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Research Article

Comparative risk of post-acute sequelae among adults following SARS-CoV-2 or influenza virus infection: A retrospective cohort study among United States adults

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Abstract

Background

Post-acute sequelae (PAS) of SARS-CoV-2 infection are well documented. However, it remains unclear whether such long-term health effects are unique to COVID-19, or also occur following other viral respiratory infections.

Methods and findings

We undertook a retrospective cohort study of 74,738 COVID-19 cases and 18,790 influenza cases within the Kaiser Permanente Southern California healthcare system diagnosed between 1 September, 2022 and 31 December, 2023. Cases received care for index infections across a spectrum of clinical settings, spanning virtual (n = 35,835; 38.3%), ambulatory (n = 26,579; 28.4%), emergency department (n = 23,388; 25.0%) and inpatient (n = 7,726; 8.3%) facilities. We compared 180-day risk of PAS-related healthcare utilization among COVID-19 cases and influenza via adjusted hazard ratios (aHRs) weighted to account for cases’ index infection type and follow-up retention. Adjustment models addressed patients’ demographic characteristics, comorbidity profiles, prior healthcare utilization patterns, and index episode severity. Risk of PAS diagnoses in any clinical setting was only modestly higher among COVID-19 cases in comparison to influenza cases within 31−90 days after cases’ initial illness (aHR = 1.04 [95% confidence interval: 0.99, 1.09]; risk difference = 0.6 [–0.1, 1.2] cases per 100 person-months). This difference was attenuated by 91−180 days (aHR = 1.01 [0.97, 1.06]; risk difference = 0.4 [–0.1, 0.9] cases per 100 person-months). However, COVID-19 cases faced higher risk of severe PAS conditions requiring hospitalization (aHR = 1.31 [1.07, 1.59] and 1.24 [1.03, 1.49] within 31−90 and 91−180 days, respectively). This excess risk of severe PAS was concentrated among COVID-19 cases hospitalized during acute-phase illness, and was attenuated among cases who received antiviral treatment, who had up-to-date vaccination status prior to infection, or who did not require inpatient admission for acute-phase illness. As a limitation, analyses included only PAS resulting in healthcare utilization; patient-reported symptoms and quality-of-life measures were not captured.

Conclusions

In this large, real-world cohort, individuals with non-severe acute respiratory illness caused by SARS-CoV-2 experienced only modestly greater risk of PAS in comparison to those whose illness was caused by influenza. However, COVID-19 cases hospitalized for their initial illness experienced greater risk of severe PAS necessitating inpatient care, and this difference persisted through 180 days of follow-up. Our findings challenge assumptions about the uniqueness of post-acute COVID-19 morbidity and suggest the long-term burden of influenza may be underrecognized.

Author summary

Why was this study done?

  • It is known that people may suffer medical complications long after COVID-19, which are called post-acute sequelae (PAS).
  • Similar sequelae are also known to happen after people are infected with influenza or other respiratory viruses.
  • It is unclear whether PAS are a unique or more prominent feature of COVID-19 in comparison to influenza.

What did the researchers find?

  • Risk of PAS was only modestly higher (4%) after COVID-19 in comparison to influenza.
  • By 91−180 days after infection, risk of PAS was similar for COVID-19 and influenza cases.
  • Differences in PAS risk were driven by the fact that people with severe COVID-19 tended to have greater risk of severe PAS afterwards.

What do these findings mean?

  • PAS risk is similar among most COVID-19 and influenza cases, although people hospitalized for COVID-19 may be at particular risk.
  • While PAS is well understood to occur after COVID-19, the burden of PAS associated with influenza may have been historically overlooked.
  • As a key limitation of the study, outcomes encompass only PAS necessitating healthcare utilization. Patient-reported symptoms and quality-of-life measures are also important outcomes when measuring PAS.

Citation: Lewnard JA, Malden DE, Hong V, Skela J, Feldstein LR, Saydah S, et al. (2025) Comparative risk of post-acute sequelae among adults following SARS-CoV-2 or influenza virus infection: A retrospective cohort study among United States adults. PLoS Med 22(10): e1004777. https://doi.org/10.1371/journal.pmed.1004777

Academic Editor: Peter MacPherson, University of Glasgow, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND

Received: June 13, 2025; Accepted: September 24, 2025; Published: October 9, 2025

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: The raw data used in this manuscript include protected health information (e.g., dates of diagnoses and testing) that cannot be shared openly without appropriate ethical oversight and approval as well as institutional data use agreements. Kaiser Permanente Southern California (KPSC) institutional policy requires a data transfer agreement to be executed between KPSC and the individual recipient entity prior to transmittal of patient-level data outside KPSC. Requests for data can be addressed to kpsc.irb@kp.org.

Funding: This work was supported by the US Centers for Disease Control and Prevention (grant 75D30123-C-18129 to SYT). Two co-authors are employed by the funder of the study (LRF and SS are employees of the US Centers for Disease Control and Prevention) and contributed to the design of the study in addition to providing critical intellectual feedback and revisions on drafts of the manuscript. The funder was not involved in the collection or analysis of data. Clearance of the manuscript by the funder of the study was required prior to submission for publication.

Competing interests: We have read the journal’s policy and the authors of this manuscript have the following competing interests: JAL has received honoraria from Pfizer, Inc.; Merck, Sharp & Dohme; Vaxcyte, Inc.; Valneva, SE; and Seqirus, Inc., all unrelated to the submitted work. SYT has received grant funding from Pfizer, Inc. unrelated to the submitted work. LSS has received grant funding from GSK, Moderna, AstraZeneca, and Dynavax, all unrelated to the submitted work. BKA has received research support from Pfizer, Moderna, GlaxoSmithKline, AstraZeneca, Merck, and F2G paid to his institution, Kaiser Permanente Southern California, for work unrelated to this study. LRT and SS are employees of the funder of the study (US Centers for Disease Control and Prevention). The remaining authors (DEM, VH, JS, IACR, RH, LSS) have no competing interests.

Abbreviations: aHR, adjusted hazard ratio; ARI, acute respiratory illness; CDC, Centers for Disease Control; EHR, electronic health record; PAS, post-acute sequelae

Introduction

Post-acute sequelae (PAS) of COVID-19 comprise a complex constellation of symptoms which may involve multiple organ systems. Longitudinal studies have estimated that as many as 30% of individuals may experience at least one post-acute symptom or PAS condition ≥30 days after SARS-CoV-2 infection [1–5], with some patients experiencing persistent PAS up to 3 years following their initial illness [6]. Conditions defining PAS are diverse and have encompassed cardiovascular, respiratory, renal, musculoskeletal, gastrointestinal, endocrine, and neurologic disorders, along with mental health and substance use-related conditions [7]. These outcomes encompass both exacerbations of pre-existing conditions and new-onset syndromes among individuals without related comorbid conditions prior to the precipitating SARS-CoV-2 infection [8]. Differences in both risk and persistence of PAS after COVID-19 have been reported by sex, by presence of pre-existing comorbidities, and by characteristics of individuals’ initial illness, including multisymptomatic presentation and severity [9–11]. Relatedly, factors preventing progression to severe COVID-19 during the acute stage, including history of COVID-19 vaccination [12–14] and receipt of antivirals during initial illness [15,16], have been reported to lessen individuals’ risk of PAS.

Whether PAS have similar prominence in disease caused by other respiratory viruses is uncertain. Consistent with observations in COVID-19, exacerbations of ischemic heart disease, congestive heart failure, and cerebrovascular disease are known to occur after influenza virus infection [17]. Neurological conditions [18,19]—including Guillain–Barré syndrome [20] and narcolepsy [21]—have also been reported as sequelae of influenza, along with chronic fatigue [22], rhabdomyolysis [23], and renal [24] and hepatic [25] conditions. Case reports have documented both new-onset diabetes associated with severe influenza [26] and exacerbations of existing disease following infection [27]. While awareness of this post-acute disease burden associated with influenza infection dates to the 1918 pandemic [28], it remains unclear whether SARS-CoV-2 and influenza infections pose comparable risk for PAS, and whether the clinical phenotypes of PAS associated with SARS-CoV-2 and influenza are distinct.

Several comparative studies [29–34] have reported greater risk of PAS after COVID-19 in comparison to influenza, albeit inconsistently [35,36]. However, a limitation of some studies has been their restriction to hospitalized COVID-19 and influenza patients, as PAS also affect patients whose preceding viral infections were not severe [37]. Persons hospitalized for COVID-19 and influenza are not representative of all people who acquire infection, and may experience different PAS outcomes in comparison to non-hospitalized patients. We therefore assessed PAS-associated healthcare utilization among a large cohort of individuals receiving care for laboratory-confirmed COVID-19 and influenza ("cases") whose acute illnesses and subsequent PAS were managed across a spectrum of clinical settings, spanning virtual to inpatient care. We quantified differences in risk of PAS according to both infecting virus and severity of acute-phase illness.

Methods

Setting

We analyzed data collected from members of Kaiser Permanente Southern California (KPSC), an integrated healthcare organization comprised of 16 hospitals and 226 medical offices providing care to >4.8 million members across Southern California. Members of KPSC health plans are enrolled through employer-provided, pre-paid, and publicly subsidized insurance plans; broadly, members reflect the socioeconomic and racial and ethnic diversity of the area’s population, have similar prevalence of chronic comorbid conditions, and reside in communities with socioeconomic characteristics mirroring the service area at large [38,39]. All aspects of clinical care received at KPSC facilities, including diagnoses, provider notes, laboratory tests, vaccinations, and prescriptions, are linked by a unique identifier in patients’ electronic health records (EHRs). Insurance claims submitted for reimbursement capture care received from external healthcare providers, enabling near-complete capture of healthcare receipt among members.

Study design

We conducted an observational cohort study of all KPSC members aged ≥18 years with a positive molecular test for SARS-CoV-2 or influenza between September 1, 2022 and December 31, 2023. We restricted analyses to individuals with accompanying acute respiratory illness (ARI) diagnoses in any clinical setting between 7 days before or after the positive test, and defined index dates as the date of the first ARI diagnosis. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist). While the study was retrospective in nature, the design and variable definitions followed a pre-specified protocol (S2 File).

Eligible participants had ≥1 year of continuous KPSC membership (allowing 45-day enrollment gap) before the positive test date to enable accurate capture of comorbid conditions and healthcare utilization. We limited analyses to the first documented ARI diagnosis associated with a positive SARS-CoV-2 or influenza test result in any 180-day period for each individual (index ARI episode). Multiple episodes were included from the same individual if these episodes occurred ≥180 days apart, and each studied episode could be associated with either SARS-CoV-2 or influenza. We excluded SARS-CoV-2 infections identified between 30 days before and 14 days after individuals received any COVID-19 vaccine dose, and influenza infections identified between 30 days before and 14 days after individuals received any influenza vaccine, so that participants’ recorded vaccination status at time of infection reflected doses from which they could be expected to have mounted a response. All individuals meeting eligibility criteria were included in analyses. As our study was retrospective in nature, there was no prespecified sample size or enrollment target.

Exposures

The primary exposure was the virus detected at each index ARI episode; we defined exposure groups as COVID-19 cases and influenza cases. We further stratified exposure groups based on respiratory viral season (October, 2022–September, 2023, and October–December, 2023) and by viral lineages (influenza type A or B, where available from testing data, and putative SARS-CoV-2 variant based on dominant lineages circulating at the time of individuals’ index date [40]). Additionally, we distinguished index episodes according to the highest-acuity care setting in which individuals received ARI diagnoses during the acute stage of their illness (through 14 days after the index date). In order from lowest to highest acuity, strata included virtual (online/telehealth) settings, ambulatory (ambulatory/urgent care) settings, emergency departments, and inpatient (hospital) facilities.

Outcomes

We defined PAS as diagnoses occurring within distinct proximal (31−90 days) and distal (91−180 days) risk periods after cases’ index date. We used prespecified International Classification of Diseases, 10th edition, Clinical Modification (ICD-10) diagnoses codes categorized into 10 disease categories with similar mechanisms and body systems affected (cardiopulmonary, hemolytic, respiratory, musculoskeletal, renal, gastrointestinal, neurological, skin, endocrine, and mental health conditions; S1 Table). Outcomes encompassed both new-onset PAS and exacerbations of pre-existing conditions resulting in post-acute healthcare utilization. Although there is no universal case definition for PAS, the study outcome encompassed all diagnosis codes included in US Centers for Disease Control (CDC) criteria for Post-COVID Conditions [2], augmented with definitions from other EHR-based studies [8,41–43]. This approach facilitates alignment with prior studies [2,16] and ongoing surveillance [44] employing CDC’s EHR-based case definition. The list of codes used by CDC to define PAS is purposefully broad to avoid missing conditions potentially occurring as PAS, and prior studies have demonstrated the included codes occur over twice as often among COVID-19 cases in the year following infection as among matched controls not known to have experienced SARS-CoV-2 infection [2]. We conducted separate analyses for PAS within each disease category, and for a composite outcome of any PAS. Consistent with our classification for index ARI diagnoses, we took the highest-acuity care setting where PAS diagnoses were assigned in each follow-up period as an indicator of severity.

We censored observations at death, disenrollment, end of study period, or ARI associated with a distinct virus, whichever occurred first.

Statistical analysis

We quantified differences in PAS risk between COVID-19 cases versus influenza cases via the adjusted hazard ratio (aHR) for time to first PAS diagnosis. We used a two-stage weighting approach to mitigate selection bias (due to differential depletion of susceptibles prior to follow-up initiation) and confounding [45–47]. First, to address differential censoring prior to the beginning of each follow-up interval among COVID-19 cases and influenza cases, we generated inverse probability of censoring weights (IPCWs) addressing each individual’s probability of remaining enrolled through the beginning of each 30-day period after index [48]. We estimated IPCWs via the Breslow estimator for survival curves from fitted Cox proportional hazards models with death or disenrollment 0–180 days after cases’ index date as the outcome. We fit these models including data from all individuals meeting eligibility criteria for analyses, including those censored before the post-acute follow-up period. Second, to account for differences in characteristics of COVID-19 cases and influenza cases, we also generated inverse probability of treatment (exposure) weights (IPTWs), addressing each individual’s probability of an index infection with SARS-CoV-2 or influenza. We computed stabilized IPTWs via logistic regression models defining infecting virus as the outcome [49]. All weighting models accounted for pre-specified covariates including individuals’ age group, sex, highest-acuity care setting for the index ARI episode, race/ethnicity, Charlson comorbidity index, history of diagnoses in each PAS disease category, history of depression, cigarette smoking, prior-year healthcare utilization (across ambulatory, emergency department, and inpatient settings), body mass index, COVID-19 and seasonal influenza vaccination status, receipt of antiviral treatment for the index ARI episode (as described below), calendar month of the index episode, and neighborhood deprivation index [50]. We included Charlson comorbidity index as a quantitative variable representing individuals’ overall health status due to collinearity of individual Charlson conditions with individuals’ history of diagnoses within the PAS disease categories. We categorized continuous variables (according to pre-specified breakpoints listed in Table 1) to avoid estimation of extreme weights in IPTW and IPCW models. We estimated risk differences for each outcome by subtracting weighted incidence rate estimates among influenza cases from weighted incidence rate estimates among COVID-19 cases.

Table 1. Characteristics of COVID-19 cases and influenza cases.

https://doi.org/10.1371/journal.pmed.1004777.t001

We estimated aHRs using Cox proportional hazards models, defining distinct observations for each 30-day period (31–60, 61–90, 91–120, 121–150, 151–180 days) after the index date to update IPCWs. Weights for each follow-up interval were the product of the individual’s (time-varying) IPCW and the individual’s (time-invariant) IPTW for infection with the identified virus [51]. We generated doubly-robust aHR estimates by including covariates used in the weighting models in analysis models, and used the sandwich variance estimator to account for repeated observations across multiple periods for each individual. We fit separate models for follow-up periods 31–90 days and 91–180 days after index. We verified the proportional hazards assumption within each follow-up period by testing for non-zero slopes of Schoenfeld residuals from fitted Cox proportional hazards models [52]. As an unplanned sensitivity analysis, we verified our estimates in analyses defining alternative interval breaks for continuous covariates, and defining these covariates with a continuous linear term, to verify findings were not sensitive to categorization schemes for continuous variables (S2 Table).

We conducted subgroup analyses restricted to individuals with or without history of prior diagnoses corresponding to PAS conditions within each organ system category in the preceding year. Among persons with such history, we interpreted PAS outcomes as exacerbations of pre-existing conditions, such that aHR estimates conveyed the differences in risk of post-viral exacerbations among COVID-19 cases and influenza cases. Among persons without history of such diagnoses, we interpreted PAS outcomes as new-onset illness, such that aHR estimates conveyed the differential risk of new-onset PAS among COVID-19 cases and influenza cases.

Last, to explore whether other factors modified the relationship between infecting virus and PAS risk, we conducted subgroup analyses stratified by age, sex, severity of the index ARI episode, and receipt of antivirals and vaccines. We defined antiviral receipt for the index infection as the dispensing of molnupiravir or nirmatrelvir–ritonavir (for COVID-19 cases) and oseltamivir, zanamivir, peramivir, or baloxavir (for influenza cases), occurring within 7 days before or after index. We defined up-to-date influenza vaccination status as receipt of seasonal influenza vaccination within 15–180 days before index. Based on US vaccination recommendations during the study period [53] and estimated durations of protection against SARS-CoV-2 infection after vaccination [54–56], we defined up-to-date COVID-19 vaccination as receipt of ≥3 COVID-19 vaccine doses, including one dose within 15–180 days before index.

We conducted analyses using R (version 4.5.1; R Foundation for Statistical Computing, Vienna, Austria, 2025). We fit Cox proportional hazards models using the survival package [57].

Ethics

This study was reviewed and approved by the Kaiser Permanente Southern California Institutional Review Board with a waiver of informed consent, and was conducted consistent with federal law and CDC policy (45 Code of Federal Regulations [CFR] part 46.101(c); 21 CFR part 56).

Results

Analyses included 74,738 eligible COVID-19 cases and 18,790 eligible influenza cases (Fig 1). In comparison to influenza cases, COVID-19 cases were older, had higher Charlson comorbidity index scores, and had higher ambulatory healthcare utilization in the year prior to index date as well as greater likelihood of hospital admission in the prior year (Tables 1 and S3). Additionally, COVID-19 cases had higher likelihood of receiving care in either inpatient settings or in virtual settings only for their initial infection.

Fig 1. Study flowchart.

Enumeration of individuals retained and excluded after applying each eligibility criterion. ARI: acute respiratory illness.

https://doi.org/10.1371/journal.pmed.1004777.g001

Overall, 97.3% and 95.3% of COVID-19 cases were retained in follow-up through 31 and 91 days after their index date, respectively (72,745 and 71,238 of 74,738, respectively), as were 97.9% and 95.7% of influenza cases (18,395 and 17,990 of 18,790, respectively; S4 Table). A total of 826 COVID-19 cases (1.1%) and 89 influenza cases (0.5%) died within 30 days after their index dates, while 1,391 COVID-19 cases (1.9%) and 149 influenza cases (0.8%) died within 90 days. Within 31−90 days after index dates, 45.2% of COVID-19 cases and 38.9% of influenza cases received PAS diagnoses, and within 91−180 days after index dates, 52.8% of COVID-19 cases and 44.0% of influenza cases received PAS diagnoses. Cardiopulmonary PAS were the most common outcome among both COVID-19 cases and influenza cases.

Absolute differences in weighted incidence of PAS diagnoses between COVID-19 cases and influenza cases were modest in analyses adjusting for individuals’ probability of infection with each virus and censoring (Table 2). Within 31–90 days after index, weighted incidence rates of PAS diagnoses per 100 person-months at risk were 26.5 (95% confidence interval: 26, 2–26.8) among COVID-19 cases and 25.9 (25.4, 26.5) among influenza cases. Within 91–180 days after index, weighted incidence rates were 23.0 (22.7, 23.2) and 22.6 (22.1, 23.0) per 100 person-months among COVID-19 and influenza cases, respectively. Risk differences totaled 0.6 (–0.1, 1.2) additional COVID-19 cases experiencing PAS per 100 person-months within 31–90 days after index, and 0.4 (–0.1, 0.9) additional COVID-19 cases experiencing PAS per 100 person-months within 91–180 days after index. We observed 1.5 (1.4, 1.5) and 1.2 (1.1, 1.4) inpatient PAS diagnoses per 100 person-months among COVID-19 and influenza cases, respectively, 31–90 days after index, and 1.1 (1.0, 1.1) and 0.9 (0.8, 1.0) inpatient PAS diagnoses per 100 person-months among COVID-19 and influenza cases, respectively, 91–180 days after index. Risk differences corresponded to 0.2 (0.1, 0.4) and 0.1 (0.0, 0.2) additional COVID-19 cases experiencing PAS requiring inpatient admission per 100 person-months within 31–90 and 91–180 days after index, respectively. These differences increased with severity of cases’ initial ARI (Table 3). Among cases whose initial ARI necessitated inpatient care, risk differences corresponded to 3.2 (2.2, 4.1) and 1.5 (0.8, 2.2) additional COVID-19 cases receiving PAS diagnoses in inpatient settings per 100 person-months within 31–90 and 91–180 days after index, respectively.

Table 2. Risk of post-acute sequelae, by disease category and severity.

https://doi.org/10.1371/journal.pmed.1004777.t002

Table 3. Risk of PAS diagnoses in inpatient settings according to severity of initial infection.

https://doi.org/10.1371/journal.pmed.1004777.t003

Based on estimates from doubly-robust Cox proportional hazards models, COVID-19 cases experienced modestly higher risk of PAS in comparison to influenza cases within 31–90 days after index (aHR = 1.04 [95% confidence interval: 0.99, 1.09]), which were attenuated by 91–180 days after index (aHR = 1.01 [0.97, 1.06]; Table 3). However, COVID-19 cases had 31% (7%, 59%) and 24% (3%, 49%) higher risk than influenza cases of receiving PAS diagnoses in inpatient settings 31–90 days and 91–180 days after index in comparison to influenza cases, respectively. Findings were consistent in sensitivity analyses with alternative handling of continuous covariates in weighting and adjustment models (S5 Table).

Point estimates favored higher risk of inpatient PAS diagnoses among COVID-19 cases versus influenza cases in the 31−90 day follow-up period across all categories of PAS diagnoses, spanning 1.23 (0.93, 1.61) for renal PAS to 7.27 (1.55, 34.09) for skin-associated PAS (Table 4). Although likewise elevated across all disease categories in the 91−180 day follow-up period, point estimates were lower in this period relative to the 31−90 day period for all categories except musculoskeletal PAS (aHR = 2.04 [1.29, 3.24]) and neurological PAS (aHR = 2.11 [1.54, 2.90]). For each syndromic category, the association of COVID-19 with increased risk of PAS was weaker for diagnoses within virtual, ambulatory, and emergency department settings than for PAS diagnoses within inpatient settings.

Differences between COVID-19 cases and influenza cases in risk of post-acute exacerbations of pre-existing conditions were also mainly evident for PAS diagnoses in inpatient settings (Table 5). Within 31−90 days after index, point estimates spanned 19%–122% higher risk for inpatient PAS exacerbations of pre-existing conditions across all syndromic categories. Within 91−180 days after index, point estimates were attenuated across all categories except musculoskeletal and neurological conditions (aHR = 3.16 [1.76, 5.69] and 2.61 [1.84, 3.71], respectively). Similarly, point estimates favored greater risk of new-onset PAS conditions necessitating inpatient care among COVID-19 cases, with aHR point estimates spanning 20%–373% greater risk within 31−90 days after cases’ index dates across all disease categories. Within 91−180 days after index, renal PAS (aHR = 2.31 [1.26, 4.24]) and skin-associated PAS (aHR = 3.25 [0.71, 14.85]) showed the greatest elevation among COVID-19 cases in comparison to influenza cases for diagnoses in inpatient settings. Generally, aHR estimates were associated with greater statistical uncertainty for comparisons of new-onset PAS diagnoses than for PAS exacerbations, reflecting the low incidence of new-onset PAS diagnoses compared to PAS exacerbations among both COVID-19 cases and influenza cases (S6 Table).

Table 5. Adjusted hazards ratios of post-acute sequelae as new-onset conditions or exacerbations of existing conditions.

https://doi.org/10.1371/journal.pmed.1004777.t005

Higher risk of inpatient PAS diagnoses among COVID-19 cases compared to influenza cases was primarily apparent among patients whose index ARI episodes necessitated inpatient admission (Table 6). Compared to influenza cases who received ARI diagnoses in inpatient settings, COVID-19 cases who received ARI diagnoses in inpatient settings had 73% (30%, 130%) and 49% (9%, 106%) higher risk of inpatient PAS diagnoses 31−90 and 91−180 days after index, respectively. Evidence of increased risk of PAS diagnoses among COVID-19 cases was inconsistent among patients whose index ARI episodes were managed in lower-acuity care settings. Differences in risk of inpatient PAS diagnoses among COVID-19 cases and influenza cases were apparent only among cases who did not receive antiviral treatment (aHR = 2.09 [1.45, 3.03] for untreated COVID-19 cases versus untreated influenza cases within 31−90 days after index). We did not identify differences in risk of inpatient PAS diagnoses among COVID-19 cases and influenza cases who were up-to-date with COVID-19 and influenza vaccines, respectively, at the time of their initial illness. Differences in risk of inpatient-diagnosed PAS conditions among COVID-19 cases versus influenza cases were more strongly apparent among female cases than male cases; additionally, differences in PAS risk among COVID-19 cases versus influenza cases appeared within multiple age groups.

Table 6. Adjusted hazard ratios of post-acute sequelae across differing subgroups.

https://doi.org/10.1371/journal.pmed.1004777.t006

Our finding that COVID-19 cases experienced greater risk than influenza cases of severe PAS necessitating hospital admission held within analyses disaggregated by season (2022−23 and 2023−24), SARS-CoV-2 variant period (BA.4/BA.5, XBB/XBB.1.5, and BA.2.86/JN.1), and influenza comparator virus (A or B), although these analyses encountered lower statistical power than primary analyses (S7 Table). Consistent with our primary analyses, we did not identify appreciable differences in risk of PAS outcomes that included diagnoses in lower-acuity settings within most subgroups (Tables 6 and S7).

Discussion

Within our study, COVID-19 cases experienced only modestly greater risk of PAS diagnoses up to 180 days after their index ARI episode in comparison to influenza cases. The primary distinguishing characteristic of PAS following COVID-19 versus influenza was that COVID-19 cases experienced higher risk of severe PAS necessitating inpatient admission. Further, these differences in risk were dependent on the severity of cases’ index infection. Whereas risk of severe PAS was comparable among COVID-19 cases and influenza cases who received care for their index episode in virtual, ambulatory, or emergency department settings, COVID-19 cases hospitalized for their index infection had higher risk of severe PAS than influenza cases who were hospitalized for their index infection. Differences in PAS risk were attenuated in strata of patients who received antiviral treatment or whose vaccination status was up-to-date against their infecting virus. Our findings demonstrate that although COVID-19 and influenza cases both experience substantial risk of PAS, the spectrum of PAS occurring after COVID-19 includes more severe manifestations than PAS occurring after influenza. This heightened risk of severe PAS is concentrated among patients who experienced severe COVID-19, and may be attenuated by vaccination or antiviral treatment.

Cardiopulmonary manifestations accounted for the greatest share of all PAS outcomes observed among both COVID-19 and influenza cases, and have been widely documented as sequelae of respiratory virus infections, not limited to SARS-CoV-2 and influenza [28–36,58–60]. Prior comparative studies of COVID-19 and influenza have reported greater PAS risk among COVID-19 across nearly all studied disease categories with the exception of respiratory conditions [30], which we likewise found to be more common among influenza cases in analyses that included non-severe PAS outcomes. One prior study which did not identify differences in PAS among hospitalized COVID-19 and influenza cases included further adjustment for disease severity during hospitalization [36]. Differences we report between hospitalized COVID-19 and influenza cases may thus relate to the greater severity of COVID-19 among patients requiring care in inpatient settings, as characterized in previous studies [61].

Among individuals with pre-existing conditions corresponding to each PAS category, COVID-19 cases experienced higher risk than influenza cases of exacerbations leading to inpatient PAS diagnoses. Point estimates of aHRs were consistent with the hypothesis that COVID-19 cases also experienced greater risk of new-onset severe PAS in comparison to influenza cases. However, given the low incidence of severe PAS among individuals without pre-existing conditions, such differences were uncertain from a statistical perspective and may not be clinically meaningful.

Comprehensive capture of healthcare interactions in an integrated system enabled us to represent a broad severity spectrum of index ARI episodes and subsequent PAS in comparison to prior studies, which have primarily addressed hospitalized COVID-19 cases and influenza cases [29,30]. Moreover, access to detailed information on cases’ demographics, comorbidities, and prior patterns of healthcare utilization enabled us to adjust rigorously for differences in characteristics of COVID-19 cases and influenza cases, as well as time-dependent sources of bias not addressed in prior studies.

Limitations of our analyses should also be considered. First, studied PAS outcomes could be more strongly associated with SARS-CoV-2 infection than with influenza infection simply because our PAS code list was assembled in part from findings of prior studies documenting that the studied outcomes occurred at elevated rates among COVID-19 survivors [2,8,41–43]. However, our consideration of a purposefully broad case definition, and our findings of only weak statistical evidence for associations of the studied outcome with COVID-19, together lessen this concern. Gold-standard case definitions for PAS remain to be established, and case definitions based on healthcare utilization recorded in EHR data differ inherently from those based on patient-reported symptoms, physical examination, and laboratory criteria [62,63]. Second, our analysis was limited to adults. Children also experience PAS associated with SARS-CoV-2 [64] and other respiratory viruses [65,66], suggesting similar studies should be undertaken in pediatric cohorts. Third, testing practices for SARS-CoV-2 and influenza differed during the study period. Whereas SARS-CoV-2 testing was widely undertaken for both inpatients and in ambulatory settings, influenza testing was less widely available and may have been reserved for individuals with more severe illness, or those receiving an initial negative SARS-CoV-2 test result. Our analyses adjusted for measured differences between COVID-19 cases and influenza cases; however, bias due to unmeasured variables and other factors may persist under our analytic framework [67]. Fourth, as our study was restricted to individuals who received ARI diagnoses, our findings did not address PAS potentially associated with asymptomatic SARS-CoV-2 and influenza infections. Fifth, our analyses did not address differences in PAS risk beyond 180 days after cases’ index dates, limiting our ability to compare differences in the lengths of time COVID-19 cases and influenza cases may be at risk for PAS. Regardless, attenuated effect estimates by 91−180 days after index suggests longer-term differences are unlikely. Considering only first PAS events over each follow-up interval diminishes our ability to compare the nature of PAS experienced among COVID-19 and influenza cases, including the frequency or persistence of clinical complaints. Sixth, although KPSC members represent the demographic and socioeconomic diversity of the surrounding population, healthcare delivery for KPSC members may not reflect patterns among other providers, impacting external generalizability of our findings. Last, while our study included adjustment and subgroup analyses for individuals receiving common outpatient antiviral therapies, the effects of earlier COVID-19 treatment strategies (including monoclonal antibodies or remdesivir) on PAS risk remain poorly understood.

We found that COVID-19 cases experienced only modestly higher risk of PAS in comparison to influenza cases, although PAS among COVID-19 cases were more likely to require hospital admission than PAS among influenza cases. Whereas PAS are widely known to occur after COVID-19, our results suggest that the risk of PAS associated with influenza may be under-appreciated and worthy of further study. For COVID-19, rigorous estimates of the burden of PAS are necessary for the contemporary context of novel circulating variants and population immunity; such analyses should adjust for counterfactual risk absent infection through control-group comparisons [68]. Aside from influenza, PAS are also known to occur in association with numerous other respiratory viruses [69], including other sarbecoviruses [70], respiratory syncytial virus [71], enteroviruses [72], and Epstein–Barr virus [73]. Improved understanding of this post-acute burden can collectively inform the value of interventions aiming to prevent or mitigate the severity of respiratory virus infections.

Disclaimer

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention (CDC). Mention of a product or company name is for identification purposes only and does not constitute endorsement by CDC.

Supporting information

S1 File. Supporting information, including S1 Table (Diagnosis codes (ICD-10-CM) used to define post-acute sequelae); S2 Table (Case counts under alternative categorization of continuous covariates); S3 Table (Individual Charlson comorbidity categories among COVID-19 cases and influenza cases); S4 Table (Cohort retention and frequency of post-acute sequelae managed in any clinical setting); S5 Table (Adjusted hazard.

Ratios of any post-acute sequelae estimated using models with alternative handling of continuous covariates); S6 Table (Cumulative incidence of post-acute sequelae as new-onset conditions or exacerbations of existing conditions, diagnosed in any clinical setting); and S7 Table (Adjusted hazard ratios of post-acute sequelae, subset by infecting virus lineage or period).

https://doi.org/10.1371/journal.pmed.1004777.s001

(PDF)

S2 File. Study protocol.

S1 Table. Diagnosis codes (ICD-10-CM) used to define post-acute sequelae.

https://doi.org/10.1371/journal.pmed.1004777.s003

(PDF)

S2 Table. Case counts under alternative categorization of continuous covariates.

https://doi.org/10.1371/journal.pmed.1004777.s004

(PDF)

S3 Table. Individual Charlson comorbidity categories among COVID-19 cases and influenza cases.

https://doi.org/10.1371/journal.pmed.1004777.s005

(PDF)

S4 Table. Cohort retention and frequency of post-acute sequelae managed in any clinical setting.

https://doi.org/10.1371/journal.pmed.1004777.s006

(PDF)

S5 Table. Adjusted hazard ratios of any post-acute sequelae estimated using models with alternative handling of continuous covariates.

https://doi.org/10.1371/journal.pmed.1004777.s007

(PDF)

S6 Table. Cumulative incidence of post-acute sequelae as new-onset conditions or exacerbations of existing conditions, diagnosed in any clinical setting.

https://doi.org/10.1371/journal.pmed.1004777.s008

(PDF)

S7 Table. Adjusted hazard ratios of post-acute sequelae, subset by infecting virus lineage or period.

https://doi.org/10.1371/journal.pmed.1004777.s009

(PDF)

References

  1. 1. Davis HE, McCorkell L, Vogel JM, Topol EJ. Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol. 2023;21(3):133–46. pmid:36639608
  2. 2. Bull-Otterson L, Baca S, Saydah S, Boehmer TK, Adjei S, Gray S, et al. Post–COVID conditions among adult COVID-19 survivors aged 18–64 and ≥ 65 years—United States, March 2020–November 2021. Morb Mortal Wkly Rep. 2022;71:713–7.
  3. 3. Ceban F, Ling S, Lui LMW, Lee Y, Gill H, Teopiz KM, et al. Fatigue and cognitive impairment in Post-COVID-19 syndrome: a systematic review and meta-analysis. Brain Behav Immun. 2022;101:93–135. pmid:34973396
  4. 4. 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(22):1934–46. pmid:37278994
  5. 5. Bowe B, Xie Y, Al-Aly Z. Postacute sequelae of COVID-19 at 2 years. Nat Med. 2023;29:2347–57.
  6. 6. Cai M, Xie Y, Topol EJ, Al-Aly Z. Three-year outcomes of post-acute sequelae of COVID-19. Nat Med. 2024;30(6):1564–73. pmid:38816608
  7. 7. Nalbandian A, Sehgal K, Gupta A, Madhavan MV, McGroder C, Stevens JS. Post-acute COVID-19 syndrome. Nat Med. 2021;27:601–15.
  8. 8. Cohen K, Ren S, Heath K, Dasmariñas MC, Jubilo KG, Guo Y, et al. Risk of persistent and new clinical sequelae among adults aged 65 years and older during the post-acute phase of SARS-CoV-2 infection: retrospective cohort study. BMJ. 2022;376:e068414. pmid:35140117
  9. 9. Greenhalgh T, Sivan M, Perlowski A, Nikolich JŽ. Long COVID: a clinical update. Lancet. 2024;404(10453):707–24. pmid:39096925
  10. 10. Sudre CH, Murray B, Varsavsky T, Graham MS, Penfold RS, Bowyer RC, et al. Attributes and predictors of long COVID. Nat Med. 2021;27(4):626–31. pmid:33692530
  11. 11. Xie Y, Bowe B, Al-Aly Z. Burdens of post-acute sequelae of COVID-19 by severity of acute infection, demographics and health status. Nat Commun. 2021;12(1):6571. pmid:34772922
  12. 12. Malden DE, Liu I-LA, Qian L, Sy LS, Lewin BJ, Asamura DT, et al. Post-COVID conditions following COVID-19 vaccination: a retrospective matched cohort study of patients with SARS-CoV-2 infection. Nat Commun. 2024;15(1):4101. pmid:38778026
  13. 13. Notarte KI, Catahay JA, Velasco JV, Pastrana A, Ver AT, Pangilinan FC, et al. Impact of COVID-19 vaccination on the risk of developing long-COVID and on existing long-COVID symptoms: a systematic review. EClinicalMedicine. 2022;53:101624. pmid:36051247
  14. 14. Byambasuren O, Stehlik P, Clark J, Alcorn K, Glasziou P. Effect of covid-19 vaccination on long covid: systematic review. BMJ Med. 2023;2(1):e000385. pmid:36936268
  15. 15. Xie Y, Choi T, Al-Aly Z. Association of treatment with nirmatrelvir and the risk of post-COVID-19 condition. JAMA Intern Med. 2023;183(6):554–64. pmid:36951829
  16. 16. Dalton AF, Baca S, Raykin J, Gregory CO, Boehmer T, Koumans EH, et al. Risk of post-COVID-19 conditions among adolescents and adults who received nirmatrelvir–ritonavir for acute COVID-19: a retrospective cohort study. Open Forum Infect Dis. 2025;12(10):ofaf567. pmid:41031103
  17. 17. Sellers SA, Hagan RS, Hayden FG, Fischer WAI. The hidden burden of influenza: a review of the extra-pulmonary complications of influenza infection. Influenza Other Respir Viruses. 2017;11:372–93.
  18. 18. Hoffman LA, Vilensky JA. Encephalitis lethargica: 100 years after the epidemic. Brain. 2017;140(8):2246–51. pmid:28899018
  19. 19. Magnus P, Gunnes N, Tveito K, Bakken IJ, Ghaderi S, Stoltenberg C, et al. Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is associated with pandemic influenza infection, but not with an adjuvanted pandemic influenza vaccine. Vaccine. 2015;33(46):6173–7. pmid:26475444
  20. 20. Sivadon-Tardy V, Orlikowski D, Porcher R, Sharshar T, Durand M-C, Enouf V, et al. Guillain–Barré syndrome and influenza virus infection. Clin Infect Dis. 2009;48(1):48–56. pmid:19025491
  21. 21. Partinen M, Kornum BR, Plazzi G, Jennum P, Julkunen I, Vaarala O. Narcolepsy as an autoimmune disease: the role of H1N1 infection and vaccination. Lancet Neurol. 2014;13(6):600–13. pmid:24849861
  22. 22. Vallings R. A case of chronic fatigue syndrome triggered by influenza H1N1 (swine influenza). J Clin Pathol. 2010;63(2):184–5. pmid:19858526
  23. 23. Singh U, Scheld WM. Infectious etiologies of rhabdomyolysis: three case reports and review. Clin Infect Dis. 1996;22(4):642–9. pmid:8729203
  24. 24. Nin N, Lorente JA, Soto L, Rios F, Hurtado J, Arancibia F, et al. Acute kidney injury in critically ill patients with 2009 influenza A (H1N1) viral pneumonia: an observational study. Intensive Care Med. 2011; 37: 768–74.
  25. 25. Papic N, Pangercic A, Vargovic M, Barsic B, Vince A, Kuzman I. Liver involvement during influenza infection: perspective on the 2009 influenza pandemic. Influenza Other Respir Viruses. 2012;6(3):e2-5. pmid:21951624
  26. 26. Sano H, Terasaki J, Tsutsumi C, Imagawa A, Hanafusa T. A case of fulminant type 1 diabetes mellitus after influenza B infection. Diabetes Res Clin Pract. 2008;79(3):e8-9. pmid:18177974
  27. 27. Bouter KP, Diepersloot RJ, van Romunde LK, Uitslager R, Masurel N, Hoekstra JB, et al. Effect of epidemic influenza on ketoacidosis, pneumonia and death in diabetes mellitus: a hospital register survey of 1976–1979 in The Netherlands. Diabetes Res Clin Pract. 1991;12(1):61–8. pmid:1906798
  28. 28. Gandhi M. Post-viral sequelae of COVID-19 and influenza. Lancet Infect Dis. 2024;24(3):218–9. pmid:38104584
  29. 29. Liu T-H, Huang P-Y, Wu J-Y, Chuang M-H, Hsu W-H, Tsai Y-W, et al. Comparison of post-acute sequelae following hospitalization for COVID-19 and influenza. BMC Med. 2023;21(1):480. pmid:38049876
  30. 30. Xie Y, Choi T, Al-Aly Z. Long-term outcomes following hospital admission for COVID-19 versus seasonal influenza: a cohort study. Lancet Infect Dis. 2024;24(3):239–55. pmid:38104583
  31. 31. Zhang V, Fisher M, Hou W, Zhang L, Duong TQ. Incidence of new-onset hypertension post-COVID-19: comparison with influenza. Hypertension. 2023;80(10):2135–48. pmid:37602375
  32. 32. Al-Aly Z, Xie Y, Bowe B. High-dimensional characterization of post-acute sequelae of COVID-19. Nature. 2021;594(7862):259–64. pmid:33887749
  33. 33. Lu JY, Wilson J, Hou W, Fleysher R, Herold BC, Herold KC, et al. Incidence of new-onset in-hospital and persistent diabetes in COVID-19 patients: comparison with influenza. EBioMedicine. 2023;90:104487. pmid:36857969
  34. 34. Iosifescu AL, Hoogenboom WS, Buczek AJ, Fleysher R, Duong TQ. New-onset and persistent neurological and psychiatric sequelae of COVID-19 compared to influenza: A retrospective cohort study in a large New York City healthcare network. Int J Methods Psychiatr Res. 2022;31(3):e1914. pmid:35706352
  35. 35. Wee LE, Ho RWL, Lim JT, Chiew CJ, Lye DCB, Tan KB. Long-term multisystemic sequelae post-hospitalisation for omicron COVID-19 vs influenza: a retrospective cohort study. Int J Infect Dis. 2025;158:107946. pmid:40499677
  36. 36. Quinn KL, Stukel TA, Huang A, Abdel-Qadir H, Altaf A, Bell CM, et al. Comparison of medical and mental health sequelae following hospitalization for COVID-19, influenza, and sepsis. JAMA Intern Med. 2023;183(8):806–17. pmid:37338892
  37. 37. Peter RS, Nieters A, Kräusslich H-G, Brockmann SO, Göpel S, Kindle G, et al. Post-acute sequelae of covid-19 six to 12 months after infection: population based study. BMJ. 2022;379:e071050. pmid:36229057
  38. 38. Koebnick C, Langer-Gould AM, Gould MK, Chao CR, Iyer RL, Smith N, et al. Sociodemographic characteristics of members of a large, integrated health care system: comparison with US Census Bureau data. Perm J. 2012;16(3):37–41. pmid:23012597
  39. 39. Davis AC, Voelkel JL, Remmers CL, Adams JL, McGlynn EA. Comparing Kaiser permanente members to the general population: implications for generalizability of research. Perm J. 2023;27(2):87–98. pmid:37170584
  40. 40. Lewnard JA, Mahale P, Malden D, Hong V, Ackerson BK, Lewin BJ, et al. Immune escape and attenuated severity associated with the SARS-CoV-2 BA.2.86/JN.1 lineage. Nat Commun. 2024;15(1):8550. pmid:39362845
  41. 41. Daugherty SE, Guo Y, Heath K, Dasmariñas MC, Jubilo KG, Samranvedhya J, et al. Risk of clinical sequelae after the acute phase of SARS-CoV-2 infection: retrospective cohort study. BMJ. 2021;373:n1098. pmid:34011492
  42. 42. Taquet M, Dercon Q, Harrison PJ. Six-month sequelae of post-vaccination SARS-CoV-2 infection: a retrospective cohort study of 10,024 breakthrough infections. Brain Behav Immun. 2022;103:154–62. pmid:35447302
  43. 43. Tartof SY, Malden DE, Liu I-LA, Sy LS, Lewin BJ, Williams JTB, et al. Health care utilization in the 6 months following SARS-CoV-2 infection. JAMA Netw Open. 2022;5(8):e2225657. pmid:35960522
  44. 44. Jones RM, Andrews JG, Dalton AF, Dixon BE, Dzomba BJ, Fernando SI, et al. Tracking the burden, distribution, and impact of post-COVID conditions in diverse populations for children, adolescents, and adults (Track PCC): passive and active surveillance protocols. BMC Public Health. 2024;24(1):2345. pmid:39210333
  45. 45. Hernán MA, Wang W, Leaf DE. Target trial emulation: a framework for causal inference from observational data. JAMA. 2022;328(24):2446–7. pmid:36508210
  46. 46. Robins JM, Mark SD, Newey WK. Estimating exposure effects by modelling the expectation of exposure conditional on confounders. Biometrics. 1992;48(2):479–95. pmid:1637973
  47. 47. Robins JM, Finkelstein DM. Correcting for noncompliance and dependent censoring in an AIDS Clinical Trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics. 2000;56(3):779–88. pmid:10985216
  48. 48. Robins JM, Rotnitzky A, Zhao LP. Estimation of regression coefficients when some regressors are not always observed. J Am Stat Assoc. 1994;89:846–66.
  49. 49. Cole SR, Hernán MA. Adjusted survival curves with inverse probability weights. Comput Methods Programs Biomed. 2004;75(1):45–9. pmid:15158046
  50. 50. Messer LC, Laraia BA, Kaufman JS, Eyster J, Holzman C, Culhane J, et al. The development of a standardized neighborhood deprivation index. J Urban Health. 2006;83(6):1041–62. pmid:17031568
  51. 51. Chesnaye NC, Stel VS, Tripepi G, Dekker FW, Fu EL, Zoccali C, et al. An introduction to inverse probability of treatment weighting in observational research. Clin Kidney J. 2021;15(1):14–20. pmid:35035932
  52. 52. Schoenfeld D. Partial residuals for the proportional hazards regression model. Biometrika. 1982;69(1):239–41.
  53. 53. Oliver SE, Wallace M, Twentyman E, Moulia DL, Godfrey M, Link-Gelles R, et al. Development of COVID-19 vaccine policy — United States, 2020–2023. Vaccine. 2024; 42:125512.
  54. 54. Tartof SY, Slezak JM, Puzniak L, Hong V, Frankland TB, Xie F, et al. Effectiveness and durability of BNT162b2 vaccine against hospital and emergency department admissions due to SARS-CoV-2 omicron sub-lineages BA.1 and BA.2 in a large health system in the USA: a test-negative, case–control study. Lancet Respir Med. 2023;11(2):176–87. pmid:36216013
  55. 55. Tartof SY, Slezak JM, Puzniak L, Hong V, Xie F, Ackerson BK, et al. Durability of BNT162b2 vaccine against hospital and emergency department admissions due to the omicron and delta variants in a large health system in the USA: a test-negative case–control study. Lancet Respir Med. 2022;10(7):689–99. pmid:35468336
  56. 56. Tartof SY, Slezak JM, Fischer H, Hong V, Ackerson BK, Ranasinghe ON, et al. Effectiveness of mRNA BNT162b2 COVID-19 vaccine up to 6 months in a large integrated health system in the USA: a retrospective cohort study. Lancet. 2021;398(10309):1407–16. pmid:34619098
  57. 57. Ackerson B, Tseng HF, Sy LS, Solano Z, Slezak J, Luo Y, et al. Severe morbidity and mortality associated with respiratory syncytial virus versus influenza infection in hospitalized older adults. Clin Infect Dis. 2019;69(2):197–203. pmid:30452608
  58. 58. Thernau TM, Lumley T. Survival: Survival analysis, version 3.8-3. 2024; https://doi.org/10.32614/cran.package.survival
  59. 59. To KKW, Lau SKP, Chan K-H, Mok K-Y, Luk HKH, Yip CCY, et al. Pulmonary and extrapulmonary complications of human rhinovirus infection in critically ill patients. J Clin Virol. 2016;77:85–91. pmid:26921740
  60. 60. Hennawi HA, Khan A, Shpilman A, Mazzoni JA. Acute myocarditis secondary to human metapneumovirus: a case series and mini-review. Glob Cardiol Sci Pract. 2024;2024(6):e202452. pmid:40026582
  61. 61. Talbot HK, Martin ET, Gaglani M, Middleton DB, Ghamande S, Silveira FP, et al. Coronavirus disease 2019 (COVID-19) versus influenza in hospitalized adult patients in the united states: differences in demographic and severity indicators. Clin Infect Dis. 2021;73(12):2240–7. pmid:34050659
  62. 62. Zang C, Zhang Y, Xu J, Bian J, Morozyuk D, Schenck EJ, et al. Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative. Nat Commun. 2023;14(1):1948. pmid:37029117
  63. 63. Wisk LE, L’Hommedieu M, Diaz Roldan K, Ebna Mannan I, Spatz ES, Weinstein RA, et al. Variability in long COVID definitions and validation of published prevalence rates. JAMA Netw Open. 2025;8(8):e2526506. pmid:40794409
  64. 64. Say D, Crawford N, McNab S, Wurzel D, Steer A, Tosif S. Post-acute COVID-19 outcomes in children with mild and asymptomatic disease. Lancet Child Adolesc Health. 2021;5(6):e22–3. pmid:33891880
  65. 65. Verwey C, Nunes MC, Dangor Z, Madhi SA. Pulmonary function sequelae after respiratory syncytial virus lower respiratory tract infection in children: a systematic review. Pediatr Pulmonol. 2020;55(7):1567–83. pmid:32364320
  66. 66. Baltagi SA, Shoykhet M, Felmet K, Kochanek PM, Bell MJ. Neurological sequelae of 2009 influenza A (H1N1) in children: a case series observed during a pandemic. Pediatr Crit Care Med. 2010;11(2):179–84. pmid:20081552
  67. 67. Gabriel EE, Sachs MC, Waernbaum I, Goetghebeur E, Blanche PF, Vansteelandt S, et al. Propensity weighting plus adjustment in proportional hazards model is not doubly robust. Biometrics. 2024;80(3):ujae069. pmid:39036984
  68. 68. Høeg TB, Ladhani S, Prasad V. How methodological pitfalls have created widespread misunderstanding about long COVID. BMJ Evid Based Med. 2024;29(3):142–6. pmid:37748921
  69. 69. Narasimhan H, Wu Y, Goplen NP, Sun J. Immune determinants of chronic sequelae after respiratory viral infection. Sci Immunol. 2022;7(73):eabm7996. pmid:35857581
  70. 70. Lombardi AF, Afsahi AM, Gupta A, Gholamrezanezhad A. Severe acute respiratory syndrome (SARS), middle east respiratory syndrome (MERS), influenza, and COVID-19, beyond the lungs: a review article. Radiol Med. 2021;126(4):561–9. pmid:33242204
  71. 71. Ubamadu E, Betancur E, Gessner BD, Menon S, Vroling H, Curcio D, et al. Respiratory syncytial virus sequelae among adults in high-income countries: a systematic literature review and meta-analysis. Infect Dis Ther. 2024;13(7):1399–417. pmid:38789901
  72. 72. Aliabadi N, Messacar K, Pastula DM, Robinson CC, Leshem E, Sejvar JJ, et al. Enterovirus D68 infection in children with acute flaccid myelitis, Colorado, USA, 2014. Emerg Infect Dis. 2016;22(8):1387–94. pmid:27434186
  73. 73. Bjornevik K, Cortese M, Healy BC, Kuhle J, Mina MJ, Leng Y, et al. Longitudinal analysis reveals high prevalence of Epstein–Barr virus associated with multiple sclerosis. Science. 2022;375(6578):296–301. pmid:35025605

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