Seasonal Variation in Demand for Smoking Cessation Treatment and Clinical Outcomes
Bibliographic record
Abstract
INTRODUCTION: Smoking behaviour shows seasonal variation, with cigarette consumption and youth smoking onset highest in summer and smoking-related web searches and sales of nicotine replacement products highest in winter. Variation in demand for clinical care and in outcomes has not been explored. AIMS AND METHODS: We measure seasonal variation in enrolments, total clinical visits, visits per enrolment, and treatment outcome (7-day abstinence at 6-month follow-up) from 2015 to 2018 in a large (n = 85 869) clinical cohort from 454 clinics across Ontario, Canada. We model seasonality using harmonic logistic and negative binomial regression. For individual-level outcomes, we adjust for variables, selected a priori, known to be associated with treatment use or outcomes. Data are nearly complete for 3 outcomes, but 6m abstinence is missing for 45% of participants. We use multiple imputation to adjust for missing data. RESULTS: All four outcomes showed significant seasonal variation (all p <.001). Total enrolments and visits were 20%-25% higher in January-April than in June-September. Visits per enrolment varied slightly, with lowest levels from May-July. Abstinence at 6 months was lowest among individuals enrolled from February-May and highest for those enrolled from July-November, with an absolute peak-trough difference of 4.3% (95% CI = 3.2% to 5.5%). CONCLUSIONS: There is meaningful seasonal variation in demand for, and outcomes of, smoking cessation treatment. Climate and weather may be indirectly responsible. Seasonal differences underscore the general importance of contextual factors in smoking cessation, may be useful in program promotion, and may explain some variability in outcomes in evaluation and research. IMPLICATIONS: Demand for tobacco cessation treatment and clinical outcomes vary seasonally. This underscores the importance of context in substance-related problems, and implies that some variability in research and evaluation results may be due to the time of year data were collected. Promotion efforts might usefully consider seasonal effects to smooth out demand and possibly improve outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".