Measurement Error and Environmental Epidemiology: a Policy Perspective

Abstract

Purpose of review: Measurement error threatens public health by producing bias in estimates of the population impact of environmental exposures. Quantitative methods to account for measurement bias can improve public health decision making.

Recent findings: We summarize traditional and emerging methods to improve inference under a standard perspective, in which the investigator estimates an exposure-response function, and a policy perspective, in which the investigator directly estimates population impact of a proposed intervention. Under a policy perspective, the analyst must be sensitive to errors in measurement of factors that modify the effect of exposure on outcome, must consider whether policies operate on the true or measured exposures, and may increasingly need to account for potentially dependent measurement error of two or more exposures affected by the same policy or intervention. Incorporating approaches to account for measurement error into such a policy perspective will increase the impact of environmental epidemiology.

Keywords: Bias (epidemiology); Environmental epidemiology; Measurement error.

Figure 1

Diagrams showing how results from environmental epidemiology inform policy under A) the standard perspective, and B) a policy perspective. Legend: j indexes individual studies of environmental exposures. X_j represents the exposure distribution in study j, Z_j represents the covariate distribution in study j, and β_j is the estimated exposure response function from study j. g indexes specific target populations where interventions or policies may be applied. X_g is the distribution of exposure in the population g, Z_g is the distribution of covariates in population g. π represents assumptions and information about the measurement error. ψ̄_gC, is the estimated population impact under the standard framework presented in panel A, which is estimated using β̄ averaged across all studies. ψ̄_gM is the estimated population impact under the policy approach in panel B, based on the average ψ_jg estimated across all studies.