Does use of tobacco or alcohol contribute to impoverishment from hospitalization costs in India?
Bibliographic record
Abstract
The study investigates the association between tobacco and alcohol use, and the potential risk of impoverishment from borrowing and distress selling of assets for meeting costs of hospitalization in India. Data from the fifty-second round of the National Sample Survey, a representative survey of 120,942 households across India, were used to investigate the likelihood and the levels of borrowing and distress selling of assets to cover hospitalization expenditures among regular users of tobacco and/or alcohol, non-users from households where there was use, and non-users from households with no use. The data were analyzed by bivariate comparisons and multivariate logistic and ordinary least square regression. The study found a higher risk of borrowing/distress selling during hospitalization for individuals who use tobacco (OR 1.35, p<0.05), who were non-users but belong to households that use tobacco (OR 1.38, p<0.05), and non-users from households that use both tobacco and alcohol (OR 1.51, p<0.05), even after controlling for socio-economic and demographic factors. The same groups also met a higher percentage of hospitalization expenditures through borrowing/distress selling of assets. The adjusted population-attributable risk proportion of borrowing/distress selling to meet hospital expenditures for tobacco and alcohol use was 16%. The study suggests that there is an association between use of tobacco and alcohol, and impoverishment through borrowing and distress selling of assets due to costs of hospitalization. While reduction of poverty is the overarching goal of developing countries and multilateral development organizations, very little is mentioned about control of tobacco and alcohol in the framework of development. It might be necessary to include strategies for control of tobacco and alcohol in the larger framework of poverty reduction.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".