Evidence map›Paper›PMID 39466241›Full record

ArticleJAMA network open2024

Body Mass Index and Postacute Sequelae of SARS-CoV-2 Infection in Children and Young Adults.

Ting Zhou, Bingyu Zhang, Dazheng Zhang, Qiong Wu, Jiajie Chen, Lu Li, Yiwen Lu, Michael J Becich, Saul Blecker, Nymisha Chilukuri and 25 more

Abstract read
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
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

35 authors.

Ting ZhouThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia.
Bingyu ZhangThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia.
Dazheng ZhangThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia.
Qiong WuThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia.
Jiajie ChenThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia.
Lu LiThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia.
Yiwen LuThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia.
Michael J BecichDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
Saul BleckerDepartment of Population Health, New York University Grossman School of Medicine, New York.
Nymisha ChilukuriDepartment of Pediatrics, Stanford University School of Medicine, Stanford, California.
Elizabeth A ChrischillesDepartment of Epidemiology, College of Public Health, The University of Iowa, Iowa City.
Haitao ChuDivision of Biostatistics, University of Minnesota School of Public Health, Minneapolis.
Leonor CorsinoDepartment of Medicine, Duke University School of Medicine, Durham, North Carolina.
Carol R GearyDepartment of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha.
Mady HornigRECOVER Patient, Caregiver, or Community Advocate Representative, New York, New York.
Maxwell M Hornig-RohanRECOVER Patient, Caregiver, or Community Advocate Representative, New York, New York.
Susan KimDepartment of Pediatrics, Division of Rheumatology, University of California San Francisco Benioff Children's Hospital, San Francisco.
David M LiebovitzDivision of General Internal Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.
Vitaly LormanApplied Clinical Research Center, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Chongliang LuoDivision of Public Health Sciences, Washington University School of Medicine in St Louis, St Louis, Missouri.
Hiroki MorizonoCenter for Genetic Medicine Research, Children's Research Institute, Children's National Hospital, Washington DC.
Abu S M MosaDepartment of Biomedical Informatics, Biostatistics, and Medical Epidemiology, University of Missouri School of Medicine, Columbia.
Nathan M PajorDivision of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Suchitra RaoDepartment of Pediatrics, University of Colorado School of Medicine, Aurora.
Hanieh RazzaghiApplied Clinical Research Center, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Srinivasan SureshDivisions of Health Informatics & Emergency Medicine, Department of Pediatrics, University of Pittsburgh, Pittsburgh, Pennsylvania.
Yacob G TedlaDivision of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.
Leah Vance UtsetDivision of Primary Care Pediatrics, Nationwide Children's Hospital, Columbus, Ohio.
Youfa WangGlobal Health Institute, Xi'an Jiaotong University, Xi'an, China.
David A WilliamsDepartment of Anesthesiology, University of Michigan, Ann Arbor.
Margot Gage WitvlietRECOVER Patient, Caregiver, or Community Advocate Representative, New York, New York.
Caren MangarelliDivision of Advanced General Pediatrics and Primary Care, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois.
Ravi JhaveriDivision of Pediatric Infectious Diseases, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois.
Christopher B ForrestApplied Clinical Research Center, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Yong ChenThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia.

Funding

Coordinating Individually Measured Phenotypes to Advance Mental Health ResearchU24MH136069 · NIMH · YALE UNIVERSITY · PI Yong Chen, Cui Tao · 2024 to 2026
$9.8M
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
A New Muscle-Brain Axis Underlying the Cognitive Benefits of Physical ActivityR01AG047820 · NIA · PALO ALTO VETERANS INSTIT FOR RESEARCH · PI RANDO, THOMAS A., WYSS-CORAY, TONY · 2013 to 2017
$3.5M
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 TR003709NEI NIH HHS R21 EY034179NIAID NIH HHS R01 AI130460NIAID NIH HHS R21 AI167418NIA NIH HHS R01 AG047820NIA NIH HHS R01 AG073435NIA NIH HHS R56 AG069880NIA NIH HHS R56 AG074604NIA NIH HHS RF1 AG077820NIMH NIH HHS U24 MH136069NLM NIH HHS R01 LM012607NLM NIH HHS R01 LM013519NLM NIH HHS R01 LM014344
6 · The paper itself

Abstract

Importance: Obesity is associated with increased severity of COVID-19. Whether obesity is associated with an increased risk of post-acute sequelae of SARS-CoV-2 infection (PASC) among pediatric populations, independent of its association with acute infection severity, is unclear. Objective: To quantify the association of body mass index (BMI) status before SARS-CoV-2 infection with pediatric PASC risk, controlling for acute infection severity. Design, Setting, and Participants: This retrospective cohort study occurred at 26 US children's hospitals from March 2020 to May 2023 with a minimum follow-up of 179 days. Eligible participants included children and young adults aged 5 to 20 years with SARS-CoV-2 infection. Data analysis was conducted from October 2023 to January 2024. Exposures: BMI status assessed within 18 months before infection; the measure closest to the index date was selected. The BMI categories included healthy weight (≥5th to <85th percentile for those aged 5-19 years or ≥18.5 to <25 for those aged >19 years), overweight (≥85th to <95th percentile for those aged 5-19 years or ≥25 to <30 for for those aged >19 years), obesity (≥95th percentile to <120% of the 95th percentile for for those aged 5-19 years or ≥30 to <40 for those aged >19 years), and severe obesity (≥120% of the 95th percentile for those aged 5-19 years or ≥40 for those aged >19 years). Main Outcomes And Measures: To identify PASC, a diagnostic code specific for post-COVID-19 conditions was used and a second approach used clusters of symptoms and conditions that constitute the PASC phenotype. Relative risk (RR) for the association of BMI with PASC was quantified by Poisson regression models, adjusting for sociodemographic, acute COVID severity, and other clinical factors. Results: A total of 172 136 participants (mean [SD] age at BMI assessment 12.6 [4.4] years; mean [SD] age at cohort entry, 13.1 [4.4] years; 90 187 female [52.4%]) were included. Compared with participants with healthy weight, those with obesity had a 25.4% increased risk of PASC (RR, 1.25; 95% CI, 1.06-1.48) and those with severe obesity had a 42.1% increased risk of PASC (RR, 1.42; 95% CI, 1.25-1.61) when identified using the diagnostic code. Compared with those with healthy weight, there was an increased risk for any occurrences of PASC symptoms and conditions among those with obesity (RR, 1.11; 95% CI, 1.06-1.15) and severe obesity (RR, 1.17; 95% CI, 1.14-1.21), and the association held when assessing total incident occurrences among those with overweight (RR, 1.05; 95% CI, 1.00-1.11), obesity (RR, 1.13; 95% CI, 1.09-1.19), and severe obesity (RR, 1.18; 95% CI, 1.14-1.22). Conclusions And Relevance: In this cohort study, elevated BMI was associated with a significantly increased PASC risk in a dose-dependent manner, highlighting the need for targeted care to prevent chronic conditions in at-risk children and young adults.

Indexed as

Body Mass IndexCOVID-19SARS-CoV-2AdolescentChildChild, PreschoolFemaleHumansMaleObesityPediatric ObesityPost-Acute COVID-19 SyndromeRetrospective StudiesRisk FactorsSeverity of Illness IndexUnited States

Identifiers

PMID39466241
PMCPMC11581483

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.