Assessing the Distribution of Household Financial Contribution to Health System: A Case Study of Iran
Bibliographic record
Abstract
BACKGROUND: Throughout the world, financing the healthcare system through households' financial contribution is a challenging issue in evaluating performance of healthcare systems. The purpose of this paper is illustrating the consequences of Iranian household to health system financial contribution in terms of burden and incomes approaches. METHOD: The Data derived from an annual survey by Statistics Center of Iran (SCI) on expenditure-income of 36,551 households in 2012 was used to analyze countrywide distribution indicators of households' medical expenses by measuring indices of Income and burden approaches based on World Health Organization (WHO) and World Bank recommended methodologies. RESULTS: The fairness in financial contribution index was 0.85 and 0.82, and the income redistributive effect index was 0.54 and 0.3 in urban and rural areas, respectively. The fairness in financial contribution index was found 0.84 and 0.83 and the income redistributive effect index was 0.48 and 0.25 for households with and without medical insurance, respectively.The percentages of household with catastrophic health payments were 2.4% and 4% and the change in the number of household falling below the poverty line due to health system payments was 0.4% and 2% in urban and rural areas, respectively. The percentages of household with catastrophic health payments were 2.8% and 3% and the change in the number of household falling below the poverty line due to health system payments was 0.008 and 0.011 for households with and without medical insurance, respectively. CONCLUSION: Distribution indicators of medical expenses were more favorable in urban areas compared to rural areas and Medical insurance has declined impoverishment risks and number of people suffered due to catastrophic health expenditure. In addition, the result showed that there are different approaches for analyzing the distribution of out of pocket payments which used to complement each other in respect of formulation and development policy making in health system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".