Estimating the health risks associated with air pollution exposure is of great importance in public health. In air pollution epidemiology, two study designs have been used mainly. Time series studies estimate acute risk associated with short-term exposure. They compare day-to-day variation of pollution concentrations and mortality rates, and have been criticized for potential confounding by time-varying covariates. Cohort studies estimate chronic effects associated with long-term exposure. They compare long-term average pollution concentrations and time-to-death across cities, and have been criticized for potential confounding by individual risk factors or city-level characteristics.

We propose a new study design and a statistical model, which use spatio-temporal information to estimate the long-term effects of air pollution exposure on life expectancy. Our approach avoids confounding by time-varying covariates and individual or city-level risk factors. By estimating the increase in life expectancy due to decreases in long-term air pollution concentrations, it provides easily interpretable values for public policy purposes. We develop a suitable backfitting algorithm that permits efficient fitting of our model to large spatio-temporal data sets. We evaluate spatio-temporal correlation in the data and obtain appropriate standard errors. We apply our methods to the Medicare Cohort Air Pollution Study, including data on fine particulate matter (PM2.5) and mortality for 18.2 million Medicare enrollees from 814 locations in the U.S. during an average of 65 months in 2000-2006. Supplemental material including R code implementing our methods is provided in a web appendix.


Epidemiology | Statistical Models

Web_appendix.zip (348 kB)
Supplementary Material, including additional graphs and R code used to implement the methods and produce the results in this paper