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.