• Pricila H Mullachery, Usama Bilal, Ran Li, and Leslie A McClure.
• Department of Health Services Administration and Policy, College of Public Health, Temple University, 1301 Cecil B. Moore Ave., Philadelphia, PA, 19122, USA. pricila.mullachery@temple.edu.
• J Urban Health. 2024 Aug 1; 101 (4): 845855845-855.

AbstractKnowledge about neighborhood characteristics that predict disease burden can be used to guide equity-based public health interventions or targeted social services. We used a case-control design to examine the association between area-level social vulnerability and severe COVID-19 using electronic health records (EHR) from a regional health information hub in the greater Philadelphia region. Severe COVID-19 cases (n = 15,464 unique patients) were defined as those with an inpatient admission and a diagnosis of COVID-19 in 2020. Controls (n = 78,600; 5:1 control-case ratio) were a random sample of individuals who did not have a COVID-19 diagnosis from the same geographic area. Retrospective data on comorbidities and demographic variables were extracted from EHR and linked to area-level social vulnerability index (SVI) data using ZIP codes. Models adjusted for different sets of covariates showed incidence rate ratios (IRR) ranging from 1.15 (95% CI, 1.13-1.17) in the model adjusted for individual-level age, sex, and marital status to 1.09 (95% CI, 1.08-1.11) in the fully adjusted model, which included individual-level comorbidities and race/ethnicity. The fully adjusted model indicates that a 10% higher area-level SVI was associated with a 9% higher risk of severe COVID-19. Individuals in neighborhoods with high social vulnerability were more likely to have severe COVID-19 after accounting for comorbidities and demographic characteristics. Our findings support initiatives incorporating neighborhood-level social determinants of health when planning interventions and allocating resources to mitigate epidemic respiratory diseases, including other coronavirus or influenza viruses.© 2024. The Author(s).

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