Evidence map›Paper›PMID 39720603›Full record

ArticleEClinicalMedicine2025

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

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 and 18 more

Abstract read
In one paragraph

Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

28 authors.

Qiong WuDepartment of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, USA.
Bingyu ZhangThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA.
Jiayi TongDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
L Charles BaileyApplied Clinical Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
H Timothy BunnellBiomedical Research Informatics Center, Nemours Children's Health, Nemours Children's Hospital, Wilmington, DE, USA.
Jiajie ChenDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Elizabeth A ChrischillesDepartment of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA, USA.
Dimitri A ChristakisCenter for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, WA, USA.
Stephen M DownsDepartment of Pediatrics, Atrium Health Wake Forest Baptist, Winston-Salem, NC, USA.
Kathryn HirabayashiApplied Clinical Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Aaron D MishkinSection of Infectious Diseases, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, USA.
Abu S M MosaBiomedical Informatics, Biostatistics and Medical Epidemiology, University of Missouri School of Medicine, Columbia, MO, USA.
Nathan M PajorDivision of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Suchitra RaoDepartment of Pediatrics, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO, USA.
Hanieh RazzaghiApplied Clinical Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Hayden T SchwenkDepartment of Pediatrics, Stanford School of Medicine, Stanford, CA, USA.
Marion R SillsDepartment of Pediatrics, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO, USA.
Huiyuan WangDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Linbo WangDepartment of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.
Yudong WangDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Dazheng ZhangDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Ting ZhouDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Ravi JhaveriDivision of Infectious Diseases, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
Eric J Tchetgen TchetgenDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Jeffrey S MorrisDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Christopher B ForrestApplied Clinical Research Center, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Yong ChenDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
RECOVER Consortium

Funding

OTA-21-015A Post-Acute Sequelae of SARS-CoV-2 Infection Initiative: NYU Langone Health Clinical Science Core, Data Resource Core, and PASC Biorepository CoreOT2HL161847 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI GROSS, RACHEL SHARON, HORWITZ, LEORA · 2021 to 2025
$651.0M
Phenotypic Diversity in COVID-19UL1TR001878 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2016 to 2025
$102.4M
PANDA-MSD: Predictive Analytics via Networked Distributed Algorithms for Multi-System DiseasesU01TR003709 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI BIAN, JIANG, CHEN, YONG · 2022 to 2025
$4.7M
TRiPOD: Toward Reusable Phenotypes in Observational Data for AD/ADRD - managing definitions and correcting biasR01AG073435 · NIA · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, XU, HUA · 2021 to 2025
$3.9M
Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
ClinEX - Clinical Evidence Extraction, Representation, and AppraisalR01LM014344 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Yong Chen, Yifan Peng · 2023 to 2026
$2.7M
AI-ADRD: Accelerating interventions of AD/ADRD via Machine learning methodsRF1AG077820 · NIA · UNIVERSITY OF PENNSYLVANIA · PI BIAN, JIANG, CHEN, YONG · 2023 to 2023
$2.3M
PheBC: bias correction methods for EHR derived phenotypeR01LM013519 · NLM · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, XU, HUA · 2021 to 2024
$1.6M
Advancing Drug Repositioning for Alzheimer’s Disease using Real-world DataR56AG069880 · NIA · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, CHEN, YONG · 2021 to 2022
$1.6M
CICADA: clinical informatics and computational approaches for drug-repositioning of AD/ADRDR56AG074604 · NIA · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, TAO, CUI · 2021 to 2022
$1.5M
A General Framework to Account for Outcome Reporting Bias in Systematic ReviewsR01LM012607 · NLM · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG · 2017 to 2020
$1.4M
Surrogate Augmented Deep Predictive Learning for Retinopathy of PrematurityR21EY034179 · NEI · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, HE, LIFANG · 2023 to 2023
$482k
NCATS NIH HHS U01 TR003709NCATS NIH HHS UL1 TR001878NEI NIH HHS R21 EY034179NHLBI NIH HHS OT2 HL161847NIAID NIH HHS R01 AI130460NIAID NIH HHS R21 AI167418NIA NIH HHS R01 AG073435NIA NIH HHS R56 AG069880NIA NIH HHS R56 AG074604NIA NIH HHS RF1 AG077820NLM NIH HHS R01 LM012607NLM NIH HHS R01 LM013519NLM NIH HHS R01 LM014344
6 · The paper itself

Abstract

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.

Indexed as

Causal mediation analysisLong COVIDPediatric researchReal-world effectivenessVaccination

Identifiers

PMID39720603
PMCPMC11667630

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.