Early on in the COVID-19 pandemic, it became clear that a subset of individuals with acute COVID-19 illness experienced a constellation of symptoms — typically fatigue and cognitive problems– after the acute symptoms resolved, with some individuals reporting symptoms at least six months after the acute infection. These persistent symptoms have been called post-acute sequelae of COVID-19, post–COVID-19 syndrome, or most commonly long COVID.
In order to estimate the prevalence of persistent COVID symptoms and to identify sociodemographic factors associated with long COVID, Roy Perlis, MD MSc, Director of the Center for Quantitative Health at MGH, and colleagues analyzed data from the COVID States Project. This is a joint project conducted by researchers from Mass General, Harvard University, Northeastern University, Rutgers University, and Northwestern University launched in order to better understand the links between social behaviors and virus transmission. (A complete archive of their reports can be found at covidstates.org.)
The current study analyzed data from 8 waves of a large-scale internet survey conducted between February 5, 2021, and July 6, 2022, among individuals aged 18 years or older across 50 States and the District of Columbia. The cohort included 16 ,091 survey respondents reporting test-confirmed COVID-19 illness at least two months prior to completing the survey.
The mean age of this group of respondents was 40.5 (SD 15.2) years; 62.6% were women, and 37.4% were men. In this cohort, 817 (5.1%) were Asian, 1826 (11.3%) were Black, 1546 (9.6%) were Hispanic, and 11 425 (71.0%) were White.
Prevalence and Symptoms of Long COVID
In this cohort, 2359 individuals (14.7%) reported COVID-19 symptoms persisting more than 2 months after experiencing the acute illness. The most common long COVID symptoms were fatigue (52.2%), brain fog or memory loss (45.7%) and loss of smell (43.7%).
Risk Factors for Long COVID
The researchers identified several sociodemographic factors associated with risk for long COVID. In logistic regression models, older age was associated with increased risk, such that age of a decade above 40 years was associated with a 15% greater risk (adjusted odds ratio [OR], 1.15; 95% CI, 1.12-1.19).
Female gender was associated with a nearly twofold increase in risk for long COVID (adjusted OR, 1.91; 95% CI, 1.73-2.13). Individuals with higher levels of education were less likely to experience long COVID (adjusted OR, 0.67; 95% CI, 0.56-0.79). In addition, urban residents were less likely than rural residents to experience long COVID (adjusted OR, 0.74; 95% CI, 0.64-0.86).
Completion of the primary vaccine series prior to acute illness was associated with diminished risk for long COVID (OR, 0.72; 95% CI, 0.60-0.86).
Next Steps
The current study from Perlis and colleagues indicates that long COVID — defined here as symptoms persisting for at least two months — is by no means uncommon and affects about 15% of adults. This number is similar to estimates generated by other studies with different methodologies.
Ideally this type of research will help us to identify individuals who are at highest risk for long COVID, so that we may be able to initiate interventions that reduce risk for persistent symptoms or lessen the severity of symptoms. At the present time, there are no evidence-based treatments for long COVID.
We still do not know what actually causes long COVID. However, this study indicates that the one thing we can do to reduce risk for long COVID is to get vaccinated.
Co-Authors: Mauricio Santillana, PhD; Katherine Ognyanova, PhD; Alauna Safarpour, PhD; Kristin Lunz Trujillo, PhD; Matthew D. Simonson, PhD; Jon Green, PhD; Alexi Quintana, BA; James Druckman, PhD; Matthew A. Baum, PhD; David Lazer, PhD
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Perlis RH, Santillana M, Ognyanova K, Safarpour A, Lunz Trujillo K, Simonson MD, Green J, Quintana A, Druckman J, Baum MA, Lazer D. Prevalence and Correlates of Long COVID Symptoms Among US Adults. JAMA Netw Open. 2022 Oct 3;5(10):e2238804.
In the News
Roy Perlis, MD, MSc is the Director of the Center for Quantitative Health at MGH and Associate Chief for Research in the Department of Psychiatry. He is the Ronald I. Dozoretz, MD Endowed Professor of Psychiatry at Harvard Medical School and Associate Editor (Neuroscience) at JAMA's new open-access journal, JAMA Network - Open. His research is focused on identifying predictors of treatment response in brain diseases, and using these biomarkers to develop novel treatments. He directs two complementary laboratory efforts, one focused on patient-derived cellular models and one applying machine learning to large clinical databases.
