Social health insurance contributes to universal coverage in South Africa, but generates inequities: survey among members of a government employee insurance scheme
Bibliographic record
Abstract
BACKGROUND: Many low- and middle-income countries are reforming their health financing mechanisms as part of broader strategies to achieve universal health coverage (UHC). Voluntary social health insurance, despite evidence of resulting inequities, is attractive to policy makers as it generates additional funds for health, and provides access to a greater range of benefits for the formally employed. The South African government introduced a voluntary health insurance scheme (GEMS) for government employees in 2005 with the aim of improving access to care and extending health coverage. In this paper we ask whether the new scheme has assisted in efforts to move towards UHC. METHODS: Using a cross-sectional survey across four of South Africa's nine provinces, we interviewed 1329 government employees, from the education and health sectors. Data were collected on socio-demographics, insurance coverage, health status and utilisation of health care. Multivariate logistic regression was used to determine if service utilisation was associated with insurance status. RESULTS: A quarter of respondents remained uninsured, even higher among 20-29 year olds (46%) and lower-skilled employees (58%). In multivariate analysis, the odds of an outpatient visit and hospital admission for the uninsured was 0.3 fold that of the insured. Cross-subsidisation within the scheme has provided lower-paid civil servants with improved access to outpatient care at private facilities and chronic medication, where their outpatient (0.54 visits/month) and inpatient utilisation (10.1%/year) approximates that of the overall population (29.4/month and 12.2% respectively). The scheme, however, generated inequities in utilisation among its members due to its differential benefit packages, with, for example, those with the most benefits having 1.0 outpatient visits/month compared to 0.6/month with lowest benefits. CONCLUSIONS: By introducing the scheme, the government chose to prioritise access to private sector care for government employees, over improving the availability and quality of public sector services available to all. Government has recently regained its focus on achieving UHC through the public system, but is unlikely to discontinue GEMS, which is now firmly established. The inequities generated by the scheme have thus been institutionalised within the country's financing system, and warrant attention. Raising scheme uptake and reducing differentials between benefit packages will ameliorate inequities within civil servants, but not across the country as a whole.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".