STATISTICAL SERVICES AND ANALYSIS RESOURCE SUMMARY Research in environmental health demonstrates that consequence of environmental exposures during critical periods in development can manifest as disease or dysfunction across the human life span. Indeed, humans are exposed to environmental factors ranging from internal physiology (e.g., oxidative stress) to external chemical (e.g., air pollution, consumer products) and lifestyle factors including exposures to environmental contaminants, poor nutrition, and stress. These factors are multi-dimensional and complex ? not simple, single exposures related to a single health outcome. Statistical methods and study designs for analyzing the complex, high-dimensional data that arise in such settings are still relatively new and require knowledge of advanced statistical techniques. The Statistical Services and Analysis Resource (SSAR) consolidates multi-disciplinary expertise (statistics, epidemiology, bioinformatics) in a single faculty made available for collaboration with the broader environmental health research community through the Data Center.
The aims of the SSAR are in three primary categories: consultation and analysis support, HHEAR mission-related methods development and implementation of novel methods, and support of collaborative research across the Environmental Health Research consortium.
Our specific aims are: 1) provide statistical, epidemiological, and bioinformatics consultation and analysis support to investigators utilizing the HHEAR Laboratory Network resources; 2) catalyze the application of novel statistical, analytical, and bioinformatics methodologies in relevant areas to the HHEAR and the Data Center; 3) catalyze the implementation of these methods in Aim 2 through statistical training for environmental health researchers by creating publicly available statistical software for analysis of the HHEAR data and developing publicly available training/example datasets to be used for didactic learning through webinars related to children's environmental health; and 4) support collaborative research across the HHEAR consortium by conducting initial meta-analyses relating environmental exposures and selected disease conditions within a consortium of relevant experts; and supporting other collaborative research projects that may initiate through the HHEAR consortium of research investigators. The achievement of these aims will advance the goals of the HHEAR Data Center and the larger HHEAR Network to maximize our understanding of the potential impact of environmental exposures on human health by providing rigorous and innovative biostatistical/bioinformatics methods for analyzing environmental health data.

Project Start
Project End
Budget Start
2020-06-01
Budget End
2021-05-31
Support Year
3
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Icahn School of Medicine at Mount Sinai
Department
Type
DUNS #
078861598
City
New York
State
NY
Country
United States
Zip Code
10029
Wright, Robert O; Teitelbaum, Susan; Thompson, Claudia et al. (2018) The child health exposure analysis resource as a vehicle to measure environment in the environmental influences on child health outcomes program. Curr Opin Pediatr 30:285-291
Kim, Stephani S; Meeker, John D; Carroll, Rachel et al. (2018) Urinary trace metals individually and in mixtures in association with preterm birth. Environ Int 121:582-590
Buckley, Jessie P; Quirós-Alcalá, Lesliam; Teitelbaum, Susan L et al. (2018) Associations of prenatal environmental phenol and phthalate biomarkers with respiratory and allergic diseases among children aged 6 and 7?years. Environ Int 115:79-88
Claus Henn, Birgit; Austin, Christine; Coull, Brent A et al. (2018) Uncovering neurodevelopmental windows of susceptibility to manganese exposure using dentine microspatial analyses. Environ Res 161:588-598
Curtin, Paul; Austin, Christine; Curtin, Austen et al. (2018) Dynamical features in fetal and postnatal zinc-copper metabolic cycles predict the emergence of autism spectrum disorder. Sci Adv 4:eaat1293
Bello, Ghalib A; Arora, Manish; Austin, Christine et al. (2017) Extending the Distributed Lag Model framework to handle chemical mixtures. Environ Res 156:253-264
Bello, Ghalib A; Arora, Manish; Austin, Christine et al. (2017) Corrigendum to ""Extending the Distributed Lag Model framework to handle chemical mixtures"" [Environ. Res. 156 (2017) 253-264]. Environ Res 159:650
Patel, Chirag J; Kerr, Jacqueline; Thomas, Duncan C et al. (2017) Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies. Cancer Epidemiol Biomarkers Prev 26:1370-1380
Rosa, Maria José; Pajak, Ashley; Just, Allan C et al. (2017) Prenatal exposure to PM2.5 and birth weight: A pooled analysis from three North American longitudinal pregnancy cohort studies. Environ Int 107:173-180
Stingone, Jeanette A; Buck Louis, Germaine M; Nakayama, Shoji F et al. (2017) Toward Greater Implementation of the Exposome Research Paradigm within Environmental Epidemiology. Annu Rev Public Health 38:315-327

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