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Copyright © 2021 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.The Pediatric Infectious Disease Journal • Volume 40, Number 4, April 2021 www.pidj.com | e137ISSN: 0891-3668/21/4004-e137
DOI: 10.1097/INF .0000000000003043Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.Original Studies
Accepted for publication December 12, 2020
*Department of Pediatrics, School of Medicine, University of Colorado, Aurora,
CO; †Department of Pathology and Laboratory Medicine, Children’ s Hospi-tal Colorado, Aurora, CO; and ‡University of Colorado School of Medicine, Aurora, CO.
Supported by the institutional funds.The authors have no conflicts of interest to disclose.K.G. and C.S. have contributed equally to this work.Address for correspondence: Kelly Graff, MD, Pediatric Infectious Diseases,
Children’ s Hospital Colorado, B055, 13123 E 16th Ave, Aurora, CO 80045. E-mail: [email protected].
Supplemental digital content is available for this article. Direct URL citations
appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’ s website (www.pidj.com).Risk Factors for Severe COVID-19 in Children
Kelly Graff, MD,* Christiana Smith, MD,* Lori Silveira, PhD,* Sarah Jung, PhD,† Shane Curran-Hays, MS,*
Jane Jarjour, MD,* Lauren Carpenter, BS,‡ Kasey Pickard, BA,‡ Michael Mattiucci, MD,* JoEllen Fresia, BA,‡
Elizabeth J. McFarland, MD,* Samuel R. Dominguez, MD, PhD,*† and Lisa Abuogi, MD*
Background: There are limited pediatric data regarding severe COVID-19
disease. Our study aims to describe the epidemiology and identify risk fac-tors for severe COVID-19 disease in children.Methods: This is a retrospective cohort study among children with positive SARS-CoV -2 PCR from March to July 2020 at Children’ s Hospital Colo-rado. Risk factors for severe disease were analyzed as defined by hospital admission, respiratory support, or critical care. Univariable and multivari-able analyses were conducted.Results: Among 454 patients identified with SARS-CoV -2, 191 (42.1%) were females, median age 11 years. Fifty-five percent of all patients iden-tified as Hispanic compared with 29% among all hospital visits in 2019 (P < 0.0001). In multivariable analyses, age 0–3 months or >20 years [adjusted odds ratio (aOR), 7.85; P < 0.0001 and aOR, 5.1; P = 0.03, respec-
tively], preterm birth history (aOR, 3.7; P = 0.03), comorbidities [including immunocompromise (aOR, 3.5; P = 0.004), gastrointestinal condition (aOR,
2.7; P = 0.009), diabetes (aOR, 6.6; P = 0.04), asthma (aOR, 2.2; P = 0.04)],
and specific symptoms at presentation were predictors for admission. Age 0–3 months or >20 years, asthma, gastrointestinal condition, and similar symptoms at presentation were also predictors for respiratory support. Ele-vated C-reactive protein was associated with the need for critical care with median of 17.7 mg/dL (IQR, 5.3–22.9) versus 1.95 mg/dL (IQR, 0.7–5.5) among patients requiring critical versus no critical care (OR, 1.2; P = 0.02).
Conclusions: Extremes of age, comorbid conditions, and elevated CRP are predictors of severe disease in children. Findings from this study can inform pediatric providers and public health officials to tailor clinical management, pandemic planning, and resource allocation.
Key Words: SARS-CoV -2, pediatrics, C-reactive protein, health disparities
(Pediatr Infect Dis J 2021;40:e137–e145)
More than 1.3 million (12%) COVID-19 cases have been
reported in children in the United States as of late November
2020.1 Several case series have described the clinical characteristics
of COVID-19 in pediatric patients 2–8 and suggest milder illness
severity in children compared with adults.4,5,7–11 However, children
can present with a wide spectrum of disease ranging from asymp-
tomatic infection to severe respiratory disease and diverse inflam-matory complications.
12–14Severe or life-threatening disease occurs in about 10%–20%
of adults with COVID-19.15,16 Increasing age, male sex, and cer-
tain comorbid conditions are risk factors for severe COVID-19 in adults.
17–20 Medically complex children may be more likely to
require critical care, but most early pediatric studies have not evalu-ated specific risk factors in further detail.
4,5,7,8,11,21 Much remains to
be learned regarding the epidemiologic, demographic, and clinical risk factors for severe COVID-19 illness in children.
Colorado has reported over 36,000 pediatric cases of SARS-
CoV -2 as of early December 2020, representing 14% of all cases in the state.
22 Children’ s Hospital Colorado (CHCO) has established
one of the largest single-institution cohorts of pediatric patients with COVID-19 disease in the United States. In this study, we eval-uated risk factors for severe disease among children with SARS-CoV -2 infection. Our findings can help pediatric providers and pub-lic health stakeholders to tailor clinical management and enhance pandemic planning and resource allocation.
MATERIALS AND METHODS
Study Design and Setting
In this retrospective cohort study, we captured clinical and
epidemiologic data from every pediatric patient with SARS-CoV -2 infection at CHCO between March 15 and July 8, 2020. CHCO is the largest pediatric referral center for children in a 7-state region and includes a 434-bed acute care hospital in Aurora, Colorado, a 111-bed acute care hospital in Colorado Springs, Colorado, and 13 additional network locations offering outpatient, specialty, and urgent care. All sites use a common electronic health record (EHR; Epic systems, Verona, WI).
SARS-CoV-2 Testing
SARS-CoV -2 molecular testing was performed on naso-
pharyngeal swabs, nasopharyngeal washes/aspirates, tracheal aspi-rates, and bronchoalveolar lavage specimens using 1 of 4 qualitative real-time reverse transcription PCR assays for molecular detection of SARS-CoV -2, all of which have been granted Emergency Use Authorization (EUA) from the Food and Drug Administration (FDA): CDC 2019-nCoV Real-Time RT -PCR Diagnostic Panel (CDC, Atlanta, GA), Simplexa COVID-19 Direct assay (DiaSorin Molecular LLC, Cypress, CA), Abbott RealTime SARS-CoV -2 assay (Abbott Molecular Inc., Des Plaines, IL), and Xpert Xpress SARS-CoV -2 test (Cepheid, Sunnyvale, CA). These 4 assays had 100% positive and negative agreement in preclinical studies, dem-onstrated no cross-reactivity to other human coronaviruses nor to a diverse panel of other common respiratory pathogens.
Study Population
All patients <21 years of age with a positive SARS-CoV -2
molecular test performed at CHCO were included. Patients ≥21 years were included only if they were followed by CHCO for a chronic medical condition. SARS-CoV -2 molecular testing first became available on March 16, 2020, with the first positive patient identified on March 19. During the first 4 weeks, testing was lim-ited to admitted patients with fever and respiratory symptoms who
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tested negative for other respiratory viruses on a multiplex panel
and symptomatic immunocompromised outpatients. On April 10, 2020, CHCO began testing all patients for SARS-CoV -2 on admis-sion. On April 18, a drive-through test site opened for preproce-dural and ambulatory patients. We excluded patients tested outside of the state of Colorado, parents/caregivers of pediatric patients, pregnant women, and health care workers.
Data Collection and Management
We received a daily report from the EHR for every patient
with a positive SARS-CoV -2 PCR result in the CHCO system. Demographic and clinical data were abstracted from the EHR in real time and entered into standardized data collection forms devel-oped in REDCap, hosted by the University of Colorado, Denver. EHR data included all available emergency department/urgent care encounters, primary care or specialty provider encounters, in-patient notes and diagnostic test results as available. Race and ethnicity were recorded as documented in the EHR. Body mass index (BMI) was calculated from the most recent height and weight documented in the EHR (measured within the last month for infants <12 months of age, within the last 6 months for those 1–5 years, and within the last year for children >5 years). Those with BMI in the 85th percentile to <95th percentile for age were considered overweight, those ≥the 95th percentile were consid-ered obese, and those ≥120% of the 95th percentile for age were considered severely obese.
23,24 Active comorbid conditions were
recorded as documented in the EHR and categorized according to body system. Exposure history, social history, and other risk factors for SARS-CoV -2 acquisition were recorded if documented in the EHR. Results of diagnostic testing were recorded for all inpatients during the period of admission or through symptom resolution, for outpatients from symptom onset through symptom resolution, and for asymptomatic patients if any laboratory testing or imaging was performed on the same day as SARS-CoV -2 PCR testing. Chest radiographs were considered abnormal if the impression included airspace opacity, consolidation, or peribronchial thickening.
25,26
We categorized patients as requiring critical care if they either (1) were admitted to the pediatric intensive care unit (ICU) for symp-tomatic COVID-19, or (2) were admitted to the neonatal ICU for symptomatic COVID-19 and required a higher level of respiratory support than low-flow nasal cannula. This study was approved by the Colorado Multiple Institutional Review Board with a waiver of informed consent.
Statistical Analysis
We described demographic characteristics using summary
statistics. Descriptive data include all eligible patients with positive SARS-CoV -2 testing and are presented as means and standard devi-ations; medians and interquartile ranges; or counts and proportions. Among all patients with symptomatic SARS-CoV -2 infection, we analyzed the odds of severe outcomes, defined as either (1) hospital admission; (2) need for critical care; or (3) need for respiratory support. Asymptomatic inpatients who were incidentally identi-fied with SARS-CoV -2 were excluded from these analyses. Sim-ple logistic regression was performed for admission, respiratory support, and critical care. Multivariable regression grouped vari-ables to identify demographic predictors, comorbidity predictors, and symptom predictors of the outcomes. Each variable within a group was tested using the following criteria: P < 0.10 for entry and P < 0.05 to remain. Those variables with >20% of missing data were excluded from the multivariable analyses; these included BMI, lab-oratory values, baseline oxygen use, and exposure history; all other variables had zero missing values, so no imputation was required. All analyses were 2-tailed and P values <0.05 were considered significant. Multivariable analysis was not done for the outcome
of critical care due to small numbers in this category. All analyses were performed in SAS 9.4 (SAS Institute, Inc., Cary, NC).
RESULTS
Study Population
A total of 454 children and youth median age 11 years (upper
limit 23 years), 42.1% female were identified with SARS-CoV -2 infection during the study period (Fig. 1 and Table 1). The vast majority (N = 427, 94%) were identified during expanded testing access in which asymptomatic and symptomatic children could be tested. The number of cases per week during the study period stead-ily rose and peaked in the second week of July (Fig. 2A). Patients identifying as Latino or Hispanic contributed 54.6% of cases fol-lowed by White non-Hispanic (22.9%), and Black non-Hispanic (5.1%) (Table 1). The proportion of patients with SARS-CoV -2 infection with Hispanic ethnicity was significantly higher than the patient population seen at CHCO in 2019 (29%, P < 0.0001) (Fig. 2B,C).
Clinical Presentation
Among all SARS-CoV -2 PCR positive patients, 315 (69%)
were symptomatic and 80 (18%) were asymptomatic (Fig. 1). The most common symptom at time of positive SARS-CoV -2 PCR result was fever (>100.4 F) (27%), followed by cough (23%), and congestion or rhinorrhea (18%) (Table 1).
The most frequently documented risk factor for COVID-19
exposure was a family member testing positive for SARS-CoV -2 (N = 114, 25%) followed by social gatherings of more than 10 peo-ple (N = 85, 19%). Symptomatic cases more commonly reported these risk factors with 98 (31%) reporting a family member with COVID-19 versus 16 (12%) of asymptomatic cases, and 79 (25%) of symptomatic cases reporting a social gathering compared with 6 (4%) of asymptomatic cases. Over a third of symptomatic cases (N = 115, 37%) had no exposure documented, while over two-thirds (N = 98, 71%) of asymptomatic cases had no documented risk factor.
Nearly half (45%) of children with SARS-CoV -2 had at least
1 comorbid condition. The most common types of comorbid condi-tions identified were pulmonary (16.7%), gastrointestinal (10.8%), and neurologic disease (10.6%) (specific diagnoses in Table, Sup-plemental Digital Content 1, http://links.lww.com/INF/E299). Of the 211 children with BMI data available, nearly half (45%) were overweight. Of these, 30% were categorized as obese and 11% were considered severely obese.
A total of 85 children (19%) were admitted of whom 66
(78%) were symptomatic. The remaining 19 patients (22%) were admitted for other reasons and were never symptomatic with COVID-19. Among the 66 symptomatic admitted children, 55% required respiratory support and 17% required critical care (Fig. 1). A total of 40 admitted patients had chest radiographs performed on admission, of which 25 (63%) had abnormal findings. Five of 39 (13%) admitted patients tested with a respiratory pathogen panel had coinfection with an additional respiratory tract virus.
Treatment and Outcomes
Of the patients admitted for symptomatic COVID-19,
median length of stay was 3.0 days (IQR, 1–6). Among the 36 children requiring respiratory support, 26 (72%) received low-flow oxygen via nasal cannula, 2 (6%) high-flow oxygen via nasal can-nula, 5 (14%) noninvasive positive pressure ventilation, 2 (6%) intubation/ventilation, and 1 (3%) ECMO. Eight patients (12%) received COVID-directed antiviral therapy and 10 (15%) received
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COVID-19-directed immunomodulatory therapy (Table, Supple-
mental Digital Content 2, http://links.lww.com/INF/E299). Five patients required readmission for COVID-19–related disease, 1 of whom was readmitted twice. Three patients (4.5%) had complica-tions of venous thromboembolism. One medically complex patient with multiple preexisting comorbidities died due to complications of ECMO.
27 All other patients were discharged from the hospital.
Risk Factors for Severe COVID-19
Risk factors associated with symptomatic hospital admission
were evaluated (Table 2). There were no sex or race/ethnicity differ-ences associated with need for admission. As compared with chil-dren age 11–15 years, infants 0–3 months [odds ratio (OR), 7.86; P < 0.001] and young adults >20 years (OR, 5.09; P = 0.03) were more likely to require admission. Comorbid conditions emerged as significant predictors of admission. The presence of any comorbid condition increased the odds of admission (OR, 2.73; P = 0.0003), and the odds increased with each additional comorbidity (OR, 1.36; P < 0.0001). Several categories of comorbidities increased the risk of admission including pulmonary, gastrointestinal, endocrine, neu-rologic, and psychiatric disease; immunocompromising conditions; and history of preterm birth (Table 2). Specific diagnoses including asthma, obstructive sleep apnea, baseline oxygen requirement, and diabetes or prediabetes were all significantly associated with admis-sion. Obesity resulted in more than twice the odds of admission and severe obesity almost 5 times the odds. Symptoms of respiratory infection (including fever, cough, shortness of breath) were predic-tive of admission as were diarrhea, abdominal pain, and fatigue.
Many of the same risk factors were associated with need
for respiratory support and critical care (Tables 2 and 3). Demo-graphic risk factors associated with the need for respiratory sup-port included Hispanic ethnicity, age 0–3 months or >20 years. Comorbid conditions including obesity and asthma were associated
with the need for respiratory support, as were several symptoms of respiratory infection at the time of SARS-CoV -2 testing. The need for critical care was associated with comorbid obstructive sleep apnea (OR, 4.7; P = 0.04) as well as elevated C-reactive protein (CRP) at time of admission (Table 3). Median CRP among patients requiring critical care was 17.7 mg/dL (IQR, 5.3–22.9) compared with those not requiring critical care (1.95 mg/dL; IQR, 0.7–5.5). For every 1-unit increase in CRP at admission, the odds of requir-ing critical care increased by 1.2 ( P = 0.02). Other laboratory values
at admission were not associated with the need for critical care, including white blood cell count, absolute neutrophil count, platelet count, procalcitonin, ferritin, D-dimer, and lactate.
In multivariable analyses (Table 2), age 0–3 months or
>20 years remained significant demographic predictors of need for admission and respiratory support. Comorbid conditions that remained significantly associated with admission included immu-nocompromising conditions, gastrointestinal disease, history of preterm birth, asthma, and diabetes/prediabetes, while only gas-trointestinal disease and asthma remained significantly associated with respiratory support. Fever, shortness of breath, and vomiting at presentation remained significantly associated with admission and respiratory support.
DISCUSSION
In this large pediatric cohort identified at a tertiary refer-
ral hospital network, we identify important factors associated with severe COVID-19 in children. Importantly, we focused on symp-tomatic children who were admitted, required respiratory support or received critical care to better inform health care providers of the population at risk for severe COVID-19. In our cohort, 1 in 5 symptomatic children with SARS-CoV -2 infection required
FIGURE 1. Summary of patients with SARS-CoV-2 at Children’s Hospital Colorado (March 19–July 8, 2020). *Patients with
respiratory support and critical care may overlap; percentages are of symptomatic admitted patients.
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hospital admission. Clear risk factors for admission or need for res-
piratory support emerged, including extremes of age, obesity, and other underlying comorbidities. Elevated CRP was predictive of the need for critical care.Our findings support demographic trends identified in lit-
erature from the United States and elsewhere.
3–5 Similar to other
studies, we found a bimodal age distribution among those requiring admission with young infants and young adults most likely to be TABLE 1. Characteristics of Children and Youth Positive for SARS-CoV-2 by Admission Status
Critical Care Hospital Admission
Symptomatic Only Symptomatic Only Total
Yes No Yes No All
(n = 11) (n = 55) (n = 66)* (n = 369) (n = 454)†
n (%) n (%) n (%) n (%) n (%)
Child’s age (yrs), median (IQR) 16.0 (12–17) 9.0 (0.2–16) 12 (1.7–17) 10 (3–16) 11 (3–11)
Age category
0–3 mo 1 (9.1) 14 (25.4) 15 (22.7) 14 (3.8) 30 (6.6)
4–12 mo 0 (0) 0 (0) 0 (0) 21 (5.7) 22 (4.8)
1–5 yrs 0 (0) 10 (18.2) 10 (15.1) 93 (25.2) 107 (23.6)
6–10 yrs 1 (9.1) 6 (10.9) 7 (10.6) 57 (15.4) 65 (14.3)
11–15 yrs 1 (9.1) 10 (18.2) 11 (16.7) 84 (22.8) 101 (22.2)
16–20 yrs 6 (54.5) 13 (23.6) 19 (28.8) 94 (25.5) 119 (26.2)
>20 yrs 2 (18.2) 2 (3.6) 4 (6.1) 6 (1.6) 10 (2.2)
Gender
Male 6 (54.5) 32 (59.3) 39 (59.1) 211 (57.2) 262 (57.7)
Female 5 (45.5) 22 (40.7) 27 (40.9) 157 (42.5) 191 (42.1)
Other 0 (0) 0 (0) 0 (0) 1 (0.3) 1 (0.2)
Race/ethnicity
White—not Hispanic/Latino 1 (9.1) 12 (21.8) 13 (19.7) 87 (23.6) 104 (22.9)
Black/African American—not Hispanic/Latino 1 (9.1) 2 (3.6) 3 (4.5) 18 (4.9) 23 (5.1)
Hispanic/Latino 8 (72.7) 32 (58.2) 40 (60.6) 196 (53.1) 248 (54.6)
Other 1 (9.1) 9 (16.4) 10 (15.1) 68 (18.4) 79 (17.4)
Body mass index‡
Normal weight 3 (37.5) 15 (46.9) 19 (46.3) 90 (58.1) 116 (55.0)
Overweight 5 (62.5) 17 (53.1) 22 (53.7) 65 (41.9) 95 (45.0)
Obese 4 (50.0) 15 (46.9) 19 (46.3) 40 (25.8) 63 (30.1)
Severely Obese 0 (0) 11 (34.4) 11 (26.8) 11 (7.1) 24 (11.4)
Symptomatic COVID-19
Yes 11 (100.0) 55 (100.0) 66 (100.0) 249 (67.5) 315 (69.4)
No 0 (0) 0 (0) 0 (0) 62 (16.8) 80 (17.6)
Unknown 0 (0) 0 (0) 0 (0) 58 (15.7) 59 (13.0)
Specific symptoms
Fever (>100.4 F) 9 (81.2) 34 (61.8) 43 (65.1) 81 (21.9) 124 (27.3)
Chills 1 (9.1) 5 (9.1) 6 (9.1) 11 (3.0) 17 (3.7)
Cough (new or above baseline) 7 (63.6) 23 (41.8) 30 (45.5) 72 (19.5) 102 (22.5)
Congestion/runny nose 1 (9.1) 16 (29.1) 17 (25.8) 65 (17.6) 82 (18.1)
Shortness of breath 7 (63.6) 12 (21.8) 19 (28.8) 18 (14.9) 37 (8.1)
Chest pain/tightness 2 (18.2) 7 (12.7) 9 (13.6) 12 (3.2) 21 (4.6)
Loss of taste/smell 0 (0) 5 (9.1) 5 (7.6) 18 (4.9) 23 (5.1)
Headache 0 (0) 12 (21.8) 12 (18.2) 38 (10.3) 50 (11.0)
Myalgia 3 (27.3) 7 (12.7) 10 (15.1) 32 (8.7) 42 (9.2)
Joint pain 0 (0) 2 (3.6) 2 (3.0) 1 (0.3) 3 (0.7)
Sore throat 2 (18.2) 6 (10.9) 8 (12.0) 37 (10.0) 45 (9.9)
Wheezing 0 (0) 4 (7.3) 4 (6.1) 1 (0.3) 5 (1.1)
Hypoxia 4 (36.4) 10 (18.2) 14 (21.2) 0 (0) 14 (3.1)
Diarrhea 0 (0) 7 (12.7) 7 (10.6) 13 (3.5) 20 (4.4)
Abdominal pain 1 (9.1) 12 (21.8) 13 (19.7) 10 (2.7) 23 (5.1)
Vomiting 3 (27.3) 12 (21.8) 15 (22.7) 12 (3.2) 27 (5.9)
Seizure 0 (0) 2 (3.6) 2 (3.0) 2 (0.5) 4 (0.9)
Rash 1 (9.1) 1 (1.8) 2 (3.0) 9 (2.4) 11 (2.4)
Conjunctivitis 0 (0) 1 (1.8) 1 (1.5) 2 (0.5) 3 (0.7)
Altered mental status 0 (0) 2 (3.6) 2 (3.0) 0 (0) 2 (0.4)
Fatigue 2 (18.2) 15 (27.3) 17 (25.8) 24 (6.5) 41 (9.0)
Other 7 (63.6) 27 (47.3) 33 (50.0) 33 (8.9) 66 (14.5)
Respiratory support
Yes 10 (90.9) 26 (47.3) 36 (54.5) 0 (0) 43 (9.5)
No 1 (9.1) 29 (52.7) 30 (45.5) 350 (94.8) 392 (86.3)
Unknown – – – 19 (5.2) 19 (5.2)
*Includes only symptomatic children, including critical care admissions.
†Includes 19 admitted patients who were asymptomatic.‡Categories may overlap, for example, overweight (BMI >85th %ile) includes obese (>95th %ile) and severely obese (>120% of 95th %ile).
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admitted.5,11,28 We detected a male predominance among children
and youth testing positive, but sex was not predictive of severe
COVID-19. Nearly 70% of the children in our cohort with posi-tive SARS-CoV -2 PCR were symptomatic, with 21% requiring hospital admission and 3.5% critical care, similar to other pediatric reports.
4,5 Encouragingly, lengths of stay were relatively short and
the need for intubation/mechanical ventilation or ECMO remained rare, consistent with the published literature on children with COVID-19.
4,29,30
We identified an ethnic disparity with overrepresentation
of children identifying as Hispanic as compared with the usual population served by CHCO and the general population of Colo-rado.
11,31 Racial and ethnic disparities have emerged as a key pre-
dictor of COVID-19 in adults and children throughout the United States.
9,32–37 In Colorado, while Hispanic and Latino populations
make up 21.7% of Colorado’ s residents, they have represented 36.9% of COVID-19 cases.
22 Our study demonstrates an even
larger disparity among Hispanic children and youth in Colorado. Additionally, Hispanic ethnicity was associated with higher odds of respiratory support in univariable analysis but not in multivariable analyses, indicating that some other factor among Hispanic chil-dren may have been confounding this association. Several factors may be contributing to these ethnic and racial disparities, which deserve further exploration, including increased prevalence of medical comorbidities, high-density housing, stigma, and barriers to accessing medical care.
38–41
Our study investigated risk factors for severe COVID-19
defined as need for admission, respiratory support, or critical care. We identified particular comorbid conditions, including asthma, diabetes and obesity, to be predictive of more severe pediatric COVID-19. There is growing evidence that children with comor-bidities (in particular medically complex patients) may be at higher risk of hospitalization and critical care.
3,5,42 One-third of children in
our study who were categorized as having a gastrointestinal comor-bidity required a gastrostomy or jejunostomy tube, which supports the association of severe COVID-19 with medical complexity. Among children with endocrine disease, one-third had diabetes mellitus or prediabetes, which are related to COVID-19 outcomes in adult studies.
43,44 Obesity has emerged as an independent risk
factor for severe COVID-19 in adults, with increasing evidence of the same trend in children.
11,17–20,45,46 Several factors may be contrib-
uting to this association, including increased prevalence of other medical comorbidities, obesity-related complications, race/ethnic-ity, and socioeconomic or behavioral factors.
46,47 Thirty percent of
patients in our cohort with BMI data were obese, which is alarm-ing as the obesity rate reported in children with 10–17 years of age in Colorado is only 10.7%.
48 Obesity and severe obesity in our
cohort were significantly associated with admission and respiratory
FIGURE 2. A, Number of SARS-CoV-2 positive children and youth by week. Red line indicates number admitted. B,
Comparison of ethnicity of patients with COVID-19 versus usual patient visits at Children’s Hospital Colorado in 2019. C, Comparison of ethnicity of patients admitted with COVID-19 versus patients admitted to Children’s Hospital Colorado in 2019.
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TABLE 2. Univariable and Multivariable Predictors of Admission and Respiratory Support in Children With COVID-19
Admitted Respiratory Support
Univariable Multivariable Univariable Multivariable
Demographic PredictorsYes No
OR (95% CI)
Yes vs. No POR‡
(95% CI) PYes No
OR (95% CI)
Yes vs. No POR‡
(95% CI) P n = 66(%) n = 369(%) n = 36(%) n = 399(%)
Age
0–3 mo 15 (23) 14 (4) 7.86 (3.0–20.4) <.0001 7.86 (3.0–20.4) <.0001 7 (19) 22 (6) 4.59 (1.4–14.6) 0.01 4.59 (1.4–14.6) 0.01
4–12 mo 0 (0) 21 (6) 0.17 (0.01–3.2) 0.24 0 (0) 21 (5) 0.32 (0.02–6.3) 0.45
1–5 yrs 10 (15) 93 (25) 0.83 (0.3–2.0) 0.67 4 (11) 99 (25) 0.62 (0.2–2.2) 0.45
6–10 yrs 7 (11) 57 (15) 0.96 (0.4–2.6) 0.93 3 (8) 61 (15) 0.78 (0.2–3.0) 0.72
16–20 yrs 19 (29) 94 (25) 1.52 (0.7–3.3) 0.30 13 (36) 100 (25) 1.85 (0.7–4.9) 0.22
> 20 yrs 4 (6) 6 (2) 5.1 (1.2–20.7) 0.03 5.1 (1.2–20.7) 0.03 3 (8) 7 (2) 6.43 (1.4–30.4) 0.02 6.43 (1.4–30.4) 0.02
11–15 yrs 11 (16) 84 (23) Reference Reference 6 (17) 89 (22) Reference Reference
Age (continuous) – – 1.00 (1.0–1.0) 0.99 – – 1.03 (0.9–1.1) 0.26
Gender
Male vs. female 39 (59) 211 (57) 1.07 (0.6–1.8) 0.83 19 (8) 231 (92) 0.81 (0.4–1.6) 0.99
Race/ethnicity
African Am./Black 3 (4) 19 (5) 1.12 (0.3–4.3) 0.87 1 (5) 20 (95) 2.45 (0.2–28.3) 0.47
Hispanic 40 (61) 196 (53) 1.37 (0.7–2.7) 0.37 28 (12) 208 (88) 6.60 (1.5–28.2) 0.01
Other 10 (15) 68 (18) 0.98 (0.4–2.4) 0.97 5 (6) 73 (94) 3.36 (0.6–17.8) 0.15
White (non-Hispanic) 13 (20) 87 (24) Reference – 2 (2) 98 (98) Reference – Household size (continuous) – – 1.12 (0.9–1.3) 0.22 – – 1.23 (0.9–1.5) 0.06
Baseline Oxygen*
Yes vs. No 9 (14) 3 (1) 13.03 (3.3–50.9) 0.0002 – – – –
Body mass index*
†
BMI (continuous) – – 1.02 (1.01–1.04) 0.0016 – – 1.02 (1.0–1.03) 0.03
Overweight 22 (25) 65 (75) 1.60 (0.8–3.2) 0.18 13 (15) 74 (85) 1.57 (0.7–3.7) 0.31
Obese 19 (32) 44 (68) 2.48 (1.2–5.1) 0.01 12 (20) 47 (80) 2.66 (1.1–6.3) 0.03
Severely obese 11 (50) 11 (50) 4.8 (1.9–12.1) 0.0009 6 (22) 16 (78) 3.25 (1.1–9.4) 0.03
Any comorbidities 23 (35) 219 (59) 2.73 (1.6–4.7) 0.0033 12 (33) 169 (42) 2.72 (1.3–5.6) 0.01
Type Comorbidities
Pulmonary disease 21 (32) 52 (14) 2.85 (1.6–5.2) 0.0006 14 (39) 59 (15) 3.67 (1.8–7.6) 0.0004
Asthma 16 (24) 37 (10) 2.87 (1.5–5.5) 0.0017 2.17 (1.1–4.5) 0.04 11 (31) 42 (11) 3.74 (1.7–8.1) 0.0009 3.1 (1.4–6.9) 0.007
Sleep Apnea 10 (15) 19 (5) 3.29 (1.5–7.4) 0.0042 5 (14) 24 (6) 2.52 (0.9–7.1) 0.08
Gastrointestinal disease 17 (26) 28 (8) 4.22 (2.2–8.3) <0.0001 2.71 (1.3–5.7) 0.0087 11 (31) 34 (9) 4.72 (2.1–10.4) 0.0001 3.96 (1.7–9.0) 0.001
Endocrine disease 13 (20) 10 (3) 8.81 (3.7–21.1) <0.0001 7 (19) 16 (4) 5.78 (2.2–15.2) 0.0004
Diabetes/prediabetes 5 (8) 2 (.5) 15.03 (2.9–79.2) 0.0014 6.6 (1.1–39.8) 0.04 2 (6) 5 (1) 4.6 (0.9–24.8) 0.07Cardiac disease 3 (5) 12 (3) 1.42 (0.4–4.2) 0.60 2 (6) 13 (3) 1.75 (0.4–8.1) 0.47
Neurologic disease 11 (17) 31 (8) 2.18 (1.0–4.6) 0.04 0.004 7 (19) 35 (9) 2.51 (1.0–6.1) 0.04
Immunocompromising 13 (20) 18 (5) 4.78 (2.2–10.3) <0.0001 3.47 (1.5–8.1) 4 (11) 27 (7) 1.72 (0.6–5.2) 0.34Psychiatric disease 7 (11) 15 (4) 2.80 (1.1–7.2) 0.03 4 (11) 18 (5) 2.65 (0.8–8.3) 0.09
Develop/Behavioral 9 (14) 29 (8) 1.85 (0.8–4.1) 0.13 6 (17) 32 (8) 2.29 (0.9–5.9) 0.09
Otolaryngolic disease 2 (3) 25 (7) 0.43 (0.1–1.9) 0.26 1 (3) 26 (7) 0.41 (0.05–3.1) 0.39
Preterm birth 5 (8) 8 (2) 3.70 (1.2–11.7) 0.03 3.48 (1.1–11.4) 0.04 2 (6) 11 (3) 2.10 (0.4–9.7) 0.36Symptom Predictors
Fever (>100.4F) 43 (65) 81 (22) 6.65 (3.8–11.7) <.0001 3.82 (2.0–7.4) <.0001 25 (69) 99 (25) 6.89 (3.3–14.5) <.0001 5.26 (2.2–12.5) 0.0002
Chills 6 (9) 11 (3) 3.26 (1.2–9.1) 0.02 5 (14) 12 (3) 5.20 (1.7–15.7) 0.0035
Cough 30 (45) 72 (20) 3.44 (2.0–6.0) <.0001 21 (58) 81 (20) 5.50 (2.7–11.1) <.0001
Congestion/runny nose 17 (26) 65 (18) 1.62 (0.9–3.0) 0.12 11 (31) 71 (18) 2.03 (0.9–4.3) 0.07
Shortness of breath 19 (29) 18 (5) 7.88 (3.9–16.1) <.0001 6.32 (2.8–14.3) <.0001 17 (47) 20 (5) 16.96 (7.7–37.5) <.0001 15.71 (6.4–38.5) <.0001
Chest pain/tightness 9 (14) 12 (3) 4.70 (1.9–11.7) 0.0008 9 (25) 12 (3) 10.75 (4.2–27.7) <.0001
Loss of taste/smell 5 (8) 18 (5) 1.60 (0.6–4.5) 0.37 3 (8) 20 (5) 1.72 (0.5–6.1) 0.40
Headache 12 (18) 38 (10) 1.94 (1.0–3.9) 0.07 7 (19) 43 (11) 2.00 (0.8–4.8) 0.12
Myalgia 10 (15) 32 (9) 1.88 (0.9–4.0) 0.11 7 (19) 35 (9) 2.51 (1.0–6.1) 0.04
Sore throat 8 (12) 37 (10) 1.24 (0.5–2.8) 0.61 5 (14) 40 (10) 1.45 (0.5–3.9) 0.47
Diarrhea 7 (11) 13 (4) 3.25 (1.2–8.5) 0.02 3 (8) 17 (4) 2.04 (0.6–7.3) 0.27
Abdominal pain 13 (20) 10 (2) 8.81 (3.7–21.1) <.0001 3.01 (1.1–8.5) 0.04 7 (19) 16 (4) 5.78 (2.2–15.2) 0.0004 3.88 (1.4–11.1) 0.01
Vomiting 15 (23) 12 (3) 8.75 (3.9–19.7) <.0001 3.89 (1.5–10.2) 0.0059 9 (25) 18 (5) 7.06 (2.9–17.2) <.0001
Fatigue 17 (26) 24 (5) 4.99 (2.5–9.9) <.0001 11 (31) 30 (8) 3.78 (1.04–13.7) 0.04
Other 33 (50) 33 (9) 10.18 (5.6–18.6) <.0001 5.17 (2.6–10.2) <.0001 16 (44) 50 (13) 5.58 (2.7–11.5) <.0001 2.63 (1.1–6.1) 0.02
*Variable excluded from multivariate analysis due to missing data.
†Body mass index categorized as overweight (>85%ile), obese (>95%ile), and severely obese (120% of the 95%ile). Categories may overlap (e.g., overweight includes obese and severely obese).‡For variables with more than 2 categories only significant Wald statistics are shown in multivariate analysis.
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support; however, due to missing data, we were not able to include
this variable in the multivariable analyses.
Importantly, elevated CRP emerged as a clear risk factor for
children requiring critical care, consistent with 2 other US studies in children.
3,49,50 A history of sleep apnea also emerged as a risk
factor for critical care in our cohort. However, we did not identify any other demographic or clinical factors associated with the need for critical care, possibly due to inadequate power to detect these associations given the small number of symptomatic critical care admissions in our cohort.
This is one of the largest pediatric cohorts reported with
over 400 children and youth. We performed a comprehensive analy-sis of factors associated with more severe COVID-19 in children, including demographics, comorbid conditions, and symptoms at TABLE 3. Association of Demographic, Clinical, and Laboratory Predictors
With Symptomatic Critical Care Compared With Noncritical Care Admissions in Children With COVID-19
Yes No Odds ratio
PN = 11 N = 55
(95% confidence
interval) N (%) N (%)
Age
0–3 mo 1 (9) 14 (25) 0.7 (0.06–8.7) 0.80 4–12 mo 0 (0) 0 (0) – – 1–5 yrs 0 (0) 10 (18) 0.3 (0.01–10.7) 0.53 6–10 yrs 1 (9) 6 (11) 1.6 (0.1–21.9) 0.72 16–20 yrs 6 (55) 13 (24) 3.4 (0.4–25.6) 0.24 >20 yrs 2 (18) 2 (4) 7.0 (0.5–99.3) 0.15 11–15 yrs 1 (9) 10 (18) referenceAge (continuous) 1.12 (1.01–1.25) 0.03Gender Male vs. female 6 (55) 32 (58) 0.83 (0.2–3.0) 0.77Race/Ethnicity Black non-Hispanic 1 (9) 2 (4) 6.0 (0.3–140.0) 0.26 Hispanic 8 (73) 32 (58) 3.0 (0.3–26.6) 0.32 Other 1 (9) 9 (16) 1.3 (0.07–24.3) 0.85 White non-Hispanic 1 (9) 12 (22) referenceHousehold size 1.2 (1.0–1.3) 0.03Comorbidities Pulmonary 6 (29) 15 (71) 3.2 (0.8–12.1) 0.09 Asthma 4 (25) 12 (75) 2.0 (0.5–8.2) 0.31 Sleep Apnea 4 (40) 6 (60) 4.7 (1.0–20.8) 0.04 GI 4 (24) 13 (76) 1.8 (0.5–7.3) 0.38 Endocrine 4 (31) 9 (69) 2.9 (0.7–12.1) 0.14 Diabetes or prediabetes 1 (20) 4 (80) 1.3 (.1–12.6) 0.84 Neurology 3 (28) 8 (73) 2.2 (0.5–10.1) 0.31 Immunocompromising 1 (8) 12 (92) 0.4 (0.04–3.1) 0.35 Prematurity 1 (20) 4 (80) 1.3 (0.1–12.6) 0.84Number of comorbidities 1.19 (0.9–1.6) 0.21Symptoms Fever (>100.4 F) 9 (81.2) 34 (61.8) 2.78 (0.5–14.1) 0.22 Cough 7 (63.6) 23 (41.8) 2.44 (0.6–9.3) 0.19 Congestion/runny nose 1 (9.1) 16 (29.1) 0.24 (0.3–2.1) 0.20 Shortness of breath 7 (63.6) 12 (21.8) 6.27 (1.6–25.0) 0.01 Chest pain/tightness 2 (18.2) 7 (12.7) 1.52 (0.3–8.5) 0.63 Diarrhea 0 (0) 7 (12.7) ––––––– –––––– Abdominal pain 1 (9.1) 12 (21.8) 0.36 (0.04–3.1) 0.35 Vomiting 3 (27.3) 12 (21.8) 1.34 (0.31–5.9) 0.69 Fatigue 2 (18) 15 (27) 0.60 (0.1–3.1) 0.53Baseline oxygen use 2 (18) 7 (13) 11.24 (0.2–7.5) 0.82Body mass index* Overweight 5 (45) 17 (31) 1.57 (0.3–7.7) 0.58 Obese 4 (36) 15 (27) 1.20 (0.3–5.6) 0.82 Severely obese 0 (0) 11 (20) ––––––– ––––––Laboratory values Median (IQR) Median (IQR)
C-reactive protein 17.7 (5.3–22.9) 1.95 (0.7–5.5) 1.22 (1.03–1.43) 0.02 White blood cell 6.1 (4.0–8.1) 7.3 (5.5–12.8) 0.91 (0.78–1.06) 0.23 Absolute neutrophil count 4.1 (2.7–6.3) 3.4 (2.4–6.1) 0.98 (0.79–1.21) 0.83 Platelet count 169.5 (102–265) 232 (216–325) 0.99 (0.99–1.00) 0.09 Procalcitonin 0.3 (0.2–3.1) 0.1 (0.1–0.5) 1.15 (0.55–2.37) 0.71 Ferritin 315 (309–453) 86 (58–673) 1.00 (1.00–1.00) 0.98 D-dimer 2.85 (1.5–6.8) 0.8 (0.5–1.0) 7.17 (0.69–74.89) 0.1 Lactate 4.8 (1.8–6.1) 1.25 (1.1–2.2) 1.06 (0.85–1.33) 0.61
*Body mass index categorized as overweight (>85%ile), obese (>95%ile), and severely obese (120% of the 95%ile).
Categories may overlap (e.g., overweight includes obese and severely obese).
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e144 | www.pidj.com © 2021 Wolters Kluwer Health, Inc. All rights reserved.Graff et al
presentation. We focused on symptomatic children to ensure that
children admitted for other reasons did not bias findings. Further, we performed a multivariable analysis to address potential con-founders. Our data were limited to those available in the EHR; while sufficient data were available for most participants, limited information was available for some outpatient cases. Changing rec-ommendations on whom to test may also have impacted our find-ings; the early focus on testing only symptomatic admitted children may have skewed our cohort toward the inclusion of more ill chil-dren, but only 6.9% of our cohort were diagnosed while testing was restricted, so we do not anticipate this created significant bias.
In conclusion, we found that age, comorbid conditions, and
elevated CRP are risk factors for severe COVID-19 in children. Findings from this study can inform pediatric providers and pub-lic health officials to tailor clinical management, pandemic plan-ning, and resource allocation. Counseling of families with children with comorbid medical conditions should include a discussion of increased risk of severe illness. Health care providers may consider screening for CRP at admission to inform the need for critical care. Additional research should evaluate approaches to mitigate these risk factors and explore associations of ethnicity and COVID-19 in children.
ACKNOWLEDGMENTS
We would like to acknowledge the patients and families
affected by COVID-19 and the Children’ s Hospital staff working to care for them. We would especially like to acknowledge the Chil-dren’ s Hospital Colorado Microbiology Laboratory, particularly Kristin Pretty, Darcy V elasquez, and Eric Mwangi for their efforts in early creation and implementation of the SARS-CoV -2 PCR test at CHCO.
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