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A statewide population-based approach to examining Long COVID symptom prevalence and predictors in Michigan.
Hirschtick, Jana L; Xie, Yanmei; Slocum, Elizabeth; Hirschtick, Robert E; Power, Laura E; Elliott, Michael R; Orellana, Robert C; Fleischer, Nancy L.
Afiliación
  • Hirschtick JL; Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA. Electronic address: janahirs@umich.edu.
  • Xie Y; Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
  • Slocum E; Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
  • Hirschtick RE; Department of Medicine, Northwestern University Feinberg School of Medicine, 676 N St. Clair, Suite 2330, Chicago, IL 60611, USA.
  • Power LE; Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
  • Elliott MR; Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA; Survey Research Center, Institute for Social Research, 426 Thompson Street, Ann Arbor, MI 48109, USA.
  • Orellana RC; CDC Foundation, COVID-19 Corps, 600 Peachtree St NE #1000, Atlanta, GA 30308, USA; Michigan Department of Health and Human Services, 333 South Grand Ave., Lansing, MI 48933, USA.
  • Fleischer NL; Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Prev Med ; 177: 107752, 2023 Dec.
Article en En | MEDLINE | ID: mdl-37944672
ABSTRACT

OBJECTIVE:

The current broad definition of Long COVID, and an overreliance on clinical and convenience samples, is leading to a wide array of Long COVID estimates with limited generalizability. Our objective was to examine Long COVID symptoms using a statewide population-based probability sample.

METHODS:

Among 8000 sampled adults with polymerase-chain-reaction-confirmed SARS-CoV-2 between June 2020 and July 2021 in the Michigan Disease Surveillance System, 2533 completed our survey (response rate 32.2%). Using modified Poisson regression, we examined sociodemographic, behavioral, and clinical predictors of eight Long COVID symptom clusters, defined as at least one applicable symptom lasting 90 or more days post COVID-19 onset.

RESULTS:

Neuropsychiatric Long COVID symptoms, including brain fog, were most prevalent (23.7%), followed by systemic symptoms (17.1%), including fatigue, musculoskeletal (11.4%), pulmonary (10.4%), dermatologic (6.7%), cardiovascular (6.1%), gastrointestinal (5.4%), and ear, nose, and throat symptoms (5.3%). In adjusted analyses, female sex, a pre-existing psychological condition, and intensive care unit admission were strong predictors of most Long COVID symptom clusters. Older age was not associated with a higher prevalence of all symptoms - cardiovascular and dermatologic symptoms were most prevalent among middle-aged adults and age was not associated with neuropsychiatric or gastrointestinal symptoms. Additionally, there were fewer associations between pre-existing conditions and cardiovascular, neuropsychiatric, and dermatologic symptoms compared to other symptom clusters.

CONCLUSIONS:

While many predictors of Long COVID symptom clusters were similar, the relationship with age and pre-existing conditions varied across clusters. Cardiovascular, neuropsychiatric, and dermatologic symptoms require further study as potentially distinct from other Long COVID symptoms.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: COVID-19 / Síndrome Post Agudo de COVID-19 Límite: Adult / Female / Humans / Middle aged País/Región como asunto: America do norte Idioma: En Revista: Prev Med Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: COVID-19 / Síndrome Post Agudo de COVID-19 Límite: Adult / Female / Humans / Middle aged País/Región como asunto: America do norte Idioma: En Revista: Prev Med Año: 2023 Tipo del documento: Article