Geographic patterns of deprivation in South Africa: informing health equity analyses and public resource allocation strategies
Bibliographic record
Abstract
There is a growing interest in the use of small area analyses in investigating the relationship between socioeconomic status and health, and in informing resource allocation decision-making. However, few such studies have been undertaken in low- and middle-income countries (LMICs). This paper reports on such a study undertaken in South Africa. It both looked at the feasibility of developing a broad-based area deprivation index in a data scarce context and considered the implications of such an index for geographic resource allocations. Despite certain data problems, it was possible to construct and compare three different indices: a general index of deprivation (GID), compiled from census data using principal component analysis; a policy-perspective index of deprivation (PID), based on groups identified as priorities within policy documents; and a single indicator of deprivation (SID), selected for relevance and feasibility of use. The findings demonstrate clearly that in South Africa deprivation is multi-faceted, is concentrated in specific areas within the country and is correlated with ill-health. However, the formula currently used by the National Treasury to allocate resources between geographic areas, biases these allocations towards less deprived areas within the country. The inclusion of the GID within this formula would dramatically alter allocations towards those areas suffering from human development deficits. The area in which analysis was undertaken was not, however, sufficiently small to identify pockets of deprivation within the less deprived metropolitan areas. These findings suggest that it is feasible to conduct small area analyses in LMICs but that specific attention needs to be given to the size of the geographic unit used in analysis. In addition, they highlight the importance of considering deprivation in resource allocation mechanisms if vertical equity goals are to be promoted through resource allocation, particularly within decentralized health systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".