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Figure 1.  Association Between Underlying Medical Conditions and Risk of Hospitalization or Severe Illness When Hospitalized in the Sample
Association Between Underlying Medical Conditions and Risk of Hospitalization or Severe Illness When Hospitalized in the Sample

Underlying medical conditions were defined as described n the Methods section. The sample was defined as children aged 18 years or younger with a COVID-19 diagnosis during an emergency department or inpatient encounter in hospitals that reported both emergency department and inpatient encounters to Premier Healthcare Database Special COVID-19 Release, March 2020 through January 2021. Each panel represents results of a single generalized linear model with Poisson distribution and log link function, that includes the following covariates: frequent (ie, prevalence >0.7%) underlying medical conditions, age group, sex, race/ethnicity, payer type, hospital urbanicity, hospital US Census region, admission month, and admission month squared. The reference group for each underlying condition was absence of that condition; the reference group for type 1 and type 2 diabetes was no diabetes.

Figure 2.  Association Between Medical Complexity and Risk of Hospitalization or Severe Illness When Hospitalized in the Sample
Association Between Medical Complexity and Risk of Hospitalization or Severe Illness When Hospitalized in the Sample

Medical complexity was defined by the Pediatric Medical Complexity Algorithm. The sample was defined as children aged 18 years or younger with a COVID-19 diagnosis during an emergency department or inpatient encounter in hospitals that reported both emergency department and inpatient encounters to Premier Healthcare Database Special COVID-19 Release, March 2020 through January 2021. Each panel represents results of a single generalized linear model with Poisson distribution and log link function, that includes the following covariates: medical complexity (presence of noncomplex chronic disease, presence of complex chronic disease, and no chronic disease as reference group), age group, sex, race/ethnicity, payer type, hospital urbanicity, hospital US Census region, admission month, and admission month squared.

Table 1.  Characteristics of the Samplea
Characteristics of the Samplea
Table 2.  The Most Frequent Underlying Medical Conditions in the Samplea
The Most Frequent Underlying Medical Conditions in the Samplea
Table 3.  Most Frequent Underlying Medical Conditions and Their Association With Risk of Hospitalization or Severe Illness When Hospitalized, Stratified by Age Groupa
Most Frequent Underlying Medical Conditions and Their Association With Risk of Hospitalization or Severe Illness When Hospitalized, Stratified by Age Groupa
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    Original Investigation
    Pediatrics
    June 7, 2021

    Underlying Medical Conditions Associated With Severe COVID-19 Illness Among Children

    Author Affiliations
    • 1COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, Georgia
    • 2Epidemic Intelligence Service, Center for Surveillance, Epidemiology and Laboratory Services, US Centers for Disease Control and Prevention, Atlanta, Georgia
    • 3US Public Health Service Commissioned Corps, Rockville, Maryland
    JAMA Netw Open. 2021;4(6):e2111182. doi:10.1001/jamanetworkopen.2021.11182
    Key Points

    Question  Among children with a COVID-19 diagnosis, what conditions are common, and which are associated with severe COVID-19 illness?

    Findings  In this cross-sectional study of 43 465 patients aged 18 years or younger with COVID-19, more than one-quarter had 1 or more underlying condition; asthma, obesity, neurodevelopmental disorders, and certain mental health conditions were most common. Certain conditions as well as medical complexity were associated with a higher risk of severe COVID-19 illness.

    Meaning  These findings expand the knowledge available regarding children with COVID-19 and could inform pediatric clinical practice and public health priorities, such as prevention and mitigation of COVID-19.

    Abstract

    Importance  Information on underlying conditions and severe COVID-19 illness among children is limited.

    Objective  To examine the risk of severe COVID-19 illness among children associated with underlying medical conditions and medical complexity.

    Design, Setting, and Participants  This cross-sectional study included patients aged 18 years and younger with International Statistical Classification of Diseases, Tenth Revision, Clinical Modification code U07.1 (COVID-19) or B97.29 (other coronavirus) during an emergency department or inpatient encounter from March 2020 through January 2021. Data were collected from the Premier Healthcare Database Special COVID-19 Release, which included data from more than 800 US hospitals. Multivariable generalized linear models, controlling for patient and hospital characteristics, were used to estimate adjusted risk of severe COVID-19 illness associated with underlying medical conditions and medical complexity.

    Exposures  Underlying medical conditions and medical complexity (ie, presence of complex or noncomplex chronic disease).

    Main Outcomes and Measures  Hospitalization and severe illness when hospitalized (ie, combined outcome of intensive care unit admission, invasive mechanical ventilation, or death).

    Results  Among 43 465 patients with COVID-19 aged 18 years or younger, the median (interquartile range) age was 12 (4-16) years, 22 943 (52.8%) were female patients, and 12 491 (28.7%) had underlying medical conditions. The most common diagnosed conditions were asthma (4416 [10.2%]), neurodevelopmental disorders (1690 [3.9%]), anxiety and fear-related disorders (1374 [3.2%]), depressive disorders (1209 [2.8%]), and obesity (1071 [2.5%]). The strongest risk factors for hospitalization were type 1 diabetes (adjusted risk ratio [aRR], 4.60; 95% CI, 3.91-5.42) and obesity (aRR, 3.07; 95% CI, 2.66-3.54), and the strongest risk factors for severe COVID-19 illness were type 1 diabetes (aRR, 2.38; 95% CI, 2.06-2.76) and cardiac and circulatory congenital anomalies (aRR, 1.72; 95% CI, 1.48-1.99). Prematurity was a risk factor for severe COVID-19 illness among children younger than 2 years (aRR, 1.83; 95% CI, 1.47-2.29). Chronic and complex chronic disease were risk factors for hospitalization, with aRRs of 2.91 (95% CI, 2.63-3.23) and 7.86 (95% CI, 6.91-8.95), respectively, as well as for severe COVID-19 illness, with aRRs of 1.95 (95% CI, 1.69-2.26) and 2.86 (95% CI, 2.47-3.32), respectively.

    Conclusions and Relevance  This cross-sectional study found a higher risk of severe COVID-19 illness among children with medical complexity and certain underlying conditions, such as type 1 diabetes, cardiac and circulatory congenital anomalies, and obesity. Health care practitioners could consider the potential need for close observation and cautious clinical management of children with these conditions and COVID-19.

    Introduction

    Our understanding of COVID-19 illness in children is limited.1 To date, most children with SARS-CoV-2 infection have been asymptomatic or had mild COVID-19 symptoms, but some children have had severe illness.2 Prior literature identified risk factors for severe COVID-19 illness in children, including being younger than 1 year or having an underlying medical condition, such as congenital heart disease, asthma, obesity, diabetes, or neurologic conditions.2-7 Approximately 1 in 4 US children have a chronic condition, including asthma, obesity, and behavioral or learning disorders.8

    Previous studies on risk factors among children were limited by small sample sizes that affected the study’s ability to detect statistically significant associations and by a limited ability to follow up children over multiple encounters.2,3 By using a large electronic administrative health care data set, we sought to describe common underlying medical conditions and medical complexity as well as their associations with the risk of hospitalization or severe illness among children seeking care in the hospital.

    Methods

    This activity was reviewed by the US Centers for Disease Control and Prevention and was deemed exempt from institutional review board oversight per 45 CFR §46.101(b)(4) and exempt from patient informed consent per 45 CFR §164.506(d)(2)(ii)(B) because the disclosed Premier Healthcare Database Special COVID-19 Release (PHD-SR) data are considered deidentified. This report was guided by and conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies.9

    Data Source

    This analysis used the PHD-SR (release date, March 15, 2021), a large, hospital-based, all-payer database.10 Among more than 900 geographically dispersed US hospitals in PHD-SR, 872 contributed both emergency department (ED) and inpatient encounters to PHD-SR and were selected for this study.

    Study Population

    The sample included patients aged 18 years or younger who had an inpatient or ED encounter with a primary or secondary COVID-19 discharge diagnosis from March 1, 2020, through January 31, 2021 (eFigure 1 in the Supplement). The International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code U07.1 (COVID-19, virus identified) was used from April 1, 2020, through January 31, 2021, and code B97.29 (other coronavirus as the cause of diseases classified elsewhere) was used from March 1 through April 30, 2020.11 Patients with missing sex (n = 56) were omitted from the analysis. Patient characteristics, including race and ethnicity categories, were provided in the information from patient medical records in the PHD-SR. Categories were defined separately for race and ethnicity by PHD-SR and were combined into a single variable (race/ethnicity) for this study. Given the known racial and ethnic disparities in incidence and severity of both COVID-19 and several of the underlying conditions analyzed, race/ethnicity is a key confounder to consider.12

    Measures and Outcomes

    Two outcomes were considered: (1) hospitalization and (2) severe illness when hospitalized, a single severity indicator for experiencing an intensive care unit (ICU) or stepdown unit admission, invasive mechanical ventilation (IMV), or death. Hospitalization was defined by having an inpatient encounter, death was defined using patient discharge status, and ICU admission and IMV were determined using patient billing records.

    This study assessed 2 exposures of interest: (1) specific underlying medical conditions and (2) medical complexity (defined later in this section). Both exposures were captured using ICD-10-CM diagnosis codes from all inpatient and hospital-based outpatient medical records in PHD-SR from January 1, 2019, up to and including a patient’s initial COVID-19 encounter (henceforth, measurement period). Three ICD-10-CM codes related to dependence on oxygen or ventilator or having received tracheostomy were excluded during the patient’s COVID-19 encounter, as they could be a part of COVID-19 treatment.

    A multistep approach was used to identify underlying medical conditions (eFigure 2 in the Supplement). First, we used the Chronic Condition Indicator (CCI) to identify chronic ICD-10-CM codes (11 803 of 73 205 total ICD-10-CM codes). Second, we used Clinical Classifications Software Refined (CCSR) to aggregate these codes into 316 meaningful categories.13,14 The CCI included some conditions that could be acute complications of COVID-19. To further differentiate and isolate truly underlying conditions from acute sequelae of COVID-19, a panel of physicians (N.T.A., J.M.N., B.B., W.R.M., K.K.W., and panel member H.G.R.) classified the 316 CCSR categories as (1) likely underlying conditions that potentially preceded the acute COVID-19 illness (275 categories); (2) likely acute conditions that were likely the result of acute COVID-19 illness (13 categories); or (3) indeterminate conditions that could be either acute or underlying or both (28 categories). We used the likely underlying CCSR categories for our analysis of underlying medical conditions and excluded the likely acute and indeterminate categories. Individuals with codes for both type 1 and type 2 diabetes were classified as having type 1 diabetes if 50% or more of their encounters included diagnosis codes for type 1 diabetes.15

    Medical complexity was defined using the validated pediatric medical complexity algorithm (PMCA) as presence of complex chronic disease (C-CD), presence of noncomplex chronic disease (NC-CD), and absence of chronic disease (reference category).16 Presence of C-CD was defined as having at least 1 encounter with a progressive condition, at least 1 encounter with malignant neoplasms, or at least 1 encounter per body system for 2 different body systems during the measurement period. Presence of NC-CD was defined as having at least 1 encounter for a single body system not flagged as progressive. Absence of chronic disease was defined as having none of the previously described encounters during the measurement period.16

    Statistical Analysis

    We used frequencies and percentages to describe the characteristics of the patient sample and hospitals they visited. We then listed the most frequently documented underlying medical conditions in the sample. We used multivariate generalized linear models with Poisson distribution, log link function, and robust standard errors to estimate the associations of frequent (ie, prevalence >0.7%) underlying medical conditions and medical complexity with the outcomes of hospitalization (reference group, patients seen in ED only) and severe illness among hospitalized patients (reference group, hospitalized patients without severe illness). Poisson distribution with robust standard errors was used instead of binomial distribution because of model convergence issues.17,18 All models used robust standard errors clustered on hospital identification19 and included frequent (ie, prevalence >0.7%) underlying medical conditions, age group, sex, race/ethnicity, payer type, hospital urbanicity, hospital US Census region, admission month, and admission month squared.

    We also conducted stratified analyses of frequent conditions by age group (frequency ≥1% within each age group) and estimated their association with risk of hospitalization and severe COVID-19 illness. The stratified analysis of children aged 1 year or younger included prematurity (gestational age <37 weeks at birth; ICD-10-CM codes, P07.2X and P07.3X) as an additional covariate and a potential risk factor for severe illness.

    Additionally, 2 sensitivity analyses were performed, using 316 CCSR categories without excluding those marked as indeterminate and likely acute by clinical review. One sensitivity analysis was performed in a full sample, another was performed with the subset of patients with at least 1 encounter before their first COVID-19 encounter. P < .05 was considered statistically significant, and all tests were 2-tailed. All analyses were conducted using R version 4.0.2 (R Project for Statistical Computing) and Stata version 15.1 (StataCorp).

    Results

    Among 3 782 157 children with ED or inpatient encounters in the PHD-SR, 43 465 (1.1%) met inclusion criteria as patients with COVID-19 (Table 1). Of children with COVID-19, 22 943 (52.8%) were female patients, 14 333 (33.0%) were Hispanic or Latino individuals, and 10 488 (24.1%) were non-Hispanic Black individuals. The median (interquartile range) age was 12 (4-16) years. Among children with COVID-19, 4302 (9.9%) were hospitalized (ie, had an inpatient visit), of whom 1273 (29.6%) had an ICU admission, 277 (6.4%) received IMV, and 38 (0.9%) died.

    Underlying medical conditions were present in 12 491 children with COVID-19 (28.7%) and 2708 hospitalized children with COVID-19 (62.9%) (Table 1). Condition frequencies stratified by care setting are in eTable 1 of the Supplement. As defined by PMCA, chronic disease, including NC-CD and C-CD, was present in 9524 children (21.9%) and 2316 hospitalized children (53.8%) (Table 1).

    We identified 18 underlying medical conditions with frequency greater than 0.7% in the sample, with the most common being asthma (4416 [10.2%]), neurodevelopmental disorders (1690 [3.9%]), anxiety and fear-related disorders (1374 [3.2%]), depressive disorders (1209 [2.8%]), and diagnosed obesity (1071 [2.5%]) (Table 2). Of these conditions, 12 were associated with at least 1 of the 2 severe COVID-19 outcomes. Type 1 diabetes, obesity, and cardiac and circulatory congenital anomalies were the strongest risk factors for hospitalization, with adjusted risk ratios (aRRs) of 4.60 (95% CI, 3.91-5.42), 3.07 (95% CI, 2.66-3.54), and 2.12 (95% CI, 1.83–2.45), respectively. Other conditions associated with a higher risk of hospitalization included epilepsy and/or convulsions (aRR, 1.97; 95% CI, 1.62-2.39); other specified status, a category indicating dependence on medical support, such as gastrostomy status (aRR, 1.96; 95% CI, 1.63-2.37); trauma and stressor-related disorders (aRR, 1.82; 95% CI, 1.51-2.18); neurodevelopmental disorders (aRR, 1.64; 95% CI, 1.47-1.83); type 2 diabetes (aRR, 1.59; 95% CI, 1.30-1.95); depressive disorders (aRR, 1.58; 95% CI, 1.34-1.87); essential hypertension (aRR, 1.51; 95% CI, 1.29-1.78); anxiety and fear-related disorders (aRR, 1.47; 95% CI, 1.27-1.70); and asthma (aRR, 1.23; 95% CI, 1.13-1.34) (Figure 1).

    Among 4302 children hospitalized with COVID-19, risk of severe COVID-19 illness (ie, experiencing ICU admission, IMV, or death) was the highest among children with type 1 diabetes (aRR, 2.38; 95% CI, 2.06-2.76), cardiac and circulatory congenital anomalies (aRR, 1.72; 95% CI, 1.48-1.99), and epilepsy and/or convulsions (aRR, 1.71; 95% CI, 1.41-2.08). Other conditions associated with higher risk were obesity (aRR, 1.42; 95% CI, 1.22-1.66); essential hypertension (aRR, 1.39; 95% CI, 1.19-1.63); sleep/wake disorders, including sleep apnea (aRR, 1.26; 95% CI, 1.09-1.45); and other specified status (aRR, 1.25; 95% CI, 1.07-1.47) (Figure 1).

    Stratified analyses showed that among children aged 1 year or younger and 2 to 5 years, cardiac and congenital anomalies were both frequent (306 of 7904 [3.9%]; 84 of 5034 [1.7%]) and associated with a higher risk of severe illness when hospitalized (aRR, 1.89; 95% CI, 1.48-2.41; aRR, 1.50; 95% CI, 1.05-2.16) (Table 3). Among children aged 1 year or younger, prematurity was a frequent (479 [6.1%]) and significant risk factor for severe COVID-19 illness (aRR, 1.83; 95% CI, 1.47-2.29). Among hospitalized patients aged 12 to 18 years, risk factors for severe COVID-19 illness were type 1 diabetes (aRR, 2.47; 95% CI, 2.12-2.87), epilepsy and/or convulsions (aRR, 1.89; 95% CI, 1.53-2.34), obesity (aRR, 1.57; 95% CI, 1.32-1.85), essential hypertension (aRR, 1.23; 95% CI, 1.01-1.51), and asthma (aRR, 1.17; 95% CI, 1.02-1.33).

    Medical complexity was associated with risk of hospitalization and severe illness when hospitalized (Figure 2). Compared with children without chronic disease, children with NC-CD and C-CD were 2.91 times (95% CI, 2.63-3.23) and 7.86 times (95% CI, 6.91-8.95) more likely to be hospitalized, respectively. Compared with hospitalized children without chronic disease, hospitalized children with NC-CD and C-CD were 1.95 times (95% CI, 1.69-2.26) and 2.86 times (95% CI, 2.47-3.32) more likely to have severe illness, respectively.

    The first sensitivity analysis, performed without excluding likely acute and indeterminate CCSR categories, found 3 frequent (>0.7%) indeterminate categories that were associated with at least 1 outcome of severe illness: coagulation and hemorrhagic disorders, diseases of white blood cells, and other nutritional and metabolic disorders (eFigure 3 in the Supplement). The risk estimates of most previously found underlying medical conditions were slightly lower with the inclusion of these 3 conditions in regression models.

    The second sensitivity analysis was performed using CCSR categories captured prior to the first COVID-19 visit in a subset of 20 773 children (eTable 2 and eTable 3 in the Supplement). Risk estimates of certain conditions were higher (eg, type 2 diabetes) or lower (eg, obesity) in magnitude compared with our main findings (eFigure 4 in the Supplement).

    Discussion

    In this cross-sectional study of 43 465 US children with a COVID-19 diagnosis in ED or inpatient encounters from March 2020 through January 2021, the most commonly documented underlying conditions were asthma, neurodevelopmental disorders, anxiety and fear-related disorders, depressive disorders, and obesity. Children with cardiac and circulatory congenital anomalies, essential hypertension, and type 1 diabetes had higher risk of both hospitalization and severe illness when hospitalized. Limited reports of small case series or cohort studies suggest that children with congenital heart disease might be at increased risk of severe COVID-19 illness.6,20 A nationwide study of adults and children in England found type 1 diabetes to be associated with higher odds of in-hospital death from COVID-19.21 Our study found that type 1 diabetes was a risk factor for severe COVID-19 illness among US children. Additionally, evidence suggests that children with type 1 diabetes have been negatively affected during the pandemic.22-26 Our observation of type 1 diabetes as a risk factor for severe COVID-19 illness could be partially explained by complications from preexisting or new-onset diabetes in the setting of SARS-CoV-2 infection or indirect pandemic-related causes (eg, delays in seeking care, delays in diagnosis, and subsequent poor glycemic control among patients with type 1 diabetes).22,23,26,27

    Anxiety and fear-related disorders, depressive disorders, and neurodevelopmental disorders (the latter driven by attention-deficit/hyperactivity disorder [ADHD] and autism spectrum disorders) were prevalent in the sample and were associated with a higher risk of hospitalization but not severe illness when hospitalized. Mental health disorders among US children are common, with ADHD (9.4%), anxiety (7.1%), and depression (3.2%) being the most commonly diagnosed.28 In addition, the COVID-19 pandemic has worsened the well-being of children and their parents,29 potentially because of ongoing social, health service, and academic disruptions (eg, loss of school-based mental health services, financial losses, food insecurity).30 It is unclear why children with mental health disorders might be hospitalized more frequently; it is possible that the effects of medications being used to treat the underlying condition are unknown or that clinicians might be more likely to hospitalize a child with a mental health disorder for closer observation or to provide additional support to the child or family.

    Asthma was the most frequent diagnosed condition, significantly associated with risk of hospitalization. Asthma was not found to be associated with a higher risk of COVID-19 illness among hospitalized children, except among those aged 12 to 18 years. A previous analysis of 454 patients younger than 21 years at Children’s Hospital Colorado found asthma to be a risk factor for hospitalization and respiratory support but not critical care.7 Current evidence suggesting asthma as a risk factor for severe COVID-19 illness among adults is mixed.31 Thus, the role of asthma in severity of infection for both children and adults remains unclear.

    Obesity is a known risk factor for severe COVID-19 illness in adults,32-34 and this study provides evidence of obesity as a risk factor among children. Prior descriptive and case series studies showed obesity as more frequent in children with severe COVID-19 illness compared with the general population.2,35,36 Obesity can have numerous negative health impacts that could explain this higher risk, including chronic inflammation, impaired immunity, and underlying cardiopulmonary disease.37

    Children with epilepsy were at higher risk of hospitalization and severe COVID-19 illness when hospitalized. Evidence regarding epilepsy or seizures and COVID-19 has been scarce, although a recent study found a higher risk of fatality among patients with epilepsy and probable or confirmed COVID-19.38

    Medical complexity and its association with severe COVID-19 illness among children is not well understood. Prior studies defined medical complexity as technology dependence (eg, gastrostomy status),7 which we also found to be associated with severe COVD-19. Our study used the PMCA definition of medical complexity, ie, as presence of NC-CD or C-CD (the latter including the presence of malignant neoplasms, progressive conditions, or conditions affecting ≥2 body systems).17 We found that children with C-CD and children with NC-CD were at a higher risk of hospitalization and severe illness when hospitalized compared with children without chronic disease. Additionally, risk ratios were higher among children with C-CD compared with children with NC-CD. Our finding that levels of medical complexity represent a risk factor for severe COVID-19 illness identifies a previously unidentified higher risk population, not clearly described in prior literature.

    More than half of our sample consisted of adolescents (aged 12-18 years), and thus our results are most reflective of that age group. Age stratified analyses showed that certain conditions (asthma, neurodevelopmental disorders) were more frequent among patients aged 2 years and older, whereas prematurity and cardiac and circulatory congenital anomalies were the most frequent and associated with the highest risk of COVID-19 illness among patients younger than 2 years.

    Premature birth affects 1 in 10 infants in the US39 and is a risk factor for long-term adverse sequelae, including those affecting respiratory system.40 Our finding supported prior findings of an association between prematurity and hospital admission,7 and showed that prematurity was also a risk factor for severe COVID-19 illness among hospitalized patients younger than 2 years. Future epidemiological analyses could shed light on the mechanisms of association between prematurity and severe COVID-19 illness and treatment approaches for this group of patients.

    The sensitivity analysis revealed 3 indeterminate conditions (ie, coagulation and hemorrhagic disorders, other nutritional and metabolic disorders, diseases of white blood cells) as both frequent and associated with at least 1 outcome indicating severe COVID-19 illness (eFigure 3 in the Supplement). These heterogeneous CCSR categories include a wide range of conditions, including underlying conditions and those that could appear in the course of COVID-19 illness.41-46 Inclusion of these factors, which were strongly associated with at least 1 outcome of severe COVID-19 illness, likely resulted in reduced magnitudes for the other covariates.47

    The second sensitivity analysis, performed on a subset of 20 773 children with at least 1 encounter prior to their first COVID-19 visit, found differences in the strength of associations between certain frequent conditions and risk of severe illness. It is possible that underascertainment of certain conditions because of using only pre–COVID-19 encounters resulted in a bias of the effect estimates.

    Limitations

    This study has limitations. First, using ICD-10-CM diagnostic codes to identify COVID-19 cases might result in misclassification, although COVID-19 codes in PHD-SR showed high sensitivity and specificity with SARS-CoV-2 test results.48 Second, this cross-sectional analysis could not determine causal relationships between the underlying conditions and severe COVID-19 illness. Third, using ICD-10-CM codes to identify underlying medical conditions may have led to their misclassification (eg, misclassification of diabetes type) or underestimation of their prevalence (eg, underdiagnosis of obesity).49,50 For example, 2.5% of children in the full sample and 10.2% of hospitalized children had an obesity diagnosis, compared with 18.5% nationally.51 Future studies could use measured body mass index to more accurately capture the prevalence of obesity among children with COVID-19 and their associated risk of severe illness. Fourth, hospitalization risk estimates could be biased if (1) coding of conditions differed by care setting or (2) a child’s hospital admission was driven by factors other than severity, such as parents’ or health care professionals’ fear of progressive severity. Fifth, we used payer type as the only available indicator of socioeconomic status, but confounding by socioeconomic indicators (such as access to care) is possible. Sixth, this analysis was limited to frequent chronic conditions, which may have caused us to omit acute or rare conditions that were risk factors for severe COVID-19 illness. Seventh, this analysis was completed among children with inpatient or ED encounters; hence, the results are not representative of all children with COVID-19. Future analyses that include a broader sample of children with COVID-19 are important to shed light on these associations.

    Conclusions

    This cross-sectional analysis showed that children with type 1 diabetes, cardiac and circulatory congenital anomalies, obesity, essential hypertension, epilepsy, neuropsychiatric disorders, and asthma as well as children with chronic disease were at an increased risk of hospitalization or severe COVID-19 illness. Children aged 1 year or younger with prematurity were at an increased risk of severe COVID-19 illness. Public health prevention and vaccine prioritization efforts might consider the potential for severe COVID-19 illness among children with these underlying medical conditions and chronic disease. Health care practitioners can consider the potential need for cautious clinical management of children with these conditions and COVID-19. Further epidemiologic investigation could provide insight into the causal pathways underlying our findings and identify other factors that place children at increased risk of severe COVID-19 illness.

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    Article Information

    Accepted for Publication: March 31, 2021.

    Published: June 7, 2021. doi:10.1001/jamanetworkopen.2021.11182

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Kompaniyets L et al. JAMA Network Open.

    Corresponding Author: Lyudmyla Kompaniyets, PhD, COVID-19 Response, US Centers for Disease Control and Prevention, 4770 Buford Hwy, MS S107-5, Atlanta, GA 30341 (LKompaniyets@cdc.gov).

    Author Contributions: Dr Kompaniyets 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.

    Concept and design: Kompaniyets, Nelson, Preston, Ko, Belay, DeSisto, Chevinsky, Schieber, Yusuf, Mac Kenzie, Wong, Gundlapalli, Goodman.

    Acquisition, analysis, or interpretation of data: Kompaniyets, Agathis, Nelson, Preston, Ko, Pennington, Danielson, Chevinsky, Schieber, Yusuf, Baggs, Mac Kenzie, Wong, Boehmer, Gundlapalli, Goodman.

    Drafting of the manuscript: Kompaniyets, Agathis, Nelson, Belay, DeSisto, Chevinsky, Wong, Gundlapalli.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Kompaniyets, Preston, Pennington, Chevinsky, Schieber, Baggs, Wong.

    Obtained funding: Boehmer, Gundlapalli.

    Administrative, technical, or material support: Kompaniyets, Agathis, Belay, Yusuf, Mac Kenzie, Boehmer, Gundlapalli.

    Supervision: Kompaniyets, Ko, Belay, Danielson, Mac Kenzie, Boehmer, Gundlapalli, Goodman.

    Conflict of Interest Disclosures: None reported.

    Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention (CDC) or the US Public Health Service.

    Additional Contributions: The authors thank Sachin Agnihotri, MS, Indira Srinivasan, MS, David Nitschke, BS, and Kimberly Riggle, BBA (CDC COVID-19 Response Data, Analytics, and Modeling Task Force), and John House, MS (Premier Inc), for facilitating access to these data. We thank Hannah G. Rosenblum, MD (CDC COVID-19 Community Interventions and Critical Populations Task Force), Catherine E. Barrett, PhD, and Joohyun Park, PhD (CDC Division of Diabetes Translation) for providing subject matter expertise on classifying underlying medical conditions. None of these individuals received compensation beyond their regular salaries for their work on this study.

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