Characterization of Long COVID by Clinical Examination and Self-Perceived Severity Stratified by Infection Wave: Beyond COVID, a Prospective, Multicenter Cohort Study in Germany

Characterization of Long COVID by Clinical Examination and Self-Perceived Severity Stratified by Infection Wave: Beyond COVID, a Prospective, Multicenter Cohort Study in Germany | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Characterization of Long COVID by Clinical Examination and Self-Perceived Severity Stratified by Infection Wave: Beyond COVID, a Prospective, Multicenter Cohort Study in Germany Alexander Mertens, Judith Smith, Ingmar Bergs, Julia Fischer, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8489939/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Long COVID refers to persistent or new-onset symptoms three months after SARS-CoV-2 infection lasting for at least two months. The prevalence of Long COVID ranges across studies, while the associated risk factors are not well understood. Methods The study population of Beyond COVID was recruited in six German cities by inviting (1) individuals registered as SARS-CoV-2 PCR positive at the local Public Health Authorities and (2) previously hospitalized patients with infection date between 1st March 2021 and 31st May 2022. Participants were allocated to the predominant variant of concern (VOC) of their first infection. Blood exams and questionnaires to assess persisting symptoms, quality of life (QOL), and psychosocial factors were performed. This publication describes the parameters at baseline visit (BV). Results We included 1258 participants (13.4% hospitalized-based; 86.6% population-based). Most participants had BA.2 (34.6%), followed by Delta (26.8%), BA.1 (18.9%), and Alpha (17.3%). The mean age was 47.1, and 59% were female. 68.8% reported at least one persisting symptom. Fatigue was the most frequent ongoing symptom (32.8%), followed by concentration disorders (25.4%) and dyspnoea (22%). Female sex, lower education, and a shorter period between infection and BV were associated with higher rates of persisting symptoms and symptom-severity. BA.1 and BA.2 had lower rates of persisting symptoms and symptom severity. Conclusion Analysis of baseline data from the Beyond-COVID cohort confirms a high percentage of persistent symptoms. Omicron variants had lower rates of persistent symptoms and symptom severity. Long-term follow-up of study participants will contribute to the characterization of Long COVID. Long COVID Long COVID-19 Post COVID Condition PCC COVID-19 Follow-up after COVID-19 COVID-long-term Gender association in post-COVID-19 Variant of Concern VOC VOCs Wave-association Persistence of COVID-19 symptoms SARS-CoV-2 Post Acute Sequelae of SARS CoV-2 infection Post-COVID-19 condition Post-COVID COVID-waves Alpha Delta Omicron Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction By December 2024, the COVID-19 pandemic led to over 777 million confirmed cases [ 1 ]. Early on in the SARS-CoV-2 pandemic patients with persistent symptoms after recovery of COVID-19 were described. In October 2021, following a Delphi consensus, the World Health Organisation (WHO) defined Long COVID as a “condition that occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually three months from the onset of COVID-19 with symptoms that last for at least two months and cannot be explained by an alternative diagnosis. Symptoms may be new onset, following initial recovery from an acute COVID-19 episode, or persist from the initial illness. Symptoms may also fluctuate or relapse over time” [ 2 ]. The definition is inclusive and acknowledges that up to 200 different Long COVID symptoms have been described and that confirmation of SARS-CoV-2 infection is not mandatory since it is not feasible in some regions (e.g., countries with limited access to healthcare) [ 3 ]. The reported prevalence of Long COVID widely differs across studies and can be estimated at around 10–30% in most studies, with an extensive range of 2–80% [ 4 – 7 ]. The Global Burden of Disease Long COVID study included 1.2 million people who had experienced an acute symptomatic SARS-CoV-2 infection and reported up to 15.1% long-lasting symptoms one year after infection [ 8 ]. Variation in Long COVID prevalence and symptom-complex definition in the literature is to a large extent due to a lack of a uniform definition, the differences in observational time by studies as well as the consideration of various variants of concern (VOCs), and the characteristics of participants included in each study (asymptomatic vs. symptomatic non-hospitalized vs. hospitalized participants vs. a mixture) [ 7 , 9 ]. In addition, most studies refer to data from highly pre-selected samples of patients referred to the participant centres or limited to online surveys [ 10 , 11 ], which are potentially biased (e.g., by self-referrals). Regarding different virus variants, SARS-CoV-2 variants emerged from the original wild-type strain to Alpha (B.1.1.7), Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529) over time [ 10 ]. The impact of different SARS-CoV-2 variants remains debatable: the majority of published studies to date focus on the characterization of one or two virus variants, as shown in the synthesis of results in the systematic reviews by Vishwakarma et al. and Du et al., stating that the difference in the prevalence of symptoms and Long COVID between various variants still needs to be explored [ 11 – 14 ]. The influence of vaccination and multiple infections on symptoms and symptom severity has also not yet been sufficiently researched [ 7 , 15 – 17 ]. The present study aims to provide further evidence regarding Long COVID symptoms and symptom severity throughout the pandemic phases. It differs from previous approaches by regarding a systematic survey of Long COVID and global symptom severity stratified by the predominant virus variants by examining randomly selected COVID-19 infected individuals from the general population together with a hospital-based sample regardless of the symptoms during or after the infection. Materials and Methods Study Population This prospective multicenter cohort study includes SARS-CoV-2-positive tested (PCR) individuals who met the inclusion criteria (see below). Six study centers (University Hospitals of Aachen, Bonn, Essen, Düsseldorf, Cologne, and Münster) took part. Two samples were defined: Individuals with laboratory-confirmed COVID-19 infection registered at the local Public Health Authorities (population-based sample) and hospitalized patients, registered at the participating study centers (hospital-based). The public health authorities drew consecutive samples from registration data limited to infections that occurred between 1 March 2021 and 31st May 2022. The authorities contacted eligible individuals and invited them by mail to participate in the study. Recruitment was conducted between 01/2022 and 09/2023. The second sample consisted of patients hospitalized in one of the six participating clinics after the cut-off date. In this case, contact was made directly by the participating study centres. Inclusion Criteria (Population Sample) Main residence in North Rhine-Westphalia in the catchment area of the cities of Aachen, Bonn, Düsseldorf, Essen, Cologne or Münster 18–75 years old Evidence of a survived SARS-CoV-2 infection (PCR test) Infection between March 1st 2021, and May 31 2022 The time of one infection was at least three months ago (reference date: date of the first positive PCR test) Patients with active COVID-19 infection or an infection that occurred less than three months ago were scheduled for later inclusion in the study The person was able to take part in the survey and the examination and has sufficient knowledge of the German language or support from an interpreter Inclusion Criteria (Hospitalized Sample) Main residence in North Rhine-Westphalia in the catchment area of the cities of Aachen, Bonn, Düsseldorf, Essen, Cologne or Münster 18–75 years old Evidence of a survived SARS-CoV-2 infection (PCR test) Infection between March 1st 2021 and May 31th 2022 The time of infection was at least three months ago (reference date: date of inpatient admission) The person was able to take part in the survey and the examination and has sufficient knowledge of the German language or support from an interpreter Study Baseline and Follow-up Both cohorts (population- and hospital-based) were examined at the Long-COVID-19 clinic of each center. Blood, urine, and saliva samples were taken following participant information and informed consent. Furthermore, participants underwent medical examinations by a study physician. Neurological tests, spirometry, transthoracic echocardiography, and abdomen ultrasound were performed (Fig. 1 ). If necessary, diffusing capacity of lung carbon monoxide (DLCO) or lung computed tomography (CT) scans, neurological evaluation, and psychological evaluation were conducted in individual cases. Aroma and taste tests were performed at the initial visit and, if pathological, on the following appointments according to the study protocol. Participants responded to questionnaires to assess general health-related information, persisting (severity and duration) symptoms, quality of life, disability, and sociodemographic and psychosocial factors at each visit. Questionnaires collected data on the date of the first and subsequent infections, vaccination, and new health issues. Follow-up visits were conducted at 3, 6,12, 18, 24, 30, and 36 months after the BV, Fig. 1 . Follow-up is ongoing. Data Assessment Persistent symptoms Persistent symptoms were assessed by questionnaire. Persistent symptoms were defined as reporting at least one self-perceived symptom due to the participants’ first SARS-CoV-2 infection still present at the baseline examination. Participants were asked for persistent symptoms that occurred initially during the acute infection and persistent symptoms that occurred later. Persistent Symptom Severity Level The overall self-perceived severity level of persistent symptoms was assessed on a scale from 0 to 10, and the participants were asked how severely they were affected by the reported symptoms at the time of the baseline examination. In cases where the severity level was missing, but at least one persistent symptom was reported, the severity level was coded to 0. The severity level of participants who reported no persistent symptoms was also coded 0. Frequency of Persisting Symptoms Regarding VOC and Symptom-Cluster Questionnaires assessed 26 different symptoms. We subsumed the frequencies of reported ongoing single symptoms into five predefined clusters [ 18 , 19 ]: chronic fatigue-like (fatigue, headache, concentration disorders), respiratory (dyspnoea, coughing, chest pain, sore throat, voice change), neurosensorial (dysgeusia, dysosmia, dizziness, tinnitus, earache), gastrointestinal (nausea, loss of appetite, diarrhoea, constipation, abdominal pain), and systemic-inflammatory chronic pain (limb/ back pain, fever, chill, cold; hair loss). Time from Infection to Study Baseline The time from the first infection to the study baseline was calculated for each participant and included as a continuous variable to account for differences in time for recovering from SARS-CoV-2 infection. Vaccination before the First Infection Participant's vaccination history was recorded in a face-to-face interview, and we determined if a vaccination occurred before or after the first infection. SARS-CoV-2 Wave of Infection (VOCs) The date of the first infection was used to assign participants to certain VOCs according to the different periods of COVID-19 in Germany [ 20 ]. This resulted in a categorical variable with five categories. The first category consisted of participants with an infection between the 9th and the 30th calendar week of 2021, representing the third COVID wave in Germany (VOC: Alpha). The second category comprises the fourth infection wave, i.e., the period from the 31st – 51st calendar week of 2021 (VOC: Delta). The third, fourth and fifth category representing sublines of Omicron: the third extends from the 52nd calendar week of 2021 to the 8th calendar week of 2022 (VOC: Omicron BA.1). Category 4 extends from the 9th calendar week of 2022 to the 21st calendar week of 2022 (VOC: Omicron BA.2). Category 5 covers the period from the 22nd calendar week of 2022 (VOC: Omicron BA.5). Educational Attainment Educational attainment was assessed as the highest general school-leaving qualification and dichotomized into a variable indicating whether general or subject-specific tertiary education entrance qualification was attained. Multiple Infections Participants provided information on every confirmed SARS-CoV-2 infection. Information was dichotomized into one confirmed infection vs. more than one confirmed infection. Statistical Analysis Descriptive statistics were calculated as contingency tables with mosaic plots. Participants with additional missing values in the covariates were only excluded from the analyses that included covariates. Logistic regression models (with N = 1117 complete observations) were fitted to calculate odds ratios (ORs) and 95%-confidence intervals (95%-CIs) for the association of SARS-CoV-2 infection waves with persistent symptoms due to first SARS-CoV-2 infection. Models were adjusted for the potential confounders: vaccination before the first infection, study center, educational attainment, multiple infections, and time from the first infection to the study baseline. In addition, linear regression models were fitted with the same covariates to calculate beta estimates and 95%-CIs for the association of SARS-CoV-2 infection waves with severity level due to the first SARS-CoV-2 infection at baseline examination. The number of observations also amounted to N = 1117. Sensitivity analyses were provided equivalent to the analysis strategy described above, using response variables defined based on all infections up to the baseline examination instead of just the first infection. In addition, primary analyses were repeated for specific persistent symptoms with high prevalence in the study sample. All analyses were conducted using SAS version 9.4 (SAS Institute). Results Study Population A total of 1185 individuals participated from the first sample (response rate: 4.7%). In the second sample, 199 participants took part (response rate: 11.4%). Ten participants (0.7%) were excluded (missing informed consent; not meeting the inclusion criteria). Overall, 1374 participants were included in the analysis with 1258 having complete information for statistical analysis on persistent symptoms (related to the first infection) and the corresponding severity level, of which 86.6% (n = 1089) were from the population-based sample. Table 1 presents the main characteristics of the sample. The mean time between the first infection and study baseline was 388.1 days (± 126.6). Most participants were female (n = 744; 59%), and the mean age was 47.1 (± 14.5) years. Regarding education level, 58.7% (n = 724) had at least a high school graduation. Most participants had at least one pre-existing condition (77%; n = 968) with a mean number of pre-existing conditions of 2.5 (± 2.7). The vast majority were vaccinated before the 1st infection (n = 1194; 96.3%; Alpha variant: 85.2%; Delta: 96.3%; BA.1: 99.1%; BA.2: 99.5%; BA.5: 100%). 24.3% (n = 306) of the participants had two or more infections at the baseline examination. Table 1 Study Population; Baseline Parameter; BV: Baseline Visit Variable Hospital-based Sample (n = 169) Population-based Sample (n = 1089) Total (n = 1258) Age (years; mean) 53.2 (± 14.1) 46.1 (± 14.4) 47.1 (± 14.5) Sex female Sex male 86 (50.6%) 83 (49.4%) 658 (60.3%) 431 (39.7%) 744 (59%) 514 (41%) High School Graduation 73 (45.6%) 651 (60.7%) 724 (58.7%) Number of Participants with ≥ 1 Pre-Existing Condition 156 (92.9%) 812 (74.6%) 968 (77%) Number of Pre-Existing Conditions 4.3 (± 3.8) 2.2 (± 2.3) 2.5 (± 2.7) Number of Vaccinated Participants (before 1st Infection) 147 (90.7%) 1047 (97.1%) 1194 (96.3%) Number of Infections at BV One Two Three Four 130 (76.9%) 37 (21.9%) 2 (1.2%) 0 822 (75.5%) 255 (23.4%) 11 (1%) 1 (0.1%) 952 (75.9%) 292 (23.2%) 13 (1%) 1 (0.1%) Number of Symptoms (during 1st Infection) Number of Participants with ≥ 1 persisting Symptom at BV (one Infection) Number of persisting Symptoms per Participant (one Infection) 7.5 (± 5.2) 134 (79.3%) 2.9 (± 3.3) 7.7 (± 4.6) 731 (67.1%) 2.1 (± 2.8) 7.7 (± 4.7) 865 (68.8%) 2.2 (± 2.9) Number of Participants with ≥ 1 persisting Symptom at BV (all infections) 136 (80.5%) 770 (70.7%) 906 (72%) Number of Persisting Symptoms per Participant (all infections) 3.2 (± 3.6) 2.7 (± 3.4) 2.7 (± 3.4) Severity of Impairment (one Infection) 3.8 (± 3) 2.7 (± 2.8) 2.9 (± 2.8) Severity of Impairment (all infections) 3.8 (± 3) 2.8 (± 2.8) 2.9 (± 2.9) Interval 1st Infection – BV (days) Interval last Infection – BV (days) 411.1 (± 171.1) 354.6 (± 175.6) 384.5 (± 118) 319.8 (± 136.9) 388.1 (± 126.6) 324.5 (± 143.1) Regarding pre-existing conditions, the most common comorbidity was overweight and obesity (48.9%), followed by cardiovascular diseases (24.9%), thyroid dysfunctions (17.6%), neurological disorders (17.6%), chronic respiratory diseases (15.9%) (Fig. 2 ). Regarding pre-existing conditions in different VOCs, we excluded BA.5 due to the low number of overall participants and the lack of appropriate statistical subgroup analyses. Ongoing Symptoms and Severity of Impairment During infection, the mean number of symptoms was 7.7 (± 4.7), while at the time of BV an overall mean number of symptoms of 2.2 (± 2.9) was reported, Table 1 . In the subgroup of participants with more than one infection, 72% (n = 906) reported at least one symptom at the study baseline. The mean number of persisting symptoms at baseline in this group was 2.7 (± 3.4), Table 1 . The severity of impairment did not differ between the subgroups with one (2.9 (± 2.8)) and more than one infection (2.9 (± 2.9)), Table 1 . Stratification by SARS-CoV-2 Wave (VOC) Participants were stratified into five subgroups based on their positive SARS-CoV-2 test date and the corresponding VOC in Germany, Table 2 . The highest number of participants was enrolled in group 4 (infection period CW 09/2022–21/2022), representing the Omicron BA.2 variant, with a total of 435 (34.6%) participants, while the lowest number of participants were included in group 5 (infection period starting with CW 22/2022 and ongoing) with a total of 30 (2.4%) participants, Table 2 . Table 2 Phase classification to describe the COVID-19 Pandemic events in Germany, 2020–2022; VOC: Variant of Concern; adapted from [ 18 ] Event Period (calender week/year) Number of Participants Hospitalized Sample / Population Sample / Total Phase 1–3 : First Covid-19 Wave (wild-type); Summer Plateau 2020; Second Covid-19 Wave (wild-type); 10/2020–8/2021 No participants (Infection before March 1st 2021: Exclusion criterion) Phase 4–5 : Third Covid-19 Wave (VOC: Alpha); Summer-Plateau 2021; 9/2021–30/2021 45 (26.6%) / 173 (15.9%) / 218 (17.3%) Phase 6 : Fourth Covid-19 Wave (VOC: Delta) 31/2021–51/2021 49 (29%) / 288 (26.5%) / 337 (26.8%) Phase 7 : Fifth Covid-19 Wave VOC: Omicron BA.1 VOC: Omicron BA.2 52/2021–8/2022 9/2022–21/2022 34 (20.1%) / 204 (18.7%) / 238 (18.9%) 38 (22.5%) / 297 (36.5%) / 435 (34.6%) Phase 8 : Sixth Covid-19 Wave (VOC: Omicron BA.5) 22/2022–5/2023 3 (1.8%) / 27 (2.5%) / 30 (2.4%) Frequency of persistent Symptoms regarding Baseline Parameter, the Number of Infections, and VOC by Multivariate Regression Persistent symptoms at baseline were analysed by multivariable regression models, Table 3 . Female sex (OR: 1.50; 95% CI: 1.14–1.97, p = 0.004), low education (OR: 2.30; 95% CI: 1.71–3.10, p = < 0.001), and a shorter period between infection and baseline examination in months (OR: 0.92, 95% CI: 0.88–0.96, p = < 0.001) were associated with the odds of reporting persisting symptoms at baseline. Odds of reporting persistent symptoms also differed by study centre. Vaccination status and the number of previous infections did not seem to affect the presence of persistent symptoms. There were differences regarding the VOCs and the chance of reporting persistent symptoms: Omicron BA.1 (OR: 0.28, 95% CI: 0.12–0.7, p = 0.01) and Omicron BA.2 (OR: 0.21, 95% CI: 0.08–0.5, p = < 0.01) revealed a much lower odds for persisting symptoms than SARS-CoV-2 Alpha variant, Table 3 . Table 3 Probability of persisting symptoms regarding baseline parameter, study center, the number of infections, and wave (multivariable logistic regression; response variable: persistent symptoms yes/no); OR: odds ratio; VOC: variant of concern; CI: confidence interval; Center 1: University Hospital of Düsseldorf; Center 2: University Hospital of Aachen; Center 3: University Hospital of Bonn; Center 4: University Hospital of Essen; Center 5: University Hospital of Cologne; Center 6: University Hospital of Münster; BV: Initial Visite Variable OR (95% CI) p-value Age (years) 1.01 [0.99;1.02] 0.27 Sex (female vs. male) 1.5 [1.14;1.97] 0.004 Low education 2.3 [1.71;3.1] < 0.001 4th Wave vs. 1st – 3rd Wave VOC: Delta vs. Alpha 5.1th Wave vs. 1st – 3rd Wave VOC: Omicron BA.1 vs. Alpha 5.2th Wave vs. 1st – 3rd Wave VOC: Omicron BA.2 vs. Alpha 6th Wave vs. 1st – 3rd Wave VOC: Omicron BA.5 vs. Alpha 0.67 [0.28;1.6] 0.28 [0.12;0.7] 0.21 [0.08;0.5] 0.36 [0.1;1.31] 0.36 0.01 < 0.01 0.12 ≥ 2 Infections 1.04 [0.75;1.45] 0.8 Vaccination before 1st Infection yes vs. no 0.91 [0.38;2.17] 0.83 Center 2 vs. Center 1 Center 3 vs. Center 1 Center 4 vs. Center 1 Center 5 vs. Center 1 Center 6 vs. Center 1 0.69 [0.43;1.09] 0.8 [0.49;1.31] 0.99 [0.58;1.68] 1.62 [0.99;2.67] 0.77 [0.5;1.19] 0.11 0.38 0.96 0.06 0.24 Interval 1st Infection to BV (month) 0.92 [0.88;0.96] < 0.001 Severity of Ongoing Symptoms regarding Baseline Parameter, the Number of Infections, and VOC by Multivariate Regression The self-perceived severity of persistent symptoms was analysed via multivariable linear regression models. Age (β: 0.009, 95% CI: -0.003-0.02, p = 0.14), the number of infections (one vs. ≥2 infections (β: -0.128, 95% CI: -0.526-0.27, p = 0.53), and vaccination before the first infection (β: -0.187, 95% CI: -1.038-0.664, p = 0.64) did not show indication for an association with the severity of persistent symptoms. A longer time period between infection and baseline examination was weakly associated with lower severity of persistent symptoms (β -0.06 per month, 95% CI: -0.11 - -0.01, p = 0.02). Instead, lower education level (β: 1.184, 95% CI: 0.854–1.515, p = < 0.001) and female sex (β: 0.567, 95% CI: 0.25–0.883, p = < 0.001) were associated with greater severity of persistent symptoms. Regarding the VOC, all Omicron variants (BA.1: β -1.276, 95% CI: -2.169 - -0.384, p = < 0.01; BA.2: β: -1.518; 95% CI: -2.387- -0.649, p = < 0.001; BA.5: β: -1.36; 95% CI: -2.704 - -0.015, p = 0.048) showed lower severity of persistent symptoms compared to the Alpha variant. Participants of the Delta wave tended to have severity of persistent symptoms in between the Alpha and Omicron variants, but the association was less strong (Delta vs. Alpha: β: -0.536, 95% CI: -1.395 - -0.323, p = 0.22) (Table 4 ). Table 4 The severity of persistent symptoms (multivariable linear regression; response variable: severity of ongoing symptoms, on a scale 0–10); β: beta estimate; VOC: variant of concern; CI: confidence interval; Center 1: University Hospital of Düsseldorf; Center 2: University Hospital of Aachen; Center 3: University Hospital of Bonn; Center 4: University Hospital of Essen; Center 5: University Hospital of Cologne; Center 6: University Hospital of Münster; BV: Baseline Visite Variable β (95% CI) p-value Age (years) 0.009 [-0.003;0.02] 0.14 Sex (female vs. male) 0.567 [0.25;0.883] < 0.001 Low education 1.184 [0.854;1.515] < 0.001 4th Wave vs. 1st – 3rd Wave VOC: Delta vs. Alpha 5.1th Wave vs. 1st – 3rd Wave VOC: Omicron BA.1 vs. Alpha 5.2th Wave vs. 1st – 3rd Wave VOC: Omicron BA.2 vs. Alpha 6th Wave vs. 1st – 3rd Wave VOC: Omicron BA.5 vs. Alpha -0.536 [-1.395;-0.323] -1.276 [-2.169;-0.384] -1.518 [-2.387;-0.649] ] -1.36 [-2.704;-0.015] 0.22 < 0.01 < 0.001 0.048 ≥ 2 Infections -0.128 [-0.526;0.27] 0.53 Vaccination before 1st Infection yes vs. no -0.187 [-1.038;0.664] 0.67 Center 2 vs. Center 1 Center 3 vs. Center 1 Center 4 vs. Center 1 Center 5 vs. Center 1 Center 6 vs. Center 1 -0.371 [-0.914;0.172] -0.409 [-0.966;0.15] 0.13 [-0.466;0.727] 0.822 [0.294;1.351] -0.538 [-1.039;-0.037] 0.18 0.15 0.67 < 0.01 0.04 Interval 1st Infection to BV (month) -0.06 [-0.11;-0.01] 0.02 Frequency of Persisting Symptoms Regarding different VOCs Fatigue was the most common symptom throughout all virus variants (Alpha, Delta, Omicron BA.1, BA.2, and BA.5) with an overall frequency of 32.8% of all participants at BV (Fig. 3 ). Concentration disorders (25.4%) and dyspnoea (22%) followed as most common persisting symptoms in all VOC (Fig. 3 , Fig. 4 ). Fatigue was less frequently reported in Omicron BA.1 (26.5%) and BA.2 (27.5%) compared to the Alpha (41.3%) and Delta (36.8%) variants (Fig. 2 , Fig. 3 ). Concentration disorders were less frequent in Omicron BA.1 (23.1%) and BA.2 (19.5%) compared to Alpha (33.9%) and Delta (28.5%). Dyspnoea was also less common in BA.1 (19.7%) and BA.2 (16.5%) compared to Alpha (30.3%) and Delta (25.2%). Dysgeusia and Dysosmia were both less common in BA.1 and BA.2. compared to Alpha and Delta. Psychiatric disorders were less frequent in BA.1 (12.6%) and BA.2. (9.6%) compared to Alpha (18.8%) and Delta (20.5%) variants (Fig. 3 , Fig. 4 ). Regarding total differences between the VOC, Alpha and Delta variants showed generally similar frequencies of persisting symptoms, with only constipation being significantly different (Alpha 3.7%, Delta 0.3%). blue shades: chronic fatigue-like (fatigue, headache, concentration disorders,); red/orange: respiratory (dyspnoea, cough, chest pain, sore throat, voice change); yellow shades: neurosensorial (dysgeusia, dysosmia, dizziness, tinnitus,); white: gastrointestinal (nausea, diarrhoea); green shades: systemic-inflammatory chronic pain (limb pain, back pain, cold; hair loss). Frequency of Persisting Symptoms Regarding VOC and Symptom-Cluster Regarding different symptom cluster, chronic fatigue-like syndrome was with 23.5% the most frequent cluster, followed by respiratory cluster (9.1%), neurosensorial syndrome cluster (6.9%), systemic-inflammatory chronic pain syndrome cluster (5.1%), and gastrointestinal cluster (2.7%) (Fig. 4 , Fig. 5 ). Regarding different VOCs, chronic fatigue-like cluster was more frequent in Alpha (29.8%) and Delta (26.1%) variants compared to Omicron BA.1 (21.4%) and BA.2 (19%) (Fig. 5 ). The respiratory cluster was more common in Alpha (11.4%) compared to Omicron BA.1 (9.1%) and BA.2 (7.3%). Delta (9.9%) had respiratory cluster frequency between Alpha and BA.1/BA.2. The gastrointestinal cluster showed overall (2.7%) low and constant frequencies throughout all VOCs (Alpha: 3%, Delta: 2.8%, BA.1: 3.5%; BA.2: 2.8%; BA.5: 2.8%). The systemic-inflammatory chronic pain cluster was present in 5.1% overall. BA.2 (3.8%) showed a lower frequency compared to Alpha (5.7%), Delta (5.4%), and BA.1 (6%). BA.5 had the highest percentage of participants with systemic-inflammatory chronic pain cluster (9.2%) (Fig. 5 ). Discussion Data analyses revealed considerable differences in reporting persisting symptoms at study baseline as well as subjective symptom severity. In general, Long COVID symptoms became less frequent with each new variant of concern. It is essential to mention that comparing epidemiological studies on Long COVID is challenging, as they have widely different recruitment strategies, follow-up periods, and heterogenous participant demographics [ 21 – 26 ]. This is particularly the case regarding frequency, timeline, and severity of symptoms, especially given the natural course of Long COVID per se, with fluctuating and relapsing symptoms and symptom severity over time [ 2 ]. In our study, 68.8% of participants reported at least one persisting symptom at BV individually attributed to SARS-CoV-2, with a higher proportion of symptomatic participants in the hospitalized sample than the population sample (79.3% vs. 67.1%). One year after infection, the reported rates of symptomatic patients are extensively heterogeneous, with a 30–60% range in most published results [ 9 , 27 – 31 ]. According to our data, female sex, lower education, and a shorter period between infection and BV were associated with higher rates of persisting symptoms and were independent predictors of symptom severity in multivariate regression models. This is in line with previously published data [ 32 ]. Interestingly, we report no age effect either on the number of symptoms or regarding symptom severity. While most of the published Long COVID studies reported higher risk in elderly patients, several studies found no age effect or only an effect in specific age subgroups (e.g., > 50 years) [ 33 , 34 ]. Furthermore, most of the studies did not record the severity of symptoms. Regarding the influence of age on symptom frequency, a recent review stated a high heterogeneity rate especially in large studies, and that high-quality studies have higher heterogeneity [ 32 ]. Along with our results, age per se is no risk factor for Long COVID (to the contrary, age may be protective for some Long COVID effects). In multivariate regression, vaccination status was not an independent risk factor for persisting symptoms and symptom severity. The vast majority (96.3%) in our study were vaccinated before the first infection; therefore, the statistical power to detect a possible protective effect is very low. This can be ascribed to the recruitment phase (infections after March 1st, 2021) and the high vaccination coverage in Germany, with an exponential increase in vaccinations between March and July 2021 [ 35 ]. Furthermore, persons refusing SARS-CoV-2 vaccination are disproportionately often skeptical about COVID-19 effects and/or healthcare authorities and, therefore, may be less interested in participating in a study trial [ 36 – 38 ]. Hospitalized participants tended to have more symptoms at BV compared to participants of the population sample and had a higher severity of impairment. Several studies, including a meta-analysis, showed a higher prevalence of Long COVID in hospitalized patients than in outpatients [ 39 – 41 ]. Our data showed both higher rates of symptoms in hospitalized participants and higher severity of impairment. The impact of SARS-CoV-2 reinfections on Long COVID (developed after the first or subsequent infections) is still a matter of debate. Most of the published data showed a higher rate of Long COVID with an increasing number of infections [ 17 , 42 ]. Interestingly despite the higher number of symptoms in the subgroup of participants with ≥ 2 infections (24.3%) our study showed no difference regarding the severity of impairment compared to participants with one infection, and multiple infections were no independent risk factor for persisting symptoms in multivariable regression. Several studies, including meta-analyses, have investigated the risk of Long COVID regarding different virus variants with inconclusive results [ 10 , 14 , 43 , 44 ]. The available data suggest that infection with an Omicron variant may result in fewer Long COVID symptoms. However, since most studies compared only two different virus variants and heterogeneous patient collectives, transferability on all variants is difficult [ 12 , 13 , 45 ]. Fernandes-de-la-Pena et al. concluded in their review that the small number of studies and the lack of control of confounders, e.g., reinfections or vaccine status, limit the generalizability of the results [ 43 ]. Vishwakarma et al. stated that the difference in the prevalence of Long COVID between various variants needs further exploration [ 10 ]. Pathophysiological differences, e.g., higher viral load (Delta > Alpha), higher transmissibility (Omicron > Alpha and Delta), and potential immune escape to vaccines among the different VOCs, were reported [ 46 – 48 ]. Our findings suggest that Alpha and Delta, on the one hand, and Omicron BA.1 and BA.2, on the other hand, have highly homogenous frequencies and distribution of persisting symptoms (single-symptoms as well as symptom-clusters). We observed a lower rate of persistent symptoms in the Omicron variants BA.1 and BA. 2 compared to the Alpha and Delta variants. Besides possible differences in the viral variants themselves this may be also explained by the increasing immunity of the population against SARS-CoV-2, which is a result of a higher number of vaccinations and natural infections in the course of the pandemic. Omicron BA.5 participants tended to have a higher symptom probability than all other VOCs, but the results were overall not significant due to the low number of participants in BA.5. We assume that the shorter period between infection and BV is the main reason for this finding. Subsequent analyses regarding the long-term follow-up will help to clarify this. Overall (all VOCs), fatigue was the most frequent ongoing symptom (32.8%), followed by concentration disorders (25.4%) and dyspnoea (22%). This is in line with previously published data, including a systematic review reporting rates of 41% for fatigue and 31% for dyspnoea 12 months after infection but making no distinction between different VOCs [ 9 ]. Fatigue, concentration disorders, and the chronic fatigue-like symptom cluster, as well as dyspnoea, were significantly less frequent in Omicron BA.1 and BA.2 compared to the Alpha and Delta variants. This finding contrasts with previously published data [ 14 , 19 , 43 ]. A series of symptoms (headache, dizziness, tinnitus, sore throat, nausea, loss of appetite, diarrhoea, abdominal pain, and hair loss) showed consistent frequencies throughout all VOCs in our data. After Omicron infection (BA.1, BA.2, and BA.5), participants were less affected by smell and taste disorders compared to Alpha and Delta, which confirms recently published data from a digital participation-only study based on self-reports [ 49 ]. Our data also show a lower frequency of the whole neurosensorial symptom cluster (dysgeusia, dysosmia, dizziness, tinnitus, earache) in BA.1 and BA.2 compared to Alpha and Delta variants. While men are at higher risk for severe COVID-19 and death, women appear more prone to developing Long COVID [ 26 , 50 , 51 ], consistent with most published studies [ 52 , 53 ]. Sex specific difference in the response to infectious are described in many contexts, a recent study showed sex-specific immune responses, with females exhibiting more severe inflammation possibly attributing to differences in regard to the likelihood and phenotype of Long COVID [ 54 ]. The higher proportion of female participants in our study may also reflect women’s generally higher response and participation rates in clinical research [ 55 ]. 24.3% of participants in our collective had more than one infection at BV, slightly higher than the reported rates in population-based analyses with 5–15% [ 56 , 57 ]. The interval between 1st or rather last (in case of ≥ 2 infections) infection and BV in the study centers in our study was roughly a year: mean of 388 days in case of one infection and 325 days in case of ≥ 2 infections. Therefore, the BV represents the time point of 12 months after infection in most participants. This study has several limitations. Despite random symptom-unrelated selection, it is probable that symptomatic patients followed the study invitation more frequently, and therefore, bias may be present. It is also evident that the response rate was rather low questioning the generalisability of findings. Furthermore, some reported symptoms are subjective (e.g., fatigue, headache), and the lack of validated scales to measure most symptoms makes it difficult to compare data between subjects. Furthermore, we could not discriminate between different virus variants per PCR test. Thus, the allocation to a specific VOC in our cohort is based on the infection time. Our results add relevant data regarding symptoms and symptom severity caused by the different virus variants in hospitalized and non-hospitalized participants stratified by different VOCs and demographical, economic, and educational participant-related context factors. Further follow-up analyses of our cohort with its long-term follow-up period of three years including laboratory tests and standardized medical examinations, will contribute to further characterizing the risk factors and natural course of Long COVID. Declarations Author Contributions: AM, ND, BS, BEOJ, and JB designed the study; AM, BEOJ, BS, and ND wrote the proposal; NK, BS, LZ, LS, MF, and SD coordinated data access; LS, BS and AM performed statistical analyses; AM, BEOJ, BS, AK and LS generated Figures and Tables; MD, MR, MM, HR, CL, PRT, JS, JR and TL provided intellectual input; AM and AK drafted the manuscript with the help of all authors. Consent for publication: All authors have read and agreed to the published version of the manuscript. Competing interests: The authors declared no conflict of interest. Study Centres: The coordinator center is the Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital, Medical Faculty of Heinrich Heine University Düsseldorf, Germany. Further centers are the Department of Pneumology and Intensive Care Medicine, RWTH University Hospital, Aachen, Germany; Department of Medicine I, University Hospital Bonn, Germany; Department of Infectious Diseases; West German Centre of Infectious Diseases, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Department I of Internal Medicine, Faculty of Medicine and University Hospital, University of Cologne, Germany; Department of Internal Medicine B, Gastroenterology, Hepatology, Endocrinology and Clinical Infectiology, University Hospital Münster, Germany. Clinical Trial registration: This study was registered in the German Clinical Trials Register under the Clinical trial number DRKS00027377 on 08.12.2021. Funding: The Beyond Covid Network responsible for this study is an investigator-initiated study and has been initiated by the six partners. The Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital of Düsseldorf, coordinates the study. The study is financed by the Ministry of Culture and Science of North Rhine-Westphalia (MKW-NRW) under Grant Agreement N° 323-8.03-153969 with a total of 4.559.655,19 euros. Ethical Approval and Consent to Participate : The Heinrich-Heine-University Düsseldorf's Ethics Committee, as the leading ethics committee, approved the study protocol, informed consent forms, and participant information materials in November 2021 (under the study registration number 2021-1688). In addition, the Ethics Committee at each clinical research site approved the study. Data Availability Statement: The results of the remote data analyses are available from the corresponding author upon reasonable request. Contact: Dr. med. Alexander Killer; E-Mail: [email protected] Participant Informed Consent: Participation in the study was voluntary and required comprehensive medical information about the meaning and purpose of the study, as well as the advantages and disadvantages of participating in the study and a signed declaration of consent. The study participant could revoke this consent in writing or verbally without giving reasons. In the event of a revocation, the study participant could decide whether the data collected for the study should be deleted, whether the samples obtained or the test results collected should be destroyed, or whether they can continue to be used for the study. The data and biomaterials collected during the study will be retained for a maximum of 10 years after completion of the study. The study participants had the opportunity to limit the use of data in the declaration of consent as part of the study information regarding other/future research purposes and time. Data Collection: Pseudonymized health data was collected by the study centers during each study visit on standardized case report forms (CRFs). This included demographic, clinical, and viro-immunological data. These health data were transferred into a clinical database system ( Clincase , Quadratek Data Solutions Ltd., Berlin, Germany), administered by the Center for Clinical Studies Essen, Germany (ZKSE). Recording and health data transfer were subject to the consent of the study participants. Data collection procedures strictly adhere to the established protocol, the recent version of the Declaration of Helsinki, general data protection regulations of the European Union (EU-DSGVO), and the International Conference on Harmonization Good Clinical Practice (ICH-GCP). Data quality and plausibility checks were carried out by trained ZKSE employees. Complex plausibility checks were programmed in SAS. If necessary, queries were sent to members of the study teams at the centers to make corrections to the data. Data Protection: The pseudonymously collected data is analyzed and evaluated for interim evaluations and finally for the legally required evaluations. All data in the study database can only be identified by a participant identification number (participant ID) corresponding to participant consent to ensure pseudonymization. The birth year is documented in the database, not the day and month. Only the local study centers maintain a separate confidential identification list to identify everyone enrolled to enable participants. This list is protected from unauthorized access stored in the respective study center. All persons who have access to pseudonymized data are subject to the general data protection regulations of the European Union (EU-DSGVO) when handling the data. Only anonymized data are passed on when the final study report is submitted to the responsible German authorities (higher federal authorities, ethics committees if applicable). Publications on the study will only be made with anonymous data. The data must be retained following legal requirements. An assessment of the necessity and proportionality of the data processing was obtained. A risk assessment regarding the possible risks caused by the processing for those affected is taken into account, and the protection goals of confidentiality, integrity, and availability were assessed. The remaining residual risk regarding the protection goals of integrity, legal control, availability, and confidentiality after implementation of technical and organizational measures are classified as low. Acknowledgments, Credits: We sincerely acknowledge all participants of the study, whose contributions and commitment were essential to the realization of this research. The authors would also like to thank everyone in the involved study centers, who contributed to the success of this project. References WHO. WHO COVID-19. dashboard 2024. https://data.who.int/dashboards/covid19/deaths?n=o Soriano JB, Murthy S, Marshall JC, Relan P, Diaz JV. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infect Dis. 2022;22:e102–7. 10.1016/S1473-3099(21)00703-9 . Hayes LD, Ingram J, Sculthorpe NF. More Than 100 Persistent Symptoms of SARS-CoV-2 (Long COVID): A Scoping Review. Front Med (Lausanne). 2021;8:750378. 10.3389/fmed.2021.750378 . Lopez-Leon S, Wegman-Ostrosky T, Perelman C, Sepulveda R, Rebolledo PA, Cuapio A, Villapol S. More than 50 long-term effects of COVID-19: a systematic review and meta-analysis. Sci Rep. 2021;11:16144. 10.1038/s41598-021-95565-8 . Chen C, Haupert SR, Zimmermann L, Shi X, Fritsche LG, Mukherjee B. Global Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review. J Infect Dis. 2022;226:1593–607. 10.1093/infdis/jiac136 . Nittas V, Gao M, West EA, Ballouz T, Menges D, Wulf Hanson S, Puhan MA. Long COVID Through a Public Health Lens: An Umbrella Review. Public Health Rev. 2022;43:1604501. 10.3389/phrs.2022.1604501 . Fernández-de-Las-Peñas C, Palacios-Ceña D, Gómez-Mayordomo V, Florencio LL, Cuadrado ML, Plaza-Manzano G, Navarro-Santana M. Prevalence of post-COVID-19 symptoms in hospitalized and non-hospitalized COVID-19 survivors: A systematic review and meta-analysis. Eur J Intern Med. 2021;92:55–70. 10.1016/j.ejim.2021.06.009 . Wulf Hanson S, Abbafati C, Aerts JG, Al-Aly Z, Ashbaugh C, Ballouz T, et al. Estimated Global Proportions of Individuals With Persistent Fatigue, Cognitive, and Respiratory Symptom Clusters Following Symptomatic COVID-19 in 2020 and 2021. JAMA. 2022;328:1604–15. 10.1001/jama.2022.18931 . Seeßle J, Waterboer T, Hippchen T, Simon J, Kirchner M, Lim A, et al. Persistent Symptoms in Adult Patients 1 Year After Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study. Clin Infect Dis. 2022;74:1191–8. 10.1093/cid/ciab611 . Vishwakarma N, Goud RB, Tirupattur MP, Katwa LC. The Eye of the Storm: Investigating the Long-Term Cardiovascular Effects of COVID-19 and Variants. Cells. 2023. 10.3390/cells12172154 Sarkar M, Madabhavi I. SARS-CoV-2 variants of concern: a review. Monaldi Arch Chest Dis. 2022. 10.4081/monaldi.2022.2337 . Antonelli M, Pujol JC, Spector TD, Ourselin S, Steves CJ. Risk of long COVID associated with delta versus omicron variants of SARS-CoV-2. Lancet. 2022;399:2263–4. 10.1016/S0140-6736(22)00941-2 . Hernández-Aceituno A, García-Hernández A, Larumbe-Zabala E. COVID-19 long-term sequelae: Omicron versus Alpha and Delta variants. Infect Dis Now. 2023;53:104688. 10.1016/j.idnow.2023.104688 . Du M, Ma Y, Deng J, Liu M, Liu J. Comparison of Long COVID-19 Caused by Different SARS-CoV-2 Strains: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health. 2022. 10.3390/ijerph192316010 . Nguyen NN, Nguyen YN, van Hoang T, Million M, Gautret P. SARS-CoV-2 Reinfection and Severity of the Disease: A Systematic Review and Meta-Analysis. Viruses. 2023. 10.3390/v15040967 . He S-T, Wu K, Cheng Z, He M, Hu R, Fan N et al. Long COVID: The latest manifestations, mechanisms, and potential therapeutic interventions. MedComm (2020). 2022;3:e196. 10.1002/mco2.196 Boufidou F, Medić S, Lampropoulou V, Siafakas N, Tsakris A, Anastassopoulou C. SARS-CoV-2 Reinfections and Long COVID in the Post-Omicron Phase of the Pandemic. Int J Mol Sci. 2023. 10.3390/ijms241612962 . Canas LS, Molteni E, Deng J, Sudre CH, Murray B, Kerfoot E, et al. Profiling post-COVID-19 condition across different variants of SARS-CoV-2: a prospective longitudinal study in unvaccinated wild-type, unvaccinated alpha-variant, and vaccinated delta-variant populations. Lancet Digit Health. 2023;5:e421–34. 10.1016/S2589-7500(23)00056-0 . Gentilotti E, Górska A, Tami A, Gusinow R, Mirandola M, Rodríguez Baño J, et al. Clinical phenotypes and quality of life to define post-COVID-19 syndrome: a cluster analysis of the multinational, prospective ORCHESTRA cohort. EClinicalMedicine. 2023;62:102107. 10.1016/j.eclinm.2023.102107 . Koch-Institut. Robert. Dritte Aktualisierung der „Retrospektiven Phaseneinteilung der COVID-19-Pandemie in Deutschland. Robert Koch-Institut; 2022. Merzon E, Weiss M, Krone B, Cohen S, Ilani G, Vinker S, et al. Clinical and Socio-Demographic Variables Associated with the Diagnosis of Long COVID Syndrome in Youth: A Population-Based Study. Int J Environ Res Public Health. 2022. 10.3390/ijerph19105993 . Charfeddine S, Ibn Hadj Amor H, Jdidi J, Torjmen S, Kraiem S, Hammami R, et al. Long COVID 19 Syndrome: Is It Related to Microcirculation and Endothelial Dysfunction? Insights From TUN-EndCOV Study. Front Cardiovasc Med. 2021;8:745758. 10.3389/fcvm.2021.745758 . Phetsouphanh C, Darley DR, Wilson DB, Howe A, Munier CML, Patel SK, et al. Immunological dysfunction persists for 8 months following initial mild-to-moderate SARS-CoV-2 infection. Nat Immunol. 2022;23:210–6. 10.1038/s41590-021-01113-x . Glynne P, Tahmasebi N, Gant V, Gupta R. Long COVID following mild SARS-CoV-2 infection: characteristic T cell alterations and response to antihistamines. J Investig Med. 2022;70:61–7. 10.1136/jim-2021-002051 . Klein J, Wood J, Jaycox JR, Dhodapkar RM, Lu P, Gehlhausen JR, et al. Distinguishing features of long COVID identified through immune profiling. Nature. 2023;623:139–48. 10.1038/s41586-023-06651-y . Su Y, Yuan D, Chen DG, Ng RH, Wang K, Choi J, et al. Multiple early factors anticipate post-acute COVID-19 sequelae. Cell. 2022;185:881–e89520. 10.1016/j.cell.2022.01.014 . Terai H, Ishii M, Takemura R, Namkoong H, Shimamoto K, Masaki K, et al. Comprehensive analysis of long COVID in a Japanese nationwide prospective cohort study. Respir Investig. 2023;61:802–14. 10.1016/j.resinv.2023.08.008 . Silverberg JI, Zyskind I, Naiditch H, Zimmerman J, Glatt AE, Pinter A, et al. Predictors of chronic COVID-19 symptoms in a community-based cohort of adults. PLoS ONE. 2022;17:e0271310. 10.1371/journal.pone.0271310 . Perisse A, de Cacqueray F, Delarbre D, Marsaa H, Bergmann C, Da Silva V, et al. Symptoms of Long-COVID 1-Year after a COVID-19 outbreak among sailors on a French aircraft carrier. Infect Dis Now. 2023;53:104673. 10.1016/j.idnow.2023.104673 . Huang L, Yao Q, Gu X, Wang Q, Ren L, Wang Y, et al. 1-year outcomes in hospital survivors with COVID-19: a longitudinal cohort study. Lancet. 2021;398:747–58. 10.1016/S0140-6736(21)01755-4 . Helmsdal G, Hanusson KD, Kristiansen MF, Foldbo BM, Danielsen ME, Steig BÁ, et al. Long COVID in the Long Run-23-Month Follow-up Study of Persistent Symptoms. Open Forum Infect Dis. 2022;9:ofac270. 10.1093/ofid/ofac270 . Tsampasian V, Elghazaly H, Chattopadhyay R, Debski M, Naing TKP, Garg P, et al. Risk Factors Associated With Post-COVID-19 Condition: A Systematic Review and Meta-analysis. JAMA Intern Med. 2023;183:566–80. 10.1001/jamainternmed.2023.0750 . Menezes AS, Botelho SM, Santos LR, Rezende AL. Acute COVID-19 Syndrome Predicts Severe Long COVID-19: An Observational Study. Cureus. 2022;14:e29826. 10.7759/cureus.29826 . Bellan M, Soddu D, Balbo PE, Baricich A, Zeppegno P, Avanzi GC, et al. Respiratory and Psychophysical Sequelae Among Patients With COVID-19 Four Months After Hospital Discharge. JAMA Netw Open. 2021;4:e2036142. 10.1001/jamanetworkopen.2020.36142 . Health GFMo. Current vaccination status 2023. https://impfdashboard.de/en/ Viskupič F, Wiltse DL, Meyer BA. Trust in physicians and trust in government predict COVID-19 vaccine uptake. Soc Sci Q. 2022;103:509–20. 10.1111/ssqu.13147 . Lindholt MF, Jørgensen F, Bor A, Petersen MB. Public acceptance of COVID-19 vaccines: cross-national evidence on levels and individual-level predictors using observational data. BMJ Open. 2021;11:e048172. 10.1136/bmjopen-2020-048172 . Sethi S, Kumar A, Mandal A, Shaikh M, Hall CA, Kirk JMW, et al. The UPTAKE study: implications for the future of COVID-19 vaccination trial recruitment in UK and beyond. Trials. 2021;22:296. 10.1186/s13063-021-05250-4 . Yuan N, Lv Z-H, Sun C-R, Wen Y-Y, Tao T-Y, Qian D, et al. Post-acute COVID-19 symptom risk in hospitalized and non-hospitalized COVID-19 survivors: A systematic review and meta-analysis. Front Public Health. 2023;11:1112383. 10.3389/fpubh.2023.1112383 . Blomberg B, Mohn KG-I, Brokstad KA, Zhou F, Linchausen DW, Hansen B-A, et al. Long COVID in a prospective cohort of home-isolated patients. Nat Med. 2021;27:1607–13. 10.1038/s41591-021-01433-3 . Pérez-González A, Araújo-Ameijeiras A, Fernández-Villar A, Crespo M, Poveda E. Long COVID in hospitalized and non-hospitalized patients in a large cohort in Northwest Spain, a prospective cohort study. Sci Rep. 2022;12:3369. 10.1038/s41598-022-07414-x . Bowe B, Xie Y, Al-Aly Z. Acute and postacute sequelae associated with SARS-CoV-2 reinfection. Nat Med. 2022;28:2398–405. 10.1038/s41591-022-02051-3 . Fernández-de-Las-Peñas C, Notarte KI, Peligro PJ, Velasco JV, Ocampo MJ, Henry BM, et al. Long-COVID Symptoms in Individuals Infected with Different SARS-CoV-2 Variants of Concern: A Systematic Review of the Literature. Viruses. 2022. 10.3390/v14122629 . Ballouz T, Menges D, Kaufmann M, Amati R, Frei A, von Wyl V, et al. Post COVID-19 condition after Wildtype, Delta, and Omicron SARS-CoV-2 infection and prior vaccination: Pooled analysis of two population-based cohorts. PLoS ONE. 2023;18:e0281429. 10.1371/journal.pone.0281429 . Magnusson K, Kristoffersen DT, Dell’Isola A, Kiadaliri A, Turkiewicz A, Runhaar J, et al. Post-covid medical complaints following infection with SARS-CoV-2 Omicron vs Delta variants. Nat Commun. 2022;13:7363. 10.1038/s41467-022-35240-2 . Teyssou E, Delagrèverie H, Visseaux B, Lambert-Niclot S, Brichler S, Ferre V, et al. The Delta SARS-CoV-2 variant has a higher viral load than the Beta and the historical variants in nasopharyngeal samples from newly diagnosed COVID-19 patients. J Infect. 2021;83:e1–3. 10.1016/j.jinf.2021.08.027 . Karim SSA, Karim QA. Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic. Lancet. 2021;398:2126–8. 10.1016/S0140-6736(21)02758-6 . Tani-Sassa C, Iwasaki Y, Ichimura N, Nagano K, Takatsuki Y, Yuasa S, et al. Viral loads and profile of the patients infected with SARS-CoV-2 Delta, Alpha, or R.1 variants in Tokyo. J Med Virol. 2022;94:1707–10. 10.1002/jmv.27479 . Diexer S, Klee B, Gottschick C, Xu C, Broda A, Purschke O, et al. Association between virus variants, vaccination, previous infections, and post-COVID-19 risk. Int J Infect Dis. 2023;136:14–21. 10.1016/j.ijid.2023.08.019 . Ancochea J, Izquierdo JL, Soriano JB. Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning. J Womens Health (Larchmt). 2021;30:393–404. 10.1089/jwh.2020.8721 . Scully EP, Haverfield J, Ursin RL, Tannenbaum C, Klein SL. Considering how biological sex impacts immune responses and COVID-19 outcomes. Nat Rev Immunol. 2020;20:442–7. 10.1038/s41577-020-0348-8 . Goldhaber NH, Kohn JN, Ogan WS, Sitapati A, Longhurst CA, Wang A, et al. Deep Dive into the Long Haul: Analysis of Symptom Clusters and Risk Factors for Post-Acute Sequelae of COVID-19 to Inform Clinical Care. Int J Environ Res Public Health. 2022. 10.3390/ijerph192416841 . Thaweethai T, Jolley SE, Karlson EW, Levitan EB, Levy B, McComsey GA, et al. Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection. JAMA. 2023;329:1934–46. 10.1001/jama.2023.8823 . Shahbaz S, Osman M, Syed H, Mason A, Rosychuk RJ, Cohen Tervaert JW, Elahi S. Integrated immune, hormonal, and transcriptomic profiling reveals sex-specific dysregulation in long COVID patients with ME/CFS. Cell Rep Med. 2025;6:102449. 10.1016/j.xcrm.2025.102449 . Fernández-Sanlés A, Smith D, Clayton GL, Northstone K, Carter AR, Millard LA, et al. Bias from questionnaire invitation and response in COVID-19 research: an example using ALSPAC. Wellcome Open Res. 2021;6:184. 10.12688/wellcomeopenres.17041.2 . Guedes AR, Oliveira MS, Tavares BM, Luna-Muschi A, Lazari CDS, Montal AC, et al. Reinfection rate in a cohort of healthcare workers over 2 years of the COVID-19 pandemic. Sci Rep. 2023;13:712. 10.1038/s41598-022-25908-6 . Eythorsson E, Runolfsdottir HL, Ingvarsson RF, Sigurdsson MI, Palsson R. Rate of SARS-CoV-2 Reinfection During an Omicron Wave in Iceland. JAMA Netw Open. 2022;5:e2225320. 10.1001/jamanetworkopen.2022.25320 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Feb, 2026 Reviews received at journal 05 Feb, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviews received at journal 07 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers invited by journal 02 Jan, 2026 Editor assigned by journal 02 Jan, 2026 Submission checks completed at journal 02 Jan, 2026 First submitted to journal 31 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8489939","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":568320396,"identity":"6ed97e0b-0944-4f28-a9f3-08bf5d580167","order_by":0,"name":"Alexander Mertens","email":"","orcid":"","institution":"Department of Gastroenterology, Hepatology, and Infectious Diseases, University Hospital Düsseldorf, Medical Faculty of Heinrich Heine University 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Cologne","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"","lastName":"Lehmann","suffix":""},{"id":568320429,"identity":"00fe5897-9030-4660-b2af-3eae77557748","order_by":18,"name":"Phil-Robin Tepasse","email":"","orcid":"","institution":"Department of Internal Medicine B, Gastroenterology, Hepatology, Endocrinology and Clinical Infectiology, University Hospital Münster","correspondingAuthor":false,"prefix":"","firstName":"Phil-Robin","middleName":"","lastName":"Tepasse","suffix":""},{"id":568320431,"identity":"875684bd-b192-426d-a43f-a9d0ee296194","order_by":19,"name":"Börge Schmidt","email":"","orcid":"","institution":"Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University Duisburg-Essen","correspondingAuthor":false,"prefix":"","firstName":"Börge","middleName":"","lastName":"Schmidt","suffix":""},{"id":568320435,"identity":"5320cd7e-f7ec-4b2b-8009-d6f56442fb3c","order_by":20,"name":"Nico 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1","display":"","copyAsset":false,"role":"figure","size":85827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStudy protocol:\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eOverview of the type of examinations carried out and their time interval. BV: Baseline Visit; TTE: transthoracic echocardiography.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8489939/v1/cfd61acb5ef62a5e86bc5868.png"},{"id":99515295,"identity":"daf958e5-5162-4738-843a-2b888ec33c97","added_by":"auto","created_at":"2026-01-05 10:10:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21341,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFrequency of pre-existing conditions regarding the VOC.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8489939/v1/179d3693d55aa9c920567f53.png"},{"id":99515292,"identity":"9772c766-b762-4f32-a004-8fde244857f5","added_by":"auto","created_at":"2026-01-05 10:10:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFrequency of ongoing symptoms regarding different VOCs.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8489939/v1/6f417786472479da50cb4360.png"},{"id":99515302,"identity":"7c41e0f2-9e96-4d59-b606-c504efc3137a","added_by":"auto","created_at":"2026-01-05 10:10:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFrequency of ongoing symptoms regarding the VOC and symptom clusters, only showing the 15 most frequent symptoms per VOC.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e§ \u003cem\u003eblue shades: chronic fatigue-like (fatigue, headache, concentration disorders,);\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e§ \u003cem\u003ered/orange: respiratory (dyspnoea, cough, chest pain, sore throat, voice change);\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e§ \u003cem\u003eyellow shades: neurosensorial (dysgeusia, dysosmia, dizziness, tinnitus,);\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e§ \u003cem\u003ewhite: gastrointestinal (nausea, diarrhoea);\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e§ \u003cem\u003egreen shades: systemic-inflammatory chronic pain (limb pain, back pain, cold; hair loss).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8489939/v1/264e8256ce731bdf5fcd175b.png"},{"id":99791796,"identity":"de4a48cd-a291-4c3b-9cba-83251c31e815","added_by":"auto","created_at":"2026-01-08 13:10:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":12961,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFrequency of ongoing symptoms regarding different VOCs and symptom clusters as a percentage of each cluster and corresponding VOC.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8489939/v1/6927e3e9e83691cc0fc5d03c.png"},{"id":99803695,"identity":"a57b101e-8b13-471a-a010-901c449ef323","added_by":"auto","created_at":"2026-01-08 14:10:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1621411,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8489939/v1/e6a4db21-b252-407c-ac43-c91bd24f884e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of Long COVID by Clinical Examination and Self-Perceived Severity Stratified by Infection Wave: Beyond COVID, a Prospective, Multicenter Cohort Study in Germany","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBy December 2024, the COVID-19 pandemic led to over 777\u0026nbsp;million confirmed cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Early on in the SARS-CoV-2 pandemic patients with persistent symptoms after recovery of COVID-19 were described. In October 2021, following a Delphi consensus, the World Health Organisation (WHO) defined Long COVID as a \u0026ldquo;condition that occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually three months from the onset of COVID-19 with symptoms that last for at least two months and cannot be explained by an alternative diagnosis. Symptoms may be new onset, following initial recovery from an acute COVID-19 episode, or persist from the initial illness. Symptoms may also fluctuate or relapse over time\u0026rdquo; [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The definition is inclusive and acknowledges that up to 200 different Long COVID symptoms have been described and that confirmation of SARS-CoV-2 infection is not mandatory since it is not feasible in some regions (e.g., countries with limited access to healthcare) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The reported prevalence of Long COVID widely differs across studies and can be estimated at around 10\u0026ndash;30% in most studies, with an extensive range of 2\u0026ndash;80% [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The Global Burden of Disease Long COVID study included 1.2\u0026nbsp;million people who had experienced an acute symptomatic SARS-CoV-2 infection and reported up to 15.1% long-lasting symptoms one year after infection [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Variation in Long COVID prevalence and symptom-complex definition in the literature is to a large extent due to a lack of a uniform definition, the differences in observational time by studies as well as the consideration of various variants of concern (VOCs), and the characteristics of participants included in each study (asymptomatic vs. symptomatic non-hospitalized vs. hospitalized participants vs. a mixture) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, most studies refer to data from highly pre-selected samples of patients referred to the participant centres or limited to online surveys [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which are potentially biased (e.g., by self-referrals). Regarding different virus variants, SARS-CoV-2 variants emerged from the original wild-type strain to Alpha (B.1.1.7), Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529) over time [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The impact of different SARS-CoV-2 variants remains debatable: the majority of published studies to date focus on the characterization of one or two virus variants, as shown in the synthesis of results in the systematic reviews by Vishwakarma et al. and Du et al., stating that the difference in the prevalence of symptoms and Long COVID between various variants still needs to be explored [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The influence of vaccination and multiple infections on symptoms and symptom severity has also not yet been sufficiently researched [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study aims to provide further evidence regarding Long COVID symptoms and symptom severity throughout the pandemic phases. It differs from previous approaches by regarding a systematic survey of Long COVID and global symptom severity stratified by the predominant virus variants by examining randomly selected COVID-19 infected individuals from the general population together with a hospital-based sample regardless of the symptoms during or after the infection.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThis prospective multicenter cohort study includes SARS-CoV-2-positive tested (PCR) individuals who met the inclusion criteria (see below). Six study centers (University Hospitals of Aachen, Bonn, Essen, D\u0026uuml;sseldorf, Cologne, and M\u0026uuml;nster) took part. Two samples were defined: Individuals with laboratory-confirmed COVID-19 infection registered at the local Public Health Authorities (population-based sample) and hospitalized patients, registered at the participating study centers (hospital-based). The public health authorities drew consecutive samples from registration data limited to infections that occurred between 1 March 2021 and 31st May 2022. The authorities contacted eligible individuals and invited them by mail to participate in the study. Recruitment was conducted between 01/2022 and 09/2023. The second sample consisted of patients hospitalized in one of the six participating clinics after the cut-off date. In this case, contact was made directly by the participating study centres.\u003c/p\u003e \u003cp\u003eInclusion Criteria (Population Sample)\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMain residence in North Rhine-Westphalia in the catchment area of the cities of Aachen, Bonn, D\u0026uuml;sseldorf, Essen, Cologne or M\u0026uuml;nster\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e18\u0026ndash;75 years old\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEvidence of a survived SARS-CoV-2 infection (PCR test)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInfection between March 1st 2021, and May 31 2022\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe time of one infection was at least three months ago (reference date: date of the first positive PCR test)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePatients with active COVID-19 infection or an infection that occurred less than three months ago were scheduled for later inclusion in the study\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe person was able to take part in the survey and the examination and has sufficient knowledge of the German language or support from an interpreter\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eInclusion Criteria (Hospitalized Sample)\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMain residence in North Rhine-Westphalia in the catchment area of the cities of Aachen, Bonn, D\u0026uuml;sseldorf, Essen, Cologne or M\u0026uuml;nster\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e18\u0026ndash;75 years old\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEvidence of a survived SARS-CoV-2 infection (PCR test)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInfection between March 1st 2021 and May 31th 2022\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe time of infection was at least three months ago (reference date: date of inpatient admission)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe person was able to take part in the survey and the examination and has sufficient knowledge of the German language or support from an interpreter\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Baseline and Follow-up\u003c/h3\u003e\n\u003cp\u003eBoth cohorts (population- and hospital-based) were examined at the Long-COVID-19 clinic of each center. Blood, urine, and saliva samples were taken following participant information and informed consent. Furthermore, participants underwent medical examinations by a study physician. Neurological tests, spirometry, transthoracic echocardiography, and abdomen ultrasound were performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). If necessary, diffusing capacity of lung carbon monoxide (DLCO) or lung computed tomography (CT) scans, neurological evaluation, and psychological evaluation were conducted in individual cases. Aroma and taste tests were performed at the initial visit and, if pathological, on the following appointments according to the study protocol. Participants responded to questionnaires to assess general health-related information, persisting (severity and duration) symptoms, quality of life, disability, and sociodemographic and psychosocial factors at each visit. Questionnaires collected data on the date of the first and subsequent infections, vaccination, and new health issues. Follow-up visits were conducted at 3, 6,12, 18, 24, 30, and 36 months after the BV, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Follow-up is ongoing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eData Assessment\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePersistent symptoms\u003c/h2\u003e \u003cp\u003ePersistent symptoms were assessed by questionnaire. Persistent symptoms were defined as reporting at least one self-perceived symptom due to the participants\u0026rsquo; first SARS-CoV-2 infection still present at the baseline examination. Participants were asked for persistent symptoms that occurred initially during the acute infection and persistent symptoms that occurred later.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePersistent Symptom Severity Level\u003c/h3\u003e\n\u003cp\u003eThe overall self-perceived severity level of persistent symptoms was assessed on a scale from 0 to 10, and the participants were asked how severely they were affected by the reported symptoms at the time of the baseline examination. In cases where the severity level was missing, but at least one persistent symptom was reported, the severity level was coded to 0. The severity level of participants who reported no persistent symptoms was also coded 0.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFrequency of Persisting Symptoms Regarding VOC and Symptom-Cluster\u003c/h2\u003e \u003cp\u003eQuestionnaires assessed 26 different symptoms. We subsumed the frequencies of reported ongoing single symptoms into five predefined clusters [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]: chronic fatigue-like (fatigue, headache, concentration disorders), respiratory (dyspnoea, coughing, chest pain, sore throat, voice change), neurosensorial (dysgeusia, dysosmia, dizziness, tinnitus, earache), gastrointestinal (nausea, loss of appetite, diarrhoea, constipation, abdominal pain), and systemic-inflammatory chronic pain (limb/ back pain, fever, chill, cold; hair loss).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTime from Infection to Study Baseline\u003c/h3\u003e\n\u003cp\u003eThe time from the first infection to the study baseline was calculated for each participant and included as a continuous variable to account for differences in time for recovering from SARS-CoV-2 infection.\u003c/p\u003e\n\u003ch3\u003eVaccination before the First Infection\u003c/h3\u003e\n\u003cp\u003eParticipant's vaccination history was recorded in a face-to-face interview, and we determined if a vaccination occurred before or after the first infection.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSARS-CoV-2 Wave of Infection (VOCs)\u003c/h2\u003e \u003cp\u003eThe date of the first infection was used to assign participants to certain VOCs according to the different periods of COVID-19 in Germany [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This resulted in a categorical variable with five categories. The first category consisted of participants with an infection between the 9th and the 30th calendar week of 2021, representing the third COVID wave in Germany (VOC: Alpha). The second category comprises the fourth infection wave, i.e., the period from the 31st \u0026ndash; 51st calendar week of 2021 (VOC: Delta). The third, fourth and fifth category representing sublines of Omicron: the third extends from the 52nd calendar week of 2021 to the 8th calendar week of 2022 (VOC: Omicron BA.1). Category 4 extends from the 9th calendar week of 2022 to the 21st calendar week of 2022 (VOC: Omicron BA.2). Category 5 covers the period from the 22nd calendar week of 2022 (VOC: Omicron BA.5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEducational Attainment\u003c/h2\u003e \u003cp\u003eEducational attainment was assessed as the highest general school-leaving qualification and dichotomized into a variable indicating whether general or subject-specific tertiary education entrance qualification was attained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultiple Infections\u003c/h2\u003e \u003cp\u003eParticipants provided information on every confirmed SARS-CoV-2 infection. Information was dichotomized into one confirmed infection vs. more than one confirmed infection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were calculated as contingency tables with mosaic plots. Participants with additional missing values in the covariates were only excluded from the analyses that included covariates. Logistic regression models (with N\u0026thinsp;=\u0026thinsp;1117 complete observations) were fitted to calculate odds ratios (ORs) and 95%-confidence intervals (95%-CIs) for the association of SARS-CoV-2 infection waves with persistent symptoms due to first SARS-CoV-2 infection. Models were adjusted for the potential confounders: vaccination before the first infection, study center, educational attainment, multiple infections, and time from the first infection to the study baseline. In addition, linear regression models were fitted with the same covariates to calculate beta estimates and 95%-CIs for the association of SARS-CoV-2 infection waves with severity level due to the first SARS-CoV-2 infection at baseline examination. The number of observations also amounted to N\u0026thinsp;=\u0026thinsp;1117. Sensitivity analyses were provided equivalent to the analysis strategy described above, using response variables defined based on all infections up to the baseline examination instead of just the first infection. In addition, primary analyses were repeated for specific persistent symptoms with high prevalence in the study sample. All analyses were conducted using SAS version 9.4 (SAS Institute).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eA total of 1185 individuals participated from the first sample (response rate: 4.7%). In the second sample, 199 participants took part (response rate: 11.4%). Ten participants (0.7%) were excluded (missing informed consent; not meeting the inclusion criteria). Overall, 1374 participants were included in the analysis with 1258 having complete information for statistical analysis on persistent symptoms (related to the first infection) and the corresponding severity level, of which 86.6% (n\u0026thinsp;=\u0026thinsp;1089) were from the population-based sample. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the main characteristics of the sample. The mean time between the first infection and study baseline was 388.1 days (\u0026plusmn;\u0026thinsp;126.6). Most participants were female (n\u0026thinsp;=\u0026thinsp;744; 59%), and the mean age was 47.1 (\u0026plusmn;\u0026thinsp;14.5) years. Regarding education level, 58.7% (n\u0026thinsp;=\u0026thinsp;724) had at least a high school graduation. Most participants had at least one pre-existing condition (77%; n\u0026thinsp;=\u0026thinsp;968) with a mean number of pre-existing conditions of 2.5 (\u0026plusmn;\u0026thinsp;2.7). The vast majority were vaccinated before the 1st infection (n\u0026thinsp;=\u0026thinsp;1194; 96.3%; Alpha variant: 85.2%; Delta: 96.3%; BA.1: 99.1%; BA.2: 99.5%; BA.5: 100%). 24.3% (n\u0026thinsp;=\u0026thinsp;306) of the participants had two or more infections at the baseline examination.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy Population; Baseline Parameter; BV: Baseline Visit\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eHospital-based Sample (n\u0026thinsp;=\u0026thinsp;169)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation-based Sample\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1089)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1258)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge (years; mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e53.2 (\u0026plusmn;\u0026thinsp;14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.1 (\u0026plusmn;\u0026thinsp;14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.1 (\u0026plusmn;\u0026thinsp;14.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSex female\u003c/p\u003e \u003cp\u003eSex male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e86 (50.6%)\u003c/p\u003e \u003cp\u003e83 (49.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e658 (60.3%)\u003c/p\u003e \u003cp\u003e431 (39.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e744 (59%)\u003c/p\u003e \u003cp\u003e514 (41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHigh School Graduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e73 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e651 (60.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e724 (58.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of Participants with \u0026ge;\u0026thinsp;1 Pre-Existing Condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e156 (92.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e812 (74.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e968 (77%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of Pre-Existing Conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.3 (\u0026plusmn;\u0026thinsp;3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2 (\u0026plusmn;\u0026thinsp;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5 (\u0026plusmn;\u0026thinsp;2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of Vaccinated Participants (before 1st Infection)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e147 (90.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1047 (97.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1194 (96.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of Infections at BV\u003c/p\u003e \u003cp\u003eOne\u003c/p\u003e \u003cp\u003eTwo\u003c/p\u003e \u003cp\u003eThree\u003c/p\u003e \u003cp\u003eFour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e130 (76.9%)\u003c/p\u003e \u003cp\u003e37 (21.9%)\u003c/p\u003e \u003cp\u003e2 (1.2%)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e822 (75.5%)\u003c/p\u003e \u003cp\u003e255 (23.4%)\u003c/p\u003e \u003cp\u003e11 (1%)\u003c/p\u003e \u003cp\u003e1 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e952 (75.9%)\u003c/p\u003e \u003cp\u003e292 (23.2%)\u003c/p\u003e \u003cp\u003e13 (1%)\u003c/p\u003e \u003cp\u003e1 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of Symptoms (during 1st Infection)\u003c/p\u003e \u003cp\u003eNumber of Participants with \u0026ge;\u0026thinsp;1 persisting Symptom at BV (one Infection)\u003c/p\u003e \u003cp\u003eNumber of persisting Symptoms per Participant (one Infection)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e7.5 (\u0026plusmn;\u0026thinsp;5.2)\u003c/p\u003e \u003cp\u003e134 (79.3%)\u003c/p\u003e \u003cp\u003e2.9 (\u0026plusmn;\u0026thinsp;3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.7 (\u0026plusmn;\u0026thinsp;4.6)\u003c/p\u003e \u003cp\u003e731 (67.1%)\u003c/p\u003e \u003cp\u003e2.1 (\u0026plusmn;\u0026thinsp;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.7 (\u0026plusmn;\u0026thinsp;4.7)\u003c/p\u003e \u003cp\u003e865 (68.8%)\u003c/p\u003e \u003cp\u003e2.2 (\u0026plusmn;\u0026thinsp;2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of Participants with \u0026ge;\u0026thinsp;1 persisting Symptom at BV (all infections)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e136 (80.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e770 (70.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e906 (72%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of Persisting Symptoms per Participant (all infections)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.2 (\u0026plusmn;\u0026thinsp;3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7 (\u0026plusmn;\u0026thinsp;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.7 (\u0026plusmn;\u0026thinsp;3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSeverity of Impairment (one Infection)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.8 (\u0026plusmn;\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7 (\u0026plusmn;\u0026thinsp;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.9 (\u0026plusmn;\u0026thinsp;2.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSeverity of Impairment (all infections)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.8 (\u0026plusmn;\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8 (\u0026plusmn;\u0026thinsp;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.9 (\u0026plusmn;\u0026thinsp;2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eInterval 1st Infection \u0026ndash; BV (days)\u003c/p\u003e \u003cp\u003eInterval last Infection \u0026ndash; BV (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e411.1 (\u0026plusmn;\u0026thinsp;171.1)\u003c/p\u003e \u003cp\u003e354.6 (\u0026plusmn;\u0026thinsp;175.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e384.5 (\u0026plusmn;\u0026thinsp;118)\u003c/p\u003e \u003cp\u003e319.8 (\u0026plusmn;\u0026thinsp;136.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e388.1 (\u0026plusmn;\u0026thinsp;126.6)\u003c/p\u003e \u003cp\u003e324.5 (\u0026plusmn;\u0026thinsp;143.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding pre-existing conditions, the most common comorbidity was overweight and obesity (48.9%), followed by cardiovascular diseases (24.9%), thyroid dysfunctions (17.6%), neurological disorders (17.6%), chronic respiratory diseases (15.9%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Regarding pre-existing conditions in different VOCs, we excluded BA.5 due to the low number of overall participants and the lack of appropriate statistical subgroup analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eOngoing Symptoms and Severity of Impairment\u003c/h2\u003e \u003cp\u003eDuring infection, the mean number of symptoms was 7.7 (\u0026plusmn;\u0026thinsp;4.7), while at the time of BV an overall mean number of symptoms of 2.2 (\u0026plusmn;\u0026thinsp;2.9) was reported, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the subgroup of participants with more than one infection, 72% (n\u0026thinsp;=\u0026thinsp;906) reported at least one symptom at the study baseline. The mean number of persisting symptoms at baseline in this group was 2.7 (\u0026plusmn;\u0026thinsp;3.4), Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The severity of impairment did not differ between the subgroups with one (2.9 (\u0026plusmn;\u0026thinsp;2.8)) and more than one infection (2.9 (\u0026plusmn;\u0026thinsp;2.9)), Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStratification by SARS-CoV-2 Wave (VOC)\u003c/h2\u003e \u003cp\u003eParticipants were stratified into five subgroups based on their positive SARS-CoV-2 test date and the corresponding VOC in Germany, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The highest number of participants was enrolled in group 4 (infection period CW 09/2022\u0026ndash;21/2022), representing the Omicron BA.2 variant, with a total of 435 (34.6%) participants, while the lowest number of participants were included in group 5 (infection period starting with CW 22/2022 and ongoing) with a total of 30 (2.4%) participants, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003ePhase classification to describe the COVID-19 Pandemic events in Germany, 2020\u0026ndash;2022; VOC: Variant of Concern; adapted from\u003c/em\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEvent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePeriod (calender week/year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNumber of Participants\u003c/p\u003e \u003cp\u003eHospitalized Sample / Population Sample / Total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhase 1\u0026ndash;3\u003c/em\u003e: First Covid-19 Wave (wild-type); Summer Plateau 2020; Second Covid-19 Wave (wild-type);\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10/2020\u0026ndash;8/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eNo participants (Infection before\u003c/p\u003e \u003cp\u003eMarch 1st 2021: Exclusion criterion)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhase 4\u0026ndash;5\u003c/em\u003e: Third Covid-19 Wave (VOC: Alpha); Summer-Plateau 2021;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e9/2021\u0026ndash;30/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e45 (26.6%) / 173 (15.9%) / 218 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhase 6\u003c/em\u003e: Fourth Covid-19 Wave (VOC: Delta)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e31/2021\u0026ndash;51/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e49 (29%) / 288 (26.5%) / 337 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhase 7\u003c/em\u003e: Fifth Covid-19 Wave\u003c/p\u003e \u003cp\u003eVOC: Omicron BA.1\u003c/p\u003e \u003cp\u003eVOC: Omicron BA.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e52/2021\u0026ndash;8/2022\u003c/p\u003e \u003cp\u003e9/2022\u0026ndash;21/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e34 (20.1%) / 204 (18.7%) / 238 (18.9%)\u003c/p\u003e \u003cp\u003e38 (22.5%) / 297 (36.5%) / 435 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhase 8\u003c/em\u003e: Sixth Covid-19 Wave (VOC: Omicron BA.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e22/2022\u0026ndash;5/2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e3 (1.8%) / 27 (2.5%) / 30 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFrequency of persistent Symptoms regarding Baseline Parameter, the Number of Infections, and VOC by Multivariate Regression\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePersistent symptoms at baseline were analysed by multivariable regression models, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Female sex (OR: 1.50; 95% CI: 1.14\u0026ndash;1.97, p\u0026thinsp;=\u0026thinsp;0.004), low education (OR: 2.30; 95% CI: 1.71\u0026ndash;3.10, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a shorter period between infection and baseline examination in months (OR: 0.92, 95% CI: 0.88\u0026ndash;0.96, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with the odds of reporting persisting symptoms at baseline. Odds of reporting persistent symptoms also differed by study centre. Vaccination status and the number of previous infections did not seem to affect the presence of persistent symptoms. There were differences regarding the VOCs and the chance of reporting persistent symptoms: Omicron BA.1 (OR: 0.28, 95% CI: 0.12\u0026ndash;0.7, p\u0026thinsp;=\u0026thinsp;0.01) and Omicron BA.2 (OR: 0.21, 95% CI: 0.08\u0026ndash;0.5, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.01) revealed a much lower odds for persisting symptoms than SARS-CoV-2 Alpha variant, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eProbability of persisting symptoms regarding baseline parameter, study center, the number of infections, and wave (multivariable logistic regression; response variable: persistent symptoms yes/no); OR: odds ratio; VOC: variant of concern; CI: confidence interval; Center 1: University Hospital of D\u0026uuml;sseldorf; Center 2: University Hospital of Aachen; Center 3: University Hospital of Bonn; Center 4: University Hospital of Essen; Center 5: University Hospital of Cologne; Center 6: University Hospital of M\u0026uuml;nster; BV: Initial Visite\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.01 [0.99;1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex (female vs. male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.5 [1.14;1.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLow education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.3 [1.71;3.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e4th Wave vs. 1st \u0026ndash; 3rd Wave\u003c/p\u003e \u003cp\u003eVOC: Delta vs. Alpha\u003c/p\u003e \u003cp\u003e5.1th Wave vs. 1st \u0026ndash; 3rd Wave\u003c/p\u003e \u003cp\u003eVOC: Omicron BA.1 vs. Alpha\u003c/p\u003e \u003cp\u003e5.2th Wave vs. 1st \u0026ndash; 3rd Wave\u003c/p\u003e \u003cp\u003eVOC: Omicron BA.2 vs. Alpha\u003c/p\u003e \u003cp\u003e6th Wave vs. 1st \u0026ndash; 3rd Wave\u003c/p\u003e \u003cp\u003eVOC: Omicron BA.5 vs. Alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.67 [0.28;1.6]\u003c/p\u003e \u003cp\u003e0.28 [0.12;0.7]\u003c/p\u003e \u003cp\u003e0.21 [0.08;0.5]\u003c/p\u003e \u003cp\u003e0.36 [0.1;1.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 Infections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.04 [0.75;1.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVaccination before 1st Infection yes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.91 [0.38;2.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCenter 2 vs. Center 1\u003c/p\u003e \u003cp\u003eCenter 3 vs. Center 1\u003c/p\u003e \u003cp\u003eCenter 4 vs. Center 1\u003c/p\u003e \u003cp\u003eCenter 5 vs. Center 1\u003c/p\u003e \u003cp\u003eCenter 6 vs. Center 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.69 [0.43;1.09]\u003c/p\u003e \u003cp\u003e0.8 [0.49;1.31]\u003c/p\u003e \u003cp\u003e0.99 [0.58;1.68]\u003c/p\u003e \u003cp\u003e1.62 [0.99;2.67]\u003c/p\u003e \u003cp\u003e0.77 [0.5;1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003cp\u003e0.38\u003c/p\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInterval 1st Infection to BV (month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.92 [0.88;0.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSeverity of Ongoing Symptoms regarding Baseline Parameter, the Number of Infections, and VOC by Multivariate Regression\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe self-perceived severity of persistent symptoms was analysed via multivariable linear regression models. Age (β: 0.009, 95% CI: -0.003-0.02, p\u0026thinsp;=\u0026thinsp;0.14), the number of infections (one vs. \u0026ge;2 infections (β: -0.128, 95% CI: -0.526-0.27, p\u0026thinsp;=\u0026thinsp;0.53), and vaccination before the first infection (β: -0.187, 95% CI: -1.038-0.664, p\u0026thinsp;=\u0026thinsp;0.64) did not show indication for an association with the severity of persistent symptoms. A longer time period between infection and baseline examination was weakly associated with lower severity of persistent symptoms (β -0.06 per month, 95% CI: -0.11 - -0.01, p\u0026thinsp;=\u0026thinsp;0.02). Instead, lower education level (β: 1.184, 95% CI: 0.854\u0026ndash;1.515, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and female sex (β: 0.567, 95% CI: 0.25\u0026ndash;0.883, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with greater severity of persistent symptoms. Regarding the VOC, all Omicron variants (BA.1: β -1.276, 95% CI: -2.169 - -0.384, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.01; BA.2: β: -1.518; 95% CI: -2.387- -0.649, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001; BA.5: β: -1.36; 95% CI: -2.704 - -0.015, p\u0026thinsp;=\u0026thinsp;0.048) showed lower severity of persistent symptoms compared to the Alpha variant. Participants of the Delta wave tended to have severity of persistent symptoms in between the Alpha and Omicron variants, but the association was less strong (Delta vs. Alpha: β: -0.536, 95% CI: -1.395 - -0.323, p\u0026thinsp;=\u0026thinsp;0.22) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe severity of persistent symptoms (multivariable linear regression; response variable: severity of ongoing symptoms, on a scale 0\u0026ndash;10); β: beta estimate; VOC: variant of concern; CI: confidence interval; Center 1: University Hospital of D\u0026uuml;sseldorf; Center 2: University Hospital of Aachen; Center 3: University Hospital of Bonn; Center 4: University Hospital of Essen; Center 5: University Hospital of Cologne; Center 6: University Hospital of M\u0026uuml;nster; BV: Baseline Visite\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.009 [-0.003;0.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex (female vs. male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.567 [0.25;0.883]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLow education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.184 [0.854;1.515]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e4th Wave vs. 1st \u0026ndash; 3rd Wave\u003c/p\u003e \u003cp\u003eVOC: Delta vs. Alpha\u003c/p\u003e \u003cp\u003e5.1th Wave vs. 1st \u0026ndash; 3rd Wave\u003c/p\u003e \u003cp\u003eVOC: Omicron BA.1 vs. Alpha\u003c/p\u003e \u003cp\u003e5.2th Wave vs. 1st \u0026ndash; 3rd Wave\u003c/p\u003e \u003cp\u003eVOC: Omicron BA.2 vs. Alpha\u003c/p\u003e \u003cp\u003e6th Wave vs. 1st \u0026ndash; 3rd Wave\u003c/p\u003e \u003cp\u003eVOC: Omicron BA.5 vs. Alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-0.536 [-1.395;-0.323]\u003c/p\u003e \u003cp\u003e-1.276 [-2.169;-0.384]\u003c/p\u003e \u003cp\u003e-1.518 [-2.387;-0.649]\u003c/p\u003e \u003cp\u003e]\u003c/p\u003e \u003cp\u003e-1.36 [-2.704;-0.015]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2 Infections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-0.128 [-0.526;0.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVaccination before 1st Infection yes vs. no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-0.187 [-1.038;0.664]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCenter 2 vs. Center 1\u003c/p\u003e \u003cp\u003eCenter 3 vs. Center 1\u003c/p\u003e \u003cp\u003eCenter 4 vs. Center 1\u003c/p\u003e \u003cp\u003eCenter 5 vs. Center 1\u003c/p\u003e \u003cp\u003eCenter 6 vs. Center 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-0.371 [-0.914;0.172]\u003c/p\u003e \u003cp\u003e-0.409 [-0.966;0.15]\u003c/p\u003e \u003cp\u003e0.13 [-0.466;0.727]\u003c/p\u003e \u003cp\u003e0.822 [0.294;1.351]\u003c/p\u003e \u003cp\u003e-0.538 [-1.039;-0.037]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003cp\u003e0.15\u003c/p\u003e \u003cp\u003e0.67\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInterval 1st Infection to BV (month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-0.06 [-0.11;-0.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFrequency of Persisting Symptoms Regarding different VOCs\u003c/h2\u003e \u003cp\u003eFatigue was the most common symptom throughout all virus variants (Alpha, Delta, Omicron BA.1, BA.2, and BA.5) with an overall frequency of 32.8% of all participants at BV (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Concentration disorders (25.4%) and dyspnoea (22%) followed as most common persisting symptoms in all VOC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Fatigue was less frequently reported in Omicron BA.1 (26.5%) and BA.2 (27.5%) compared to the Alpha (41.3%) and Delta (36.8%) variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Concentration disorders were less frequent in Omicron BA.1 (23.1%) and BA.2 (19.5%) compared to Alpha (33.9%) and Delta (28.5%). Dyspnoea was also less common in BA.1 (19.7%) and BA.2 (16.5%) compared to Alpha (30.3%) and Delta (25.2%). Dysgeusia and Dysosmia were both less common in BA.1 and BA.2. compared to Alpha and Delta. Psychiatric disorders were less frequent in BA.1 (12.6%) and BA.2. (9.6%) compared to Alpha (18.8%) and Delta (20.5%) variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Regarding total differences between the VOC, Alpha and Delta variants showed generally similar frequencies of persisting symptoms, with only constipation being significantly different (Alpha 3.7%, Delta 0.3%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eblue shades: chronic fatigue-like (fatigue, headache, concentration disorders,);\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ered/orange: respiratory (dyspnoea, cough, chest pain, sore throat, voice change);\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eyellow shades: neurosensorial (dysgeusia, dysosmia, dizziness, tinnitus,);\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ewhite: gastrointestinal (nausea, diarrhoea);\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003egreen shades: systemic-inflammatory chronic pain (limb pain, back pain, cold; hair loss).\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFrequency of Persisting Symptoms Regarding VOC and Symptom-Cluster\u003c/h2\u003e \u003cp\u003eRegarding different symptom cluster, chronic fatigue-like syndrome was with 23.5% the most frequent cluster, followed by respiratory cluster (9.1%), neurosensorial syndrome cluster (6.9%), systemic-inflammatory chronic pain syndrome cluster (5.1%), and gastrointestinal cluster (2.7%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Regarding different VOCs, chronic fatigue-like cluster was more frequent in Alpha (29.8%) and Delta (26.1%) variants compared to Omicron BA.1 (21.4%) and BA.2 (19%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The respiratory cluster was more common in Alpha (11.4%) compared to Omicron BA.1 (9.1%) and BA.2 (7.3%). Delta (9.9%) had respiratory cluster frequency between Alpha and BA.1/BA.2. The gastrointestinal cluster showed overall (2.7%) low and constant frequencies throughout all VOCs (Alpha: 3%, Delta: 2.8%, BA.1: 3.5%; BA.2: 2.8%; BA.5: 2.8%). The systemic-inflammatory chronic pain cluster was present in 5.1% overall. BA.2 (3.8%) showed a lower frequency compared to Alpha (5.7%), Delta (5.4%), and BA.1 (6%). BA.5 had the highest percentage of participants with systemic-inflammatory chronic pain cluster (9.2%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eData analyses revealed considerable differences in reporting persisting symptoms at study baseline as well as subjective symptom severity. In general, Long COVID symptoms became less frequent with each new variant of concern. It is essential to mention that comparing epidemiological studies on Long COVID is challenging, as they have widely different recruitment strategies, follow-up periods, and heterogenous participant demographics [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This is particularly the case regarding frequency, timeline, and severity of symptoms, especially given the natural course of Long COVID per se, with fluctuating and relapsing symptoms and symptom severity over time [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In our study, 68.8% of participants reported at least one persisting symptom at BV individually attributed to SARS-CoV-2, with a higher proportion of symptomatic participants in the hospitalized sample than the population sample (79.3% vs. 67.1%). One year after infection, the reported rates of symptomatic patients are extensively heterogeneous, with a 30\u0026ndash;60% range in most published results [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. According to our data, female sex, lower education, and a shorter period between infection and BV were associated with higher rates of persisting symptoms and were independent predictors of symptom severity in multivariate regression models. This is in line with previously published data [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Interestingly, we report no age effect either on the number of symptoms or regarding symptom severity. While most of the published Long COVID studies reported higher risk in elderly patients, several studies found no age effect or only an effect in specific age subgroups (e.g., \u0026gt;\u0026thinsp;50 years) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, most of the studies did not record the severity of symptoms. Regarding the influence of age on symptom frequency, a recent review stated a high heterogeneity rate especially in large studies, and that high-quality studies have higher heterogeneity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Along with our results, age per se is no risk factor for Long COVID (to the contrary, age may be protective for some Long COVID effects). In multivariate regression, vaccination status was not an independent risk factor for persisting symptoms and symptom severity. The vast majority (96.3%) in our study were vaccinated before the first infection; therefore, the statistical power to detect a possible protective effect is very low. This can be ascribed to the recruitment phase (infections after March 1st, 2021) and the high vaccination coverage in Germany, with an exponential increase in vaccinations between March and July 2021 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, persons refusing SARS-CoV-2 vaccination are disproportionately often skeptical about COVID-19 effects and/or healthcare authorities and, therefore, may be less interested in participating in a study trial [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHospitalized participants tended to have more symptoms at BV compared to participants of the population sample and had a higher severity of impairment. Several studies, including a meta-analysis, showed a higher prevalence of Long COVID in hospitalized patients than in outpatients [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Our data showed both higher rates of symptoms in hospitalized participants and higher severity of impairment.\u003c/p\u003e \u003cp\u003eThe impact of SARS-CoV-2 reinfections on Long COVID (developed after the first or subsequent infections) is still a matter of debate. Most of the published data showed a higher rate of Long COVID with an increasing number of infections [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Interestingly despite the higher number of symptoms in the subgroup of participants with \u0026ge;\u0026thinsp;2 infections (24.3%) our study showed no difference regarding the severity of impairment compared to participants with one infection, and multiple infections were no independent risk factor for persisting symptoms in multivariable regression.\u003c/p\u003e \u003cp\u003eSeveral studies, including meta-analyses, have investigated the risk of Long COVID regarding different virus variants with inconclusive results [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The available data suggest that infection with an Omicron variant may result in fewer Long COVID symptoms. However, since most studies compared only two different virus variants and heterogeneous patient collectives, transferability on all variants is difficult [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Fernandes-de-la-Pena et al. concluded in their review that the small number of studies and the lack of control of confounders, e.g., reinfections or vaccine status, limit the generalizability of the results [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Vishwakarma et al. stated that the difference in the prevalence of Long COVID between various variants needs further exploration [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Pathophysiological differences, e.g., higher viral load (Delta\u0026thinsp;\u0026gt;\u0026thinsp;Alpha), higher transmissibility (Omicron\u0026thinsp;\u0026gt;\u0026thinsp;Alpha and Delta), and potential immune escape to vaccines among the different VOCs, were reported [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Our findings suggest that Alpha and Delta, on the one hand, and Omicron BA.1 and BA.2, on the other hand, have highly homogenous frequencies and distribution of persisting symptoms (single-symptoms as well as symptom-clusters). We observed a lower rate of persistent symptoms in the Omicron variants BA.1 and BA. 2 compared to the Alpha and Delta variants. Besides possible differences in the viral variants themselves this may be also explained by the increasing immunity of the population against SARS-CoV-2, which is a result of a higher number of vaccinations and natural infections in the course of the pandemic. Omicron BA.5 participants tended to have a higher symptom probability than all other VOCs, but the results were overall not significant due to the low number of participants in BA.5. We assume that the shorter period between infection and BV is the main reason for this finding. Subsequent analyses regarding the long-term follow-up will help to clarify this.\u003c/p\u003e \u003cp\u003eOverall (all VOCs), fatigue was the most frequent ongoing symptom (32.8%), followed by concentration disorders (25.4%) and dyspnoea (22%). This is in line with previously published data, including a systematic review reporting rates of 41% for fatigue and 31% for dyspnoea 12 months after infection but making no distinction between different VOCs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Fatigue, concentration disorders, and the chronic fatigue-like symptom cluster, as well as dyspnoea, were significantly less frequent in Omicron BA.1 and BA.2 compared to the Alpha and Delta variants. This finding contrasts with previously published data [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. A series of symptoms (headache, dizziness, tinnitus, sore throat, nausea, loss of appetite, diarrhoea, abdominal pain, and hair loss) showed consistent frequencies throughout all VOCs in our data. After Omicron infection (BA.1, BA.2, and BA.5), participants were less affected by smell and taste disorders compared to Alpha and Delta, which confirms recently published data from a digital participation-only study based on self-reports [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Our data also show a lower frequency of the whole neurosensorial symptom cluster (dysgeusia, dysosmia, dizziness, tinnitus, earache) in BA.1 and BA.2 compared to Alpha and Delta variants.\u003c/p\u003e \u003cp\u003eWhile men are at higher risk for severe COVID-19 and death, women appear more prone to developing Long COVID [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], consistent with most published studies [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Sex specific difference in the response to infectious are described in many contexts, a recent study showed sex-specific immune responses, with females exhibiting more severe inflammation possibly attributing to differences in regard to the likelihood and phenotype of Long COVID [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The higher proportion of female participants in our study may also reflect women\u0026rsquo;s generally higher response and participation rates in clinical research [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. 24.3% of participants in our collective had more than one infection at BV, slightly higher than the reported rates in population-based analyses with 5\u0026ndash;15% [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The interval between 1st or rather last (in case of \u0026ge;\u0026thinsp;2 infections) infection and BV in the study centers in our study was roughly a year: mean of 388 days in case of one infection and 325 days in case of \u0026ge;\u0026thinsp;2 infections. Therefore, the BV represents the time point of 12 months after infection in most participants.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Despite random symptom-unrelated selection, it is probable that symptomatic patients followed the study invitation more frequently, and therefore, bias may be present. It is also evident that the response rate was rather low questioning the generalisability of findings. Furthermore, some reported symptoms are subjective (e.g., fatigue, headache), and the lack of validated scales to measure most symptoms makes it difficult to compare data between subjects. Furthermore, we could not discriminate between different virus variants per PCR test. Thus, the allocation to a specific VOC in our cohort is based on the infection time.\u003c/p\u003e \u003cp\u003eOur results add relevant data regarding symptoms and symptom severity caused by the different virus variants in hospitalized and non-hospitalized participants stratified by different VOCs and demographical, economic, and educational participant-related context factors. Further follow-up analyses of our cohort with its long-term follow-up period of three years including laboratory tests and standardized medical examinations, will contribute to further characterizing the risk factors and natural course of Long COVID.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions:\u003c/em\u003e\u003c/strong\u003e AM, ND, BS, BEOJ, and JB designed the study; AM, BEOJ, BS, and ND wrote the proposal; NK, BS, LZ, LS, MF, and SD coordinated data access; LS, BS and AM performed statistical analyses; AM, BEOJ, BS, AK and LS generated Figures and Tables; MD, MR, MM, HR, CL, PRT, JS, JR and TL provided intellectual input; AM and AK drafted the manuscript with the help of all authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication:\u003c/em\u003e\u003c/strong\u003eAll authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests:\u003c/em\u003e\u003c/strong\u003e The authors declared no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy Centres:\u003c/em\u003e\u003c/strong\u003eThe coordinator center is the Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital, Medical Faculty of Heinrich Heine University Düsseldorf, Germany. Further centers are the Department of Pneumology and Intensive Care Medicine, RWTH University Hospital, Aachen, Germany; Department of Medicine I, University Hospital Bonn, Germany; Department of Infectious Diseases; West German Centre of Infectious Diseases, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Department I of Internal Medicine, Faculty of Medicine and University Hospital, University of Cologne, Germany; Department of Internal Medicine B, Gastroenterology, Hepatology, Endocrinology and Clinical Infectiology, University Hospital Münster, Germany.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical Trial registration:\u003c/em\u003e\u003c/strong\u003eThis study was registered in the German Clinical Trials Register under the Clinical trial number DRKS00027377 on 08.12.2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding:\u003c/em\u003e\u003c/strong\u003e The Beyond Covid Network responsible for this study is an investigator-initiated study and has been initiated by the six partners. The Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital of Düsseldorf, coordinates the study. The study is financed by the Ministry of Culture and Science of North Rhine-Westphalia (MKW-NRW) under Grant Agreement N° 323-8.03-153969 with a total of 4.559.655,19 euros.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Approval and Consent to Participate\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e:\u003c/em\u003e\u003c/strong\u003e The Heinrich-Heine-University Düsseldorf's Ethics Committee, as the leading ethics committee, approved the study protocol, informed consent forms, and participant information materials in November 2021 (under the study registration number 2021-1688). In addition, the Ethics Committee at each clinical research site approved the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Availability Statement:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe results of the remote data analyses are available from the corresponding author upon reasonable request. Contact: Dr. med. Alexander Killer; E-Mail: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipant Informed Consent:\u003c/em\u003e\u003c/strong\u003eParticipation in the study was voluntary and required comprehensive medical information about the meaning and purpose of the study, as well as the advantages and disadvantages of participating in the study and a signed declaration of consent. The study participant could revoke this consent in writing or verbally without giving reasons. In the event of a revocation, the study participant could decide whether the data collected for the study should be deleted, whether the samples obtained or the test results collected should be destroyed, or whether they can continue to be used for the study. The data and biomaterials collected during the study will be retained for a maximum of 10 years after completion of the study. The study participants had the opportunity to limit the use of data in the declaration of consent as part of the study information regarding other/future research purposes and time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Collection:\u003c/em\u003e\u003c/strong\u003ePseudonymized health data was collected by the study centers during each study visit on standardized case report forms (CRFs). This included demographic, clinical, and viro-immunological data. These health data were transferred into a clinical database system (\u003cem\u003eClincase\u003c/em\u003e, Quadratek Data Solutions Ltd., Berlin, Germany), administered by the Center for Clinical Studies Essen, Germany (ZKSE). Recording and health data transfer were subject to the consent of the study participants. Data collection procedures strictly adhere to the established protocol, the recent version of the Declaration of Helsinki, general data protection regulations of the European Union (EU-DSGVO), and the International Conference on Harmonization Good Clinical Practice (ICH-GCP). Data quality and plausibility checks were carried out by trained ZKSE employees. Complex plausibility checks were programmed in SAS. If necessary, queries were sent to members of the study teams at the centers to make corrections to the data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Protection:\u003c/em\u003e\u003c/strong\u003eThe pseudonymously collected data is analyzed and evaluated for interim evaluations and finally for the legally required evaluations. \u0026nbsp;All data in the study database can only be identified by a participant identification number (participant ID) corresponding to participant consent to ensure pseudonymization. The birth year is documented in the database, not the day and month. Only the local study centers maintain a separate confidential identification list to identify everyone enrolled to enable participants. This list is protected from unauthorized access stored in the respective study center. All persons who have access to pseudonymized data are subject to the general data protection regulations of the European Union (EU-DSGVO) when handling the data. Only anonymized data are passed on when the final study report is submitted to the responsible German authorities (higher federal authorities, ethics committees if applicable). Publications on the study will only be made with anonymous data. The data must be retained following legal requirements. An assessment of the necessity and proportionality of the data processing was obtained. A risk assessment regarding the possible risks caused by the processing for those affected is taken into account, and the protection goals of confidentiality, integrity, and availability were assessed. The remaining residual risk regarding the protection goals of integrity, legal control, availability, and confidentiality after implementation of technical and organizational measures are classified as low.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments, Credits:\u003c/em\u003e\u003c/strong\u003e We sincerely acknowledge all participants of the study, whose contributions and commitment were essential to the realization of this research. The authors would also like to thank everyone in the involved study centers, who contributed to the success of this project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. WHO COVID-19. dashboard 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.who.int/dashboards/covid19/deaths?n=o\u003c/span\u003e\u003cspan address=\"https://data.who.int/dashboards/covid19/deaths?n=o\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoriano JB, Murthy S, Marshall JC, Relan P, Diaz JV. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infect Dis. 2022;22:e102\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1473-3099(21)00703-9\u003c/span\u003e\u003cspan address=\"10.1016/S1473-3099(21)00703-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayes LD, Ingram J, Sculthorpe NF. More Than 100 Persistent Symptoms of SARS-CoV-2 (Long COVID): A Scoping Review. Front Med (Lausanne). 2021;8:750378. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2021.750378\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2021.750378\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez-Leon S, Wegman-Ostrosky T, Perelman C, Sepulveda R, Rebolledo PA, Cuapio A, Villapol S. More than 50 long-term effects of COVID-19: a systematic review and meta-analysis. Sci Rep. 2021;11:16144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-95565-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-95565-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen C, Haupert SR, Zimmermann L, Shi X, Fritsche LG, Mukherjee B. Global Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review. J Infect Dis. 2022;226:1593\u0026ndash;607. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/infdis/jiac136\u003c/span\u003e\u003cspan address=\"10.1093/infdis/jiac136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNittas V, Gao M, West EA, Ballouz T, Menges D, Wulf Hanson S, Puhan MA. Long COVID Through a Public Health Lens: An Umbrella Review. Public Health Rev. 2022;43:1604501. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/phrs.2022.1604501\u003c/span\u003e\u003cspan address=\"10.3389/phrs.2022.1604501\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-de-Las-Pe\u0026ntilde;as C, Palacios-Ce\u0026ntilde;a D, G\u0026oacute;mez-Mayordomo V, Florencio LL, Cuadrado ML, Plaza-Manzano G, Navarro-Santana M. Prevalence of post-COVID-19 symptoms in hospitalized and non-hospitalized COVID-19 survivors: A systematic review and meta-analysis. Eur J Intern Med. 2021;92:55\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejim.2021.06.009\u003c/span\u003e\u003cspan address=\"10.1016/j.ejim.2021.06.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWulf Hanson S, Abbafati C, Aerts JG, Al-Aly Z, Ashbaugh C, Ballouz T, et al. Estimated Global Proportions of Individuals With Persistent Fatigue, Cognitive, and Respiratory Symptom Clusters Following Symptomatic COVID-19 in 2020 and 2021. JAMA. 2022;328:1604\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2022.18931\u003c/span\u003e\u003cspan address=\"10.1001/jama.2022.18931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSee\u0026szlig;le J, Waterboer T, Hippchen T, Simon J, Kirchner M, Lim A, et al. Persistent Symptoms in Adult Patients 1 Year After Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study. Clin Infect Dis. 2022;74:1191\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cid/ciab611\u003c/span\u003e\u003cspan address=\"10.1093/cid/ciab611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVishwakarma N, Goud RB, Tirupattur MP, Katwa LC. The Eye of the Storm: Investigating the Long-Term Cardiovascular Effects of COVID-19 and Variants. Cells. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells12172154\u003c/span\u003e\u003cspan address=\"10.3390/cells12172154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarkar M, Madabhavi I. SARS-CoV-2 variants of concern: a review. Monaldi Arch Chest Dis. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4081/monaldi.2022.2337\u003c/span\u003e\u003cspan address=\"10.4081/monaldi.2022.2337\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntonelli M, Pujol JC, Spector TD, Ourselin S, Steves CJ. Risk of long COVID associated with delta versus omicron variants of SARS-CoV-2. Lancet. 2022;399:2263\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(22)00941-2\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(22)00941-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHern\u0026aacute;ndez-Aceituno A, Garc\u0026iacute;a-Hern\u0026aacute;ndez A, Larumbe-Zabala E. COVID-19 long-term sequelae: Omicron versus Alpha and Delta variants. Infect Dis Now. 2023;53:104688. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.idnow.2023.104688\u003c/span\u003e\u003cspan address=\"10.1016/j.idnow.2023.104688\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu M, Ma Y, Deng J, Liu M, Liu J. Comparison of Long COVID-19 Caused by Different SARS-CoV-2 Strains: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph192316010\u003c/span\u003e\u003cspan address=\"10.3390/ijerph192316010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen NN, Nguyen YN, van Hoang T, Million M, Gautret P. SARS-CoV-2 Reinfection and Severity of the Disease: A Systematic Review and Meta-Analysis. Viruses. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/v15040967\u003c/span\u003e\u003cspan address=\"10.3390/v15040967\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe S-T, Wu K, Cheng Z, He M, Hu R, Fan N et al. Long COVID: The latest manifestations, mechanisms, and potential therapeutic interventions. MedComm (2020). 2022;3:e196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/mco2.196\u003c/span\u003e\u003cspan address=\"10.1002/mco2.196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoufidou F, Medić S, Lampropoulou V, Siafakas N, Tsakris A, Anastassopoulou C. SARS-CoV-2 Reinfections and Long COVID in the Post-Omicron Phase of the Pandemic. Int J Mol Sci. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms241612962\u003c/span\u003e\u003cspan address=\"10.3390/ijms241612962\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanas LS, Molteni E, Deng J, Sudre CH, Murray B, Kerfoot E, et al. Profiling post-COVID-19 condition across different variants of SARS-CoV-2: a prospective longitudinal study in unvaccinated wild-type, unvaccinated alpha-variant, and vaccinated delta-variant populations. Lancet Digit Health. 2023;5:e421\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2589-7500(23)00056-0\u003c/span\u003e\u003cspan address=\"10.1016/S2589-7500(23)00056-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGentilotti E, G\u0026oacute;rska A, Tami A, Gusinow R, Mirandola M, Rodr\u0026iacute;guez Ba\u0026ntilde;o J, et al. Clinical phenotypes and quality of life to define post-COVID-19 syndrome: a cluster analysis of the multinational, prospective ORCHESTRA cohort. EClinicalMedicine. 2023;62:102107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eclinm.2023.102107\u003c/span\u003e\u003cspan address=\"10.1016/j.eclinm.2023.102107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoch-Institut. Robert. Dritte Aktualisierung der \u0026bdquo;Retrospektiven Phaseneinteilung der COVID-19-Pandemie in Deutschland. Robert Koch-Institut; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerzon E, Weiss M, Krone B, Cohen S, Ilani G, Vinker S, et al. Clinical and Socio-Demographic Variables Associated with the Diagnosis of Long COVID Syndrome in Youth: A Population-Based Study. Int J Environ Res Public Health. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph19105993\u003c/span\u003e\u003cspan address=\"10.3390/ijerph19105993\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharfeddine S, Ibn Hadj Amor H, Jdidi J, Torjmen S, Kraiem S, Hammami R, et al. Long COVID 19 Syndrome: Is It Related to Microcirculation and Endothelial Dysfunction? Insights From TUN-EndCOV Study. Front Cardiovasc Med. 2021;8:745758. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2021.745758\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2021.745758\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhetsouphanh C, Darley DR, Wilson DB, Howe A, Munier CML, Patel SK, et al. Immunological dysfunction persists for 8 months following initial mild-to-moderate SARS-CoV-2 infection. Nat Immunol. 2022;23:210\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41590-021-01113-x\u003c/span\u003e\u003cspan address=\"10.1038/s41590-021-01113-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlynne P, Tahmasebi N, Gant V, Gupta R. Long COVID following mild SARS-CoV-2 infection: characteristic T cell alterations and response to antihistamines. J Investig Med. 2022;70:61\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/jim-2021-002051\u003c/span\u003e\u003cspan address=\"10.1136/jim-2021-002051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein J, Wood J, Jaycox JR, Dhodapkar RM, Lu P, Gehlhausen JR, et al. Distinguishing features of long COVID identified through immune profiling. Nature. 2023;623:139\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-023-06651-y\u003c/span\u003e\u003cspan address=\"10.1038/s41586-023-06651-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu Y, Yuan D, Chen DG, Ng RH, Wang K, Choi J, et al. Multiple early factors anticipate post-acute COVID-19 sequelae. Cell. 2022;185:881\u0026ndash;e89520. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2022.01.014\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2022.01.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerai H, Ishii M, Takemura R, Namkoong H, Shimamoto K, Masaki K, et al. Comprehensive analysis of long COVID in a Japanese nationwide prospective cohort study. Respir Investig. 2023;61:802\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.resinv.2023.08.008\u003c/span\u003e\u003cspan address=\"10.1016/j.resinv.2023.08.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilverberg JI, Zyskind I, Naiditch H, Zimmerman J, Glatt AE, Pinter A, et al. Predictors of chronic COVID-19 symptoms in a community-based cohort of adults. PLoS ONE. 2022;17:e0271310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0271310\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0271310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerisse A, de Cacqueray F, Delarbre D, Marsaa H, Bergmann C, Da Silva V, et al. Symptoms of Long-COVID 1-Year after a COVID-19 outbreak among sailors on a French aircraft carrier. Infect Dis Now. 2023;53:104673. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.idnow.2023.104673\u003c/span\u003e\u003cspan address=\"10.1016/j.idnow.2023.104673\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang L, Yao Q, Gu X, Wang Q, Ren L, Wang Y, et al. 1-year outcomes in hospital survivors with COVID-19: a longitudinal cohort study. Lancet. 2021;398:747\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(21)01755-4\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(21)01755-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelmsdal G, Hanusson KD, Kristiansen MF, Foldbo BM, Danielsen ME, Steig B\u0026Aacute;, et al. Long COVID in the Long Run-23-Month Follow-up Study of Persistent Symptoms. Open Forum Infect Dis. 2022;9:ofac270. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ofid/ofac270\u003c/span\u003e\u003cspan address=\"10.1093/ofid/ofac270\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsampasian V, Elghazaly H, Chattopadhyay R, Debski M, Naing TKP, Garg P, et al. Risk Factors Associated With Post-COVID-19 Condition: A Systematic Review and Meta-analysis. JAMA Intern Med. 2023;183:566\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamainternmed.2023.0750\u003c/span\u003e\u003cspan address=\"10.1001/jamainternmed.2023.0750\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenezes AS, Botelho SM, Santos LR, Rezende AL. Acute COVID-19 Syndrome Predicts Severe Long COVID-19: An Observational Study. Cureus. 2022;14:e29826. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7759/cureus.29826\u003c/span\u003e\u003cspan address=\"10.7759/cureus.29826\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellan M, Soddu D, Balbo PE, Baricich A, Zeppegno P, Avanzi GC, et al. Respiratory and Psychophysical Sequelae Among Patients With COVID-19 Four Months After Hospital Discharge. JAMA Netw Open. 2021;4:e2036142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2020.36142\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2020.36142\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealth GFMo. Current vaccination status 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://impfdashboard.de/en/\u003c/span\u003e\u003cspan address=\"https://impfdashboard.de/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViskupič F, Wiltse DL, Meyer BA. Trust in physicians and trust in government predict COVID-19 vaccine uptake. Soc Sci Q. 2022;103:509\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ssqu.13147\u003c/span\u003e\u003cspan address=\"10.1111/ssqu.13147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindholt MF, J\u0026oslash;rgensen F, Bor A, Petersen MB. Public acceptance of COVID-19 vaccines: cross-national evidence on levels and individual-level predictors using observational data. BMJ Open. 2021;11:e048172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2020-048172\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2020-048172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSethi S, Kumar A, Mandal A, Shaikh M, Hall CA, Kirk JMW, et al. The UPTAKE study: implications for the future of COVID-19 vaccination trial recruitment in UK and beyond. Trials. 2021;22:296. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13063-021-05250-4\u003c/span\u003e\u003cspan address=\"10.1186/s13063-021-05250-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan N, Lv Z-H, Sun C-R, Wen Y-Y, Tao T-Y, Qian D, et al. Post-acute COVID-19 symptom risk in hospitalized and non-hospitalized COVID-19 survivors: A systematic review and meta-analysis. Front Public Health. 2023;11:1112383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2023.1112383\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2023.1112383\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlomberg B, Mohn KG-I, Brokstad KA, Zhou F, Linchausen DW, Hansen B-A, et al. Long COVID in a prospective cohort of home-isolated patients. Nat Med. 2021;27:1607\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-021-01433-3\u003c/span\u003e\u003cspan address=\"10.1038/s41591-021-01433-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026eacute;rez-Gonz\u0026aacute;lez A, Ara\u0026uacute;jo-Ameijeiras A, Fern\u0026aacute;ndez-Villar A, Crespo M, Poveda E. Long COVID in hospitalized and non-hospitalized patients in a large cohort in Northwest Spain, a prospective cohort study. Sci Rep. 2022;12:3369. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-022-07414-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-07414-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowe B, Xie Y, Al-Aly Z. Acute and postacute sequelae associated with SARS-CoV-2 reinfection. Nat Med. 2022;28:2398\u0026ndash;405. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-022-02051-3\u003c/span\u003e\u003cspan address=\"10.1038/s41591-022-02051-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-de-Las-Pe\u0026ntilde;as C, Notarte KI, Peligro PJ, Velasco JV, Ocampo MJ, Henry BM, et al. Long-COVID Symptoms in Individuals Infected with Different SARS-CoV-2 Variants of Concern: A Systematic Review of the Literature. Viruses. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/v14122629\u003c/span\u003e\u003cspan address=\"10.3390/v14122629\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBallouz T, Menges D, Kaufmann M, Amati R, Frei A, von Wyl V, et al. Post COVID-19 condition after Wildtype, Delta, and Omicron SARS-CoV-2 infection and prior vaccination: Pooled analysis of two population-based cohorts. PLoS ONE. 2023;18:e0281429. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0281429\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0281429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagnusson K, Kristoffersen DT, Dell\u0026rsquo;Isola A, Kiadaliri A, Turkiewicz A, Runhaar J, et al. Post-covid medical complaints following infection with SARS-CoV-2 Omicron vs Delta variants. Nat Commun. 2022;13:7363. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-022-35240-2\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-35240-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeyssou E, Delagr\u0026egrave;verie H, Visseaux B, Lambert-Niclot S, Brichler S, Ferre V, et al. The Delta SARS-CoV-2 variant has a higher viral load than the Beta and the historical variants in nasopharyngeal samples from newly diagnosed COVID-19 patients. J Infect. 2021;83:e1\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jinf.2021.08.027\u003c/span\u003e\u003cspan address=\"10.1016/j.jinf.2021.08.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarim SSA, Karim QA. Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic. Lancet. 2021;398:2126\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(21)02758-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(21)02758-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTani-Sassa C, Iwasaki Y, Ichimura N, Nagano K, Takatsuki Y, Yuasa S, et al. Viral loads and profile of the patients infected with SARS-CoV-2 Delta, Alpha, or R.1 variants in Tokyo. J Med Virol. 2022;94:1707\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jmv.27479\u003c/span\u003e\u003cspan address=\"10.1002/jmv.27479\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiexer S, Klee B, Gottschick C, Xu C, Broda A, Purschke O, et al. Association between virus variants, vaccination, previous infections, and post-COVID-19 risk. Int J Infect Dis. 2023;136:14\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijid.2023.08.019\u003c/span\u003e\u003cspan address=\"10.1016/j.ijid.2023.08.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAncochea J, Izquierdo JL, Soriano JB. Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning. J Womens Health (Larchmt). 2021;30:393\u0026ndash;404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/jwh.2020.8721\u003c/span\u003e\u003cspan address=\"10.1089/jwh.2020.8721\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScully EP, Haverfield J, Ursin RL, Tannenbaum C, Klein SL. Considering how biological sex impacts immune responses and COVID-19 outcomes. Nat Rev Immunol. 2020;20:442\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41577-020-0348-8\u003c/span\u003e\u003cspan address=\"10.1038/s41577-020-0348-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldhaber NH, Kohn JN, Ogan WS, Sitapati A, Longhurst CA, Wang A, et al. Deep Dive into the Long Haul: Analysis of Symptom Clusters and Risk Factors for Post-Acute Sequelae of COVID-19 to Inform Clinical Care. Int J Environ Res Public Health. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph192416841\u003c/span\u003e\u003cspan address=\"10.3390/ijerph192416841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThaweethai T, Jolley SE, Karlson EW, Levitan EB, Levy B, McComsey GA, et al. Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection. JAMA. 2023;329:1934\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2023.8823\u003c/span\u003e\u003cspan address=\"10.1001/jama.2023.8823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahbaz S, Osman M, Syed H, Mason A, Rosychuk RJ, Cohen Tervaert JW, Elahi S. Integrated immune, hormonal, and transcriptomic profiling reveals sex-specific dysregulation in long COVID patients with ME/CFS. Cell Rep Med. 2025;6:102449. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.xcrm.2025.102449\u003c/span\u003e\u003cspan address=\"10.1016/j.xcrm.2025.102449\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-Sanl\u0026eacute;s A, Smith D, Clayton GL, Northstone K, Carter AR, Millard LA, et al. Bias from questionnaire invitation and response in COVID-19 research: an example using ALSPAC. Wellcome Open Res. 2021;6:184. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.12688/wellcomeopenres.17041.2\u003c/span\u003e\u003cspan address=\"10.12688/wellcomeopenres.17041.2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuedes AR, Oliveira MS, Tavares BM, Luna-Muschi A, Lazari CDS, Montal AC, et al. Reinfection rate in a cohort of healthcare workers over 2 years of the COVID-19 pandemic. Sci Rep. 2023;13:712. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-022-25908-6\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-25908-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEythorsson E, Runolfsdottir HL, Ingvarsson RF, Sigurdsson MI, Palsson R. Rate of SARS-CoV-2 Reinfection During an Omicron Wave in Iceland. JAMA Netw Open. 2022;5:e2225320. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2022.25320\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2022.25320\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"infection","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infe","sideBox":"Learn more about [Infection](http://link.springer.com/journal/15010)","snPcode":"15010","submissionUrl":"https://submission.nature.com/new-submission/15010/3","title":"Infection","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Long COVID, Long COVID-19, Post COVID Condition, PCC, COVID-19, Follow-up after COVID-19, COVID-long-term, Gender association in post-COVID-19, Variant of Concern, VOC, VOCs, Wave-association, Persistence of COVID-19 symptoms, SARS-CoV-2, Post Acute Sequelae of SARS CoV-2 infection, Post-COVID-19 condition, Post-COVID, COVID-waves, Alpha, Delta, Omicron","lastPublishedDoi":"10.21203/rs.3.rs-8489939/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8489939/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLong COVID refers to persistent or new-onset symptoms three months after SARS-CoV-2 infection lasting for at least two months. The prevalence of Long COVID ranges across studies, while the associated risk factors are not well understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study population of \u003cem\u003eBeyond COVID\u003c/em\u003e was recruited in six German cities by inviting (1) individuals registered as SARS-CoV-2 PCR positive at the local Public Health Authorities and (2) previously hospitalized patients with infection date between 1st March 2021 and 31st May 2022. Participants were allocated to the predominant variant of concern (VOC) of their first infection. Blood exams and questionnaires to assess persisting symptoms, quality of life (QOL), and psychosocial factors were performed. This publication describes the parameters at baseline visit (BV).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe included 1258 participants (13.4% hospitalized-based; 86.6% population-based). Most participants had BA.2 (34.6%), followed by Delta (26.8%), BA.1 (18.9%), and Alpha (17.3%). The mean age was 47.1, and 59% were female. 68.8% reported at least one persisting symptom. Fatigue was the most frequent ongoing symptom (32.8%), followed by concentration disorders (25.4%) and dyspnoea (22%). Female sex, lower education, and a shorter period between infection and BV were associated with higher rates of persisting symptoms and symptom-severity. BA.1 and BA.2 had lower rates of persisting symptoms and symptom severity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAnalysis of baseline data from the Beyond-COVID cohort confirms a high percentage of persistent symptoms. Omicron variants had lower rates of persistent symptoms and symptom severity. Long-term follow-up of study participants will contribute to the characterization of Long COVID.\u003c/p\u003e","manuscriptTitle":"Characterization of Long COVID by Clinical Examination and Self-Perceived Severity Stratified by Infection Wave: Beyond COVID, a Prospective, Multicenter Cohort Study in Germany","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-05 10:10:07","doi":"10.21203/rs.3.rs-8489939/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-06T05:20:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-05T21:27:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216527949764459554277347469363644569133","date":"2026-01-26T15:17:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T16:37:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102937108529266112280201433649552791453","date":"2026-01-05T15:21:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-02T09:07:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-02T08:19:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-02T08:10:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Infection","date":"2025-12-31T12:46:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"infection","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infe","sideBox":"Learn more about [Infection](http://link.springer.com/journal/15010)","snPcode":"15010","submissionUrl":"https://submission.nature.com/new-submission/15010/3","title":"Infection","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ca513b11-0993-4b67-841a-eae60f2f7005","owner":[],"postedDate":"January 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-03T08:53:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-05 10:10:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8489939","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8489939","identity":"rs-8489939","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}