Main Determinants of Catastrophic Health Expenditures: A Bayesian Logit Approach on Iranian Household Survey Data (2010)
Bibliographic record
Abstract
BACKGROUND: During recent decades, increase in both health care expenditures and improvement of the awareness as well as health expectations have created some problems with regard to finance healthcare expenditures so that the issue of health financing by households has been determined as a major challenge in health sector. According to the definition by the World Health Organization, catastrophic health expenditure is considered if financial contribution for health service is more than 40% of income remaining after subsistence needs have been met. OBJECTIVES: The purpose of our study was determination of Main factors on catastrophic health expenditures in Iranian households. PATIENTS & METHODS: In this study, using an econometrics Bayesian logit model, determinants of the appearance of catastrophic health expenditure based on household budget data collected in 2010 were evaluated. RESULTS: Among Iranian households, the following groups were more likely to encounter with unsustainable health expenditures: rural households, households with the numbers of the elderly more than 65 years, illiterate householders, unemployed householders, households with some unemployed persons, households in upper rank and households with larger equivalent household size were higher than the average of community could significantly predict catastrophic health expenditures. CONCLUSIONS: About 2.1% of households were faced with catastrophic health expenditures in 2010. Thus, the implemented policies could not make considerable and significant change in improving justice in financing in health systems.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".