Estimating the impact of ambient air pollution on lung cancer risk in the presence of preferential sampling and measurement error using electronic health record data

Music City Center 

A network of monitoring sites is often not well-designed for accurately mapping ambient (outdoor) air pollution due to external factors, such as budget constraints and public opinion. As such, naively using point measurements from the monitoring network can lead to biased mapping. This can have profound downstream implications for environmental health studies that rely on this map to estimate ambient air pollution exposure at participants' locations. In this talk, we will address this potential bias due to preferential sampling in the design of a monitoring network for mapping ambient air pollution in California. We will utilize a recently developed spatio-temporal statistical framework that simultaneously models the air pollution field and monitoring site selection process. Further, we will examine the downstream implications in estimating the effects of ambient air pollution on lung cancer risk using electric health record data (N>44,000) from Stanford Health Care, an academic medical center, and Sutter Health, a multisite community practice. We will employ a Bayesian cause-specific Cox regression model to incorporate the competing risk of death as well as the measure error in the air pollution exposure.

Preferential sampling

Measurement error

Environmental health studies

Air pollution

Bayesian