Evaluation of a hospital-initiated tobacco dependence treatment service: uptake, smoking cessation, readmission and mortality
Bibliographic record
Abstract
Abstract Background The National Health Service in England aims to implement tobacco dependency treatment services in all hospitals by 2024. We aimed to assess the uptake of a new service, adapted from the Ottawa Model of Smoking Cessation, and its impact on 6-month quit rates and readmission or death at 1-year follow-up. Methods We conducted a pragmatic service evaluation of a tobacco dependency service implemented among 2067 patients who smoked who were admitted to 2 acute hospitals in London, England, over a 12-month period from July 2020. The intervention consisted of the systematic identification of smoking status, automatic referral to tobacco dependence specialists, provision of pharmacotherapy and behavioural support throughout the hospital stay, and telephone support for 6 months after discharge. The outcomes were (i) patient acceptance of the intervention during admission, (ii) quit success at 6 months after discharge, (iii) death, or (iv) readmission up to 1 year following discharge. Multivariable logistic regression was used to estimate the impact of a range of clinical and demographic variables on these outcomes. Results The majority (79.4%) of patients accepted support at the first assessment. Six months after discharge, 35.1% of successfully contacted patients reported having quit smoking. After adjustment, odds of accepting support were 51–61% higher among patients of all non-White ethnicity groups, relative to White patients, but patients of Mixed, Asian, or Other ethnicities had decreased odds of quit success (adjusted odds ratio (AOR) = 0.32, 95%CI = 0.15–0.66). Decreased odds of accepting support were associated with a diagnosis of cardiovascular disease or diabetes; however, diabetes was associated with increased odds of quit success (AOR = 1.88, 95%CI = 1.17–3.04). Intention to make a quit attempt was associated with a threefold increase in odds of quit success, and 60% lower odds of death, compared to patients who did not intend to quit. A mental health diagnosis was associated with an 84% increase in the odds of dying within 12 months. Conclusions The overall quit rates were similar to results from Ottawa models implemented elsewhere, although outcomes varied by site. Outcomes also varied according to patient demographics and diagnoses, suggesting personalised and culturally tailored interventions may be needed to optimise quit success.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".