Vol. 131, No. 3 Invited PerspectiveOpen AccessInvited Perspective: The Potential of Potential Outcomes in Air Pollution Epidemiologyis companion ofUsing 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) Andreas M. Neophytou Andreas M. Neophytou Address correspondence to Andreas Neophytou, 1681 Campus Delivery, Fort Collins, CO 80523-1681 USA. Email: E-mail Address: [email protected] https://orcid.org/0000-0001-7327-5361 Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA Search for more papers by this author Published:15 March 2023CID: 031305https://doi.org/10.1289/EHP12209AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InReddit The potential outcomes framework1 allows epidemiologists to mathematically define causal effects of interest as a contrast between two potential (or counterfactual) outcomes.2 This, in turn, has allowed the use of observational data in efforts to answer questions relating to hypothetical interventions. The causal interpretation of contrasting potential outcomes relies on assumptions that the potential outcomes framework also allows investigators to explicitly state. Whether or not those assumptions actually hold, however, will typically not be entirely testable or ultimately known. Nevertheless, the appeal of using observational data for causal inference, especially in an area where experimental data are limited, has generated excitement in air pollution epidemiology and has spurred the adoption of methodology from this framework in recent years.3In one such example, Chen et al. report in this issue of Environmental Health Perspectives on an application of the parametric g-formula to assess the effect of hypothetical interventions on fine particulate matter [particulate matter less than or equal to 2.5 micrometers≤2.5μm in aerodynamic diameter (particulate matter begin subscript 2.5 end subscriptPM2.5)] exposures in a Canadian cohort.4 They report findings of reduced mortality outcomes associated with hypothetical reductions in exposure while identifying several advantages of the approach compared with a more traditional analysis approach using a survival framework, the Cox proportional hazards model. The parametric g-formula approach is indeed advantageous because it can address situations of exposure–confounder feedback in time-varying settings and does not suffer from the built-in selection bias of the Cox model.5However, these are not likely to be major sources of bias in the study by Chen et al.4 Exposure–confounder feedback is not generally a feature in air pollution epidemiology settings and, in the current example, depletion of susceptible individuals leading to crossing of hazards also is not expected to be a major source of bias, with approximately 90 percent∼90% of participants still alive at the end of follow-up. Overall, this is confirmed by the sensitivity analysis results fitting a Cox model and yielding qualitatively similar (though not directly comparable) effect estimates as the parametric g-formula. The use of this method is, therefore, not likely to greatly improve upon the internal validity of effect estimates and, as previously stated, we should be cautious about attributing causal interpretations of findings owing simply to the statistical method in any particular study.6,7The parametric g-formula, nevertheless, is still advantageous in this setting compared with more traditional regression approaches. Unlike the Cox model, which yields target parameters based on the hazard, the parametric g-formula reports findings based on risk (cumulative incidence) and carries advantages over the hazard, such as collapsibility and the increased applicability with respect to public health relevance, especially when focusing on the risk difference.5,8 The approach further yields marginal effect estimates as opposed to conditional. However, in my opinion, the major advantage of the approach is the framing of the parameters of interest in terms of the dynamic interventions considered. Rather than focus target parameters based simply on exposure–response relationships, such as associations for a particular increase in exposure from level A to level B, these hypothetical interventions essentially compare the effect as a contrast of two counterfactual distributions of exposure in the same population. This is far more representative of how an intervention or policy could actually affect exposure and, by extension, health outcomes on the population level in a real-world setting.Despite this advantage, hypothetical interventions of the type that Chen at al.4 considered do have some perhaps fewer obvious limitations relating to key assumptions required for causal interpretation of these findings. Among these are the assumption of consistency and the “no multiple versions of treatment” assumption, part of Rubin’s stable unit treatment value assumption.9,10 Briefly, the assumptions require that a well-defined intervention is contrasted in the counterfactual effect estimate, and that there do not exist multiple versions of the treatment of interest (here, exposure to particulate matter begin subscript 2.5 end subscriptPM2.5).It is arguable that neither holds here. Depending on the intervention or policy change that would lead to a reduction in exposure levels, we may expect different sources of pollution (e.g., transportation, energy, agriculture) to be affected to varying degrees. That, in turn, may affect who experiences a greater change in pollution levels. However, even if we somehow achieved reductions according to some threshold or percentage-based intervention on the individual level, the differing composition of individual-level particulate matter begin subscript 2.5 end subscriptPM2.5 exposures may mean that the same concentration could have different effects in otherwise perfectly exchangeable individuals.11 This would constitute a violation of “no multiple versions of treatment” and would also lead to bias if unmeasured common causes exist between particle composition (version of treatment) and the outcome.10Along the same lines, an intervention to reduce particulate matter begin subscript 2.5 end subscriptPM2.5 will likely lead to differential changes in other pollutants as well, given the common sources many air pollutants share. In that regard, the true intervention effect would be higher than estimated simply by considering particulate matter begin subscript 2.5 end subscriptPM2.5 alone. As the health burden of air pollution is, in essence, a result of joint effects of multiple pollutants, the solution maximizing potential health benefits should be one of joint interventions. On the other hand, because individual pollutants are regulated separately, it could be argued that reporting of individual effects is still useful for policy purposes. However, those, too, would probably be more accurate if the effect of other pollutants was also taken into account. One application of an exposure mixture approach has been proposed within the g-formula framework,12 although combining some of the work involving multipollutant models and causal inference is an area that requires more applied examples.Future work leveraging the potential outcomes framework could incorporate knowledge gained from studies on exposure mixtures and source apportionment to help address some of the aforementioned limitations when envisioning hypothetical interventions. Importantly, the potential outcomes framework allows for estimation of causal effects even in the presence of multiple versions of treatment.10 This framework can also be leveraged to assess the effects of less hypothetical, and therefore better defined, interventions as demonstrated in a 2018 study leveraging principal stratification based on potential outcomes to assess the effect of the U.S. Environmental Protection Agency’s National Ambient Air Quality Standards nonattainment designations.13In conclusion, I applaud Chen et al.4 for their contribution, which brings a more realistic representation of the health effects of potential interventions to reduce air pollution exposures. The adoption of the potential outcomes framework in air pollution epidemiology can be a valuable tool in risk assessment and aid in the interpretability of epidemiological findings in terms of policy, but I would also caution readers to carefully consider the assumptions required for causal inference in each case.
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