The Impact of Exposure Measurement Error on the Estimated Concentration–Response Relationship between Long-Term Exposure to PM2.5 and Mortality

Abstract

Background: Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed. Objectives: We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration–response relationships between annual exposure to fine particulate matter [PM in aerodynamic diameter ( )] and all-cause mortality. Methods: In this simulation study, we use daily predictions at spatial resolution to estimate annual exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration–response relationship between exposure and mortality (linear, quadratic, or soft-threshold—i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration–response relationship between exposure and mortality, adjusting for the confounders. Results: We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration–response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels. Discussion: Our findings suggest that the causal determination for long-term exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389

Invited Perspective: Impact of Exposure Measurement Error on Effect Estimates—An Important and Neglected Problem in Air Pollution Epidemiology

“… 8 The bias in the effect estimates associated with variance ratios farther from 1, and the associated correlations, in these prior studies were much larger than that found by Wei et al. 1 It can also be observed that investigating short-term exposures, where the observed correlations are higher, variance ratios are rarely found, thus the upward bias is only rarely observed. It should be noted that the ME problem gains further complexity in studies of multi-exposures.…”

“…We would like to note that some aspects of the paper by Wei et al. 1 are unique and not common in many situations and lead to a smaller reported magnitude of bias, as described below.…”

“…reported variance ratios between 0.97 and 1.03. 1 In other reported work assessing typical modeling strategies, variance ratios differed depending on the exposure model. For example, variance ratios for background sites for exposure to nitrogen dioxide ranged from 0.76 to 1.38 for short-term exposures (with associated correlation coefficients ) and from 1.5 to 11.6 for long-term exposures (correlation coefficients from 0.44 to 0.97) 7 ; variance ratios for exposure to PM in aerodynamic diameter ( ) ranged from 0.37 to 0.97 for short-term exposures (correlation coefficients from 0.71 to 0.95) and 0.51 to 1.18 for long-term exposures (correlation coefficients from 0.13 to 0.39).…”

“…The paper by Wei et al. 1 addresses this issue, investigating the bias from ME in long-term estimates of fine particulate matter [PM in aerodynamic diameter ( )] in residential areas of study participants on the mortality effect estimates. This paper has several exceptional methodological features and is an excellent addition to the limited relevant literature.…”

“… 2 It is evident that the estimation of ME depends on what is defined as the true measure. In Wei et al., 1 as in several other cases, 3 the true measure is defined as the modeled ambient concentration at the cohort member’s residence. In other situations, the true measure may be defined as the personal exposure to pollutants from ambient sources or even total personal exposure from any source.…”