Examining the association between public health unit funding per capita and conventional tobacco and e-cigarette use in Ontario, Canada
Bibliographic record
Abstract
Background: Conventional tobacco and e-cigarette use are behavioural risk factors for many chronic diseases, including cardiovascular and pulmonary diseases. Public health has played a crucial role in reducing all types of tobacco use, employing comprehensive tobacco control approaches. One such approach is the Smoke-Free Ontario (SFO) Act, which was enacted in 2017 to regulate the sale, supply, display, promotion, and public use of tobacco, e-cigarettes, and cannabis among Ontarians, of which Ontario’s public health units (PHUs) play a crucial role in maintaining. Despite these population-level approaches, little is known about the association between public health funding and tobacco or e-cigarette use, despite their detrimental economic and healthcare costs to Canada’s publicly funded healthcare system. This thesis aims to (1) examine the association between SFO-related PHU funding per capita and the odds of conventional tobacco and e-cigarette use among individuals aged 18 and older living in Ontario, Canada in 2018 and 2023, and (2) determine whether the observed associations are heterogeneous across sex, household income, and household education. Methods: Repeated cross-sectional data were obtained from the 2018 and 2023 Canadian Community Health Surveys (CCHS). The case-complete weighted sample was comprised of 9,818,000 individuals across 33 PHUs in 2018 and 10,561,000 individuals across 31 PHUs in 2023. SFO-specific PHU funding per capita was obtained from the 2018 and 2023 Ontario Public Health Information Databases (OPHID) and categorized ordinally (low, low-moderate, moderate-high, and high funding). Conventional tobacco and/or e-cigarette use was defined as a binary measure based on self-reported use from the CCHS. Individual-level confounders, including sex, BMI, age, marital status, household income, household education, urbanicity, and racialized group status, were extracted from the CCHS. In contrast, area-level confounders, including material deprivation and ethnic concentration, were taken from the 2016 and 2021 Ontario Marginalization Indices (ON-Marg). Multilevel logistic regression analyses (MLMs) were used to estimate the association between SFO-specific PHU funding and tobacco and e-cigarette use. Findings: In 2018, the proportion of respondents who self-reported conventional tobacco use, e-cigarette use, and concurrent use of both was 14.9%, 1.2%, and 2.1%, respectively. By 2023, these proportions shifted to 8.5% for conventional tobacco use, 2.9% for e-cigarette use, and 2.6% for concurrent use. While no associations were found between SFO-specific PHU funding and tobacco use status in 2018, in 2023, low-moderate SFO-specific PHU funding per capita was associated with higher odds of conventional tobacco use (OR: 1.77; 95% CI: 1.19, 2.62) compared to the lowest funding per capita category, whereas high funding per capita was associated with lower odds of e-cigarette use (OR: 0.33, 95% CI: 0.11, 0.94). Analysis of cross-level interactions revealed that, in 2018, all levels of SFO-specific PHU funding per capita were associated with increased odds of conventional tobacco use among females compared to males, while high funding areas were associated with increased odds of e-cigarette use among individuals in the middle-high income quartile (OR: 4.41; 95% CI: 1.08, 18.02) compared to individuals in the highest income quartile. High SFO funding areas were also associated with lower odds of e-cigarette use among those with a high school household education (OR: 0.22, 95% CI: 0.05, 0.98). In 2023, low-moderate SFO-specific funding per capita was associated with lower odds of conventional tobacco in the lowest (OR: 0.65; 95% CI: 0.46, 0.93) and low-middle (OR: 0.52; 95% CI: 0.31, 0.89) household income quartiles, while high funding per capita was associated with higher odds of e-cigarette use among those in the lowest income quartile (OR: 3.31; 95% CI: 1.05, 10.47). Conclusion: In 2023, low-moderate SFO-related PHU funding per capita was associated with higher odds of conventional tobacco use compared to low funding, while high funding per capita was associated with lower odds of e-cigarette use. Further research should aim to identify the complex longitudinal patterns and mechanisms associated with PHU funding per capita as it pertains to conventional tobacco and e-cigarette use.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".