Estimating the effect of long-term exposure to PM2.5 on mortality in Canadian Community Health Survey Cohort using parametric g-computation
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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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".