Using parametric g-computation to estimate the effect of long-term exposure to air pollution on mortality risk and simulate the benefits of hypothetical policies: the Canadian Community Health Survey cohort (2005 to 2015)
Bibliographic record
Abstract
Abstract Background Numerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter (PM 2.5 ) on mortality even at relatively low levels. However, methodological challenges remain to consider potential regulatory intervention’s complexity and provide actionable evidence on the predicted benefits of interventions. We propose the parametric g-computation as an alternative analytical approach to such challenges. Method We applied the parametric g-computation to estimate the cumulative risks of non-accidental death under different hypothetical intervention strategies targeting long-term exposure to PM 2.5 in the Canadian Community Health Survey cohort from 2005 to 2015. On both relative and absolute scales, we explored benefits of hypothetical intervention strategies compared to the natural course that 1) set the simulated exposure value at each follow-up year to a threshold value if exposure was above the threshold (8.8 µg/m 3 , 7.04 µg/m 3 , 5 µg/m 3 , and 4 µg/m 3 ); and 2) reduce the simulated exposure value by a percentage (5% and 10%) at each follow-up year. We used the three-year average PM 2.5 concentration with one-year lag at the postal code of respondents’ annual mailing addresses as their long-term exposure to PM 2.5 . We considered baseline and time-varying confounders including demographics, behavior characteristics, income level, and neighborhood socioeconomic status. We also included the R syntax for reproducibility and replication. Results All hypothetical intervention strategies explored led to lower 11-year cumulative mortality risks than the estimated value under natural course without intervention, with the smallest reduction of 0.20 per 1000 participants (95% CI: 0.06 to 0.34) under the threshold of 8.8 µg/m 3 , and the largest reduction of 3.40 per 1000 participants (95% CI: -0.23 to 7.03) under the relative reduction of 10% per interval. The reductions in cumulative risk, or numbers of deaths that would have been prevented if the intervention was employed instead of maintaining status quo, increased over time but flattened towards the end of follow-up. Estimates among those ≥65 years were greater with a similar pattern. Our estimates were robust to different model specifications. Discussion We found evidence that any intervention further reducing the long-term exposure to PM 2.5 would reduce the cumulative mortality risk, with greater benefits in the older population, even in a population already exposed to low levels of ambient PM 2.5 . The parametric g-computation used in this study provides flexibilities in simulating real world interventions, accommodates time-varying exposure and confounders, and estimates adjusted survival curves with clearer interpretation and more information than a single hazard ratio, making it a valuable analytical alternative in air pollution epidemiological research.
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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 | 0.017 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".