FormalPara Key Points for Decision Makers

The escalating prevalence of type 2 diabetes (T2D) in South Africa, affecting 9.5% of the working-age population, is concurrently associated with a decline in both workforce participation and productivity while at work.

Using a novel measure of productivity-adjusted life years (PALYs), our study demonstrated a significant cumulative productivity loss across the working age lifetime of those with diabetes in South Africa. The simulated loss of 13 million PALYs due to T2D equates to US$223 billion, highlighting the long-term economic consequences of diabetes.

Studies of this nature provide a theoretical benchmark against which to evaluate interventions in terms of return on investment, as investing in T2D prevention can yield significant economic benefits, indicating the need for resource allocation to preventive interventions for substantial returns in both population health and economic growth.

1 Introduction

Type 2 diabetes (T2D) is a chronic disease characterised by chronic hyperglycaemia resulting from variable combinations of defective insulin secretion and/or action [1]. It poses significant public health challenges that substantially impact society [1, 2]. Recent data indicate that over one-third of diabetes-related deaths occur in those under 60, adversely affecting labour force participation and diminishing the productivity of affected workers [2].

Africa, currently home to 1 billion people, is projected to reach 2.4 billion by 2050, with more than half of the population expected to be under the age of 25, as per United Nations forecasts. [3]. The World Bank economists further projected massive human capital gain from investments in young people if efficiently handled [4]. It has been suggested that to maximise developmental prospects, countries in the region should focus on alleviating poverty, creating new jobs, and improving education and health [5]. The World Health Organization (WHO) Commission on Macroeconomics and Health further highlighted the two-way causal relationship between health and economic development [6]. This two-way recognition by WHO stresses investments in health to reduce the burden of preventable diseases, stimulating economic growth and raising a society's ability to invest in public health [4, 7]. At the same time, the severity of diseases can also affect individuals' productivity and cause premature death [8].

The Sub-Saharan Africa (SSA) region is known to have a heavy burden of non-communicable diseases (NCDs) [9]. South Africa (SA) is undergoing an epidemiological transition led by rapid urbanisation, changes in diets and physical activity levels, and population ageing [9, 10]. The country faces an epidemic of NCDs, including T2D [11, 12]. The 10th Edition of the International Diabetes Federation (IDF) reports that 4.2 million adults in SA aged 20–79 years are living with T2D [13]. At 11.3%, SA has one of the highest rates of T2D prevalence in the SSA region [13, 14]. One study projected the financial cost (i.e., direct medical costs) of diagnosed T2D to the SA public healthcare system would reach over 35.1 billion SA Rands (ZAR), equivalent to US$2.5 billion, by 2030 [15]. However, according to the IDF, diabetes-related health expenditures in SA reached US$1700 per person in 2021, contributing to 96,000 deaths, with an estimated total ZAR109 billion, equivalent to US$7.2 billion, in health expenditures for that year [16]. The loss in productivity, stemming from a combination of premature mortality, morbidity leading to workforce dropouts, and, notably, reductions in effective performance while at work (known as presenteeism), is substantial [17, 18].

Economic theory assesses productivity as output, a function of capital and labour input [17, 19]. Neglecting this productivity aspect and the related costs during economic evaluations implies that these costs are zero [17, 19]. Studies on societal costs suggest that cost-effectiveness analyses incorporate the total resource implications of mortality changes [20, 22]. This includes evaluating the productivity of individuals on a scale from entirely unproductive (0) to fully productive (1) in relation to their health conditions and taking into account the segment of productivity impairment [19, 21].

A population-level study assessing productivity loss due to diabetes in China concluded that T2D significantly contributes to the loss of productivity-adjusted life years (PALYs), resulting in a total loss of $2.6 trillion in gross domestic productivity (GDP) due to reduced productivity, with an average of $45,959 lost per person in 2017 [19]. Diabetes-associated complications resulted in an approximate loss of $89.9 billion in the USA in 2017, including absenteeism ($3.3 billion), presenteeism ($26.9 billion), diabetes-related decreased labour force participation ($37.5 billion), and premature deaths ($19.9 billion) [20]. Such estimates of productivity loss are essential to encompass the broader economic impact of T2D and provide evidence for investing in its prevention and disease management.

In SA, the burden of T2D presents significant challenges, especially in the context of the proposed National Health Insurance. A deeper understanding of T2D-related productivity loss is critical, not just locally but globally, and is particularly pressing in SSA. However, there is a notable lack of comprehensive data on the impact of T2D on productivity, especially among the working-age population. This gap includes limited insights into the disease's contribution to excess mortality, years of life lost (YLL), and PALYs lost. Our study aimed to fill this crucial gap by providing detailed evidence on the extent of productivity loss due to T2D in SA. Such evidence is vital to inform effective, scalable interventions in resource-constrained settings, responding to an urgent call by researchers for data-driven strategies to manage the growing T2D epidemic.

2 Methods

This study created a life table modelling approach with a single-year cycle length and estimated the health and productivity burden of T2D in working-age South Africans. Life table modelling in combination with decision analysis were utilised to simulate the follow-up of the T2D cohort.

We specifically focused on T2D due to its prevalence, healthcare costs, and impact on the burden of labour productivity [18,19,20,21,22]. In this study, we used life table modelling to project the health outcomes of the working-age population in SA. The models were developed in Microsoft Excel version 2021. We have constructed two models within Excel: Model 1 simulated a cohort with T2D, and Model 2 re-simulated the same cohort under the hypothetical absence of T2D. Model 1 is depicted in Fig. 1. Notably, our life table models did not account for transitions between non-diabetic and diabetic states or the possibility of T2D recovery. Instead, we relied on baseline prevalence data and population demographics to simulate cohort progression to mortality transitions, omitting changes between diabetic and healthy states.

Fig. 1
figure 1

Model description for cohort with type 2 diabetes (T2D), including two health states

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By contrasting the cohorts—one with T2D (presented in Fig. 1) and the other hypothetically without T2D (presented in Fig. 2)—we quantified the impact of T2D on life years lived and workforce productivity. First, the difference in the total mortality (excess mortality) and YLL between the two models in labour force participation reflected the impact of diabetes-related productivity loss in labour dropout—accounting for the shortfall in labour force participation in those with T2D compared to those without [16,17,18]. Second, the PALY metric, which depends on productivity indices and YLL, was calculated [17].

Fig. 2
figure 2

Model description for cohort without type 2 diabetes (hypothetically)

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Data were derived both from publicly available databases and published sources. We used population labour force data from Statistics South Africa (Stats SA) for 2019 [23], shown in the online resources—see Electronic Supplementary Material (ESM) Table 2. The 5-year age group and sex-stratified data of working-age population prevalence and annual mortality rate (AMR) were obtained from the IDF Atlas 10th Edition and the vital statistics department in Stats SA, respectively [11, 13, 23]. All monetary values were converted from ZAR to US dollars using the 2019 average exchange rate of ZAR14.4496 to US$1 to allow comparability with other populations [24].

2.1 Type 2 Diabetes (T2D) Prevalence

Five-year age group and sex-stratified T2D prevalence data for the SA population were obtained from the 2021 IDF Atlas [12]. These data were then applied to the midpoint of each age band to estimate single-year age prevalence rates. Two distinct scenarios were established in separate sheets: Model 1, using the actual T2D prevalence from IDF data, and Model 2, assuming zero T2D prevalence. These data points were extrapolated over time, incorporating demographic shifts. Our method used life table models to simulate the cohort’s progression to mortality transitions based on the existing prevalence data and population demographics, as detailed online in ESM Table 1. This approach enabled us to assess the impact of the presence and absence of T2D on the SA population.

Table 1 Age- and sex-specific prevalence of T2D among the working-age population in SA, 2019 [12, 23]
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2.2 Annual Mortality Rates

The 5-year age and sex band mortality rates for the SA population in 2019 were obtained from Stats SA [10, 30]. We analysed mortality rates attributed to individuals with and without T2D based on age, sex, and T2D prevalence. In the current context of T2D, the 2021 IDF Atlas method of age-related estimation of T2D mortality in conjunction with age was utilised.

This study applied the average annual proportional reduction in adult mortality for the past decade in SA of 1.8% per year data obtained from the WHO progressively across the model time horizon to project temporal risk trends in population mortality [25]. AMR was used, assuming that death occurs half way through the cycle. Information regarding the burden of mortality and excess deaths related to diabetes is often limited and unreliable, especially in low- and middle-income countries (LMICs). This is also the case for estimates in SA, which have primarily relied on death certificate data from the Department of Home Affairs submitted to Stats SA. However, death certificate data alone may not account for the complex nature of deaths influenced by multiple causes. Formulas used to calculate AMRs for individuals with and without T2D, categorised by age group and sex, can be found in the online ESM.

All-cause mortality associated with T2D was sourced from a study conducted within the Asia Pacific population [19]. The decision to utilise data from the Asia Pacific region was driven by the lack of comprehensive, specific mortality risk data related to T2D in the SA population and the cultural similarity between the two populations [26, 36]. Through the process, we quantified the excess deaths caused by T2D and the YLL. The YLL is a metric that quantifies the effects of premature death by estimating the years a person would have lived had they not died early [17,18,19]. This calculation is derived by subtracting the age at death from a predetermined life expectancy value, known as the standard life expectancy (SLE). Hence, the formula for YLL is expressed as the difference between the years of life lived in the ‘T2D cohort’ assuming no T2D and the years lived in the T2D cohort. The model estimation included the SLE and the age at which the person died. In SA, at birth in 2019, men had a lower life expectancy of 64 years, while women had a higher life expectancy of 70 years [27].

2.3 Productivity Indices

Productivity indices signify a productivity index that can be calculated by dividing an output index by an index of hours worked [17, 28]. While the productivity index for T2D patients is expected to be less than 1, in those without diabetes, it was assumed to be 1 (fully productive).

Absenteeism, here the number of lost workdays in a year owing to diabetes, is commonly represented as a percentage of the total number of working days in a year [21]. Both women and men of all age groups in SSA were estimated to miss 8.6 days on average [22].

Presenteeism, the self-assessed productivity loss indicator while at work, was expressed as a percentage of total productivity. Data estimated by the ‘Global Economic Burden of Diabetes’ in adults aged 20–79 years for SSA indicate that 1.0% of women and 0.6% of men experienced a decrease in production due to presenteeism related to T2D [22].

To determine the PALYs for the T2D cohort, we multiplied the time period that the cohort spent in the workforce by a productivity index. The index was determined based on estimates of absenteeism and presenteeism related to diabetes. This process is similar to multiplying the years of life lived by a measure of utility to calculate quality-adjusted life years [29]. Thus:

$${\text{PALYs }} = \, \left( {^{{1}} {\text{productivity}}\;{\text{ index}}} \right)\left( {^{{2}} {\text{each }}\;{\text{year}}\;{\text{ lived }}\;{\text{in}}\;{\text{ the}}\;{\text{ labour}}\;{\text{ force }}\left( {{\text{YLL}}_{{\text{d}}} } \right)\;{\text{ by}}\;{\text{ T2D}}\;{\text{ cohort}}} \right)$$

1Absenteeism and presenteeism caused by T2D-related impairment

2((YLLd)(labour force participation rate of SA))

There were 250 calendar working days in 2019 in SA. This study estimated the combined productivity shortfall related to diabetes, including absenteeism and presenteeism, which was 4.44% in women (a T2D productivity index of 0.9556) and 4.04% in men (with a productivity index of 0.9596), with the formulas employed for estimation found in the online ESM. On the other hand, the productivity index among individuals without T2D was assumed to be 1 (fully productive) for simplicity of calculations.

2.4 Labour Force Participation

Five-year age group, sex-stratified, population-level employment was obtained from the Labour Department of Stats SA [27]. These data were used to quantify the inability to work and early retirement among people with T2D compared to those without T2D. The labour force participation was quantified using a full-time equivalent (FTE) employment rate. The FTE considers the total hours worked by individuals in a given age group relative to the complete standard full-time working hours, thus adjusting the proportion of employed individuals in that group. In the general population of SA, the lowest labour force participation rates were observed in the 60–64 age group, with 4.68% for males and 5.78% for females. Conversely, the highest participation rate was in the 25–29 age bracket for males at 16% and in the 20–24 age bracket for females at 15.4% (Table 1) [23].

Diabetes-related labour force dropout was expressed as a difference in labour force participation rates, varying from 7.0% in females and 5.2% in males aged 20–29 years with T2D to 12.8% in females and 8.3% in males over 40 years [21]. These relative reductions were applied to 2019 sex- and age-group-specific SA labour force participation rates to derive the participation rates in those with and without T2D. The decline in labour force participation is often triggered by an inability to work and early retirement among individuals with T2D compared to individuals without, accounting for absenteeism and presenteeism [17].

2.5 Gross Domestic Product

The data on GDP per capita in SA were sourced from the World Bank and stood at ZAR96,584.38 ($6684.04) in 2019 [30]. The projected temporal trends in GDP growth rate of 1.6% for 2019 were obtained from the Organisation for Economic Co-operation and Development (OECD) Compendium [31]. In this study, we assumed that the economic value of each PALY corresponded to the annual GDP per effective full-time (EFT) worker, based on a constant GDP per EFT worker of $6684.04 [30]. This calculation reflects the potential GDP loss incurred when a worker drops out of the workforce due to T2D and does not reflect the actual annual earnings of an EFT worker. Furthermore, this does not account for the indirect costs incurred by the worker when forced out of the workforce due to diabetes, which is currently under investigation in this study for the year 2019. The GDP per EFT worker was held constant throughout the model time horizon.

2.6 Sensitivity and Scenario Analysis

To assess the robustness of our model, we conducted deterministic sensitivity analyses. These analyses examined the impact of uncertainties in diabetes-related mortality risk, productivity indices, economic data inputs, and potential PALYs lost due to T2D. We incorporated the upper and lower 95% confidence intervals around the risk ratios of T2D to estimate the bounds for all-cause mortality risk associated with T2D. Additionally, we assessed the uncertainty limits around productivity indices by varying absenteeism, presenteeism, and labour force dropout estimates by 25%. Scenario analyses were also conducted to explore the effects of different model assumptions. These included scenarios where the mortality risk doubled from the population's average annual reduction in mortality risk of 1.8–3.6% per year [25], maintaining 2019 mortality risks over the model's time horizon, and varying trends in GDP per worker by either doubling the average annual GDP growth rate from 1.6 to 3.2% or keeping it constant throughout the model [30]. Lastly, we explored the effect of applying a 5% discount rate, in contrast to the standard WHO annual discount rate of 3% [32], to evaluate its impact on our findings.

3 Results

3.1 T2D Prevalence

Working-age individuals living with T2D in SA accounted for 9.5% of the total working-age population (7.7% for men and 11.4% for women), equivalent to 3.2 million aged 20–65 years in SA in 2019 (Table 1). Further details regarding people (men and women) living with T2D aged between 20 and 65 and the data sources are found in online ESM Tables 4 and 5.

3.2 Quantifying Excess Death and Years of Life Lost to Diabetes

Our life table model, detailing the follow-up of the current working-age SA population until retirement, is presented in Table 2. The projected number of deaths in individuals with T2D compared to those without diabetes was 669,427. The male cohort is estimated to have 315,558 excess deaths, and the female cohort 353,839. Additionally, Table 2 indicates that an estimated 6.2 million YLL would be lost due to T2D over the model’s time horizon. This translates to 1.9 years of life lost per person during their working lifetime, with a higher impact observed among men (2.5 years) compared to women (1.6 years).

Table 2 Excess mortality and YLL across the working-age lifespan of South Africans with T2D, as modelled in a life table
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3.3 Evaluating Productivity-Adjusted Life Years Lost to Diabetes

An estimated 13 million PALYs were lost among the working-age cohort living with T2D in SA over their working lifetimes, with 4.4 million lost in men and 8.7 million in women. This represents 30.6% of PALYs lost for the total cohort (22.5% for men and 37.5% for women), as detailed in Table 3. This equates to a loss of 3.1 PALYs per individual (2.6 for men and 3.6 for women). The present study used the human capital approach, whereby GDP is an oft-reported economic metric, similar to data on workforce participation [33]. Using the measure of GDP per person employed, which reflects labour productivity as the economic output generated by each employed individual, the calculation is based on dividing SA's GDP by the total number of people employed. Assuming a constant GDP per EFT employee of ZAR248,472 (US$17,136) per annum [31], this results in an estimated productivity loss due to diabetes in SA of ZAR3.2 trillion (US$223 billion) in GDP, as shown in Table 4. This equates to ZAR1,009,693 (US$69,875) per person with T2D over the working lifetime in SA.

Table 3 PALYs lost during the working lifespan of South Africans with T2D, as simulated in the life table
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Table 4 Sensitivity and scenario analyses for evaluating uncertainty of key data inputs
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3.4 Sensitivity and Scenario Analyses

Table 4 illustrates the sensitivity and scenario analyses performed by altering one input variable while holding all other model inputs constant (Ceteris paribus) as done elsewhere [20, 22].

In sensitivity analyses, a 25% increase in labour force dropout, representing the upper uncertainty bounds of the 95% confidence interval, led to an additional 14.8 million PALYs lost (a 14% increase). Conversely, with a 25% decrease in dropout and the lower uncertainty bound, the estimate of PALYs lost was reduced to 11.2 million, reflecting a 14% decrease. This highlights the significant effects on workforce productivity from early retirement, which we used as a proxy for T2D-related complications.

In the scenario analyses, altering mortality risk and economic growth rates further illustrated the sensitivity of our model to external economic factors. When the temporal trend in population mortality risk was doubled from 1.8 to 3.6% annually, PALYs lost increased to 287,000, as opposed to 165,000 PALYs lost without a mortality trend.

Adjusting for the upper and lower uncertainty bounds in absenteeism and presenteeism, PALYs lost due to T2D fluctuated by ±2.14% from the base case. Similarly, varying the all-cause mortality risk associated with T2D resulted in a 4.2% increase and a 5.4% decrease in PALYs lost. Economically, doubling the annual GDP growth rate from 1.6 to 3.2% escalated the estimated economic impact of T2D-related productivity loss to US$230 billion. Conversely, maintaining 2019 GDP levels throughout the model's timeline reduced the estimated loss to US$219 billion. Adjusting the annual discount rate to 5% and 1.5% varied the PALYs lost by − 18.1% and + 18.4%, respectively.

Figure 3 shows that labour force dropout and mortality account for the majority of productivity loss at 47.1% and 37.2%, respectively. Absenteeism and presenteeism contributed 15.8% of the loss. Cost-wise, labour force dropout leads with US$77.1 billion, followed by mortality at US$60.9 billion and absenteeism and presenteeism at US$25.8 billion. PALYs lost are higher among men (21.7%) than women (15.4%) for mortality, while dropout and absenteeism rates are higher for women, at 27.8% and 9.7%, respectively, versus men’s 19.3% and 6%.

Fig. 3
figure 3

Economic impact of reduced productivity due to type 2 diabetes-related absenteeism, presenteeism, premature mortality, and labour force dropout in the South African working population

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4 Discussion

This study underscores the substantial burden of T2D, resulting in reduced years of life and productivity for the working-age population in SA. Through the simulated follow-up of the T2D population, hypothetically considering a scenario with ‘no T2D ’ and following until the retirement age of 65 years, it was estimated that there would be 669,427 excess deaths and a loss of 6.2 million life years (9.3%) and 13 million PALYs (30.6%) in SA.

Decreased productivity is commonly associated with various factors, including missed workdays due to absenteeism, reduced workforce participation due to disability caused by disease, and the premature loss of working years due to diabetes-related mortality during an individual's working lifetime [18, 20, 35]. Hence, PALYs are a novel and well-tested approach to measuring health outcomes and the population-level impact of disease (such as diabetes) on productivity [17, 33].

The findings of this study are consistent with prior research conducted in Bangladesh [21] and China [19] utilising a similar modelling approach. The study underscores the significant impact of T2D on YLL, particularly noticeable among younger individuals. This phenomenon is attributed to the cumulative losses linked to early-onset T2D among younger age groups.

The results from the life table model estimated the loss of 13 million PALYs among those with T2D, or 3.1 PALYs lost per person, over the working lifetime. The per-person PALYs loss in SA was higher than the previously reported outcomes from China [19], Australia [33], and Bangladesh [21], with 1.3, 1.6, and 1.4, respectively, with further details found in online ESM Table 3. In SA, the retirement age is 65 for both genders. However, when compared to patients in China, SA patients experienced higher productivity loss. Men in SA faced a 5-year greater loss, while women incurred a 15-year higher loss than their Chinese counterparts. This discrepancy stems from differences in the working-age population structure; in China, it ranges from 20 to 59 years for men and 20–49 years for women [19]. Moreover, the prevalence of T2D-related YLL in SA is more than twice that of Australia, reflecting elevated mortality risk and labour force dropout in SA compared to Australia [33]. It is crucial to note that the Australian PALY study did not discount its results. While these findings offer valuable insights for comparison, it is important to acknowledge the potential influence of unaccounted variables on the overall data interpretation. However, the model structure in both studies is similar. Furthermore, with its higher Economic Complexity Index (ECI) and higher per capita income, SA experiences more significant losses in its population than Bangladesh, which has a lower ECI and lower per capital income [33].

In terms of absolute numbers, men experienced greater PALYs lost compared to women, primarily due to higher labour force participation among men (64.9%) than women (54.3%) in SA [37]. However, the relative decrease in productivity caused by T2D was more pronounced in women (37.5%) compared to men (12.25%), primarily attributable to a higher rate of labour force dropouts (70.8% in women versus 40.3% in men).

We further estimated that the lost productivity incurred a loss of ZAR3.2 trillion (US$223 billion) in GDP. This translates to an average of ZAR1,009,693 (US$ 69,875) per person with T2D over their working lifetime. At the same time, T2D prevalence (9.5%) is significantly higher in SA compared to other populous countries, such as Nigeria, with a 4.3% rate [34]. We further estimated that spending on the prevention of T2D strategy can generate financial gains by enhancing work productivity and decreasing the direct costs of T2D treatment and management. Considering that T2D is not entirely preventable, if an intervention could prevent 10% of diabetes cases, the break-even investment would amount to US$6627 per working-age individual, making it a cost-saving initiative. This estimation is derived solely from the perspective of saved productivity, making it a conservative figure. Additional economic benefits are likely, especially from the savings in direct costs associated with diabetes management [15]. Therefore, framing the allocation of resources towards T2D prevention should be considered an investment, not an expenditure.

The present study's strength lies in its use of gender and age stratification, which sharpens the precision of our data analysis. This approach enables more accurate identification of the impact of diabetes across different demographics. To address uncertainties in the model, we conducted sensitivity and scenario analyses, ensuring a comprehensive and robust examination of the impact of diabetes on productivity. The life table model proved more sensitive to variations in labour force dropout (± 13.7%) than fluctuations in productivity indices (absenteeism and presenteeism) by 25%, which had a minimal effect on the estimates of PALYs (± 2.14%). Notably, the life table model did not consider individuals in the SA population with T2D who remain undiagnosed or unaware of their condition (52.4% are estimated to have T2D but undiagnosed) [38]. Consequently, our findings may underestimate the impact of T2D on work productivity, as it can still affect those with undiagnosed T2D. Additionally, we assumed that projections for temporal trends in mortality and GDP growth rates would remain constant throughout the model's time horizon for simplification and practicality. However, GDP growth was considered in the scenario analysis.

4.1 Limitations

In adhering to the two-model approach—one representing a cohort with T2D and the other without—the study acknowledges a fundamental limitation: the sole transition probability considered is towards death, excluding other potential transitions such as onset or remission from T2D. This simplification, while streamlining the model, may not fully capture the dynamic nature of diabetes progression and its varied impacts on different population segments. Second, we could not classify the productivity indices predictions for different age groups because the required data on absenteeism and presenteeism were lacking.

Due to the unavailability of data from Stats SA on the division of the labour force into full- and part-time based on disease status, we treated all labour force participants as full-time employed in both the T2D and simulated cohorts. Consequently, this study did not account for the impact of T2D on unpaid work (caregiving, child-rearing, household, and community roles), potentially missing a proportion of the overall impact. The life table model was used to simulate the progress of the current cohort of South Africans with known T2D but could not account for those who had undiagnosed T2D at the time of data extraction. Finally, we also assumed that the economic value of one PALY is equivalent to that of annual GDP per employee, as done elsewhere [19, 21, 33]. We acknowledge that these assumptions and limitations could lead to unaccounted costs, thus underestimating the burden.

Furthermore, the influence of diabetes-related comorbidities, such as a high prevalence of obesity and hypertension among SA populations [39], on productivity loss could not be disentangled from these projections. This limitation stems from the need for more data. We further assumed that work pertains to paid employment, presuming that the entire population worked full-time, and we did not account for unpaid and part-time workers. Despite these limitations, it is improbable that our study's core findings and conclusions would be substantially altered.

In addition, Stats SA, the national statistical service provider, and Diabetes South Africa, the T2D Association of SA did not have T2D prevalence data—citing that the Department of Health does not mandate them to keep T2D statistics. In addition to the lack of a local country depository for confirmed T2D or prevalence, the IDF indicated that the quality and number of data sources examining the prevalence of T2D in adults was very low. Second, the relative risk (RR) and utility scores were extrapolated with predictions using the Asia Pacific region and Ethiopian studies, respectively, without available data from SA. Utilising real-time data would significantly enhance accuracy, generalisability, and reproducibility of the evidence. Consequently, it is paramount for SA to prioritise the collection and analysis of diabetes-related data. This focus will enable more precise and relevant analyses in future research.

5 Conclusion

T2D is a major contributor to mortality and morbidity in SA. This study brings forth new insights from the South African context, specifically focusing on health, well-being, and productivity losses associated with T2D. Our research also elucidates that quantifying the lifetime earnings lost due to T2D is a complex endeavour influenced by various factors. It emphasises the need for enhanced allocation of public health resources through informed decision-making, particularly by assessing projected lost production opportunities. In light of these findings, we emphasise the importance of viewing prevention, treatment, and efficient management of T2D not merely as healthcare expenditures but as valuable investments. This perspective is crucial for shaping strategies that effectively address the broader economic implications of this disease in SA. This study calls for adequate interventions to control and mitigate the burden of T2D in SA.