Socioeconomic-related inequalities in self-rated health status in Kermanshah city, Islamic Republic of Iran: a decomposition analysis
Bibliographic record
Abstract
BACKGROUND: Socioeconomic-related inequalities in health are a major public health challenge in both developed and developing countries. Little evidence is available on socioeconomic-related inequalities in health in different regions of the Islamic Republic of Iran. AIMS: This study aimed to determine socioeconomic-related inequality in poor self-rated health in adults in Kermanshah city, western Islamic Republic of Iran. METHODS: This cross-sectional study with stratified sampling obtained data on socioeconomic status, demographic characteristics, behavioural risk factors and self-rated health of 2040 adults (≥ 18 years) in Kermanshah city. A self-administrated questionnaire was used to collect data from the participants. The concentration (C) index and C curve were used to determine the socioeconomic-related inequality in poor self-rated health. A decomposition analysis of the C index was done to identify the factors explaining socioeconomic-related inequality in poor self-rated health. RESULTS: The crude and age-adjusted prevalence of poor self-rated health was 13.8% and 18.1%, respectively. The estimated C for the whole sample was -0.295, indicating that poor SRH was concentrated in the poor. The decomposition results suggested that socioeconomic status (45.5%), having a chronic health condition (11.9%) and smoking (7.3%) were the main factors contributing to the concentration of poor self-rated health among those of lower socioeconomic status. CONCLUSION: The concentration of poor self-rated health among the poor in Kermanshah city warrants policy attention. Policies aimed at reducing inequality in wealth distribution and risky health behaviour and preventing chronic health conditions among the poor may mitigate socioeconomic-related inequalities in poor self-rated health in Kermanshah.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".