Association between smoking cessation and short‐term health‐care use: results from an international prospective cohort study ( <scp>ATTEMPT</scp> )
Bibliographic record
Abstract
BACKGROUND AND AIMS: Previous studies have found that smoking cessation is associated with a short-term increase in health-care use. This may be because 'sicker' smokers are more likely to stop smoking. The current study assessed the association between smoking cessation and health-care use, adjusting for pre-cessation physical and mental health conditions. DESIGN/SETTING: Data came from the ATTEMPT cohort, a multi-national prospective survey of smokers in the United States, Canada, United Kingdom, France and Spain, that lasted 18 months (with follow-ups every 3 months). PARTICIPANTS: A total of 3645 smokers completed the baseline questionnaire. All participants smoked at least five cigarettes per day, intended to quit smoking within the next 3 months and were between 35 and 65 years of age. MEASUREMENTS: Participants were asked questions about their socio-demographic and smoking characteristics, as well previous smoking-related morbidities. Participants were also asked to report their health-care use in the previous 3 months i.e. emergency room (ER) visits, hospitalization, whether hospitalization required surgery, and health-care appointments. FINDINGS: A total of 8252, 4779 and 1954 baseline episodes of smoking were available for 3, 6 and 12 months, respectively. Of these, 2.8% (n = 230), 0.9% (n = 40) and 0.7% (n = 14) were followed by 3, 6 and 12 months of abstinence. No significant differences were found among 3, 6 or 12 months of abstinence and ER visits, hospitalization and whether hospitalization required surgery or health-care visits. However, 6-month smoking cessation episodes were associated with higher odds of reporting an appointment with a dietician. CONCLUSION: Smoking cessation does not appear to be associated with a substantial short-term increase or decrease in health-care use after adjusting for pre-cessation morbidities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".