Associations between cigarette smoking, obesity, sociodemographic characteristics and remote-sensing-derived estimates of ambient PM<sub>2.5</sub>: results from a Canadian population-based survey
Bibliographic record
Abstract
OBJECTIVES: Long-term exposure to ambient fine particles (PM2.)) has been shown to increase mortality. Variables measured on the same spatial scales of air pollution may confound associations, and so the authors' objectives were to evaluate the associations between PM2.5 and individual-level measures of smoking, obesity and sociodemographic status. The authors present an approach to evaluate the impact that uncontrolled confounding from smoking may have on associations between PM2.5 and mortality. METHODS: Individual-level behavioural and sociodemographic data were obtained from a 2003 national survey of 122,548 Canadians. Estimates of ground-level PM2.5 at a resolution of 10×10 km between 2001 and 2006 were derived from satellite remote sensing. Exposures were assigned to the residence of the participants at the time of the survey. Differences in the prevalence of smoking across concentrations of PM2.5 and RRs drawn from the literature were used to model the bias on rate ratios. RESULTS: Participants in areas with higher concentrations of PM2.5 had a higher income and educational attainment, smoked less and were more likely immigrants. Smoking had a negative confounding effect on the associations between PM2.5) and mortality. To compensate for this bias, for a 10 μg/m3 increase in PM2.5, mortality from lung cancer and heart disease in the referent exposure group needed to be increased by 6.9% and 3.2%, respectively. CONCLUSIONS: Associations were found between sociodemographic and lifestyle characteristics and PM2.5 at a resolution of 10×10 km. The authors present a model to adjust for uncontrolled confounding of smoking that can be readily adapted to exposures measured at different spatial resolutions.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".