Availability Does Not Mean Utilisation: Analysis of a Large Micro Health Insurance Programme in Pakistan
Bibliographic record
Abstract
In recent years, several Micro Health Insurance (MHI) schemes have been initiated in low- and middle-income countries (LMIC) to meet the universal health coverage targets. Evidence on the utilization of these MHI schemes is scarce. Field experiences and lesson learning is crucial to effectively increase access to health care and offer protection against catastrophic health expenditure to the poorest population through the MHI schemes. This paper analyzes community utilization and factors affecting utilization of an MHI provided to the poorest rural households in eight districts of Sindh province of Pakistan. This initiative is part of a larger pro-poor European Union (EU) funded Sindh Union Council and Community Economic Strengthening Support (SUCCESS) Programme implemented by the Rural Support Programs (RSPs). The analysis draws on insurance utilization records and an internal assessment report by the RSPs Network (RSPN). The analysis provides qualitative experiences of the community, empanelled health care providers, the insurance agency and frontline management staff. Our analysis revealed that the overall utilization was very low (0.42%) and the highest number of cases treated at the hospital were of women utilizing obstetric and gynaecology related care. The scheme was noted to prevent catastrophic health expenditure in households that were able to successfully utilize the scheme. Key factors affecting utilization were identified to be around i) awareness creation, ii) distance to empanelled hospitals, and iii) access issues at the health facility level. We aim to add to the knowledge base around MHI for policy makers to design and implement more informed initiatives in the future.
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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 | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".