This is a joint collaborative effort between North Carolina State University, Duke University, and the University of North Carolina at Chapel Hill. The expertise at the 3 institutions complements each other, and brings synergy. We will achieve the following objectives: (1) The development of broad spatial-temporal statistical models to study the impact under climatic change conditions of air pollution on human health. We will improve upon existing methods, by introducing Bayesian multivariate spatio-temporal statistical models that characterize simultaneously complex spatial and temporal dependence structures in the environmental stressors, climatic variables, and health outcomes, while taking into account different sources of uncertainty in models and data. We will develop novel spatial quantile regression models for the climatic and pollution variables for better characterization of extremes, tail behavior, and complex dependences between weather and pollution. (2) The development of Bayesian hierarchical shrinkage methods for assessing spatial associations between complex pollutant mixtures and health outcomes. We will improve upon existing approaches by simultaneously accounting for different pollutant types, such as ozone and particulate matter (PM) or speciated PM, characterizing the spatial temporal structure of the susceptible periods of fetal development (pregnancy outcomes) and the exposure lag (mortality outcome), while taking into account different sources of uncertainty in models and data. (3) We will build neighborhood deprivation and environment indices for linkage to health outcomes. We will use the statistical frameworks above and data on birth weight and gestational age at delivery in the Pregnancy, Infection, and Nutrition (PIN) study, which examines neighborhood factors concerning the built and perceived physical environment in relation to pregnancy outcomes, to bring together GIS capabilities, deterministic models for air pollution, climate and weather, and novel spatial statistical modeling approaches for dimension reduction. (4) We will combine the statistical models in aims 1-3 to study the impact of air pollution and extreme weather on human health under projected future climatic conditions. Health data to be examined include the following: U.S. daily mortality in 2001-2006 at the county level (and geocoded at the street level for the states of NC and NY). Birth weight (small-for-gestational age) and gestational age at delivery (preterm birth) in a sample of infants born in 10 U.S. states who participated as controls in the National Birth Defects Prevention Study (NBDPS), for whom geocoded latitude and longitude at delivery are available. Individual-level cardiovascular birth defects geocoded data are available, as well as individual-level geocoded cardiovascular birth defects data for 15,000 cases and controls in NBDPS. We will make this new methodology broadly applicable and disseminated by developing free-access software and conducting extensive validation and diagnostics of our approaches, as well as presenting measures of goodness-of-fit. PHS SF424 (Updated 12/09) Page 1 Continuation Format Page

Public Health Relevance

This project addresses the critical need to improve the scientific understanding of the health impacts associated to climate change, as well the local characterization of environmental factors and exposure assessment, while quantifying the uncertainty associated with this understanding. We provide the statistical tools to facilitate policy making and more efficient management of air quality and other environmental agents under limited information and changing climatic conditions. PHS SF424 (Updated 12/09) Page 1 Continuation Format Page

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Research Project (R01)
Project #
Application #
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Gray, Kimberly A
Project Start
Project End
Budget Start
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Fiscal Year
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North Carolina State University Raleigh
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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Wilson, Ander; Reich, Brian J; Nolte, Christopher G et al. (2017) Climate change impacts on projections of excess mortality at 2030 using spatially varying ozone-temperature risk surfaces. J Expo Sci Environ Epidemiol 27:118-124
Kaufeld, Kimberly A; Fuentes, Montse; Reich, Brian J et al. (2017) A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes. Int J Environ Res Public Health 14:
Warren, Joshua L; Stingone, Jeanette A; Herring, Amy H et al. (2016) Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects. Stat Med 35:2786-801
Berchuck, Samuel I; Warren, Joshua L; Herring, Amy H et al. (2016) Spatially Modelling the Association Between Access to Recreational Facilities and Exercise: The 'Multi-Ethnic Study of Atherosclerosis'. J R Stat Soc Ser A Stat Soc 179:293-310
Schnell, Patrick; Bandyopadhyay, Dipankar; Reich, Brian J et al. (2015) A marginal cure rate proportional hazards model for spatial survival data. J R Stat Soc Ser C Appl Stat 64:673-691
Langley, Ricky L; Kao, Yimin; Mort, Sandra A et al. (2015) Adverse neurodevelopmental effects and hearing loss in children associated with manganese in well water, North Carolina, USA. J Environ Occup Sci 4:62-69
Boehm Vock, Laura F; Reich, Brian J; Fuentes, Montserrat et al. (2015) Spatial variable selection methods for investigating acute health effects of fine particulate matter components. Biometrics 71:167-177
Smith, Luke B; Reich, Brian J; Herring, Amy H et al. (2015) Multilevel quantile function modeling with application to birth outcomes. Biometrics 71:508-19
Chang, Howard H; Warren, Joshua L; Darrow, Lnydsey A et al. (2015) Assessment of critical exposure and outcome windows in time-to-event analysis with application to air pollution and preterm birth study. Biostatistics 16:509-21
Smith, Luke B; Fuentes, Montserrat; Gordon-Larsen, Penny et al. (2015) QUANTILE REGRESSION FOR MIXED MODELS WITH AN APPLICATION TO EXAMINE BLOOD PRESSURE TRENDS IN CHINA. Ann Appl Stat 9:1226-1246

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