Socioeconomic - related inequalities in overweight and obesity: findings from the PERSIAN cohort study
Bibliographic record
Abstract
Abstract Background Overweight and obesity are major health concerns worldwide, with adverse health consequences during the life span. This study measured socioeconomic inequality in overweight and obesity among Iranian adults. Methods Data were extracted from 129,257 Iranian adults (aged 35 years and older) participated in the Prospective Epidemiologic Research Studies in IrAN (PERSIAN) in 14 provinces of Iran in 2014. Socioeconomic-related inequality in overweight and obesity was estimated using the Concentration Index (C n ). The C n further decomposed to find factors explaining the variability within the Socioeconomic related inequality in overweight and obesity. Results Of the total number of participants, 1.98, 26.82, 40.76 and 30.43% had underweight, normal weight, overweight and obesity respectively. The age-and sex standardized prevalence of obesity was higher in females than males (39.85% vs 18.79%). People with high socioeconomic status (SES) had a 39 and 15% higher chance of being overweight and obese than low SES people, respectively. The positive value of C n suggested a higher concentration of overweight (0.081, 95% confidence interval [CI]; 0.074–0.087) and obesity (0.027, 95% CI; 0.021–0.034) among groups with high SES. There was a wide variation in socioeconomic-related inequality in overweight and obesity rate across 14 provinces. The decomposition results suggested that SES factor itself explained 66.77 and 89.07% of the observed socioeconomic inequalities in overweight and obesity among Iranian adults respectively. Following SES, province of residence, physical activity, using hookah and smoking were the major contributors to the concentration of overweight and obesity among the rich. Conclusions Overall, we found that overweight and obesity is concentrated among high SES people in the study population. . Accordingly, it seems that intersectional actions should be taken to control and prevent overweight and obesity among higher socioeconomic groups.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 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".