Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae

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 monit...

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Autores principales: Click, Eleanor S., Malec, Donald, Chevinsky, Jennifer R., Tao, Guoyu, Melgar, Michael, Giovanni, Jennifer E., Gundlapalli, Adi V., Datta, S. Deblina, Wong, Karen K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Centers for Disease Control and Prevention 2023
Materias:
Dispatch
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881771/
https://www.ncbi.nlm.nih.gov/pubmed/36564152
http://dx.doi.org/10.3201/eid2902.220712
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author Click, Eleanor S.
Malec, Donald
Chevinsky, Jennifer R.
Tao, Guoyu
Melgar, Michael
Giovanni, Jennifer E.
Gundlapalli, Adi V.
Datta, S. Deblina
Wong, Karen K.
author_facet Click, Eleanor S.
Malec, Donald
Chevinsky, Jennifer R.
Tao, Guoyu
Melgar, Michael
Giovanni, Jennifer E.
Gundlapalli, Adi V.
Datta, S. Deblina
Wong, Karen K.
author_sort Click, Eleanor S.
collection PubMed
description 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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spelling pubmed-98817712023-02-08 Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae Click, Eleanor S. Malec, Donald Chevinsky, Jennifer R. Tao, Guoyu Melgar, Michael Giovanni, Jennifer E. Gundlapalli, Adi V. Datta, S. Deblina Wong, Karen K. Emerg Infect Dis Dispatch 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. Centers for Disease Control and Prevention 2023-02 /pmc/articles/PMC9881771/ /pubmed/36564152 http://dx.doi.org/10.3201/eid2902.220712 Text en https://creativecommons.org/licenses/by/4.0/Emerging Infectious Diseases is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited.
spellingShingle Dispatch
Click, Eleanor S.
Malec, Donald
Chevinsky, Jennifer R.
Tao, Guoyu
Melgar, Michael
Giovanni, Jennifer E.
Gundlapalli, Adi V.
Datta, S. Deblina
Wong, Karen K.
Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae
title Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae
title_full Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae
title_fullStr Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae
title_full_unstemmed Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae
title_short Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae
title_sort longitudinal analysis of electronic health information to identify possible covid-19 sequelae
topic Dispatch
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881771/
https://www.ncbi.nlm.nih.gov/pubmed/36564152
http://dx.doi.org/10.3201/eid2902.220712
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