Research Article: Lifestyle and socio-economic inequalities in diabetes prevalence in South Africa: A decomposition analysis

Date Published: January 30, 2019

Publisher: Public Library of Science

Author(s): Chipo Mutyambizi, Frederik Booysen, Andrew Stokes, Milena Pavlova, Wim Groot, Brecht Devleesschauwer.


Inequalities in diabetes are widespread and are exacerbated by differences in lifestyle. Many studies that have estimated inequalities in diabetes make use of self-reported diabetes which is often biased by differences in access to health care and diabetes awareness. This study adds to this literature by making use of a more objective standardised measure of diabetes in South Africa. The study estimates socio-economic inequalities in undiagnosed diabetes, diagnosed diabetes (self-reported), as well as total diabetes (undiagnosed diabetics + diagnosed diabetics). The study also examines the contribution of lifestyle factors to diabetes inequalities in South Africa.

This cross sectional study uses data from the 2012 South African National Health and Nutrition Examination Survey (SANHANES-1) and applies the Erreygers Concentration Indices to assess socio-economic inequalities in diabetes. Contributions of lifestyle factors to inequalities in diabetes are assessed using a decomposition method.

Self-reported diabetes and total diabetes (undiagnosed diabetics + diagnosed diabetics) were significantly concentrated amongst the rich (CI = 0.0746; p < 0.05 and CI = 0.0859; p < 0.05). The concentration index for undiagnosed diabetes was insignificant but pro-poor. The decomposition showed that lifestyle factors contributed 22% and 35% to socioeconomic inequalities in self-reported and total diabetes, respectively. Diabetes in South Africa is more concentrated amongst higher socio-economic groups when measured using self-reported diabetes or clinical data. Our findings also show that the extent of inequality is worse in the total diabetes outcome (undiagnosed diabetics + diagnosed diabetics) when compared to the self-reported diabetes outcome. Although in comparison to other determinants, the contribution of lifestyle factors was modest, these contributions are important in the development of policies that address socio-economic inequalities in the prevalence of diabetes.

Partial Text

Non-communicable diseases (NCDs) are currently the leading cause of death globally. According to the World Health Organisation, NCDs are projected to overtake all other causes of death in Africa by the year 2030 [1]. In the last two decades, the prevalence of diabetes has increased from 4.7% in 1980 to 8.5% of the total world population in 2014 and is expected to further increase especially in lower and middle income countries [2]. Between 1990 and 2013, the years of life lost to diabetes globally have increased by 67% [3]. Historically diabetes was a burden of developed countries but a huge increase has now been reported in developing countries [2], countries that often do not have the resources for the prevention, diagnosis, treatment and management of the disease [4]. In South Africa, the International Diabetes Federation (IDF) estimates that in 2015, almost 2.3 million people had diabetes [5]. The magnitude of the diabetes burden is further reflected in the mortality and causes of death statistics, which show that diabetes has moved from being the fifth leading underlying cause of death in 2013 to being the third and second leading underlying cause of death in 2014 and 2015, respectively [6].

Our paper provides evidence on the socio-economic inequalities in various diabetes outcomes using the CI and identifies the contribution of lifestyle factors to socio-economic inequalities in diabetes prevalence by conducting a decomposition analysis. To the best of our knowledge this is the first paper to incorporate biomarker analysis in the measurement of diabetes inequalities in South Africa and the first to attempt to measure the contribution of various lifestyle factors to socio-economic related inequalities in diabetes. Consistent with the study by Stokes et al., our study documents the high levels of undiagnosed diabetes in South Africa [18]. This study showed that the total prevalence of diabetes in South Africa was 11%, of which 38% were undiagnosed. The poor rates of diagnosis are largely a result of insufficient access to health care and poor health systems [17]. The prevalence of self-reported diabetes was 6.86% of which 61% were on treatment and 31% of those on treatment had controlled diabetes. The poor rates of treatment and control have also been previously reported by Stokes et al. [18] and have been attributed to poor diabetes education and medication adherence [17, 18].

A major strength of this study is that it made use of an HbA1c test, an objective measure of diabetes. This measure allowed us to measure the prevalence of undiagnosed and total diabetes. The study has some limitations that must be acknowledged. Whilst there are several regression based decomposition methods within the literature our study makes use of the Wagstaff method. Results may differ depending on the decomposition method applied [31]. The American Diabetes Association states that although the risk of developing diabetes increases with age, there is no exact age for the onset of type 1 or type 2 diabetes, thus we were unable to separate type 2 from type 1 diabetics [67]. Despite this, lifestyle factors such as alcohol, physical activity and fruit consumption which are common risk factors for type 2 diabetes were included in our analysis as explanatory variables. Another limitation of the study is the low number of individuals who went to the testing centres and provided a blood sample. Our analytical sample may be prone to self-selection of individuals that went to get blood samples taken as well as those who completed the adult and household questionnaires. We therefore compared our final analytical sample to the 2011 South African census across sex, age, race and province. Compared to the 2011 census our analytic sample contained a larger sample of non-Africans (27% versus 23) and a smaller sample of Africans (72% versus 77%). Our sample also contained fewer individuals within the age category of 15 to 35 years (51% versus 55%). The analytical sample employed in this study therefore is not nationally representative, which means caution is necessary in drawing generalisations from the empirical results. It is also possible that our self-reported data on lifestyle factors suffered from social desirability bias. For example an under reporting of smoking patterns or alcohol consumption could potentially influence the contributions made by these factors to diabetes inequalities.

This paper provides an analysis of the socio-economic inequalities in the prevalence of diabetes and determines the sources of these inequalities with a focus on modifiable lifestyle factors. The paper contributes to the literature on diabetes by making use of a more objective measure of diabetes and highlighting the magnitude of undiagnosed diabetes in South Africa. The study provides evidence that inequality in self-reported and total diabetes is concentrated among the rich. The magnitude of inequality estimates based on self-reported data only would be different when compared to inequality estimates based both on self-reported plus clinical data. The measured inequalities are mostly explained by residence and wealth. The contributions made by lifestyle factors to inequalities in diabetes, are less than the overall contributions of other factors within our model. Although modest, the contributions made by lifestyle factors to inequalities in diabetes provide important information for use in planning of interventions to reduce the burden of diabetes. Our study shows that in comparison to all other lifestyle factors obesity, alcohol consumption and vegetable consumption make large contributions to inequalities in diabetes. These findings are important to policy makers in terms of informing the design of effective strategies and policies for encouraging healthy lifestyles. Future national health surveillance surveys that capture larger numbers of individuals who provide blood samples are an ideal conduit for the monitoring of diabetes and the tracking of socio-economics inequalities in the prevalence, diagnosis and treatment of diabetes.




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