OBJECTIVES

Throughout the pandemic, children with COVID-19 have experienced hospitalization, ICU admission, invasive respiratory support, and death. Using a multisite, national dataset, we investigate risk factors associated with these outcomes in children with COVID-19.

METHODS

Our data source (Optum deidentified COVID-19 Electronic Health Record Dataset) included children aged 0 to 18 years testing positive for COVID-19 between January 1, 2020, and January 20, 2022. Using ordinal logistic regression, we identified factors associated with an ordinal outcome scale: nonhospitalization, hospitalization, or a severe composite outcome (ICU, intensive respiratory support, death). To contrast hospitalization for COVID-19 and incidental positivity on hospitalization, we secondarily identified patient factors associated with hospitalizations with a primary diagnosis of COVID-19.

RESULTS

In 165 437 children with COVID-19, 3087 (1.8%) were hospitalized without complication, 2954 (1.8%) experienced ICU admission and/or intensive respiratory support, and 31 (0.02%) died. We grouped patients by age: 0 to 4 years old (35 088), and 5 to 11 years old (75 574), 12 to 18 years old (54 775). Factors positively associated with worse outcomes were preexisting comorbidities and residency in the Southern United States. In 0- to 4-year-old children, there was a nonlinear association between age and worse outcomes, with worse outcomes in 0- to 2-year-old children. In 5- to 18-year-old patients, vaccination was protective. Findings were similar in our secondary analysis of hospitalizations with a primary diagnosis of COVID-19, though region effects were no longer observed.

CONCLUSIONS

Among children with COVID-19, preexisting comorbidities and residency in the Southern United States were positively associated with worse outcomes, whereas vaccination was negatively associated. Our study population was highly insured; future studies should evaluate underinsured populations to confirm generalizability.

As of November 2022, more than 15 million children have been infected with COVID-19, representing 18.3% of US cases.1  Early in the pandemic, information on pediatric clinical characteristics and outcomes were limited, possibly because of infrequent testing and interventions (eg, school closures). As variants emerged, reports of children with severe COVID-19 increased, accompanied by a rise in multisystem inflammatory syndrome in children.2,3 

Early data suggested that children with chronic medical conditions suffered worse outcomes.413  Yet hospitalization, ICU admission, intensive respiratory support, and death rates were difficult to assess because of inconsistent illness severity and outcome definitions.1,14  Small sample sizes and minimal geographic diversity limited generalizability of clinical and demographic factors associated with poor outcomes in children with COVID-19.413  Additionally, COVID-19 vaccination rates in children vary by geography and age, necessitating exploring the impact of vaccination across ages.1524 

Harnessing a large, multisite, national, US-based electronic health record (EHR)-based dataset, we evaluated which clinical and demographic factors (including variant and vaccination) are associated with severe outcomes in children with COVID-19.

In this observational, retrospective study using the Optum COVID-19 EHR dataset (Optum Inc., Eden Prairie, Minnesota), we evaluated the probability of severe outcomes within 90 days of diagnosis as a function of clinical and demographic factors in children with COVID-19. Ninety days was chosen to be inclusive given the rarity of severe outcomes in children with COVID-19. The primary outcome was all-cause, ordinal severity with 3 categories: no hospitalization; hospitalization without intensive care (hospitalization henceforth); or a composite including ICU admission, intensive respiratory support, or death (critical illness or death). These categories were chosen because of insufficient data on items such as hypoxia and radiography in our dataset, and because of rarity of death.

We included children aged 0 to 18 years testing positive for COVID-19 on antigen or polymerase chain reaction test between January 1, 2020, and January 20, 2022. We also identified children hospitalized with a primary diagnosis of COVID-19 to compare children admitted “for COVID” to children “with COVID.” We divided our cohort into 0- to 4-, 5- to 11-, and 12- to 18-year-old groups because clinical differences, vaccine availability differences during the study period, and consistency with previous literature on pediatric COVID-19.3,25,26 

Optum’s deidentified COVID-19 EHR dataset is a national low-latency pipeline leveraging data from inpatient and ambulatory EHRs, practice management systems, and other internal systems.27  The dataset captures approximately 4.5 million adults and children tested for COVID-19 older than age 2 years, derived from more than 700 hospitals and 7000 clinics in the United States. Unique identifiers link encounters to patients. An independent statistical expert certified the data’s deidentification. This release includes patients with a diagnosis of COVID-19 or acute respiratory illness after January 1, 2020, and/or COVID-19 testing (positive or negative result) between January 1, 2020, and January 20, 2022.

For each patient, we used the first positive test for COVID-19 to define the diagnosis date and extracted diagnosis International Classification of Diseases-10 codes, severity outcomes, clinical characteristics, and demographic factors.

Hospitalization and ICU admission were identified using encounter location. Intensive respiratory support (high-flow nasal cannula, nonintensive positive pressure ventilation, and mechanical ventilation) was identified using Form UB-04 Revenue Codes (31500, 94002, 31502, 94660, 94640, 31605, 31603) or current procedure terminology/health care common procedure coding system codes (A4615, A4620, A4619, E0562). Death was identified by death date. Out-of-health-system death information (eg, death certificates) was not available.

Optum deidentification removed birth month and date; thus, age was estimated as year of first COVID-19 diagnosis minus birth year. Sex options were male or female. Race and ethnicity were included not as biologic differences but to evaluate effects of social constructs often associated with poorer access to health care on outcomes. Options were race (African American, Asian, Caucasian, and Other/Unknown) and ethnicity (Hispanic, Not Hispanic, and Unknown). We created 1 variable for race and ethnicity: patients identified as Hispanic were classified as such or classified by their reported race.28  Insurance options were commercial, public (Medicaid, Medicare, or Medicare Advantage), other, uninsured, and unknown. If multiple insurances were documented at COVID-19 diagnosis, the option with greater coverage was hierarchically selected (eg, for a patient documented with Medicaid and uninsured, Medicaid was selected). Hospital/clinic region (region henceforth) was defined using US census (Midwest, Northeast, South, West, and other/unknown).

Body mass index (BMI) was reported from the first COVID-19 diagnosis or the most recent BMI recorded within 6 months of the first COVID-19 diagnosis. BMI was excluded for 0- to 4-year-old children because of Centers for Disease Control and Prevention recommendations against using BMI-for-age in children younger than aged 2 years and high missingness in this group.29  Comorbidity data documented before COVID-19 diagnosis were aggregated using a validated index predicting hospitalization risk, referred to as pediatric comorbidity index (PCI) score.30  PCI score is a continuous variable representing increased hospitalization risk in children using International Classification of Diseases-10 codes.

Vaccination categories were: at least 1 vaccine received (documented dose of messenger RNA vaccine before COVID-19 diagnosis) or no known vaccination because of rarity of vaccination in this cohort and period. Vaccinations were extracted from vaccination encounter or self-report. No external data sources on vaccination were available. Predominant variant at diagnosis was determined using public data by region and date from outbreak information.31,32 

We identified missingness in sex (0.1%), insurance type (4.7%), and BMI (17.9%). We performed random forest imputation using the missRanger R package with 16 candidate nonmissing values and noncandidate variables to sample from in predictive mean matching steps and 100 trees to account for missingness while reducing bias and increasing precision.3336 

We described severity outcomes, clinical factors, and demographic factors in the study population. To explore associations with severe outcomes, we fit multivariable ordinal logistic regressions using proportional odds for each age group. Models for 5- to 11- and 12- to 18-year-old patients evaluate association of outcomes with age, gender, BMI, PCI score, vaccination status, predominant variant, race/ethnicity, region, and insurance status. The 0- to 4-year-old model uses these variables except BMI and vaccination status because of insufficiently granular age data and unavailability of COVID-19 vaccines for this age group during the study period, respectively. We fit models including interaction terms between region and insurance and region and race/ethnicity to adjust for confounding between race, ethnicity, and insurance status with region. Continuous variables (age, PCI score, and BMI) were modeled using restricted cubic splines with 3 knots for nonlinear relationships.37  All analysis was performed using R version 4.1.2 (R Core Team, Vienna, Austria) and rms R package, α = 0.05.37,38  Model significance (compared with null model and between models) was assessed using likelihood ratio χ2 tests. Discrimination significance was measured by concordance indices. We report odds ratios (ORs) and 95% confidence intervals (95% CI) for each factor. For factors modeled by restricted cubic splines, ORs compare 25th and 75th percentiles (interquartile range).

A secondary analysis was done using only severity outcomes (hospitalization, ICU admission, intensive respiratory support, or death) in which admissions had a primary encounter diagnosis of COVID-19 to attempt to account for differences between admissions “for COVID” and admissions “with COVID” detected on routine screening.

In our dataset, 165 437 children tested positive for COVID-19 through January 20, 2022. Most children were 12 to 18 years old (45.7%) followed by 5 to 11 years old (33.1%) and 0 to 4 years old (21.2%) (Fig 1).

FIGURE 1

Consort diagram for patient selection. Critical care outcome represents ICU admission or use of intensive respiratory support, defined as high-flow nasal cannula, nonintensive positive pressure ventilation, and mechanical ventilation.

FIGURE 1

Consort diagram for patient selection. Critical care outcome represents ICU admission or use of intensive respiratory support, defined as high-flow nasal cannula, nonintensive positive pressure ventilation, and mechanical ventilation.

Close modal

We report outcomes, clinical, and demographic factors in Table 1. Our cohort was primarily Caucasian (49.7%), followed by Hispanic (18.1%), African American (9.2%), and Asian (1.8%). Sex was split between male (50.2%) and female (49.7%). Children were primarily in the Midwest (32.8%), followed by Northeast (22.7%), South (21.9%), and West (6.5%). Most children (82.5%) had commercial insurance, followed by public insurance (10.3%).

TABLE 1

Demographics, Clinical Factors, and Outcomes for Children in this Cohort With COVID-19 by Age Group

0–4 Year Olds (N = 35 088)5–11 Year Olds (N = 75 574)12–18 Year Olds (N = 54 775)Overall (N = 16 5437)
Race/ethnicity, n (%)     
 African American 3090 (8.8) 5107 (9.3) 6974 (9.2) 15171 (9.2) 
 Asian 735 (2.1) 1105 (2.0) 1139 (1.5) 2979 (1.8) 
 Caucasian 13 608 (38.8) 27 094 (49.5) 41 533 (55.0) 82 235 (49.7) 
 Hispanic 8353 (23.8) 9552 (17.4) 12 061 (16.0) 29 966 (18.1) 
 Other/unknown 9302 (26.5) 11917 (21.8) 13 867 (18.3) 35 086 (21.2) 
Sex, n (%)     
 Female 16 185 (46.1) 26 704 (48.8) 39 252 (51.9) 82 141 (49.7) 
 Male 18 814 (53.6) 27 996 (51.1) 36 246 (48.0) 83 056 (50.2) 
 Missing 89 (0.3) 75 (0.1) 76 (0.1) 240 (0.1) 
Insurance category, n (%)     
 Commercial 27 501 (78.4) 45 476 (83.0) 63 570 (84.1) 136 547 (82.5) 
 Other 679 (1.9) 1111 (2.0) 1461 (1.9) 3251 (2.0) 
 Public 4511 (12.9) 5342 (9.8) 7141 (9.4) 16994 (10.3) 
 Uninsured 121 (0.3) 259 (0.5) 526 (0.7) 906 (0.5) 
 Missing 2276 (6.5) 2587 (4.7) 2876 (3.8) 7739 (4.7) 
Region, n (%)     
 Midwest 8693 (24.8) 16 971 (31.0) 28 673 (37.9) 54 337 (32.8) 
 Northeast 8244 (23.5) 13 791 (25.2) 15 575 (20.6) 37 610 (22.7) 
 South 9255 (26.4) 10 829 (19.8) 16 151 (21.4) 36 235 (21.9) 
 West 1947 (5.5) 3912 (7.1) 4950 (6.5) 10 809 (6.5) 
 Other/unknown 6949 (19.8) 9272 (16.9) 10 225 (13.5) 26 446 (16.0) 
Predominant variant, n (%)     
 Original 8042 (22.9) 10 012 (18.3) 17 390 (23.0) 35 444 (21.4) 
 Alpha 8585 (24.5) 11 485 (21.0) 19 922 (26.4) 39 992 (24.2) 
 Delta 6705 (19.1) 15 510 (28.3) 17 083 (22.6) 39 298 (23.8) 
 Omicron 5067 (14.4) 9002 (16.4) 11 826 (15.6) 25 895 (15.7) 
 Unknown/no predominant variant 6689 (19.1) 8766 (16.0) 9353 (12.4) 24 808 (15.0) 
BMI     
 Mean (SD) 18.1 (5.56) 19.5 (5.48) 24.7 (6.77) 21.7 (6.79) 
 Median (IQR) 17.0 (10.1–54.9) 17.8 (10.1–54.9) 23.0 (10.3–54.9) 19.9 (10.1–54.9) 
 Missing, n (%) 9934 (28.3) 9326 (17.0) 10 422 (13.8) 29 682 (17.9) 
PCI Score     
 Mean (SD) 2.00 (2.60) 2.28 (2.81) 3.08 (3.71) 2.59 (3.25) 
 Median (IQR) 1.00 (0.0–27.0) 1.00 (0.0–29.0) 2.00 (0.0–31.0) 2.00 (0.0–31.0) 
Vaccination status, n (%)     
 No known vaccination 35 088 (100) 51 405 (93.8) 63 330 (83.8) 149 823 (90.6) 
 At least 1 vaccine received 0 (0) 3370 (6.2) 12 244 (16.2) 15 614 (9.4) 
Outcome, n (%)     
 All other outcomes 32 853 (93.6) 53 463 (97.6) 73 049 (96.7) 159 365 (96.3) 
 Hospitalization without critical care 854 (2.4) 583 (1.1) 1650 (2.2) 3087 (1.9) 
 Hospitalization with critical care 1365 (3.9) 726 (1.3) 863 (1.1) 2954 (1.8) 
 Deceased 16 (0.05) 3 (0.005) 12 (0.02) 31 (0.02) 
0–4 Year Olds (N = 35 088)5–11 Year Olds (N = 75 574)12–18 Year Olds (N = 54 775)Overall (N = 16 5437)
Race/ethnicity, n (%)     
 African American 3090 (8.8) 5107 (9.3) 6974 (9.2) 15171 (9.2) 
 Asian 735 (2.1) 1105 (2.0) 1139 (1.5) 2979 (1.8) 
 Caucasian 13 608 (38.8) 27 094 (49.5) 41 533 (55.0) 82 235 (49.7) 
 Hispanic 8353 (23.8) 9552 (17.4) 12 061 (16.0) 29 966 (18.1) 
 Other/unknown 9302 (26.5) 11917 (21.8) 13 867 (18.3) 35 086 (21.2) 
Sex, n (%)     
 Female 16 185 (46.1) 26 704 (48.8) 39 252 (51.9) 82 141 (49.7) 
 Male 18 814 (53.6) 27 996 (51.1) 36 246 (48.0) 83 056 (50.2) 
 Missing 89 (0.3) 75 (0.1) 76 (0.1) 240 (0.1) 
Insurance category, n (%)     
 Commercial 27 501 (78.4) 45 476 (83.0) 63 570 (84.1) 136 547 (82.5) 
 Other 679 (1.9) 1111 (2.0) 1461 (1.9) 3251 (2.0) 
 Public 4511 (12.9) 5342 (9.8) 7141 (9.4) 16994 (10.3) 
 Uninsured 121 (0.3) 259 (0.5) 526 (0.7) 906 (0.5) 
 Missing 2276 (6.5) 2587 (4.7) 2876 (3.8) 7739 (4.7) 
Region, n (%)     
 Midwest 8693 (24.8) 16 971 (31.0) 28 673 (37.9) 54 337 (32.8) 
 Northeast 8244 (23.5) 13 791 (25.2) 15 575 (20.6) 37 610 (22.7) 
 South 9255 (26.4) 10 829 (19.8) 16 151 (21.4) 36 235 (21.9) 
 West 1947 (5.5) 3912 (7.1) 4950 (6.5) 10 809 (6.5) 
 Other/unknown 6949 (19.8) 9272 (16.9) 10 225 (13.5) 26 446 (16.0) 
Predominant variant, n (%)     
 Original 8042 (22.9) 10 012 (18.3) 17 390 (23.0) 35 444 (21.4) 
 Alpha 8585 (24.5) 11 485 (21.0) 19 922 (26.4) 39 992 (24.2) 
 Delta 6705 (19.1) 15 510 (28.3) 17 083 (22.6) 39 298 (23.8) 
 Omicron 5067 (14.4) 9002 (16.4) 11 826 (15.6) 25 895 (15.7) 
 Unknown/no predominant variant 6689 (19.1) 8766 (16.0) 9353 (12.4) 24 808 (15.0) 
BMI     
 Mean (SD) 18.1 (5.56) 19.5 (5.48) 24.7 (6.77) 21.7 (6.79) 
 Median (IQR) 17.0 (10.1–54.9) 17.8 (10.1–54.9) 23.0 (10.3–54.9) 19.9 (10.1–54.9) 
 Missing, n (%) 9934 (28.3) 9326 (17.0) 10 422 (13.8) 29 682 (17.9) 
PCI Score     
 Mean (SD) 2.00 (2.60) 2.28 (2.81) 3.08 (3.71) 2.59 (3.25) 
 Median (IQR) 1.00 (0.0–27.0) 1.00 (0.0–29.0) 2.00 (0.0–31.0) 2.00 (0.0–31.0) 
Vaccination status, n (%)     
 No known vaccination 35 088 (100) 51 405 (93.8) 63 330 (83.8) 149 823 (90.6) 
 At least 1 vaccine received 0 (0) 3370 (6.2) 12 244 (16.2) 15 614 (9.4) 
Outcome, n (%)     
 All other outcomes 32 853 (93.6) 53 463 (97.6) 73 049 (96.7) 159 365 (96.3) 
 Hospitalization without critical care 854 (2.4) 583 (1.1) 1650 (2.2) 3087 (1.9) 
 Hospitalization with critical care 1365 (3.9) 726 (1.3) 863 (1.1) 2954 (1.8) 
 Deceased 16 (0.05) 3 (0.005) 12 (0.02) 31 (0.02) 

Public insurance category includes Medicaid, Medicare, and Medicare Advantage. Critical care represents ICU admission or use of intensive respiratory support, defined as high-flow nasal cannula, nonintensive positive pressure ventilation, and mechanical ventilation. IQR, interquartile range.

Within 90 days of diagnosis with COVID-19, 2985 (1.8%) children were hospitalized with ICU admission, intensive respiratory support, or death; 3087 (1.9%) children were hospitalized without critical care; and 159 635 (96.3%) children were not hospitalized. The 0- to 4-year-old children experienced the highest incidence of hospitalization with critical care of all age groups. A total of 1.9% of unvaccinated children experienced the composite outcome of hospitalization with ICU admission (1.0%), intensive respiratory support (0.98%), or death (0.02%). No child receiving at least 1 vaccine dose died within 90 days of diagnosis.

Overall model likelihood ratio χ2 tests were statistically significant (P < .001) in all models (Supplemental Table 2). Overall model concordance indices were 0.807 for 0- to 4-, 0.881 for 5- to 11-, and 0.876 for 12- to 18-year-old patients.

Figure 2 depicts ORs and 95% CIs for the multivariable ordinal regression model between factors and severe outcomes in 0- to 4-year-old children with COVID-19. Independent factors most associated with ICU admission, intensive respiratory support, or death were residency in the Southern United States compared with the Midwest (OR, 3.61; 95% CI, 3.15–4.15), PCI score (OR, 2.59; 95% CI, 2.28–2.93), and African American compared with Caucasian race (OR, 1.44; 95% CI, 1.23–1.68). Residency in the Northeast United States compared with the Midwest (OR, 1.3; 95% CI, 1.09–1.54) and Hispanic ethnicity (OR, 1.16; 95% CI, 1.03–1.29) were positively associated with severe outcomes. Compared with the original strain, the Alpha variant was more associated with severe outcomes (OR, 1.25; 95% CI, 1.13–1.39). The Delta (OR, 0.47; 95% CI, 0.4–0.56) and Omicron (OR, 0.26; 95% CI, 0.2–0.34) variants were negatively associated with severe outcomes. Female sex (OR, 0.82; 95% CI, 0.75–0.9) and 1-year-old age compared with 3-year-old age (OR, 0.58; 95% CI, 0.54–0.63) were negatively associated with severe outcomes. We produced a partial-effects plot to predict age effects holding other variables constant, showing highest relative risk in infants with a nonlinear decrease as age approaches 2 years (Supplemental Fig 5).

FIGURE 2

Odds ratios with 95% confidence intervals from multivariable ordinal regression model between risk factors and severe outcomes in 0- to 4-year-old patients with COVID-19. For continuous variables represented by restricted cubic splines (age and PCI score), we present adjusted odds ratios comparing the 75th to 25th percentiles.

FIGURE 2

Odds ratios with 95% confidence intervals from multivariable ordinal regression model between risk factors and severe outcomes in 0- to 4-year-old patients with COVID-19. For continuous variables represented by restricted cubic splines (age and PCI score), we present adjusted odds ratios comparing the 75th to 25th percentiles.

Close modal

Figure 3 depicts ORs and 95% CIs for the multivariable ordinal regression model between factors and severe outcomes in 5- to 11-year-old patients with COVID-19. Independent factors most associated with ICU admission, intensive respiratory support, or death were residency in the Southern United States compared with the Midwest (OR, 7.16; 95% CI, 5.94–8.63), PCI score (OR, 4.58; 95% CI, 3.78–5.55), and African American compared with Caucasian race (OR, 1.85; 95% CI, 1.52–2.24). Asian compared with Caucasian race (OR, 1.69; 95% CI, 1.18–2.42), residency in the Northeast United States compared with the Midwest (OR, 1.67; 95% CI, 1.32–2.11) and Hispanic ethnicity (OR, 1.65; 95% CI, 1.43–1.9) was positively associated with severe outcomes. Female sex (OR, 0.83; 95% CI, 0.74–0.94), 10-year-old age compared with 6-year-old age (OR, 0.78; 95% CI, 0.69–0.88), and receiving at least 1 vaccine dose (OR, 0.48; 95% CI, 0.33–0.71) were negatively associated with severe outcomes. Compared with the original strain, Delta (OR, 0.26; 95% CI, 0.21–0.32) and Omicron (OR, 0.11; 95% CI, 0.08–0.16) variants were negatively associated with severe outcomes.

FIGURE 3

Odds ratios with 95% confidence intervals from multivariable ordinal regression model between risk factors and severe outcomes in 5- to 11-year-old patients with COVID-19. For continuous variables represented by restricted cubic splines (age, BMI [kg/m2], and PCI score), we present adjusted odds ratios comparing the 75th to 25th percentiles.

FIGURE 3

Odds ratios with 95% confidence intervals from multivariable ordinal regression model between risk factors and severe outcomes in 5- to 11-year-old patients with COVID-19. For continuous variables represented by restricted cubic splines (age, BMI [kg/m2], and PCI score), we present adjusted odds ratios comparing the 75th to 25th percentiles.

Close modal

Figure 4 depicts ORs and 95% CIs for the multivariable ordinal regression model between factors and severe outcomes in 12- to 18-year-old patients with COVID-19. Independent factors most associated with ICU admission, intensive respiratory support, or death were PCI score (OR, 6.67; 95% CI, 5.72–7.78), residency in the Southern United States compared with the Midwest (OR, 5.8; 95% CI, 5.17–6.52), and Asian compared with Caucasian race (OR, 1.97; 95% CI, 1.45–2.69). Hispanic ethnicity (OR, 1.93; 95% CI, 1.74–2.14), African American compared with Caucasian race (OR, 1.76; 95% CI, 1.53–2.03), residency in the West United States compared with the Midwest (OR, 1.58; 95% CI, 1.23–2.03), and residency in the Northeast United States compared with the Midwest (OR, 1.31; 95% CI, 1.1–1.56) were positively associated with severe outcomes. Compared with the original strain, Delta (OR, 0.27; 95% CI, 0.23–0.31) and Omicron (OR, 0.08; 95% CI, 0.06–0.11) variants were negatively associated with severe outcomes. Receiving at least 1 vaccine dose was negatively associated with severe outcomes (OR, 0.59; 95% CI, 0.51–0.68).

FIGURE 4

Odds ratios with 95% confidence intervals from multivariable ordinal regression model between risk factors and severe outcomes in 12- to 18-year-old patients with COVID-19. For continuous variables represented by restricted cubic splines (age, BMI [kg/m2], and PCI score), we present adjusted odds ratios comparing the 75th to 25th percentiles.

FIGURE 4

Odds ratios with 95% confidence intervals from multivariable ordinal regression model between risk factors and severe outcomes in 12- to 18-year-old patients with COVID-19. For continuous variables represented by restricted cubic splines (age, BMI [kg/m2], and PCI score), we present adjusted odds ratios comparing the 75th to 25th percentiles.

Close modal

Models with linear interactions between region and insurance type and region and race/ethnicity were significant and had similar discrimination per C indices (Supplemental Table 3). Although these factors do interact, the models demonstrate independently significant region, insurance type, and race/ethnicity effects after accounting for interactions.

Supplemental Figs 6, 7, and 8 depict ORs and 95% CIs for the secondary analysis of multivariable ordinal regressions between risk factors and hospitalizations with a primary encounter diagnosis of COVID-19 in 0- to 4-, 5- to 11-, and 12- to 18-year-old patients, respectively. We report outcomes, clinical, and demographic factors for this analysis in Supplemental Table 4. Limiting criteria for hospitalization, ICU admission, intensive respiratory support, or death to only such outcomes with a primary encounter diagnosis of COVID-19 reduced the number of children experiencing these outcomes from 6072 in our initial analysis to 2232 in the secondary analysis. Most factors associated with ICU admission, intensive respiratory support, and death are shared between children hospitalized with COVID-19 and those hospitalized for COVID-19 as the primary encounter diagnosis; however, some are not. In 0- to 4-year-old patients, Hispanic ethnicity, Alpha variant, and were no longer associated with severe outcomes. Residence in the US South was negatively associated with severe outcomes. In 5- to 11-year-old patients, Asian race, residence in the US South, and residence in the US West were no longer associated with severe outcomes. In 12- to 18-year-old patients, residence in the US South and residence in the US West were no longer associated with severe outcomes.

In this nationwide cohort of 165 437 children with COVID-19, several variables were positively associated with severe outcomes. Across all ages, residency in the US South, pre–COVID-19 comorbidities, African American race, Hispanic ethnicity, and predominant variant at test positivity were positively associated with severe outcomes. Infancy and male sex were positively associated with severe outcomes in the 0- to 4-year-old group. Asian race and lack of vaccination were positively associated with severe outcomes in the 5- to 18-year-old group.

Hospitalization and mortality rates in our dataset were consistent with American Academy of Pediatrics–reported rates in US children, and our analysis substantiates previous findings that infancy, non-Caucasian race, Hispanic ethnicity, and preexisting comorbidities are associated with severe outcomes in COVID-19.1,410  Proportionally more children in our 0- to 4-year-old cohort experienced severe outcomes compared with the older age groups. The greatest risk for severe outcomes was in those aged younger than 2 years old, contrasting previous observations that young infants were protected from severe COVID-19.39,40  The Delta and Omicron variants were negatively associated with ICU admission, intensive respiratory support, and death compared with the original strain in our cohort, conflicting with studies showing increased hospitalizations and severe infections resulting from the Omicron and Delta variants.2,3  This may reflect our highly insured cohort, resulting in increased health care access that may have reduced severe outcomes as the pandemic progressed and more effective treatments were identified.

Residency in the Southern United States was a significant, independent factor positively associated with severe outcomes, even after accounting for interactions with insurance status and race/ethnicity, aligning with another published regional analysis.41  This may be explained by poverty rates and reduced access to social programs and health care, but the reason for this disparity is unknown.41,42  Our results confirm that vaccination is negatively associated with severe outcomes in 5- to 18-year-old patients, supporting increased vaccination rates in children, particularly in vulnerable regions and populations.

This study has several strengths. First, longitudinal data available throughout the pandemic allowed for assessment through shifts in variant predominance, infection rates, and vaccine eligibility. Second, presence of many variables from the EHR data allowed inclusion of a wide range of clinical and demographic patient characteristics. Third, the dataset produced sufficient sample sizes to include uncommon severe outcomes in children with COVID-19, allowing for many clinical variables without exceeding allowable statistical degrees of freedom, and to describe incidence of COVID-19 in partially or fully vaccinated children.

Like any observational study using a large EHR dataset, our study suffers limitations. First, loss of birth date and month allowed only for age estimation. Though the estimation was consistent, this limits prediction in individual patients. However, our findings of a nonlinearly increased severe outcome rate in youngest patients supports clinical practice.

Second, our dataset period limits understanding vaccination effects. Vaccines were available to children during a short period in our dataset (from December 2020 for 16- to 18-year-old patients, May 2021 for 12- to 15-year-old patients, and November 2021 for 5- to 11-year-old patients). Despite this, vaccination was independently associated with less severe outcomes. Our study cutoff occurred before Omicron BA.4, BA.5, and other subvariants, and only contained vaccinations reported from a clinical encounter or self-report, potentially leading to underreporting. However, we believe our findings bolster vaccine outreach, especially among the most vulnerable.

Third, some variables were coded as “other/unknown.” We treated “other/unknown” as a separate category rather than missing to account for potential bias in these categories. Fourth, few children experienced severe outcomes in our cohort. Though fortunate, this limited our ability to evaluate severe outcomes individually. Fifth, vaccine types, polymerase chain reaction test names, and procedure codes were documented differently between sites. Computational and manual methods were used to include these, but some information could have been missed.

Sixth, outcomes were limited by their availability in the dataset. Patients hospitalized outside the Optum-associated network were not recorded. Because the database is amalgamated by an analytics company associated with a national insurer, only 10% of our cohort had public insurance, which is well below the reported 37.5% in US children.43  Thus, our results may not be generalizable among patients with low access to care and emerging treatments. Future studies using Medicaid data or multisite data from public hospitals may address this gap. Seventh, clinical site and EHR system heterogeneity in our study prevented use of standard-of-care risk scores (eg, Pediatric Risk of Mortality, Pediatric Logistic Organ Dysfunction, Pediatric Early Warning Signs) to confirm infection severity.

Finally, some patients in our study were likely admitted “with COVID” identified on routine screening on admission for reasons other than “for COVID,” leading to inclusion of patients whose outcomes may not have been related to COVID-19. Our secondary analysis of patients with a primary encounter diagnosis of COVID-19 attempts to account for this. However, we acknowledge the limitations of EHR-designated primary encounter diagnosis to represent the true primary clinical diagnosis given the complexity and variability in primary diagnosis documentation.

Our study demonstrates population level associations between demographic and clinical factors and severe outcomes in pediatric COVID-19. Our findings demonstrate the protective effect of vaccines, increased association with severe outcomes among infants, and disparities related to race, ethnicity, insurance, and geography. We identified populations in need of outreach for vaccination and other preventive efforts.

Ms Ho and Dr Turer conceptualized and designed the study, conducted the analyses, drafted the initial manuscript, and reviewed and revised the manuscript; Drs Most, Perl, Radunsky, Hanna, and Thakur conceptualized and designed the study and reviewed and revised the manuscript critically for important intellectual content; Mr Diaz, Ms Casazza, Dr Saleh, and Ms Pickering designed and conducted analyses and reviewed and revised the manuscript critically for important intellectual content; Drs Medford and Lehmann contributed substantially to conception and design of the study, acquisition of data, supervised the analyses, and reviewed and revised the manuscript critically for important intellectual content; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: This work was supported by the UT Southwestern Medical Center’s Clinical Informatics Center. The funder/sponsor did not participate in the work.

CONFLICT OF INTEREST DISCLOSURES: Dr Medford is funded through the Texas Health Resources Clinical Scholar Program, the Centers for Disease Control and Prevention (grant U01CK000590) and has received research funding through Verily Life Sciences and the Sergey Brin Family Foundation. All other authors have indicated they have no potential conflicts of interest to disclose.

1.
American Academy of Pediatrics
.
Children and COVID-19: state-level data report
.
2.
Kuehn
BM
.
Delta variant linked with spike in youth hospitalizations
.
JAMA
.
2021
;
326
(
14
):
1366
3.
Wang
L
,
Berger
NA
,
Kaelber
DC
,
Davis
PB
,
Volkow
ND
,
Xu
R
.
COVID infection severity in children under 5 years old before and after Omicron emergence in the US
.
MedRxiv
.
2022
;
2022.01.12.22269179
.
4.
Badal
S
,
Thapa Bajgain
K
,
Badal
S
,
Thapa
R
,
Bajgain
BB
,
Santana
MJ
.
Prevalence, clinical characteristics, and outcomes of pediatric COVID-19: a systematic review and meta-analysis
.
J Clin Virol
.
2021
;
135
:
104715
5.
Alsohime
F
,
Temsah
MH
,
Al-Nemri
AM
,
Somily
AM
,
Al-Subaie
S
.
COVID-19 infection prevalence in pediatric population: etiology, clinical presentation, and outcome
.
J Infect Public Health
.
2020
;
13
(
12
):
1791
1796
6.
Tsankov
BK
,
Allaire
JM
,
Irvine
MA
, et al
.
Severe COVID-19 infection and pediatric comorbidities: a systematic review and meta-analysis
.
Int J Infect Dis
.
2021
;
103
:
246
256
7.
Yasuhara
J
,
Kuno
T
,
Takagi
H
,
Sumitomo
N
.
Clinical characteristics of COVID-19 in children: a systematic review
.
Pediatr Pulmonol
.
2020
;
55
(
10
):
2565
2575
8.
Cui
X
,
Zhao
Z
,
Zhang
T
, et al
.
A systematic review and meta-analysis of children with coronavirus disease 2019 (COVID-19)
.
J Med Virol
.
2021
;
93
(
2
):
1057
1069
9.
Ludvigsson
JF
.
Systematic review of COVID-19 in children shows milder cases and a better prognosis than adults
.
Acta Paediatr
.
2020
;
109
(
6
):
1088
1095
10.
Blumfield
E
,
Levin
TL
.
COVID-19 in pediatric patients: a case series from the Bronx, NY
.
Pediatr Radiol
.
2020
;
50
(
10
):
1369
1374
10.1007/s00247-020-04782-2
11.
Bellino
S
,
Punzo
O
,
Rota
MC
, et al
;
COVID-19 WORKING GROUP
.
COVID-19 disease severity risk factors for pediatric patients in Italy
.
Pediatrics
.
2020
;
146
(
4
):
e2020009399
10.1542/peds.2020-009399
12.
Chen
J
,
Zhang
ZZ
,
Chen
YK
, et al
.
The clinical and immunological features of pediatric COVID-19 patients in China
.
Genes Dis
.
2020
;
7
(
4
):
535
541
13.
Mehta
NS
,
Mytton
OT
,
Mullins
EWS
, et al
.
SARS-CoV-2 (COVID-19): what do we know about children? A systematic review
.
Clin Infect Dis
.
2020
;
71
(
9
):
2469
2479
14.
Arvisais-Anhalt
S
,
Lehmann
CU
,
Park
JY
, et al
.
What the coronavirus disease 2019 (COVID-19) pandemic has reinforced: the need for accurate data
.
Clin Infect Dis
.
2021
;
72
(
6
):
920
923
15.
American Academy of Pediatrics
.
Children and COVID-19 vaccination trends
.
16.
Troiano
G
,
Nardi
A
.
Vaccine hesitancy in the era of COVID-19
.
Public Health
.
2021
;
194
:
245
251
17.
Aw
J
,
Seng
JJB
,
Seah
SSY
,
Low
LL
.
COVID-19 vaccine hesitancy-a scoping review of literature in high-income countries
.
Vaccines (Basel)
.
2021
;
9
(
8
):
900
18.
Klein
NP
,
Stockwell
MS
,
Demarco
M
, et al
.
Effectiveness of COVID-19 Pfizer-BioNTech BNT162b2 mRNA vaccination in preventing COVID-19-associated emergency department and urgent care encounters and hospitalizations among nonimmunocompromised children and adolescents aged 5-17 Years - VISION Network, 10 States, April 2021-January 2022
.
MMWR Morb Mortal Wkly Rep
.
2022
;
71
(
9
):
352
358
19.
Lutrick
K
,
Rivers
P
,
Yoo
YM
, et al
.
Interim estimate of vaccine effectiveness of BNT162b2 (Pfizer-BioNTech) vaccine in preventing SARS-CoV-2 infection among adolescents aged 12-17 years - Arizona, July-December 2021
.
MMWR Morb Mortal Wkly Rep
.
2021
;
70
(
5152
):
1761
1765
20.
Olson
SM
,
Newhams
MM
,
Halasa
NB
, et al
;
Overcoming Covid-19 Investigators
.
Effectiveness of BNT162b2 vaccine against critical Covid-19 in adolescents
.
N Engl J Med
.
2022
;
386
(
8
):
713
723
21.
Reis
BY
,
Barda
N
,
Leshchinsky
M
, et al
.
Effectiveness of BNT162b2 vaccine against delta variant in adolescents
.
N Engl J Med
.
2021
;
385
(
22
):
2101
2103
22.
Hause
AM
,
Marquez
P
,
Zhang
B
, et al
.
COVID-19 mRNA vaccine safety among children aged 6 months-5 years - United States, June 18, 2022-August 21, 2022
.
MMWR Morb Mortal Wkly Rep
.
2022
;
71
(
35
):
1115
1120
23.
Cohen-Stavi
CJ
,
Magen
O
,
Barda
N
, et al
.
BNT162b2 vaccine effectiveness against omicron in children 5 to 11 years of age
.
N Engl J Med
.
2022
;
387
(
3
):
227
236
24.
Tan
SHX
,
Cook
AR
,
Heng
D
,
Ong
B
,
Lye
DC
,
Tan
KB
.
Effectiveness of BNT162b2 vaccine against omicron in children 5 to 11 years of age
.
N Engl J Med
.
2022
;
387
(
6
):
525
532
25.
Martin
B
,
DeWitt
PE
,
Russell
S
, et al
.
Characteristics, outcomes, and severity risk factors associated with SARS-CoV-2 infection among children in the US National COVID Cohort Collaborative
.
JAMA Netw Open
.
2022
;
5
(
2
):
e2143151
26.
Bourgeois
FT
,
Gutiérrez-Sacristán
A
,
Keller
MS
, et al
;
Consortium for Clinical Characterization of COVID-19 by EHR (4CE)
.
International analysis of electronic health records of children and youth hospitalized with COVID-19 infection in 6 countries
.
JAMA Netw Open
.
2021
;
4
(
6
):
e2112596
27.
Chomistek
AK
,
Liang
C
,
Doherty
MC
, et al
.
Predictors of critical care, mechanical ventilation, and mortality among hospitalized patients with COVID-19 in an electronic health record database
.
BMC Infect Dis
.
2022
;
22
(
1
):
413
28.
Flanagin
A
,
Frey
T
,
Christiansen
SL
;
AMA Manual of Style Committee
.
Updated guidance on the reporting of race and ethnicity in medical and science journals
.
JAMA
.
2021
;
326
(
7
):
621
627
10.1001/jama.2021.13304
29.
Centers for Disease Control and Prevention
.
Division of Nutrition, Physical Activity, and Obesity
.
Growth chart training: using the WHO growth charts
.
30.
Sun
JW
,
Bourgeois
FT
,
Haneuse
S
, et al
.
Development and validation of a Pediatric Comorbidity Index
.
Am J Epidemiol
.
2021
;
190
(
5
):
918
927
31.
Gangavarapu
K
,
Latif
AA
,
Mullen
J
, et al
.
Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations
.
Res Sq
.
2022
;
rs.3.rs-1723829
32.
Tsueng
G
,
Mullen
J
,
Alkuzweny
M
, et al
.
Outbreak.info Research Library: a standardized, searchable platform to discover and explore COVID-19 resources
.
BioRxiv
.
2022
;
2022.01.20.477133
33.
Mayer
M
.
missRanger: fast imputation of missing values
.
Available at: https://CRAN.R-project.org/package=missRanger. Accessed June 5, 2022
34.
Shah
AD
,
Bartlett
JW
,
Carpenter
J
,
Nicholas
O
,
Hemingway
H
.
Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study
.
Am J Epidemiol
.
2014
;
179
(
6
):
764
774
35.
Goto
T
,
Camargo
CA
Jr
,
Faridi
MK
,
Freishtat
RJ
,
Hasegawa
K
.
Machine learning-based prediction of clinical outcomes for children during emergency department triage
.
JAMA Netw Open
.
2019
;
2
(
1
):
e186937
36.
Murray
JS
.
Multiple imputation: a review of practical and theoretical findings
.
Stat Sci
.
2018
;
33
(
2
):
142
159
37.
Harrell
FE
Jr
.
Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis
.
New York
:
Springer International Publishing
;
2015
38.
Harrell
FE
Jr
.
rms: Regression modeling strategies
.
Available at: https://CRAN.R-project.org/package=rms. Accessed September 26, 2022
39.
Marks
KJ
,
Whitaker
M
,
Anglin
O
, et al
;
COVID-NET Surveillance Team
.
Hospitalizations of children and adolescents with laboratory-confirmed COVID-19 - COVID-NET, 14 states, July 2021-January 2022
.
MMWR Morb Mortal Wkly Rep
.
2022
;
71
(
7
):
271
278
40.
CDC COVID-19 Response Team
.
Severe outcomes among patients with coronavirus disease 2019 (COVID-19) - United States, February 12-March 16, 2020
.
MMWR Morb Mortal Wkly Rep
.
2020
;
69
(
12
):
343
346
41.
Stoto
MA
,
Schlageter
S
,
Kraemer
JD
.
COVID-19 mortality in the United States: it’s been two Americas from the start
.
PLoS One
.
2022
;
17
(
4
):
e0265053
42.
Vestal
C
.
The south may see the largest share of coronavirus misery
.
Available at: https://pew.org/2JU13yT. Accessed June 15, 2022
43.
KFF
.
Health insurance coverage of children 0-18
.
Available at: https://www.kff.org/other/state-indicator/children-0-18/. Accessed July 19, 2022

Supplementary data