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.
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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.
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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.
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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
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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.
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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).
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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).
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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.
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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, ή2 = .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, ή2 = .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, ή2 = .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.
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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
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References
Abbas, Z. G., & Archibald, L. K. (2007). Challenges for management of the diabetic foot in
Africa: doing more with less. International Wound Journal, 4(4), 305-313. doi:
10.1111/j.1742-481X.2007.00376.x
Abbas, Z. G., Lutale, J. K., Game, F. L., & Jeffcoate, W. J. (2008). Comparison of four systems
of classification of diabetic foot ulcers in Tanzania. Diabetic Medicine, 25(2), 134-137.
doi: 10.1111/j.1464-5491.2007.02308.x
Agardh, E., Allebeck, P., Hallqvist, J., Moradi, T., & Sidorchuk, A. (2011). Type 2 diabetes
incidence and socio-economic position: a systematic review and meta-analysis.
International Journal of Epidemiology, 40(3), 804-818. doi: 10.1093/ije/dyr029
Akanji, A. O. (1990). Malnutrition-related diabetes mellitus in your adult diabetic patietns
attending a Nigerian diabetic clinic. Journal of Tripical Medicince and Hygiene, 93(1),
35-38.
American Diabetes Association. (2007). Diagnosis and Classification of Diabetes Mellitus.
Diabetes Care, 30(suppl 1), S42-S47. doi: 10.2337/dc07-S042
American Diabetes Association. (2012). Diagnosis and Classification of Diabetes Mellitus.
Diabetes Care, 35(Supplement 1), S64-S71. doi: 10.2337/dc12-s064
Amoah, A. G. B., Owusu, S. K., & Adjei, S. (2002). Diabetes in Ghana: a community based
prevalence study in Greater Accra. Diabetes Research and Clinical Practice, 56(3), 197-
205. doi: 10.1016/s0168-8227(01)00374-6
Aspray, T. J., Mugusi, F., Rashid, S., Whiting, D., Edwards, R., Alberti, K. G., & Unwin, N. C.
(2000). Rural and urban differences in diabetes prevalence in Tanzania: the role of
73
obesity, physical inactivity and urban living. Transactions of the Royal Society of
Tropical Medicine and Hygiene, 94(6), 637-644.
Assah, F. K., Ekelund, U., Brage, S., Mbanya, J. C., & Wareham, N. J. (2011). Urbanization,
Physical Activity, and Metabolic Health in Sub-Saharan Africa. Diabetes Care, 34(2),
491-496. doi: 10.2337/dc10-0990
Baldé, N. M., Diallo, I., Baldé, M. D., Barry, I. S., Kaba, L., Diallo, M. M., . . . Maugendre, D.
(2007). Diabetes and impaired fasting glucose in rural and urban populations in Futa
Jallon (Guinea): prevalence and associated risk factors. Diabetes & Metabolism,
33(2), 114-120. doi: 10.1016/j.diabet.2006.10.001
Barrett-Connor, E. (1989). Epidemiology, obesity, and non-insulin dependent diabetes mellitus.
Epidemiologic Reviews, 11(1), 172-181.
Benson, J. S. (2001). The impact of privatization on access in Tanzania. Social Science &
Medicine, 52(12), 1903-1915.
Bionime. (2012). Users manual Righttest GM300 101-3GM300-701 EN. Retrieved March 25,
2012, from
http://data.bionime.com/Manual_download/GM300/Users_Manual/GM300_Users_Manu
al-EN%28101-3GM300-701%29.pdf
Bonnefond, A., Froguel, P., & Vaxillaire, M. (2010). The emerging genetics of type 2 diabetes.
Trends in Molecular Medicine, 16(9), 407-416. doi: 10.1016/j.molmed.2010.06.004
Burgess, L. J., & Sulzer, N. U. (2010). The role of print advertising in clinic trial recruitement:
Lessons from a South African site. Open Access Journal of Clinical Trials, 2010(2), 83-
87. doi: 10.2147/OAJCT.S10027
74
Campbell, M. C., & Tishkoff, S. A. (2008). African Genetic Diversity: Implications for Human
Demographic History, Modern Human Origins, and Complex Disease Mapping. Annual
Review of Genomics and Human Genetics, 9(1), 403-433. doi:
doi:10.1146/annurev.genom.9.081307.164258
Ceesay, M. M., Morgan, M. W., Kamanda, M. O., Willoughby, V. R., & Lisk, D. R. (1997).
Prevalence of diabetes in rural and urban populations in southern Sierra Leone: a
preliminary survey. Tropical Medicine & International Health, 2(3), 272-277. doi:
10.1046/j.1365-3156.1997.d01-265.x
Centers for Medicare and Medicaid Services. (2009). National Health Expenditure Fact Sheet
Retrieved March 19, 2012, from
https://www.cms.gov/NationalHealthExpendData/25_NHE_Fact_Sheet.asp#TopOfPage
Chege, P., M. (2009). Diabetes Mellitus: An overview. CHAK Times, May-August.
Chen, G., Adeyemo, A., Zhou, J., Chen, Y., Huang, H., Doumatey, A., . . . Rotimi, C. (2007).
Genome-wide search for susceptibility genes to type 2 diabetes in West Africans:
Potential role of C-peptide. Diabetes Research and Clinical Practice, 78(3), e1-e6. doi:
10.1016/j.diabres.2007.04.010
Christensen, D. L., Friis, H., Mwaniki, D. L., Kilonzo, B., Tetens, I., Boit, M. K., . . . Borch-
Johnsen, K. (2009). Prevalence of glucose intolerance and associated risk factors in rural
and urban populations of different ethnic groups in Kenya. Diabetes Research and
Clinical Practice, 84(3), 303-310. doi: 10.1016/j.diabres.2009.03.007
CIA. (2009). The world factbook. Washington DC: Central Intelligence Agency Retrieved from
https://www.cia.gov/library/publications/the-world-factbook/index.html.
75
Cruickshank, J., Mbanya, J., Wilks, R., Balkau, B., McFarlane-Anderson, N., & Forrester, T.
(2001). Sick genes, sick individuals or sick populations with chronic disease? The
emergence of diabetes and high blood pressure in African-origin populations.
International Journal of Epidemiology, 30(1), 111-117. doi: 10.1093/ije/30.1.111
Cubbins, L. A., Kasprzyk, D., Montano, D., Jordan, L. P., & Woelk, G. (2012). Alcohol use and
abuse among rural Zimbabwean adults: A test of a community-level intervention. Drug
and Alcohol Dependence, 124(3), 333-339. doi:
http://dx.doi.org/10.1016/j.drugalcdep.2012.02.002
Dalal, S., Beunza, J. J., Volmink, J., Adebamowo, C., Bajunirwe, F., Njelekela, M., . . . Holmes,
M. D. (2011). Non-communicable diseases in sub-Saharan Africa: what we know now.
International Journal of Epidemiology, 40(4), 885-901. doi: 10.1093/ije/dyr050
Danaei, G., Finucane, M. M., Lu, Y., Singh, G. M., Cowan, M. J., Paciorek, C. J., . . . Ezzati, M.
(2011). National, regional, and global trends in fasting plasma glucose and diabetes
prevalence since 1980: systematic analysis of health examination surveys and
epidemiological studies with 370 country-years and 2·7 million participants. The Lancet,
378(9785), 31-40. doi: 10.1016/s0140-6736(11)60679-x de-Graft Aikins, A., Unwin, N., Agyemang, C., Allotey, P., Campbell, C., & Arhinful, D. (2010).
Tackling Africa's chronic disease burden: from the local to the global. Globalization and
Health, 6(1), 5.
Delisle, H., Ntandou-Bouzitou, G., Agueh, V., Sodjinou, R., & Fauomi, B. (2011). Urbanization,
nutrition transition and cardiometabolic risk: The Benin study. British Journal of
Nutrition, 25, 1-11.
76
Douglas, M. (1964). P. H. Gulliver: Social control in an African society. A study of the Arusha:
agricultural Masai of northern Tanganyika. (International Library of Sociology and
Social Reconstruction.) xiv, 306 pp., 8 plates. London: Routledge and Kegan Paul, 1963.
35s. Bulletin of the School of Oriental and African Studies, 27(03), 676-677. doi:
doi:10.1017/S0041977X00118877
Ducorps, M., Ndong, W., Jupkwo, B., Poirier, J. M., Mayaudon, H., & Bauduceau, B. (2002).
Epidemiological aspects of diabetes in Cameroon: what is the role of tropical diabetes?
Diabetes & Metabolism, 23(1), 61-67.
Ekow, J.-M., & Shipp, J. (2001). Malnutrition-related Diabetes Mellitus: Myth or Reality. In J.-
M. Ekoe, P. Zimmet & R. Williams (Eds.), The Epidemiology of Diabetes Mellitus an
International Perspective (1 ed., pp. 263-272). Chichester: John Wiley & Sons Ltd.
Emanuel, Ezekiel J., Wendler, D., Killen, J., & Grady, C. (2004). What Makes Clinical Research
in Developing Countries Ethical? The Benchmarks of Ethical Research. The Journal of
Infectious Diseases, 189(5), 930-937. doi: doi:10.1086/381709
Evaristo-Neto, A., Foss-Freitas, M., & Foss, M. (2010). Prevalence of diabetes mellitus and
impaired glucose tolerance in a rural community of Angola. Diabetology & Metabolic
Syndrome, 2(1), 63.
Field, A. (2009). Discovering statistics using SPSS (3rd Eds ed.). London: Sage.
Friedman, G. D. (2004). Primer of Epidemiology (5 ed.). New York: McGraw-Hill.
Gallagher, E. J., Leroith, D., & Karnieli, E. (2011). The Metabolic Syndrome- from insulin
resistance to obesity and diabetes. Medical Clinic of North America, 95(5), 855-873.
77
Gavin, J. R., III, Davidson, M. B., & DeFronzo, R. A. (1997). Report of the Expert Committee
on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care, 20(7), 1183-
1197.
Habib, S. H., & Saha, S. (2010). Burden of non-communicable disease: Global Review. Diabetes
& Metabolic Syndrome: Clinical Research& Reviews, 4(1), 41-47.
Hall, V., Thomsen, R. W., Henriksen, O., & Lohse, N. (2011). Diabetes in Sub Saharan Africa
1999-2011: epidemiology and public health implications. A systematic review. BMC
Public Health, 11, 564-564.
Harbuwono, D. S. (2011). Redefining diabetes: Is it really necessary. Acta Medica Indonesia,
43(2), 79-81.
Hargreaves, J., Morison, L., Gear, J., Kim, J., Makhubele, M., Porter, J., . . . Pronyk, P. (2007).
Assessing household wealth in health studies in developing countries: a comparison of
participatory wealth ranking and survey techniques from rural South Africa. Emerging
Themes in Epidemiology, 4(1), 4.
Harris, M. I., Hadden, W. C., Knowler, W. C., & Bennett, P. H. (1985). International criteria for
the diagnosis of diabetes and impaired glucose tolerance. Diabetes Care, 8(6), 562-567.
Hartge, P. (2001). Epidemiologic Tools for Today and Tomorrow. Annals of the New York
Academy of Sciences, 954(1), 295-310. doi: 10.1111/j.1749-6632.2001.tb02757.x
Hightower, J. D., Hightower, C. M., Vázquez, B. Y., & Intaglietta, M. (2011). Incident
prediabetes/diabetes and blood pressure in urban and rural communities in the
Democratic Republic of Congo Vascular Health and Risk Management, 7(1), 483-489.
doi: : http://dx.doi.org/10.2147/VHRMS22707
78
Hillbom, E. (2010). From Millet to Tomatoes: Productivity Increase by Means of Introducing
High Value Agricultural Products in Meru, Tanzania. Paper presented at the
Development Research Day, Lund Sweden.
Huffman, M. D., Rao, K. D., Pichon-Riviere, A., Zhao, D., Harikrishnan, S., Ramaiya, K., . . .
Prabhakaran, D. (2011). A Cross-Sectional Study of the Microeconomic Impact of
Cardiovascular Disease Hospitalization in Four Low- and Middle-Income Countries.
PLoS ONE, 6(6), e20821. doi: 10.1371/journal.pone.0020821
Huxley, R., Mendis, S., Zheleznyakov, E., Reddy, S., & Chan, J. (2009). Body mass index, waist
circumference and waist:hip ratio as predictors of cardiovascular risk-a review of the
literature. Eur J Clin Nutr, 64(1), 16-22.
Idemyor, V. (2010). Diabetes in Sub-Saharan Africa: Health care perspectives, challenges, and
the economic burden of disease. Journal of the national Medical Associations, 102(7),
650-653.
Ikem, I., & Sumpio, B. E. (2011). Cardiovascular disease: the new epidemic in sub-Saharan
Africa. Vascular, 19(6), 301-307. doi: 10.1258/vasc.2011.ra0049
Joint United Nations Programme on HIV/AIDS WHO. (2006). AIDS epidemic update. UNAIDS
2006.
Jones-Smith, J. C., Gordon-Larsen, P., Siddiqi, A., & Popkin, B. (2011). Is the burden of
overweight shifting to the poor across the globe[quest] Time trends among women in 39
low- and middle-income countries (1991-2008). Int J Obes. doi:
http://www.nature.com/ijo/journal/vaop/ncurrent/suppinfo/ijo2011179s1.html
79
Justin-Temu, M., Nondo, R. S. O., Wiedenmayer, K., Ramaiya, K. L., & Teuscher, A. (2009).
Anti-diabetic drugs in the private and public sector in Dar Es Salaam, Tanzania. East
African Medical Journal, 85(3), 110-114.
Kapiga, S. (2011). Commentary: Non-communicable diseases in sub-Saharan Africa: a new
global health priority and opportunity. International Journal of Epidemiology, 40(4),
902-903. doi: 10.1093/ije/dyr098
Kauh, E., Mixson, L., Malice, M.-P., Mesens, S., Ramael, S., Burke, J., . . . Ruddy, M. (2012).
Prednisone affects inflammation, glucose tolerance, and bone turnover within hours of
treatment in healthy individuals. European Journal of Endocrinology, 166(3), 459-467.
doi: 10.1530/eje-11-0751
Khan, M. M., Hotchkiss, D. R., Berruti, A. A., & Hutchinson, P. L. (2006). Geographic aspects
of poverty and health in Tanzania: does living in a poor area matter? Health Policy Plan.,
21(2), 110-122. doi: 10.1093/heapol/czj008
Kirigia, J., Sambo, H., Sambo, L., & Barry, S. (2009). Economic burden of diabetes mellitus in
the WHO African region. BMC International Health and Human Rights, 9(1), 6.
Kolling, M., Winkley, K., & von Deden, M. (2010). "For someone who's rich, it's not a
problem". Insights from Tanzania on diabetes health-seeking and medical pluralism
among Dar es Salaam's urban poor. Globalization and Health, 6(1), 8.
Krieger, N. (1994). Epidemiology and the web of causation: Has anyone seen the spider? Social
Science Medicine, 39(7), 887-903.
Krieger, N. (2001). Theories for social epidemiology in the 21st century: an ecosocial
perspective. International Journal of Epidemiology, 30(4), 668-677. doi:
10.1093/ije/30.4.668
80
Krieger, N. (2011). Epidemiology and the peoples health: Theory and Context. Oxford: Oxford
press.
Kruk, M. E., Mbaruku, G., Rockers, P. C., & Galea, S. (2008). User fee exemptions are not
enough: Out-of-pocket payments for "free" delievery services in rural Tanzania. Tropical
Medicine & International Health, 13(12), 1442-1451. doi: 10.1111/j1365-
3156.2008.02173.x
Kusumayati, A., & Gross, R. (1998). Ecological and geographical characteristics predict
nutritional status of cummunities: Rapid assessment for poor villages. Health Policy and
Planning, 13(4), 408-416.
Leroith, D. (2012). Pathophysiology of metabolic syndrome: Implications for the
cardiometabolic risks associated with Type 2 Diabetes. American Journal of Medical
Sciences, 343(1), 13-16.
Levitt, N. S. (2008). Diabetes in Africa: epidemiology, management and healthcare challenges.
Heart, 94(11), 1376-1382. doi: 10.1136/hrt.2008.147306
Levitt, N. S., Steyn, K., Dave, J., & Bradshaw, D. (2011). Chronic noncommunicable diseases
and HIV-AIDS on a collision course: relevance for health care delivery, particularly in
low-resource settings—insights from South Africa. The American Journal of Clinical
Nutrition, 94(6), 1690S-1696S. doi: 10.3945/ajcn.111.019075
Levitt, N. S., Unwin, N. C., Bradshaw, D., Kitange, H. M., Mbanya, J.-C. N., Mollentze, W. F., .
. . Machibya, H. (2000). Application of the new ADA criteria for the diagnosis of
diabetes to population studies in sub-Saharan Africa. Diabetic Medicine, 17(5), 381-385.
Lopez, A. D., Mathers, C. D., Ezzati, M., Jamison, D., T., & Murray, C. J. L. (2006). Global
Burden of Disease and Risk Factors. Washington DC: The World Bank
81
Oxford Press.
Lugongo, B. (2010). Prevalence of diabetes in tanzania worrisome, say experts. The Citizen
Retrieved 11/26, 2010, from http://thecitizen.co.tz/business/-/5540-preference-of-
diabetes-in-tanzania-worrisome-say-experts
Lutale, J., Thordarson, H., Abbas, Z., & Vetvik, K. (2007). Microalbuminuria among Type 1 and
Type 2 diabetic patients of African origin in Dar Es Salaam, Tanzania. BMC Nephrology,
8(1), 2.
Lynch, J., & Smith, G. D. (2005). A life course approach to chronic disease. Annual Review of
Public Health, 26(1), 1-35. doi: 10.1146/annurev.publhealth.26.021304.144505
Maher, D., Smeeth, L., & Sekajugo, J. (2010). Health transition in Africa: practical policy
proposals for primary care. Bulletin of the World helath Organization, 88(12), 943-948.
Malecki, M. T. (2005). Genetics of type 2 diabetes mellitus. Diabetes Research and Clinical
Practice, 68, Supplement 1(0), S10-S21. doi: 10.1016/j.diabres.2005.03.003
Maletnlema, T. (2002). A Tanzanian perspective on the nutrition transition and its implications
for health. Public Health Nutrition, 5(1a), 163-168. doi: doi:10.1079/PHN2001289
Maruapula, S. D., Jackson, J. C., Holsten, J., Shaibu, S., Malete, L., Wrotniak, B., . . . Compher,
C. (2011). Socio-economic status and urbanization are linked to snacks and obesity in
adolescents in Botswana. Public Health Nutrition, 14(12), 2260-2267. doi:
doi:10.1017/S1368980011001339
Masalu, J., Kikwilu, E., Kahabuka, F., Senkoro, A., & Kida, I. (2009). Oral health related
behaviors among adult Tanzanians: a national pathfinder survey. BMC Oral Health, 9(1),
22.
82
Mathenge, W., Foster, A., & Kuper, H. (2010). Urbanization, ethnicity and cardiovascular risk in
a population in transition in Nakuru, Kenya: a population-based survey. BMC Public
Health, 10(1), 569.
Mazze, R., Yogev, Y., & Langer, O. Measuring glucose exposure and variability using
continuous glucose monitoring in normal and abnormal glucose metabolism in
pregnancy. Journal of Maternal-Fetal and Neonatal Medicine, 0(ja), 1-17. doi:
doi:10.3109/14767058.2012.670413
Mbanya, J. C. N., Cruickshank, J. K., Forrester, T., Balkau, B., Ngogang, J. Y., Riste, L., . . .
Wilks, R. (1999). Standardized comparison of glucose intolerance in west African-origin
populations of rural and urban Cameroon, Jamaica, and Caribbean migrants to Britain.
Diabetes Care, 22(3), 434-440. doi: 10.2337/diacare.22.3.434
Mbanya, J. C. N., Ngogang, J., Salah, J. N., Minkoulou, E., & Balkau, B. (1997). Prevalence of
NIDDM and impaired glucose tolerance in a rural and an urban population in Cameroon.
Diabetologia, 40(7), 824-829. doi: 10.1007/s001250050755
McCarthy, M. I. (2010). Genomics, Type 2 Diabetes, and Obesity. New England Journal of
Medicine, 363(24), 2339-2350. doi: doi:10.1056/NEJMra0906948
McLarty, D. G., Kinabo, L., & Swai, A. B. M. (1990). Diabetes in tropical Africa: a prospective
study, 1981-7. II. Course and prognosis. British Medical Journal, 300(6732), 1107-1110.
McLarty, D. G., Kitange, H. M., Mtinangi, B. L., Makene, W. J., Swai, A. B. M., Masuki, G., . . .
Alberti, K. G. (1989). Prevalence of diabetes and impaired glucose tolerance in rural
Tanzania. The Lancet, 333(8643), 871-875.
Miller, B. J. (2013). Diabetes Mellitus. In L. C. Copstead & J. Banasik (Eds.), Pathophysiology
(5 ed.). St Louis Missouri: Sauders.
83
Molyneux, S., Kamuya, D., & Marsh, V. (2010). Community Members Employed on Research
Projects Face Crucial, Often Under-Recognized, Ethical Dilemmas. The American
Journal of Bioethics, 10(3), 24 - 26.
Montgomery, M. R. (2008). The Urban Transformation of the Developing World. Science,
319(5864), 761-764. doi: 10.1126/science.1153012
Morris, J. N. (2007). Uses of epidemiology. International Journal of Epidemiology, 36(6), 1165-
1172. doi: 10.1093/ije/dym227
Motala, A. A. (2002). Diabetes trends in Africa. Diabetes/Metabolism Research and Reviews,
18(S3), S14-S20. doi: 10.1002/dmrr.284
Motala, A. A., Esterhuizen, T., Gouws, E., Pirie, F. J., & Omar, M. A. K. (2008). Diabetes and
Other Disorders of Glycemia in a Rural South African Community. Diabetes Care, 31(9),
1783-1788. doi: 10.2337/dc08-0212
Munga, M. A., Songstad, N., Blystad, A., & Maestad, O. (2009). The decentralisation-
centralisation dilemma: recruitment and distribution of health workers in remote districts
of Tanzania. BMC International Health and Human Rights, 9(1), 9.
Mungi, R. (2011). The Little Data book on Africa
Narayan, K. M. V., Echouffo-Tcheugui, J. B., Mohan, V., & Ali, M. K. (2012). Global
Prevention And Control Of Type 2 Diabetes Will Require Paradigm Shifts In Policies
Within And Among Countries. Health Affairs, 31(1), 84-92. doi:
10.1377/hlthaff.2011.1040
National Bureau of Statistics. (2003). Household Budget Survey 2000/01. Dar es Salaam.
National Bureau of Statistics. (2009). Household Budget Survey 2007. Dar es Salaam.
84
National Bureau of Statistics. (2011). Tanzania in Figures: 2010. Dar es Salaam: Ministry of
Finance.
National Diabetes Data Group. (1979). Classification and diagnosis of diabetes mellitus and
other categories of glucose intolerance. Diaebtes, 28, 1039-1057.
National High Blood Pressure Education Program. (2004). The Seventh Report of the Joint
National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood
Pressure. Bethesda MD.
Nesto, R., Nelinson, D., & Pagotto, U. (2009). Obesity as a disease state: Guiding clinic
decisions on abdominal obesity and cardiometabolic risk. Clinical Cornerstone, 9(4), 43-
52.
Neuhann, H. F., Warter-Neuhann, C., Lyaruu, I., & Msuya, L. (2002). Diabetes care in
Kilimanjaro region: clinical presentation and problems of patients of the diabetes clinic at
the regional referral hospital—an inventory before structured intervention. Diabetic
Medicine, 19(6), 509-513. doi: 10.1046/j.1464-5491.2002.00673.x
Neuman, M., Finlay, J. E., Davey Smith, G., & Subramanian, S. (2011). The poor stay thinner:
stable socioeconomic gradients in BMI among women in lower- and middle-income
countries. The American Journal of Clinical Nutrition. doi: 10.3945/ajcn.111.018127
Ngowi, H. P. (2009). Economic development and change in Tanzania since independence: The
political leadership factor. African Journal of Political Science and International
Relations, 3(4), 259-267.
Nube, M., Asenso-Okyere, W. K., & van den Bloom, G. J. M. (1998). Body mass index as
indicator of standard of living in developing countries. European Journal of Clinical
Nutrition, 52(2), 136-144.
85
Nyamdorj, R. (2010). Anthropometric measures of obesity-their association with type 2 diabetes
and hypertension across ethnic groups. Ph.D., University of Helsinki, Helsinki Finland.
Nyenwe, E. A., Odia, O. J., Ihekwaba, A. E., Ojule, A., & Babatunde, S. (2003). Type 2 diabetes
in adult Nigerians: a study of its prevalence and risk factors in Port Harcourt, Nigeria.
Diabetes Research and Clinical Practice, 62(3), 177-185. doi:
10.1016/j.diabres.2003.07.002
Oladapo, O. O., Salako, L., Sodiq, O., Shoyinka, K., Adedapo, K., & Falase, A. O. (2010). A
prevalence of cardiometabolic risk factors amoung a rural Yoruba south-western Nigerian
population: A population -based survey. Cardiovascular Journal of Africa, 21(1), 26-31.
Osei, K., Schuster, D., Amoah, A. G. B., & Owusu, S. K. (2003). Pathogensis of type 1 and type
2 diabetes millitus in sub-Saharan Africa: Implications for transitional populations.
Journal of Cardiovascular Risk, 10(1), 85-96.
Petursson, H., Sigurdsson, J. A., Bengtsson, C., Nilsen, T. I. L., & Getz, L. (2011). Body
Configuration as a Predictor of Mortality: Comparison of Five Anthropometric Measures
in a 12 Year Follow-Up of the Norwegian HUNT 2 Study. PLoS ONE, 6(10), e26621.
doi: 10.1371/journal.pone.0026621
Polito, A., Brouland, J.-P., Porcher, R., Sonneville, R., Siami, S., Stevens, R., . . . Sharshar, T.
(2011). Hyperglycaemia and apoptosis of microglial cells in human septic shock. Critical
Care, 15(3), R131.
Popkin, B. (1999). Urbanization, lifestyle changes, and the nutritional transition. World
Development, 27(1), 1905-1916.
86
Popkin, B. (2002). Part II. What is unique about the experience in lower-and middle-income less-
industrialised countries compared with the very-highincome industrialised countries?
Public Health Nutrition, 5(1a), 205-214. doi: doi:10.1079/PHN2001295
Popkin, B., Adair, L. S., & Ng, S. W. (2012). Global nutrition transition and the pandemic of
obesity in developing countries. Nutrition Reviews, 70(1), 3-21. doi: 10.1111/j.1753-
4887.2011.00456.x
Prinsloo, J., Malan, L., de Ridder, J. H., Potgieter, J. C., & Steyn, H. S. (2011). Determining the
Waist Circumference Cut off which Best Predicts the Metabolic Syndrome components
in urban Africans: The SABPA study. Exp Clin Endocrinol Diabetes, 119(10), 599,603.
doi: 10.1055/s-0031-1280801
Prokopenko, I., McCarthy, M. I., & Lindgren, C. M. (2008). Type 2 diabetes: new genes, new
understanding. Trends in Genetics, 24(12), 613-621. doi: 10.1016/j.tig.2008.09.004
Renzaho, A., M. N. (2004). Fat, rich and beautiful: changing socio-cultural paradigms associated
with obesity risk, nutritional status and refugee children from sub-Saharan Africa. Health
and Place, 10, 105-113. doi: 10.1016/S1353-8292(03)00051-0
Roglic, G., & Unwin, N. (2010). Mortality attributable to diabetes: Estimates for the year 2010.
Diabetes Research and Clinical Practice, 87(1), 15-19. doi:
10.1016/j.diabres.2009.10.006
Schulze, M. B., Heidemann, C., Schienkiewitz, A., Bergmann, M. M., Hoffmann, K., & Boeing,
H. (2006). Comparison of Anthropometric Characteristics in Predicting the Incidence of
Type 2 Diabetes in the EPIC-Potsdam Study. Diabetes Care, 29(8), 1921-1923. doi:
10.2337/dc06-0895
Selembo, G. (2009). [Obesity in Sakila Village].
87
Seto, K. C., Fragkias, M., Güneralp, B., & Reilly, M. K. (2011). A Meta-Analysis of Global
Urban Land Expansion. PLoS ONE, 6(8), e23777. doi: 10.1371/journal.pone.0023777
Silva-Matos, C., Gomes, A., Azevedo, A., Damasceno, A., Prista, A., & Lunet, N. (2011).
Diabetes in Mozambique: Prevalence, management and healthcare challenges. Diabetes
& Metabolism, 37(3), 237-244. doi: 10.1016/j.diabet.2010.10.006
Sluik, D., Boeing, H., Montonen, J., Pischon, T., Kaaks, R., Teucher, B., . . . Nöthlings, U.
(2011). Associations Between General and Abdominal Adiposity and Mortality in
Individuals With Diabetes Mellitus. American Journal of Epidemiology, 174(1), 22-34.
doi: 10.1093/aje/kwr048
Sobngwi, E., Mauvais-Jarvis, F., Vexiau, P., Mbanya, J. C., & Gautier, J.-F. (2002). Diabetes in
Africans. Part 2: Ketosis-prone atypical diabetes mellitus. Diabetes & Metabolism, 28(1),
5-12.
Sobngwi, E., Mbanya, J.-C., Unwin, N. C., Porcher, R., Kengne, A.-P., Fezeu, L., . . . Alberti, K.
G. (2004). Exposure over the life course to an urban environment and its relation with
obesity, diabetes, and hypertension in rural and urban Cameroon. International Journal of
Epidemiology, 33(4), 769-776. doi: 10.1093/ije/dyh044
Sobngwi, E., Mbanya, J. C., Unwin, N., Kengne, A.-P., fezeu, L., Minkoulou, E., . . . Alberti, K.
G. (2002). Physical activity and its relationship with obesity, hypertension and diabetes in
urban and rural Cameroon. International Journal of Obesity, 26(7), 1009-1016.
Sobngwi, E., Ndour-Mbaye, M., Boateng, K. A., Ramaiya, K. L., Njenga, E. W., Diop, S. N., . . .
Ohwovoriole, A. E. (2012). Type 2 diabetes control and complications in specialised
diabetes care centres of six sub-Saharan African countries: The Diabcare Africa study.
88
Diabetes Research and Clinical Practice, 95(1), 30-36. doi:
10.1016/j.diabres.2011.10.018
Solet, J. L., Baroux, N., Pochet, M., Benoit-Cattin, T., De Montera, A. M., Sissoko, D., . . .
Fagot-Campagna, A. (2011). Prevalence of type 2 diabetes and other cardiovascular risk
factors in Mayotte in 2008: The MAYDIA study. Diabetes & Metabolism, 37(3),
201-207. doi: 10.1016/j.diabet.2010.09.007
Stitzel, M., Sethupathy, P., Pearson, D., Chines, P. S., Song, L., Erdos, M. R., . . . Collins, F. S.
(2010). Global Epigenomic Analysis of Primary Human Pancreatic Islets Provides
Insights into Type 2 Diabetes Susceptibility Loci. Cell Metabolism, 12(5), 443-455.
Subramanian, S., Perkins, J. M., Özaltin, E., & Davey Smith, G. (2011). Weight of nations: a
socioeconomic analysis of women in low- to middle-income countries. The American
Journal of Clinical Nutrition, 93(2), 413-421. doi: 10.3945/ajcn.110.004820
Swai, A. B. M., Kitange, H. M., Masuki, G., Kilima, P. M., Alberti, K. G., & McLarty, D. G.
(1992). Is diabetes mellitus related to undernutrition in rural Tanzania? British Medical
Journal, 305(6861), 1057-1062.
Swai, A. B. M., Lutale, J., & McLarty, D. G. (1990). Diabetes in tropical Africa: a prospective
study, 1981-7. Characteristics of newly presenting patients in Dar es Salaam, Tanzania,
1981-7. BMJ: British Medical Journal, 300(6732), 1103-1106.
Tesfaye, S., & Gill, G. (2011). Chronic diabetic complications in Africa. African Journal of
Diabetes Medicine, 19(1), 4-8.
The World Bank. (2011). 2011 World Development Indicators
Travers, M., & McCarthy, M. (2011). Type 2 diabetes and obesity: genomics and the clinic.
Human Genetics, 130(1), 41-58. doi: 10.1007/s00439-011-1023-8
89
United Nations. (2007). World Urbanization Prospects: The 2007 revision population database
Retrieved March 18, 2012, from http://esa.un.org/unup,
United Nations. (2010). World Population Prospects, the 2010 revision. In P. D. o. t. U. n. D. o.
E. a. S. Affairs (Ed.). New York.
Unwin, N., James, P., McLarty, D., Machybia, H., Nkulila, P., Tamin, B., . . . McNally, R.
(2010). Rural to urban migration and changes in cardiovascular risk factors in Tanzania: a
prospective cohort study. BMC Public Health, 10(1), 272.
Van Der Sande, M. A. B., Bailey, R., Faal, H., Banya, W. A. S., Dolin, P., Nyan, O. A., . . .
McAdam, K. P. W. J. (1997). Nationwide prevalence study of hypertension and related
non-communicable diseases in The Gambia. Tropical Medicine & International Health,
2(11), 1039-1048. doi: 10.1046/j.1365-3156.1997.d01-184.x
Viswanathan, V., Wadud, J. R., Madhavan, S., Rajasekar, S., Kumpatla, S., Lutale, J. K., &
Abbas, Z. G. (2010). Comparison of post amputation outcome in patients with type 2
diabetes from specialized foot care centres in three developing countries. Diabetes
Research and Clinical Practice, 88(2), 146-150.
Wamala, S., Merlo, J., & Bostrom, G. (2006). Inequity in access to dental care services explains
current socioeconomic disparities in oral health: the Swedish national Surveys of Public
health 2004-2005. J Epidemiol Community Health, 60, 1027 - 1033.
Weatherall, D. J. (2012). The definition and epidemiology of non-transfusion-dependent
thalassemia. Blood Reviews, 26, Supplement 1(0), S3-S6. doi:
http://dx.doi.org/10.1016/S0268-960X(12)70003-6
Weed, D. L. (2001). Theory and Practice in Epidemiology. Annals of the New York Academy of
Sciences, 954(1), 52-62. doi: 10.1111/j.1749-6632.2001.tb02746.x
90
Welch Allyn. (2001). Service Manual: Aneroid Sphygmomanometers (Vol. Service Manual
95P504Rev. D).
Whiting, D. R., Guariguata, L., Weil, C., & Shaw, J. (2011). IDF Diabetes Atlas: Global
estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Research and
Clinical Practice, 94(3), 311-321. doi: 10.1016/j.diabres.2011.10.029
WHO. (2000). Obesity: Preventing and managing the global epidemic. Report of a WHO
consultation (Vol. WHO Technical Report Series 894,). Geneva: World Health
Organziation.
WHO. (2003). Screening for Type 2 Diabetes: Report of the World Health Organization and the
International Diabetes Federation. In Department of Noncommunicable Disease
Management (Ed.): World Health Organization.
WHO. (2011). Use of Glycated Haemoglobin (HbA1c) in the diagnosis of diabetes mellitus.
Geneva: World Health Organization.
Whole Village Project. (2011). Village reports for Leguruki, Kingori, Malula, Samaria and Njoro
in AruMeru District: University of Minnesota.
Wild, S., Roglic, G., Green, A., Sicree, R., & King, H. (2004). Global Prevalence of Diabetes:
Estimates for the year 2000 and projections for 2030. Diabtes Care, 27(5), 1047-1053.
World Diabetes Foundation. (2008). Diabetes Clinics in Tanzania Retrieved February 2, 2012,
from http://www.worlddiabetesfoundation.org/composite-127.htm
World Health Organization. (2008). Waist circumference and waist–hip ratio: report of a WHO
expert consultation (pp. 1-47). Geneva.
91
Yancey, A. K., Ortega, A. N., & Kumanyika, S. K. (2006). Effective recruitement and retention
of minority research particpants. . Annual Review of Public Health, 27(1), 1-28. doi:
doi:10.1146/annurev.publhealth.27.021405.102113
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
Zimmet, P. (2000). Globalization, coca-colonization and the chronic disease epidemic: can the
Doomsday scenario be averted? Journal of Internal Medicine, 247(3), 301-310. doi:
10.1046/j.1365-2796.2000.00625.x
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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.
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Data collection tool: Swahili with English subtitles.
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Results sheet provided to participant.
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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 Male: 29 Yes: 3 (3%) Yes: 2 (2%) Yes: 7(8%) Yes: 5 (5%) Mean: 54.6 (32%) sd = 18.3 Female: 63 (68%) CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension
Age groups Crude rates of Crude rates Crude rates of Crude rates of Pre-diabetes of Diabetes HTN 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 Lifelong non tobacco use 71 77 % Tobacco Use Former tobacco use 16 17 % Current tobacco use 5 5 % No alcohol use 88 96 % Alcohol use Rare alcohol use (<3 drinks per month) 1 1 % Regular alcohol use (drinks weekly) 3 3 % Less than 3 sweet drinks per week 4 4 % Sweet drink 4-10 sweet drinks per week 24 26 % consumption 11-20 sweet drinks per week 44 48 % 21 or more sweet drinks per week 20 22 % Group variable Count per variable Low 43 46.7 % Income group Medium 40 43.5 % High: 9 9.8 % No school: 10 10.9 % Education level: Primary school: 74 80.4 % Secondary school or higher: 8 8.7 % Ambulatory: 88 95.7 % Mode of Bicycle: 1 1.1 % transportation Motorized (motorcycle/automobile): 3 3.3 % River/stream: 45 48.9 % Source of cooking Unprotected well: 32 34.8 % water Protected well: 15 16.3 % Indoor plumbing: 0 0 % Earthen/dirt flooring: 22 23.9 % Household flooring Wooden plank flooring: 52 56.5 % construction 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 Male: 23 Yes: 1 (2%) Yes: 0 Yes: 2 (4%) Yes: 3 (6%) Mean: 50.62 (43%) sd = 18.63 Female: 30 (57%) CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension
Age groups Crude rates of Crude rates Crude rates of Crude rates of Pre-diabetes of Diabetes HTN 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 Lifelong non tobacco use 42 80 % Tobacco Use Former tobacco use 8 15 % Current tobacco use 3 5 % No alcohol use 44 83 % Alcohol use Rare alcohol use (<3 drinks per month) 3 6 % Regular alcohol use (drinks weekly) 6 11 % Less than 3 sweet drinks per week 3 6 % Sweet drink 4-10 sweet drinks per week 14 26 % consumption 11-20 sweet drinks per week 15 28 % 21 or more sweet drinks per week 21 40 % Group variable Count per variable Percentage Low 8 15.1 % Income group Medium 15 28.3 % High: 30 56.6 % No school: 11 20.8 % Education level: Primary school: 33 62.3 % Secondary school or higher: 9 17 % Ambulatory: 45 84.9 % Mode of Bicycle: 5 9.4 % transportation Motorized (motorcycle/automobile): 3 5.7 % River/stream: 1 1.9 % Source of cooking Unprotected well: 3 5.7 % water Protected well: 45 84.9 % Indoor plumbing: 4 7.5 % Earthen/dirt flooring: 16 30.2 % Household flooring Wooden plank flooring: 4 7.5 % construction 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 Male: 28 Yes: 1 Yes: 0 Yes: 0 Yes: 3 Mean: 44.0 (44%) (2%) (5%) sd = 13.09 Female: 36 (56%) CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension
Age groups Crude rates Crude rates Crude rates of Crude rates of of Pre- of Diabetes HTN Excessive diabetes 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 Lifelong non tobacco use 46 71.9 % Tobacco Use Former tobacco use 14 21.9 % Current tobacco use 4 6.3 % No alcohol use 61 95.3 % Alcohol use Rare alcohol use (<3 drinks per month) 0 0 % Regular alcohol use (drinks weekly) 3 4.7 % Less than 3 sweet drinks per week 3 4.7 % Sweet drink 4-10 sweet drinks per week 17 26.6 % consumption 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 Low 6 9.4 % Income group Medium 21 32.8 % High: 37 57.8 % No school: 10 15.6 % Education level: Primary school: 47 73.4 % Secondary school or higher: 7 10.9 % Ambulatory: 51 79.7 % Mode of Bicycle: 3 4.7 % transportation Motorized (motorcycle/automobile): 10 15.6 % River/stream: 2 3.1 % Source of cooking Unprotected well: 0 0 % water Protected well: 57 89.1 % Indoor plumbing: 6 7.8 % Earthen/dirt flooring: 24 37.5 % Household flooring Wooden plank flooring: 0 0 % construction 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 Male: 30 Yes: 5 (5%) Yes: 0 Yes: 1 (1%) Yes: 10 Mean: 50.6 (25%) (8%) sd = 15.54 Female: 90 (75%) CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension
Age groups Crude rates of Crude rates Crude rates of Crude rates of Pre-diabetes of Diabetes HTN 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 Lifelong non tobacco use 93 77.5 % Tobacco Use Former tobacco use 19 15.8 % Current tobacco use 8 6.7 % No alcohol use 106 88.3 % Alcohol use Rare alcohol use (<3 drinks per month) 6 5 % Regular alcohol use (drinks weekly) 8 6.7 % Less than 3 sweet drinks per week 6 5 % Sweet drink 4-10 sweet drinks per week 33 27.5 % consumption 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 Low 9 7.5 % Income group Medium 36 30 75 High: 75 62.5 % No school: 21 17.5 % Education level: Primary school: 82 68.3 % Secondary school or higher: 17 14.2 % Ambulatory: 110 91.7 % Mode of Bicycle: 2 1.7 % transportation Motorized (motorcycle/automobile): 8 6.7 % River/stream: 1 0.8 % Source of cooking Unprotected well: 2 1.7 % water Protected well: 112 93.3 % Indoor plumbing: 5 4.2 % Household Earthen/dirt flooring: 26 21.7 % flooring Wooden plank flooring: 12 10 % construction 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 Male: 36 Yes: 2 (2%) Yes: 0 Yes: 0 Yes: 1 Mean: 52.04 (36%) (1%) sd = 18.55 Female: 64 (64%) CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension
Age groups Crude rates of Crude rates Crude rates of Crude rates of Pre-diabetes of Diabetes HTN 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 Lifelong non tobacco use 60 60 % Tobacco Use Former tobacco use 28 28 % Current tobacco use 12 12 % No alcohol use 82 82 % Alcohol use Rare alcohol use (<3 drinks per month) 5 5 % Regular alcohol use (drinks weekly) 13 13 % Less than 3 sweet drinks per week 12 12 % Sweet drink 4-10 sweet drinks per week 26 26 % consumption 11-20 sweet drinks per week 20 20 % 21 or more sweet drinks per week 42 42 % Group variable Count per variable Percentage Low 14 14 % Income group Medium 63 63 % High: 23 23 % No school: 27 27 % Education level: Primary school: 67 67 % Secondary school or higher: 6 6 % Ambulatory: 96 96 % Mode of Bicycle: 2 2 % transportation Motorized (motorcycle/automobile): 2 2 % River/stream: 4 4 % Source of Unprotected well: 8 8 % cooking water Protected well: 88 88 % Indoor plumbing: 0 0 % Household Earthen/dirt flooring: 38 38 % flooring Wooden plank flooring: 33 33 % construction 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 Male: 46 Yes: 6 (5%) Yes: 1 (1%) Yes: 6 (5%) Yes: 6 (5%) Mean: 51.2 (38%) sd = 17.63 Female: 76 (62%) CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension
Age groups Crude rates of Crude rates Crude rates of Crude rates of Pre-diabetes of Diabetes HTN 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 Lifelong non tobacco use 101 82.8 % Tobacco Use Former tobacco use 20 16.4 % Current tobacco use 1 0.8 % No alcohol use 117 95.9 % Alcohol use Rare alcohol use (<3 drinks per month) 3 2.5 % Regular alcohol use (drinks weekly) 2 1.6 % Less than 3 sweet drinks per week 13 10.7 % Sweet drink 4-10 sweet drinks per week 51 41.8 % consumption 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 Low 12 9.8 % Income group Medium 45 36.9 % High: 65 53.3 % No school: 25 20.5 % Education level: Primary school: 81 66.4 % Secondary school or higher: 16 13.1 % Ambulatory: 106 86.9 % Mode of Bicycle: 5 4.1 % transportation Motorized (motorcycle/automobile): 11 9.0 % River/stream: 12 9.8 % Source of cooking Unprotected well: 10 8.2 % water Protected well: 96 78.7 % Indoor plumbing: 4 3.3 % Earthen/dirt flooring: 25 20.5 % Household flooring Wooden plank flooring: 18 14.8 % construction 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 Male: 41 Yes: 4 (4%) Yes: 0 Yes: 3 (3%) Yes: 6 (6%) Mean: 50.35 (44%) sd = 16.40 Female: 53 (56%) CVA= Cerebrovascular disease, CAD= coronary artery disease, HTN= Hypertension
Age groups Crude rates of Crude rates Crude rates of Crude rates of Pre-diabetes of Diabetes HTN 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 Lifelong non tobacco use 80 85.1 % Tobacco Use Former tobacco use 12 12.8 % Current tobacco use 2 2.1 % No alcohol use 88 93.6 % Alcohol use Rare alcohol use (<3 drinks per month) 2 2.1 % Regular alcohol use (drinks weekly) 4 4.3 % Less than 3 sweet drinks per week 5 5.3 % Sweet drink 4-10 sweet drinks per week 24 25.5 % consumption 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 Low 26 27.7 % Income group Medium 38 40.4 % High: 30 31.9 % No school: 23 24.5 % Education level: Primary school: 70 74.5 % Secondary school or higher: 1 1.1 % Ambulatory: 66 70.2 % Mode of Bicycle: 21 22.3 % transportation Motorized (motorcycle/automobile): 7 7.4 % River/stream: 3 3.2 % Source of Unprotected well: 32 34 % cooking water Protected well: 56 59.6 % Indoor plumbing: 3 3.2 % Household Earthen/dirt flooring: 49 52.1 % flooring Wooden plank flooring: 11 11.7 % construction Concrete/Tile flooring: 34 36.2 %
117
Table 1
Distribution of diabetes and impaired glucose tolerance prevalence 2011 2030 Increase in the No of No of Comparative Comparative no. of people people Population diabetes Population diabetes people with with prevalence prevalence with diabetes diabetes diabetes 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 Spending Mean on diabetes health as a % of expenditure Health expenditure for diabetes in 2010 total health per person Region expenditure with in 2010 diabetes in R=2 2010 R=2 US Dollars (USD) International Dollars (ID) US ID R=2 R=3 R=2 R=3 D 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 Women who are pregnant analysis 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 Groups 18-29 years (n= 75) 30-39 years (n=105) Age 40-49 years (n=125) 50-59 years (n=127) 60+ years (n=212)
Hypertension Normal = SBP < 140 mm/Hg (n=487) Systolic blood pressure (SBP) Hypertension = SBP ≥ 140 mm/Hg (n=158)
Adiposity Normal = BMI < 25.0 (n=478) Body mass index (BMI) Obesity = BMI ≥ 25.0 (n=165)
Diabetes groups Glucose Metabolic Groups Normal (n=540) Normal glucose metabolism (NGM) (n=540) Pre-diabetes (n=46) Impaired glucose metabolism (IGM) (n=105) Diabetes (n=59)
126
Table 8
Recoding of socioeconomic variables Original variable New variable Education level Education level
No formal education (n=127) 0 No formal education (n=127) Primary school (n=454) 1 Primary school education (n=454) Secondary school (n=54) 2 Secondary school or higher (n=64) Trade or vocational school (n=5) 3 College/University education (n=5) 4
Household flooring construction Household flooring construction
Earthen/ dirt floors (n=200) 1 Earthen/dirt floors (n=200) Wooden plank floors (n=130) 2 Wooden plank floors (n=130) Concrete slab floors (n=314) 3 Concrete/tile floors (n=315) Tile floors (n=1) 4
Mode of transportation Mode of transportation
Walk/public transportation (n=562) 1 Walk/public transportation (n=562) Bicycle (n=39) 2 Bicycle (n=39) Motorcycle (n=38) 3 Motorized vehicle (n=44) Automobile- car (n=6) 4
Source of cooking water Unchanged
Stream/river (n=68) 1 Unprotected well (n=87) 2 Protected well (n=469) 3 Indoor plumbing (n=21) 4
Income Score = Sum of each category above
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Table 9
Recoding of lifestyle variables Original variable New variable Tobacco Use Unchanged
Lifelong non-smoker (n=493) Former smoker (n=117) Current smoker (n=35)
Alcohol use Alcohol use
Does not drink alcohol (n=586) Does not drink alcohol (n=586) Rare- less than 4 drinks per month (n=20) Rare- less than 4 drinks per month (n=20) 1-2 drinks per week (n=15) Regular alcohol consumption (n=39) 3-7 drinks per week (n=6) 8-14 drinks per week (n=9) 15 or more drinks per week (n=9)
Sweet drink consumption Sweet drink consumption
Less than 3 sweet drinks per week (n=46) Less than 3 sweet drinks per week (n=46) 4-10 sweet drinks per week (n=189) 4-10 sweet drinks per week (n=189) 11-15 sweet drinks per week (n=142) 11-20 sweet drinks per week (n=210) 16-20 sweet drinks per week (n=68) 21 or more sweet drinks per week (n=200) 21- 25 sweet drinks per week (n=130) 26 or more sweet drinks per week (n=70)
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Table 10
Description of village statistics All villages combined Age (Mean) 50.8 (range 18-103, sd 17.1) Male n=233 (36%) Gender 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) No formal education (n=127) Education Level Primary school education (n=454) Secondary school or higher (n=64)
Walk/public transportation (n=562) Mode of Transportation Bicycle (n=39) Motorized vehicle (n=44)
Earthen/dirt floors (n=200) Household Flooring construction Wooden plank floors (n=130) Concrete/tile floors (n=315)
Stream/river (n=68) Unprotected well (n=87) Source of Cooking water Protected well (n=469) Indoor plumbing (n=21)
Never 493 Tobacco use Former 117 Current 35
Does not drink alcohol (n=586) Alcohol use Rare- less than 4 drinks per month (n=20) Regular alcohol consumption (n=39)
Less than 3 sweet drinks per week (n=46) 4-10 sweet drinks per week (n=189) Average Sweet drink use 11-20 sweet drinks per week (n=210) 21 or more sweet drinks per week (n=200)
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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 *Rural Sample Event Event Crude Crude Age- Age group in Population Size rate rate Rate Rate adjusted adjusted years percentage Pre-DM DM Pre-DM DM rate, rate, Pre-DM 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
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Table 12
Examining the association between impaired glucose metabolism, hypertension, and adiposity IGM HTN Adiposity χ2 = 10.86, p = .001, χ2 = 8.67, p = .003, IGM - Eta2= .13 Eta2 = .12
HTN - - χ2 = 2.82, p = .09*
Age Groups χ2=15.5, p = .004 χ2 (4)=43.43, p < .001 χ2 (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
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Table 13
The strength of association of biometric indices on FPG, SBP, BMI Fasting Plasma Systolic blood pressure Body Mass Index Glucose F = 2.06, p < .001, F = 3.86, p < .001, F 4.14, p < .001, Overall Model Eta2 = .12 Eta2 = .182 Eta2 = .2.2 F = 10.44, p = .003 Gender ns ns Eta2 = .017 F = 3.03, p = .017 F = 5.84, p < .001 Age groups ns Eta2 = .02 Eta2 = .037 F = 10.63, p = .001 IGM - ns Eta2 = .02 F = 12.86, p < .001 HTN ns - Eta2 = .021 F = 11.36, p = .001 F = 10.47, p = .001 Adiposity - Eta2 = .02 Eta2 = .017 Univariate ANOVA
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Table 14
Odds assessment of biometric variables associated with the development of Impaired Glucose Tolerance Independent Logistic regression p- SE OR 95% CI variable coefficient value 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 0.50 0.24 .037 1.64 [1.03, 2.62] mm/Hg Adiposity BMI ≥ 25.0 0.64 0.24 .006 1.90 [1.2, 3.02] Forward conditional Variable removed: gender Note. CI= confidence interval
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Table 15 Odds assessment of biometric variables associated with the development of hypertension Independent Logistic regression SE p- value OR 95% CI variable coefficient 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
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Table 16
Odds assessment of biometric variables associated with the development of adiposity Independent Logistic regression SE P- value OR 95% CI variable coefficient 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 Impaired Glucose Indicator Hypertension Adiposity Metabolism 2 2 2 χ Sig χ Sig χ 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 - ns - ns - ns consumption 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 Systolic blood pressure Body Mass Index Glucose F = 1.83, p = .004, F = 1.93, p = .002, Overall Model Fit ns Eta2 = .09 Eta2 = .095 Alcohol Use ns ns ns F = 6.99, p = .001* F = 7.65, p = .001 Tobacco Use ns Eta2 = .022 Eta2 = .025 Sweet drink ns** ns ns consumption 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)
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Table 19 Associated Socioeconomic factors and the development of elevated FPG, SBP, BMI Fasting plasma glucose Systolic blood pressure Body mass index
F (54,587) = 2.05, p Overall F (54,580) = 5.82, p < .001, p = .06 < .001 model fit Eta2 = .352 Eta2 = .158 F (2,580) = 3.43, p = .033 F (2,590) = 7.35, p = .001 Education ns Eta2 = .012 Eta2 = .024 Flooring F (2,580) = 17.23, p < .001 ns ns construction Eta2 = .056 F (3,580) = 33.36, p < .001 Water source ns ns Eta2 = .147 Mode of F (2,580) = 23.21, p < .001 ns ns transportation Eta2 = .074 Two way interaction: Two way Education level and source interaction: of water, Education level F (3,580) = 3.50, p = .015 and mode of Eta2 = .018 transportation, Mode of transportation and F (2,587) = 3.78, p source of water, = .023 F (3,580) = 67.72, p < .001 Eta2 = .013 Eta2 = .259 Univariate ANOVA
138
Table 20 Odds assessment of lifestyle and economic variables and the development of IGM Independent Logistic regression SE P- value OR 95% CI variable coefficient Education Level .022 Primary School -0.64 0.25 .009 0.53 [0.33, 0.85] Secondary school -0.816 0.43 .058 0.44 [0.19, 1.03] or higher 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 Logistic Independent regression SE p- value OR 95% CI variable coefficient 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 Logistic regression SE P- value OR 95% CI variable coefficient 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
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
Number of participants N=645
148
Figure 8
Average number of sweet drinks consumed per week