Real-world effectiveness and causal mediation study of BNT162b2 on long COVID risks in children and adolescentsResearch in context

Qiong Wu; Bingyu Zhang; Jiayi Tong; L. Charles Bailey; H. Timothy Bunnell; Jiajie Chen; Elizabeth A. Chrischilles; Dimitri A. Christakis; Stephen M. Downs; Kathryn Hirabayashi; Aaron D. Mishkin; Abu S.M. Mosa; Nathan M. Pajor; Suchitra Rao; Hanieh Razzaghi; Hayden T. Schwenk; Marion R. Sills; Huiyuan Wang; Linbo Wang; Yudong Wang; Dazheng Zhang; Ting Zhou; Ravi Jhaveri; Eric J. Tchetgen Tchetgen; Jeffrey S. Morris; Christopher B. Forrest; Yong Chen

EClinicalMedicine (Jan 2025)

Real-world effectiveness and causal mediation study of BNT162b2 on long COVID risks in children and adolescentsResearch in context

Qiong Wu,
Bingyu Zhang,
Jiayi Tong,
L. Charles Bailey,
H. Timothy Bunnell,
Jiajie Chen,
Elizabeth A. Chrischilles,
Dimitri A. Christakis,
Stephen M. Downs,
Kathryn Hirabayashi,
Aaron D. Mishkin,
Abu S.M. Mosa,
Nathan M. Pajor,
Suchitra Rao,
Hanieh Razzaghi,
Hayden T. Schwenk,
Marion R. Sills,
Huiyuan Wang,
Linbo Wang,
Yudong Wang,
Dazheng Zhang,
Ting Zhou,
Ravi Jhaveri,
Eric J. Tchetgen Tchetgen,
Jeffrey S. Morris,
Christopher B. Forrest,
Yong Chen

Affiliations

Qiong Wu: Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, USA; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA
Bingyu Zhang: The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA; The Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
Jiayi Tong: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
L. Charles Bailey: Applied Clinical Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
H. Timothy Bunnell: Biomedical Research Informatics Center, Nemours Children’s Health, Nemours Children’s Hospital, Wilmington, DE, USA
Jiajie Chen: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA
Elizabeth A. Chrischilles: Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA, USA
Dimitri A. Christakis: Center for Child Health, Behavior and Development, Seattle Children’s Research Institute, Seattle, WA, USA
Stephen M. Downs: Department of Pediatrics, Atrium Health Wake Forest Baptist, Winston-Salem, NC, USA
Kathryn Hirabayashi: Applied Clinical Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Aaron D. Mishkin: Section of Infectious Diseases, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, USA
Abu S.M. Mosa: Biomedical Informatics, Biostatistics and Medical Epidemiology, University of Missouri School of Medicine, Columbia, MO, USA
Nathan M. Pajor: Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH, USA
Suchitra Rao: Department of Pediatrics, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora, CO, USA
Hanieh Razzaghi: Applied Clinical Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Hayden T. Schwenk: Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
Marion R. Sills: Department of Pediatrics, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora, CO, USA; Department of Research, OCHIN, Inc., Portland, OR, USA
Huiyuan Wang: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA
Linbo Wang: Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
Yudong Wang: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA
Dazheng Zhang: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA
Ting Zhou: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA
Ravi Jhaveri: Division of Infectious Diseases, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA
Eric J. Tchetgen Tchetgen: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
Jeffrey S. Morris: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Corresponding author.
Christopher B. Forrest: Applied Clinical Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA; Corresponding author.
Yong Chen: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA; The Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA; Leonard Davis Institute of Health Economics, Philadelphia, PA, USA; Penn Medicine Center for Evidence-based Practice (CEP), Philadelphia, PA, USA; Penn Institute for Biomedical Informatics (IBI), Philadelphia, PA, USA; Corresponding author. Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Journal volume & issue: Vol. 79
p. 102962

Abstract

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Summary: Background: The impact of pre-infection vaccination on the risk of long COVID remains unclear in the pediatric population. We aim to 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. We further explore if such pre-infection vaccination can mitigate the risk of long COVID beyond its established protective benefits against SARS-CoV-2 infection using causal mediation analysis. Methods: We conducted real-world vaccine effectiveness study and mediation analysis using data from twenty health systems in the RECOVER PCORnet electronic health record (EHR) Program. Three independent cohorts were constructed including adolescents (12–20 years) during the Delta phase (July 1–November 30, 2021), children (5–11 years) and adolescents (12–20 years) during the Omicron phase (January 1–November 30, 2022). The intervention is first dose of the BNT162b2 vaccine in comparison with no receipt of COVID-19 vaccine. The outcomes 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. Findings: 112,590 adolescents (88,811 vaccinated) were included in the cohort for the analysis against Delta variant, and 188,894 children (101,277 vaccinated), and 84,735 adolescents (37,724 vaccinated) were included for the analysis against Omicron variant. During the Delta period, the estimated effectiveness of the BNT162b2 vaccine against long COVID among adolescents was 95.4% (95% CI: 90.9%–97.7%). During the Omicron phase, the estimated effectiveness against long COVID among children was 60.2% (95% CI: 40.3%–73.5%) and 75.1% (95% CI: 50.4%–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 estimated relative risk of 1.08 (95% CI: 0.75–1.55) in the Delta study among adolescents, 1.24 (95% CI: 0.92–1.66) among children and 0.91 (95% CI: 0.69–1.19) 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: 0.03–0.05) among adolescents during Delta phase, 0.31 (95% CI: 0.23–0.42) among children and 0.21 (95% CI: 0.16–0.27) among adolescents during the Omicron period. Interpretation: Our 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 vaccine’s effectiveness is primarily derived from its role in reducing the risk of SARS-CoV-2 infection. Funding: National Institutes of Health.

Published in EClinicalMedicine

ISSN: 2589-5370 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Medicine: Medicine (General)
Website: https://www.thelancet.com/journals/eclinm/home

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