Smoking trajectories over the first year of the pandemic in UK middle-aged adults: evidence from the UKHLS COVID-19 study
Bibliographic record
Abstract
ABSTRACT Background The COVID-19 pandemic has altered the conditions leading people to smoke. Multiple studies have examined changes in population levels of smoking at the start of the pandemic. However, conclusions remain mixed due to the high proportion of studies with poor methods and short follow-up periods. Methods This study used longitudinal data from the UKHLS COVID-19 study to derive smoking trajectories among 4,130 UK adults aged 35-64 across four time points over the first year of the pandemic (2018-19, April 2020, September 2020, and January 2021). Random-effects models were used to examine subject-specific changes across time points. Results Between the pre-pandemic estimate and January 2021, there was a significant decline in smoking from 14.8% to 13.1% (PR = 0.89, 95%CI 0.83-0.95). The number of cigarettes smoked per day among smokers increased in April 2020 ( B = 0.5, 95%CI 0.0, 1.0) and September 2020 ( B = 1.0, 95%CI 0.4, 1.5), but declined back to pre-pandemic levels in January 2021 ( B = 0.3, 95%CI -0.3, 0.8). These changes did not vary by sex, ethnicity, relationship status, education, occupation, or household income. Conclusion Among UK adults aged 35-64, there has been a slight decrease in smoking which was maintained up to January 2021. Whereas there was an increase in cigarette consumption among smokers at the start of the pandemic, this was no longer observable in January 2021. The findings support the argument that the first year of the pandemic is unlikely to have had a negative effect of most middle-aged adult smokers’ trajectory.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".