Changes in socioeconomic inequality in self-assessed health in South Africa: The contributions of changes in inequalities between and within socioeconomic groups
Bibliographic record
Abstract
Globally, poor health is associated with lower socioeconomic status (i.e., the gradient). While significant socio-demographic drivers of socioeconomic inequalities have been documented in South Africa, little is known about changes in socioeconomic inequalities in health between and within socioeconomic groups, an essential consideration for closing the gaps between socioeconomic groups. This paper assesses changes in health inequalities in South Africa, using self-assessed health (SAH) to uncover the relative contributions of inequalities between and within socioeconomic groups to changes in socioeconomic inequalities in SAH. It uses data from five waves (2008, 2010/11, 2012, 2014/15, and 2017) of South Africa's nationally representative National Income Dynamics Study (NIDS) as cross-sectional with a final sample size ranging between 13,732 and 21,303 adults (>18 years). Based on five categories, SAH was recategorised and dichotomised as "good health" with SAH = 1. Socioeconomic status and quintiles were based on per capita household expenditure. The standard concentration index measured socioeconomic inequality in SAH. A recent methodology decomposes changes in the concentration index of SAH into changes in inequality within and between socioeconomic groups. A pro-poor shift or change is when socioeconomic inequality in health (including for between- and within-socioeconomic groups) reduces between two time periods, while an increase in inequalities means a pro-rich shift or change. The results show a significant pro-rich gradient in SAH among adults in South Africa (concentration index ranging between 0.0053 and 0.0327), with good health reported more by relatively wealthier adults than their more socioeconomically deprived counterparts. This pro-rich gradient declined overall between 2008 and 2017 (a pro-poor shift), associated mainly (between 96% and 100%) with reduced inequalities between socioeconomic groups, i.e., closing gaps between socioeconomic groups. Addressing health inequalities in South Africa requires a multisectoral approach prioritising socioeconomically deprived individuals and policy to reduce health disparities between groups that leave no one behind.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".