Dr. Jeanette Stingone is a formally-trained environmental epidemiologist with a focus on perinatal and pediatric health. She conducts research that couples data science techniques with epidemiologic methods to investigate how prenatal and early-life environmental exposures affect health and development throughout childhood and beyond. Currently, she is investigating how machine learning approaches can be used to uncover the combinations of multiple environmental exposures that contribute to disease and disability in children including birth defects, adverse neurodevelopment and early puberty. Dr. Stingone also has a strong interest in the use of collective science initiatives to advance public health research, and works to develop methods and approaches for data harmonization across diverse studies of environmental health.
BS, Boston University
MPH, Mount Sinai School of Medicine
PhD, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
Adjunct Assistant Professor, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai
Member, Society for Epidemiologic Research
Member, Society for Perinatal and Pediatric Epidemiologic Research
Member, International Society for Environmental Epidemiology
Areas of Expertise
Data Science, Child Health and Development, Neurodevelopmental Disorders, Pollution--Air/Ground/Water, Environmental Epidemiology, Birth Outcomes, Perinatal Epidemiology, Urban Health
Select Urban Health Activities
NYCDOHMH Longitudinal Study of Early Development: Dr. Stingone has a longstanding collaboration with the New York City Department of Health and Mental Hygiene, working to link EPA databases of air quality to the Health Department's Longitudinal Study of Early Development (LSED). LSED Project is a data linkage of five administrative databases containing information on New York City children, including birth, health and education records. By linking these data to air pollution data, Dr. Stingone and her colleagues at the Health Department have been able to investigate how higher levels of common urban air pollutants are associated with adverse school outcomes. Additionally, they have been able to evaluate how children exposed to environmental pollutants early in life may benefit from academic support services.
Stingone JA, Pandey OP, Claudio L, Pandey G. Using machine learning to identify air pollution profiles associated with early cognitive skills in U.S. children. Environmental Pollution 2017; 230:730-740
Stingone JA, Buck Louis GM, Nakayama SF, Vermeulen RC et al. Toward greater implementation of the exposome research paradigm within environmental epidemiology. Annual Review of Public Health 2017; 38:315-327.
Stingone JA, McVeigh KH, Claudio L. Early-life exposure to air pollution and greater use of academic support services in childhood: a population-based cohort study of urban children. Environmental Health 2017; 16:2.
Stingone JA, McVeigh KH, Claudio L. Association between prenatal exposure to ambient diesel particulate matter and perchloroethylene with children's 3rd grade standardized test scores. Environmental Research 2016; 148:144-53.
Rashid SM, Chastain K, Stingone JA, McGuinness DL, McCusker JP. The semantic data dictionary approach to data annotation and integration. In: CEUR Workshop Proceedings, v1931. Proceedings of the First Workshop on Enabling Open Semantic Science, Vienna AUT. October 2017