Socioeconomic inequality in catastrophic healthcare expenditures in Western Iran
Bibliographic record
Abstract
Purpose Financial protection of households against catastrophic healthcare expenditure (CHE) is defined as one of the main goals in health systems. The purpose of this paper is to measure and decompose socioeconomic inequality in CHE among households in Kermanshah province, Western of Iran. Design/methodology/approach This cross-sectional study was carried out among 1,188 households in 2017. Data were extracted from the Household Income and Expenditure Survey which is conducted by the Statistical Center of Iran. The CHE is defined as household healthcare expenditure greater than or equal to the 40 percent of household’s “capacity to pay.” The concentration curve and the Wagstaff (W) and Erreygers (E) indexes were used to illustrate and measure the extent of socioeconomic inequality in CHE. In addition, the authors decomposed the W and E indexes to identify the main determinants of socioeconomic inequality in CHE. Findings The results indicated that the prevalence of CHE among households was 4.12 percent (95% confidence interval (CI): 3.13 to 5.42 percent). The estimated value of the W and E indexes were −0.2849 (95% CI: −0.4493 to −0.1205) and −0.0451 (95% CI: −0.0712 to −0.0190), respectively; suggesting the concentration of CHE prevalence among the poor households. Decomposition analyses indicated socioeconomic status as the most important factor contributing to the concentration of CHE among the poor. In contrast, health insurance coverage was found to increase the concentration of CHE among the rich in Iran. Originality/value The current study demonstrated a higher concentration of CHE among the poor households in Kermanshah province. These results call for the government’s efforts to reduce healthcare expenditure among socioeconomically disadvantaged populations. Further studies are required to understand the mechanisms through which health insurance coverage increased the probability of CHE among rich in Kermanshah province.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".