We hypothesize that analyzing individual-level secondary data with instrumental variable (IV) methods can advance knowledge of the long-term effects of air pollution on dementia. We discuss issues in measurement using secondary data and how IV estimation can overcome biases due to measurement error and unmeasured variables. We link air-quality data from the Environmental Protection Agency's monitors with Medicare claims data to illustrate the use of secondary data to document associations. Additionally, we describe results from a previous study that uses an IV for pollution and finds that PM2.5's effects on dementia are larger than non-causal associations.
Keywords: Air pollution; aged; dementia; instrumental variables; research design; selection bias.