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Assessment of 135 794 Pediatric Patients Tested for Severe Acute Respiratory Syndrome Coronavirus 2 Across the United States

Educational Objective
To identify the key insights or developments described in this article
1 Credit CME
Key Points

Question  What is the epidemiology across the United States of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among pediatric patients undergoing diagnostic testing for the virus?

Findings  In this cohort study using electronic health records for 135 794 US pediatric patients in 7 children’s health systems, 96% of patients tested had negative results, and rates of severe cardiorespiratory presentation of coronavirus disease 2019 (COVID-19) illness were low. Minority race/ethnicity, chronic illness, and increasing age were associated with SARS-CoV-2 infection.

Meaning  This study suggests that for most pediatric patients, the risk of SARS-CoV-2 infection appears low, but higher concern may be warranted for patients with medically complex conditions or those of minority race/ethnicity.

Abstract

Importance  There is limited information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing and infection among pediatric patients across the United States.

Objective  To describe testing for SARS-CoV-2 and the epidemiology of infected patients.

Design, Setting, and Participants  A retrospective cohort study was conducted using electronic health record data from 135 794 patients younger than 25 years who were tested for SARS-CoV-2 from January 1 through September 8, 2020. Data were from PEDSnet, a network of 7 US pediatric health systems, comprising 6.5 million patients primarily from 11 states. Data analysis was performed from September 8 to 24, 2020.

Exposure  Testing for SARS-CoV-2.

Main Outcomes and Measures  SARS-CoV-2 infection and coronavirus disease 2019 (COVID-19) illness.

Results  A total of 135 794 pediatric patients (53% male; mean [SD] age, 8.8 [6.7] years; 3% Asian patients, 15% Black patients, 11% Hispanic patients, and 59% White patients; 290 per 10 000 population [range, 155-395 per 10 000 population across health systems]) were tested for SARS-CoV-2, and 5374 (4%) were infected with the virus (12 per 10 000 population [range, 7-16 per 10 000 population]). Compared with White patients, those of Black, Hispanic, and Asian race/ethnicity had lower rates of testing (Black: odds ratio [OR], 0.70 [95% CI, 0.68-0.72]; Hispanic: OR, 0.65 [95% CI, 0.63-0.67]; Asian: OR, 0.60 [95% CI, 0.57-0.63]); however, they were significantly more likely to have positive test results (Black: OR, 2.66 [95% CI, 2.43-2.90]; Hispanic: OR, 3.75 [95% CI, 3.39-4.15]; Asian: OR, 2.04 [95% CI, 1.69-2.48]). Older age (5-11 years: OR, 1.25 [95% CI, 1.13-1.38]; 12-17 years: OR, 1.92 [95% CI, 1.73-2.12]; 18-24 years: OR, 3.51 [95% CI, 3.11-3.97]), public payer (OR, 1.43 [95% CI, 1.31-1.57]), outpatient testing (OR, 2.13 [1.86-2.44]), and emergency department testing (OR, 3.16 [95% CI, 2.72-3.67]) were also associated with increased risk of infection. In univariate analyses, nonmalignant chronic disease was associated with lower likelihood of testing, and preexisting respiratory conditions were associated with lower risk of positive test results (standardized ratio [SR], 0.78 [95% CI, 0.73-0.84]). However, several other diagnosis groups were associated with a higher risk of positive test results: malignant disorders (SR, 1.54 [95% CI, 1.19-1.93]), cardiac disorders (SR, 1.18 [95% CI, 1.05-1.32]), endocrinologic disorders (SR, 1.52 [95% CI, 1.31-1.75]), gastrointestinal disorders (SR, 2.00 [95% CI, 1.04-1.38]), genetic disorders (SR, 1.19 [95% CI, 1.00-1.40]), hematologic disorders (SR, 1.26 [95% CI, 1.06-1.47]), musculoskeletal disorders (SR, 1.18 [95% CI, 1.07-1.30]), mental health disorders (SR, 1.20 [95% CI, 1.10-1.30]), and metabolic disorders (SR, 1.42 [95% CI, 1.24-1.61]). Among the 5374 patients with positive test results, 359 (7%) were hospitalized for respiratory, hypotensive, or COVID-19–specific illness. Of these, 99 (28%) required intensive care unit services, and 33 (9%) required mechanical ventilation. The case fatality rate was 0.2% (8 of 5374). The number of patients with a diagnosis of Kawasaki disease in early 2020 was 40% lower (259 vs 433 and 430) than in 2018 or 2019.

Conclusions and Relevance  In this large cohort study of US pediatric patients, SARS-CoV-2 infection rates were low, and clinical manifestations were typically mild. Black, Hispanic, and Asian race/ethnicity; adolescence and young adulthood; and nonrespiratory chronic medical conditions were associated with identified infection. Kawasaki disease diagnosis is not an effective proxy for multisystem inflammatory syndrome of childhood.

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Article Information

Accepted for Publication: September 30, 2020.

Published Online: November 23, 2020. doi:10.1001/jamapediatrics.2020.5052

Corresponding Author: L. Charles Bailey, MD, PhD, Department of Pediatrics, Children’s Hospital of Philadelphia, 2716 South St, 11th Floor, Philadelphia, PA 19146 (baileyc@chop.edu).

Author Contributions: Dr Bailey had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr Bailey and Ms Razzaghi contributed equally to the work reported here.

Concept and design: Bailey, Razzaghi, Christakis, Rao, Sofela, Forrest.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Bailey, Razzaghi, Camacho, Rao, Sofela, Bruno, Forrest.

Critical revision of the manuscript for important intellectual content: Bailey, Razzaghi, Burrows, Bunnell, Christakis, Eckrich, Kitzmiller, Lin, Magnusen, Newland, Pajor, Ranade, Rao, Sofela, Zahner, Bruno.

Statistical analysis: Bailey, Razzaghi, Burrows, Bunnell, Forrest.

Obtained funding: Forrest.

Administrative, technical, or material support: Bailey, Burrows, Bunnell, Camacho, Christakis, Eckrich, Kitzmiller, Lin, Magnusen, Pajor, Ranade, Rao, Sofela, Zahner, Bruno.

Supervision: Bailey, Razzaghi, Forrest.

Conflict of Interest Disclosures: Drs Bailey, Bunnell, Magnusen, and Pajor and Mss Razzaghi and Zahner reported receiving grants from the Patient-Centered Outcomes Research Institute (PCORI) during the conduct of the study. Dr Magnusen reported receiving grants from People Centered Research Foundation during the conduct of the study. Ms Ranade reported receiving grants from PEDSnet during the conduct of the study. No other disclosures were reported.

Funding/Support: This work was funded by PCORI (RI-CRN-2020-007).

Role of the Funder/Sponsor: Neither PCORI nor its representatives participated directly in any of the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Disclaimer: Dr Christakis is editor of JAMA Pediatrics; he was not involved in the editorial review and decision for this manuscript.

Additional Contributions: The authors would like to thank the following people from the PEDSnet Data Coordinating Center at the Children’s Hospital of Philadelphia: Susan Hague, MS, and Shweta Chavan, MSEE, for managing the data operations and ensuring the availability of the data used for analyses; and Kimberley Dickinson, BS, and Levon Utidjian, MD, for their contributions in reviewing data quality for analyses. They were not compensated for their contributions.

References
1.
World Health Organization. WHO Director-General’s opening remarks at the media briefing on COVID-19—11 March 2020. Published March 11, 2020. Accessed June 29, 2020. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
2.
Johns Hopkins University & Medicine. COVID-19 dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Accessed June 29, 2020. https://coronavirus.jhu.edu/map.html
3.
Centers for Disease Control and Prevention. COVIDView: a weekly surveillance summary of U.S. COVID-19 activity. Updated October 9, 2020. Accessed October 12, 2020. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html
4.
Wu  Z , McGoogan  JM .  Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention.   JAMA. 2020;323(13):1239-1242. doi:10.1001/jama.2020.2648 PubMedGoogle ScholarCrossref
5.
Tagarro  A , Epalza  C , Santos  M ,  et al.  Screening and severity of coronavirus disease 2019 (COVID-19) in children in Madrid, Spain.   JAMA Pediatr. 2020. Published online April 8, 2020. doi:10.1001/jamapediatrics.2020.1346PubMedGoogle Scholar
6.
Parri  N , Lenge  M , Buonsenso  D ; Coronavirus Infection in Pediatric Emergency Departments (CONFIDENCE) Research Group.  Children with COVID-19 in pediatric emergency departments in Italy.   N Engl J Med. 2020;383(2):187-190. doi:10.1056/NEJMc2007617 PubMedGoogle ScholarCrossref
7.
CDC COVID-19 Response Team.  Coronavirus disease 2019 in children—United States, February 12–April 2, 2020.   MMWR Morb Mortal Wkly Rep. 2020;69(14):422-426. doi:10.15585/mmwr.mm6914e4PubMedGoogle ScholarCrossref
8.
Dong  Y , Mo  X , Hu  Y ,  et al.  Epidemiology of COVID-19 among children in China.   Pediatrics. 2020;145(6):e20200702. doi:10.1542/peds.2020-0702 PubMedGoogle Scholar
9.
Castagnoli  R , Votto  M , Licari  A ,  et al.  Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children and adolescents: a systematic review.   JAMA Pediatr. 2020. doi:10.1001/jamapediatrics.2020.1467 PubMedGoogle Scholar
10.
Lu  X , Zhang  L , Du  H ,  et al; Chinese Pediatric Novel Coronavirus Study Team.  SARS-CoV-2 infection in children.   N Engl J Med. 2020;382(17):1663-1665. doi:10.1056/NEJMc2005073 PubMedGoogle ScholarCrossref
11.
DeBiasi  RL , Song  X , Delaney  M ,  et al.  Severe coronavirus disease-2019 in children and young adults in the Washington, DC, metropolitan region.   J Pediatr. 2020;223:199-203. doi:10.1016/j.jpeds.2020.05.007PubMedGoogle ScholarCrossref
12.
Shekerdemian  LS , Mahmood  NR , Wolfe  KK ,  et al; International COVID-19 PICU Collaborative.  Characteristics and outcomes of children with coronavirus disease 2019 (COVID-19) infection admitted to US and Canadian pediatric intensive care units.   JAMA Pediatr. 2020;174(9):868-873. doi:10.1001/jamapediatrics.2020.1948 PubMedGoogle ScholarCrossref
13.
Centers for Disease Control and Prevention. Multisystem inflammatory syndrome in children (MIS-C) associated with coronavirus disease 2019 (COVID-19). Published May 14, 2020. Accessed June 29, 2020. https://emergency.cdc.gov/han/2020/han00432.asp
14.
Verdoni  L , Mazza  A , Gervasoni  A ,  et al.  An outbreak of severe Kawasaki-like disease at the Italian epicentre of the SARS-CoV-2 epidemic: an observational cohort study.   Lancet. 2020;395(10239):1771-1778. doi:10.1016/S0140-6736(20)31103-X PubMedGoogle ScholarCrossref
15.
New York State Department of Health. Childhood inflammatory disease related to COVID-19. Accessed June 29, 2020. https://coronavirus.health.ny.gov/childhood-inflammatory-disease-related-covid-19
16.
Forrest  CB , Margolis  PA , Bailey  LC ,  et al.  PEDSnet: a national pediatric learning health system.   J Am Med Inform Assoc. 2014;21(4):602-606. doi:10.1136/amiajnl-2014-002743 PubMedGoogle ScholarCrossref
17.
Forrest  CB , Margolis  P , Seid  M , Colletti  RB .  PEDSnet: how a prototype pediatric learning health system is being expanded into a national network.   Health Aff (Millwood). 2014;33(7):1171-1177. doi:10.1377/hlthaff.2014.0127 PubMedGoogle ScholarCrossref
18.
von Elm  E , Altman  DG , Egger  M , Pocock  SJ , Gøtzsche  PC , Vandenbroucke  JP ; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.   PLoS Med. 2007;4(10):e296. doi:10.1371/journal.pmed.0040296 PubMedGoogle Scholar
19.
PEDSnet. COVID-19 cohort ETL guidance. Accessed June 29, 2020. https://github.com/PEDSnet/Data_Models_Public/blob/master/PEDSnet/docs/COVID-19%20Cohort.md
20.
Khare  R , Utidjian  L , Ruth  BJ ,  et al.  A longitudinal analysis of data quality in a large pediatric data research network.   J Am Med Inform Assoc. 2017;24(6):1072-1079. doi:10.1093/jamia/ocx033 PubMedGoogle ScholarCrossref
21.
Khare  R , Ruth  BJ , Miller  M ,  et al.  Predicting causes of data quality issues in a clinical data research network.   AMIA Jt Summits Transl Sci Proc. 2018;2017:113-121.PubMedGoogle Scholar
22.
White  PH , Cooley  WC ; Transitions Clinical Report Authoring Group; American Academy of Pediatrics; American Academy of Family Physicians; American College of Physicians.  Supporting the health care transition from adolescence to adulthood in the medical home.   Pediatrics. 2018;142(5):e20182587. doi:10.1542/peds.2018-2587 PubMedGoogle Scholar
23.
U.S. Department of Health & Human Services. Young adult coverage. Accessed June 29, 2020. https://www.hhs.gov/healthcare/about-the-aca/young-adult-coverage/index.html
24.
Simon  TD , Cawthon  ML , Stanford  S ,  et al; Center of Excellence on Quality of Care Measures for Children with Complex Needs (COE4CCN) Medical Complexity Working Group.  Pediatric medical complexity algorithm: a new method to stratify children by medical complexity.   Pediatrics. 2014;133(6):e1647-e1654. doi:10.1542/peds.2013-3875 PubMedGoogle ScholarCrossref
25.
Simon  TD , Cawthon  ML , Popalisky  J , Mangione-Smith  R ; Center of Excellence on Quality of Care Measures for Children with Complex Needs (COE4CCN).  Development and validation of the Pediatric Medical Complexity Algorithm (PMCA) version 2.0.   Hosp Pediatr. 2017;7(7):373-377. doi:10.1542/hpeds.2016-0173 PubMedGoogle ScholarCrossref
26.
SNOMED International. Accessed October 16, 2020. https://snomed.org/
27.
R Development Core Team.  R: a language and environment for statistical computing. R Foundation for Statistical Computing; 2019.
28.
Vandenbroucke  JP .  A shortcut method for calculating the 95 per cent confidence interval of the standardized mortality ratio.   Am J Epidemiol. 1982;115(2):303-304. doi:10.1093/oxfordjournals.aje.a113306 Google ScholarCrossref
29.
Greene  SM , Reid  RJ , Larson  EB .  Implementing the learning health system: from concept to action.   Ann Intern Med. 2012;157(3):207-210. doi:10.7326/0003-4819-157-3-201208070-00012 PubMedGoogle ScholarCrossref
30.
Wu  D , Lu  J , Liu  Y , Zhang  Z , Luo  L .  Positive effects of COVID-19 control measures on influenza prevention.   Int J Infect Dis. 2020;95:345-346. doi:10.1016/j.ijid.2020.04.009 PubMedGoogle ScholarCrossref
31.
Baker  MA , Baer  B , Kulldorff  M ,  et al.  Kawasaki disease and 13-valent pneumococcal conjugate vaccination among young children: a self-controlled risk interval and cohort study with null results.   PLoS Med. 2019;16(7):e1002844. doi:10.1371/journal.pmed.1002844 PubMedGoogle Scholar
32.
Denburg  MR , Razzaghi  H , Bailey  LC ,  et al.  Using electronic health record data to rapidly identify children with glomerular disease for clinical research.   J Am Soc Nephrol. 2019;30(12):2427-2435. doi:10.1681/ASN.2019040365 PubMedGoogle ScholarCrossref
33.
Phillips  CA , Razzaghi  H , Aglio  T ,  et al.  Development and evaluation of a computable phenotype to identify pediatric patients with leukemia and lymphoma treated with chemotherapy using electronic health record data.   Pediatr Blood Cancer. 2019;66(9):e27876. doi:10.1002/pbc.27876 PubMedGoogle Scholar
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