Association Between Smoking Cessation Treatment and Healthcare Costs in a Single-Payer Public Healthcare System
Bibliographic record
Abstract
INTRODUCTION: There has been little investigation of whether the clinical effectiveness of smoking cessation treatments translates into differences in healthcare costs, using real-world cost data, to determine whether anticipated benefits of smoking cessation treatment are being realized. AIMS AND METHODS: We sought to determine the association between smoking cessation treatment and healthcare costs using linked administrative healthcare data. In total, 4752 patients who accessed a smoking cessation program in Ontario, Canada between July 2011 and December 2012 (treatment cohort) were each matched to a smoker who did not access these services (control cohort). The primary outcome was total healthcare costs in Canadian dollars, and secondary outcomes were sector-specific costs, from one year prior to the index date until December 31, 2017, or death. Costs were partitioned into four phases: pretreatment, treatment, posttreatment, and end-of-life for those who died. RESULTS: Among females, total healthcare costs were similar between cohorts in pretreatment and posttreatment phases, but higher for the treatment cohort during the treatment phase ($4,554 vs. $3,237, p < .001). Among males, total healthcare costs were higher in the treatment cohort during pretreatment ($3,911 vs. $2,784, p < .001), treatment ($4,533 vs. $3,105, p < .001) and posttreatment ($5,065 vs. $3,922, p = .001) phases. End-of-life costs did not differ. Healthcare sector-specific costs followed a similar pattern. CONCLUSIONS: Five-year healthcare costs were similar between females who participated in a treatment program versus those that did not, with a transient increase during the treatment phase only. Among males, treatment was associated with persistently higher healthcare costs. Further study is needed to address the implications with respect to long-term costs. IMPLICATIONS: The clinical effectiveness of pharmacological and behavioral smoking cessation treatments is well established, but whether such treatments are associated with healthcare costs, using real-world data, has received limited attention. Our findings suggest that the use of a smoking cessation treatment offered by their health system is associated with persistent higher healthcare costs among males but a transient increase among females. Given increasing access to evidence-based smoking cessation treatments is an important component in national tobacco control strategies, these data highlight the need for further exploration of the relations between smoking cessation treatment engagement and healthcare costs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".