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)

BACKGROUND: Numerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter [particulate matter [Formula: see text] in aerodynamic diameter ([Formula: see text])] 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 nonaccidental death under different hypothetical intervention strategies targeting long-term exposure to [Formula: see text] in the Canadian Community Health Survey cohort from 2005 to 2015. On both relative and absolute scales, we explored the benefits of hypothetical intervention strategies compared with the natural course that a) set the simulated exposure value at each follow-up year to a threshold value if exposure was above the threshold ([Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text]), and b) reduced the simulated exposure value by a percentage (5% and 10%) at each follow-up year. We used the 3-y average [Formula: see text] concentration with 1-y lag at the postal code of respondents’ annual mailing addresses as their long-term exposure to [Formula: see text]. 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-y cumulative mortality risks than the estimated value under the natural course without intervention, with the smallest reduction of 0.20 per 1,000 participants (95% CI: 0.06, 0.34) under the threshold of [Formula: see text] , and the largest reduction of 3.40 per 1,000 participants (95% CI: [Formula: see text] , 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 the status quo, increased over time but flattened toward the end of the follow-up period. Estimates among those [Formula: see text] years of age 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 [Formula: see text] would reduce the cumulative mortality risk, with greater benefits in the older population, even in a population already exposed to low levels of ambient [Formula: see text]. 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. https://doi.org/10.1289/EHP11095