Exposure measurement error is a likely source of bias in nearly all environmental and epidemiological studies, typically leading to under-estimation of relative risks and loss of statistical power to detect effects. In the previous cycle of this project, we extended the regression calibration method for adjustment for measurement error in multivariate regression models, including Cox models and logistic models, to accommodate the study designs and data structures encountered in environmental epidemiology. We featured these new methods in a number of publications on the health effects of environmental exposure to endotoxin, methyl tert-butyl ether, lead, and indoor NO2, and developed publicly available software. In the next cycle of this project, we will focus on issues in air pollution epidemiology - in particular, the chronic effects of particulate exposure and elemental carbon on all-cause mortality, cardio-vascular mortality and lung cancer mortality. Having assembled an inter-disciplinary team of leading statisticians, environmental scientists and environmental epidemiologists from around the world, we will develop methods to adjust for measurement error in Cox regression models suitable for the prospective cohort designs of the Six Cities Study, Nurses'Health Study, Netherlands Cohort Study, and MESA-Air in relation to cumulative and 12 month running average exposure metrics. Careful attention will be paid to the important issues surrounding timing of exposure and potential non-linearity of the dose-response curves of the exposure metrics, in particular, removing bias in the quantification of these features due to exposure measurement error. The biomarker data available in NHS and MESA-AIR will be used to improve the exposure validation. User- friendly software will be posted on the web, facilitating widescale application of the new methods.

Public Health Relevance

Extrapolation from recent studies has suggested that approximately 100,000 premature deaths in the United States may be associated with exposure to airborne particles each year. Interpretation of the limited data on exposure to constituents of air pollution suggests that the effects of long-term exposure appear to be substantially greater than those of acute exposure. Measurement error in alternative exposure metrics has been found to be substantial, yet the statistical tools are not available to adjust for this source of bias explicitly in analysis. We propose to develop methods for doing this, and apply them to four major studies on air pollution in relation to mortality: the Six Cities Study, the Nurses'Health Study, MESA-Air, and the Netherlands Cohort Study. These analyses will substantially improve our understanding of the health effects of exposure to air pollution, including identification of the critical times of exposure and potential non-linearity in the exposure-response relationship, and will be useful in future risk assessment and policy development.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Research Project (R01)
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Cardiovascular and Sleep Epidemiology (CASE)
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Dilworth, Caroline H
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Harvard University
Public Health & Prev Medicine
Schools of Public Health
United States
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