Measuring and decomposing socioeconomic inequality in catastrophic healthcare expenditures in Iran
Bibliographic record
Abstract
OBJECTIVES: Equity in financial protection against healthcare expenditures is one the primary functions of health systems worldwide. This study aimed to quantify socioeconomic inequality in facing catastrophic healthcare expenditures (CHE) and to identify the main factors contributing to socioeconomic inequality in CHE in Iran. METHODS: A total of 37 860 households were drawn from the Households Income and Expenditure Survey, conducted by the Statistical Center of Iran in 2017. The prevalence of CHE was measured using a cut-off of spending at least 40% of the capacity to pay on healthcare services. The concentration curve and concentration index (C) were used to illustrate and measure the extent of socioeconomic inequality in CHE among Iranian households. The C was decomposed to identify the main factors explaining the observed socioeconomic inequality in CHE in Iran. RESULTS: The prevalence of CHE among Iranian households in 2017 was 5.26% (95% confidence interval [CI], 5.04 to 5.49). The value of C was -0.17 (95% CI, -0.19 to -0.13), suggesting that CHE was mainly concentrated among socioeconomically disadvantaged households in Iran. The decomposition analysis highlighted the household wealth index as explaining 71.7% of the concentration of CHE among the poor in Iran. CONCLUSIONS: This study revealed that CHE is disproportionately concentrated among poor households in Iran. Health policies to reduce socioeconomic inequality in facing CHE in Iran should focus on socioeconomically disadvantaged households.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".