Trends in obesity defined by body mass index among adults before and during the COVID-19 pandemic: a repeated cross-sectional study of the 2009–2023 Canadian Community Heath Surveys
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic affected health behaviours and the social determinants of health. We sought to describe trends in the prevalence in body mass index (BMI) categories before and during the COVID-19 pandemic among adults in Canada. METHODS: We conducted a repeated cross-sectional study of adults in the 2009-2023 Canadian Community Health Surveys. We compared changes after the onset of the COVID-19 pandemic (April 2020 to December 2023) to an 11-year prepandemic period (January 2009 to March 2020). We calculated odds ratios (ORs) and absolute percentages from, respectively, weighted logistic and linear regression models. RESULTS: Our unweighted analytic sample included 746 250 adults from the 2009-2023 surveys. The prevalence of BMI-defined obesity increased from 24.95% in 2009 to 32.69% in 2023 (absolute increase 7.74%). The COVID-19 pandemic period was associated with an adjusted annual increase in the relative odds of obesity that was 1.02 (95% confidence interval [CI] 1.01-1.04) times greater than the prepandemic period. The absolute rate of increase of BMI-defined obesity nearly doubled during the pandemic, with an annual average excess rate of 0.44 (95% CI 0.14-0.74) percentage points. Class II and III obesity increased at a greater absolute rate than class I, indicating a shift toward more severe obesity. The relative increase in class III obesity was greater among young adults and females. INTERPRETATION: Since the COVID-19 pandemic, the prevalence of BMI-defined obesity, and especially class III obesity, increased at a faster rate than before the pandemic. Some groups that historically had lower levels of obesity were disproportionately affected during the pandemic.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".