Estimating the effect of long-term exposure to PM2.5 on mortality in Canadian Community Health Survey Cohort using parametric g-computation
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Bibliographic record
Abstract
BACKGROUND AND AIM: Numerous epidemiological studies reported the adverse health impact of long-term exposure to fine particulate matter (PM2.5) on mortality across populations. However, previous studies mostly utilized traditional outcome regression approaches, which may fail under certain circumstances (e.g., if exposure-confounder feedback exists). We aim to explore this health impact using g-computation, which could validate traditional regression approaches and refine the effect estimates by considering more complex circumstances in the identification. METHODS: We utilize a cohort of ~540,000 respondents to the Canadian Community Health Survey from 2001 to 2012, whose death records and residential history were ascertained till 2016. Annual postal code specific three-year average PM2.5 concentration with one-year lag was derived from satellite measurements and linked to cohort respondents, with quintiles of exposure calculated for each calendar year. We apply parametric g-computation with pooled logistic regression adjusted for socio-economic, behavioral, and time-varying covariates to estimate 1) the effect on mortality by changing the long-term PM2.5 exposure level from the higher quintiles to the lowest quintile; and 2) the effect on mortality by reducing the long-term PM2.5 exposure levels from the observed values to below the national standard. We also evaluate the influence of exposure-confounder feedback and discuss whether other identification assumptions hold in assessing health impacts of air pollution. RESULTS:Our preliminary results confirm an increase in the risk of premature mortality in relation to long-term exposure to PM2.5. CONCLUSIONS:These results provide evidence on the effect of long-term exposure to PM2.5 on mortality in the presence of time-varying exposures and confounders. It also provides an alternative analytical strategy highly useful to air pollution epidemiological research, especially for evaluating specific intervention strategies. KEYWORDS: g-computation, casual infrence, chronic exposure to PM2.5, mortality
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it