tetano
Editor, Senior Moderator
Emerg Infect Dis
. 2022 Dec 23;29(2).
doi: 10.3201/eid2902.220712. Online ahead of print.
Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae
Eleanor S Click, Donald Malec, Jennifer Chevinsky, Guoyu Tao, Michael Melgar, Jennifer Giovanni, Adi Gundlapalli, Deblina Datta, Karen K Wong
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.
Keywords: COVID-19; SARS-CoV-2; United States; coronavirus disease; coronaviruses; electronic health information sequelae; longitudinal analysis; respiratory infections; severe acute respiratory syndrome coronavirus 2; viruses; zoonoses.
. 2022 Dec 23;29(2).
doi: 10.3201/eid2902.220712. Online ahead of print.
Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae
Eleanor S Click, Donald Malec, Jennifer Chevinsky, Guoyu Tao, Michael Melgar, Jennifer Giovanni, Adi Gundlapalli, Deblina Datta, Karen K Wong
- PMID: 36564152
- DOI: 10.3201/eid2902.220712
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.
Keywords: COVID-19; SARS-CoV-2; United States; coronavirus disease; coronaviruses; electronic health information sequelae; longitudinal analysis; respiratory infections; severe acute respiratory syndrome coronavirus 2; viruses; zoonoses.