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Publications

Journal Article Review Ppublish 2 citations

PubMed: 39251872 DOI: 10.1038/s41370-024-00712-8

A review of geospatial exposure models and approaches for health data integration.

J Expo Sci Environ Epidemiol 35(2):131-148

Authors: Lara P Clark, Daniel Zilber, Charles Schmitt, David C Fargo, David M Reif, Alison A Motsinger-Reif, Kyle P Messier

Affiliations

  • National Institute of Environmental Health Sciences, Office of the Scientific Director, Office of Data Science, Durham, NC, USA.
  • National Institute of Environmental Health Sciences, Division of Translational Toxicology, Predictive Toxicology Branch, Durham, NC, USA.
  • National Institute of Environmental Health Sciences, Office of the Director, Office of Environmental Science Cyberinfrastructure, Durham, NC, USA.
  • National Institute of Environmental Health Sciences, Division of Intramural Research, Biostatistics and Computational Biology Branch, Durham, NC, USA.
  • National Institute of Environmental Health Sciences, Division of Translational Toxicology, Predictive Toxicology Branch, Durham, NC, USA.
  • National Institute of Environmental Health Sciences, Division of Intramural Research, Biostatistics and Computational Biology Branch, Durham, NC, USA.

Abstract

BACKGROUND

Geospatial methods are common in environmental exposure assessments and increasingly integrated with health data to generate comprehensive models of environmental impacts on public health.

OBJECTIVE

Our objective is to review geospatial exposure models and approaches for health data integration in environmental health applications.

METHODS

We conduct a literature review and synthesis.

RESULTS

First, we discuss key concepts and terminology for geospatial exposure data and models. Second, we provide an overview of workflows in geospatial exposure model development and health data integration. Third, we review modeling approaches, including proximity-based, statistical, and mechanistic approaches, across diverse exposure types, such as air quality, water quality, climate, and socioeconomic factors. For each model type, we provide descriptions, general equations, and example applications for environmental exposure assessment. Fourth, we discuss the approaches used to integrate geospatial exposure data and health data, such as methods to link data sources with disparate spatial and temporal scales. Fifth, we describe the landscape of open-source tools supporting these workflows.

MeSH

Environmental Exposure, Workflow, Water Quality

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