Trends in Obesity Among Canadian Adults: A Population-Level Database Analysis
Bibliographic record
Abstract
Background and objective Obesity continues to pose a critical public health challenge in Canada, contributing to the growing burden of chronic diseases such as type 2 diabetes, cardiovascular disorders, and mental health conditions. It is associated with reduced quality of life and increased healthcare expenditures. Despite awareness and policy efforts, national prevalence rates have remained elevated over the past decade. This study aims to assess 12-year trends in adult obesity in Canada from 2007 to 2019 and examine differences in prevalence by sex and age groups using national survey data and BMI-based classifications. A secondary objective was to benchmark Canada's adult obesity prevalence against Organisation for Economic Co-operation and Development (OECD) countries (using the most recent national estimates available through 2023) and to identify how Canada compares with peer nations. Methods A cross-sectional, population-level analysis was conducted using data from Statistics Canada's Canadian Health Measures Survey, cycles 1-6 (2007-2019), and complementary Statistics Canada reports. Adults aged 18 to 79 years were included. Descriptive statistics and 95% CIs were used to analyze prevalence trends over time and between demographic groups. OECD data from 2023 and earlier were used for international benchmarking. Results In 2019, the overall obesity prevalence among Canadian adults was 24.3% (95% CI: 18.0-32.0). Males had a higher prevalence (26.7%) than females (22.0%). Obesity increased with age: 19.8% in ages 18-39, 25.5% in 40-59, and 29.3% in 60-79. From 2007 to 2019, obesity varied within a narrow range, peaking in 2014-2015 (25.9%) before declining slightly. Compared internationally, Canada's rate was similar to the OECD average (25%) but lower than countries like the US (42.8%). Conclusions Obesity remains a prevalent and complex issue in Canada, with significant variation across sex and age groups. Canada's 2019 prevalence (24.3%) is close to the OECD average but contrasts with both higher-prevalence countries (e.g., the US) and lower-prevalence countries (e.g., several East Asian nations), underscoring the need for integrated clinical care and policy action tailored to national and subpopulation needs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.022 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".