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

Author:

Manning Erika Rasnick1,Duan Qing1,Taylor Stuart2,Ray Sarah3,Corley Alexandra M S34,Michael Joseph5,Gillette Ryan2,Unaka Ndidi236,Hartley David35,Beck Andrew F234567,Brokamp Cole13ORCID, ,Anyigbo Chidiogo,Crosby Lori,de Leon Magdely Diaz,Egbo John,Foley Ben,Henize Adrienne,Jones Margaret,Jones Nana-Hawa Yayah,Kahn Robert,Krantz Landon,Lipps Lauren,Power-Hayes Alexandra,Quinn Charles,Quinonez Elizabeth,Riley Carley,Sandoval Laura,Shook Lisa,Steller Jeffrey

Affiliation:

1. Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center , Cincinnati, OH 45229, United States

2. Office of Population Health, Cincinnati Children’s Hospital Medical Center , Cincinnati, OH 45229, United States

3. Department of Pediatrics, University of Cincinnati College of Medicine , Cincinnati, OH 45219, United States

4. Division of General and Community Pediatrics, Cincinnati Children’s Hospital Medical Center , Cincinnati, OH 45229, United States

5. James M Anderson Center for Health Systems Excellence, Cincinnati Children’s Hospital Medical Center , Cincinnati, OH 45229, United States

6. Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center , Cincinnati, OH 45229, United States

7. Michael Fisher Child Health Equity Center, Cincinnati Children’s Hospital Medical Center , Cincinnati, OH 45229, United States

Abstract

Abstract 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.

Funder

National Institutes of Health

Agency for Healthcare Research and Quality

Publisher

Oxford University Press (OUP)

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