Project Summary Long-term exposure to air pollutants is recognized as a risk factor for cardiovascular disease (CVD); exposure to ambient fine particulates (PM2.5) is considered among the top ten global risk factors for premature death and morbidity primarily due to CVD effects. Public health action on a national and global level still requires better information on the nature of the association between pollutants and CVD: Is there a threshold for exposure, below which effects do not occur? Is the effect linear or does it follow a more biologically likely function? This proposal addresses these critical questions with two approaches: 1) pooling high-quality cohort studies; and 2) analyzing health system records based on electronic health records (EHR). The former approach is the epidemiological standard while the latter represents the ?big data? future. The combination and comparison of these two approaches in the same research program affords a unique opportunity to address another key public health question: Can ?big data? provide the same answer as traditional cohort approaches? This proposal addresses low-level air pollution health effects using state-of-the-art exposure assessment, fine- scale hybrid modeling of concentrations (PM2.5, oxides of nitrogen, and ozone) and advanced statistical methods. The cohorts permit optimal minimization of bias due to confounding and misclassification by pooling information from a set of well-established cohorts in the US totaling nearly one million participants, each with appropriate outcome, home address, and individual level covariate detail. The consortium cohorts feature geographic and exposure diversity. This project will also study one unusually well-characterized large-scale integrated health delivery system, Kaiser Permanente Northern California, with detailed EHR on more than 4.7 million members. Results of this study will provide critically important knowledge to guide policy in the United States and globally. Further, this proposal will directly compare traditional cohort and new ?big data? approaches for answering complex epidemiological research questions, allowing for better understanding of the ability of ?big data? to replace and/or supplement traditional approaches.

R01ES027696

2017-09-15

2021-06-30