Racial/Ethnic Differences in Long-COVID-Associated Symptoms among Pediatrics Population: Findings from Difference-in-differences Analyses in RECOVER Program (preprint)

Racial/ethnic differences are associated with the potential symptoms and conditions of post-acute sequelae SARS-CoV-2 infection (PASC) in adults. These differences may exist among children and warrant further exploration. We conducted a retrospective cohort study for children and adolescents under the age of 21 from the thirteen institutions in the RECOVER Initiative. The cohort is 225,723 patients with SARS-CoV-2 infection or COVID-19 diagnosis and 677,448 patients without SARS-CoV-2 infection or COVID-19 diagnosis between March 2020 and October 2022. The study compared minor racial/ethnic groups to Non-Hispanic White (NHW) individuals, stratified by severity during the acute phase of COVID-19. Within the severe group, Asian American/Pacific Islanders (AAPI) had a higher prevalence of fever/chills and respiratory symptoms, Hispanic patients showed greater hair loss prevalence in severe COVID-19 cases, while Non-Hispanic Black (NHB) patients had fewer skin symptoms in comparison to NHW patients. Within the non-severe group, AAPI patients had increased POTS/dysautonomia and respiratory symptoms, and NHB patients showed more cognitive symptoms than NHW patients. In conclusion, racial/ethnic differences related to COVID-19 exist among specific PASC symptoms and conditions in pediatrics, and these differences are associated with the severity of illness during acute COVID-19.

Real-world Effectiveness and Causal Mediation Study of BNT162b2 on Long COVID Risks in Children and Adolescents (preprint)

BackgroundThe impact of pre-infection vaccination on the risk of long COVID remains unclear in the pediatric population. Further, it is unknown if such pre-infection vaccination can mitigate the risk of long COVID beyond its established protective benefits against SARS-CoV-2 infection. ObjectiveTo assess the effectiveness of BNT162b2 on long COVID risks with various strains of the SARS-CoV-2 virus in children and adolescents, using comparative effectiveness methods. To disentangle the overall effectiveness of the vaccine on long COVID outcomes into its independent impact and indirect impact via prevention of SARS-CoV-2 infections, using causal mediation analysis. DesignReal-world vaccine effectiveness study and mediation analysis in three independent cohorts: adolescents (12 to 20 years) during the Delta phase, children (5 to 11 years) and adolescents (12 to 20 years) during the Omicron phase. SettingTwenty health systems in the RECOVER PCORnet electronic health record (EHR) Program. Participants112,590 adolescents (88,811 vaccinated) in the Delta period, 188,894 children (101,277 vaccinated), and 84,735 adolescents (37,724 vaccinated) in the Omicron period. ExposuresFirst dose of the BNT162b2 vaccine vs. no receipt of COVID-19 vaccine. MeasurementsOutcomes of interest include conclusive or probable diagnosis of long COVID following a documented SARS-CoV-2 infection, and body-system-specific condition clusters of post-acute sequelae of SARS-CoV-2 infection (PASC), such as cardiac, gastrointestinal, musculoskeletal, respiratory, and syndromic categories. The effectiveness was reported as (1-relative risk)*100 and mediating effects were reported as relative risks. ResultsDuring the Delta period, the estimated effectiveness of the BNT162b2 vaccine against long COVID among adolescents was 95.4% (95% CI: 90.9% to 97.7%). During the Omicron phase, the estimated effectiveness against long COVID among children was 60.2% (95% CI: 40.3% to 73.5%) and 75.1% (95% CI: 50.4% to 87.5%) among adolescents. The direct effect of vaccination, defined as the effect beyond their impact on SARS-CoV-2 infections, was found to be statistically non-significant in all three study cohorts, with estimates of 1.08 (95% CI: 0.75 to 1.55) in the Delta study among adolescents, 1.24 (95% CI: 0.92 to 1.66) among children and 0.91 (95% CI: 0.69 to 1.19) among adolescents in the Omicron studies. Meanwhile, the estimated indirect effects, which are effects through protecting SARS-CoV-2 infections, were estimated as 0.04 (95% CI: 0.03 to 0.05) among adolescents during Delta phase, 0.31 (95% CI: 0.23 to 0.42) among children and 0.21 (95% CI: 0.16 to 0.27) among adolescents during the Omicron period. LimitationsObservational study design and potentially undocumented infection. ConclusionsOur study suggests that BNT162b2 was effective in reducing risk of long COVID outcomes in children and adolescents during the Delta and Omicron periods. The mediation analysis indicates the vaccines effectiveness is primarily derived from its role in reducing the risk of SARS-CoV-2 infection. Primary Funding SourceNational Institutes of Health.

Real-world Effectiveness of BNT162b2 in Children and Adolescents in Preventing Infection and Severe Diseases with SARS-CoV-2 During the Delta and Omicron Periods: Findings from Trial Emulation Using an EHR-based Cohort (preprint)

BACKGROUND The current understanding of the long-term effectiveness of the BNT162b2 vaccine for a range of outcomes across diverse U.S. pediatric populations is limited. In this study, we assessed the effectiveness of BNT162b2 against various strains of the SARS-CoV-2 virus using data from a national collaboration of pediatric health systems (PEDSnet). METHODS We emulated three target trials to assess the real-world effectiveness of BNT162b2: adolescents aged 12 to 20 years during the Delta variant period (Target trial 1), children aged 5 to 11 years (Target trial 2) and adolescents aged 12 to 20 years during the Omicron variant period (Target trial 3). The outcomes included documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and two cardiac-related outcomes, myocarditis and pericarditis. In the U.S., immunization records are often captured and stored across multiple disconnected sources, resulting in incomplete vaccination records in patients' electronic health records (EHR). We implemented a novel trial emulation pipeline accounting for possible misclassification bias in vaccine documentation in EHRs. The effectiveness of the BNT162b2 vaccine was estimated from the Poisson regression model with confounders balanced via propensity score stratification. RESULTS During the Delta period, the BNT162b2 vaccine demonstrated an overall effectiveness 98.4% (95% CI, 98.1 to 98.7) against documented infection among adolescents, with no significant waning after receipt of the first dose. During the Omicron period, the overall effectiveness was estimated to be 74.3% (95% CI, 72.2 to 76.2) in preventing documented infection among children, which was higher against moderate or severe COVID-19 (75.5%; 95% CI, 69.0 to 81.0) and ICU admission with COVID-19 (84.9%; 95% CI, 64.8 to 93.5). In the adolescent population, the overall effectiveness against documented Omicron infection was 85.5% (95% CI, 83.8 to 87.1), with effectiveness of 84.8% (95% CI, 77.3 to 89.9) against moderate or severe COVID-19, and 91.5% (95% CI, 69.5 to 97.6) against ICU admission with COVID-19. The effectiveness of the BNT162b2 vaccine against the Omicron variant declined after 4 months following the first dose and then stabilized with higher levels of uncertainty. Across all three cohorts, the risk of cardiac outcomes was approximately 65% to 85% lower in the vaccinated group than that of the unvaccinated group accounting for possible misclassification bias. CONCLUSIONS This study suggests BNT162b2 was effective among children and adolescents in Delta and Omicron periods for a range of COVID-19-related outcomes and is associated with a lower risk for cardiac complications. Waning effectiveness over time suggests that revaccination may be needed in the future.

Clinical Subphenotypes of Multisystem Inflammatory Syndrome in Children: An EHR-based cohort study from the RECOVER program (preprint)

Background: Multi-system inflammatory syndrome in children (MIS-C) represents one of the most severe post-acute sequelae of SARS-CoV-2 infection in children, and there is a critical need to characterize its disease patterns for improved recognition and management. Our objective was to characterize subphenotypes of MIS-C based on presentation, demographics and laboratory parameters. Methods: We conducted a retrospective cohort study of children with MIS-C from March 1, 2020 - April 30, 2022 and cared for in 8 pediatric medical centers that participate in PEDSnet. We included demographics, symptoms, conditions, laboratory values, medications and outcomes (ICU admission, death), and grouped variables into eight categories according to organ system involvement. We used a heterogeneity-adaptive latent class analysis model to identify three clinically-relevant subphenotypes. We further characterized the sociodemographic and clinical characteristics of each subphenotype, and evaluated their temporal patterns. Findings: We identified 1186 children hospitalized with MIS-C. The highest proportion of children (44.4%) were aged between 5-11 years, with a male predominance (61.0%), and non-Hispanic white ethnicity (40.2%). Most (67.8%) children did not have a chronic condition. Class 1 represented children with a severe clinical phenotype, with 72.5% admitted to the ICU, higher inflammatory markers, hypotension/shock/dehydration, cardiac involvement, acute kidney injury and respiratory involvement. Class 2 represented a moderate presentation, with 4-6 organ systems involved, and some overlapping features with acute COVID-19. Class 3 represented a mild presentation, with fewer organ systems involved, lower CRP, troponin values and less cardiac involvement. Class 1 initially represented 51.1% of children early in the pandemic, which decreased to 33.9% from the pre-delta period to the omicron period. Interpretation: MIS-C has a spectrum of clinical severity, with degree of laboratory abnormalities rather than the number of organ systems involved providing more useful indicators of severity. The proportion of severe/critical MIS-C decreased over time.

A New Approach for Timely Systematic Reviews of Rapidly Evolving Research: Identifying Evidence in Which We Can Place Our Confidence (preprint)

ABSTRACT Coronavirus disease of 2019 (COVID-19) has impacted the world in unprecedented ways since first emerging in December 2019. In the last two years, the scientific community has made an enormous effort to understand COVID-19 and potential interventions. As of June 15, 2021, there were more than 140,000 COVID-19 focused manuscripts on PubMed and preprint servers, such as medRxiv and BioRxiv . Preprints, which constitute more than 15% of all manuscripts, may contain more up-to-date research findings compared to published papers, due to the sometimes lengthy timeline between manuscript submission and publication. Including preprints in systematic reviews and meta-analyses thus has the potential to improve the timeliness of reviews. However, there is no clear guideline on whether preprints should be included in systematic reviews and meta-analyses. Using a prototypical example of a rapid systematic review examining the comparative effectiveness of COVID-19 therapeutics, we propose including all preprints in the systematic review by assigning them a weight we term the “confidence score”. Motivated by our observation that, unlike the traditional journal submission process which is unobserved, the timeline from submission to publication for a preprint can be observed and can be modeled as a time-to-event outcome. This observation provides a unique opportunity to model and quantify the probability that a preprint will be published, which can be used as a confidence score to weight preprints in systematic reviews and meta-analyses. To obtain the confidence score, we propose a novel survival cure model, which incorporates both the time from posting to publication for a preprint, and key characteristics of the study described in the content of the preprint. Using meta data from 158 preprints on evaluating therapeutic options for COVID-19 posted through 09/03/2020, we demonstrate the utility of the confidence score in weighting of preprints in a systematic review. Our proposed method has the potential to advance timely systematic reviews of the evidence examining COVID-19 and other clinical conditions with rapidly evolving evidence bases by providing an approach for inclusion of unpublished manuscripts.

An efficient distributed algorithm with application to COVID-19 data from heterogeneous clinical sites (preprint)

ObjectivesIntegrating electronic health records (EHR) data from several clinical sites offers great opportunities to improve estimation with a more general population compared to analyses based on a single clinical site. However, sharing patient-level data across sites is practically challenging due to concerns about maintaining patient privacy. The objective of this study is to develop a novel distributed algorithm to integrate heterogeneous EHR data from multiple clinical sites without sharing patient-level data. Materials and MethodsThe proposed distributed algorithm for binary regression can effectively account for between-site heterogeneity and is communication-efficient. Our method is built on a pairwise likelihood function in the extended Mantel-Haenszel regression, which is known to be statistically highly efficient. We construct a surrogate pairwise likelihood function through approximating the target pairwise likelihood by its surrogate. We show that the proposed surrogate pairwise likelihood leads to a consistent and asymptotically normal estimator by effective communication without sharing individual patient-level data. We study the empirical performance of the proposed method through a systematic simulation study and an application with data of 14,215 COVID-19 patients from 230 clinical sites at UnitedHealth Group Clinical Research Database. ResultsThe proposed method was shown to perform close to the gold standard approach under extensive simulation settings. When the event rate is <5%, the relative bias of the proposed estimator is 30% smaller than that of the meta-analysis estimator. The proposed method retained high accuracy across different sample sizes and event rates compared with meta-analysis. In the data evaluation, the proposed estimate has a relative bias <9% when the event rate is <1%, whereas the meta-analysis estimate has a relative bias at least 10% higher than that of the proposed method. ConclusionsOur simulation study and data application demonstrate that the proposed distributed algorithm provides an estimator that is robust to heterogeneity in event rates when effectively integrating data from multiple clinical sites. Our algorithm is therefore an effective alternative to both meta-analysis and existing distributed algorithms for modeling heterogeneous multi-site binary outcomes.

Lossless Distributed Linear Mixed Model with Application to Integration of Heterogeneous Healthcare Data (preprint)

Linear mixed models (LMMs) are commonly used in many areas including epidemiology for analyzing multi-site data with heterogeneous site-specific random effects. However, due to the regulation of protecting patients’ privacy, sensitive individual patient data (IPD) are usually not allowed to be shared across sites. In this paper we propose a novel algorithm for distributed linear mixed models (DLMMs). Our proposed DLMM algorithm can achieve exactly the same results as if we had pooled IPD from all sites, hence the lossless property. The DLMM algorithm requires each site to contribute some aggregated data (AD) in only one iteration. We apply the proposed DLMM algorithm to analyze the association of length of stay of COVID-19 hospitalization with demographic and clinical characteristics using the administrative claims database from the UnitedHealth Group Clinical Research Database.