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Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae.
Click, Eleanor S; Malec, Donald; Chevinsky, Jennifer; Tao, Guoyu; Melgar, Michael; Giovanni, Jennifer; Gundlapalli, Adi; Datta, Deblina; Wong, Karen K.
Emerg Infect Dis ; 29(2): 389-392, 2023 Feb.
Article in English | MEDLINE | ID: covidwho-2198457
ABSTRACT
Ongoing symptoms might follow acute COVID-19. Using electronic health information, we compared pre‒ and post‒COVID-19 diagnostic codes to identify symptoms that had higher encounter incidence in the post‒COVID-19 period as sequelae. This method can be used for hypothesis generation and ongoing monitoring of sequelae of COVID-19 and future emerging diseases.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Middle East Respiratory Syndrome Coronavirus / COVID-19 Type of study: Observational study / Prognostic study Topics: Long Covid Limits: Humans Language: English Journal: Emerg Infect Dis Journal subject: Communicable Diseases Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Middle East Respiratory Syndrome Coronavirus / COVID-19 Type of study: Observational study / Prognostic study Topics: Long Covid Limits: Humans Language: English Journal: Emerg Infect Dis Journal subject: Communicable Diseases Year: 2023 Document Type: Article