Missing Middle: Extending Health Insurance Coverage in India
Bibliographic record
Abstract
Abstract: In India’s stride towards achieving its goal of Universal Health Coverage, an important and sometimes neglected aspect is that of health insurance. The pandemic has served to highlight the state of healthcare infrastructure along with the impact of government spending on the healthcare sector. However, in the current Union Health Budget (2022-23) there has been only a marginal increase of 0.2 percent over the revised estimates of 2021-22 which clearly indicates that the financial protection extended by the government does not amount too much. Consequently, the public is directed towards the private sector which results in high out-of-pocket expenditures. Though the government schemes include insurance coverage for the ultra-poor, and there is a portion of the population that is covered by private and voluntary insurance, that leaves 30% of the population devoid of any insurance. They have been referred to as the “missing middle.” This paper looks at the health insurance landscape of countries like the USA, China, and Canada. We also look at the data regarding coverage of different schemes and took inputs from hospitals and private insurance providers to gain a perspective on how health insurance coverage in India can be expanded and be made more inclusive. Factors determining demand and supply are analyzed. We recommend that both the private and public sectors need to collaborate to achieve this outcome. Keywords: Universal Health Coverage, Missing Middle, Health Insurance, Employee State Insurance Corporation (ESIC), Private Health Insurance, Pradhan Mantri Jan Arogya Yojna (PMJAY).
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".