Does More Accurate Exposure Prediction Necessarily Improve Health Effect Estimates?

A unique challenge in air pollution cohort studies and similar applications in environmental epidemiology is that exposure is not measured directly at subjects ’ locations. Instead, pollution data from monitoring stations at some distance from the study subjects are used to predict exposures, and these predicted exposures are used to estimate the health effect parameter of interest. It is usually assumed that minimizing the error in predicting the true exposure will improve health effect estimation. We show in a simulation study that this is not always the case. We interpret our results in light of recently developed statistical theory for measurement error, and we discuss implications for the design and analysis of epidemiologic research. There has been a major effort in air pollution epidemiology research to develop statistical models to predict exposures at subjects ’ locations in situations where measurements at the desired locations are not available.1–7 These efforts assume that exposure predictions with less measurement error relative to the unknown true values will improve health effect estimation.8–10 We demonstrate in a simulation study that this assumption is not always true, and we interpret our results using recently developed statistical theory for measuremen