Pattern and determinants of health care use and expenditures at the end-of-life in India
Bibliographic record
Abstract
The burden of out-of-pocket expenditure on health care is unduly heavy on poor and vulnerable households. A national study shows that almost one quarter of households fall into poverty as a direct consequence of the medical expenses they pay after being hospitalized. Further, more than two-fifths of individuals who were hospitalized during the last year borrowed money or sold assets to cover the hospital expenses. Health and social insurance mechanisms in India have not been adequately developed to mitigate such adverse impact. The consequences on those households get elevated further when the hospitalization eventually results into a death event. One possible outcome could be pushing these families into a zone of permanent poverty. The main objectives of the study are: • to examine the type of medical attention received at the end-of-life • to analyse differentials in the use of hospital care and expenditure on treatment at the end-of-life by socio-economic groups • to compare financial burden of treatment (direct and indirect) on households reporting fatal and non-fatal outcomes. Conclusions 1. The poor and rural population persistently report lower levels of medical attention and use of hospital care at the end-of-life, thus pinpointing accessibility and equity concerns. 2.An incidence of hospitalization puts severe financial burden on a household and the burden becomes unduly heavy when resulting into death. In both rural and urban areas the burden rises with expenditure class, much sharply among fatal than non-fatal cases. Impoverishment burden is felt much more for rural than urban population. 3. There is need for a comprehensive health insurance coverage for poor and rural population to mitigate the adverse impact of meeting hospitalization costs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".