Prevalence of obesity and elevated body mass index along a progression of rurality: A cross-sectional study – The Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Introduction: Obesity is an important public health concern, and large studies of rural–urban differences in prevalence of obesity are lacking. Our purpose is to compare body mass index (BMI) and obesity in Canada using an expanded definition of rurality. Methods: A cross-sectional analysis of self-reported BMI across diverse communities of Canadians aged 45–85 years was conducted using data from the Canadian Longitudinal Study on Aging (CLSA), a national sample representative of community-dwelling residents. Rurality was identified in the CLSA based on residential postal codes, which were divided into 4 categories: urban, peri-urban, mixed and rural. Logistic regression models were constructed to calculate adjusted odds ratios (aORs) with 95% confidence intervals (95% CIs) between obesity (BMI ≥30 kg/m 2 from self-reported weight and height) and rurality, adjusting for age, sex, province, marital status, number of residents in household and household income. Results: Twenty-one thousand one hundred and twenty-six Canadian residents aged 45–85 years, surveyed during 2010–2015, were included. 26.8% were obese. Obesity was less prevalent amongst urban (25.2%) than rural (30.3%, P < 0.0001), mixed (28.7%, P < 0.0001) or peri-urban communities (28.1%, P < 0.0001). When compared to urban areas, the aOR (95% CI) for obesity was 1.09 (1.00–1.20) in rural regions and 1.20 (1.08–1.35) in peri-urban settings. In areas of mixed urban and rural residence, the aOR was 1.12 (0.99–1.27). Conclusion: One in four Canadian adults were obese. Living in a non-urban setting is an independent risk factor for obesity. Rural–urban health disparities could underlie rural–urban differences, but further research is needed.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".