Variability in patient sociodemographics, clinical characteristics, and healthcare service utilization among 107,302 treatment seeking smokers in Ontario: A cross-sectional comparison
Bibliographic record
Abstract
BACKGROUND: Since 2005, the Smoking Treatment for Ontario Patients (STOP) program has provided smoking cessation treatment of varying form and intensity to smokers through 11 distinct treatment models, either in-person at partnering healthcare organizations or remotely via web or telephone. We aimed to characterize the patient populations reached by different treatment models. METHODS: We linked self-report data to health administrative databases to describe sociodemographics, physical and mental health comorbidity, healthcare utilization and costs. Our sample consisted of 107,302 patients who enrolled between 18Oct2005 and 31Mar2016, across 11 models operational during different time periods. RESULTS: Patient populations varied on sociodemographics, comorbidity burden, and healthcare usage. Enrollees in the Web-based model were youngest (median age: 39; IQR: 29-49), and enrollees in primary care-based Family Health Teams were oldest (median: 51; IQR: 40-60). Chronic Obstructive Pulmonary Disease and hypertension were the most common physical health comorbidities, twice as prevalent in Family Health Teams (32.3% and 30.8%) than in the direct-to-smoker (Web and Telephone) and Pharmacy models (13.5%-16.7% and 14.7%-17.7%). Depression, the most prevalent mental health diagnosis, was twice as prevalent in the Addiction Agency (52.1%) versus the Telephone model (25.3%). Median healthcare costs in the two years up to enrollment ranged from $1,787 in the Telephone model to $9,393 in the Addiction Agency model. DISCUSSION: While practitioner-mediated models in specialized and primary care settings reached smokers with more complex healthcare needs, alternative settings appear better suited to reach younger smokers before such comorbidities develop. Although Web and Telephone models were expected to have fewer barriers to access, they reached a lower proportion of patients in rural areas and of lower socioeconomic status. Findings suggest that in addition to population-based strategies, embedding smoking cessation treatment into existing healthcare settings that reach patient populations with varying disparities may enhance equitable access to treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".