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 moni...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Centers for Disease Control and Prevention
2023-02-01
|
| Series: | Emerging Infectious Diseases |
| Subjects: | |
| Online Access: | https://wwwnc.cdc.gov/eid/article/29/2/22-0712_article |
| _version_ | 1797945148940746752 |
|---|---|
| author | Eleanor S. Click Donald Malec Jennifer R. Chevinsky Guoyu Tao Michael Melgar Jennifer E. Giovanni Adi V. Gundlapalli S. Deblina Datta Karen K. Wong |
| author_facet | Eleanor S. Click Donald Malec Jennifer R. Chevinsky Guoyu Tao Michael Melgar Jennifer E. Giovanni Adi V. Gundlapalli S. Deblina Datta Karen K. Wong |
| author_sort | Eleanor S. Click |
| collection | DOAJ |
| 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.
|
| first_indexed | 2024-04-10T20:49:38Z |
| format | Article |
| id | doaj.art-e623877fb0424c2f9be6414ce2ab1e44 |
| institution | Directory Open Access Journal |
| issn | 1080-6040 1080-6059 |
| language | English |
| last_indexed | 2024-04-10T20:49:38Z |
| publishDate | 2023-02-01 |
| publisher | Centers for Disease Control and Prevention |
| record_format | Article |
| series | Emerging Infectious Diseases |
| spelling | doaj.art-e623877fb0424c2f9be6414ce2ab1e442023-01-23T18:21:58ZengCenters for Disease Control and PreventionEmerging Infectious Diseases1080-60401080-60592023-02-0129238939210.3201/eid2902.220712Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 SequelaeEleanor S. ClickDonald MalecJennifer R. ChevinskyGuoyu TaoMichael MelgarJennifer E. GiovanniAdi V. GundlapalliS. Deblina DattaKaren K. Wong 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. https://wwwnc.cdc.gov/eid/article/29/2/22-0712_articleCOVID-19coronavirus diseasesevere acute respiratory syndrome coronavirus 2SARS-CoV-2coronavirusesviruses |
| spellingShingle | Eleanor S. Click Donald Malec Jennifer R. Chevinsky Guoyu Tao Michael Melgar Jennifer E. Giovanni Adi V. Gundlapalli S. Deblina Datta Karen K. Wong Longitudinal Analysis of Electronic Health Information to Identify Possible COVID-19 Sequelae Emerging Infectious Diseases COVID-19 coronavirus disease severe acute respiratory syndrome coronavirus 2 SARS-CoV-2 coronaviruses viruses |
| 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 | COVID-19 coronavirus disease severe acute respiratory syndrome coronavirus 2 SARS-CoV-2 coronaviruses viruses |
| url | https://wwwnc.cdc.gov/eid/article/29/2/22-0712_article |
| work_keys_str_mv | AT eleanorsclick longitudinalanalysisofelectronichealthinformationtoidentifypossiblecovid19sequelae AT donaldmalec longitudinalanalysisofelectronichealthinformationtoidentifypossiblecovid19sequelae AT jenniferrchevinsky longitudinalanalysisofelectronichealthinformationtoidentifypossiblecovid19sequelae AT guoyutao longitudinalanalysisofelectronichealthinformationtoidentifypossiblecovid19sequelae AT michaelmelgar longitudinalanalysisofelectronichealthinformationtoidentifypossiblecovid19sequelae AT jenniferegiovanni longitudinalanalysisofelectronichealthinformationtoidentifypossiblecovid19sequelae AT adivgundlapalli longitudinalanalysisofelectronichealthinformationtoidentifypossiblecovid19sequelae AT sdeblinadatta longitudinalanalysisofelectronichealthinformationtoidentifypossiblecovid19sequelae AT karenkwong longitudinalanalysisofelectronichealthinformationtoidentifypossiblecovid19sequelae |