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)
Authors: Chen Chen , Chen Hong , van Donkelaar Aaron , Burnett Richard T. , Martin Randall V. , Chen Li , Tjepkema Michael , Kirby-McGregor Megan , Li Yi , Kaufman Jay S. , Benmarhnia Tarik 
Published: February 2023

Background

Numerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter (PM2.5) 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 PM2.5in 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/m3, 7.04 µg/m3, 5 µg/m3, and 4 µg/m3); and 2) reduce the simulated exposure value by a percentage (5% and 10%) at each follow-up year. We used the three-year average PM2.5concentration with one-year lag at the postal code of respondents’ annual mailing addresses as their long-term exposure to PM2.5. 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/m3, 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 PM2.5would reduce the cumulative mortality risk, with greater benefits in the older population, even in a population already exposed to low levels of ambient PM2.5. 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.