Many environmental pollutants have distinct patterns in both space and time. One of the main challenges in epidemiologic studies of the health effects of these pollutants is characterizing those patterns accurately enough to rank study participants by the amount of pollutant exposure at any particular point in time or cumulatively over a given period of time. We propose new Bayesian statistical models to combine spatial and temporal exposure information from a variety of sources including source emissions, fate and transport models, exposure questionnaires, and biochemical measurements such as pollutant concentrations in participant's blood samples. Bayesian methods also produce quantitative estimates of the extent of uncertainty regarding each participant's amount of exposure. This characterization of uncertainty is critical for understanding the reliability of spatial epidemioloic analyses linking pollutant exposures to adverse health effects. We propose to further develop and implement these new Bayesian models using data from the C8 Health Project, a recent study of over 69,030 community residents exposed to perfluorooctanoate released from a major production facility in West Virginia. Previous analyses have associated perfluorooctanoate exposures in these study participants to pregnancy-induced hypertension/preeclampsia, testicular cancer, and kidney cancer;numerous other epidemiologic analyses are currently underway. These analyses all rely on the same set of exposure estimates, which estimate the extent of past exposure to each individual based on his/her residential and occupational history and tap water consumption, in conjunction with a complex set of linked spatial fate and transport models that estimate the past movement of perfluorooctanoate in the environment. Probability distributions will be used to characterize the uncertainty contributed by the various exposure model components for each consented participant in the C8 Health Project. Monte Carlo techniques will then be used to determine the combined effects of those uncertainties on the exposure estimates. These induced prior distributions will then be combined with perfluorooctanoate blood serum measurements obtained for the study participants in 2005-2006;the resulting combined posterior exposure estimates will be used to reanalyze the pregnancy-induced hypertension/preeclampsia association. These Bayesian models should 1.) improve exposure rankings for epidemiologic analyses and 2.) allow for direct characterization of the effects of spatial uncertainty on the reliability of epidemiologic findings.
Many health studies for environmental pollutants depend on spatial information about the location of participants and pollutants over time, but it's generally impossible to obtain perfectly accurate spatial information. We propose to develop new statistical models to assess the accuracy of that spatial information and improve it by allowing its direct combination with biological measurements such as pollutant concentrations in blood. We will apply these new models to data from a large study associating perfluorooctanoate (a widespread environmental pollutant) with pregnancy-induced hypertension/preeclampsia (medical conditions that are life- threatening in extreme cases).
|Avanasi, Raghavendhran; Shin, Hyeong-Moo; Vieira, Veronica M et al. (2016) Variability and epistemic uncertainty in water ingestion rates and pharmacokinetic parameters, and impact on the association between perfluorooctanoate and preeclampsia in the C8 Health Project population. Environ Res 146:299-307|
|Avanasi, Raghavendhran; Shin, Hyeong-Moo; Vieira, VerÃ³nica M et al. (2016) Impact of Exposure Uncertainty on the Association between Perfluorooctanoate and Preeclampsia in the C8 Health Project Population. Environ Health Perspect 124:126-32|
|Shin, Hyeong-Moo; Steenland, Kyle; Ryan, P Barry et al. (2014) Biomarker-based calibration of retrospective exposure predictions of perfluorooctanoic acid. Environ Sci Technol 48:5636-42|