Tobacco advertising and oral health among never smokers: the mediating role of secondhand smoke exposure.
Bibliographic record
Abstract
OBJECTIVE: To determine whether the association between tobacco advertising (TA) exposure and poor self-rated oral health (SROH) is mediated through secondhand smoke (SHS) exposure in Brazilian adults who have never smoked. METHODS: Secondary cross-sectional analysis of The Brazilian National Health Survey 2019 data. The daily, weekly, or monthly exposure to SHS at home or at work was set as the mediator. Mediation analysis within a counterfactual approach used adjusted binary logistic regressions for both poor SROH and SHS exposure, to estimate the natural direct effect (NDE), natural indirect effect (NIE) through SHS exposure, and marginal total effect (MTE) of TA exposure on poor SROH. To assess the robustness of the results, we calculated the E-value for the MTE. RESULTS: The sample comprised 53,295 never smoker adults. The MTE of TA exposure on poor SROH was 1.09 (1.03, 1.16), with the indirect effect through SHS exposure responsible for only 16.6% of the total (NIE: 1.01 [1.01, 1.02] and NDE: 1.08 [1.02, 1.14]). An effect of 1.42 would be required for an unmeasured confounder to explain away the association between TA and SROH. CONCLUSION: More individuals exposed to TA have poor SROH than those unexposed, with secondhand smoke exposure explaining only a small portion of this effect. Upstream tobacco policies should consider oral health outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".