provide informed consent Currently taking antibiotics, anti-

malarial, or anti-viral medications

Willing to provide a sample of blood for

analysis

Women who are pregnant

Women who are currently lactating

People who are currently taking a

glucocorticoid/mineralocorticoid steroid

125

Table 7

Recoding of demographic and biometric variables

Original variable New Variable

Age

Age Groups

18-29 years (n= 75)

30-39 years (n=105)

40-49 years (n=125)

50-59 years (n=127)

60+ years (n=212)

Systolic blood pressure (SBP)

Hypertension

Normal = SBP < 140 mm/Hg (n=487)

Hypertension = SBP ≥ 140 mm/Hg (n=158)

Body mass index (BMI)

Adiposity

Normal = BMI < 25.0 (n=478)

Obesity = BMI ≥ 25.0 (n=165)

Diabetes groups

Normal (n=540)

Pre-diabetes (n=46)

Diabetes (n=59)

Glucose Metabolic Groups

Normal glucose metabolism (NGM) (n=540)

Impaired glucose metabolism (IGM) (n=105)

126

Table 8

Recoding of socioeconomic variables

Original variable New variable

Education level

No formal education (n=127)

Primary school (n=454)

Secondary school (n=54)

Trade or vocational school (n=5)

College/University education (n=5)

0

1

2

3

4

Education level

No formal education (n=127)

Primary school education (n=454)

Secondary school or higher (n=64)

Household flooring construction

Earthen/ dirt floors (n=200)

Wooden plank floors (n=130)

Concrete slab floors (n=314)

Tile floors (n=1)

1

2

3

4

Household flooring construction

Earthen/dirt floors (n=200)

Wooden plank floors (n=130)

Concrete/tile floors (n=315)

Mode of transportation

Walk/public transportation (n=562)

Bicycle (n=39)

Motorcycle (n=38)

Automobile- car (n=6)

1

2

3

4

Mode of transportation

Walk/public transportation (n=562)

Bicycle (n=39)

Motorized vehicle (n=44)

Source of cooking water

Stream/river (n=68)

Unprotected well (n=87)

Protected well (n=469)

Indoor plumbing (n=21)

1

2

3

4

Unchanged

Income Score = Sum of each category above

127

Table 9

Recoding of lifestyle variables

Original variable New variable

Tobacco Use

Lifelong non-smoker (n=493)

Former smoker (n=117)

Current smoker (n=35)

Unchanged

Alcohol use

Does not drink alcohol (n=586)

Rare- less than 4 drinks per month (n=20)

1-2 drinks per week (n=15)

3-7 drinks per week (n=6)

8-14 drinks per week (n=9)

15 or more drinks per week (n=9)

Alcohol use

Does not drink alcohol (n=586)

Rare- less than 4 drinks per month (n=20)

Regular alcohol consumption (n=39)

Sweet drink consumption

Less than 3 sweet drinks per week (n=46)

4-10 sweet drinks per week (n=189)

11-15 sweet drinks per week (n=142)

16-20 sweet drinks per week (n=68)

21- 25 sweet drinks per week (n=130)

26 or more sweet drinks per week (n=70)

Sweet drink consumption

Less than 3 sweet drinks per week (n=46)

4-10 sweet drinks per week (n=189)

11-20 sweet drinks per week (n=210)

21 or more sweet drinks per week (n=200)

128

Table 10

Description of village statistics

All villages combined

Age (Mean) 50.8 (range 18-103, sd 17.1)

Gender Male n=233 (36%)

Female n=412 (64%)

BMI (Mean) 23 (range 15.2-43.5, sd 4.5)

SBP (Mean) 128 (range 81-249, sd 23.2)

FPG (Mean) 100 (range 39-600, sd 37.5)

Income score (Mean) 7 (range 3-15, sd 1.6)

Education Level

No formal education (n=127)

Primary school education (n=454)

Secondary school or higher (n=64)

Mode of Transportation

Walk/public transportation (n=562)

Bicycle (n=39)

Motorized vehicle (n=44)

Household Flooring construction

Earthen/dirt floors (n=200)

Wooden plank floors (n=130)

Concrete/tile floors (n=315)

Source of Cooking water

Stream/river (n=68)

Unprotected well (n=87)

Protected well (n=469)

Indoor plumbing (n=21)

Tobacco use

Never 493

Former 117

Current 35

Alcohol use

Does not drink alcohol (n=586)

Rare- less than 4 drinks per month (n=20)

Regular alcohol consumption (n=39)

Average Sweet drink use

Less than 3 sweet drinks per week (n=46)

4-10 sweet drinks per week (n=189)

11-20 sweet drinks per week (n=210)

21 or more sweet drinks per week (n=200)

129

Table 11

Crude and age-adjusted prevalence rates by age group of pre-diabetes and diabetes in the

rural area of the AruMeru district of northern Tanzania

Age

group in

years

*Rural

Population

percentage

Sample

Size

Event

rate

Pre-DM

Event

rate

DM

Crude

Rate

Pre-DM

Crude

Rate

DM

Age-

adjusted

rate,

Pre-DM

Age

adjusted

rate,

DM

15-19 9.50% 8 0 0 0% 0% 0.00% 0.00%

20-24 6.60% 33 1 1 0.15% 0.15% 0.20% 0.20%

25-29 5.80% 34 1 2 0.15% 0.31% 0.17% 0.34%

30-34 5.20% 43 4 0 0.62% 0% 0.48% 0.00%

35-39 5.00% 62 2 4 0.31% 0.62% 0.16% 0.32%

40-44 3.70% 54 6 4 0.93% 0.62% 0.41% 0.27%

45-49 3.10% 71 5 3 0.78% 0.47% 0.22% 0.13%

50-54 2.80% 86 11 10 1.71% 1.55% 0.36% 0.33%

55-59 2.10% 41 3 6 0.47% 0.93% 0.15% 0.31%

60-64 2.00% 54 1 6 0.15% 0.93% 0.04% 0.22%

65-69 1.50% 56 4 7 0.62% 1.09% 0.11% 0.19%

70-74 1.50% 35 1 8 0.15% 1.24% 0.04% 0.34%

75-79 0.80% 25 1 2 0.15% 0.31% 0.03% 0.06%

80+ 1.00% 42 6 5 0.93% 0.78% 0.14% 0.12%

Total 50.60% 644 46 58 7.1% 9.0% 2.52% 2.84%

*Age standardization were based on national 2010 Demographic and Health Survey, rural

population

130

Table 12

Examining the association between impaired glucose metabolism, hypertension, and

adiposity

IGM HTN Adiposity

IGM - χ

2 = 10.86, p = .001,

Eta2= .13

χ2 = 8.67, p = .003,

Eta2 = .12

HTN - - χ2 = 2.82, p = .09*

Age Groups χ2=15.5, p = .004 χ

2 (4)=43.43, p < .001 χ

2 (4)=24.1, p < .001

IGM= Normal Glucose metabolism v IGM (pre-diabetes and diabetes)

HTN = Normal blood pressure v. hypertension (SBP≥140 mm/Hg)

Adiposity = healthy levels of adiposity, less than 25 v. excess adiposity, BMI ≥ 25

*Not statistically significant

131

Table 13

The strength of association of biometric indices on FPG, SBP, BMI

Fasting Plasma

Glucose Systolic blood pressure Body Mass Index

Overall Model F = 2.06, p < .001,

Eta2 = .12

F = 3.86, p < .001,

Eta2 = .182

F 4.14, p < .001,

Eta2 = .2.2

Gender ns ns F = 10.44, p = .003

Eta2 = .017

Age groups ns F = 3.03, p = .017

Eta2 = .02

F = 5.84, p < .001

Eta2 = .037

IGM - F = 10.63, p = .001

Eta2 = .02

ns

HTN ns - F = 12.86, p < .001

Eta2 = .021

Adiposity F = 11.36, p = .001

Eta2 = .02

F = 10.47, p = .001

Eta2 = .017

-

Univariate ANOVA

132

Table 14

Odds assessment of biometric variables associated with the development of Impaired

Glucose Tolerance

Independent

variable

Logistic regression

coefficient SE

p-

value OR 95% CI

Age .03

30-39 - - ns - -

40-49 - - ns - -

50-59 1.17 0.52 .025 3.21 [1.15, 8.86]

60-69 1.01 0.53 .044 2.76 [1.03, 7.39]

HTN

SBP ≥ 140

mm/Hg

0.50 0.24 .037 1.64 [1.03, 2.62]

Adiposity

BMI ≥ 25.0 0.64 0.24 .006 1.90 [1.2, 3.02]

Forward conditional

Variable removed: gender

Note. CI= confidence interval

133

Table 15

Odds assessment of biometric variables associated with the development of hypertension

Independent

variable

Logistic regression

coefficient SE p- value OR 95% CI

Age <.001

30-39 - - ns - -

40-49 1.45 0.56 .01 4.25 [1.42, 12.78]

50-59 2.12 0.55 <.001 8.32 [2.84, 24.42]

60-69 2.14 0.54 <.001 8.50 [2.98, 24.26]

IGM

Pre-DM, DM 0.53 0.24 .026 1.69 [1.06, 2.67]

Forward conditional

Variable removed: gender, adiposity

Note. CI= confidence interval

134

Table 16

Odds assessment of biometric variables associated with the development of adiposity

Independent

variable

Logistic regression

coefficient SE P- value OR 95% CI

Gender

Female 1.27 0.23 < .001 3.56 [2.27, 5.59]

Age < .001

30-39 1.32 0.44 .003 3.73 [1.58, 8.80]

40-49 1.56 0.43 < .001 4.77 [2.07, 11.0]

50-59 1.57 0.43 < .001 4.81 [2.07, 11.2]

60-69 - - ns* - -

IGM

Pre DM- DM 0.70 0.42 .004 2.02 [1.25, 3.25]

Forward conditional

Variable removed: HTN

*p=.059

Note. CI= confidence interval

135

Table 17

Association between lifestyle indicators and IGM, HTN and Adiposity

Indicator Impaired Glucose

Metabolism Hypertension Adiposity

χ 2 Sig χ

2 Sig χ

2 Sig

Income Group - ns - ns 10.95 p = .004

Water Source - ns 8.11 p = .044 - ns

Education Level 7.84 p = .02 5.42 p =.06 9.28 p = .01

Flooring type - ns - ns 15.99 p < .001

Transportation Mode - ns - ns 10.44 p = 0.034

Sweet drink

consumption - ns - ns - ns

Alcohol use - ns - ns - ns

Tobacco use - ns 13.63 p = .001 16.4 p <.001

Chi-square statistic

136

Table 18

Association of lifestyle behaviors on FPG, SBP, and BMI

Fasting Plasma

Glucose Systolic blood pressure Body Mass Index

Overall Model Fit ns F = 1.83, p = .004,

Eta2 = .09

F = 1.93, p = .002,

Eta2 = .095

Alcohol Use ns ns ns

Tobacco Use ns F = 6.99, p = .001*

Eta2 = .022

F = 7.65, p = .001

Eta2 = .025

Sweet drink

consumption ns** ns ns

Univariate ANOVA

* post-hoc Bonferroni correction demonstrates the most significant difference is between life-long

non-tobacco users and current tobacco users

** post-hoc Bonferroni correction demonstrates a significant association between people who

consume 4 or more drinks per week and elevated fasting plasma glucose levels (p = .007)

137

Table 19

Associated Socioeconomic factors and the development of elevated FPG, SBP, BMI

Fasting plasma glucose Systolic blood pressure Body mass index

Overall

model fit

F (54,580) = 5.82, p < .001,

Eta2 = .352

p = .06

F (54,587) = 2.05, p

< .001

Eta2 = .158

Education F (2,580) = 3.43, p = .033

Eta2 = .012

F (2,590) = 7.35, p = .001

Eta2 = .024

ns

Flooring

construction

F (2,580) = 17.23, p < .001

Eta2 = .056

ns ns

Water source F (3,580) = 33.36, p < .001

Eta2 = .147

ns ns

Mode of

transportation

F (2,580) = 23.21, p < .001

Eta2 = .074

ns ns

Two way interaction:

Education level and source

of water,

F (3,580) = 3.50, p = .015

Eta2 = .018

Mode of transportation and

source of water,

F (3,580) = 67.72, p < .001

Eta2 = .259

Two way

interaction:

Education level

and mode of

transportation,

F (2,587) = 3.78, p

= .023

Eta2 = .013

Univariate ANOVA

138

Table 20

Odds assessment of lifestyle and economic variables and the development of IGM

Independent

variable

Logistic regression

coefficient SE P- value OR 95% CI

Education Level .022

Primary School -0.64 0.25 .009 0.53 [0.33, 0.85]

Secondary school

or higher

-0.816 0.43 .058 0.44 [0.19, 1.03]

Forward conditional

Variable removed: tobacco, alcohol, sweet drink, floor type, water source, mode of

transportation

Note. CI= confidence interval

139

Table 21

Odds assessment on lifestyle and economic variables and the development of HTN

Independent

variable

Logistic

regression

coefficient

SE p- value OR 95% CI

Tobacco .001

Former tobacco 0.82 0.22 < .001 2.26 [1.46, 3.5]

Current tobacco - - ns - -

Water source .041

Unprotected well - - ns - -

Protected well - - ns - -

Indoor plumbing - - ns - -

Forward conditional

Variable removed: alcohol, sweet drink, floor type, education level, household flooring, and

mode of transportation

Note. CI= confidence interval

140

Table 22

Odds assessment of lifestyle and economic variables and the development of excess

adiposity

Independent

variable

Logistic regression

coefficient SE P- value OR 95% CI

Tobacco .001

Former tobacco -0.873 .283 .002 0.42 [0.24, 0.07]

Current tobacco -1.32 .62 .032 0.27 [0.08, 0.89]

Flooring type .001

Wooden plank 0.68 0.28 .015 1.98 [1.14, 3.44]

Concrete-tile 0.84 0.23 <.001 2.32 [1.47, 3.66]

Forward conditional

Variable removed: alcohol, sweet drink, education level, water source, and mode of

transportation

Note. CI= confidence interval

141

Figure 1

Life expectancy by income status

142

Figure 2

Factors contributing to chronic disease

143

Figure 3

Perspective of African Continent

144

Figure 4

Map of Tanzania

145

Figure 5

Map of Arusha region

The AruMeru district is located north and northeast of the town of Arusha

146

Figure 6

Capillary blood sample size

From user guide manual for the Righttest GM300 glucose monitor

Bionime. (2012). User’s manual Righttest GM300 101-3GM300-701 EN. Retrieved March 25,

2012, from

http://data.bionime.com/Manual_download/GM300/Users_Manual/GM300_Users_Manual-

EN%28101-3GM300-701%29.pdf

147

Figure 7

Participant screening

Initially Screened N=709

Number of participants

N=645

Excluded from Analysis n=64

Breast feeding n=38

Use of antibiotics or antiviral agents, n=16

Pregnant, n=7

Use of oral steriods, n=2

Temp > 101.4, n=1

148

Figure 8

Average number of sweet drinks consumed per week