Cross-national comparison of socioeconomic inequalities in obesity in the United States and Canada
Bibliographic record
Abstract
INTRODUCTION: Prior cross-national studies of socioeconomic inequalities in obesity have only compared summary indices of inequality but not specific, policy-relevant dimensions of inequality: (a) shape of the socioeconomic gradient in obesity, (b) magnitude of differentials in obesity across socioeconomic levels and, (c) level of obesity at any given socioeconomic level. We use unique data on two highly comparable societies - U.S. and Canada - to contrast each of these inequality dimensions. METHODS: Data came from the 2002/2003 Joint Canada/U.S. Survey of Health. We calculated adjusted prevalence ratios (APRs) for obesity (compared to normal weight) by income quintile and education group separately for both nations and, between Canadians and Americans in the same income or education group. RESULTS: In the U.S., every socioeconomic group except the college educated had significant excess prevalence of obesity. By contrast in Canada, only those with less than high school were worse off, suggesting that the shape of the socioeconomic gradient differs in the two countries. U.S. differentials between socioeconomic levels were also larger than in Canada (e.g., PR quintile 1 compared to quintile 5 was 1.82 in the U.S. [95 % CI: 1.52-2.19] but 1.45 in Canada [95 % CI: 1.10-1.91]). At the lower end of the socioeconomic gradient, obesity was more prevalent in the U.S. than in Canada. CONCLUSIONS: Our results suggest there is variation between U.S. and Canada in different dimensions of socioeconomic inequalities in obesity. Future research should examine a broader set of nations and test whether specific policies or environmental exposures can explain these differences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".