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Background: Over the last 30 years, South Africa has experienced four â € colliding epidemics’ of HIV and tuberculosis, chronic illness and mental health, injury and violence, and maternal, neonatal, and child mortality, which have had substantial effects on health and well-being. Using data from the 2019 Global Burden of Diseases, Injuries and Risk Factors Study (GBD 2019), we evaluated national and provincial health trends and progress towards important Sustainable Development Goal targets from 1990 to 2019. Methods: We analysed GBD 2019 estimates of mortality, non-fatal health loss, summary health measures and risk factor burden, comparing trends over 1990-2007 and 2007-2019. Additionally, we decomposed changes in life expectancy by cause of death and assessed healthcare system performance. Results: Across the nine provinces, inequalities in mortality and life expectancy increased over 1990-2007, largely due to differences in HIV/AIDS, then decreased over 2007-2019. Demographic change and increases in non-communicable diseases nearly doubled the number of years lived with disability between 1990 and 2019. From 1990 to 2019, risk factor burdens generally shifted from communicable and nutritional disease risks to non-communicable disease and injury risks; unsafe sex remained the top risk factor. Despite widespread improvements in healthcare system performance, the greatest gains were generally in economically advantaged provinces. Conclusions: Reductions in HIV/AIDS and related conditions have led to improved health since 2007, though most provinces still lag in key areas. To achieve health targets, provincial governments should enhance health investments and exchange of knowledge, resources and best practices alongside populations that have been left behind, especially following the COVID-19 pandemic.
The analytic framework, methods and data sources used for GBD 2019 are described extensively in prior publications.12–14 This study complies with GATHER recommendations (online supplemental table S1). Briefly, GBD is a comprehensive effort to identify and use all available data sources, evaluate quality and correct for known biases in each source, implement standardised statistical methods, and produce estimates with 95% uncertainty intervals (UIs) that are propagated throughout all stages of modelling. This report includes estimates of fatal and non-fatal health outcomes and risk factors as well as summary measures of health—years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life-years (DALYs) and healthy life expectancy (HALE). jech-2021-217480supp001.pdf We analysed subnational data in our models by treating provinces as unique geographies nested hierarchically within South Africa.15 We drew on census data (1951, 1960, 1970, 1980, 1985, 1991, 1996, 2001 and 2011) and a wide range of data sources including surveys, vital registration (VR) data, disease surveillance data, registries and published scientific literature. A detailed list of data sources used for the GBD South Africa estimates can be found at the Global Health Data Exchange (GHDx)16 and in online supplemental appendix 1. Statistical methods relevant to this paper are summarised briefly in the following paragraphs and described in detail in online supplemental information section 2 with references in online supplemental information section 5.12–14 We used demographic methods to estimate all-cause, under-5 and adult mortality based on surveys and census data. We then used various South African data sources including provincial-level VR data (1997–2016) in GBD 2019 to produce cause-specific mortality fractions for 286 unique causes of death after correcting for incomplete registration and ‘garbage-coded’ causes. Data were analysed using the Cause of Death Ensemble model, which employs a wide range of predictive models that draw on combinations of covariates correlated with specific causes of death and whose composition is determined by tests of in-sample and out-of-sample performance. Further adjustments were made for discontinuities in long-term trends, including HIV/AIDS as well as improvements in implied pattern of VR completeness by age and sex (online supplemental information section 2). Mortality rates were then scaled to all-cause mortality estimates. Finally, we calculated YLLs and age-standardised rates using a global reference life table. To estimate non-fatal outcomes (incidence, prevalence, and YLDs), data sources of relevance to South Africa were mapped to 369 unique causes of disease or injury and 3473 unique sequelae. Data were analysed in DisMod-MR 2.1, a Bayesian meta-regression tool that produces internally consistent estimates of disease parameters (incidence, prevalence, remission, excess mortality and cause-specific mortality). The prevalence or incidence of each sequela was then multiplied by its corresponding disability weight to calculate YLDs, and sequelae were aggregated by disease or injury to obtain total YLDs for each cause. DALYs were calculated by summing YLLs and YLDs for each cause. Separately, the disease burden attributable to specific risk factors was calculated for each cause using the comparative risk assessment method. HALE was calculated using multiple decrement life tables that incorporated estimates of per-capita YLDs. All estimates were produced separately by 5-year age interval, sex, province and year. For the purposes of this report, we generally present estimates for 1990, 2007 and 2019; 2007 is highlighted here as an approximate turning point (peak) in HIV/AIDS-related mortality in South Africa. We assessed provincial-level health system performance using the Healthcare Access and Quality Index (HAQ Index), a measure developed for the GBD studies that incorporates age-standardised and risk-standardised mortality rates for 32 causes that are amenable to healthcare.17 The HAQ Index is scaled from 0 to 100 across all countries (including subnational units) and years included in the GBD study, allowing direct comparisons across locations and time. We compared HAQ Index values and changes over 1990–2019 among the nine provinces and compared with other Southern African Development Community (SADC) countries. This analytical multilevel approach helps contextualise and benchmark the performance of the South African health system subnationally, nationally and regionally, in the efforts to improve population access to healthcare. Finally, we also assessed progress towards achievement of key health-related SDG targets (namely: maternal, neonatal mortality and under-5 mortality; HIV and tuberculosis incidence and lastly NCD mortality) in South Africa and at provincial level. We assessed observed annualised rate of change (ARC) in the indicator over 2015–2019, the start of the SDG period and contextualised this ARC in terms of that would be required to achieve SDG target by 2030, both nationally and by province. This paper summarises key findings from our analysis of GBD 2019 estimates. Data files containing all GBD 2019 subnational estimates are available on the GHDx (http://ghdx.healthdata.org/gbd-2019).16 International Classification of Disease codes mapped to the GBD cause list are found in online supplemental table S6. Select additional results are presented in online supplemental information section 6; all additional results can be explored through online interactive data visualisations (http://www.healthdata.org/gbd/data-visualizations).
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