# Statistical Methods for Precision Environmental Health with Mixture Exposures

> **NIH NIH R01** · COLORADO STATE UNIVERSITY · 2024 · $528,194

## Abstract

Project Summary/Abstract
Precision environmental health focuses on individual risk assessment to inform targeted disease prevention strate­
gies. Identifying individuals with increased sensitivity to environmental exposures is especially challenging with
mixture exposures. The health effects of exposure to mixtures are likely to depend on the composition of the
mixture, characteristics specific to the espoused individual including individual­ and neighborhood­level factors,
and the developmental stage at which an individual is exposed. We propose to develop statistical methods for
precision environmental health with mixture exposures. The proposed methods will estimate mixture­exposure­
response relationships that are individualized based on multiple candidate modifying factors. The framework we
develop will allow for data­driven discovery of novel combinations of individual­ and neighborhood­level factors
that define susceptible subgroups. We will address three specific data settings. In Aim 1 we propose a general
framework for effect heterogeneity using established mixture methods including Bayesian multiple index models.
This will include heterogeneous versions of Bayesian kernel machine regression and linear index models. In Aim
2 we develop methods to identify critical windows of susceptibility to mixtures that are assessed longitudinally.
The methods will allow for identification of individualized windows of susceptibility to a mixture and estimation of
individualized mixture­exposure­time­response functions. In Aim 3 we develop heterogeneous mixture methods
for multiple outcomes. The multiple outcome methods will apply to trajectories defined by repeated measures of
common endpoint or pathway as well as shared information across multiple related endpoints, such as multiple
measures of a common pathway. In Aim 4 we will develop software to implement the methods, along with vignettes
and tutorials. We will use the methods developed to analyze air pollution mixtures in a large administrative birth
cohort and in a Northeastern United States longitudinal perinatal cohort drawing from multiple source populations.
We will estimate individualized mixture­exposure­response functions for birth weight and multiple neurodevelop­
mental endpoints assessed at multiple times. The methods we develop will allow for new avenues of precision
environmental health to better identify individuals at increased risk of adverse effects of the environment, which
will better inform targeted disease prevention strategies.

## Key facts

- **NIH application ID:** 10981999
- **Project number:** 1R01ES035735-01A1
- **Recipient organization:** COLORADO STATE UNIVERSITY
- **Principal Investigator:** Thomas Ander Wilson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $528,194
- **Award type:** 1
- **Project period:** 2024-07-06 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10981999

## Citation

> US National Institutes of Health, RePORTER application 10981999, Statistical Methods for Precision Environmental Health with Mixture Exposures (1R01ES035735-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10981999. Licensed CC0.

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