Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, United States.
Advocate Aurora Research Institute, Advocate Health, 3075 Highland Parkway, Downers Grove, IL, 60515, United States, 414-219-4763.
JMIR Public Health Surveill. 2024 Oct 1;10:e55697. doi: 10.2196/55697.
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.
This study aimed to examine associations between acute COVID-19 symptom profiles and long COVID prevalence using a population-based sample.
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.
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.
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.
越来越多的证据表明，严重的急性 COVID-19 疾病会增加长新冠（也称为 COVID-19 后状况）的风险。然而，很少有研究检查急性症状与长新冠发病之间的关联。
本研究旨在使用基于人群的样本，检查急性 COVID-19 症状谱与长新冠患病率之间的关联。
我们使用了一种基于双模式（电话和网络）的基于人群的概率调查，对 2020 年 6 月至 2022 年 5 月期间在密歇根疾病监测系统中聚合酶链反应确诊的 SARS-CoV-2 成年患者进行了调查，以检查：（1）使用潜在类别分析，急性 COVID-19 症状如何聚类；（2）使用多项逻辑回归，考虑分类不确定性，检查社会人口统计学和临床预测因子与症状聚类的关系；（3）使用修正泊松回归，检查症状聚类与长新冠患病率之间的关联。
在我们的样本（n=4169）中，15.9%（n=693）患有长新冠，定义为 SARS-CoV-2 感染后至少 90 天出现新的或恶化的症状。我们从潜在类别分析中确定了 6 个急性 COVID-19 症状聚类，我们的样本中最常见的是流感样症状（24.7%）和发热（23.6%），其次是鼻塞（16.4%）、多症状（14.5%）、疲劳为主（10.8%）和呼吸急促为主（10%）聚类。多症状（39.7%）和呼吸急促为主（22.4%）聚类的长新冠患病率最高，其次是流感样症状（15.8%）、疲劳为主（14.5%）、发热（6.4%）和鼻塞（5.6%）聚类。调整后，女性（与男性相比）更有可能属于多症状、流感样症状和疲劳为主的聚类，而西班牙裔或其他种族或民族（与非西班牙裔白人相比）更有可能属于多症状聚类。与鼻塞聚类相比，多症状聚类的长新冠患病率最高（调整后的患病率比[aPR]6.1，95%置信区间[CI]4.3-8.7），其次是呼吸急促为主（aPR 3.7，95% CI 2.5-5.5）、流感样症状（aPR 2.8，95% CI 1.9-4.0）和疲劳为主（aPR 2.2，95% CI 1.5-3.3）聚类。
研究人员和临床医生在评估长新冠的后续风险时，应考虑急性 COVID-19 症状谱，包括研究背景下的潜在机制途径，并在提供临床护理时主动筛查高风险患者。
