A spatial and contextual exposome-wide association study and polyexposomic score of COVID-19 hospitalization

Author:

Hu Hui1ORCID,Laden Francine123,Hart Jaime12,James Peter24,Fishe Jennifer5,Hogan William6ORCID,Shenkman Elizabeth6,Bian Jiang6ORCID

Affiliation:

1. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School , Boston, MA, USA

2. Department of Environmental Health, Harvard T.H. Chan School of Public Health , Boston, MA, USA

3. Department of Epidemiology, Harvard T.H. Chan School of Public Health , Boston, MA, USA

4. Department of Population Medicine, Harvard Pilgrim Healthcare , Boston, MA, USA

5. Department of Emergency Medicine, University of Florida College of Medicine—Jacksonville, Jacksonville , FL, USA

6. Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida , Gainesville, FL, USA

Abstract

Abstract Environmental exposures have been linked to COVID-19 severity. Previous studies examined very few environmental factors, and often only separately without considering the totality of the environment, or the exposome. In addition, existing risk prediction models of severe COVID-19 predominantly rely on demographic and clinical factors. To address these gaps, we conducted a spatial and contextual exposome-wide association study (ExWAS) and developed polyexposomic scores (PES) of COVID-19 hospitalization leveraging rich information from individuals’ spatial and contextual exposome. Individual-level electronic health records of 50 368 patients aged 18 years and older with a positive SARS-CoV-2 PCR/Antigen lab test or a COVID-19 diagnosis between March 2020 and October 2021 were obtained from the OneFlorida+ Clinical Research Network. A total of 194 spatial and contextual exposome factors from 10 data sources were spatiotemporally linked to each patient based on geocoded residential histories. We used a standard two-phase procedure in the ExWAS and developed and validated PES using gradient boosting decision trees models. Four exposome measures significantly associated with COVID-19 hospitalization were identified, including 2-chloroacetophenone, low food access, neighborhood deprivation, and reduced access to fitness centers. The initial prediction model in all patients without considering exposome factors had a testing-area under the curve (AUC) of 0.778. Incorporation of exposome data increased the testing-AUC to 0.787. Similar findings were observed in subgroup analyses focusing on populations without comorbidities and aged 18–24 years old. This spatial and contextual exposome study of COVID-19 hospitalization confirmed previously reported risk factor but also generated novel predictors that warrant more focused evaluation.

Funder

National Institute of Environmental Health Sciences

Publisher

Oxford University Press (OUP)

Subject

General Economics, Econometrics and Finance

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