Impact of exposure measurement error (ME) on the health effect estimates: introduction

Abstract

Background/Aim: Although it is widely accepted that air pollution exposure, at least to pollutants of ambient origin, has adverse health effects, the quantitative effect estimates incorporate considerable uncertainty, especially because the relevant exposure (namely individual or population exposure to pollutants of ambient origin) is so difficult to measure accurately. The resulting exposure Measurement Error (ME) is a neglected but potentially important problem resulting in bias and loss of accuracy in health effect estimates. This Symposium will focus on recently published results on the bias in effect estimates due to exposure measurement error. Methods: Measurements of air pollutants concentrations from fixed site monitors or ad-hoc campaigns have been used in epidemiological studies assessing the associations between exposure to pollutants and multiple health outcomes, and also models (statistical, dispersion or hybrid) have been extensively developed and applied. The magnitude and direction of bias resulting from ME in exposure estimation has mainly been evaluated in simulation or validation studies. Results: Recent publications show that bias from ME of commonly applied exposure models is mainly towards the null and can be substantial. Only under specific and relatively rarely observed values of correlations and variance ratios upward bias is observed. Other studies which use more sophisticated "hybrid” models show smaller bias. Conclusion: In this Symposium we will attempt to quantify the importance of exposure ME in the valid estimation of health effects of air pollutants. We will present results from recent studies which quantified the impact of various types of exposure measurement error on the effect estimates for health outcomes and evaluated correction methods. Key words: exposure measurement error; air pollution; bias; effect estimates; correction methods