ENSIGN GLOBAL COLLEGE 

KPONG, EASTERN REGION, GHANA 

 

DEPARTMENT OF COMMUNITY HEALTH 

 

PROGRESSION OF TYPE TWO DIABETES AMONG THE AGED IN AGINCOURT 

MPUMALANGA PROVINCE, SOUTH AFRICA 

 

 

BY 

 

GRACIA HELARIE FRAIKUE 

 

 

 

JUNE, 2024 

 



 
 

 

ENSIGN GLOBAL COLLEGE  

KPONG, EASTERN REGION, GHANA 

 

FACULTY OF PUBLIC HEALTH 

DEPARTMENT OF COMMUNITY HEALTH 

 

PROGRESSION OF TYPE TWO DIABETES AMONG THE AGED IN 

AGINCOURT MPUMALANGA PROVINCE, SOUTH AFRICA 

 

 

BY 

 

GRACIA HELARIE FRAIKUE 

(237100245) 

 

 

 

 

 

 

JUNE, 2024



i 
 

DECLARATION 

I, GRACIA HELARIE FRAIKUE hereby declare that with the exception of the references 

made to other people’s work which I duly acknowledged, this research submitted to the De-

partment of Community Health, Ensign Global College is my original work and has neither in 

whole nor in part been presented to the University or elsewhere for another degree. 

 

GRACIA HELARIE FRAIKUE 

(ID-237100245)                                                      Signature: …………….        Date: …………… 

(Student) 

 

(Certified by) 

DR. SANDRA BOATEMAA KUSHITOR          Signature: ……………         Date: ……………. 

(Supervisor) 

 

(Certified by) 

DR. STEPHEN MANORTEY                              Signature: ……………         Date: ……………. 

(Head of Academic Program) 

 

 

 



ii 
 

DEDICATION 

I dedicate this work to my parents, Ing. Mr. and Dr. Mrs. Fraikue, my brother Kwow Nyan 

Fraikue and Grandmother and Betty Biney for their immense support throughout the period of 

my studies. I am forever grateful. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



iii 
 

 

ACKNOWLEDGEMENT 

 I would like to express my profound gratitude to the Almighty God for granting me the grace 

and strength to successfully complete this dissertation. I am genuinely thankful for the guidance 

and support provided by my supervisor, Dr. Sandra Boatemaa Kushitor, in completing this 

thesis. Furthermore, I want to extend my appreciation to my parents, Ing. Mr. and Dr. Mrs. 

Fraikue, my brother Kwow Nyan Fraikue, and my Grandmother, Betty Biney, for their unwa-

vering support throughout my studies. I am forever grateful for their encouragement. 

 Additionally, I am sincerely thankful to all the faculty members of Ensign Global College for 

their contributions in bringing this study to fruition. I particularly want to express my gratitude 

to Miss Mariam Yenipuni Kombat for her steadfast support throughout my project journey; 

you have been nothing but an epitome of good luck. 

Furthermore, I want to acknowledge the support of my fellow colleagues at Ensign Global 

College, especially those in the F23 cohort (weekend), for their encouragement and assistance 

throughout this study. 

 

 

 

 

 

 

 

 

 



iv 
 

ABBREVIATION/ACRONYMS 

ABBREVIATION MEANING 

ADA                              American Diabetic Association 

BMI                              Body Mass Index 

CAPI                            Computer-Assisted Personal Interview 

DM                               Diabetes Mellitus 

GDM                            Gestational Diabetes Mellitus 

HAALSI                       Health and Aging in Africa 

HDSS                            Health and Demographic Surveillance System 

HIV                              Human Immunodeficiency Virus 

HTN                             Hypertension 

IDF                               International Diabetes Federation 

NCDs                            Non-communicable Diseases 

SA                                 South Africa 

SSA                               Sub-Saharan Africa 

T2DM                           Type two Diabetes Mellitus 

TB                                 Tuberculosis 

WHO                            World Health Organisation 

 

 



v 
 

ABSTRACT 

 

BACKGROUND 

Diabetes Mellitus, including Type 2, is of global concern, particularly among aging populations 

in rural South Africa. T2DM is prevalent among the elderly, yet research on its progression in 

Agincourt. This study aimed to assess T2DM progression among South Africa's elderly over 

eight years, using data from the Health and Aging in Africa: A Longitudinal study of INDEPTH 

(HAALSI). 

METHODS 

The study utilized a quantitative, cross-sectional design within the HAALSI study, a 

retrospective longitudinal cohort study, to assess type two diabetes progression among the 

elderly in rural Agincourt, Mpumalanga Province, South Africa. It employed surveys and 

structured questionnaires to collect data on demographics, health conditions, lifestyle, and 

healthcare utilization.  

RESULTS 

 The prevalence of diabetes increased from 8.02% in Wave 1 to 11.49% in Wave 2.  In Wave 

1, the majority of participants were in the 50-69 age groups, with significant representation 

from those aged 70 and above. Also, about 90% of the people who did not have Diabetes at 

baseline progressed to have it at the end of the follow up stage.  

CONCLUSION 

This study reveals a concerning escalation in diabetes prevalence among the elderly in rural 

Agincourt, Mpumalanga Province, South Africa, over eight years. Factors such as age, marital 

status, and employment correlate with diabetes prevalence, alongside health indicators like 

waist circumference and body mass index. Lifestyle factors including diet, smoking, and 

alcohol intake also influence prevalence, necessitating targeted interventions to address these 

contributors. 

 

 

 



vi 
 

 

Table of Contents 

DECLARATION.................................................................................................................................... i 

DEDICATION....................................................................................................................................... ii 

ACKNOWLEDGEMENT ................................................................................................................... iii 

ABBREVIATION/ACRONYMS ........................................................................................................ iv 

ABSTRACT ........................................................................................................................................... v 

LIST OF TABLES ............................................................................................................................. viii 

LIST OF FIGURES ............................................................................................................................. ix 

LIST OF MAPS..................................................................................................................................... x 

LIST OF APPENDICES ..................................................................................................................... xi 

CHAPTER ONE ................................................................................................................................... 1 

1.0 INTRODUCTION ....................................................................................................................... 1 

1.1 Background of the study......................................................................................................... 1 

1.2 Problem statement .................................................................................................................. 2 

1.3 Rationale of the study ............................................................................................................. 4 

1.4 Conceptual framework ........................................................................................................... 5 

1.5 Research questions .................................................................................................................. 7 

1.6 Objectives ................................................................................................................................. 8 

1.7 Profile of the study area.......................................................................................................... 8 

1.8 Scope of the Study ................................................................................................................. 10 

CHAPTER TWO ................................................................................................................................ 12 

2.0 LITERATURE REVIEW ........................................................................................................ 12 

2.1 Introduction ........................................................................................................................... 12 

2.2 Global Prevalence of Type two Diabetes Mellitus. ............................................................. 12 

2.3 Prevalence of Type two Diabetes Mellitus in Sub-Saharan Africa ................................... 14 

2.4 Prevalence of Type two Diabetes Mellitus in South Africa ............................................... 16 

2.5 Risk factors of Type two Diabetes Mellitus ........................................................................ 19 

2.6. Implications of type two Diabetes Mellitus ........................................................................ 20 

2.7 The progression of Type two Diabetes Mellitus in South Africa ...................................... 22 

2.8 Relationship Between the Progression and Risk Factors of Type two Diabetes Mellitus

 ...................................................................................................................................................... 23 

2.9 Varied theories used for the study of type two Diabetes Mellitus .................................... 25 

CHAPTER THREE ............................................................................................................................ 28 

3.0 METHODOLOGY ................................................................................................................... 28 

3.1 Introduction ........................................................................................................................... 28 

3.2 Study design and source of data .......................................................................................... 28 



vii 
 

3.3 Data collection method and instruments............................................................................. 30 

3.4 Data handling ........................................................................................................................ 32 

3.5 Statistical analysis ................................................................................................................. 33 

3.6 Ethical issues .......................................................................................................................... 33 

3.7 Limitations of the study ........................................................................................................ 33 

3.8 Assumptions ........................................................................................................................... 35 

CHAPTER 4 ........................................................................................................................................ 36 

4.0 RESULTS .................................................................................................................................. 36 

4.1. Progression of type two diabetes among the aged in Agincourt Mpumalanga province, 

South Africa ................................................................................................................................. 36 

4.2 Socio-demographic characteristics of the HAALSI population ........................................ 36 

4.3 Prevalence of health risk factors of diabetes ...................................................................... 39 

4.4 Diabetes prevalence Wave 1 and 2 ...................................................................................... 42 

4.5 Progression of T2DM in Wave 1 and 2 ............................................................................... 43 

4.6 Multivariate logistic regression ............................................................................................ 44 

CHAPTER 5 ........................................................................................................................................ 49 

5.0 DISCUSSION ............................................................................................................................ 49 

5.1 Discussion of results .............................................................................................................. 49 

5.2 Socio Demographic factors of the HAALSI population .................................................... 49 

5.3 Progression of Diabetes prevalence and risk factors ......................................................... 52 

5.4 Association between socio-demographic and health risk factors and diabetes ............... 56 

CHAPTER SIX ................................................................................................................................... 60 

6.0 Conclusions and Recommendations ........................................................................................ 60 

6.1 Conclusion ............................................................................................................................. 60 

6.2 Recommendations ................................................................................................................. 60 

REFERENCES .................................................................................................................................... 62 

APPENDIX ONE: ETHICAL CLEARANCE LETTER ................................................................ 71 

 

 

 

 

 

 

 

 

 



viii 
 

 

LIST OF TABLES 

Table 4.1 – Socio demographic factors of HAALSI population ………………………...38 

Table 4.2– Prevalence of health and lifestyle risk factors ……………………................40 

Table 4.3.– Fruits and vegetables intake among the HAALSI population………………41 

Table 4.4. – Multivariate logistic regression (Wave 1) …………………………………45 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



ix 
 

 

LIST OF FIGURES 

 

Figure 1.1: Conceptual Framework…………………………………………………………7 

Figure 4.1: Diabetes prevalence in Wave 1&2 of the HAALSI study……………………...41 

Figure 4.2: Progression of T2DM in Wave 1&2 from the HAALSI study………………….43 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



x 
 

 

 

LIST OF MAPS 

MAP1.1………………………………………………………………………………….10 

MAP3.1…………………………………………………………………………….…… 40 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



xi 
 

 

LIST OF APPENDICES 

 

Appendix 1: Ethical Clearance Letter…………………………………………………71 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



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CHAPTER ONE 

1.0 INTRODUCTION 

1.1 Background of the study 

Diabetes Mellitus is a metabolic disorder marked by high blood sugar levels and related health 

issues (WHO, 2021. It is a significant global health concern, classified into Type 1 and Type 2 

diabetes (Mansoori, 2023). The prevalence of type 2 diabetes (T2DM) is rising, primarily as a 

result of aging populations, urbanization, and lifestyle modifications. T2DM is especially rising 

in the over-65 age group.  This metabolic disorder is often characterized by polydipsia, 

polyuria, and polyphagia (Nigro et al., 2018). The World Health Organization (WHO) links 

T2DM with serious health issues like kidney failure, heart attacks, stroke, and blindness, which 

resulted in 1.6 million deaths in 2016 (WHO, 2020). T2DM is more common among older 

adults and individuals with risk factors including insufficient physical activity, unhealthy 

eating habits, high body mass index (BMI), excess weight, and smoking (Aschner, 2016).  

In the year 2021, it was estimated that around 10.5% of individuals aged 20 to 79 years 

worldwide had diabetes, which amounts to about 536.6 million people. This percentage is 

projected to increase to 12.2% by the year 2045, encompassing approximately 783.2 million 

individuals (Sun, 2022). Diabetes is a significant challenge, particularly in low and middle-

income countries like Ethiopia, where about 77% of people with diabetes lack proper care 

(Manne-Goehler, 2016). In Africa, nearly half of diabetes-related deaths occur in individuals 

under 60 years, with the African Region facing the highest proportion. Additionally, diabetes 

in Africa is compounded by the coexistence of infectious and chronic diseases. While 

manageable, untreated high blood sugar can lead to severe complications and even death 

(Wang, 2018). Poor blood sugar control is linked to shorter lifespans, significant health issues, 

and a reduced quality of life (Haung et al., 2017). Effective diabetes management and 

prevention are crucial to address these issues.  



2 
 

Scientists have shown that there is a strong link between obesity and Type 2 diabetes, often 

called 'diabesity' (Toplak, 2019). Being very overweight, especially around the belly, makes 

the body less responsive to insulin, a key problem in Type 2 diabetes (Chadt, 2018). In other 

words, obesity increases the risk of getting Type 2 diabetes. According to International 

Diabetes Federation (2021), T2DM is a big problem in sub-Saharan Africa (SSA) and is getting 

worse quickly. The number of people with diabetes in SSA is predicted to go up from 24 million 

in 2021 to 55 million by 2045. Among SSA countries, South Africa (SA) has the second highest 

number of people with diabetes. In 2017, there were 1,826,100 adults in SA with diabetes, and 

the issue is most serious in poorer areas where resources are limited.  

This study focuses on T2DM's rise in the elderly, aiming to understand its progression and its 

implications over a period of eight years. 

1.2 Problem statement 

The escalating prevalence of Type 2 Diabetes Mellitus (T2DM) in Sub-Saharan Africa (SSA), 

especially South Africa poses a significant and growing public health concern. Recent data 

from WHO reveals a worrisome increase in T2DM cases in South Africa (WHO, 2021). This 

rise is not confined to a single nation, as SSA as a whole experiences a rising tide of T2DM, as 

documented by the International Diabetes Federation (IDF, 2021). In 2019, IDF estimated a 

global prevalence of 463 million adults living with diabetes, projected to rise to 700 million by 

2045 (International Diabetes Federation, 2019). In SSA, diabetes prevalence is increasing due 

to urbanization, lifestyle changes, and dietary shifts. While SSA traditionally had lower rates, 

countries like South Africa and Mauritius report prevalence comparable to developed nations 

(Atun et al., 2017; Peer et al., 2020). However, accurate assessment faces challenges like 

limited healthcare infrastructure and under reporting (Mayige et al., 2019). Despite this, 

consensus exists among experts that diabetes burden is rising in SSA. Urgent attention from 



3 
 

policymakers and healthcare providers is needed to implement preventive measures and 

enhance access to care. 

In South Africa, the prevalence of diabetes mellitus (DM) has been increasing, as indicated by 

findings from the Health and Aging in Africa: A Longitudinal Study of an INDEPTH 

Community in South Africa (HAALSI). The HAALSI study, conducted in the Agincourt sub-

district of Mpumalanga Province, demonstrated a concerning trend of rising DM prevalence 

over time among adults aged 40 years and older (Gómez-Olivé et al., 2017). Data from the 

study's baseline survey in 2015-2016 revealed a DM prevalence of 14.2%, with projections 

indicating a notable upward trend in subsequent years. Factors contributing to this trend include 

lifestyle changes, urbanization, and an aging population (Gómez-Olivé et al., 2017). The 

HAALSI study serves as a valuable resource for monitoring DM prevalence trends in SA and 

underscores the importance of targeted interventions to address this growing public health 

concern.  

Diabetes imposes significant implications on healthcare systems and individuals. Managing 

diabetes requires regular medical check-ups, medication, and lifestyle adjustments, leading to 

increased healthcare costs and resource allocation (American Diabetes Association, 2022). 

Individuals with diabetes often face complications like heart disease, kidney failure, and vision 

problems, reducing their quality of life and life expectancy (Center for Disease Control and 

Prevention, 2021). Moreover, diabetes can hinder productivity due to frequent hospital visits 

and sick days, impacting economic stability for both individuals and society (Sloan et al., 

2018). Effective management through education, early detection, and access to affordable 

healthcare services is crucial to mitigate these burdens. The progression of Type 2 Diabetes 

Mellitus (T2DM) among the elderly population in South Africa over an eight-year period, as 

documented in the HAALSI study's successive waves, represents a critical health concern that 

demands investigation and intervention. 



4 
 

1.3 Rationale of the study 

This research sought understand the rapid progression of Type 2 Diabetes Mellitus (T2DM) 

among the elderly is crucial in addressing the growing burden of this condition, particularly in 

countries like South Africa where the elderly population is increasing. This research aims to 

shed light on how T2DM progresses over time in older individuals in South Africa. Such 

insights are vital for tailoring healthcare strategies to effectively manage and prevent T2DM 

complications in this demographic group. One intervention that can be implemented based on 

this study is the promotion of healthy aging through lifestyle modifications. Encouraging older 

adults to adopt healthier dietary habits, engage in regular physical activity, and maintain a 

healthy weight can help prevent the onset of T2DM or slow its progression. This can be 

achieved through community-based programs, education campaigns, and support groups 

targeting the elderly population. Additionally, improving access to diabetes screening and early 

detection services is essential. Mobile clinics, community health screenings, and outreach 

programs can be organized to reach elderly individuals in underserved areas. Regular health 

check-ups and screenings can help identify individuals at risk of developing T2DM or those 

who may need additional support in managing their condition. Furthermore, enhancing 

diabetes management and care services for older adults is crucial. This includes ensuring access 

to affordable medications, regular monitoring, and specialized care for complications 

associated with T2DM. Collaborative care models involving healthcare professionals, 

community health workers, and caregivers can provide comprehensive support to elderly 

individuals living with T2DM. 

Education and awareness programs targeting both older adults and healthcare providers are 

also essential. Older individuals need to be educated about the importance of managing T2DM 

and adhering to treatment plans, while healthcare providers should receive training on geriatric 

diabetes care and treatment guidelines. Also addressing social determinants of health such as 



5 
 

poverty, inequality, and access to healthcare is fundamental in tackling T2DM among the 

elderly. Implementing policies aimed at improving socioeconomic conditions, providing 

financial assistance for healthcare expenses, and strengthening healthcare infrastructure in 

underserved areas can help reduce disparities in diabetes care and outcomes among older 

adults. Understanding the progression of T2DM among the elderly in South Africa is essential 

for developing targeted interventions and policies to address this growing public health 

challenge. By implementing interventions such as promoting healthy aging, improving access 

to screening and care services, enhancing diabetes management, and addressing social 

determinants of health, we can effectively manage and prevent T2DM complications in older 

individuals, not only in South Africa but also in other countries facing similar demographic 

changes and diabetes challenges globally. 

 

1.4 Conceptual framework 

This study adapts the conceptual framework of Grundlingh et al.  Diabetes prevalence has been 

explained by this framework (Figure1). They proposed three main factors that influence the 

prevalence of diabetes and pre-diabetes in South Africa. The following demographic, lifestyle 

and health factors derived from the framework will be examined in this study. Among the 

demographic factors include age, marital status, educational level and employment status. 

Health-related variables comprise of some anthropometric measures including; height, weight, 

hip and waist measurements, waist-to-hip ratio and body mass index, as well as measurements 

of blood pressure, haemoglobin, blood glucose. Lifestyle factors include daily consumption of 

fruits and vegetables, history of smoking or alcohol consumption. 

As individuals age, the risk of developing T2DM increases due to physiological changes and 

cumulative exposure to risk factors.  



6 
 

 

Variations in T2DM prevalence exist between males and females, influenced by hormonal 

differences and health-seeking behaviours. Disparities in income, education, and access to 

healthcare contribute to variations in T2DM prevalence among different socioeconomic 

groups. This study adapts the conceptual framework of Grundlingh et al. The following 

demographic, lifestyle and health factors will be examined.  

 

 

 

 

 

 

 

 

 

 

 

 

 



7 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1.1: Conceptual Framework 

Source: Authors Own Construct 

 

 

1.5 Research questions  

1. What are the risk factors of T2DM among the aged in South Africa at each wave? 

2. What is the progression of T2DM among the aged over a period of 8 years? 

3. What is the relationship between the risk factors of T2DM and it’s progression? 



8 
 

1.6 Objectives 

1.6.1 General objective 

This study aimed to access the progression of T2DM among the aged in South Africa. 

1.6.2 Specific objectives 

1. To describe the proportion of adults living with T2DM in Wave 1  

2. To monitor and document the development and progression of T2DM among the 

elderly over an 8-year period. 

3. To examine the correlation between identified risk factors and the progression of T2DM 

among the elderly population. 

1.7 Profile of the study area 

This study used data from the HAALSI research project. HAALSI was conducted by the 

Harvard Center for Population and Development Studies in collaboration with the Public 

Health and Health Transitions Research Unit (Agincourt) of the University of the 

Witwatersrand.  The aim of HAALSI is to examine and characterize the population of older 

men and women in rural South Africa, specifically in the Mpumalanga province. The study 

area is located in the Mpumalanga province, South Africa. Mpumalanga is one of the country's 

provinces, known for its diverse landscapes, including mountains, valleys, and wildlife.  

The study area encompasses Agincourt, a community within rural Mpumalanga, and it includes 

a diverse population of older individuals, both men and women, aged 40 years and older. 

Mpumalanga, located in eastern South Africa, is a province known for its natural beauty and 

cultural heritage. Often referred to as the "Place Where the Sun Rises," Mpumalanga offers a 

unique blend of breath-taking landscapes, rich biodiversity, and a diverse cultural tapestry. Yet, 

amid its many attractions, the province faces a pressing health concern – the increased 

prevalence of type 2 diabetes mellitus (T2DM) among its aging population. 



9 
 

  

Geographically, it is characterized by its diverse topography, featuring towering mountains, 

deep valleys, and lush Lowveld plains. Biodiversity thrives in this province, providing refuge 

to a wide array of flora and fauna. Mpumalanga is home to remarkable wildlife, including the 

celebrated Big Five and numerous bird species. Each group contributes its unique traditions, 

music, dance, and art to the cultural fabric of the province, offering visitors a chance to immerse 

themselves in local customs. Mpumalanga thrives through agriculture, mining, forestry, and 

tourism. It plays a pivotal role in South Africa's agricultural sector, notably in citrus and 

subtropical fruit production. Additionally, the province is a significant coal producer, which is 

essential for the country's energy sector. Tourism is a key driver of the province's economy, 

with attractions like the Kruger National Park, known for its remarkable wildlife, and the 

Panorama Route, offering travellers an opportunity to explore sites like God's Window and 

Bourke's Luck Potholes. Nonetheless, the province faces typical challenges seen in many South 

African regions, including unemployment, poverty, and disparities in healthcare and education 

access. 

However, the high prevalence of T2DM among the elderly population poses a notable health 

challenge, with potential economic repercussions for the healthcare system. Notably, the 

prevalence of T2DM among the elderly population is a significant health concern, driving 

initiatives for healthcare improvements and public awareness campaigns. Mpumalanga, with 

its natural beauty, cultural diversity, and economic significance, serves as a microcosm of 

South Africa's marvels and challenges. While it continues to captivate with its splendours, the 

province also highlights the need to address pressing health issues, particularly the prevalence 

of T2DM among the aging population. The study is conducted within the framework of the 

Agincourt health and socio-demographic surveillance system (AHDSS). This system provides 

a foundation for tracking and monitoring health and demographic trends within the study area. 



10 
 

 

 

MAP 1.1: MAP OF MPUMALANGA 

 

MAP OF MPUMALANGA (assessed from Global Africa Network) 

1.8 Scope of the Study  

This study used HAALSI (Health and Aging in Africa: A Longitudinal Study of an INDEPTH 

Community in South Africa) datasets to investigate the progression of type two diabetes among 

the aged in Agincourt Mpumalanga province, South Africa. The dataset was used to identify 

the risk factors associated with type two Diabetes Mellitus within the elderly population in 

South Africa during each survey wave and explore the relationship between the risk factors 

identified and the progression of type two Diabetes Mellitus among the elderly population. The 

study's target population included individuals who were aged 40 and older as of July 1, 2014 

and individuals who were permanent residents of the study area for the 12 months preceding 

the 2013 Health and Demographic Surveillance System (HDSS) census. The use of the 



11 
 

HAALSI, a reliable and well-known data source, to support the conclusions made from this 

study is a strength of this study. 

1.9 Organization of thesis 

The study is organized into six chapters. Chapter One, the introduction, covers the background 

of the study, problem statement, justification for the investigation, conceptual framework, 

research questions, study objectives, profile of the study area, scope of the study, and the study's 

organization. The second chapter discusses literature relating to the study. The assessment of 

this chapter also considers the application of theories and factual research pertinent to the topic 

under consideration. The study's methodology is covered in the third chapter. In particular, the 

research design, research instrument, data collection process, and data analysis are covered. 

The analysis of the data and the presentation of the findings are the key topics of Chapter 4. 

This chapter will be presented in a manuscript style. Chapter Five contains a discussion of the 

results. Chapter Six presents the key findings and recommendations for the progression of type 

two diabetes among the aged in Agincourt Mpumalanga province, South Africa. 

 

 

 

 

 

 

 

 



12 
 

CHAPTER TWO 

2.0 LITERATURE REVIEW 

2.1 Introduction 

This chapter offers evidence on the progression of type two diabetes among the aged in Agin-

court Mpumalanga province, South Africa. The global prevalence of T2DM, prevalence of 

T2DM In Sub-Saharan Africa, prevalence of T2DM In South Africa, risk factors of diabetes, 

implications of diabetes, progression of T2DM in South Africa, varied theories that have been 

used to study diabetes as well as the relationship between the Progression and risk factors of 

T2DM will be discussed in this section and the conceptual framework that has been used to 

study diabetes 

2.2 Global Prevalence of Type two Diabetes Mellitus. 

Globally, there are 415 million individuals coping with diabetes mellitus, and approximately 

193 million people are believed to have diabetes that has not yet been diagnosed (Chatterjee, 

2017). Type 2 diabetes is the primary form of diabetes, encompassing over 90% of all diabetes 

cases. It results in significant emotional and physical suffering for both patients and their 

caregivers while also placing a substantial strain on healthcare systems. Although there has 

been a growing understanding of the risk factors for type 2 diabetes and the effectiveness of 

preventive measures, the occurrence and spread of this disease continue to escalate on a global 

scale (Chatterjee, 2017). The prevalence and occurrence of type 2 diabetes mellitus (T2DM) 

have surged significantly in recent decades and have reached epidemic levels worldwide. The 

global prevalence of type 2 diabetes has risen dramatically over the past few decades, becoming 

a pressing global health concern.  

In 2019, the International Diabetes Federation (IDF) reported that approximately 463 million 

adults aged 20 to 79 were living with diabetes worldwide. This figure, however, represents 

only a snapshot of a larger and more concerning picture. Projections indicate that by 2045, the 



13 
 

number of people with diabetes could surge to nearly 700 million, signifying a substantial 51% 

increase in just over two and a half decades (International Diabetes Federation, 2019). This 

alarming upward trajectory can be attributed to a complex interplay of various factors. 

Urbanization has led to more sedentary lifestyles and the consumption of unhealthy, calorie-

rich diets, has contributed to the higher incidence of obesity, which is a primary risk factor for 

type 2 diabetes. Additionally, the aging global population has introduced a significant 

demographic shift. Older individuals are more susceptible to type 2 diabetes, making it a 

condition closely tied to the process of aging (Chen, 2012).  

The surge in global diabetes prevalence has placed an enormous burden on healthcare systems 

worldwide. Managing diabetes necessitates a significant financial commitment, as individuals 

living with the condition require ongoing medical care, a variety of medications, and frequent 

monitoring. This financial strain is particularly conspicuous in low- and middle-income 

countries where healthcare resources are often limited, and budget constraints have a more 

profound impact (Bommer, 2017). Moreover, the cost of addressing diabetes-related 

complications, such as cardiovascular disease, kidney failure, and lower limb amputations, 

contributes substantially to the economic load borne by healthcare systems. These 

complications demand specialized care and recurrent medical visits, increasing the overall 

demand for healthcare services and the strain on healthcare infrastructure (IDF, 2017). The 

burden is not solely economic.  

The morbidity and mortality associated with diabetes have a substantial impact on healthcare 

delivery and outcomes. Patients with diabetes often require specialized care, sometimes in 

multidisciplinary teams. Additionally, chronic diseases, including diabetes, can drain 

healthcare resources and hinder the delivery of comprehensive healthcare to individuals with 

other health concerns, adding a layer of complexity to healthcare systems worldwide 

(Ogurtsova, 2017). The elderly population faces a unique set of challenges in managing type 2 



14 
 

diabetes on a global scale. As age is one of the predominant risk factors for developing type 2 

diabetes, the aging global population contributes to the increasing prevalence of the condition 

among older individuals.  

Beyond age itself, elderly individuals often have multiple comorbidities, making the 

management of diabetes more complex, as it must be integrated with the care for other health 

conditions (Sinclair, 2017). Elderly individuals may experience physical limitations and 

cognitive decline, further complicating their ability to adhere to treatment regimens and self-

manage their diabetes effectively. Access to healthcare can be problematic, especially in 

regions with limited healthcare infrastructure, and elderly patients may find it challenging to 

consistently adhere to prescribed medications and monitoring schedules (American Diabetes 

Association, 2019).  

In addition to these issues, the elderly is more susceptible to the complications of diabetes, such 

as neuropathy and retinopathy, which can significantly impact their quality of life. The risk of 

falls and fractures due to neuropathy, coupled with visual impairment from retinopathy, can 

make daily living more challenging for older individuals with diabetes (Luo, 2018). Cultural 

and socio-economic factors also play a vital role in diabetes management among the elderly. 

Dietary preferences, which are often deeply rooted in cultural traditions, can affect the ability 

of elderly individuals to adhere to recommended dietary modifications. Financial constraints 

and limited access to healthcare services further complicate the management of diabetes in this 

population. 

2.3 Prevalence of Type two Diabetes Mellitus in Sub-Saharan Africa 

Sub-Saharan Africa (SSA) has witnessed a significant shift in the epidemiology of diseases in 

recent decades, with the increasing prevalence of non-communicable diseases (NCDs) like type 

2 diabetes taking centre stage (WHO, 2020). The region comprises diverse countries with 



15 
 

varying levels of economic development, urbanization, and healthcare infrastructure, which 

contributes to disparities in diabetes prevalence. According to the World Health Organization 

(WHO), by 2019, the prevalence of diabetes in the SSA region had reached approximately 

3.1% of the adult population, representing a noteworthy increase from previous years (WHO, 

2020). In SSA Countries, about 98% of adults with T2DM carry at least one or two co-

morbidities.  

In SSA, the prevalence of T2DM in older persons 55 years of age and older is approximately 

13% greater in urban than in rural areas (Losso et al., 2021). Numerous studies have identified 

prevailing dietary patterns in US, European and Asian populations, and investigated their 

association with the risk of diabetes (Odegaard et al., 2011). The trends in diabetes prevalence 

across Sub-Saharan Africa are far from uniform. Some countries have experienced a more rapid 

increase in diabetes rates than others. This variability can be attributed to multiple factors, 

including differences in lifestyle, urbanization rates, and genetic predisposition (Hall, 2011).  

Urbanization has led to significant shifts in dietary habits and physical activity levels. As rural 

populations migrate to urban centres, traditional diets rich in whole grains, vegetables, and 

fruits are often replaced by high-calorie, low-nutrient diets. These dietary changes, coupled 

with increasingly sedentary lifestyles, have contributed to rising obesity rates, a well-

established risk factor for type 2 diabetes (Hall, 2011). Furthermore, genetic factors play a role 

in diabetes prevalence in SSA. The region has a diverse genetic landscape, with various ethnic 

groups exhibiting different susceptibilities to diabetes. Genetic predisposition interacts with 

lifestyle factors, contributing to the variability in prevalence. The impact of lifestyle changes 

and urbanization on diabetes prevalence is substantial.  

As mentioned, urbanization is often accompanied by shifts in dietary patterns, with an 

increased consumption of processed and high-sugar foods, reduced physical activity, and an 



16 
 

elevated risk of obesity. The transition from physically demanding rural work to more 

sedentary urban jobs has further exacerbated the problem (Agyeman, 2016). These shifts in 

lifestyle have, in turn, played a key role in the increasing prevalence of type 2 diabetes. SSA 

faces numerous challenges in its efforts to address the growing burden of diabetes. Healthcare 

infrastructure in many countries in the region is often inadequate, with limited resources and 

healthcare personnel.  

Diabetes management requires consistent access to medical care, laboratory tests, and 

medications, which may be unavailable or unaffordable for many individuals, particularly those 

living in rural areas (Atun, 2010). Access to care is further hampered by economic disparities 

and transportation difficulties. The cost of healthcare, including medications, can be prohibitive 

for many individuals in SSA, leading to poor adherence to treatment regimens. Moreover, there 

are issues of awareness and education, as many individuals may not be aware of the importance 

of early diabetes detection and management. 

2.4 Prevalence of Type two Diabetes Mellitus in South Africa 

In South Africa (SA), the elderly population is particularly susceptible to type 2 diabetes, and 

this susceptibility remains a critical health concern. While specific data on diabetes prevalence 

among the elderly in SA post-2013 is limited, it is widely recognized that age constitutes a 

significant risk factor for the development of type 2 diabetes. With the ongoing increase in life 

expectancy, the elderly population in SA is on the rise, and this demographic shift plays a 

pivotal role in the heightened prevalence of diabetes among the aged (IDF, 2019). According 

to the International Diabetes Federation (IDF), the 2019 statistics for SA indicate an overall 

diabetes prevalence rate of 4.6%. These figures encompass both urban and rural areas, 

signifying a nation grappling with the diabetes challenge on multiple fronts (IDF, 2019).  



17 
 

Unfortunately, precise figures specifically targeting the elderly population are not readily 

available in recent data. Nevertheless, it is well-established that the prevalence of diabetes 

among the elderly exceeds that among younger age groups, emphasizing the pronounced 

impact on this demographic. This increase in life expectancy coupled with lifestyle changes 

has resulted in a substantial number of elderly South Africans living with type 2 diabetes (Peer, 

2012). Recent trends in SA's diabetes landscape have unveiled a troubling upward trajectory in 

prevalence, driven by a multitude of factors. The shift towards more sedentary lifestyles and 

the transformation of dietary habits, characterized by the consumption of high-calorie, low-

nutrient diets, have disproportionately affected the elderly.  

The process of urbanization, which often leads to alterations in diet and physical activity 

patterns, has significantly contributed to these trends. While regional variations may exist due 

to disparities in lifestyle, genetics, and healthcare access, the overarching trend in SA indicates 

a surge in diabetes prevalence, particularly among the elderly (Levett, 2011). SA's healthcare 

system confronts considerable challenges in addressing the growing burden of type 2 diabetes 

among the elderly. The healthcare infrastructure faces mounting pressure due to the surging 

demand for diabetes care and management. Elderly individuals often necessitate more 

extensive and specialized care to effectively manage their diabetes. Given the intricate 

healthcare needs of the elderly population, which may encompass comorbidities and age-

related complications, the healthcare system must be adaptable and expand its capacity (Levett, 

2011).  

The elderly in SA encounter formidable challenges in accessing healthcare, medications, and 

regular monitoring, particularly those residing in rural areas. Socioeconomic disparities further 

compound the issues related to accessing healthcare services, resulting in disparities in diabetes 

management within the elderly population. SA is witnessing an escalating prevalence of type 

2 diabetes among the elderly, a trend that mirrors the global situation. The aging population, 



18 
 

combined with evolving lifestyles, underscores the gravity of this issue. While precise statistics 

focusing on the elderly segment may be somewhat elusive, the overall burden of diabetes in 

SA remains substantial. The prevalence of DM in South Africa varies across communities, 

impacting healthcare and individuals differently. Urban areas, such as Johannesburg and Cape 

Town, often exhibit higher DM prevalence due to factors like sedentary lifestyles and poor 

dietary habits (Mutyambizi et al., 2020). These communities face increased healthcare costs 

and strain on healthcare systems in managing DM and its complications (Mutyambizi et al., 

2020). Conversely, rural communities, such as those in the Eastern Cape or Limpopo provinces, 

may encounter challenges in accessing healthcare services, leading to undiagnosed and 

untreated DM cases (Mayosi et al., 2012). Additionally, socio-economic disparities contribute 

to differential impacts, with marginalized communities experiencing greater barriers to 

diabetes care and management (Mayosi et al., 2012). Addressing these disparities requires 

tailored interventions focused on improving access to healthcare, promoting healthy lifestyles, 

and addressing social determinants of health. In South Africa, the expanding prevalence of 

T2DM goes beyond immediate health implications, placing substantial pressure on healthcare 

systems and adversely affecting patient outcomes. Recent research by Saeedi et al. (2020) 

underscores that mortality linked to diabetes is a significant concern in South Africa, 

necessitating effective intervention strategies.  

Furthermore, healthcare disparities within South Africa compounds the T2DM challenge, 

disproportionately affecting underserved communities. The healthcare system in South Africa 

reflects many issues common to other Southern African countries, such as disease burden, lack 

of infrastructure, and societal inequalities (Padarath et al., 2020). However, ongoing reforms 

in South Africa, particularly in preparation for a National Healthcare Insurance, offer 

opportunities to learn from effective care models in other African contexts. Kenya's innovative 

approach to primary healthcare, including significant private sector involvement, is worth 



19 
 

studying (Chuma et al., 2018). Collaboration between the government and non-state actors is 

crucial for integrated and effective care delivery (Bhatia & Rifkin, 2014). Grassroots social 

innovation should be supported and connected to policymakers (Shah et al., 2020). South 

Africa, despite facing significant challenges like inequality and poverty, possesses strong 

infrastructure, media freedom, civil society engagement, and economic stability (Economist 

Intelligence Unit, 2021). With these assets, it can make significant progress in addressing 

healthcare and other societal challenges, contributing to African solutions for African 

problems. Understanding these dynamics and identifying regional commonalities and 

differences among South Africa, SSA, and Ghana is essential for devising targeted 

interventions to mitigate T2DM progression and enhance health outcomes across these regions.  

Effectively addressing this challenge necessitates a healthcare system that can adapt and 

expand to meet the distinctive healthcare needs of the elderly diabetic population. 

2.5 Risk factors of Type two Diabetes Mellitus 

Type two Diabetes Mellitus poses a substantial global health challenge, marked by insulin 

resistance and impaired insulin secretion. Recognizing the risk factors linked to T2DM is 

imperative for prevention, timely detection, and efficient management of the disease.  Obesity 

and sedentary behaviour stand out as a prominent risk factors for T2DM. Excessive adiposity, 

especially abdominal obesity, contributes to insulin resistance—a pivotal precursor to T2DM 

(Abdul-Ghani et al., 2015). Sedentary lifestyles exacerbate this risk by fostering weight gain 

and diminishing insulin sensitivity (Hu et al., 2018). Genetic predisposition also significantly 

influences T2DM susceptibility. Family history serves as a robust predictor of the disease, with 

specific genetic variants affecting insulin signalling pathways or beta-cell function (Khera et 

al., 2019; Zeggini et al., 2017). Unhealthy dietary habits play a substantial role in T2DM risk. 

High consumption of processed foods, sugary beverages, and saturated fats elevates the 

likelihood of T2DM development. Diets deficient in fibre and rich in refined carbohydrates 



20 
 

further exacerbate insulin resistance and T2DM risk (Schulze et al., 2016). Advancing age is a 

well-established risk factor for T2DM, with prevalence escalating notably in older populations 

(International Diabetes Federation, 2019). Ethnicity also contributes, with specific ethnic 

groups—such as African Americans, Hispanics, and Indigenous populations—having a higher 

risk of T2DM compared to Caucasians (Narayan et al., 2016). Early-life factors, including low 

birth weight and exposure to gestational diabetes, contribute to T2DM risk later in life (Harder 

et al., 2017). Individuals born with low birth weight or those exposed to gestational diabetes 

during fetal development are more prone to developing T2DM due to programmed metabolic 

abnormalities. 

Understanding these risk factors is imperative for implementing preventive strategies and 

targeted interventions to alleviate the burden of T2DM on individuals and healthcare systems. 

Addressing these risk factors through lifestyle modifications, genetic screening, and early 

intervention programs can effectively mitigate the impact of T2DM and enhance health 

outcomes globally. 

2.6. Implications of type two Diabetes Mellitus 

Type two Diabetes Mellitus exerts significant implications on both individuals and healthcare 

systems globally. Firstly, T2DM significantly increases the risk of various comorbidities and 

complications, including cardiovascular diseases, neuropathy, nephropathy, and retinopathy 

(ADA, 2019). These complications not only reduce the quality of life but also pose substantial 

economic burdens on healthcare systems due to increased hospitalizations and long-term care 

requirements (Kovacs et al., 2020). Moreover, T2DM is associated with higher mortality rates 

compared to the general population, primarily due to its cardiovascular complications (Mata-

Cases et al., 2016). Individuals with T2DM face a shortened life expectancy, highlighting the 

severity of the disease and the importance of effective management strategies. 



21 
 

T2DM also impacts mental health, leading to increased rates of depression and anxiety among 

affected individuals (Nouwen et al., 2019). The psychological burden of managing a chronic 

condition coupled with the fear of complications can significantly diminish overall well-being. 

Furthermore, T2DM imposes considerable economic consequences on both individuals and 

society. Direct medical costs associated with T2DM management, including medications, 

hospitalizations, and physician visits, are substantial (Zhou et al., 2018). Additionally, indirect 

costs, such as loss of productivity and disability, further exacerbate the economic burden of the 

disease (Bommer et al., 2018). The implications of T2DM extend beyond the individual level 

to societal and healthcare system levels. The increasing prevalence of T2DM strains healthcare 

resources, leading to overwhelmed healthcare facilities and longer wait times for essential 

services (Khunti et al., 2019). Moreover, disparities in access to healthcare exacerbate existing 

inequalities, particularly among marginalized populations. 

Addressing the implications of T2DM requires a multifaceted approach. Effective management 

strategies, including lifestyle interventions, pharmacotherapy, and regular monitoring, are 

essential for preventing complications and improving outcomes (Cosentino et al., 2020). 

Moreover, public health initiatives aimed at promoting healthy behaviours, early detection, and 

access to affordable healthcare are crucial for reducing the burden of T2DM on individuals and 

society (Boyle et al., 2017). T2DM poses significant implications on individuals, healthcare 

systems, and society as a whole. Addressing the multifaceted consequences of T2DM requires 

comprehensive strategies that encompass prevention, early detection, and effective 

management. By prioritizing public health initiatives and ensuring equitable access to 

healthcare, we can mitigate the impact of T2DM and improve outcomes for affected 

individuals. 



22 
 

2.7 The progression of Type two Diabetes Mellitus in South Africa 

T2DM has emerged as a critical public health concern globally, and SA is no exception to this 

alarming trend. SA has witnessed a substantial increase in the prevalence of T2DM over the 

past few decades (Levitt et al.,2016), highlighting the escalating burden of the disease across 

both urban and rural areas and attributing this rise to the shift from traditional to more 

westernized lifestyles. This transition is characterized by sedentary behaviours and unhealthy 

dietary patterns, contributing significantly to the increasing prevalence of T2DM (Mutyambizi 

et al., 2019). According to Mayosi et al. (2012), there is an emphasis on the disproportionate 

impact on individuals with lower socioeconomic status, limited access to healthcare, and 

inadequate health literacy.  

Economic disparities exacerbate the prevalence, diagnosis, and management of T2DM, posing 

challenges to public health initiatives. Genetic predisposition interacts intricately with 

environmental factors in the development of T2DM. Matsha et al. (2017) underscore the 

complex interplay of genetic susceptibility, including polymorphisms in genes related to 

insulin resistance and beta-cell function. Meanwhile, urbanization, dietary choices, and 

physical inactivity further compound genetic risks, contributing to the surging rates of T2DM 

in SA. The progression of T2DM in South Africa is hampered by significant healthcare 

challenges.  

Hall et al. (2011) point out that limited resources, inadequate infrastructure, and workforce 

shortages impede effective prevention and management strategies. Access to diabetes care, 

particularly in rural areas, is a critical issue that necessitates urgent attention to ensure equitable 

healthcare provision. However, cultural and behavioural factors contribute significantly to 

T2DM progression in SA. Raubenheimer et al. (2018) emphasize the impact of traditional 

beliefs about health, cultural norms regarding food choices, and lifestyle behaviours on 

diabetes prevalence. Understanding and addressing these cultural influences are crucial for the 



23 
 

development of effective and culturally sensitive interventions. Efforts to mitigate the 

progression of T2DM in SA involve implementing preventive strategies and interventions.  

Oosthuizen et al. (2020) discuss community-based education, lifestyle modification programs, 

and policy changes as essential components of a comprehensive approach. Early detection 

through screening and effective management strategies are imperative in addressing the 

growing challenge of T2DM in the country. The progression of T2DM in SA is a multifaceted 

issue influenced by epidemiological trends, socioeconomic disparities, genetic and 

environmental factors, healthcare challenges, and cultural dimensions. Addressing this 

escalating public health concern requires a holistic approach that integrates preventive 

strategies, healthcare improvements, and culturally sensitive interventions. Future research and 

policy measures should focus on evaluating the effectiveness of targeted interventions to 

alleviate the impact of T2DM on the SA population. 

2.8 Relationship Between the Progression and Risk Factors of Type two Diabetes Mellitus  

T2DM represents a significant and growing global health challenge, characterized by a 

complex interplay of genetic, lifestyle, and environmental factors. The progression of T2DM 

is a dynamic continuum, encompassing a series of metabolic alterations that culminate in 

impaired glucose metabolism and the manifestation of clinical symptoms (ADA, 2022). This 

progression typically involves the initial development of insulin resistance, where cells become 

less responsive to insulin, and the subsequent failure of pancreatic beta cells to produce 

adequate insulin to compensate for this resistance.  

Genetic factors play a pivotal role in T2DM susceptibility, with heritability estimated to be 

around 40-70% (Fuchsberger et al., 2016). Family history, coupled with specific genetic 

variants, significantly influences an individual's likelihood of developing T2DM. Obesity, 

particularly abdominal or visceral adiposity, is a prominent modifiable risk factor for T2DM. 



24 
 

Adipose tissue, especially in the abdominal region, secretes adipokines, contributing to insulin 

resistance and metabolic dysfunction. Sedentary lifestyles and lack of regular physical activity 

contribute significantly to the progression of T2DM. Exercise improves insulin sensitivity and 

plays a crucial role in weight management (Colberg et al., 2016).  

Dietary habits, especially those high in refined carbohydrates, sugars, and saturated fats, are 

linked to insulin resistance and contribute to the development of obesity and T2DM (Ley et al., 

2014). Advancing age is a non-modifiable risk factor, with T2DM prevalence increasing with 

age (Gregg et al., 2014). Additionally, ethnicity influences susceptibility, with certain 

populations, such as African Americans and Hispanics, having a higher risk. Women with a 

history of gestational diabetes mellitus (GDM) face an elevated risk of developing T2DM later 

in life. GDM serves as an early indicator, highlighting both genetic and environmental 

influences. Hypertension and cardiovascular disease are closely intertwined with T2DM, 

forming a complex relationship where each condition exacerbates the progression of the others 

(Gregg et al., 2014).  

The relationship between T2DM progression and its risk factors is intricate, with each factor 

contributing to the development and exacerbation of the disorder. Genetic predisposition 

interacts with environmental factors, influencing an individual's susceptibility to insulin 

resistance and beta-cell dysfunction (Colberg et al., 2016; Ley et al., 2014). Lifestyle factors, 

such as obesity, physical inactivity, and poor dietary choices, directly impact insulin sensitivity 

and contribute to the metabolic dysfunction observed in T2DM. Age and ethnicity further shape 

the risk profile, emphasizing the importance of considering demographic factors in preventive 

and management strategies. Gestational diabetes not only poses immediate risks during 

pregnancy but also serves as a predictive factor for future T2DM risk.  



25 
 

The association between T2DM and cardiovascular complications underlines the systemic 

nature of the disease and the need for comprehensive management strategies. Given the 

multifactorial nature of T2DM, preventive strategies must address the various risk factors 

involved. Implementing lifestyle modifications, including promoting regular physical activity 

and encouraging healthy dietary patterns, is crucial (Colberg et al., 2016; Ley et al., 2014). 

Early identification of individuals with a genetic predisposition or a history of gestational 

diabetes allows for targeted interventions and close monitoring. Public health initiatives should 

focus on raising awareness about the impact of obesity, emphasizing the importance of 

maintaining a healthy body weight. Additionally, healthcare professionals play a vital role in 

promoting regular screening, particularly in high-risk populations, to facilitate early diagnosis 

and intervention.  

The relationship between the progression of T2DM and its risk factors is intricate, involving a 

dynamic interplay of genetic, lifestyle, and environmental influences. Recognizing the role of 

genetic predisposition, obesity, physical inactivity, unhealthy dietary patterns, age, ethnicity, 

gestational diabetes, and cardiovascular complications is essential for developing effective 

preventive and management strategies. By understanding these relationships, healthcare 

professionals can tailor interventions to address individualized risk profiles, emphasizing the 

importance of early identification and comprehensive lifestyle modifications. Further research 

into the interactions between these factors and their cumulative impact on T2DM progression 

will contribute to refining preventive measures and therapeutic interventions, ultimately 

alleviating the global burden of this pervasive metabolic disorder. 

2.9 Varied theories used for the study of type two Diabetes Mellitus 

Understanding Type two Diabetes Mellitus (T2DM) requires a multifaceted approach that 

integrates various theoretical frameworks. One prominent theoretical framework used in 

studying T2DM is the biopsychosocial model. This model considers biological, psychological, 



26 
 

and social factors in understanding the development and progression of T2DM (Adler et al., 

2016). It acknowledges the complex interplay between genetic predisposition, psychosocial 

stressors, and environmental influences on T2DM risk and outcomes. Another theoretical lens 

commonly employed is the social determinants of health (SDH) framework. This framework 

emphasizes how social, economic, and environmental factors shape health outcomes, including 

T2DM prevalence and disparities (Berkowitz et al., 2017). By examining factors such as 

socioeconomic status, access to healthcare, and neighbourhood characteristics, researchers can 

better understand the social determinants driving T2DM disparities. 

In addition, the health behaviour change theory is integral to understanding T2DM prevention 

and management. The transtheoretical model (TTM), for example, delineates stages of 

behaviour change, such as precontemplation, contemplation, preparation, action, and 

maintenance (Prochaska et al., 2016). Applying TTM helps elucidate individual readiness to 

adopt and maintain health-promoting behaviours, such as dietary changes and physical activity, 

crucial for T2DM prevention and control. The ecological systems theory provides a 

comprehensive framework for examining T2DM within the context of broader environmental 

influences. This theory considers the interaction between individuals and their microsystems 

(e.g., family, peers), mesosystems (e.g., community, healthcare settings), ecosystems (e.g., 

policies, societal norms), and macrosystems (e.g., cultural values, economic structures) 

(Bronfenbrenner, 2017). By analysing these multiple levels of influence, researchers can 

identify strategies for promoting health at various ecological levels and mitigating T2DM risk 

factors. The precision medicine approach has gained traction in T2DM research, emphasizing 

personalized interventions based on individual genetic, clinical, and lifestyle factors (Kolb et 

al., 2017). By integrating genomic data, biomarkers, and patient characteristics, researchers 

aim to tailor interventions to the specific needs of individuals with T2DM, optimizing treatment 

outcomes and reducing adverse effects. The study of T2DM encompasses diverse theoretical 



27 
 

frameworks that illuminate different aspects of the disease. From the biopsychosocial model to 

the social determinants of health framework, and from health behaviour change theories to 

ecological systems theory and precision medicine approaches, researchers utilize a wide array 

of theoretical perspectives to advance our understanding of T2DM. By integrating these 

theories, researchers can develop comprehensive strategies for preventing, managing, and 

mitigating the impact of T2DM on individuals and populations. 

 

 

 

 

 

 

 

 

 

 

 



28 
 

 

 

CHAPTER THREE 

3.0 METHODOLOGY 

3.1 Introduction 

The strategies, techniques, and analyses employed to accomplish the objectives of the study 

are described in this chapter.  

3.2 Study design and source of data  

This was a quantitative study with a longitudinal design to identify the progression of type two 

diabetes among the aged in Agincourt Mpumalanga Province, South Africa. The HAALSI 

(Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South 

Africa) study was designed as a retrospective longitudinal cohort study. This study was 

quantitative and was done through surveys and structured questionnaires.  

The HAALSI study in South Africa explores the correlation between health and aging by 

scrutinizing various factors, including demographics, health conditions, lifestyle choices, 

healthcare utilization, social dynamics, cognitive function, mortality, and biological markers. 

Details such as age, education, chronic diseases, and mental health are considered, alongside 

lifestyle aspects like diet and physical activity. The study provides a comprehensive 

understanding of participants' well-being.  but its evolving nature requires continuous updates 

for the latest insights into the dynamic relationship between health and aging in the studied 

population. 

The HAALSI research took place in the Agincourt sub-district, which is located in rural South 

Africa's Mpumalanga Province (Figure 3.2). Mpumalanga, South Africa, grapples with various 

health conditions beyond diabetes mellitus (DM), including tuberculosis (TB), HIV/AIDS, 



29 
 

hypertension, respiratory diseases, maternal and child health issues, malnutrition, non-

communicable diseases (NCDs), and mental health disorders. These challenges stem from 

factors such as poverty, limited access to healthcare services, environmental conditions, and 

lifestyle factors. Addressing these health conditions requires comprehensive strategies that 

focus on prevention, early detection, and access to quality healthcare services tailored to the 

unique needs of the population in Mpumalanga. 

 

MAP 3.1: MAP OF AGINCOURT STUDY AREA, MPUMLANGA, SOUTH AFRICA 

 

 

MAP OF AGINCOURT STUDY AREA, MPUMLANGA, SOUTH AFRICA  

Data from the HAALSI study was used in this investigation. Data were drawn from the baseline 

and second wave of HAALSI (“Health and Aging in Africa: A Longitudinal study of an 

INDEPTH community”) cohort study. In order to estimate the prevalence and incidence of 

major chronic illnesses in persons 50 years of age and older, HAALSI set out to create 

longitudinal surveys of health, aging, and wellbeing. The eligibility criteria was individuals 

aged 40 and older as of July 1, 2014, and to have lived in the study site continuously for the 12 

 

 

 



30 
 

months preceding the 2013 Health and Demographic Surveillance System (HDSS) census. This 

sample size was determined through a random selection process from those who met the study's 

eligibility criteria. A total of 6,281 persons were selected for recruitment. To maximize the 

linkages with previous HIV and NCD studies in Agincourt, those who participated in earlier 

studies and met the eligibility criteria were selected with 100 percent probability. The 

remainder of the sample was selected randomly from the 2013 census HDSS database, 

stratifying on sex in order to achieve equal numbers of women and men. 

In 2014–2015, the HAALSI baseline round was carried out. A baseline response rate of 87% 

was used to randomly sample about 40% of all residents in 27 villages who were 40 years of 

age or older. In particular, 5,059 out of 6,281 men and women who were sampled from these 

villages completed house interviews; the remaining individuals were either unable to 

participate or refused (478), could not be located (353), or were disqualified because of death 

or out-migration (391) (Gómez-Olivé et al., 2018). The study included blood draws, 

anthropometric and psychological testing. The interviews were administered using a structured 

Computer-Assisted Personal Interview (CAPI) during a roughly three-hour home visit. In 2018 

and 2021, the second and third waves were carried out in succession. The study population for 

the HAALSI research included 12,875 men and women.  

 

3.3 Data collection method and instruments 

The HAALSI study employed a multifaceted approach to collect data. It used various tools and 

methods to comprehensively capture information related to health, aging, and other relevant 

factors. Here is a breakdown of the data collection components: 

Surveys and Questionnaires: The researchers assessed information from structured surveys 

and questionnaires. These were carefully designed to collect self-reported data on a wide range 



31 
 

of topics, such as health status, lifestyle, socioeconomic factors, and more. The questions were 

tailored to the study's specific research goals, allowing participants to share their experiences 

and insights. 

Clinical Assessments: The researcher assessed information from the study which included 

physical health assessments. Instruments like blood pressure cuffs, scales for measuring weight 

and height, and devices for calculating body mass index (BMI) were used for the assessment. 

These assessments provided objective and quantitative data on participants' physical well-

being, offering insights into their health status. 

The study was divided into three sections: (i) gathering data on households, socioeconomic 

status, and individual participants; (ii) performing assessments of physical, clinical, and 

cognitive functioning; and (iii) collecting blood samples in the form of dried blood spots 

(DBSs) for point-of-care testing. Participants were asked to take off all outer layers of clothes 

and sit for a minimum of five minutes before having their blood pressure measured. Next, using 

an Omron M6W automated cuff, the field worker took three blood pressure readings every two 

minutes. The second and third blood pressure values were used to compute the mean blood 

pressure. Field personnel obtained droplets of blood, which were then examined for HIV 

infection status (one to three drops) and preserved DBS on Whatman 903 paper. 

HTN was identified as self-reported current antihypertensive medication use or a mean systolic 

blood pressure (SBP) of ≥140 mm Hg or mean diastolic blood pressure (DBP) of ≥90 mm Hg. 

We sought to include all individuals who fit the criteria for HTN at either the baseline or follow-

up, thus 12 contradictory responses for self-reported use of anti-HTN medication were recorded 

as positive at follow-up if the respondent claimed use at baseline but denied use at follow-up 

(n = 51). 



32 
 

Diabetes was a glucose measurement that met the threshold (≥ 7 mmol/l [126 mg/dL] in fasting 

group [defined as > 8 hours], and ≥11.1 mmol/l [200 mg/dL] in non-fasting [“random or 

casual”] group. Criteria used to define overweight for the HAALSI study were the ones of the 

WHO, which considers overweight when BMI ≥25 to 29,9 kg/m2 and obesity when BMI ≥30 

kg/m. 

Bio specimen Collection:  The researchers assessed biological samples, such as blood and 

urine, that was collected from participants. Specialized instruments and tools, including 

needles, vials, and containers for specimen storage, was used to ensure the safe and effective 

collection of these samples. These bio specimens were essential for analysing biomarkers and 

genetic factors related to health and aging. 

3.4 Data handling  

The HAALSI study demonstrated a strong commitment to responsible data management, 

security, and participant confidentiality. It employed a wide array of data collection methods, 

ensure data accuracy through rigorous quality control, and conducted meaningful data analysis 

using appropriate statistical techniques. Robust data security measures were put in place, 

including encryption, restricted access, and safeguards against cyber threats. Participant 

confidentiality was upheld through anonymization, ethical reviews, and data sharing 

agreements. Research findings was presented in aggregate or anonymized form to prevent 

individual identification. Informed consent was obtained from participants, in accordance with 

ethical standards. Data access was restricted to authorized personnel who adhered to strict 

confidentiality agreements. The study's practices served as a model for ethical and responsible 

research, emphasizing the importance of safeguarding participant privacy and data integrity 

while pursuing valuable insights into health and aging dynamics. The data was cleaned 

thoroughly with Stata 18 software which helped achieved all statistics for T2DM in SA. 



33 
 

3.5 Statistical analysis 

The data collected was organised and cleaned using Stata 18 software. Starting with descriptive 

statistics to summarize data, regression analysis explored the relationships between variables, 

allowing for the identification of key predictors of health and aging. Frequency distribution 

tables was done for all variables of interest such as age, gender and so on. Chi square analysis 

was used to determine if there are associations between the progression of T2DM and the 

factors (health, demographics and lifestyle) over the 8-year period. Again, logistic regression 

was used to determine the strength of significant associations between the progression of 

T2DM among the aged and the several factors affecting them. 

3.6 Ethical issues  

Ethics are simply principles of right conduct that must guide researchers in all phases of a 

research. Before the study began, ethical clearance was obtained from the Ethical Review 

Committee of Ensign Global College(SN-245). Also, administrative permission was sought 

from the National Institute on Aging conducted by the Harvard Centre for Population and 

Development Studies in partnership with Witwatersrand University. Throughout the research 

process, ethical principles were observed to ensure that the outcomes of the study meet set 

standards. 

3.7 Limitations of the study 

Wave 1 

1.Measurement Methods 

• Reliance on single measurements of fasting glucose levels and HbA1c to diagnose 

T2DM, which might not capture temporal variations or provide a comprehensive 

assessment. 



34 
 

• Self-reported data on medication use and medical history might have introduced recall 

bias. 

2.Population and Sampling 

• The study focused on an older, rural population, which might limit the generalizability 

of the findings to other demographics, such as urban or younger populations. 

3. Incomplete Data 

• Missing data due to non-response or inability to measure fasting glucose in some 

participants could bias results. 

• Lack of detailed dietary and physical activity data which are crucial in understanding 

T2DM risk factors. 

Wave 2 

1.Follow-up Issues 

• Attrition over time, as some participants were lost to follow-up, moved away, or died, 

potentially leading to bias in longitudinal analysis. 

• Potential for survivor bias, as those who remained in the study might differ significantly 

in health status compared to those who did not. 

2.Measurement Consistency 

• Variability in measurement techniques or tools used between waves could affect 

comparability of data over time. 

• Continued reliance on self-reported data, which might still introduce bias. 

3.Environmental and Social Factors 



35 
 

• Limited data on environmental and social determinants of health, such as detailed 

socioeconomic status, cultural practices, and local healthcare infrastructure, which 

could impact T2DM risk and management. 

3.8 Assumptions 

• The HAALSI study assumed that the prevalence of T2DM in rural South Africa would 

be influenced by traditional risk factors such as age, obesity, and physical inactivity. It 

also assumed that cultural and lifestyle differences in rural populations would affect 

T2DM incidence and management differently compared to urban areas. 

• The study assumed that self-reported data on medication use and medical history would 

be reliable indicators for assessing T2DM prevalence and management among 

participants. It also presumed that the methods used to measure fasting glucose levels 

and HbA1c were sufficient for diagnosing T2DM accurately in this population. 

• HAALSI assumed that the demographic and health data collected from older, rural 

South Africans would provide meaningful insights into the broader patterns of T2DM 

prevalence and risk factors. The study also assumed that longitudinal follow-up data 

would help identify trends and causal relationships in T2DM development over time. 

 

 

 

 

 

 



36 
 

CHAPTER 4 

4.0 RESULTS 

4.1. Progression of type two diabetes among the aged in Agincourt Mpumalanga 

province, South Africa 

The study's results, which were formulated in accordance with the specified objectives, are 

presented in this chapter. It comprises lifestyle, health, and sociodemographic aspects of the 

respondents. The chapter also lists the risk factors for both waves 1 and 2 as well as the 

prevalence of type 2 diabetes. This section also looks at the relationship between diabetes and 

some selected risk factors. 

4.2 Socio-demographic characteristics of the HAALSI population 

According to Table 4.1, the socio demographic factors of the HAALSI population in Wave 1 

for age distribution, the majority of participants fall within the age groups of 50-59 (27.85%) 

and 60-69 (26.56%). There is a notable proportion of participants aged 70 and above, with 

18.6% falling into the 70-79 age group and 7.43% aged 80 and above. The largest marital status 

category is currently married/partnered, comprising 55.05% of participants. A significant 

portion of the sample is widowed, representing 27.74% of participants. Separated/divorced 

individuals make up 12.29% of the sample, while never-married individuals constitute only 

4.92%. 

The majority of participants have received no formal education (42.44%) or have completed 

some primary education (36.66%). A smaller percentage have attained some secondary 

education (13.15%), while only 7.75% have completed secondary education or more. The 

majority of participants are from South Africa, accounting for 69.43% of the sample. 

Participants from Mozambique or other countries constitute 30.57% of the sample. 



37 
 

The largest employment status category is "Retired," with 71.08% of participants falling into 

this group. Employed individuals make up 17.42% of the sample, while homemakers represent 

11.49%.  The socio-demographic analysis of participants at follow-up in the HAALSI study 

wave 2 reveals distinctive trends in marital and employment statuses. 

A significant proportion, comprising 47.51% of participants, were currently married, indicating 

prevalent stable relationships within the community. Additionally, 33.62% of participants are 

widowed, reflecting experiences of spousal loss among a substantial segment of the population. 

Separated/divorced individuals constitute 12.03% of participants, underscoring instances of 

relationship dissolution or disruption. A smaller fraction, representing 6.84% of participants, 

have never married, highlighting a segment of the population yet to enter into marital unions. 

The majority of participants, accounting for 83.43%, are retired, signalling prevalent economic 

challenges and disengagement from the labour market within the community. Conversely, 

14.97% of participants are employed, indicating a subset of individuals actively engaged in 

remunerative work or occupations. A smaller yet notable proportion, constituting 1.6% of 

participants, are categorized as homemakers, primarily involved in domestic duties and 

household management. Demographic variables don’t change between the Waves, it is only 

the permanent variables that were used and according to the study, the report is on only those 

that changed between the surveys which are marital status and employment status for Wave 2. 

 

 

 

 

 



38 
 

Table 4.1 Socio-Demographic factors of the HAALSI population 

VARIABLE Wave 1 n (%) Wave 2 n (%) 

AGE 

40-49 366(19.56%)  

50-59 521(27.85%)  

60-69 497(26.56%)  

70-79 348(18.6%)  

80+ 139(7.43%)  

MARITAL STATUS 

Never married  92(4.92%) 128(6.84%) 

Separated/Divorced 230(12.92%) 225(12.03%) 

Widowed 519(27.74%) 625(33.62%) 

Currently married 1030(55.05%) 889(47.51%) 

YEARS OF EDUCATION 

No formal education 794(42.44%)  

Some primary (1-7) 686(36.66%)  

Some Secondary (8-11) 246(13.15%)  

Secondary/more (12+) 149(7.75%)  

COUNTRY OF ORIGIN 

South Africa 1299(69.43%)  

Mozambique/others 572(30.57%)  

EMPLOYMENT STATUS 

Employed 326(17.42%) 280(14.97%) 

Retired 1330(71.08%) 1561(83.43%) 

Homemaker 215(11.49%) 30(1.6%) 



39 
 

 

4.3 Prevalence of health risk factors of diabetes  

In Wave 1, the prevalence of obesity, as indicated by the BMI category, shows that 11.99% of 

participants fell into the obese category. This translates to 70 individuals out of the total. In 

Wave 2, the prevalence of obesity remains similar, with 14.31% of participants falling into the 

obese category, representing 80 individuals. Hypertension is explained in two folds, systolic 

and diastolic blood pressure. For systolic blood pressure in Wave 1, 8.62% of participants had 

values ≥140 whereas in Wave 2, this proportion increased to 17.11%. For diastolic blood 

pressure in Wave 1, 7.46% of participants had diastolic blood pressure of ≥90. While in Wave 

2, this proportion remained relatively stable at 11.90%. The prevalence of obesity remains high 

and relatively stable between the two waves, with a slight increase in Wave 2. However, 

concerning trends observed in hypertension, both systolic and diastolic blood pressure levels 

indicate an increase in the prevalence of hypertension in both Waves. According to Table 4.2 

the diabetes prevalence and associated risk factors at Wave 1 and follow-up in the HAALSI 

study for Wave 2, for all participants, the prevalence of diabetes at follow-up is 11.49%, with 

215 individuals affected and 1656 unaffected. 

Individuals who are widowed have the highest prevalence of diabetes (11.92%), followed 

closely by those who are currently married/partnered (12.04%). Retired individuals have a 

higher prevalence of diabetes (12.36%) compared to employed individuals (7.86%). 

Participants with high-risk waist circumference and obesity have higher prevalence rates of 

diabetes. Individuals with systolic blood pressure ≥140 mmHg and diastolic blood pressure 

≥90 mmHg exhibit elevated prevalence rates of diabetes. Participants with glucose levels ≥11 

mmol/L have a prevalence rate of 100%, indicating that all individuals with elevated glucose 

levels have been diagnosed with diabetes. Participants with lower daily fruit and vegetable 



40 
 

intake tend to have higher prevalence rates of diabetes, especially those consuming 0 servings 

per day. 

 

Table 4.2 Prevalence of health and lifestyle risk factors of Diabetes 

 

VARIABLE Wave 1(%) Wave 2(%) 

BODY MASS INDEX 

Underweight  0 2.41 

Normal weight  5.40 7.63 

Overweight  8.14 14.98 

Obese 11.99 14.31 

ALCOHOL INTAKE 

Yes 6.35 9.98 

No 9.27 12.41 

SMOKING STATUS 

Yes 5.41 7.67 

No 8.70 12.18 

HEMOGLOBIN 

<8 15.00 6.76 

<11 > 8 12.73 11.37 

>11 7.01 11.79 

GLUCOSE LEVELS 

< 11 2.55 2.53 

> 11 100 100 



41 
 

BLOOD PRESSURE LEVELS 

Systole   

<140 7.60 9.98 

>140 8.62 17.11 

Diastol   

<90 8.20 11.40 

>90 7.46 11.90 

WAIST CIRCUMFERENCE 

Low risk 2.39 3.99 

High risk 9.24 12.93 

 

 

Table 4.3. Fruits and vegetables intake among the HAALSI population 

VARIABLES Wave 1(%) Wave 2(%) 

FRUITS INTAKE 

0 6.79 12.29 

1-2 15.71 25.39 

3-4 16.76 17.87 

5-6 25.52 27.59 

7 7.75 13.04 

VEGETABLES INTAKE 

0 6.10 6.10 

1-2 17,75 17.75 

3-4 13.19 13.19 



42 
 

5-6 21.23 21.23 

7 8.33 8.33 

 

4.4 Diabetes prevalence Wave 1 and 2 

According to Figure 4.1, the diabetes prevalence of participants at baseline in the HAALSI 

study (Wave 1) indicates that that 8.02% (150 participants) were diagnosed with diabetes, while 

the majority 91.98% (1721 participants) did not have the condition. Also, Wave 2 indicates 

that 11.49% (215 participants) were diagnosed with diabetes, while 88.51% did not have the 

condition. A notable trend was indicated by the fact that the overall prevalence of diabetes rose 

from 8.02% in Wave 1 to 11.49% in Wave 2. From the analysis the difference in diabetes 

prevalence in both waves is 3.47. 

 

Figure 4.1 Diabetes Prevalence in Wave 1 & 2 of the HAALSI study 

 

8.02%
11.49%

91.98%
88.51%

0

10

20

30

40

50

60

70

80

90

100

WAVE 1 WAVE 2

P
er

ce
n
ta

g
es

Diabetes Prevalence

OVERALL DIABETES PREVALENCE

 YES

NO



43 
 

 

4.5 Progression of T2DM in Wave 1 and 2 

According to figure 4.2of the HAALSI study, the progression of type 2 diabetes mellitus 

(T2DM) among participants reveals notable trends. Out of the 1,871 individuals studied, 

97.59% were newly diagnosed with T2DM at baseline (wave 1), indicating a significant 

incidence rate. Notably, 97% of those initially without diabetes in wave 1 developed T2DM by 

wave 2, highlighting the rapid progression within the HAALSI cohort. Moreover, during the 

follow-up period, 2.41% of the initially identified cases advanced, totalling 45 individuals.  

 

Figure 4.2: Progression of T2DM in Wave 1 and 2 from the HAALSI study 

 

 

0

20

40

60

80

100

120

New cases Progressed cases

P
er

ce
n
ta

g
es

Cases

Progression of T2DM

End of follow up stage (Wave 2)



44 
 

4.6 Multivariate logistic regression 

In this study, the factors that predicted the presence of diabetes in older adults were body mass 

index, waist circumference, and participant age. From the multivariate analysis run, 

participants aged 40-49 years were 0.6 (AOR=0.35; CI=0.14-0.90) times less likely to be 

diabetic when compared to those who were less than the age of 40 adjusting for all the other 

variables. Those aged 70-79 years were 1.47 times more likely to be diabetic (AOR=1.47; 

CI=0.71-3.05). 

Compared to participants with a waist circumference below 80cm, high-risk persons or those 

with a waist circumference >80cm had a 2.39 (AOR=2.39; CI=1.09-5.23) times higher 

likelihood of having diabetes. Furthermore, compared to their underweight counterparts, those 

with normal and overweight body mass indices were 0.47 (AOR=0.53; CI=0.33-0.85) and 0.34 

(AOR=0.66; CI=0.44-0.99) times less likely to have diabetes, respectively. 

According to Table 4.4 below, this is the multivariate logistic regression analysis conducted at 

Wave 1 of the HAALSI study. 

Individuals aged 40-49 years have significantly lower odds of diabetes compared to those under 

40 years (AOR: 0.35, p-value: 0.030). There are no significant differences in diabetes odds 

among other age groups compared to those under 40 years. No significant associations are 

observed between marital status categories and diabetes odds. There are no significant 

associations between different levels of education and diabetes odds. Participants from 

Mozambique/Others have lower odds of diabetes compared to those from South Africa, but the 

difference is not statistically significant. There are no significant associations between 

employment status categories and diabetes odds. Participants with high-risk waist 

circumference have significantly higher odds of diabetes compared to those with low-risk waist 

circumference (AOR: 2.39, p-value: 0.030). Normal BMI is associated with significantly lower 



45 
 

odds of diabetes compared to being underweight (AOR: 0.53, p-value: 0.009). Being 

overweight also shows lower odds of diabetes, but the association is marginally significant (p-

value: 0.050). No significant associations are observed between blood pressure, hemoglobin 

levels, smoking, alcohol consumption, daily fruit intake, and daily vegetable intake, and 

diabetes odds. These findings highlight the significant associations between health-related 

characteristics, such as waist circumference and BMI, and diabetes odds, while other factors 

like age, education, marital status, country of origin, employment status, and lifestyle 

characteristics show no significant associations with diabetes odds at Wave 1 of the HAALSI 

study. 

Table 4.4 Multivariate logistic regression (wave 1) 

 

Multivariate logistic regression 

  
Variables Categories AOR (CI) P-value 

Age group < 40 Ref 

 

 

40-49 0.35(0.14-0.90) 0.030* 

 

50-59 0.71(0.33-1.55) 0.389 

 

60-69 1.16(0.56-2.41) 0.683 

 

70-79 1.47(0.71-3.05) 0.55 

 

80+ 

  
Marital status Never married Ref 

 

 

Separated/divorced 0.88(0.26-2.90) 0.829 

 

Widowed 0.83(0.27-2.54) 0.741 



46 
 

 

Currently married/ partnered 1.17(0.40-3.45) 0.776 

Years of education No formal education Ref 

 

 

Some primary (1-7) 0.92(0.61-1.40) 0.713 

 

Some secondary (8-11) 0.89(0.46-1.69) 0.713 

 

Secondary or more (12+) 1.39(0.63-3.04) 0.415 

Country of origin South Africa Ref 

 

 

Mozambique/Others 0.73(0.46-1.48) 0.172 

Employment status Employed Ref 

 

 

Retired 1.31(0.73-2.36) 0.361 

 

Homemaker 1.36(0.65-2.85) 0.411 

Health-related 

characteristics 

  
Waist circumference Low risk Ref 

 

High risk 2.39(1.09-5.23) 0.030* 

Body mass index Underweight Ref 

 

 

Normal 0.53(0.33-0.85) 0.009* 

 

Overweight 0.66(0.44-0.99) 0.050* 

 

Obese                                                           

  



47 
 

Blood pressure (mean 

systolic) <140 Ref 

 

 

≥140 0.95(0.63-1.45) 0.839 

Blood pressure (mean 

diastolic) <90 Ref 

 

 

≥90 0.93(0.58-1.51) 0.782 

Hemoglobin <8 Ref 

 

 

< 8 ≥ 11 0.83(0.31-2.23) 0.76 

 

≥11 0.41(0.16-1.06) 0.782 

Lifestyle 

characteristics 

   
Smoking (ever 

smoked) Yes Ref 

 

 

No 1.18(0.66-2.11) 0.57 

Alcohol (ever 

consumed alcohol) Yes Ref 

 

 

No 1.25(0.81-1.91) 0.311 

Diet (daily fruit 

intake) 0 Ref 

 

 

1-2 0.99(0.54-1.84) 0.982 



48 
 

 

2 1.16(0.68-1.98) 0.596 

 

3 1.26((0.71-2.21) 0.431 

 

4 0.90(0.43-1.89) 0.787 

 

5 1.17(0.56-2.61) 0.698 

 

6 1.94(0.57-6.58) 0.285 

 

7 0.83(0.39-1.78) 0.632 

Diet (daily vegetable 

intake) 0 Ref 

 

 

1 1.12(0.37-3.36) 0.837 

 

2 1.27(0.46-3.49) 0.645 

 

3 0.82(0.30-2.28) 0.704 

 

4 0.77(0.26-2.26) 0.637 

 

5 1.37(0.46-4.09) 0.571 

 

6 1.53(0.42-5.55) 0.52 

 

7 1.11(0.33-3.73) 0.869 

 

 

 

 



49 
 

CHAPTER 5 

5.0 DISCUSSION 

5.1 Discussion of results 

The study aimed to assess the progression of type 2 diabetes mellitus (T2DM) among the 

elderly population in South Africa, specifically in Mpumalanga, utilizing data from the 

HAALSI study spanning from 2014 to 2022. Here's a breakdown of the objectives and methods 

outlined in the study: 

• Describe the proportion of adults living with diabetes during the initial phase (Wave 1). 

• Monitor and document the development and progression of T2DM among the elderly 

over an 8-year period. 

• Examine the correlation between identified risk factors and the progression of T2DM 

within the elderly population. 

A descriptive method was used and secondary HAALSI was used and cleaned using Stata 

18. A descriptive analysis provided an overview of the data while multivariate analysis was 

likely used to explore the relationship between identified risk factors and the progression 

of T2DM among the elderly. It appears the study aimed to provide insights into the 

prevalence, progression, and associated risk factors of T2DM among the elderly population 

in South Africa over an extended period. The forthcoming chapter will likely present a 

detailed interpretation and summary of the results obtained from the analysis. 

5.2 Socio Demographic factors of the HAALSI population  

The socio-demographic characteristics of participants at baseline (Wave 1) of the HAALSI 

shed light on the diverse composition of the study population. The distribution of participants 

across different age groups revealed a significant representation of older adults in the HAALSI 

cohort. Notably, those aged 50-59 years represented the largest segment (27.85%) (Gómez-



50 
 

Olivé et al., 2018), followed closely by individuals aged 60-69 years (26.56%) and 70-79 years 

(18.6%). This age distribution underscored the relevance of studying health issues, such as 

T2DM, among older age groups in South Africa. Participants' marital status reflected varied 

relationship statuses within the HAALSI cohort. A substantial proportion of individuals were 

currently married or partnered (55.05%), indicating stable unions. However, significant 

percentages were widowed (27.74%) or separated/divorced (12.29%) (Gómez-Olivé et al., 

2018), reflecting experiences of marital dissolution or loss. These marital status categories may 

have influenced health outcomes, including T2DM progression, through factors such as social 

support and lifestyle changes.  

Education levels among HAALSI participants demonstrated considerable diversity, with 

implications for health literacy and access to resources. A portion of participants had received 

no formal education (42.44%) or only completed primary education (36.66%) (Gómez-Olivé 

et al., 2018). Relatively fewer individuals had attained some secondary education (13.15%), 

while the smallest proportion had completed secondary education or higher (7.75%). Education 

played a crucial role in understanding and managing T2DM, influencing factors such as self-

care practices and healthcare utilization. The majority of HAALSI participants originated from 

South Africa (69.43%), reflecting the study's focus on the Agincourt Health and Socio-

Demographic Surveillance System (HDSS) site. However, a significant minority (30.57%) 

hailed from Mozambique or other countries, highlighting the international diversity within the 

cohort (Gómez-Olivé et al., 2018).  

This diversity may have influenced T2DM progression through cultural factors, healthcare 

access, and migration-related stressors. Participants' employment status provided insights into 

their economic circumstances and potential barriers to healthcare access. A considerable 

proportion were unemployed (71.08%), indicating socioeconomic challenges. Additionally, a 

smaller fraction was employed (17.42%), while a notable percentage identified as homemakers 



51 
 

(11.49%) (Gómez-Olivé et al., 2018). Unemployment and financial strain may have impacted 

T2DM management through limitations in accessing medications, healthy foods, and 

healthcare services. The HAALSI research underscored disparities in T2DM burden across 

socio-demographic groups. Individuals with lower education levels, limited access to 

healthcare, and socioeconomic disadvantages were at higher risk of developing and 

experiencing complications from T2DM.  

These findings highlighted the importance of addressing social determinants of health in T2DM 

prevention and management strategies. Furthermore, HAALSI emphasized the need for early 

detection and intervention to mitigate the impact of T2DM on older adults' health and well-

being. Integrated healthcare approaches, community-based interventions, and health promotion 

initiatives were essential for supporting T2DM management among the elderly in South Africa. 

By addressing socio-demographic factors and health disparities, HAALSI aimed to contribute 

to more effective T2DM prevention and control efforts in the region. 

The HAALSI study's follow-up (Wave 2) reveals shifts in socio-demographic characteristics 

among elderly participants in South Africa, shedding light on the evolving dynamics and 

implications for T2DM progression (Gómez-Olivé et al., 2018). Marital status changes notably, 

with a decrease in currently married/partnered individuals (from 55.05% to 47.51%) and an 

increase in widowed participants (from 27.74% to 33.62%) (Gómez-Olivé et al., 2018). 

Additionally, slight increases are observed in the percentages of never married and 

separated/divorced individuals. These changes impact social support networks crucial for 

managing chronic conditions like T2DM (Gómez-Olivé et al., 2018). 

 

Similarly, employment status experiences shifts, with a decrease in unemployment (from 

71.08% to 83.43%) but a slight rise in employment rates (from 17.42% to 14.97%) (Gómez-



52 
 

Olivé et al., 2018). These alterations may influence access to resources and healthcare, 

affecting T2DM management among the elderly (Gómez-Olivé et al., 2018). Persistent high 

unemployment underscores socioeconomic challenges faced by South Africa's elderly, 

exacerbating T2DM outcome disparities (Gómez-Olivé et al., 2018). 

In summary, findings from Wave 2 of the HAALSI study underscore the dynamic socio-

demographic characteristics' implications for T2DM progression among South Africa's elderly 

population (Gómez-Olivé et al., 2018). Addressing these factors is essential in developing 

effective strategies for T2DM prevention and management (Gómez-Olivé et al., 2018). 

 

5.3 Progression of Diabetes prevalence and risk factors 

The baseline findings from the HAALSI study shed light on the prevalence of diabetes and its 

associated risk factors among the elderly population. At baseline, the overall prevalence of 

diabetes among participants was 8.02%, indicating a significant presence of this chronic 

condition within the HAALSI cohort. This finding underscores the importance of addressing 

diabetes as a public health concern in South Africa. According to Indigo Wellness Index in 

2019, South Africa is one of the unhealthiest countries with a high surge in glucose levels, high 

BMI and obesity among its inhabitants. There was a notable increase in diabetes prevalence 

with advancing age. Participants aged 70-79 years and 80+ exhibited the highest prevalence 

rates at 11.78% and 7.91%, respectively, compared to younger age groups (Gómez-Olivé et