Change in prevalence of smoking during the first year of the COVID-19 pandemic among middle-aged and older adults in Canada: a cohort study of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Objective: The primary objective was to evaluate the change in the prevalence of daily or occasional tobacco smoking during the first year of the COVID-19 pandemic and to assess if socioeconomic factors were associated with changes in smoking. The secondary objective was to evaluate the association of smoking with adherence to public health measures. Methods: In this prospective cohort study, using Canadian Longitudinal Study on Aging data (44 139 middle-aged and older-aged adults), the prevalence of smoking during the COVID-19 pandemic (2020) and prepandemic (2011-2018) was estimated using weighted generalised estimating equations. ORs and 95% CIs for the association between smoking and adherence to public health measures (a derived score) were estimated using multinomial logistic regression. Results: Time (during vs prepandemic) was a significant predictor of smoking (adjusted OR (aOR) 1.12; 95% CI 1.07, 1.17). The adjusted prevalence of smoking during the beginning of the pandemic was 11.2% (95% CI 10.1%, 12.4%), compared with the prepandemic prevalence of 10.1% (95% CI 9.1%, 11.2%), with p<0.001. Factors associated with an increase in smoking were male sex, being aged 55 to 74 years, residing in Ontario or British Columbia, immigrant background, belonging to a racialised group, higher income and being married/common-law relationship. Smoking was associated with increased odds of high adherence to public health measures (aOR 1.53; 95% CI 1.31, 1.78). Conclusion: Among middle-aged and older adults in Canada, there was a small increase in the adjusted prevalence of smoking early in the pandemic. Daily or occasional smoking was associated with greater adherence to public health measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".