Using Data Science and a Health Equity Lens to Identify Long-COVID Sequelae Among Medically Underserved Populations

J Health Care Poor Underserved. 2023;34(2):521-534. doi: 10.1353/hpu.2023.0047.

Abstract

Understanding how post-acute COVID-19 syndrome (PACS or long COVID) manifests among underserved populations, who experienced a disproportionate burden of acute COVID-19, can help providers and policymakers better address this ongoing crisis. To identify clinical sequelae of long COVID among underserved populations treated in the primary care safety net, we conducted a causal impact analysis with electronic health records (EHR) to compare symptoms among community health center patients who tested positive (n=4,091) and negative (n=7,118) for acute COVID-19. We found 18 sequelae with statistical significance and causal dependence among patients who had a visit after 60 days or more following acute COVID-19. These sequelae encompass most organ systems and include breathing abnormalities, malaise and fatigue, and headache. This study adds to current knowledge about how long COVID manifests in a large, underserved population.

MeSH terms

  • COVID-19* / epidemiology
  • Data Science
  • Disease Progression
  • Health Equity*
  • Humans
  • Medically Underserved Area
  • Post-Acute COVID-19 Syndrome