Environmental epidemiological data need to be collected over time and across different geographic domains. These data need to be analyzed in order to determine important aspects of national environmental policy, aspects that protect the health of citizens and prevent damage to infrastructure and the environment. The purpose of this research is to develop a statistical framework and methodology for integrated analyses of spatial temporal data on air pollution concentrations and other environmental agents, exposure, health outcomes and covariate information. Generally, these various data layers are temporally misaligned and are observed at different spatial scales. The focus of this research is: ? ? [1] the development of new statistical methods and models for the investigation of the spatial and temporal association between environmental stressors, taking into account human activity, and adverse human health outcomes in the context of two case studies: *study of the impact of ozone and PM (fine, course and ultrafine) on cardiovascular mortality across the conterminuous U.S. *study of the impact of ozone and PM (fine, course and ultra fine) on asthma, cardiovascular and cerebrovascular diseases in the state of Wisconsin. ? ? [2] The development of a broad statistical framework to study the association of environmental factors and adverse health outcomes. This general framework incorporates parametric and nonparametric ial dependence structure for environmental processes, taking into account spatial misalignment, spatial and temporal change of support, and lack of stationarity and lack of separability in the space-time covariance function. An exposure simulator model is used to characterize population exposure levels. ? ? [3] The model fitting, estimation and prediction of multivariate space-time environmental epidemiological data. ? ? [4] The statistical assessment of the performance of deterministic and stochastic models, and model diagnostics.
In aims 2 -4 we establish general statistical frameworks that will be implemented to the case studies introduced in aim 1. ? ? ? ? ?

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-HOP-E (02))
Program Officer
Gray, Kimberly A
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
North Carolina State University Raleigh
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
Zip Code
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

Showing the most recent 10 out of 104 publications