Associations between acute COVID-19 symptom profiles and long COVID prevalence: Population-based cross-sectional study

Authors

Jana L. Hirschtick, Advocate Health - MidwestFollow
Elizabeth Slocum, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States.
Yanmei Xie, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States.
Laura E. Power, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States.
Michael R. Elliott, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States.
Robert C. Orellana, Centers for Disease Control and Prevention Foundation, Atlanta, GA, United States.
Nancy L. Fleischer, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States.

Recommended Citation

Hirschtick JL, Slocum E, Xie Y, et al. Associations Between Acute COVID-19 Symptom Profiles and Long COVID Prevalence: Population-Based Cross-Sectional Study. JMIR Public Health Surveill. 2024;10:e55697. Published 2024 Oct 1. doi:10.2196/55697

Abstract

Background:Growing evidence suggests that severe acute COVID-19 illness increases the risk of long COVID (also known as post-COVID-19 condition). However, few studies have examined associations between acute symptoms and long COVID onset.

Objective:This study aimed to examine associations between acute COVID-19 symptom profiles and long COVID prevalence using a population-based sample.

Methods:We used a dual mode (phone and web-based) population-based probability survey of adults with polymerase chain reaction-confirmed SARS-CoV-2 between June 2020 and May 2022 in the Michigan Disease Surveillance System to examine (1) how acute COVID-19 symptoms cluster together using latent class analysis, (2) sociodemographic and clinical predictors of symptom clusters using multinomial logistic regression accounting for classification uncertainties, and (3) associations between symptom clusters and long COVID prevalence using modified Poisson regression.

Results:In our sample (n=4169), 15.9% (n=693) had long COVID, defined as new or worsening symptoms at least 90 days post SARS-CoV-2 infection. We identified 6 acute COVID-19 symptom clusters resulting from the latent class analysis, with flu-like symptoms (24.7%) and fever (23.6%) being the most prevalent in our sample, followed by nasal congestion (16.4%), multi-symptomatic (14.5%), predominance of fatigue (10.8%), and predominance of shortness of breath (10%) clusters. Long COVID prevalence was highest in the multi-symptomatic (39.7%) and predominance of shortness of breath (22.4%) clusters, followed by the flu-like symptom (15.8%), predominance of fatigue (14.5%), fever (6.4%), and nasal congestion (5.6%) clusters. After adjustment, females (vs males) had greater odds of membership in the multi-symptomatic, flu-like symptom, and predominance of fatigue clusters, while adults who were Hispanic or another race or ethnicity (vs non-Hispanic White) had greater odds of membership in the multi-symptomatic cluster. Compared with the nasal congestion cluster, the multi-symptomatic cluster had the highest prevalence of long COVID (adjusted prevalence ratio [aPR] 6.1, 95% CI 4.3-8.7), followed by the predominance of shortness of breath (aPR 3.7, 95% CI 2.5-5.5), flu-like symptom (aPR 2.8, 95% CI 1.9-4.0), and predominance of fatigue (aPR 2.2, 95% CI 1.5-3.3) clusters.

Conclusions:Researchers and clinicians should consider acute COVID-19 symptom profiles when evaluating subsequent risk of long COVID, including potential mechanistic pathways in a research context, and proactively screen high-risk patients during the provision of clinical care.

Document Type

Article

PubMed ID

39352725