Has the Rajiv Aarogyasri Community Health Insurance Scheme of Andhra Pradesh Addressed the Educational Divide in Accessing Health Care?
Bibliographic record
Abstract
BACKGROUND: Equity of access to healthcare remains a major challenge with families continuing to face financial and non-financial barriers to services. Lack of education has been shown to be a key risk factor for 'catastrophic' health expenditure (CHE), in many countries including India. Consequently, ways to address the education divide need to be explored. We aimed to assess whether the innovative state-funded Rajiv Aarogyasri Community Health Insurance Scheme of Andhra Pradesh state launched in 2007, has achieved equity of access to hospital inpatient care among households with varying levels of education. METHODS: We used the National Sample Survey Organization 2004 survey as our baseline and the same survey design to collect post-intervention data from 8623 households in the state in 2012. Two outcomes, hospitalisation and CHE for inpatient care, were estimated using education as a measure of socio-economic status and transforming levels of education into ridit scores. We derived relative indices of inequality by regressing the outcome measures on education, transformed as a ridit score, using logistic regression models with appropriate weights and accounting for the complex survey design. FINDINGS: Between 2004 and 2012, there was a 39% reduction in the likelihood of the most educated person being hospitalised compared to the least educated, with reductions observed in all households as well as those that had used the Aarogyasri. For CHE the inequality disappeared in 2012 in both groups. Sub-group analyses by economic status, social groups and rural-urban residence showed a decrease in relative indices of inequality in most groups. Nevertheless, inequalities in hospitalisation and CHE persisted across most groups. CONCLUSION: During the time of the Aarogyasri scheme implementation inequalities in access to hospital care were substantially reduced but not eliminated across the education divide. Universal access to education and schemes such as Aarogyasri have the synergistic potential to achieve equity of access to healthcare.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.002 |
| 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".