@inbook{b74c8a620fc0470b9138fac696a0de39,
title = "Analyzing Individual-Level Secondary Data with Instrumental Variable Methods Is Useful for Studying the Effects of Air Pollution on Dementia",
abstract = "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 PM 2.5 's effects on dementia are larger than non-causal associations.",
keywords = "Air pollution, aged, dementia, instrumental variables, research design, selection bias",
author = "Bishop, {Kelly C.} and Sehba Husain-Krautter and Ketcham, {Jonathan D.} and Kuminoff, {Nicolai V.} and Corbett Schimming",
note = "Funding Information: We thank the editor and two anonymous reviewers for helpful comments and suggestions. Bishop, Ketcham, and Kuminoff received partial support for this study from National Institute of Aging Grant #P30AG012810. We thank Nirman Saha for research assistance. Publisher Copyright: {\textcopyright} 2021 The authors and IOS Press. All rights reserved.",
year = "2020",
doi = "10.3233/AIAD210044",
language = "English",
series = "Advances in Alzheimer's Disease",
publisher = "IOS Press BV",
pages = "531--539",
editor = "Lilian Calderon-Garciduenas",
booktitle = "Alzheimer{\~A}¯{\^A}¿{\^A}½s Disease and Air Pollution",
address = "Netherlands",
}