Health trends, inequalities and opportunities in South Africa’s provinces, 1990-2019: findings from the Global Burden of Disease 2019 Study

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Study Justification:
The study titled “Health trends, inequalities and opportunities in South Africa’s provinces, 1990-2019: findings from the Global Burden of Disease 2019 Study” aims to evaluate national and provincial health trends in South Africa over a 30-year period. The study addresses the impact of four colliding epidemics (HIV and tuberculosis, chronic illness and mental health, injury and violence, and maternal, neonatal, and child mortality) on health and well-being. The findings from this study provide valuable insights into the progress made towards important Sustainable Development Goal targets and highlight the areas where improvements are still needed.
Highlights:
1. Inequalities in mortality and life expectancy increased between 1990 and 2007 due to differences in HIV/AIDS, but decreased between 2007 and 2019.
2. The number of years lived with disability nearly doubled between 1990 and 2019, primarily due to demographic change and increases in non-communicable diseases.
3. Risk factor burdens shifted from communicable and nutritional diseases to non-communicable diseases and injuries over the study period, with unsafe sex remaining the top risk factor.
4. Healthcare system performance improved, but the greatest gains were observed in economically advantaged provinces.
Recommendations:
1. Provincial governments should enhance health investments to address the lagging areas and achieve health targets.
2. Exchange of knowledge, resources, and best practices should be promoted among provinces to ensure equitable improvements in health outcomes.
3. Special attention should be given to populations that have been left behind, especially in the aftermath of the COVID-19 pandemic.
Key Role Players:
1. Provincial governments
2. Health departments and ministries
3. Healthcare providers and professionals
4. Public health organizations and NGOs
5. Community leaders and advocates
Cost Items for Planning Recommendations:
1. Increased funding for healthcare infrastructure and services
2. Investments in healthcare workforce training and development
3. Research and data collection to monitor progress and inform decision-making
4. Implementation of health promotion and disease prevention programs
5. Support for knowledge exchange and collaboration initiatives
6. Targeted interventions to address specific health challenges
7. Monitoring and evaluation systems to assess the impact of interventions
Please note that the cost items provided are general categories and not actual cost estimates. The actual costs will depend on the specific context and strategies adopted to address the recommendations.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on data from the 2019 Global Burden of Diseases, Injuries and Risk Factors Study (GBD 2019). The study used a comprehensive analytic framework, methods, and data sources, including census data, surveys, vital registration data, disease surveillance data, registries, and published scientific literature. The study complied with GATHER recommendations and employed standardized statistical methods. The report includes estimates of fatal and non-fatal health outcomes, risk factors, and summary measures of health. The study analyzed subnational data, treating provinces as unique geographies nested hierarchically within South Africa. The evidence is further supported by the use of statistical models and adjustments for known biases. To improve the evidence, the authors could provide more specific details about the statistical methods used and the sources of data, as well as include information about the sample size and representativeness of the study population.

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).

Based on the provided description, it is difficult to identify specific innovations for improving access to maternal health. The description mainly focuses on analyzing health trends and inequalities in South Africa, rather than proposing specific innovations. To identify potential innovations, it would be helpful to have more information about the current challenges and barriers to accessing maternal health in South Africa.
AI Innovations Description
Based on the description provided, the recommendation to improve access to maternal health in South Africa is to enhance health investments and exchange of knowledge, resources, and best practices among provincial governments and populations that have been left behind. This recommendation is based on the findings from the Global Burden of Disease 2019 Study, which highlighted the colliding epidemics and health inequalities in South Africa.

To achieve health targets, it is crucial for provincial governments to prioritize maternal health and allocate sufficient resources to address the challenges faced in this area. This includes investing in healthcare infrastructure, ensuring availability of essential maternal health services, and improving the quality of care provided to pregnant women.

Additionally, exchanging knowledge, resources, and best practices can help provinces learn from each other’s successes and challenges in improving maternal health. This can involve sharing innovative approaches, successful interventions, and lessons learned in addressing maternal health issues. Collaboration and cooperation among provinces can lead to more effective strategies and interventions to improve access to maternal health services.

Furthermore, it is important to address the disparities and inequalities in access to maternal health services. Efforts should be made to reach populations that have been left behind, especially those in economically disadvantaged areas. This can involve targeted interventions, outreach programs, and community engagement to ensure that all pregnant women have equal access to quality maternal health services.

Overall, by enhancing health investments and promoting knowledge exchange, resources sharing, and addressing inequalities, South Africa can make significant progress in improving access to maternal health and achieving the Sustainable Development Goal targets in this area.
AI Innovations Methodology
Based on the provided description, it seems that you are looking for innovations to improve access to maternal health in South Africa. Additionally, you are interested in a methodology to simulate the impact of these recommendations on improving access to maternal health. Unfortunately, the provided text does not explicitly mention any specific recommendations or innovations. However, I can provide you with a general approach to developing recommendations and simulating their impact.

1. Identify potential innovations: Start by conducting a thorough review of existing literature, research studies, and best practices in the field of maternal health. Look for innovative approaches, technologies, or interventions that have shown promise in improving access to maternal health in similar contexts. Some examples of potential innovations could include telemedicine for remote consultations, mobile health applications for prenatal care, community-based health worker programs, or improved transportation systems for pregnant women.

2. Assess feasibility and suitability: Evaluate the feasibility and suitability of each potential innovation in the context of South Africa. Consider factors such as infrastructure, resources, cultural norms, and the specific challenges faced by pregnant women in accessing maternal health services. Narrow down the list of potential innovations to those that are most likely to be effective and feasible in the South African context.

3. Develop a simulation model: Once you have identified the potential innovations, you can develop a simulation model to estimate their potential impact on improving access to maternal health. The simulation model should take into account various factors such as the target population, geographic distribution, current healthcare infrastructure, and the specific outcomes you want to measure (e.g., reduction in maternal mortality, increase in antenatal care coverage).

4. Collect data: Gather relevant data to inform the simulation model. This may include demographic data, health facility data, population health indicators, and any other data sources that are necessary to accurately represent the current state of maternal health access in South Africa.

5. Define parameters and assumptions: Define the parameters and assumptions for the simulation model. This includes factors such as the uptake rate of the innovation, the potential reach of the intervention, and the expected impact on maternal health outcomes. These parameters should be based on available evidence and expert input.

6. Run simulations: Use the simulation model to run various scenarios that simulate the impact of the recommended innovations on improving access to maternal health. Compare the outcomes of different scenarios to understand the potential benefits and trade-offs of each innovation.

7. Analyze results: Analyze the results of the simulations to assess the potential impact of the recommended innovations on improving access to maternal health. Look for trends, patterns, and key insights that can inform decision-making and prioritize the most effective interventions.

8. Refine recommendations: Based on the simulation results, refine the recommendations for improving access to maternal health. Consider factors such as cost-effectiveness, scalability, and sustainability of the interventions. Prioritize the recommendations that are most likely to have a significant impact on improving access to maternal health in South Africa.

It is important to note that the methodology for simulating the impact of recommendations may vary depending on the specific innovations and context. The steps outlined above provide a general framework for developing recommendations and simulating their impact on improving access to maternal health.

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