Social inequalities in obesity and overweight in 11 OECD countries
Bibliographic record
Abstract
BACKGROUND: Evidence of inequalities in obesity and overweight is available mostly from national studies. This article provides a broad international comparison of inequalities by education level and socio-economic status, in men and women and over time. METHODS: Data from national health surveys of 11 OECD countries were used. The size of inequalities was assessed on the basis of absolute and relative inequality indexes. A regression-analysis approach was used to assess differences between social groups in trends over time. RESULTS: Of the countries examined, USA and England had the highest rates of obesity and overweight. Large social inequalities were consistently detected in all countries, especially in women. Absolute inequalities were largest in Hungary and Spain with a difference of 11.6 and 10% in obesity rates in men, and 18.3 and 18.9% in women, respectively, across the education spectrum. Relative inequalities were largest in France and Sweden with poorly educated men 3.2 and 2.8 times as likely to be obese as men with the highest education (18 and 17 times for women in Spain and Korea, respectively). Pro-poor inequalities in overweight were observed for men in USA, Canada, Korea, Hungary, Australia and England. Inequalities remained virtually stable during the last 15 years, with only small variations in England, Korea, Italy and France. CONCLUSION: Large and persistent social inequalities in obesity and overweight by education level and socio-economic status exist in OECD countries. These are consistently larger in women than in men.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".