Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities

Combined Exposures and Mixtures Research: An Enduring NIEHS Priority

BACKGROUND

The National Institute of Environmental Health Sciences (NIEHS) continues to prioritize research to better understand the health effects resulting from exposure to mixtures of chemical and nonchemical stressors. Mixtures research activities over the last decade were informed by expert input during the development and deliberations of the 2011 NIEHS Workshop "Advancing Research on Mixtures: New Perspectives and Approaches for Predicting Adverse Human Health Effects." NIEHS mixtures research efforts since then have focused on key themes including a) prioritizing mixtures for study, b) translating mixtures data from in vitro and in vivo studies, c) developing cross-disciplinary collaborations, d) informing component-based and whole-mixture assessment approaches, e) developing sufficient similarity methods to compare across complex mixtures, f) using systems-based approaches to evaluate mixtures, and g) focusing on management and integration of mixtures-related data.

OBJECTIVES

We aimed to describe NIEHS driven research on mixtures and combined exposures over the last decade and present areas for future attention.

RESULTS

Intramural and extramural mixtures research projects have incorporated a diverse array of chemicals (e.g., polycyclic aromatic hydrocarbons, botanicals, personal care products, wildfire emissions) and nonchemical stressors (e.g., socioeconomic factors, social adversity) and have focused on many diseases (e.g., breast cancer, atherosclerosis, immune disruption). We have made significant progress in certain areas, such as developing statistical methods for evaluating multiple chemical associations in epidemiology and building translational mixtures projects that include both in vitro and in vivo models.

DISCUSSION

Moving forward, additional work is needed to improve mixtures data integration, elucidate interactions between chemical and nonchemical stressors, and resolve the geospatial and temporal nature of mixture exposures. Continued mixtures research will be critical to informing cumulative impact assessments and addressing complex challenges, such as environmental justice and climate change. https://doi.org/10.1289/EHP14340.

Development of a multimodal geomarker pipeline to assess the impact of social, economic, and environmental factors on pediatric health outcomes

OBJECTIVES

We sought to create a computational pipeline for attaching geomarkers, contextual or geographic measures that influence or predict health, to electronic health records at scale, including developing a tool for matching addresses to parcels to assess the impact of housing characteristics on pediatric health.

MATERIALS AND METHODS

We created a geomarker pipeline to link residential addresses from hospital admissions at Cincinnati Children's Hospital Medical Center (CCHMC) between July 2016 and June 2022 to place-based data. Linkage methods included by date of admission, geocoding to census tract, street range geocoding, and probabilistic address matching. We assessed 4 methods for probabilistic address matching.

RESULTS

We characterized 124 244 hospitalizations experienced by 69 842 children admitted to CCHMC. Of the 55 684 hospitalizations with residential addresses in Hamilton County, Ohio, all were matched to 7 temporal geomarkers, 97% were matched to 79 census tract-level geomarkers and 13 point-level geomarkers, and 75% were matched to 16 parcel-level geomarkers. Parcel-level geomarkers were linked using our exact address matching tool developed using the best-performing linkage method.

DISCUSSION

Our multimodal geomarker pipeline provides a reproducible framework for attaching place-based data to health data while maintaining data privacy. This framework can be applied to other populations and in other regions. We also created a tool for address matching that democratizes parcel-level data to advance precision population health efforts.

CONCLUSION

We created an open framework for multimodal geomarker assessment by harmonizing and linking a set of over 100 geomarkers to hospitalization data, enabling assessment of links between geomarkers and hospital admissions.

STROBE-GEMA: a STROBE extension for reporting of geographically explicit ecological momentary assessment studies

CONTEXT

While a growing body of research has been demonstrating how exposure to social and built environments relate to various health outcomes, specific pathways generally remain poorly understood. But recent technological advancements have enabled new study designs through continuous monitoring using mobile sensors and repeated questionnaires. Such geographically explicit momentary assessments (GEMA) make it possible to link momentary subjective states, behaviors, and physiological parameters to momentary environmental conditions, and can help uncover the pathways linking place to health. Despite its potential, there is currently no review of GEMA studies detailing how location data is used to measure environmental exposure, and how this in turn is linked to momentary outcomes of interest. Moreover, a lack of standard reporting of such studies hampers comparability and reproducibility.

AIMS

The objectives of this research were twofold: 1) conduct a systematic review of GEMA studies that link momentary measurement with environmental data obtained from geolocation data, and 2) develop a STROBE extension guideline for GEMA studies.

METHOD

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Inclusion criteria consisted of a combination of repeated momentary measurements of a health state or behavior with GPS coordinate collection, and use of these location data to derive momentary environmental exposures. To develop the guideline, the variables extracted for the systematic review were compared to elements of the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and CREMAS (CRedibility of Evidence from Multiple Analyses of the Same data) checklists, to provide a new guideline for GEMA studies. An international panel of experts participated in a consultation procedure to collectively develop the proposed checklist items. RESULTS AND DEVELOPED TOOLS: A total of 20 original GEMA studies were included in the review. Overall, several key pieces of information regarding the GEMA methods were either missing or reported heterogeneously. Our guideline provides a total of 27 categories (plus 4 subcategories), combining a total of 70 items. The 22 categories and 32 items from the original STROBE guideline have been integrated in our GEMA guideline. Eight categories and 6 items from the CREMAS guideline have been included to our guideline. We created one new category (namely "Consent") and added 32 new items specific to GEMA studies.

CONCLUSIONS AND RECOMMENDATIONS

This study offers a systematic review and a STROBE extension guideline for the reporting of GEMA studies. The latter will serve to standardize the reporting of GEMA studies, as well as facilitate the interpretation of results and their generalizability. In short, this work will help researchers and public health professionals to make the most of this method to advance our understanding of how environments influence health.

Lifetime residential history collection and processing for environmental data linkages in the ABCD study

By using geospatial information such as participants' residential history along with external datasets of environmental exposures, ongoing studies can enrich their cohorts to investigate the role of the environment on brain-behavior health outcomes. However, challenges may arise if clear guidance and key quality control steps are not taken at the outset of data collection of residential information. Here, we detail the protocol development aimed at improving the collection of lifetime residential address information from the Adolescent Brain Cognitive Development (ABCD) Study. This protocol generates a workflow for minimizing gaps in residential information, improving data collection processes, and reducing misclassification error in exposure estimates.

Not just crop or forest: an integrated land cover map for agricultural and natural areas

Due to the key role surrounding landscape plays in ecological processes, a detailed characterization of land cover is critical for researchers and conservation practitioners. Unfortunately, in the United States, land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this gap, we merged two datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated 'Spatial Products for Agriculture and Nature' (SPAN). Our workflow leveraged strengths of the NVC and the CDL to create detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN annually from 2012-2021 for the conterminous United States, quantified agreement and accuracy of SPAN, and published the complete computational workflow. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved most conflicts, leaving only 0.6% of agricultural pixels unresolved in SPAN. These ready-to-use rasters characterizing both agricultural and natural land cover will be widely useful in environmental research and management.

Community-level exposomics: a population-centered approach to address public health concerns

Environmental factors affecting health and vulnerability far outweigh genetics in accounting for disparities in health status and longevity in US communities. The concept of the exposome, the totality of exposure from conception onwards, provides a paradigm for researchers to investigate the complex role of the environment on the health of individuals. We propose a complementary framework, community-level exposomics, for population-level exposome assessment. The goal is to bring the exposome paradigm to research and practice on the health of populations, defined by various axes including geographic, social, and occupational. This framework includes the integration of community-level measures of the built, natural and social environments, environmental pollution-derived from conventional and community science approaches, internal markers of exposure that can be measured at the population-level and early responses associated with health status that can be tracked using population-based monitoring. Primary challenges to the implementation of the proposed framework include needed advancements in population-level measurement, lack of existing models with the capability to produce interpretable and actionable evidence and the ethical considerations of labeling geographically-bound populations by exposomic profiles. To address these challenges, we propose a set of recommendations that begin with greater engagement with and empowerment of affected communities and targeted investment in community-based solutions. Applications to urban settings and disaster epidemiology are discussed as examples for implementation.