Statistical methods to characterize causal mechanisms by which air pollution affects the recurrence of cardiovascular events

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Project Summary One of our era's greatest scourges is air pollution, and it is well documented that exposure to fine particles (e.g., PM2.5) increases the risk of cardiovascular disease (CVD) and death. However, there are two critical knowledge gaps. First, existing studies have mainly considered the occurrence of the first adverse health event as health out- comes; hence, the impact of overall disease burden and of PM2.5 exposure on disease progression both remain unknown. Second, to our knowledge, there are no studies assessing the causal pathways by which exposure to air pollutants impacts recurrent cardiovascular events. Without a better understanding of disease progression and clinical mediators, our ability to inform regulatory policy and prevent disease is severely hampered. Limited attention has been given to developing methods for assessing the causal effect of time varying ex- posures, especially in the presence of a terminating event like death. A related methodological gap is the ability to identify (time varying) mediators or estimating mediated effects of time varying exposure for a recurrent event outcome. This proposal addresses these two critical methodological gaps in causal inference, the overarching goals being to elucidate (i) the impact of PM2.5 on the burden and progression of CVD; and, (ii) the key causal pathways by which air pollution exposure impacts such events. Accomplishing such goals will be facilitated by new analyses of an unprecedented data collection consisting of (a) an already harmonized and linked Part A Medicare data (33+ million subjects 2000 to 2019) at both the individual (e.g., age, gender, race, date of hospital- ization for any of the possible causes for hospitalizations, date of death) and zip code (e.g., daily PM2.5, O3 and NO2 levels; many potential confounders) levels; and, (b) an augmented version of these data including individu- ally linked Part B data (doctor visits, and outpatient procedures for any cause) for a representative sample of over 15 million Medicare enrollees (2012 to 2019) and, (c) the Medicare Current Beneficiary Survey (MCBS) data for the same study period, as a secondary analysis to account for possible bias due to unmeasured confounding. In methodological terms, we propose robust marginal and structural nested models that allow estimation of both instantaneous and delayed effects of time varying exposure to PM2.5 on recurrence of CVD events (Aim 1). We further propose a semiparametric approach to identify clinically important pathways by which exposure to air pollutants increases the risk of recurrence of CVD hospitalizations and death, and estimate the corresponding mediated effects (Aim 2). We will implement our methods and apply them to the above-described rich data source, focusing specifically on (Aim 3): (i) estimating the causal effects of PM2.5 exposure on two relevant causal estimands for recurrent events in the presence of mortality; (ii) identifying key mediators and characterize clinical pathways through which exposure to air pollution increases risks of CVD progression and death; and, (iii) identifying and investigating the disease groups that are biologically plausible. Finally, we will create and disseminate open-source, peer-reviewed statistical software to ensure ease-of-use and accessibility (Aim 4).