Article Text

The impact of diabetes on the productivity and economy of Bangladesh
1. Afsana Afroz1,
2. Thomas R Hird1,2,
3. Ella Zomer1,
4. Alice Owen1,
5. Lei Chen2,
7. Danny Liew1,
8. Dianna J Magliano1,2,
9. Baki Billah1
1. 1Department of Epidemiology and Preventive Medicine, Monash University School of Public Health and Preventive Medicine, Melbourne, Victoria, Australia
2. 2Diabetes and Population Health, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia
1. Correspondence to Dr Afsana Afroz; afsana.afroz{at}monash.edu

## Abstract

Aims To estimate the impact of type 2 diabetes in terms of mortality, years of life lost (YLL) and productivity-adjusted life years (PALY) lost in Bangladesh.

### Scenario analyses

First, the individual contribution of absenteeism, labour force dropout and premature mortality to productivity loss were calculated and considered as the base case. Second, to assess the impact of uncertainty around diabetes-related mortality risk, productivity indices and economic data inputs, deterministic sensitivity analyses were performed on the model to calculate PALYs lost in the Bangladeshi population with diabetes. These included using the upper and lower 95% CIs around estimates of all-cause mortality associated with diabetes,28 and varying estimates of absenteeism and labour force dropout by 25%.24 Finally, scenario analyses were undertaken to explore other model assumptions: doubling the average annual reduction in mortality risk from the UN WPP (1.0% per year) to a 2% reduction per year; removing the temporal trend in mortality risk; doubling the annual GDP growth rate from 1.8% per year (OECD forecast average annual GDP growth rate) to 3.6% per year; removing the temporal trend in GDP per worker26; and varying the annual discount rate to 5% and 1.5%.19

### Patient and public involvement

Patients or the public were not involved in the development or implementation of this study.

## Results

As estimated by the IDF,4 the prevalence of diabetes was 7.0% in women and 5.7% in men (table 1).

Table 1

The age and gender-specific population and number of people living with diabetes in Bangladesh in 2017

### Excess mortality and years of life lost to diabetes

The projected deaths in each 5 years age group and sex group are shown in table 2. It was estimated that of the current working age population followed up until retirement, there were 813 807 extra deaths due to diabetes (304 682 and 509 125 among men and women, respectively). Over the modelled time horizon (over 40 years), 3.9 million years of life (5.5%) would be lost to diabetes, which equated to overall 0.7 years (for both men and women) of life lost per person in the diabetes population.

Table 2

Excess deaths and years of life lived in those with diabetes, and in the same cohort assuming no diabetes, over the working lifetime of the Bangladeshi population simulated from life table modelling

It was estimated that 9.2 million (20.4%) PALYs were lost due to diabetes, which equated to 1.6 PALYs lost per person (1.7 in men and 1.5 in women) (table 3). Assuming a constant GDP per EFT worker of BDT701 062 (US$8763), the economic impact of productivity lost due to diabetes in Bangladesh would be BDT7.7 trillion (US$97.4 billion) loss in GDP. This is equivalent to an average GDP loss of BDT1 358 969 (US$16 987) per person with diabetes over the working lifespan. Table 3 Productivity-adjusted life years (PALY) lived in those with diabetes, and in the same cohort assuming no diabetes, over the working lifetime of the Bangladeshi population simulated from life table modelling ### Scenario analyses Figure 1 shows the contribution of labour force dropout, mortality and absenteeism to diabetes-related productivity loss. Labour force dropout (64.9%) was the major contributor to productivity loss followed by mortality (26.8%) and absenteeism (8.3%). Accordingly, the majority of costs associated with productivity losses were caused by diabetes-related labour force dropout (BDT5.1 trillion or US$63.2 billion) followed by mortality (BDT2.1 trillion or US$26.1 billion) and absenteeism (BDT648 billion or US$8.1 billion). The proportion of PALYs lost to diabetes-related mortality and absenteeism were higher in men (37.0% and 10.2%, respectively) than in women (18.0% and 6.7%, respectively), while the proportion of PALYs lost to labour force dropout was higher in women (75.2%) than in men (52.8%).

Figure 1

Economic burden of productivity loss in those with diabetes due to diabetes-related absenteeism, premature mortality and labour force dropout over the working lifespan in the Bangladeshi population.

Table 4 includes results from scenario analyses. The model was sensitive to a number of inputs such as productivity indices, diabetes-related labour force dropout, mortality risk and model assumptions including temporal trends in mortality risk and the annual discount rate. The PALYs lost due to diabetes were reduced by 5.4% and increased by 1.6%, respectively, for the upper and lower uncertainty bounds of absenteeism estimate compared with the base case. At the hypothetical 25% change of diabetes-related labour force dropout, PALYs lost changed by 16.7%. Applying the upper and lower bounds of 95% CI around estimates of all-cause mortality risk associated with diabetes, PALYs lost were increased by 20.5% and decreased by 8.7%, respectively.

Table 4

Sensitivity and scenario analyses to assess the impact of the uncertainties around productivity, mortality and economic data inputs on productivity-adjusted life years (PALY) lost in those with diabetes in the Bangladeshi population and the associated economic impact

In scenario analyses, the annual reduction in population mortality risk to 2% and removal of the temporal trends in population mortality risk resulted to ±1.2% change in PALYs lost. Doubling the annual GDP growth rate to 3.6% leads to an increase in the estimate of GDP lost to BDT9.0 trillion (US$112.9 billion), while removing all temporal trends in GDP decreased the estimate of GDP lost to BDT6.6 trillion (US$81.9 billion). Altering the annual discount rate to 5% and 1.5% corresponded to a 17.1% reduction in PALYs lost and a 17.3% increment in PALYs lost, respectively (table 4).

## Discussion

This study highlights that diabetes in the working age Bangladeshi population is projected to profound loss in years of life lived and productivity. Follow-up of this population until the retirement age leads to an estimated 813 807 excess deaths, and a loss of 4.0 million years of life (5.5%) and 9.2 million PALYs (20.4%) due to diabetes.

The productivity loss attributable to diabetes represents a combined effect of premature mortality, diabetes-related labour force dropout and absenteeism. Over the working lifespan of the diabetes cohort, higher all-cause mortality risk in those with diabetes resulted in a 5.5% reduction in years of life lived and was similar in men and women. This is consistent with the previous study conducted in India21 but is contrary to the findings of a study conducted in China, which showed a higher mortality risk among working age Chinese men than women.29 The relative impact of diabetes on years of life lost was higher among Bangladeshi younger people due to their longer working lifespan. On the other hand, the prevalence of diabetes and labour force dropout among younger age groups was lower, which highlights the importance of prevention before onset of diabetes. The literature supports that there is a strong association of mortality with duration of diabetes and diabetes complications among young Asian population.30

A recent study conducted in Bangladesh showed that 40% of people with type 2 diabetes had the disease before the age of 40 years.32 This study showed that the relative impact of diabetes on productivity loss was greater in younger people, which reflects the greater cumulative losses associated with early onset of diabetes. The absolute number of PALYs lost was greater in men (1.7) than in women (1.5). This may be due to the higher labour force participation of men than women in Bangladesh,23 which is related to higher diabetes-related productivity losses among men. However, the relative reduction in productivity due to diabetes was higher in women (32.5%) than men (14.5%), driven by higher labour force dropouts (75.2% in women vs 52.8% in men). There is evidence of an employment shortfall in people with diabetes compared with those without diabetes.11 33 34 Results from studies in the USA showed that diagnosis of diabetes was associated with approximately double the labour force participation shortfall and compared with men with diabetes, more workdays lost among women with diabetes.35 36

We estimated an average GDP loss of BDT1 358 969 (US$16 987) per person with diabetes over the working lifespan. The recent literature reports the median monthly income of Bangladeshi population is US$375,25 which is equivalent to 42 months of the gross household median income. Thus, in Bangladesh, where nearly one-third (31.5%) of the population lives below the poverty line,25 diabetes is placing a noticeable burden on its economy. According to Islam et al, Bangladesh spends a much lower percentage (3.5%) of its GDP on health compared with neighbouring counties like Maldives (10.8%) or countries with similar economic status like South Africa (8.9%) and Tanzania (7.3%).37 A study conducted in Bangladesh in 201725 showed that 3.5% of people with type 2 diabetes had income less than the estimated average annual cost of US$865 for diabetes management. Overall, a person with type 2 diabetes in Bangladesh spent 9% of his/her annual household income on management of the disease. Acknowledging that diabetes is not 100% preventable, if even 10% of diabetes was prevented, BDT13 589 (US$1698) could be spent per working age person as, at least, a break-even investment. This study also emphasises the importance of strengthening the current strategies on prevention of diabetes in Bangladesh.

This study quantifies the macroeconomic burden of diabetes-related productivity loss using contemporary age and sex-specific estimates of diabetes prevalence by the IDF, mortality risk and labour force participation.4 The major strength of the study is the use of PALYs to calculate productivity losses. PALYs are able to ascribe a financial value in terms of GDP and net costs. PALYs represent a useful measure alongside QALYs which require the calculation of incremental cost-effectiveness ratios16 to estimate the impact of health interventions. Strengths of our study include the use of productivity indices stratified by age, sex and the type of work that people undertake and were sourced from a study conducted on Bangladeshi population.25 Thus, estimates of the impact of diabetes on the productivity of specific subgroups were more precise and are generalisable to the total Bangladeshi population. In addition, we used contemporary age and sex-specific estimates of diabetes prevalence, mortality risk, labour force participation and in-work productivity, which can inform the targeting of interventions. Life table modelling allowed us to capture the impact of diabetes-related productivity loss across the working age lifespan.

Life table modelling is a well-recognised tool used in epidemiological and demographic studies but is not without limitations. One limitation of this study was the mortality rates, which were estimated based on published data. The uncertainty around productivity indices was explored in sensitivity and scenario analyses and found that the model was more sensitive to variation in labour force dropout (±16.7%) while varying absenteeism by 25% had a minimum effect on estimates of PALYs lost (+1.6%, −5.4%). Our model did not account for the large proportion of people (56%) who are unaware of the presence of diabetes.38 A previous study showed that 63.4% of people with diabetes have diabetes-related complications32 but in this model an assumption was made that they are as productive as without diabetes. Thus, these results may be considered an underestimation of the effect of diabetes on work productivity as diabetes could still exert an impact on those with undiagnosed diabetes. We also assumed that current projections in temporal trends in mortality rates and GDP growth held true across the modelling time horizon. However, in scenario analyses, the doubling and removal of the trend in population mortality rates affected the model output by <2%, although estimates of GDP lost were more sensitive to the equivalent changes in GDP growth rate. The contribution of comorbidities of diabetes such as obesity and hypertension on productivity loss could not be distinguished from these estimates. Due to the absence of available data, an assumption was made that work will relate to paid employment and the population worked full time. Furthermore, diabetes might impact on GDP in ways other than through productivity losses.10 12 While these limitations may affect the current estimates of our model, the overall conclusion of the study is unlikely to be changed.

## Conclusion

Diabetes creates a substantial burden on the Bangladeshi population, in terms of health and well-being and lost productivity. By quantifying the economic burden of diabetes in terms of missed production opportunities, rather than health expenditure only, our findings highlight the importance of prevention, treatment and adequate control of diabetes in Bangladesh as an investment. An economic pay-off through gains in productivity can be achieved by interventions aimed to prevent and adequately control diabetes. Future studies should aim to describe the dynamics of the balance between the economic benefits arising from productivity gains and the greater investment in prevention and health services for diabetes.

## Footnotes

• Handling editor Lei Si

• Contributors AA and TRH performed the analysis and interpretation of data. BB, DL and DJM jointly conceived the study and made a substantial contribution to the interpretation of data. EZ and ZA made a substantial contribution to the analytical strategy. AA wrote the first draft of the manuscript and TRH, EZ, AO, LC, ZA, DL, DJM and BB reviewed and revised the manuscript. All authors approved the final version of the article.

• Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

• Competing interests None declared.

• Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

• Patient consent for publication Not required.

• Ethics approval This study was approved by the Monash University Human Research Ethics Committee (Ref No 1469) as a low-risk project.

• Provenance and peer review Not commissioned; externally peer reviewed.

• Data availability statement Data are available upon request. All data have been obtained from publicly available sources. AA and BB are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.