Long COVID incidence in adults and children between 2020 and 2023: a real-world data study from the RECOVER Initiative

doi:10.21203/rs.3.rs-4124710/v1

Estimates of post-acute sequelae of SARS-CoV-2 infection (PASC) incidence, also known as Long COVID, have varied across studies and changed over time. We estimated PASC incidence among adult and pediatric populations in three nationwide research networks of electronic health records (EHR) participating in the RECOVER Initiative using different classification algorithms (computable phenotypes). Overall, 7% of children and 8.5%-26.4% of adults developed PASC, depending on computable phenotype used. Excess incidence among SARS-CoV-2 patients was 4% in children and ranged from 4-7% among adults, representing a lower-bound incidence estimation based on two control groups - contemporary COVID-19 negative and historical patients (2019). Temporal patterns were consistent across networks, with peaks associated with introduction of new viral variants. Our findings indicate that preventing and mitigating Long COVID remains a public health priority. Examining temporal patterns and risk factors of PASC incidence informs our understanding of etiology and can improve prevention and management.

Health sciences/Medical research/Epidemiology

Health sciences/Diseases/Infectious diseases/Viral infection

Health sciences/Medical research/Translational research

Question: What proportion of adults and children with known acute SARS-CoV-2 infection develop symptoms and conditions consistent with Long COVID within 180 days post-infection, and how has that proportion changed over time?

Findings: In this electronic health record-based study of COVID-19 positive patients across three national research networks, crude incidence of Long COVID, was 7% in children and ranged from 8.5% to 26.4% in adults, depending on approach used to detect Long COVID. Excess incidence of these symptoms or conditions was 4.2% in children and 7% in adults compared to uninfected individuals. Temporal patterns were consistent across networks.

Meaning: Findings indicate that preventing and mitigating Long COVID remains a public health priority.

Post-acute sequelae of SARS-CoV-2 infection (PASC), commonly referred to as Long COVID, has emerged as a significant clinical and public health concern following the global COVID-19 pandemic.¹ PASC refers to ongoing, intermittent, or new symptoms or conditions resulting from acute SARS-CoV-2 infection, beginning 1-3 months after onset of mild or severe COVID-19 illness.^2–4 These prolonged effects can profoundly impact quality of life and lead to functional limitations, psychological distress, and higher numbers of healthcare visits.⁵

Characterization of the epidemiology of PASC to guide the larger public health response has remained challenging due to the wide range of symptoms. Various studies have quantified how many patients with SARS-CoV-2 infections may subsequently develop PASC, resulting in a wide range of estimates. A recent systematic review found that 45% of patients with COVID-19 developed at least one unresolved symptom, with higher prevalence among hospitalized patients; however, estimates ranged from 2-99.9% across studies.⁶ Differences in PASC definitions, data sources, study populations, and methodology have contributed to inconsistent results.^7,8 In the absence of a gold standard, multiple case definitions for PASC have been operationalized by researchers.^3,4

Another potential source of variation is the study period. Some studies have centered on a brief period, focusing on symptom prevalence over a specified time after acute infection.^9–12 Fewer studies have explored fluctuations in PASC frequency over variant waves. Some of these suggest that risk of PASC has declined with recent COVID-19 variants,^13–19 while others have found similar or higher levels of risk with Omicron.^20,21 Larger studies are needed to systematically elucidate whether variations in PASC incidence are associated with variant differences, and whether incidence has declined after introduction of acute COVID-19 treatments, vaccination efforts, and immunity supplied by prior infection.

As part of the National Institutes of Health (NIH)-funded REsearching COVID to Enhance Recovery (RECOVER) Initiative, we leveraged electronic health record (EHR) data from three large EHR-based research networks to estimate PASC incidence. Our analysis considered demographic factors, including age, sex, race, ethnicity, and residential setting. We also examined how temporal trends varied across viral variant waves. Given the lack of a unified definition of PASC, our analyses leveraged three definitions developed by each network. By exploring patterns of PASC across networks over time using a range of definitions, our study contributes to the growing body of knowledge on sequelae of SARS-CoV-2 infection to inform ongoing healthcare planning, intervention strategies, and patient care.

Participating Institutions

RECOVER is a large NIH-funded initiative designed to investigate the long-term effects of COVID-19, including EHR-based research that comprises three participating research networks: the National COVID Cohort Collaborative (N3C), a centralized repository of COVID-19 positive patients matched to non-infected controls;²² the National Patient-Centered Clinical Research Network (PCORnet); and PEDSnet, a pediatric learning health system within PCORnet. Cross-network studies are coordinated by a Clinical Science Core at NYU Langone Health. For these analyses, N3C selected 18,406,079 unique patient records across 68 sites, PCORnet selected 18,893,900 records across 27 sites, and PEDSnet selected 8,200,977 records across 38 sites. See more information about data sources in Supplement eMethods.

Study Population

We identified adult and pediatric patients with evidence of SARS-CoV-2, including a: (1) positive polymerase chain reaction (PCR) or antigen test; (2) International Classification of Diseases, Tenth Revision (ICD-10) diagnosis code representing COVID-19 (U07.1); (3) prescription for nirmatrelvir/ritonavir (Paxlovid) or Remdesivir; (4) post-acute evidence of SARS-CoV-2 infection, including ICD-10 codes M35.81 (Multisystem Inflammatory Syndrome in Children, or MIS-C), U09.9 (PASC), or B94.8 (Sequelae of other specified infectious and parasitic diseases),²³ and pediatric positive nucleocapsid IgG test results. We defined the index event as the first documented evidence, and for patients with post-acute evidence only we imputed index events as 59 days prior to the earliest, placing the window for PASC onset after the acute infection period but minimizing possibility of the window being associated with reinfection.²⁴

Pediatric patients were included if ≤21 years old at index event (PEDSnet), and adult patients were included if ≥22 (N3C, PCORnet). Patients with an index event between March 2020 and February 2023 were included, leaving at least six months before the end of the study period (September 2023) to allow time for PASC to be documented and mitigate lags in data reporting. We also required patients to have at least one visit within the health system prior to index event, and at least one follow-up visit occurring during the study period and 90 or more days after the index event. See patient attrition details in Supplement eFigure 2.

Outcomes

EHR networks have iteratively developed separate PASC definitions using different populations and technical infrastructures. Given the lack of a gold standard, we saw this as an opportunity to look across definitions. Each network applied its own computable phenotype for probable PASC (herein ‘PASC’). N3C’s definition identified a) earliest documentation of a U09.9 / B94.8 diagnosis code or b) patients with predicted PASC by a machine-learning based algorithm trained on patients with a U09.9 diagnosis.²⁵ PCORnet and PEDSnet applied rules-based definitions based on a combination of clinical input and data-driven analysis of new-onset diagnoses more common in patients with COVID-19 than without.^26–28 Generally, PCORnet classified patients based on the presence of a U09.9 or B94.8 code, or at least one incident PASC diagnosis.^26,29 PEDSnet classified patients based on diagnoses (one U09.9, B94.8, M35.81 code, or two or more PASC-associated features at least 28 days apart). See Supplement eMethods for network-specific descriptions.

All networks examined the proportion of COVID-19 patients developing PASC within 30-180 days after the index event (incidence proportion) and defined PASC onset as the earliest date that the computable phenotype identified PASC. Rates were presented as percentages (per 100 COVID-19 positive patients) over the entire study timeframe and by month or variant wave.

Timeframes and covariates

We defined the following time periods as distinct waves for COVID-19 variants: Ancestral (March-December 2020), Alpha (January-May 2021), and Delta (June-November 2021). We identified three separate Omicron waves: BA.1.1 - BA.2 (December 2021-April 2022), BA.2.12.1 - BA.5 (May-November 2022), and BQ.1.1 - XBB.1.5 (December 2022-August 2023).³⁰

We also explored incidence by patient age, sex, race/ethnicity (categorized as either Hispanic; non-Hispanic Asian, Black, other, or white; or missing/unknown), and rurality. Rurality was assessed by linking patient 5-digit ZIP codes to an urban commuting area (RUCA) classification (metropolitan, micropolitan, small town, or rural).³¹ Finally, we examined incidence by 1) severity of infection, 2) documentation of vaccination or treatment for COVID-19, and 3) pre-existing conditions prior to index event (see Supplement eMethods).

Statistical analysis

We summarized categorical data as counts and percentages and described continuous data as mean values with standard deviations (SDs) or median values with interquartile ranges (IQRs). We analyzed 180-day incidence proportions using the beta distribution to calculate 95% confidence intervals (CIs) and represented findings visually as time-series plots and heatmaps, including summing patients with PASC by month of PASC onset. Unadjusted and adjusted hazard ratios with 95% CIs were generated using multivariable Cox Proportional Hazards regression models with PASC onset as an endpoint and presented by age group, sex, race/ethnicity, pre-existing conditions, rurality, COVID severity, vaccination status, and index month. P-values <0.05 were considered statistically significant. All analysis and visualizations were created using R, including ggplot2, survival, and survminer packages.

Control group analysis
Because many PASC symptoms are non-specific, we compared PASC-positivity within a COVID test-negative control group against COVID-19 positive patients within the same period. "Negative” controls were required to have a negative PCR or antigen test within the first five months of 2021 (when testing was available but before widespread home testing), and no evidence of COVID-19 prior to or within 180 days afterwards. We included patients if their first documented negative test fell within the period and considered this the index event. We repeated this approach using a “historical” control group selected from the first five months of 2019 to estimate how many patients would be identified as PASC-positive using our algorithms during that period, selecting the first visit within that timeframe as the index event. To calculate ‘excess incidence’ for the SARS-CoV-2 study population, we considered the incidence among COVID-19 negative outpatients to approximate a baseline (“COVID-free”) burden of PASC-like sequelae and subtracted this from the overall incidence among COVID-19 positive patients.

In total, 5,445,776 COVID-19 positive patients were identified: 3,736,427 adults within N3C (mean age 51 years, SD 17; 61% female), 1,126,467 adults within PCORnet (mean age 53 years, SD 17; 62% female), and 582,882 pediatric patients within PEDSnet (mean age 9 years, SD 7; 50% female) (Supplement eTable 1). Most adults (58-67%) and 42% of pediatric patients were non-Hispanic white. The majority resided within urban settings (N3C: 71%, PCORnet: 72%, PEDSnet: 60%). Severity of COVID-19 index infections was similar across networks. Few patients were hospitalized for their index events (with or without ICU-level care: N3C: 6%, PCORnet: 8%, PEDSnet: 2%). Few (0.4%) children had evidence of MIS-C (data not shown).

Between 7% and 26% of patients were identified as having PASC, with the PCORnet estimation being the highest (PCORnet: 26.4%, N3C: 8.5%, PEDSnet: 7.0%) (Table 1). Of these, less than 10% lacked data on acute infection timing and had index dates imputed (PEDSnet: 9.6%, N3C: 8.9%, PCORnet: 6.1%). Patients with PASC were more likely to have been hospitalized for COVID-19 than those without, and to have a higher burden of pre-existing conditions, particularly in N3C. Nearly all children with MIS-C (95.5%) were categorized as having PASC (data not shown).

Because most patients had COVID-19 prior to widespread use of Paxlovid around Spring 2022,³² few had Paxlovid prescribed for their index infection (N3C: 4%, PCORnet: 9%, PEDSnet: 1%) (eTable 1). In a sub-analysis over June 2022-September 2023, excluding 19 sites not reporting Paxlovid data to N3C, monthly percentages of adults treated with Paxlovid surpassed 20% (Figure 1E). The proportion of patients developing PASC was similar for treated and untreated patients (Table 1).

Documented vaccinations were consistently low across adult EHR networks, with 24% of N3C patients and 26% of PCORnet patients having at least one COVID-19 vaccination prior to index event (Figure 1F, eTable 1). This number was lower (12%) for PEDSnet patients. In unadjusted analyses, proportions of patients developing PASC were similar by vaccination status (Table 1).

Monthly trends for COVID-19 and PASC cases

Figure 1 shows the occurrence of COVID-19 and PASC from March 2020 to February 2023, together with changes in related covariates. Monthly count patterns were concordant across PCORnet and N3C, including spikes in cases around the beginning of 2022 (Figure 1A), while PASC case counts among children were lower. COVID-19 case counts were also closely aligned across networks (Figure 1B), including surges in cases following the onset of new variant waves. While absolute PASC incidence proportions differed, temporal patterns were strikingly similar (Figure 1C). A decline in incidence towards the end of the study period was noted across networks. However, analysis of source data newer than those used for this manuscript confirms that this apparent decline is unreliable (data not shown), and likely an artifact of incomplete follow-up times for some of our population due to heterogeneous follow-up and reporting schedules across sites. Monthly percentages of severe COVID-19 cases were concordant (Figure 1D).

Across networks, PASC incidence was higher for female patients (Figure 2A) and, among adults, for older age groups and Black patients. Notably, incidence increased across variant waves: it was lowest during the Ancestral and Alpha waves, followed by Delta and early Omicron, and was highest for recent Omicron variants. Incidence was frequently greater with higher Charlson Comorbidity Index (CCI) scores, Pediatric Medical Complexity Algorithm (PMCA) category, and COVID-19 severity (Figure 2B).

Multivariable analysis

We constructed time-to-PASC multivariable Cox regression models (Figure 2C). Unadjusted and adjusted hazard ratios (HR and aHRs; 95% CI) for PASC incidence within 180 days were summarized over the study period (Table 2). Among adults, older age groups had a significantly higher PASC risk, with highest risk among patients 65 and older (N3C HR: 2.66 (2.63-2.69), PCORnet HR: 1.31 (1.30-1.33)). This attenuated but remained significant after adjustment (N3C aHR: 1.45 (1.43-1.47), PCORnet aHR: 1.21 (1.20-1.22)). In children, children aged 6-11 had significantly lower PASC risk than other age groups, including after adjustment. Overall, male patients had significantly lower risk of PASC than female patients (N3C HR: 0.76 (0.76-0.77), PCORnet HR: 0.94 (0.93-0.95), PEDSnet HR: 0.96 (0.93-0.98)), an association that was strengthened after adjusting for covariates.

Risk of PASC varied by race and ethnicity, with Asian patients having a slightly lower PASC risk compared to white patients (Table 2). Results were otherwise inconsistent across networks. After adjustment, adult patients with documented prior vaccination had a significantly lower risk of PASC compared to those with no evidence, with a stronger observed effect among PCORnet patients (aHR: 0.84, p<0.001) than N3C (aHR: 0.98, p<0.001). In N3C, patients vaccinated within 90 days post-index event also had slightly lower risk of PASC (aHR: 0.97, p<0.001).Prior vaccination was associated with a slightly higher risk of PASC among pediatric patients (aHR: 1.1, p<0.001).

Acute COVID-19 illness severity was the strongest predictor of PASC risk (Table 2). Patients hospitalized with ICU-level care had the highest risk, even after adjustment (N3C aHR: 2.53, p<0.001; PCORnet aHR: 2.31, p<0.001; PEDSnet aHR: 2.29, p<0.001). Burden of pre-existing conditions was also an important risk factor, especially within N3C, where patients with a CCI score of 0 had lower PASC risk than patients scoring 1-3 (aHR: 2.15, p<0.001) or 4+ (aHR: 3.38, p<0.001).

Control group analysis

PASC-like sequelae were found in both 2019 (“historical”) and 2021 (“contemporary” COVID-19 test negative) control groups (Figure 3), but at lower levels than among COVID-19 positive patients, demonstrating the non-specific nature of symptoms associated with PASC. Rates among historical controls were higher than contemporary controls, potentially reflecting higher levels of interaction with the healthcare system in 2019 compared to 2021. Heatmaps for each control group showed the increased propensity for PASC with greater burden of pre-existing conditions.

Resulting excess PASC incidence estimates (N3C: 4.2%, PCORnet: 6.7%, PEDSnet: 4.2%) may represent a lower bound on the excess burden posed by COVID-19.

Our study explored crude and excess incidence of PASC among a large cohort of COVID-19-infected adults and children using nationwide EHR network data. Crude incidence estimates of PASC ranged from a low of 8.5% to a high of 26.4% in adults and was 7.0% in children. After accounting for potential background levels of PASC-like symptoms, excess incidence was estimated to be 4% in children and between 4-7% among adults. Incidence was highest in older adults, women, and patients with pre-existing comorbidities or more severe acute COVID-19 illness. While magnitude of PASC incidence varied depending on definition used, temporal patterns and risk factors were largely consistent across the EHR networks. Temporal peaks in PASC cases aligned with emergence of new viral variants, suggesting a potential association between viral dynamics and the development of PASC. Peaks aside, we did not see sizable secular decreases in PASC risk over time, suggesting that PASC remains a public health priority. However, other factors such as increasing recognition of PASC clinical features and use of U09.9 codes over time PASC over time may be influencing detection of PASC diagnoses in EHRs. Higher prevalence of PASC among older adults and individuals of certain racial or ethnic backgrounds underscores the importance of considering these factors in PASC risk stratification.

Our study relied on three distinct definitions of PASC, including a broader definition from PCORnet and a more restrictive definition from N3C. Our pediatric definition was also more restrictive (requiring two or more diagnoses indicative of PASC). Thus, we present findings as a range of potential PASC incidence estimates. Our analyses further contextualize these estimates by applying PASC definitions to two “control” groups. As expected, PASC-like sequelae were highest among COVID-19 patients, especially when considering pre-event health of patients and severity of the index encounter. We note that PASC-like symptoms may be over-represented in both control groups, which could underestimate excess incidence; contemporary controls may include patients with false-negative tests or undocumented infections, and patients had more frequent interactions with the healthcare system in 2019 compared to 2021.³³

Consistent with other studies, our findings highlight severity of initial COVID-19 infection as a risk factor for PASC, with elevated rates of PASC among patients hospitalized during their initial COVID-19 infection,^6,34,35 though we recognize that patients with inpatient care may receive closer follow-up, leading to more PASC diagnosis opportunity.

Consistent with a prior N3C publication,³⁶ our adjusted analyses found that previously vaccinated adults had a lower risk of PASC compared to unvaccinated patients. This finding was not replicated within the pediatric cohort, perhaps because higher-risk pediatric patients are more likely to seek out vaccination. However, the sample of pediatric patients with known vaccination was also small and we were not able to differentiate unvaccinated patients from vaccinated patients lacking documentation. The impact of vaccination on PASC incidence remains an important area for investigation.

In this study, we applied a consistent analytic approach across three research networks using different PASC definitions. Despite concordance of most findings, this study had several limitations. First, diagnoses of COVID-19 and PASC may be missing in EHR data. Indeed, we suspect the high levels of PASC observed at the beginning of the pandemic reflect underdetection of mild COVID-19 while testing was rationed. Similarly, higher PASC incidence during recent variant waves could reflect growing recognition and diagnosis of PASC.²¹ Findings may also be influenced by restriction to patients with multiple healthcare encounters, underrepresentation of data from non-academic medical centers, and variation in healthcare access by gender, race/ethnicity, rurality, and other structural or social determinants of health. Our study also lacked complete data on vaccination status.

Nonetheless, our study contributes valuable insights into PASC incidence in a large cohort of COVID-19 patients. Our cross-network approach leveraged differing definitions delivered a plausible range of estimates and demonstrated remarkably similar profiles and temporal patterns of PASC incidence. Future prospective studies incorporating comprehensive assessment protocols, diverse healthcare settings, and detailed variant and vaccination data are warranted to further elucidate the complex nature of PASC and inform strategies for its prevention, management, and long-term care.

Acknowledgements

Disclaimer: Authorship was determined using ICMJE recommendations. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, N3C, PCORI, or RECOVER.

Author Contributions: RM and YY had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Mandel, Yoo, Allen, Abedian, Karlson, Kleinman, Mudumbi, Oliveira, Muszynski, Gross, Carton, Kim, Taylor, Park, Divers, Kelly, Arnold, Geary, Zang, Tantisira, Rhee, Koropsak, Mohandas, Vasey, Mosa, Murphy, O’Brien, Szmuszkovicz, Guthe, Santana, De, Bogie, Halabi, Mohanraj, Kinser, Packard, Tuttle, Thorpe, Moffitt

Acquisition, analysis, or interpretation of data: Mandel, Yoo, Allen, Abedian, Verzani, Karlson, Carton, Zang, Koropsak, Weiner, Haendel, Chute, Thorpe, Moffitt

Drafting of the manuscript: Mandel, Yoo, Thorpe, Moffitt

Critical review of the manuscript for important intellectual content: All authors

Statistical analysis: Yoo, Allen, Abedian, Verzani, Moffitt

Supervision: Thorpe, Moffitt

Conflict of Interest Disclosures: No disclosures were reported.

Funding/Support: This study is part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, which seeks to understand, treat, and prevent the post-acute sequelae of SARS-CoV-2 infection (PASC). For more information on RECOVER, visit https://recovercovid.org/. This research was funded by the National Institutes of Health (NIH) Agreement OTA OT2HL161847 as part of the RECOVER program.

Role of the Funder/Sponsor: The funders/sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2.

Additional Contributions

We would like to thank the National Community Engagement Group (NCEG), all patient, caregiver and community Representatives, and all the participants enrolled in the RECOVER Initiative. For contributions to manuscript methods: Allison A. Lambert, MD; Michelle Stevenson, MD, MS. The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution & Publication Policy v 1.2-2020-08-25b supported by NCATS U24 TR002306, Axle Informatics Subcontract: NCATS-P00438-B. This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the on-going development of this community resource.²²

We gratefully acknowledge the following core contributors to N3C:

Adam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew Southerland, Andrew T. Girvin, Anita Walden, Anjali A. Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, Caleb Alexander, Carolyn Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego Mazzotti, Don Brown, Eilis Boudreau, Elaine Hill, Elizabeth Zampino, Emily Carlson Marti, Emily R. Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold Lehmann, Heidi Spratt, Hemalkumar Mehta, Hongfang Liu, Hythem Sidky, J.W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Islam, Jin Ge, Joel Gagnier, Joel H. Saltz, Joel Saltz, Johanna Loomba, John Buse, Jomol Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M. Walters, Ken Wilkins, Kenneth R. Gersing, Kenrick Dwain Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Allan Pyles, Leonie Misquitta, Lesley Cottrell, Lili Portilla, Mariam Deacy, Mark M. Bissell, Marshall Clark, Mary Emmett, Mary Morrison Saltz, Matvey B. Palchuk, Melissa A. Haendel, Meredith Adams, Meredith Temple-O'Connor, Michael G. Kurilla, Michele Morris, Nabeel Qureshi, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Peter Robinson, Philip R.O. Payne, Rafael Fuentes, Randeep Jawa, Rebecca Erwin-Cohen, Rena Patel, Richard A. Moffitt, Richard L. Zhu, Rishi Kamaleswaran, Robert Hurley, Robert T. Miller, Saiju Pyarajan, Sam G. Michael, Samuel Bozzette, Sandeep Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O'Neil, Soko Setoguchi, Stephanie S. Hong, Steve Johnson, Tellen D. Bennett, Tiffany Callahan, Umit Topaloglu, Usman Sheikh, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang. Details of contributions available at covid.cd2h.org/core-contributors

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Hershey Medical Center — UL1TR002014: Penn State Clinical and Translational Science Institute • Rush University Medical Center — UL1TR002389: The Institute for Translational Medicine (ITM) • Rutgers, The State University of New Jersey — UL1TR003017: New Jersey Alliance for Clinical and Translational Science • Stony Brook University — U24TR002306 • The Alliance at the University of Puerto Rico, Medical Sciences Campus — U54GM133807: Hispanic Alliance for Clinical and Translational Research (The Alliance) • The Ohio State University — UL1TR002733: Center for Clinical and Translational Science • The State University of New York at Buffalo — UL1TR001412: Clinical and Translational Science Institute • The University of Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • The University of Iowa — UL1TR002537: Institute for Clinical and Translational Science • The University of Miami Leonard M. Miller School of Medicine — UL1TR002736: University of Miami Clinical and Translational Science Institute • The University of Michigan at Ann Arbor — UL1TR002240: Michigan Institute for Clinical and Health Research • The University of Texas Health Science Center at Houston — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • The University of Texas Medical Branch at Galveston — UL1TR001439: The Institute for Translational Sciences • The University of Utah — UL1TR002538: Uhealth Center for Clinical and Translational Science • Tufts Medical Center — UL1TR002544: Tufts Clinical and Translational Science Institute • Tulane University — UL1TR003096: Center for Clinical and Translational Science • The Queens Medical Center — None (Voluntary) • University Medical Center New Orleans — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • University of Alabama at Birmingham — UL1TR003096: Center for Clinical and Translational Science • University of Arkansas for Medical Sciences — UL1TR003107: UAMS Translational Research Institute • University of Cincinnati — UL1TR001425: Center for Clinical and Translational Science and Training • University of Colorado Denver, Anschutz Medical Campus — UL1TR002535: Colorado Clinical and Translational Sciences Institute • University of Illinois at Chicago — UL1TR002003: UIC Center for Clinical and Translational Science • University of Kansas Medical Center — UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute • University of Kentucky — UL1TR001998: UK Center for Clinical and Translational Science • University of Massachusetts Medical School Worcester — UL1TR001453: The UMass Center for Clinical and Translational Science (UMCCTS) • University Medical Center of Southern Nevada — None (voluntary) • University of Minnesota — UL1TR002494: Clinical and Translational Science Institute • University of Mississippi Medical Center — U54GM115428: Mississippi Center for Clinical and Translational Research (CCTR) • University of Nebraska Medical Center — U54GM115458: Great Plains IDeA-Clinical & Translational Research • University of North Carolina at Chapel Hill — UL1TR002489: North Carolina Translational and Clinical Science Institute • University of Oklahoma Health Sciences Center — U54GM104938: Oklahoma Clinical and Translational Science Institute (OCTSI) • University of Pittsburgh — UL1TR001857: The Clinical and Translational Science Institute (CTSI) • University of Pennsylvania — UL1TR001878: Institute for Translational Medicine and Therapeutics • University of Rochester — UL1TR002001: UR Clinical & Translational Science Institute • University of Southern California — UL1TR001855: The Southern California Clinical and Translational Science Institute (SC CTSI) • University of Vermont — U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • University of Virginia — UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • University of Washington — UL1TR002319: Institute of Translational Health Sciences • University of Wisconsin-Madison — UL1TR002373: UW Institute for Clinical and Translational Research • Vanderbilt University Medical Center — UL1TR002243: Vanderbilt Institute for Clinical and Translational Research • Virginia Commonwealth University — UL1TR002649: C. Kenneth and Dianne Wright Center for Clinical and Translational Research • Wake Forest University Health Sciences — UL1TR001420: Wake Forest Clinical and Translational Science Institute • Washington University in St. Louis — UL1TR002345: Institute of Clinical and Translational Sciences • Weill Medical College of Cornell University — UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center • West Virginia University — U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI)  Submitted: Icahn School of Medicine at Mount Sinai — UL1TR001433: ConduITS Institute for Translational Sciences • The University of Texas Health Science Center at Tyler — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • University of California, Davis — UL1TR001860: UCDavis Health Clinical and Translational Science Center • University of California, Irvine — UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS) • University of California, Los Angeles — UL1TR001881: UCLA Clinical Translational Science Institute • University of California, San Diego — UL1TR001442: Altman Clinical and Translational Research Institute • University of California, San Francisco — UL1TR001872: UCSF Clinical and Translational Science Institute  Pending: Arkansas Children’s Hospital — UL1TR003107: UAMS Translational Research Institute • Baylor College of Medicine — None (Voluntary) • Children’s Hospital of Philadelphia — UL1TR001878: Institute for Translational Medicine and Therapeutics • Cincinnati Children’s Hospital Medical Center — UL1TR001425: Center for Clinical and Translational Science and Training • Emory University — UL1TR002378: Georgia Clinical and Translational Science Alliance • HonorHealth — None (Voluntary) • Loyola University Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • Medical College of Wisconsin — UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin • MedStar Health Research Institute — None (Voluntary) • Georgetown University — UL1TR001409: The Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) • MetroHealth — None (Voluntary) • Montana State University — U54GM115371: American Indian/Alaska Native CTR • NYU Langone Medical Center — UL1TR001445: Langone Health’s Clinical and Translational Science Institute • Ochsner Medical Center — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • Regenstrief Institute — UL1TR002529: Indiana Clinical and Translational Science Institute • Sanford Research — None (Voluntary) • Stanford University — UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education • The Rockefeller University — UL1TR001866: Center for Clinical and Translational Science • The Scripps Research Institute — UL1TR002550: Scripps Research Translational Institute • University of Florida — UL1TR001427: UF Clinical and Translational Science Institute • University of New Mexico Health Sciences Center — UL1TR001449: University of New Mexico Clinical and Translational Science Center • University of Texas Health Science Center at San Antonio — UL1TR002645: Institute for Integration of Medicine and Science • Yale New Haven Hospital — UL1TR001863: Yale Center for Clinical Investigation

We gratefully acknowledge sites and principal investigators within the PCORnet Consortium:

ADVANCE Collaborative

OCHIN, Inc.

Marion Sills, PI

CAPriCORN

Northwestern University

David Liebovitz, PI

Greater Plains Collaborative

Intermountain Healthcare

Benjamin D. Horne, PI

Medical College of Wisconsin

Bradley Taylor, PI

University of Iowa

Elizabeth Chrischilles, PI

University of Missouri

Abu Mosa, PI

University of Nebraska Medical Center

Carol Geary, PI

University of Utah

Mollie Cummins, PI

University of Texas Southwestern Medical Center

Lindsay Cowell, PI

INSIGHT Clinical Research Network

Albert Einstein College of Medicine Montefiore Medical Center

Parsa Mirhaji, PI

Columbia University

Soumitra Sengupta, PI

New York University Langone Health

Saul Blecker, PI

Weill Cornell Medicine

Rainu Kaushal, PI

OneFlorida+ Clinical Research Consortium

Emory University

Rishikesan Kamaleswaran, PI

Gari Clifford, PI

Rachel Patzer, PI

Nicklaus Children’s Hospital

Daria Salyakina, PI

University of Florida Health

Mei Liu, PI

University of Miami

Nick Tsinoremas, PI

University of South Florida Tampa

Asa Oxner, PI

PaTH Clinical Research Network

Pennsylvania State College of Medicine

Cynthia Chuang, PI

Temple University

Sharon Herring, PI

The Ohio State University

Soledad Fernandez, PI

University of Pittsburgh

Srinivasan Suresh, PI

Jonathan Arnold, PI

Michael Bechich, PI

University of Michigan

David Williams, PI

PEDSnet

Ann & Robert H. Lurie Children's Hospital of Chicago

Ravi Jhaveri, PI

Children's Hospital of Philadelphia

Chris Forrest, PI

Charles Bailey, PI

Children's National Medical Center

Hiroki Morizono, PI

Cincinnati Children's Hospital Medical Center

Nathan Pajor, PI

Nationwide Children's Hospital

Kelly Kelleher, PI

Nemours Children’s Health

H Timothy Bunnell, PI

Seattle Children's Hospital

Dimitri Christakis, PI

Stanford Medicine Children’s Health

Alan Schroeder, PI

University of Denver Children's Hospital of Colorado

Suchitra Rao, PI

REACHnet

Ochsner Health System

Dan Fort, PI

University of California San Francisco

Susan Kim, PI

University Medical Center New Orleans Louisiana Public Health Institute

Thomas Carton, PI

STAR Clinical Research Network

Duke University Health System

Schuyler Jones, PI

Medical University of South Carolina

Leslie Lenert, PI

Vanderbilt University Medical Center

Yacob Telda, PI

Wake Forest University Health Sciences

Stephen Downs, PI

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Davis HE, McCorkell L, Vogel JM, Topol EJ. Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol. 2023;21(3):133-146. doi:10.1038/s41579-022-00846-2
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Diexer S, Klee B, Gottschick C, et al. Association between virus variants, vaccination, previous infections, and post-COVID-19 risk. Int J Infect Dis. 2023;136:14-21. doi:10.1016/j.ijid.2023.08.019
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Hastie CE, Lowe DJ, McAuley A, et al. True prevalence of long-COVID in a nationwide, population cohort study. Nat Commun. 2023;14(1):7892. doi:10.1038/s41467-023-43661-w
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Table 1. Descriptive characteristics of COVID-19-positive patients with and without PASC across three electronic health record research networks

Characteristic	N3C (N=3,736,427)		PCORnet (N= 1,126,467)		PEDSnet (N= 582,882)
Characteristic	COVID-19+ without PASC No. (%)	COVID-19+ with PASC No. (%)	COVID-19+ without PASC No. (%)	COVID-19+ with PASC No. (%)	COVID-19+ without PASC No. (%)	COVID-19+ with PASC No. (%)
Total N	3,424,079	316,268	829,419	297,048	542,185	40,697
Age, mean [SD], years	50.18 (17)	55.59 (16)	52 (17)	54 (18)	8.94 (6)	8.42 (7)
Age groups
0-5	NA				201,993 (37)	17,671 (43)
6-11					132,008 (24)	7,438 (18)
12-17					141,252 (26)	10,993 (27)
18-21					66,932 (12)	4,595 (11)
22-34	795,624 (23)	36,706 (12)	167,198 (20)	51,323 (17)	NA
35-44	1,221,080 (36)	131,495 (42)	144,817 (17)	47,446 (16)
45-64	622,892 (18)	48,303 (15)	296,980 (36)	106,229 (36)
65+	784,483 (23)	99,826 (32)	220,424 (27)	92,050 (31)
Sex
Female	2,062,017 (60)	211,394 (67)	508,194 (61)	187,162 (63)	272,206 (50)	20,605 (51)
Male	1,362,062 (40)	104,874 (33)	321,225 (39)	109,886 (37)	269,979 (50)	20,092 (49)
Race/ethnicity
Hispanic	369,126 (11)	27,981 (9)	89,224 (11)	33,936 (11)	132,276 (24)	9,964 (25)
Non-Hispanic Asian	93,602 (3)	7,186 (2)	26,706 (3)	8,100 (3)	23,288 (4)	1,577 (4)
Non-Hispanic Black	426,623 (12)	46,859 (15)	129,257 (16)	48,860 (16)	99,326 (18)	6,973 (17)
Non-Hispanic Other	55,809 (2)	4,069 (1)	43,780 (5)	15,960 (5.4)	10,181 (2)	919 (2)
Non-Hispanic White	2,280,984 (67)	216,331 (68)	484,757 (58)	169,506 (57)	224,280 (41)	17,903 (44)
Missing / Unknown	197,935 (6)	13,842 (4)	55,695 (7)	20,686 (7)	52,834 (10)	3,361 (8)
Rurality
Micropolitan	304,507 (9)	23,385 (7)	37,616 (5)	15,141 (5)	15,301 (3)	1,403 (3)
Rural	71,445 (2)	6,383 (2)	10,154 (1)	4,047 (1)	2,805 (0.5)	287 (0.7)
Small town	127,772 (4)	10,933 (3)	17,118 (2)	6,343 (2)	5,034 (0.9)	509 (1)
Urban	2,425,780 (71)	223,567 (71)	599,529 (72)	217,090 (73)	326,845 (60)	24,027 (59)
Missing	494,575 (14)	52,000 (16)	165,002 (20)	54,427 (18)	192,200 (35)	14,471 (36)
Charlson Comorbidity Index Score
0	1,831,295 (53)	96,376 (30)	585,722 (71)	199,612 (67)	NA
1-3	929,078 (27)	125376 (40)	222,672 (27)	90,716 (31)
4+	313,154 (9)	78,830 (25)	14,487 (1.7)	5,950 (2)
Unknown	350,552 (10)	15,686 (5)	6,538 (0.8)	770 (0.3)
Pediatric Medical Complexity Algorithm Category
None	NA				479,533 (88)	29,633 (73)
Non-complex Chronic					27,562 (5)	2,450 (6)
Complex Chronic					35,090 (7)	8,614 (21)
COVID-19 severity
Not hospitalized	2,704,944 (79)	207,118 (65)	635,955 (77)	205,931 (69)	411,314 (76)	22,688 (56)
Emergency Department	425,038 (12)	42,923 (14)	114,524 (14)	42,351 (14)	82,086 (15)	5,473 (13)
Hospitalized	149,055 (4)	38,426 (12)	51,044 (6)	28,190 (10)	6,660 (1)	1,962 (5)
Hospitalized and COVID-19 positive	112,268 (3)	17,479 (6)	21,901 (3)	15,114 (5)	41,648 (8)	10,261 (25)
Hospitalized with Intensive Care Unit-level care	32,774 (0.96)	10,322 (3)	5,995 (0.7)	5,462 (2)	477 (0.1)	313 (0.8)
Predominant variant at index event
Ancestral	946,001 (28)	68,886 (22)	205,107 (25)	66,317 (22)	37,114 (7)	1,858 (5)
Alpha	420,400 (12)	37,570 (12)	97,613 (12)	33,598 (11)	123,496 (23)	6,996 (17)
Delta	517,090 (15)	52,452 (17)	108,148 (13)	39,521 (13)	85,533 (16)	6,565 (16)
Omicron BA.1.1 - BA.2	868,111 (25)	81,787 (26)	193,210 (23)	68,813 (23)	178,851 (33)	13,505 (33)
Omicron BA.2.12.1 - BA.5	569,702 (17)	66,740 (21)	189,174 (23)	74,031 (25)	96,350 (18)	9,734 (24)
Omicron BQ.1.1 - XBB.1.5	102,775 (3)	10,505 (3)	36,167 (4)	14,768 (5)	20,841 (4)	2,039 (5)
Paxlovid ordered at index event	134,170 (4)	13,944 (4)	80,105 (10)	25,012 (8)	3,127 (0.6)	169 (0.4)
Evidence of vaccination
Prior to index event	815,242 (24)	82,811 (26)	217,403 (26)	71,799 (24)	65,613 (12)	5,795 (14)
Close to index event	176,763 (5)	14,235 (5)	34,491 (4)	13,774 (5)	21,596 (4)	1,672 (4)
None documented	2,432,074 (71)	219,438 (69)	577,525 (70)	211,475 (71)	454,976 (84)	33,230 (82)

Table 2. Multivariable hazard ratios for development of PASC

N3C

PCORnet

PEDSnet

Unadjusted^a

Adjusted^b

Unadjusted^a

Adjusted^b

Unadjusted^a

Adjusted^b

Age Group

0-5

NA

1.45^c

(1.4 - 1.5)

1.38^c

(1.31 - 1.44)

6-11

Ref

12-17

1.39^c

(1.34 - 1.44)

1.24^c

(1.18 - 1.29)

18-21

1.17^c

(1.12 - 1.23)

1.16^c

(1.09 - 1.23)

22-34

Ref

NA

35-44

1.65^c

(1.63 - 1.67)

1.51^c

(1.49 - 1.53)

1.06^c

(1.05 - 1.08)

1.06^c

(1.05 - 1.07)

45-64

2.26^c

(2.23 - 2.28)

1.7^c

(1.68 - 1.72)

1.15^c

(1.14 - 1.16)

1.12^c

(1.11 - 1.14)

65+

2.66^c

(2.63 - 2.69)

1.45^c

(1.43 - 1.47)

1.31^c

(1.30 - 1.33)

1.21^c

(1.20 - 1.23)

Sex

Female

Ref

Male

0.76^c

(0.76 - 0.77)

0.68^c

(0.68 - 0.69)

0.94^c

(0.93 - 0.95)

0.92^c

(0.91 - 0.92)

0.96^c

(0.93 - 0.98)

0.87^c

(0.84 - 0.9)

Race/Ethnicity

Hispanic

0.81^c

(0.8 - 0.82)

0.88^c

(0.86 - 0.89)

1.08^c

(1.06 - 1.09)

1.05^c

(1.04 - 1.07)

0.88^c

(0.85 - 0.91)

1

(0.96 - 1.04)

Non-Hispanic Asian

0.82^c

(0.8 - 0.84)

0.82^c

(0.8 - 0.84)

0.88^c

(0.86 - 0.90)

0.92^c

(0.90 - 0.94)

0.85^c

(0.8 - 0.91)

0.9^d

(0.82 - 0.98)

Non-Hispanic Black

1.15^c

(1.14 - 1.16)

0.95^c

(0.94 - 0.96)

1.07^c

(1.06 - 1.08)

1.03^c

(1.02 - 1.04)

0.86^c

(0.83 - 0.89)

0.81^c

(0.77 - 0.84)

Non-Hispanic Other

0.78^c

(0.76 - 0.8)

0.87^c

(0.84 - 0.89)

1.04^c

(1.02 - 1.06)

1.05^c

(1.03 - 1.06)

1.05

(0.98 - 1.13)

0.95

(0.86 - 1.05)

Non-Hispanic White

Ref

Missing/Unknown

0.75^c

(0.74 - 0.76)

0.84^c

(0.83 - 0.86)

1.05^c

(1.04 - 1.07)

1.05^c

(1.03 - 1.06)

0.82^c

(0.78 - 0.87)

0.94

(0.87 - 1.01)

Rurality

Micropolitan

0.84^c

(0.83 - 0.85)

0.82^c

(0.81 - 0.83)

1.1^c

(1.08 - 1.11)

1.08^c

(1.06 - 1.10)

1.2^c

(1.14 - 1.27)

0.99

(0.92 - 1.07)

Rural

0.97^e

(0.94 - 0.99)

0.88^c

(0.86 - 0.9)

1.09^c

(1.06 - 1.12)

1.06^c

(1.03 - 1.09)

1.33^c

(1.18 - 1.51)

1.08

(0.93 - 1.26)

Small town

0.93^c

(0.91 - 0.95)

0.87^c

(0.85 - 0.89)

1.02

(1.00- 1.05)

1.01

(0.98 - 1.03)

1.38^c

(1.26 - 1.5)

1.17^e

(1.04 - 1.31)

Urban

Ref

Missing

1.13^c

(1.12 - 1.15)

1.0

(0.99 - 1.01)

0.92^c

(0.91 - 0.93)

0.95^c

(0.94 - 0.96)

1.11^f

(1.03 - 1.19)

1.05

(0.95 - 1.16)

Charlson Comorbidity Index Score

0

Ref

NA

1-3

2.46^c

(2.44-2.48)

2.15^c

(2.13 - 2.17)

1.17^c

(1.16 - 1.18)

1.04^c

(1.04 - 1.05)

4+

4.37^c

(4.32 - 4.41)

3.38^c

(3.34 - 3.41)

1.17^c

(1.15 - 1.21)

1.01

(0.99 - 1.04)

Unknown

0.86^c

(0.84 - 0.87)

0.94^c

(0.93 - 0.96)

0.38^c

(0.35 - 0.41)

0.42^c

(0.39 - 0.45)

Pediatric Medical Complexity Algorithm Category

Not complex

NA

Ref

Non-Complex Chronic

1.47^c

(1.4 - 1.55)

1.04

(0.99 - 1.1)

Complex chronic

3.61^c

(3.5 - 3.72)

1.39^c

(1.33 - 1.45)

Vaccination

No evidence

Ref

Prior to index event

1.13^c

(1.12 - 1.14)

0.98^c

(0.97 - 0.98)

0.91^c

(0.90 - 0.92)

0.84^c

(0.83 - 0.85)

1.24^c

(1.2 – 1.28)

1.1^c

(1.05 – 1.15)

Close to index event

0.88^c

(0.87 - 0.9)

0.97^c

(0.95 - 0.98)

1.09^c

(1.07 - 1.11)

1.12^c

(1.10 - 1.14)

1.05

(0.99 – 1.11)

1.14^c

(1.06 – 1.23)

COVID-19 Severity

Not Hospitalized

Ref

Emergency Department

1.31^c

(1.29 - 1.32)

1.21^c

(1.2 - 1.22)

1.12^c

(1.10 - 1.13)

1.09^c

(1.08 - 1.10)

1.2^c

(1.16 - 1.25)

1.15^c

(1.09 – 1.21)

Hospitalized

3.14^c

(3.11 - 3.17)

2.12^c

(2.1 - 2.15)

1.6^c

(1.58 - 1.62)

1.53^c

(1.51 - 1.55)

3.78^c

(3.55 – 4.03)

2.12^c

(1.77 - 2.54)

Hospitalized with Intensive Care Unit-level care

3.84^c

(3.76 - 3.91)

2.53^c

(2.48 - 2.58)

2.41^c

(2.35 - 2.48)

2.31^c

(2.24 - 2.37)

5.26^c

(4.42 - 6.25)

2.29^c

(2.12 - 2.47)

Hospitalized and COVID-19 positive

1.99^c

(1.96 - 2.02)

1.52^c

(1.5 - 1.55)

1.92^c

(1.89 - 1.95)

1.81^c

(1.78 - 1.84)

4.12^c

(4.0 - 4.24)

2.56^c

(2.46 - 2.67)

^aHazard ratios generated by a Cox Proportional Hazards regression model, unadjusted.

^bHazard ratios generated by a multivariable Cox Proportional Hazards regression model, adjusted for age group, sex, race/ethnicity, pre-existing conditions, rurality, COVID-19 severity, vaccination status, and month of the index event.

^cp-value <0.001

^dp = 0.018

^ep = 0.008

^fp = 0.007

There is NO Competing Interest.

Long COVID incidence in adults and children between 2020 and 2023: a real-world data study from the RECOVER Initiative

Status:

Version 1

Abstract

Figures

Key Points

Introduction

Methods

Results

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1