What Explains Socioeconomic Inequality in Health-related Quality of Life in Iran? A Blinder-Oaxaca Decomposition
Bibliographic record
Abstract
OBJECTIVES: This study aimed to explain the health-related quality of life (HRQoL) gap between the poorest and the wealthiest quintiles in the capitals of Kermanshah and Kurdistan Provinces (Kermanshah and Sanandaj), in western Iran. METHODS: This was a cross-sectional study conducted among 1772 adults. Data on socio-demographic characteristics, socioeconomic status (SES), lifestyle factors, body mass index, and HRQoL of participants were collected using a self-administered questionnaire. The slope and relative indices of inequality (SII and RII, respectively) were employed to examine socioeconomic inequality in poor HRQoL. Blinder-Oaxaca (BO) decomposition was used to quantify the contribution of explanatory variables to the gap in the prevalence of poor HRQoL between the wealthiest and the poorest groups. RESULTS: The overall crude and age-adjusted prevalence of poor HRQoL among adults was 32.0 and 41.8%, respectively. The SII and RII indicated that poor HRQoL was mainly concentrated among individuals with lower SES. The absolute difference (%) in the prevalence of poor HRQoL between the highest and lowest SES groups was 28.4. The BO results indicated that 49.9% of the difference was explained by different distributions of age, smoking behavior, physical inactivity, chronic health conditions, and obesity between the highest and lowest SES groups, while the remaining half of the gap was explained by the response effect. CONCLUSIONS: We observed a pro-rich distribution of poor HRQoL among adults in the capitals of Kermanshah and Kurdistan Provinces. Policies and strategies aimed at preventing and reducing smoking, physical inactivity, chronic health conditions, and obesity among the poor may reduce the gap in poor HRQoL between the highest and lowest SES groups in Iran.
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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 | 0.030 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".