# A DATA SCIENCE APPROACH TO AIR TOXICS AND CHILDREN'S ENVIRONMENTAL HEALTH

> **NIH NIH R00** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $247,463

## Abstract

PROJECT SUMMARY: This proposal aims to characterize the associations between prenatal exposure to
interpretable combinations of air toxics and children’s cognitive health through the efficient use of big public
health data. With guidance from multidisciplinary advisors, the candidate will develop skills in data science,
machine learning and advanced biostatistics to supplement her training in epidemiologic methods. This will
allow her to progress in her career and advance research on combined environmental exposures and
children’s health. Previous research has found associations between prenatal exposure to single air pollutants
and children’s cognitive health but has lacked the ability to investigate combined impacts of multiple pollutants,
including the synergistic/antagonistic interactions between pollutants that have been observed in experimental
studies. Understanding the effects of combined exposures is a strategic goal of the National Institute of
Environmental Health Sciences, and the field of environmental health is transitioning from single-pollutant
approaches to more holistic paradigms, such as the exposome. Identifying associations and interactions within
the context of high-dimensional exposure data presents a computational challenge. Methods from domains
such as data science, including machine learning methods, can be incorporated into the epidemiologic toolbox
for addressing environmental mixtures and multiple exposures. The goal of this Career Development Award is
to advance the candidate into an independent research career at the intersection of big data science and
children’s environmental health. Through formal coursework, directed learning and field rotations, the
candidate will gain skills in data science, machine learning and advanced biostatistics. Mentors, advisors and
consultants have been selected for their complementary expertise, relevant research experience and
mentoring abilities. The proposed research will leverage the skills gained from the training plan and apply them
to characterize associations between prenatal exposure to interpretable combinations of air toxics and 3rd
grade standardized test scores, a school-based measure of cognitive outcomes. Residence at birth will be
used to link data on air toxics, a subset of air pollutants, to an administrative data linkage of public health
registries and education data for approximately 220,000 children born in New York City from 1994-1998. The
candidate will develop and validate a two-stage approach of hypotheses generation followed by targeted
analyses in order to identify combinations of air toxics associated with children’s test scores within the context
of high-dimensional exposure data (Aim 1). Targeted analyses using well-established epidemiologic methods
for effect estimation and assessment of interaction between air toxics will be performed. (Aim2) Potential
mediators of the relationship between air toxics and test scores can then be identified using statist...

## Key facts

- **NIH application ID:** 9999949
- **Project number:** 5R00ES027022-05
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Jeanette A Stingone
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $247,463
- **Award type:** 5
- **Project period:** 2018-09-25 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999949

## Citation

> US National Institutes of Health, RePORTER application 9999949, A DATA SCIENCE APPROACH TO AIR TOXICS AND CHILDREN'S ENVIRONMENTAL HEALTH (5R00ES027022-05). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/9999949. Licensed CC0.

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