Premium
Error in estimating area‐level air pollution exposures for epidemiology
Author(s) -
Keller Joshua P.,
Peng Roger D.
Publication year - 2019
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2573
Subject(s) - statistics , estimator , particulates , mean squared error , environmental science , environmental epidemiology , exposure assessment , air pollution , health effect , small area estimation , econometrics , environmental health , mathematics , medicine , ecology , chemistry , organic chemistry , biology
Abstract In air pollution epidemiology, measurements of relevant exposure concentrations are typically made at point locations, resulting in spatial misalignment between the exposure data and health outcomes aggregated at the area level. To obtain values that match the spatial units of the health data, observations can be averaged directly or prediction models developed. We present a framework for evaluating the error in aggregating exposure concentrations to the area unit. We present estimators of mean squared error that can be used for model selection. We find that exposure prediction models, even when misspecified, outperform monitor averages in settings with realistic numbers of monitors and that important reductions in error of the health effect estimate can be obtained when restricting to areas with a monitor. In an analysis of long‐term particulate matter concentrations across the United States, we estimated the error of the prediction model approach to be less than that of the monitor averaging approach on average across counties. We present health effect estimates about particulate matter exposure and pediatric asthma morbidity in the Medicaid population using each approach. Our findings support the use of a prediction model for estimating area‐wide averages, even when restricting to areas that contain a monitor.