Prevalence of age-specific and sex-specific overweight and obesity in Ontario and Quebec, Canada: a cross-sectional study using direct measures of height and weight
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether combining three cycles of the Canadian Health Measures Survey (CHMS) produces provincially representative and valid estimates of overweight and obesity in Ontario and Quebec. SETTING: An ongoing, nationally representative health survey in Canada, with data released every 2 years. Objective measures of height and weight were taken at mobile examination centres located within 100 km of participants' residences. To increase sample size, we combined three cycles completed during 2007-2013. PARTICIPANTS: 5740 Ontario residents and 3980 Quebec residents aged 6-79, with birth dates and directly measured height and weight recorded in the CHMS. Pregnant females were excluded. Sociodemographic characteristics of the Ontario and Quebec portions of the CHMS appeared similar to characteristics from the 2006 Canada Census. PRIMARY OUTCOME MEASURES: Objectively measured overweight and obesity prevalence overall and among males and females in the following age groups: 6-11, 12-19, 20-39, 40-59 and 60-79. We compared these with provincially representative and objectively measured estimates from the 2015 Canadian Community Health Survey (CCHS)-Nutrition. RESULTS: 57.1% (95% CI 52.8% to 61.4%) of Ontarians were classified overweight or obese and 24.0% (95% CI 20.3% to 27.6%) obese, while Quebec's corresponding percentages were 56.2% (95% CI 51.3% to 61.1%) and 24.4% (95% CI 20.6% to 28.3%). Generally, overweight and obesity combined was higher in older age groups and males. Comparisons with the CCHS-Nutrition did not yield unexplainable differences between surveys. CONCLUSIONS: Combining three CHMS cycles can produce estimates of overweight and obesity in populations representative of Ontario and Quebec. As new CHMS data are collected, these estimates can be updated and used to evaluate trends.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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".