Impact of the Elderly on Household Health Expenditure in Bihar and Kerala, India
Bibliographic record
Abstract
Ageing in India is leading to an increase in chronic diseases. Given the limited health insurance coverage, this could lead to a variety of economic- and access-related consequences for the households. Against this backdrop, this article aims at examining the impact of the presence of the elderly on household health expenditure, avoidance of treatment, loss of income and use of alternate sources of funding to pay for care. The article uses data from 2004 National Sample Survey Organisation survey on healthcare for two Indian states, namely, Bihar and Kerala. The rate of catastrophic health expenditure (CHE) is found to be higher in Kerala and is associated with a higher proportion of households having elderly members, who, in turn, have higher incidence of chronic disease. While the presence of elderly in the household, incidence of chronic disease and treatment from private sources are linked to CHE, our results suggest that other groups, such as households without elderly, may simply be delaying the economic consequences of paying for healthcare by borrowing. Though the ageing population is leading to increased health expenditure for households due to increased chronic illness, the impact of using private treatment is much less clear.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".