Using Data Science and a Health Equity Lens to Identify Long-COVID Sequelae Among Medically Underserved Populations.
In: Journal of Health Care for the Poor & Underserved, Jg. 34 (2023-05-01), Heft 2, S. 521-534
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Zugriff:
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. [ABSTRACT FROM AUTHOR]
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Using Data Science and a Health Equity Lens to Identify Long-COVID Sequelae Among Medically Underserved Populations.
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Autor/in / Beteiligte Person: | Nasir, Md ; Cook, Nicole ; Parras, Daniel ; Mukherjee, Sumit ; Miller, Geralyn ; Ferres, Juan Lavista ; Chung-Bridges, Katherine |
Zeitschrift: | Journal of Health Care for the Poor & Underserved, Jg. 34 (2023-05-01), Heft 2, S. 521-534 |
Veröffentlichung: | 2023 |
Medientyp: | academicJournal |
ISSN: | 1049-2089 (print) |
DOI: | 10.1353/hpu.2023.0047 |
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