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Activity Number: 320 - Electronic Health Records, Causal Inference and Miscellaneous
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #318849
Title: Avoidance of Care: How Socioeconomic Inequities Impact COVID-19 Severity and Outcome
Author(s): Chinyere J Okpara* and Jasmin Divers and Megan D Winner and Meredith Akerman and Shahidul Islam
Companies: NYU Langone Health Long Island and NYU Long Island School of Medicine and NYU Langone Health Long Island and NYU Long Island School of Medicine and NYU Long Island School of Medicine
Keywords: COVID-19; Determinants of Health; Socioeconomic Inequities; Payer Mix; Hierarchical Logistic Regression Model

Individuals with several comorbidities have a higher risk for poor COVID-19 outcomes, including death. The under- and uninsured comprise a huge proportion of that population. For multiple reasons, including fear of the financial consequences, under- and uninsured individuals tend to delay or even avoid seeking medical attention, which could lead to even worse outcomes. Adopting the political determinants of health framework, we will examine the effect of socioeconomic status, measured by payer type and census data, on COVID-19 severity at presentation and death. We will apply a hierarchical logistic regression model assessing death as the outcome (first-level). At the second level, parameters will be modeled as functions of initial disease severity, payer types, and socioeconomic data at the census-tract level. Data will be sourced from an institutional COVID-19 data lake of >500,000 patients. We will focus on 5,479 PCR positive inpatients admitted between March and June 2020. Initial analysis show higher death rates in self-payers (n = 508, 19%), compared to privately insured (n = 1,615, 19%) and Medicaid recipients (n = 847, 12%), (p-value < 0.001).

Authors who are presenting talks have a * after their name.

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