Background: Incidence estimates of post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, also known as long COVID, have varied across studies and changed over time. We estimated long COVID incidence among adult and pediatric populations in 3 nationwide research networks of electronic health records (EHRs) participating in the RECOVER (Researching COVID to Enhance Recovery) Initiative using different classification algorithms (computable phenotypes). Methods: This EHR-based retrospective cohort study included adult and pediatric patients with documented acute SARS-CoV-2 infection and 2 control groups: contemporary coronavirus disease 2019 (COVID-19)–negative and historical patients (2019). We examined the proportion of individuals identified as having symptoms or conditions consistent with probable long COVID within 30–180 days after COVID-19 infection (incidence proportion). Each network (the National COVID Cohort Collaborative [N3C], National Patient-Centered Clinical Research Network [PCORnet], and PEDSnet) implemented its own long COVID definition. We introduced a harmonized definition for adults in a supplementary analysis. Results: Overall, 4% of children and 10%–26% of adults developed long COVID, depending on computable phenotype used. Excess incidence among SARS-CoV-2 patients was 1.5% in children and ranged from 5% to 6% among adults, representing a lower-bound incidence estimation based on our control groups. Temporal patterns were consistent across networks, with peaks associated with introduction of new viral variants. Conclusions: Our findings indicate that preventing and mitigating long COVID remains a public health priority. Examining temporal patterns and risk factors for long COVID incidence informs our understanding of etiology and can improve prevention and management.

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.

Background: People with human immunodeficiency virus (HIV) may be at increased risk for long COVID after acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We investigated the association between HIV and long COVID in 2 large electronic health record databases. Methods: Using data from the Patient-Centered Clinical Research Network (PCORnet) and the National Clinical Cohort Collaborative (N3C) from 1 January 2018 to 30 April 2024, our analytic sample included individuals aged ≥21 years with SARS-CoV-2. All individuals were classified as having HIV or not. We estimated the adjusted odds ratio (aOR) of long COVID by HIV status using logistic regression. Multivariable models controlled for potential associated factors and used 2 cohort definitions: a computed phenotype definition or ICD-10 code–based definition. Results: We included 1 369 896 patients from PCORnet (11 964 with and 1 357 932 without HIV) and 3 312 355 patients from N3C (23 931 with and 3 288 424 without HIV). Using the computed phenotype definition of long COVID, we noted a small, but significant, increase in odds of developing long COVID among people with compared to those without HIV (PCORnet: aOR, 1.09 [95% confidence interval {CI}, 1.04–1.14]; N3C: aOR, 1.18 [95% CI, 1.13–1.23]). Using the ICD-10 definition of long COVID, there was no association between HIV and long COVID (PCORnet: aOR, 1.01 [95% CI, .88–1.16]; N3C: aOR, 1.07 [95% CI, .97–1.18]). Conclusions: In this large multicenter study, people with HIV had a modestly increased risk of long COVID when defined by a computed phenotype, but not when using ICD-10 codes. These findings suggest that long COVID may be underrecognized in people with HIV and underscore challenges in diagnosing long COVID in populations with baseline chronic conditions.