Exposure measurement error is a likely source of bias in nearly all environmental health studies, typically leading to an under-estimation of relative risks and a loss of statistical power to detect effects. In this proposed research, we wil take a life course approach, as consistent with NIEHS strategic priorities, focusing on methodological needs in several critical areas of environmental health, including the effects of constituents of air pollution and of aspects of the neighborhood environment on cardiovascular disease and its precursors and consequences, including all-cause mortality, obesity, type 2 diabetes and subclinical cardiovascular biomarkers. Having assembled a strong multi-disciplinary team of leading theoretical and applied statisticians, environmental epidemiologists and environmental exposure assessment experts, consistent with another NIEHS strategic objective, we will make significant contributions to novel areas of pressing environmental health policy importance, with a major focus on developing methods to accurately quantify the effects of complex single and multiple, simultaneous exposure effects across space and time, responding to another NIEHS strategic priority, reducing if not eliminating the bias and loss of efficiency otherwise present due to the presence of substantial exposure measurement error. In this work, careful attention will be paid to removing bias due to spatial and temporal confounding as well as to adjusting for confounding by indoor sources of air pollution. Currently available validation data on air pollution constituents and on features of the neighborhood environment will be assembled and used to develop measurement error models relating personal exposure to measured ambient exposure as suitable for the data at hand. Mixed longitudinal and Cox survival data regression models will underlie the analytic framework. User-friendly software implementing the methods will be posted on the web, facilitating wide-scale application of the new methods to a broad range of environmental health problems.
This research is intended to improve our understanding of environmental risks, leading to improved disease prevention, through the development and application of new statistical methods to adjust for bias due to exposure measurement error within a multi-disciplinary team comprised of biostatisticians, epidemiologists and environmental exposure assessment experts following one of NIEHS' strategic objectives. Consistent with another NIEHS strategic objective, we will take a life course approach, focusing on methodologic innovations in several critical areas of environmental health related to the effects of multiple constituents of air pollution and of aspects of the neighborhood environment on risks of cardiovascular disease incidence, its precursors and consequences as well as the development of obesity and cognitive decline. The methodologic innovations to be developed will address the unique measurement error features of these data, and will make it possible to accurately quantify the effects of complex single and multiple simultaneous exposures across space and time, responding to another NIEHS strategic priority, while controlling for spatial confounding and confounding by indoor sources using advanced statistical methods, on critical health outcomes across the life course, overcoming bias and loss of efficiency otherwise present due to substantial measurement error.