Health-related quality of life variation by socioeconomic status: Evidence from an Iranian population-based study
Bibliographic record
Abstract
BACKGROUND: Health-Related Quality of Life (HRQoL) values based on the accurate and reliable European Quality of Life Five Dimension (EQ-5D) questionnaire gives health-state utilities as a helpful data set for studying socio-demographic and socio-economic inequalities in health status in the general population. We aimed to do a population-based study to see how HRQoL varies by socio-demographics and socioeconomic status (SES). MATERIALS AND METHODS: The study was a cross-sectional population-based study in Shiraz, Iran's southwest. Data was gathered utilizing a personal digital assistant (PDA). A trained interviewer administered the EQ-5D questionnaire to a representative sample of 1036 inhabitants. Principal component analysis (PCA) was used to create SES indices. Because of the skewed distribution, quantile regression was utilized to model the quartiles of HRQoL values. STATA 12.0 was used to perform all statistical analyses. P <0.05 was considered statistically significant. RESULTS: In 1036 study respondents, women had a mean HRQoL of 0.67 ± 0.28, whereas men had a mean HRQoL of 0.78 ± 0.25. Gender and age remained significant in all quartiles of HRQoL value. Participants with insurance showed 0.14 and 0.08 higher HRQoL values in the first and second HRQoL quartiles than those without coverage, respectively. Education [95% CI: 0.034, 0.111)], economy [95% CI: 0.013, 0.077], and assets [95% CI: 0.003, 0.069] all had an impact on HRQoL value in the lowest quintile. CONCLUSION: In all quartiles of HRQoL value, women had lower reported HRQoL than men. Insurance programs aimed at more disadvantaged groups with poorer HRQoL may help to minimize inequity. Education, economics, and assets all had an impact on the lower quartiles of HRQoL value, emphasizing the importance of general policies in determining public health status.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".