Measuring and Explaining Socioeconomic Inequalities in Public Healthcare Utilization in Western Iran: Evidence from a Cross-sectional Survey.
Bibliographic record
Abstract
BACKGROUND: Equity in healthcare utilization is a major health policy goal in all healthcare systems. This study aimed to examine socioeconomic inequalities in public healthcare utilization in Kermanshah City, western Iran. STUDY DESIGN: A cross-sectional study. METHODS: Using convenience sampling method, 2040 adult aged 18-65 yr were enrolled from Kermanshah City in 2017. A self-administrated questionnaire was used to collect data on socio-demographic characteristics, socioeconomic status, behavioral factors, and utilization of public healthcare services (inpatient and outpatient care) over the period between from May to Aug 2017. The concentration index (C) was used to measure and decompose socioeconomic inequalities in the utilization inpatient and outpatient care in public sector. The indirect standardization method was used to estimate the horizontal inequity (HI) indices in inpatient and outpatient care use. RESULTS: The utilization outpatient (C=-0.121, 95% CI: -0.171, -0.071) and inpatient care in public sector (C=-0.165, 95% CI: -0.229, -0.101) were concentrated among the poor in Kermanshah, Iran. Socioeconomic status, health-related quality of life, marital status and having a chronic health condition were the main determinants of socioeconomic-related inequalities in the utilization of inpatient and outpatient care in public sector among adults. The distributions of outpatient (HI=-0.045, CI: -0.093 to 0.003) and inpatient care (HI= -0.044 95% CI: -0.102, 0.014) in Kermanshah were pro-poor. These results were not statistically significant (P<0.05). CONCLUSIONS: The utilization of public healthcare services in Iran are pro-poor. The pro-poor distribution of inpatient and outpatient care in public facilities calls for initiatives to increase the allocation of resources to public facilities in Iran that may greatly benefit the health outcomes of the poor.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".