Chén C Kenyon, MD, MSHP

The COVID-19 pandemic has led to more than 40 million infections and more than 650,000 deaths in the United States alone.¹ Morbidity and mortality have disproportionately affected older adults.^2-4 However, acute infection and delayed effects, such as multisystem inflammatory syndrome in children (MIS-C), occur and can lead to severe complications, hospitalization, and death in pediatric patients.^5,6 Due to higher clinical disease prevalence and morbidity in the adult population, we have learned much about the clinical factors associated with severe adult COVID-19 disease.^5,7-9 Such clinical factors include older age, concurrent comorbidities, smoke exposure, and Black race or Hispanic ethnicity, among others.^5,7-10 However, there is a paucity of data on severe COVID-19 disease in pediatric patients.^5,11,12 In addition, most immunization strategies and pharmacologic treatments for COVID-19 have not been evaluated or approved for use in children.¹³ To guide targeted prevention and treatment strategies, there is a critical need to identify children and adolescents—who are among the most vulnerable patient populations—at high risk for severe disease.

Identifying the clinical factors associated with severe COVID-19 disease will help with prioritizing and allocating vaccines when they are approved for use in patients younger than 12 years. It also can provide insight for clinicians and families faced with decisions wherein individual risk assessment is crucial (eg, in-person schooling, other group activities). The objective of this study was to determine the clinical factors associated with severe COVID-19 among children and adolescents in the United States.

Study Design

We conducted a multicenter retrospective cohort study of patients presenting for care at pediatric hospitals that report data to the Pediatric Health Information System (PHIS) database. The PHIS administrative database includes billing and utilization data from 45 US tertiary care hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Data quality and reliability are ensured through a joint validation effort between the Children’s Hospital Association and participating hospitals. Hospitals submit discharge data, including demographics, diagnoses, and procedures using International Classification of Diseases, 10th Revision (ICD-10) codes, along with daily detailed information on pharmacy, location of care, and other services.

Study Population

Patients 30 days to 18 years of age discharged from the emergency department (ED) or inpatient setting with a primary diagnosis of COVID-19 (ICD-10 codes U.071 and U.072) between April 1, 2020, and September 30, 2020, were eligible for inclusion.¹⁴ In a prior study, the positive predictive value of an ICD-10–coded diagnosis of COVID-19 among hospitalized pediatric patients was 95.5%, compared with reverse transcription polymerase reaction results or presence of MIS-C.¹⁵ The diagnostic code for COVID-19 (ICD-10-CM) also had a high sensitivity (98.0%) in the hospitalized population.¹⁶ Acknowledging the increasing practice of screening patients upon admission, and in an attempt to minimize potential misclassification, we did not include encounters with secondary diagnoses of COVID-19 in our primary analyses. Pediatric patients with surgical diagnoses and neonates who never left the hospital were also excluded.

Factors Associated With Severe COVID-19 Disease

Exposures of interest were determined a priori based on current evidence in the literature and included patient age (0-4 years, 5-11 years, and 12-18 years), sex, race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, other non-White race [defined as Pacific Islander, Native American, or other]), payor type, cardiovascular complex chronic conditions (CCC), neuromuscular CCC, obesity/type 2 diabetes mellitus (DM), pulmonary CCC, asthma (defined using ICD-10 codes¹⁷), and immunocompromised CCC. Race and ethnicity were included as covariates based on previous studies reporting differences in COVID-19 outcomes among racial and ethnic groups.⁹ The CCC covariates were defined using the pediatric CCC ICD-10 classification system version 2.¹⁸

Pediatric Complications and Conditions Associated With COVID-19

Based on current evidence and expert opinion of study members, associated diagnoses and complications co-occurring with a COVID-19 diagnosis were defined a priori and identified through ICD-10 codes (Appendix Table 1). These included acute kidney injury, acute liver injury, aseptic meningitis, asthma exacerbation, bronchiolitis, cerebral infarction, croup, encephalitis, encephalopathy, infant fever, febrile seizure, gastroenteritis/dehydration, Kawasaki disease/MIS-C, myocarditis/pericarditis, pneumonia, lung effusion or empyema, respiratory failure, sepsis, nonfebrile seizure, pancreatitis, sickle cell complications, and thrombotic complications.

Outcomes

COVID-19 severity outcomes were assessed as follows: (1) mild = ED discharge; (2) moderate = inpatient admission; (3) severe = intensive care unit (ICU) admission without mechanical ventilation, shock, or death; and (4) very severe = ICU admission with mechanical ventilation, shock, or death.¹⁹ This ordinal ranking system did not violate the proportional odds assumption. Potential reasons for admission to the ICU without mechanical ventilation, shock, or death include, but are not limited to, need for noninvasive ventilation, vital sign instability, dysrhythmias, respiratory insufficiency, or complications arising from concurrent conditions (eg, thrombotic events, need for continuous albuterol therapy). We examined several secondary, hospital-based outcomes, including associated diagnoses and complications, all-cause 30-day healthcare reutilization (ED visit or rehospitalization), length of stay (LOS), and ICU LOS.

Statistical Analysis

Demographic characteristics were summarized using frequencies and percentages for categorical variables and geometric means with SD and medians with interquartile ranges (IQR) for continuous variables, as appropriate. Factors associated with hospitalization (encompassing severity levels 2-4) vs ED discharge (severity level 1) were assessed using logistic regression. Factors associated with increasing severity among hospitalized pediatric patients (severity levels 2, 3, and 4) were assessed using ordinal logistic regression. Covariates in these analyses included race and ethnicity, age, sex, payor, cardiovascular CCC, neurologic/neuromuscular CCC, obesity/type 2 DM, pulmonary CCC, asthma, and immunocompromised CCC. Adjusted odds ratios (aOR) and corresponding 95% CI for each risk factor were generated using generalized linear mixed effects models and random intercepts for each hospital. Given the potential for diagnostic misclassification of pediatric patients with COVID-19 based on primary vs secondary diagnoses, we performed sensitivity analyses defining the study population as those with a primary diagnosis of COVID-19 and those with a secondary diagnosis of COVID-19 plus a concurrent primary diagnosis of a condition associated with COVID-19 (Appendix Table 1).

All analyses were performed using SAS version 9.4 (SAS Institute, Inc), and P < .05 was considered statistically significant. The Institutional Review Board at Vanderbilt University Medical Center determined that this study of de-identified data did not meet the criteria for human subjects research.

Study Population

A total of 19,976 encounters were included in the study. Of those, 15,913 (79.7%) were discharged from the ED and 4063 (20.3%) were hospitalized (Table 1). The most common race/ethnicity was Hispanic (9741, 48.8%), followed by non-Hispanic White (4217, 21.1%). Reference race/ethnicity data for the overall 2019 PHIS population can be found in Appendix Table 2.

The severity distribution among the hospitalized population was moderate (3222, 79.3%), severe (431, 11.3%), and very severe (380, 9.4%). The frequency of COVID-19 diagnoses increased late in the study period (Figure). Among those hospitalized, the median LOS for the index admission was 2 days (IQR, 1-4), while among those admitted to the ICU, the median LOS was 3 days (IQR, 2-5).

Overall, 10.1% (n = 2020) of the study population had an all-cause repeat encounter (ie, subsequent ED encounter or hospitalization) within 30 days following the index discharge. Repeat encounters were more frequent among patients hospitalized than among those discharged from the ED (Appendix Table 3).

Prevalence of Conditions and Complications Associated With COVID-19

Overall, 3257 (16.3%) patients had one or more co-occurring diagnoses categorized as a COVID-19–associated condition or complication. The most frequent diagnoses included lower respiratory tract disease (pneumonia, lung effusion, or empyema; n = 1415, 7.1%), gastroenteritis/dehydration (n = 1068, 5.3%), respiratory failure (n = 731, 3.7%), febrile infant (n = 413, 2.1%), and nonfebrile seizure (n = 425, 2.1%). Aside from nonfebrile seizure, neurological complications were less frequent and included febrile seizure (n = 155, 0.8%), encephalopathy (n = 63, 0.3%), aseptic meningitis (n = 16, 0.1%), encephalitis (n = 11, 0.1%), and cerebral infarction (n = 6, <0.1%). Kawasaki disease and MIS-C comprised 1.7% (n = 346) of diagnoses. Thrombotic complications occurred in 0.1% (n = 13) of patients. Overall, these conditions and complications associated with COVID-19 were more frequent in hospitalized patients than in those discharged from the ED (P < .001) (Table 2).

Factors Associated With COVID-19 Disease Severity

Compared to pediatric patients with COVID-19 discharged from the ED, factors associated with increased odds of hospitalization included private payor insurance; obesity/type 2 DM; asthma; and cardiovascular, immunocompromised, neurologic/neuromuscular, and pulmonary CCCs (Table 3). Factors associated with decreased risk of hospitalization included Black race or Hispanic ethnicity compared with White race; female sex; and age 5 to 11 years and age 12 to 17 years (vs age 0-4 years). Among children and adolescents hospitalized with COVID-19, factors associated with greater disease severity included Black or other non-White race; age 5 to 11 years; age 12 to 17 years; obesity/type 2 DM; immunocompromised conditions; and cardiovascular, neurologic/neuromuscular, and pulmonary CCCs (Table 3).

Sensitivity Analysis

We performed a sensitivity analysis that expanded the study population to include those with a secondary diagnosis of COVID-19 plus a diagnosis of a COVID-19–associated condition or complication. Analyses using the expanded population (N = 21,247) were similar to the primary analyses (Appendix Table 4 and Appendix Table 5).

In this large multicenter study evaluating COVID-19 disease severity in more than 19,000 patients presenting for emergency care at US pediatric hospitals, approximately 20% were hospitalized, and among those hospitalized almost a quarter required ICU care. Clinical risk factors associated with increased risk of hospitalization include private payor status and selected comorbidities (obesity/type 2 DM; asthma; and cardiovascular, pulmonary, immunocompromised, neurologic/neuromuscular CCCs), while those associated with decreased risk of hospitalization include older age, female sex, and Black race or Hispanic ethnicity. Factors associated with severe disease among hospitalized pediatric patients include Black or other non-White race, school age (≥5 years), and certain chronic conditions (cardiovascular disease, obesity/type 2 DM, neurologic or neuromuscular disease). Sixteen percent of patients had a concurrent diagnosis for a condition or complication associated with COVID-19.

While the study population (ie, children and adolescents presenting to the ED) represents a small fraction of children and adolescents in the community with SARS-CoV-2 infection, the results provide important insight into factors of severe COVID-19 in the pediatric population. A report from France suggested ventilatory or hemodynamic support or death were independently associated with older age (≥10 years), elevated C-reactive protein, and hypoxemia.¹² An Italian study found that younger age (0-4 years) was associated with less severe disease, while preexisting conditions were more likely in patients with severe disease.¹¹ A single-center case series of 50 patients (aged ≤21 years) hospitalized at a children’s hospital in New York City found respiratory failure (n = 9) was more common in children older than 1 year, patients with elevated inflammatory markers, and patients with obesity.²⁰

Our study confirms several factors for severe COVID-19 found in these studies, including older age,^11,12,20 obesity,²⁰ and preexisting conditions.¹¹ Our findings also expand on these reports, including identification of factors associated with hospitalization. Given the rate of 30-day re-encounters among pediatric patients with COVID-19 (10.1%), identifying risk factors for hospitalization may aid ED providers in determining optimal disposition (eg, home, hospital admission, ICU). We also identified specific comorbidities associated with more severe disease in those hospitalized with COVID-19, such as cardiovascular disease, obesity/type 2 DM, and pulmonary, neurologic, or neuromuscular conditions. We also found that asthma increased the risk for hospitalization but not more severe disease among those hospitalized. This latter finding also aligns with recent single-center studies,^21,22 whereas a Turkish study of pediatric patients aged 0 to 18 years found no association between asthma and COVID-19 hospitalizations.²³We also examined payor type and racial/ethnic factors in our analysis. In 2019, patients who identified as Black or Hispanic comprised 52.3% of all encounters and 40.7% of hospitalizations recorded in the PHIS database. During the same year, encounters for influenza among Black or Hispanic pediatric patients comprised 58.7% of all influenza diagnoses and 47.0% of pediatric influenza hospitalizations (Appendix Table 2). In this study, patients who identified as Black or Hispanic race represented a disproportionately large share of patients presenting to children’s hospitals (68.5%) and of those hospitalized (60.8%). Hispanic ethnicity, in particular, represented a disproportionate share of patients seeking care for COVID-19 compared to the overall PHIS population (47.7% and 27.1%, respectively). After accounting for other factors, we found Black and other non-White race—but not of Hispanic ethnicity—were independently associated with more disease severity among those hospitalized. This contrasts with findings from a recent adult study by Yehia et al,²⁴ who found (after adjusting for other clinical factors) no significant difference in mortality between Black patients and White patients among adults hospitalized due to COVID-19. It also contrasts with a recent large population-based UK study wherein pediatric patients identifying as Asian, but not Black or mixed race or ethnicity, had an increased risk of hospital admission and admission to the ICU compared to children identifying as White. Children identifying as Black or mixed race had longer hospital admissions.²⁵ However, as the authors of the study note, residual confounders and ascertainment bias due to differences in COVID testing may have influenced these findings.

Our findings of differences in hospitalization and disease severity among those hospitalized by race and ethnicity should be interpreted carefully. These may reflect a constellation of factors that are difficult to measure, including differences in healthcare access, inequalities in care (including hospital admission inequalities), and implicit bias—all of which may reflect structural racism. For example, it is possible that children who identify as Black or Hispanic have different access to care compared to children who identify as White, and this may affect disease severity on presentation.² Alternatively, it is possible that White pediatric patients are more likely to be hospitalized as compared to non-White pediatric patients with similar illness severity. Our finding that pediatric patients who identify as Hispanic or Black had a lower risk of hospitalization should be also interpreted carefully, as this may reflect higher utilization of the ED for SARS-CoV-2 testing, increased use of nonemergency services among those without access to primary care, or systematic differences in provider decision-making among this segment of the population.² Further study is needed to determine specific drivers for racial and ethnic differences in healthcare utilization in children and adolescents with COVID-19.²⁶

Complications and co-occurring diagnoses in adults with COVID-19 are well documented.^27-30 However, there is little information to date on the co-occurring diagnoses and complications associated with COVID-19 in children and adolescents. We found that complications and co-occurring conditions occurred in 16.3% of the study population, with the most frequent conditions including known complications of viral infections such as pneumonia, respiratory failure, and seizures. Acute kidney and liver injury, as well as thrombotic complications, occurred less commonly than in adults.^26-29 Interestingly, neurologic complications were also uncommon compared to adult reports^8,31 and less frequent than in other viral illnesses in children and adolescents. For example, neurologic complications occur in approximately 7.5% of children and adolescents hospitalized with influenza.³²

Limitations of the present study include the retrospective design, as well as incomplete patient-level clinical data in the PHIS database. The PHIS database only includes children’s hospitals, which may limit the generalizability of findings to community hospitals. We also excluded newborns, and our findings may not be generalizable to this population. We only included children and adolescents with a primary diagnosis of COVID-19, which has the potential for misclassification in cases where COVID-19 was a secondary diagnosis. However, results of our sensitivity analysis, which incorporated secondary diagnoses of COVID-19, were consistent with findings from our main analyses. Our study was designed to examine associations between certain prespecified factors and COVID-19 severity among pediatric patients who visited the ED or were admitted to the hospital during the COVID-19 pandemic. Thus, our findings must be interpreted in light of these considerations and may not be generalizable outside the ED or hospital setting. For example, it could be that some segments of the population utilized ED resources for testing, whereas others avoided the ED and other healthcare settings for fear of exposure to SARS-CoV-2. We also relied on diagnosis codes to identify concurrent diagnoses, as well as mechanical ventilation in our very severe outcome cohort, which resulted in this classification for some of these diagnoses. Despite these limitations, our findings represent an important step in understanding the risk factors associated with severe clinical COVID-19 disease in pediatric patients.

Our findings may inform future research and clinical interventions. Future studies on antiviral therapies and immune modulators targeting SARS-CoV-2 infection in children and adolescents should focus on high-risk populations, such as those identified in the study, as these patients are most likely to benefit from therapeutic interventions. Similarly, vaccine-development efforts may benefit from additional evaluation in high-risk populations, some of which may have altered immune responses. Furthermore, with increasing vaccination among adults and changes in recommendations, societal mitigation efforts (eg, masking, physical distancing) will diminish. Continued vigilance and COVID-19–mitigation efforts among high-risk children, for whom vaccines are not yet available, are critical during this transition.

Among children with COVID-19 who received care at children’s hospitals and EDs, 20% were hospitalized, and, of those, 21% were admitted to the ICU. Older children and adolescent patients had a lower risk of hospitalization; however, when hospitalized, they had greater illness severity. Those with selected comorbidities (eg, cardiovascular, obesity/type 2 DM, pulmonary and neurologic or neuromuscular disease) had both increased odds of hospitalization and in-hospital illness severity. While there were observed differences in COVID-19 severity by race and ethnicity, additional research is needed to clarify the drivers of such disparities. These factors should be considered when prioritizing mitigation strategies to prevent infection (eg, remote learning, avoidance of group activities, prioritization of COVID-19 vaccine when approved for children aged <12 years).

The COVID-19 pandemic has led to more than 40 million infections and more than 650,000 deaths in the United States alone.¹ Morbidity and mortality have disproportionately affected older adults.^2-4 However, acute infection and delayed effects, such as multisystem inflammatory syndrome in children (MIS-C), occur and can lead to severe complications, hospitalization, and death in pediatric patients.^5,6 Due to higher clinical disease prevalence and morbidity in the adult population, we have learned much about the clinical factors associated with severe adult COVID-19 disease.^5,7-9 Such clinical factors include older age, concurrent comorbidities, smoke exposure, and Black race or Hispanic ethnicity, among others.^5,7-10 However, there is a paucity of data on severe COVID-19 disease in pediatric patients.^5,11,12 In addition, most immunization strategies and pharmacologic treatments for COVID-19 have not been evaluated or approved for use in children.¹³ To guide targeted prevention and treatment strategies, there is a critical need to identify children and adolescents—who are among the most vulnerable patient populations—at high risk for severe disease.

Identifying the clinical factors associated with severe COVID-19 disease will help with prioritizing and allocating vaccines when they are approved for use in patients younger than 12 years. It also can provide insight for clinicians and families faced with decisions wherein individual risk assessment is crucial (eg, in-person schooling, other group activities). The objective of this study was to determine the clinical factors associated with severe COVID-19 among children and adolescents in the United States.

Study Design

We conducted a multicenter retrospective cohort study of patients presenting for care at pediatric hospitals that report data to the Pediatric Health Information System (PHIS) database. The PHIS administrative database includes billing and utilization data from 45 US tertiary care hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Data quality and reliability are ensured through a joint validation effort between the Children’s Hospital Association and participating hospitals. Hospitals submit discharge data, including demographics, diagnoses, and procedures using International Classification of Diseases, 10th Revision (ICD-10) codes, along with daily detailed information on pharmacy, location of care, and other services.

Study Population

Patients 30 days to 18 years of age discharged from the emergency department (ED) or inpatient setting with a primary diagnosis of COVID-19 (ICD-10 codes U.071 and U.072) between April 1, 2020, and September 30, 2020, were eligible for inclusion.¹⁴ In a prior study, the positive predictive value of an ICD-10–coded diagnosis of COVID-19 among hospitalized pediatric patients was 95.5%, compared with reverse transcription polymerase reaction results or presence of MIS-C.¹⁵ The diagnostic code for COVID-19 (ICD-10-CM) also had a high sensitivity (98.0%) in the hospitalized population.¹⁶ Acknowledging the increasing practice of screening patients upon admission, and in an attempt to minimize potential misclassification, we did not include encounters with secondary diagnoses of COVID-19 in our primary analyses. Pediatric patients with surgical diagnoses and neonates who never left the hospital were also excluded.

Factors Associated With Severe COVID-19 Disease

Exposures of interest were determined a priori based on current evidence in the literature and included patient age (0-4 years, 5-11 years, and 12-18 years), sex, race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, other non-White race [defined as Pacific Islander, Native American, or other]), payor type, cardiovascular complex chronic conditions (CCC), neuromuscular CCC, obesity/type 2 diabetes mellitus (DM), pulmonary CCC, asthma (defined using ICD-10 codes¹⁷), and immunocompromised CCC. Race and ethnicity were included as covariates based on previous studies reporting differences in COVID-19 outcomes among racial and ethnic groups.⁹ The CCC covariates were defined using the pediatric CCC ICD-10 classification system version 2.¹⁸

Pediatric Complications and Conditions Associated With COVID-19

Based on current evidence and expert opinion of study members, associated diagnoses and complications co-occurring with a COVID-19 diagnosis were defined a priori and identified through ICD-10 codes (Appendix Table 1). These included acute kidney injury, acute liver injury, aseptic meningitis, asthma exacerbation, bronchiolitis, cerebral infarction, croup, encephalitis, encephalopathy, infant fever, febrile seizure, gastroenteritis/dehydration, Kawasaki disease/MIS-C, myocarditis/pericarditis, pneumonia, lung effusion or empyema, respiratory failure, sepsis, nonfebrile seizure, pancreatitis, sickle cell complications, and thrombotic complications.

Outcomes

COVID-19 severity outcomes were assessed as follows: (1) mild = ED discharge; (2) moderate = inpatient admission; (3) severe = intensive care unit (ICU) admission without mechanical ventilation, shock, or death; and (4) very severe = ICU admission with mechanical ventilation, shock, or death.¹⁹ This ordinal ranking system did not violate the proportional odds assumption. Potential reasons for admission to the ICU without mechanical ventilation, shock, or death include, but are not limited to, need for noninvasive ventilation, vital sign instability, dysrhythmias, respiratory insufficiency, or complications arising from concurrent conditions (eg, thrombotic events, need for continuous albuterol therapy). We examined several secondary, hospital-based outcomes, including associated diagnoses and complications, all-cause 30-day healthcare reutilization (ED visit or rehospitalization), length of stay (LOS), and ICU LOS.

Statistical Analysis

Demographic characteristics were summarized using frequencies and percentages for categorical variables and geometric means with SD and medians with interquartile ranges (IQR) for continuous variables, as appropriate. Factors associated with hospitalization (encompassing severity levels 2-4) vs ED discharge (severity level 1) were assessed using logistic regression. Factors associated with increasing severity among hospitalized pediatric patients (severity levels 2, 3, and 4) were assessed using ordinal logistic regression. Covariates in these analyses included race and ethnicity, age, sex, payor, cardiovascular CCC, neurologic/neuromuscular CCC, obesity/type 2 DM, pulmonary CCC, asthma, and immunocompromised CCC. Adjusted odds ratios (aOR) and corresponding 95% CI for each risk factor were generated using generalized linear mixed effects models and random intercepts for each hospital. Given the potential for diagnostic misclassification of pediatric patients with COVID-19 based on primary vs secondary diagnoses, we performed sensitivity analyses defining the study population as those with a primary diagnosis of COVID-19 and those with a secondary diagnosis of COVID-19 plus a concurrent primary diagnosis of a condition associated with COVID-19 (Appendix Table 1).

All analyses were performed using SAS version 9.4 (SAS Institute, Inc), and P < .05 was considered statistically significant. The Institutional Review Board at Vanderbilt University Medical Center determined that this study of de-identified data did not meet the criteria for human subjects research.

Study Population

A total of 19,976 encounters were included in the study. Of those, 15,913 (79.7%) were discharged from the ED and 4063 (20.3%) were hospitalized (Table 1). The most common race/ethnicity was Hispanic (9741, 48.8%), followed by non-Hispanic White (4217, 21.1%). Reference race/ethnicity data for the overall 2019 PHIS population can be found in Appendix Table 2.

The severity distribution among the hospitalized population was moderate (3222, 79.3%), severe (431, 11.3%), and very severe (380, 9.4%). The frequency of COVID-19 diagnoses increased late in the study period (Figure). Among those hospitalized, the median LOS for the index admission was 2 days (IQR, 1-4), while among those admitted to the ICU, the median LOS was 3 days (IQR, 2-5).

Overall, 10.1% (n = 2020) of the study population had an all-cause repeat encounter (ie, subsequent ED encounter or hospitalization) within 30 days following the index discharge. Repeat encounters were more frequent among patients hospitalized than among those discharged from the ED (Appendix Table 3).

Prevalence of Conditions and Complications Associated With COVID-19

Overall, 3257 (16.3%) patients had one or more co-occurring diagnoses categorized as a COVID-19–associated condition or complication. The most frequent diagnoses included lower respiratory tract disease (pneumonia, lung effusion, or empyema; n = 1415, 7.1%), gastroenteritis/dehydration (n = 1068, 5.3%), respiratory failure (n = 731, 3.7%), febrile infant (n = 413, 2.1%), and nonfebrile seizure (n = 425, 2.1%). Aside from nonfebrile seizure, neurological complications were less frequent and included febrile seizure (n = 155, 0.8%), encephalopathy (n = 63, 0.3%), aseptic meningitis (n = 16, 0.1%), encephalitis (n = 11, 0.1%), and cerebral infarction (n = 6, <0.1%). Kawasaki disease and MIS-C comprised 1.7% (n = 346) of diagnoses. Thrombotic complications occurred in 0.1% (n = 13) of patients. Overall, these conditions and complications associated with COVID-19 were more frequent in hospitalized patients than in those discharged from the ED (P < .001) (Table 2).

Factors Associated With COVID-19 Disease Severity

Compared to pediatric patients with COVID-19 discharged from the ED, factors associated with increased odds of hospitalization included private payor insurance; obesity/type 2 DM; asthma; and cardiovascular, immunocompromised, neurologic/neuromuscular, and pulmonary CCCs (Table 3). Factors associated with decreased risk of hospitalization included Black race or Hispanic ethnicity compared with White race; female sex; and age 5 to 11 years and age 12 to 17 years (vs age 0-4 years). Among children and adolescents hospitalized with COVID-19, factors associated with greater disease severity included Black or other non-White race; age 5 to 11 years; age 12 to 17 years; obesity/type 2 DM; immunocompromised conditions; and cardiovascular, neurologic/neuromuscular, and pulmonary CCCs (Table 3).

Sensitivity Analysis

We performed a sensitivity analysis that expanded the study population to include those with a secondary diagnosis of COVID-19 plus a diagnosis of a COVID-19–associated condition or complication. Analyses using the expanded population (N = 21,247) were similar to the primary analyses (Appendix Table 4 and Appendix Table 5).

In this large multicenter study evaluating COVID-19 disease severity in more than 19,000 patients presenting for emergency care at US pediatric hospitals, approximately 20% were hospitalized, and among those hospitalized almost a quarter required ICU care. Clinical risk factors associated with increased risk of hospitalization include private payor status and selected comorbidities (obesity/type 2 DM; asthma; and cardiovascular, pulmonary, immunocompromised, neurologic/neuromuscular CCCs), while those associated with decreased risk of hospitalization include older age, female sex, and Black race or Hispanic ethnicity. Factors associated with severe disease among hospitalized pediatric patients include Black or other non-White race, school age (≥5 years), and certain chronic conditions (cardiovascular disease, obesity/type 2 DM, neurologic or neuromuscular disease). Sixteen percent of patients had a concurrent diagnosis for a condition or complication associated with COVID-19.

While the study population (ie, children and adolescents presenting to the ED) represents a small fraction of children and adolescents in the community with SARS-CoV-2 infection, the results provide important insight into factors of severe COVID-19 in the pediatric population. A report from France suggested ventilatory or hemodynamic support or death were independently associated with older age (≥10 years), elevated C-reactive protein, and hypoxemia.¹² An Italian study found that younger age (0-4 years) was associated with less severe disease, while preexisting conditions were more likely in patients with severe disease.¹¹ A single-center case series of 50 patients (aged ≤21 years) hospitalized at a children’s hospital in New York City found respiratory failure (n = 9) was more common in children older than 1 year, patients with elevated inflammatory markers, and patients with obesity.²⁰

Our study confirms several factors for severe COVID-19 found in these studies, including older age,^11,12,20 obesity,²⁰ and preexisting conditions.¹¹ Our findings also expand on these reports, including identification of factors associated with hospitalization. Given the rate of 30-day re-encounters among pediatric patients with COVID-19 (10.1%), identifying risk factors for hospitalization may aid ED providers in determining optimal disposition (eg, home, hospital admission, ICU). We also identified specific comorbidities associated with more severe disease in those hospitalized with COVID-19, such as cardiovascular disease, obesity/type 2 DM, and pulmonary, neurologic, or neuromuscular conditions. We also found that asthma increased the risk for hospitalization but not more severe disease among those hospitalized. This latter finding also aligns with recent single-center studies,^21,22 whereas a Turkish study of pediatric patients aged 0 to 18 years found no association between asthma and COVID-19 hospitalizations.²³We also examined payor type and racial/ethnic factors in our analysis. In 2019, patients who identified as Black or Hispanic comprised 52.3% of all encounters and 40.7% of hospitalizations recorded in the PHIS database. During the same year, encounters for influenza among Black or Hispanic pediatric patients comprised 58.7% of all influenza diagnoses and 47.0% of pediatric influenza hospitalizations (Appendix Table 2). In this study, patients who identified as Black or Hispanic race represented a disproportionately large share of patients presenting to children’s hospitals (68.5%) and of those hospitalized (60.8%). Hispanic ethnicity, in particular, represented a disproportionate share of patients seeking care for COVID-19 compared to the overall PHIS population (47.7% and 27.1%, respectively). After accounting for other factors, we found Black and other non-White race—but not of Hispanic ethnicity—were independently associated with more disease severity among those hospitalized. This contrasts with findings from a recent adult study by Yehia et al,²⁴ who found (after adjusting for other clinical factors) no significant difference in mortality between Black patients and White patients among adults hospitalized due to COVID-19. It also contrasts with a recent large population-based UK study wherein pediatric patients identifying as Asian, but not Black or mixed race or ethnicity, had an increased risk of hospital admission and admission to the ICU compared to children identifying as White. Children identifying as Black or mixed race had longer hospital admissions.²⁵ However, as the authors of the study note, residual confounders and ascertainment bias due to differences in COVID testing may have influenced these findings.

Our findings of differences in hospitalization and disease severity among those hospitalized by race and ethnicity should be interpreted carefully. These may reflect a constellation of factors that are difficult to measure, including differences in healthcare access, inequalities in care (including hospital admission inequalities), and implicit bias—all of which may reflect structural racism. For example, it is possible that children who identify as Black or Hispanic have different access to care compared to children who identify as White, and this may affect disease severity on presentation.² Alternatively, it is possible that White pediatric patients are more likely to be hospitalized as compared to non-White pediatric patients with similar illness severity. Our finding that pediatric patients who identify as Hispanic or Black had a lower risk of hospitalization should be also interpreted carefully, as this may reflect higher utilization of the ED for SARS-CoV-2 testing, increased use of nonemergency services among those without access to primary care, or systematic differences in provider decision-making among this segment of the population.² Further study is needed to determine specific drivers for racial and ethnic differences in healthcare utilization in children and adolescents with COVID-19.²⁶

Complications and co-occurring diagnoses in adults with COVID-19 are well documented.^27-30 However, there is little information to date on the co-occurring diagnoses and complications associated with COVID-19 in children and adolescents. We found that complications and co-occurring conditions occurred in 16.3% of the study population, with the most frequent conditions including known complications of viral infections such as pneumonia, respiratory failure, and seizures. Acute kidney and liver injury, as well as thrombotic complications, occurred less commonly than in adults.^26-29 Interestingly, neurologic complications were also uncommon compared to adult reports^8,31 and less frequent than in other viral illnesses in children and adolescents. For example, neurologic complications occur in approximately 7.5% of children and adolescents hospitalized with influenza.³²

Limitations of the present study include the retrospective design, as well as incomplete patient-level clinical data in the PHIS database. The PHIS database only includes children’s hospitals, which may limit the generalizability of findings to community hospitals. We also excluded newborns, and our findings may not be generalizable to this population. We only included children and adolescents with a primary diagnosis of COVID-19, which has the potential for misclassification in cases where COVID-19 was a secondary diagnosis. However, results of our sensitivity analysis, which incorporated secondary diagnoses of COVID-19, were consistent with findings from our main analyses. Our study was designed to examine associations between certain prespecified factors and COVID-19 severity among pediatric patients who visited the ED or were admitted to the hospital during the COVID-19 pandemic. Thus, our findings must be interpreted in light of these considerations and may not be generalizable outside the ED or hospital setting. For example, it could be that some segments of the population utilized ED resources for testing, whereas others avoided the ED and other healthcare settings for fear of exposure to SARS-CoV-2. We also relied on diagnosis codes to identify concurrent diagnoses, as well as mechanical ventilation in our very severe outcome cohort, which resulted in this classification for some of these diagnoses. Despite these limitations, our findings represent an important step in understanding the risk factors associated with severe clinical COVID-19 disease in pediatric patients.

Our findings may inform future research and clinical interventions. Future studies on antiviral therapies and immune modulators targeting SARS-CoV-2 infection in children and adolescents should focus on high-risk populations, such as those identified in the study, as these patients are most likely to benefit from therapeutic interventions. Similarly, vaccine-development efforts may benefit from additional evaluation in high-risk populations, some of which may have altered immune responses. Furthermore, with increasing vaccination among adults and changes in recommendations, societal mitigation efforts (eg, masking, physical distancing) will diminish. Continued vigilance and COVID-19–mitigation efforts among high-risk children, for whom vaccines are not yet available, are critical during this transition.

Among children with COVID-19 who received care at children’s hospitals and EDs, 20% were hospitalized, and, of those, 21% were admitted to the ICU. Older children and adolescent patients had a lower risk of hospitalization; however, when hospitalized, they had greater illness severity. Those with selected comorbidities (eg, cardiovascular, obesity/type 2 DM, pulmonary and neurologic or neuromuscular disease) had both increased odds of hospitalization and in-hospital illness severity. While there were observed differences in COVID-19 severity by race and ethnicity, additional research is needed to clarify the drivers of such disparities. These factors should be considered when prioritizing mitigation strategies to prevent infection (eg, remote learning, avoidance of group activities, prioritization of COVID-19 vaccine when approved for children aged <12 years).

The COVID-19 pandemic has led to more than 40 million infections and more than 650,000 deaths in the United States alone.¹ Morbidity and mortality have disproportionately affected older adults.^2-4 However, acute infection and delayed effects, such as multisystem inflammatory syndrome in children (MIS-C), occur and can lead to severe complications, hospitalization, and death in pediatric patients.^5,6 Due to higher clinical disease prevalence and morbidity in the adult population, we have learned much about the clinical factors associated with severe adult COVID-19 disease.^5,7-9 Such clinical factors include older age, concurrent comorbidities, smoke exposure, and Black race or Hispanic ethnicity, among others.^5,7-10 However, there is a paucity of data on severe COVID-19 disease in pediatric patients.^5,11,12 In addition, most immunization strategies and pharmacologic treatments for COVID-19 have not been evaluated or approved for use in children.¹³ To guide targeted prevention and treatment strategies, there is a critical need to identify children and adolescents—who are among the most vulnerable patient populations—at high risk for severe disease.

Identifying the clinical factors associated with severe COVID-19 disease will help with prioritizing and allocating vaccines when they are approved for use in patients younger than 12 years. It also can provide insight for clinicians and families faced with decisions wherein individual risk assessment is crucial (eg, in-person schooling, other group activities). The objective of this study was to determine the clinical factors associated with severe COVID-19 among children and adolescents in the United States.

Study Design

We conducted a multicenter retrospective cohort study of patients presenting for care at pediatric hospitals that report data to the Pediatric Health Information System (PHIS) database. The PHIS administrative database includes billing and utilization data from 45 US tertiary care hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Data quality and reliability are ensured through a joint validation effort between the Children’s Hospital Association and participating hospitals. Hospitals submit discharge data, including demographics, diagnoses, and procedures using International Classification of Diseases, 10th Revision (ICD-10) codes, along with daily detailed information on pharmacy, location of care, and other services.

Study Population

Patients 30 days to 18 years of age discharged from the emergency department (ED) or inpatient setting with a primary diagnosis of COVID-19 (ICD-10 codes U.071 and U.072) between April 1, 2020, and September 30, 2020, were eligible for inclusion.¹⁴ In a prior study, the positive predictive value of an ICD-10–coded diagnosis of COVID-19 among hospitalized pediatric patients was 95.5%, compared with reverse transcription polymerase reaction results or presence of MIS-C.¹⁵ The diagnostic code for COVID-19 (ICD-10-CM) also had a high sensitivity (98.0%) in the hospitalized population.¹⁶ Acknowledging the increasing practice of screening patients upon admission, and in an attempt to minimize potential misclassification, we did not include encounters with secondary diagnoses of COVID-19 in our primary analyses. Pediatric patients with surgical diagnoses and neonates who never left the hospital were also excluded.

Factors Associated With Severe COVID-19 Disease

Exposures of interest were determined a priori based on current evidence in the literature and included patient age (0-4 years, 5-11 years, and 12-18 years), sex, race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, other non-White race [defined as Pacific Islander, Native American, or other]), payor type, cardiovascular complex chronic conditions (CCC), neuromuscular CCC, obesity/type 2 diabetes mellitus (DM), pulmonary CCC, asthma (defined using ICD-10 codes¹⁷), and immunocompromised CCC. Race and ethnicity were included as covariates based on previous studies reporting differences in COVID-19 outcomes among racial and ethnic groups.⁹ The CCC covariates were defined using the pediatric CCC ICD-10 classification system version 2.¹⁸

Pediatric Complications and Conditions Associated With COVID-19

Based on current evidence and expert opinion of study members, associated diagnoses and complications co-occurring with a COVID-19 diagnosis were defined a priori and identified through ICD-10 codes (Appendix Table 1). These included acute kidney injury, acute liver injury, aseptic meningitis, asthma exacerbation, bronchiolitis, cerebral infarction, croup, encephalitis, encephalopathy, infant fever, febrile seizure, gastroenteritis/dehydration, Kawasaki disease/MIS-C, myocarditis/pericarditis, pneumonia, lung effusion or empyema, respiratory failure, sepsis, nonfebrile seizure, pancreatitis, sickle cell complications, and thrombotic complications.

Outcomes

COVID-19 severity outcomes were assessed as follows: (1) mild = ED discharge; (2) moderate = inpatient admission; (3) severe = intensive care unit (ICU) admission without mechanical ventilation, shock, or death; and (4) very severe = ICU admission with mechanical ventilation, shock, or death.¹⁹ This ordinal ranking system did not violate the proportional odds assumption. Potential reasons for admission to the ICU without mechanical ventilation, shock, or death include, but are not limited to, need for noninvasive ventilation, vital sign instability, dysrhythmias, respiratory insufficiency, or complications arising from concurrent conditions (eg, thrombotic events, need for continuous albuterol therapy). We examined several secondary, hospital-based outcomes, including associated diagnoses and complications, all-cause 30-day healthcare reutilization (ED visit or rehospitalization), length of stay (LOS), and ICU LOS.

Statistical Analysis

Demographic characteristics were summarized using frequencies and percentages for categorical variables and geometric means with SD and medians with interquartile ranges (IQR) for continuous variables, as appropriate. Factors associated with hospitalization (encompassing severity levels 2-4) vs ED discharge (severity level 1) were assessed using logistic regression. Factors associated with increasing severity among hospitalized pediatric patients (severity levels 2, 3, and 4) were assessed using ordinal logistic regression. Covariates in these analyses included race and ethnicity, age, sex, payor, cardiovascular CCC, neurologic/neuromuscular CCC, obesity/type 2 DM, pulmonary CCC, asthma, and immunocompromised CCC. Adjusted odds ratios (aOR) and corresponding 95% CI for each risk factor were generated using generalized linear mixed effects models and random intercepts for each hospital. Given the potential for diagnostic misclassification of pediatric patients with COVID-19 based on primary vs secondary diagnoses, we performed sensitivity analyses defining the study population as those with a primary diagnosis of COVID-19 and those with a secondary diagnosis of COVID-19 plus a concurrent primary diagnosis of a condition associated with COVID-19 (Appendix Table 1).

All analyses were performed using SAS version 9.4 (SAS Institute, Inc), and P < .05 was considered statistically significant. The Institutional Review Board at Vanderbilt University Medical Center determined that this study of de-identified data did not meet the criteria for human subjects research.

Study Population

A total of 19,976 encounters were included in the study. Of those, 15,913 (79.7%) were discharged from the ED and 4063 (20.3%) were hospitalized (Table 1). The most common race/ethnicity was Hispanic (9741, 48.8%), followed by non-Hispanic White (4217, 21.1%). Reference race/ethnicity data for the overall 2019 PHIS population can be found in Appendix Table 2.

The severity distribution among the hospitalized population was moderate (3222, 79.3%), severe (431, 11.3%), and very severe (380, 9.4%). The frequency of COVID-19 diagnoses increased late in the study period (Figure). Among those hospitalized, the median LOS for the index admission was 2 days (IQR, 1-4), while among those admitted to the ICU, the median LOS was 3 days (IQR, 2-5).

Overall, 10.1% (n = 2020) of the study population had an all-cause repeat encounter (ie, subsequent ED encounter or hospitalization) within 30 days following the index discharge. Repeat encounters were more frequent among patients hospitalized than among those discharged from the ED (Appendix Table 3).

Prevalence of Conditions and Complications Associated With COVID-19

Overall, 3257 (16.3%) patients had one or more co-occurring diagnoses categorized as a COVID-19–associated condition or complication. The most frequent diagnoses included lower respiratory tract disease (pneumonia, lung effusion, or empyema; n = 1415, 7.1%), gastroenteritis/dehydration (n = 1068, 5.3%), respiratory failure (n = 731, 3.7%), febrile infant (n = 413, 2.1%), and nonfebrile seizure (n = 425, 2.1%). Aside from nonfebrile seizure, neurological complications were less frequent and included febrile seizure (n = 155, 0.8%), encephalopathy (n = 63, 0.3%), aseptic meningitis (n = 16, 0.1%), encephalitis (n = 11, 0.1%), and cerebral infarction (n = 6, <0.1%). Kawasaki disease and MIS-C comprised 1.7% (n = 346) of diagnoses. Thrombotic complications occurred in 0.1% (n = 13) of patients. Overall, these conditions and complications associated with COVID-19 were more frequent in hospitalized patients than in those discharged from the ED (P < .001) (Table 2).

Factors Associated With COVID-19 Disease Severity

Compared to pediatric patients with COVID-19 discharged from the ED, factors associated with increased odds of hospitalization included private payor insurance; obesity/type 2 DM; asthma; and cardiovascular, immunocompromised, neurologic/neuromuscular, and pulmonary CCCs (Table 3). Factors associated with decreased risk of hospitalization included Black race or Hispanic ethnicity compared with White race; female sex; and age 5 to 11 years and age 12 to 17 years (vs age 0-4 years). Among children and adolescents hospitalized with COVID-19, factors associated with greater disease severity included Black or other non-White race; age 5 to 11 years; age 12 to 17 years; obesity/type 2 DM; immunocompromised conditions; and cardiovascular, neurologic/neuromuscular, and pulmonary CCCs (Table 3).

Sensitivity Analysis

We performed a sensitivity analysis that expanded the study population to include those with a secondary diagnosis of COVID-19 plus a diagnosis of a COVID-19–associated condition or complication. Analyses using the expanded population (N = 21,247) were similar to the primary analyses (Appendix Table 4 and Appendix Table 5).

In this large multicenter study evaluating COVID-19 disease severity in more than 19,000 patients presenting for emergency care at US pediatric hospitals, approximately 20% were hospitalized, and among those hospitalized almost a quarter required ICU care. Clinical risk factors associated with increased risk of hospitalization include private payor status and selected comorbidities (obesity/type 2 DM; asthma; and cardiovascular, pulmonary, immunocompromised, neurologic/neuromuscular CCCs), while those associated with decreased risk of hospitalization include older age, female sex, and Black race or Hispanic ethnicity. Factors associated with severe disease among hospitalized pediatric patients include Black or other non-White race, school age (≥5 years), and certain chronic conditions (cardiovascular disease, obesity/type 2 DM, neurologic or neuromuscular disease). Sixteen percent of patients had a concurrent diagnosis for a condition or complication associated with COVID-19.

While the study population (ie, children and adolescents presenting to the ED) represents a small fraction of children and adolescents in the community with SARS-CoV-2 infection, the results provide important insight into factors of severe COVID-19 in the pediatric population. A report from France suggested ventilatory or hemodynamic support or death were independently associated with older age (≥10 years), elevated C-reactive protein, and hypoxemia.¹² An Italian study found that younger age (0-4 years) was associated with less severe disease, while preexisting conditions were more likely in patients with severe disease.¹¹ A single-center case series of 50 patients (aged ≤21 years) hospitalized at a children’s hospital in New York City found respiratory failure (n = 9) was more common in children older than 1 year, patients with elevated inflammatory markers, and patients with obesity.²⁰

Our study confirms several factors for severe COVID-19 found in these studies, including older age,^11,12,20 obesity,²⁰ and preexisting conditions.¹¹ Our findings also expand on these reports, including identification of factors associated with hospitalization. Given the rate of 30-day re-encounters among pediatric patients with COVID-19 (10.1%), identifying risk factors for hospitalization may aid ED providers in determining optimal disposition (eg, home, hospital admission, ICU). We also identified specific comorbidities associated with more severe disease in those hospitalized with COVID-19, such as cardiovascular disease, obesity/type 2 DM, and pulmonary, neurologic, or neuromuscular conditions. We also found that asthma increased the risk for hospitalization but not more severe disease among those hospitalized. This latter finding also aligns with recent single-center studies,^21,22 whereas a Turkish study of pediatric patients aged 0 to 18 years found no association between asthma and COVID-19 hospitalizations.²³We also examined payor type and racial/ethnic factors in our analysis. In 2019, patients who identified as Black or Hispanic comprised 52.3% of all encounters and 40.7% of hospitalizations recorded in the PHIS database. During the same year, encounters for influenza among Black or Hispanic pediatric patients comprised 58.7% of all influenza diagnoses and 47.0% of pediatric influenza hospitalizations (Appendix Table 2). In this study, patients who identified as Black or Hispanic race represented a disproportionately large share of patients presenting to children’s hospitals (68.5%) and of those hospitalized (60.8%). Hispanic ethnicity, in particular, represented a disproportionate share of patients seeking care for COVID-19 compared to the overall PHIS population (47.7% and 27.1%, respectively). After accounting for other factors, we found Black and other non-White race—but not of Hispanic ethnicity—were independently associated with more disease severity among those hospitalized. This contrasts with findings from a recent adult study by Yehia et al,²⁴ who found (after adjusting for other clinical factors) no significant difference in mortality between Black patients and White patients among adults hospitalized due to COVID-19. It also contrasts with a recent large population-based UK study wherein pediatric patients identifying as Asian, but not Black or mixed race or ethnicity, had an increased risk of hospital admission and admission to the ICU compared to children identifying as White. Children identifying as Black or mixed race had longer hospital admissions.²⁵ However, as the authors of the study note, residual confounders and ascertainment bias due to differences in COVID testing may have influenced these findings.

Our findings of differences in hospitalization and disease severity among those hospitalized by race and ethnicity should be interpreted carefully. These may reflect a constellation of factors that are difficult to measure, including differences in healthcare access, inequalities in care (including hospital admission inequalities), and implicit bias—all of which may reflect structural racism. For example, it is possible that children who identify as Black or Hispanic have different access to care compared to children who identify as White, and this may affect disease severity on presentation.² Alternatively, it is possible that White pediatric patients are more likely to be hospitalized as compared to non-White pediatric patients with similar illness severity. Our finding that pediatric patients who identify as Hispanic or Black had a lower risk of hospitalization should be also interpreted carefully, as this may reflect higher utilization of the ED for SARS-CoV-2 testing, increased use of nonemergency services among those without access to primary care, or systematic differences in provider decision-making among this segment of the population.² Further study is needed to determine specific drivers for racial and ethnic differences in healthcare utilization in children and adolescents with COVID-19.²⁶

Complications and co-occurring diagnoses in adults with COVID-19 are well documented.^27-30 However, there is little information to date on the co-occurring diagnoses and complications associated with COVID-19 in children and adolescents. We found that complications and co-occurring conditions occurred in 16.3% of the study population, with the most frequent conditions including known complications of viral infections such as pneumonia, respiratory failure, and seizures. Acute kidney and liver injury, as well as thrombotic complications, occurred less commonly than in adults.^26-29 Interestingly, neurologic complications were also uncommon compared to adult reports^8,31 and less frequent than in other viral illnesses in children and adolescents. For example, neurologic complications occur in approximately 7.5% of children and adolescents hospitalized with influenza.³²

Limitations of the present study include the retrospective design, as well as incomplete patient-level clinical data in the PHIS database. The PHIS database only includes children’s hospitals, which may limit the generalizability of findings to community hospitals. We also excluded newborns, and our findings may not be generalizable to this population. We only included children and adolescents with a primary diagnosis of COVID-19, which has the potential for misclassification in cases where COVID-19 was a secondary diagnosis. However, results of our sensitivity analysis, which incorporated secondary diagnoses of COVID-19, were consistent with findings from our main analyses. Our study was designed to examine associations between certain prespecified factors and COVID-19 severity among pediatric patients who visited the ED or were admitted to the hospital during the COVID-19 pandemic. Thus, our findings must be interpreted in light of these considerations and may not be generalizable outside the ED or hospital setting. For example, it could be that some segments of the population utilized ED resources for testing, whereas others avoided the ED and other healthcare settings for fear of exposure to SARS-CoV-2. We also relied on diagnosis codes to identify concurrent diagnoses, as well as mechanical ventilation in our very severe outcome cohort, which resulted in this classification for some of these diagnoses. Despite these limitations, our findings represent an important step in understanding the risk factors associated with severe clinical COVID-19 disease in pediatric patients.

Our findings may inform future research and clinical interventions. Future studies on antiviral therapies and immune modulators targeting SARS-CoV-2 infection in children and adolescents should focus on high-risk populations, such as those identified in the study, as these patients are most likely to benefit from therapeutic interventions. Similarly, vaccine-development efforts may benefit from additional evaluation in high-risk populations, some of which may have altered immune responses. Furthermore, with increasing vaccination among adults and changes in recommendations, societal mitigation efforts (eg, masking, physical distancing) will diminish. Continued vigilance and COVID-19–mitigation efforts among high-risk children, for whom vaccines are not yet available, are critical during this transition.

Among children with COVID-19 who received care at children’s hospitals and EDs, 20% were hospitalized, and, of those, 21% were admitted to the ICU. Older children and adolescent patients had a lower risk of hospitalization; however, when hospitalized, they had greater illness severity. Those with selected comorbidities (eg, cardiovascular, obesity/type 2 DM, pulmonary and neurologic or neuromuscular disease) had both increased odds of hospitalization and in-hospital illness severity. While there were observed differences in COVID-19 severity by race and ethnicity, additional research is needed to clarify the drivers of such disparities. These factors should be considered when prioritizing mitigation strategies to prevent infection (eg, remote learning, avoidance of group activities, prioritization of COVID-19 vaccine when approved for children aged <12 years).

In the United States, respiratory illnesses are the most common cause of emergency department (ED) visits and hospitalizations in children.¹ In response to the ongoing COVID-19 pandemic, several public health interventions, including school and business closures, stay-at-home orders, and mask mandates, were implemented to limit transmission of SARS-CoV-2.^2,3 Studies have shown that children can contribute to the spread of SARS-CoV-2 infections, especially within households.^4-6 Recent data suggest that COVID-19, and the associated public health measures enacted to slow its spread, may have affected the transmission of other respiratory pathogens.⁷ Similarly, the pandemic has likely affected healthcare utilization for nonrespiratory illnesses through adoption of social distancing recommendations, suspension and delays in nonemergent elective care, avoidance of healthcare settings, and the effect of decreased respiratory disease on exacerbation of chronic illness.⁸ The objective of this study was to examine associations between the COVID-19 pandemic and healthcare utilization for pediatric respiratory and nonrespiratory illnesses at US pediatric hospitals.

Study Design

This is a multicenter, cross-sectional study of encounters at 44 pediatric hospitals that reported data to the Pediatric Health Information System (PHIS) database maintained by the Children’s Hospital Association (Lenexa, Kansas).

Study Population

Children 2 months to 18 years of age discharged from ED or inpatient settings with a nonsurgical diagnosis from January 1 to September 30 over a 4-year period (2017-2020) were included.

Exposure

The primary exposure was the 2020 COVID-19 pandemic time, divided into three periods: pre-COVID-19 (January-February 2020, the period prior to the pandemic in the United States), early COVID-19 (March-April 2020, coinciding with the first reported US pediatric case of COVID-19 on March 2, 2020), and COVID-19 (May-September 2020, marked by the implementation of at least two of the following containment measures in every US state: stay-at-home/shelter orders, school closures, nonessential business closures, restaurant closures, or prohibition of gatherings of more than 10 people).²

Outcomes

Respiratory illness diagnoses were classified into mutually exclusive subgroups following a prespecified hierarchy: influenza, pneumonia, croup, bronchiolitis, asthma, unspecified influenza-like illness, and “other respiratory diagnoses” (Appendix Table 1). To assess the impact of COVID-19 after its International Classification of Diseases, Tenth Revision code was established on March 25, 2020, the “other respiratory” subgroup was divided into other respiratory illnesses with and without COVID-19. Nonrespiratory illness diagnoses were defined as all diagnoses not included in the respiratory illness cohort.

Statistical Analysis

Categorical variables were summarized using frequencies and percentages and compared using chi-square tests. Continuous variables were summarized as median and interquartile range (IQR) and compared using Wilcoxon rank sum tests. Weekly observed-to-expected (O:E) ratios were calculated for each hospital by dividing the number of observed respiratory illness and nonrespiratory illness encounters in a given week in 2020 (observed) by the average number of encounters for that same week during 2017-2019 (expected). O:E ratios were then aggregated over the three COVID-19 study periods, and 95% confidence intervals were established around mean O:E ratios across individual hospitals. Outcomes were then stratified by respiratory illness subgroups, geographic region, and age. Additional details can be found in the Supplemental Methods in the Appendix.

Study Population

A total of 9,051,980 encounters were included in the study, 6,811,799 with nonrespiratory illnesses and 2,240,181 with respiratory illnesses. Median age was 5 years (IQR, 1-11 years), and 52.7% of the population was male (Appendix Table 2 and Appendix Table 3).

Respiratory vs Nonrespiratory Illness During the COVID-19 Pandemic

Over the study period, fewer respiratory and nonrespiratory illness encounters were observed than expected, with a larger decrease in respiratory illness encounters (Table, Appendix Table 4).

Respiratory Subgroup Analyses

The O:E ratio decreased for all respiratory subgroups over the study period (Table, Appendix Table 4). There were significant differences in specific respiratory subgroups, including asthma, bronchiolitis, croup, influenza, and pneumonia (Appendix Figure 1A). Temporal trends in respiratory encounters were consistent across hospital settings, ages, and geographic regions (Appendix Figure 1B-D). When comparing the with and without COVID-19 subgroups in the “other respiratory illnesses” cohort, other respiratory illness without COVID-19 decreased and remained lower than expected over the rest of the study period, while other respiratory illness with COVID-19 increased markedly during the summer months and declined thereafter (Appendix Figure 2).

All age groups had reductions in respiratory illness encounters during the early COVID-19 and COVID-19 periods, although the decline was less pronounced in the 12- to 17-year-old group (Appendix Figure 1B). Similarly, while all age groups experienced increases in encounters for respiratory illnesses during the summer months, only children in the 12- to 17-year-old group experienced increases beyond pre-COVID-19 levels. Importantly, this increase in respiratory encounters was largely driven by COVID-19 diagnoses (Appendix Figure 3). The trend in nonrespiratory illness encounters stratified by age is shown in Appendix Figure 4.

When patients were stratified by hospital setting, there were no differences between those hospitalized and those discharged from the ED (Appendix Figure 1C). Patterns in respiratory illnesses by geographic location were qualitatively similar until the beginning of the summer 2020, after which geographical variation became more evident (Appendix Figure 1D).

In this large, multicenter study evaluating ED visits and hospitalizations for respiratory and nonrespiratory illnesses at US pediatric hospitals during the 2020 COVID-19 pandemic, we found a significant and substantial decrease in healthcare encounters for respiratory illnesses. A rapid and marked decline in encounters for respiratory illness in a relatively short period of time (March 12-April 2) was observed across all hospitals and US regions. Declines were consistent across common respiratory illnesses. More modest, yet still substantial, declines were also observed for nonrespiratory illnesses.

There are likely multiple underlying reasons for the observed reductions. Social distancing measures almost certainly played an important role in interrupting respiratory infection transmission. Rapid reduction in influenza transmission during the early COVID-19 period has been attributed to social distancing measures,³ and influenza transmission in children decreases with school closures.⁹ It is also possible that some families delayed seeking care at hospitals due to COVID-19, leading to less frequent encounters but more severe illness. The similar decrease in O:E ratio for ED visits and hospitalizations, however, is inconsistent with this explanation. It is also possible that nonurgent conditions cared for in the hospital settings were diverted to other care settings. For example, during this pandemic, telehealth and telephone visits for pediatric asthma increased by 61% and 19%, respectively, while ED and outpatient visits decreased concurrently.¹⁰Similar changes in location of care may also contribute to the decline in nonrespiratory illness encounters. Decreased use of hospital resources for nonurgent care diagnoses during the pandemic would suggest that, prior to COVID-19, there was overutilization of ambulatory services at children’s hospitals. Therefore, the pandemic may be driving care to more appropriate settings.

We also found relative differences in changes in encounters for respiratory illness by age. Adolescents’ levels of respiratory healthcare use declined less and recovered at a faster rate than those of younger children, returning to pre-COVID-19 levels by the end of the study period. The reason for this age differential is likely multifaceted. Infections, such as bronchiolitis and pneumonia, are more likely to be a source of respiratory illness in younger than in older children. It is also likely that disproportionate relaxation of social distancing measures among adolescents, who are known to have a stronger pattern of social interaction, contributed to the faster rise in respiratory illness–related encounters in this age group.¹¹ Adolescents have been reported to be more susceptible to, and more likely to transmit, SARS-CoV-2 compared to younger age groups.¹² More modest, albeit similar, age-based changes were observed in encounters for nonrespiratory illnesses. It is possible that pandemic-related stressors resulted in a subsequent increase in mental health encounters among this age group.¹³ While the reason for this also is likely multifactorial, adolescent behavior, as well as transmission of infectious illness that can exacerbate nonrespiratory conditions, may be a factor.

Emerging evidence suggests that school-age children may play an important role in SARS-CoV-2 transmission in the community.^4,14 Our finding that, compared to younger children, adolescents had significantly fewer reductions in respiratory illness encounters is concerning. These findings suggest that community-based efforts to help prevent respiratory illnesses, especially COVID-19, should focus on adolescents, who are most likely to maintain social interactions and transmit respiratory infections in the school setting and their households.

This study is limited by the inclusion of only tertiary care children’s hospitals, which may not be nationally representative, and the inability to assess the precise timing of when specific public health interventions were introduced. Moreover, previous studies suggest that social distancing behaviors may have changed even before formal recommendations were enacted.¹⁵ Future studies should investigate the local impact of state- and municipality-specific mandates on the burden of COVID-19 and other respiratory illnesses.

The COVID-19 pandemic was associated with substantial reductions in encounters for respiratory diseases, and also with more modest but still sizable reductions in encounters for nonrespiratory diseases. These reductions varied by age. Encounters among adolescents declined less and returned to previous levels faster compared with those of younger children.

This publication is dedicated to the memory of our coauthor, Dr. Michael Bendel-Stenzel. Dr. Bendel-Stenzel was dedicated to bettering the lives of children and advancing our knowledge of pediatrics through his research.

In the United States, respiratory illnesses are the most common cause of emergency department (ED) visits and hospitalizations in children.¹ In response to the ongoing COVID-19 pandemic, several public health interventions, including school and business closures, stay-at-home orders, and mask mandates, were implemented to limit transmission of SARS-CoV-2.^2,3 Studies have shown that children can contribute to the spread of SARS-CoV-2 infections, especially within households.^4-6 Recent data suggest that COVID-19, and the associated public health measures enacted to slow its spread, may have affected the transmission of other respiratory pathogens.⁷ Similarly, the pandemic has likely affected healthcare utilization for nonrespiratory illnesses through adoption of social distancing recommendations, suspension and delays in nonemergent elective care, avoidance of healthcare settings, and the effect of decreased respiratory disease on exacerbation of chronic illness.⁸ The objective of this study was to examine associations between the COVID-19 pandemic and healthcare utilization for pediatric respiratory and nonrespiratory illnesses at US pediatric hospitals.

Study Design

This is a multicenter, cross-sectional study of encounters at 44 pediatric hospitals that reported data to the Pediatric Health Information System (PHIS) database maintained by the Children’s Hospital Association (Lenexa, Kansas).

Study Population

Children 2 months to 18 years of age discharged from ED or inpatient settings with a nonsurgical diagnosis from January 1 to September 30 over a 4-year period (2017-2020) were included.

Exposure

The primary exposure was the 2020 COVID-19 pandemic time, divided into three periods: pre-COVID-19 (January-February 2020, the period prior to the pandemic in the United States), early COVID-19 (March-April 2020, coinciding with the first reported US pediatric case of COVID-19 on March 2, 2020), and COVID-19 (May-September 2020, marked by the implementation of at least two of the following containment measures in every US state: stay-at-home/shelter orders, school closures, nonessential business closures, restaurant closures, or prohibition of gatherings of more than 10 people).²

Outcomes

Respiratory illness diagnoses were classified into mutually exclusive subgroups following a prespecified hierarchy: influenza, pneumonia, croup, bronchiolitis, asthma, unspecified influenza-like illness, and “other respiratory diagnoses” (Appendix Table 1). To assess the impact of COVID-19 after its International Classification of Diseases, Tenth Revision code was established on March 25, 2020, the “other respiratory” subgroup was divided into other respiratory illnesses with and without COVID-19. Nonrespiratory illness diagnoses were defined as all diagnoses not included in the respiratory illness cohort.

Statistical Analysis

Categorical variables were summarized using frequencies and percentages and compared using chi-square tests. Continuous variables were summarized as median and interquartile range (IQR) and compared using Wilcoxon rank sum tests. Weekly observed-to-expected (O:E) ratios were calculated for each hospital by dividing the number of observed respiratory illness and nonrespiratory illness encounters in a given week in 2020 (observed) by the average number of encounters for that same week during 2017-2019 (expected). O:E ratios were then aggregated over the three COVID-19 study periods, and 95% confidence intervals were established around mean O:E ratios across individual hospitals. Outcomes were then stratified by respiratory illness subgroups, geographic region, and age. Additional details can be found in the Supplemental Methods in the Appendix.

Study Population

A total of 9,051,980 encounters were included in the study, 6,811,799 with nonrespiratory illnesses and 2,240,181 with respiratory illnesses. Median age was 5 years (IQR, 1-11 years), and 52.7% of the population was male (Appendix Table 2 and Appendix Table 3).

Respiratory vs Nonrespiratory Illness During the COVID-19 Pandemic

Over the study period, fewer respiratory and nonrespiratory illness encounters were observed than expected, with a larger decrease in respiratory illness encounters (Table, Appendix Table 4).

Respiratory Subgroup Analyses

The O:E ratio decreased for all respiratory subgroups over the study period (Table, Appendix Table 4). There were significant differences in specific respiratory subgroups, including asthma, bronchiolitis, croup, influenza, and pneumonia (Appendix Figure 1A). Temporal trends in respiratory encounters were consistent across hospital settings, ages, and geographic regions (Appendix Figure 1B-D). When comparing the with and without COVID-19 subgroups in the “other respiratory illnesses” cohort, other respiratory illness without COVID-19 decreased and remained lower than expected over the rest of the study period, while other respiratory illness with COVID-19 increased markedly during the summer months and declined thereafter (Appendix Figure 2).

All age groups had reductions in respiratory illness encounters during the early COVID-19 and COVID-19 periods, although the decline was less pronounced in the 12- to 17-year-old group (Appendix Figure 1B). Similarly, while all age groups experienced increases in encounters for respiratory illnesses during the summer months, only children in the 12- to 17-year-old group experienced increases beyond pre-COVID-19 levels. Importantly, this increase in respiratory encounters was largely driven by COVID-19 diagnoses (Appendix Figure 3). The trend in nonrespiratory illness encounters stratified by age is shown in Appendix Figure 4.

When patients were stratified by hospital setting, there were no differences between those hospitalized and those discharged from the ED (Appendix Figure 1C). Patterns in respiratory illnesses by geographic location were qualitatively similar until the beginning of the summer 2020, after which geographical variation became more evident (Appendix Figure 1D).

In this large, multicenter study evaluating ED visits and hospitalizations for respiratory and nonrespiratory illnesses at US pediatric hospitals during the 2020 COVID-19 pandemic, we found a significant and substantial decrease in healthcare encounters for respiratory illnesses. A rapid and marked decline in encounters for respiratory illness in a relatively short period of time (March 12-April 2) was observed across all hospitals and US regions. Declines were consistent across common respiratory illnesses. More modest, yet still substantial, declines were also observed for nonrespiratory illnesses.

There are likely multiple underlying reasons for the observed reductions. Social distancing measures almost certainly played an important role in interrupting respiratory infection transmission. Rapid reduction in influenza transmission during the early COVID-19 period has been attributed to social distancing measures,³ and influenza transmission in children decreases with school closures.⁹ It is also possible that some families delayed seeking care at hospitals due to COVID-19, leading to less frequent encounters but more severe illness. The similar decrease in O:E ratio for ED visits and hospitalizations, however, is inconsistent with this explanation. It is also possible that nonurgent conditions cared for in the hospital settings were diverted to other care settings. For example, during this pandemic, telehealth and telephone visits for pediatric asthma increased by 61% and 19%, respectively, while ED and outpatient visits decreased concurrently.¹⁰Similar changes in location of care may also contribute to the decline in nonrespiratory illness encounters. Decreased use of hospital resources for nonurgent care diagnoses during the pandemic would suggest that, prior to COVID-19, there was overutilization of ambulatory services at children’s hospitals. Therefore, the pandemic may be driving care to more appropriate settings.

We also found relative differences in changes in encounters for respiratory illness by age. Adolescents’ levels of respiratory healthcare use declined less and recovered at a faster rate than those of younger children, returning to pre-COVID-19 levels by the end of the study period. The reason for this age differential is likely multifaceted. Infections, such as bronchiolitis and pneumonia, are more likely to be a source of respiratory illness in younger than in older children. It is also likely that disproportionate relaxation of social distancing measures among adolescents, who are known to have a stronger pattern of social interaction, contributed to the faster rise in respiratory illness–related encounters in this age group.¹¹ Adolescents have been reported to be more susceptible to, and more likely to transmit, SARS-CoV-2 compared to younger age groups.¹² More modest, albeit similar, age-based changes were observed in encounters for nonrespiratory illnesses. It is possible that pandemic-related stressors resulted in a subsequent increase in mental health encounters among this age group.¹³ While the reason for this also is likely multifactorial, adolescent behavior, as well as transmission of infectious illness that can exacerbate nonrespiratory conditions, may be a factor.

Emerging evidence suggests that school-age children may play an important role in SARS-CoV-2 transmission in the community.^4,14 Our finding that, compared to younger children, adolescents had significantly fewer reductions in respiratory illness encounters is concerning. These findings suggest that community-based efforts to help prevent respiratory illnesses, especially COVID-19, should focus on adolescents, who are most likely to maintain social interactions and transmit respiratory infections in the school setting and their households.

This study is limited by the inclusion of only tertiary care children’s hospitals, which may not be nationally representative, and the inability to assess the precise timing of when specific public health interventions were introduced. Moreover, previous studies suggest that social distancing behaviors may have changed even before formal recommendations were enacted.¹⁵ Future studies should investigate the local impact of state- and municipality-specific mandates on the burden of COVID-19 and other respiratory illnesses.

The COVID-19 pandemic was associated with substantial reductions in encounters for respiratory diseases, and also with more modest but still sizable reductions in encounters for nonrespiratory diseases. These reductions varied by age. Encounters among adolescents declined less and returned to previous levels faster compared with those of younger children.

This publication is dedicated to the memory of our coauthor, Dr. Michael Bendel-Stenzel. Dr. Bendel-Stenzel was dedicated to bettering the lives of children and advancing our knowledge of pediatrics through his research.

In the United States, respiratory illnesses are the most common cause of emergency department (ED) visits and hospitalizations in children.¹ In response to the ongoing COVID-19 pandemic, several public health interventions, including school and business closures, stay-at-home orders, and mask mandates, were implemented to limit transmission of SARS-CoV-2.^2,3 Studies have shown that children can contribute to the spread of SARS-CoV-2 infections, especially within households.^4-6 Recent data suggest that COVID-19, and the associated public health measures enacted to slow its spread, may have affected the transmission of other respiratory pathogens.⁷ Similarly, the pandemic has likely affected healthcare utilization for nonrespiratory illnesses through adoption of social distancing recommendations, suspension and delays in nonemergent elective care, avoidance of healthcare settings, and the effect of decreased respiratory disease on exacerbation of chronic illness.⁸ The objective of this study was to examine associations between the COVID-19 pandemic and healthcare utilization for pediatric respiratory and nonrespiratory illnesses at US pediatric hospitals.

Study Design

This is a multicenter, cross-sectional study of encounters at 44 pediatric hospitals that reported data to the Pediatric Health Information System (PHIS) database maintained by the Children’s Hospital Association (Lenexa, Kansas).

Study Population

Children 2 months to 18 years of age discharged from ED or inpatient settings with a nonsurgical diagnosis from January 1 to September 30 over a 4-year period (2017-2020) were included.

Exposure

The primary exposure was the 2020 COVID-19 pandemic time, divided into three periods: pre-COVID-19 (January-February 2020, the period prior to the pandemic in the United States), early COVID-19 (March-April 2020, coinciding with the first reported US pediatric case of COVID-19 on March 2, 2020), and COVID-19 (May-September 2020, marked by the implementation of at least two of the following containment measures in every US state: stay-at-home/shelter orders, school closures, nonessential business closures, restaurant closures, or prohibition of gatherings of more than 10 people).²

Outcomes

Respiratory illness diagnoses were classified into mutually exclusive subgroups following a prespecified hierarchy: influenza, pneumonia, croup, bronchiolitis, asthma, unspecified influenza-like illness, and “other respiratory diagnoses” (Appendix Table 1). To assess the impact of COVID-19 after its International Classification of Diseases, Tenth Revision code was established on March 25, 2020, the “other respiratory” subgroup was divided into other respiratory illnesses with and without COVID-19. Nonrespiratory illness diagnoses were defined as all diagnoses not included in the respiratory illness cohort.

Statistical Analysis

Categorical variables were summarized using frequencies and percentages and compared using chi-square tests. Continuous variables were summarized as median and interquartile range (IQR) and compared using Wilcoxon rank sum tests. Weekly observed-to-expected (O:E) ratios were calculated for each hospital by dividing the number of observed respiratory illness and nonrespiratory illness encounters in a given week in 2020 (observed) by the average number of encounters for that same week during 2017-2019 (expected). O:E ratios were then aggregated over the three COVID-19 study periods, and 95% confidence intervals were established around mean O:E ratios across individual hospitals. Outcomes were then stratified by respiratory illness subgroups, geographic region, and age. Additional details can be found in the Supplemental Methods in the Appendix.

Study Population

A total of 9,051,980 encounters were included in the study, 6,811,799 with nonrespiratory illnesses and 2,240,181 with respiratory illnesses. Median age was 5 years (IQR, 1-11 years), and 52.7% of the population was male (Appendix Table 2 and Appendix Table 3).

Respiratory vs Nonrespiratory Illness During the COVID-19 Pandemic

Over the study period, fewer respiratory and nonrespiratory illness encounters were observed than expected, with a larger decrease in respiratory illness encounters (Table, Appendix Table 4).

Respiratory Subgroup Analyses

The O:E ratio decreased for all respiratory subgroups over the study period (Table, Appendix Table 4). There were significant differences in specific respiratory subgroups, including asthma, bronchiolitis, croup, influenza, and pneumonia (Appendix Figure 1A). Temporal trends in respiratory encounters were consistent across hospital settings, ages, and geographic regions (Appendix Figure 1B-D). When comparing the with and without COVID-19 subgroups in the “other respiratory illnesses” cohort, other respiratory illness without COVID-19 decreased and remained lower than expected over the rest of the study period, while other respiratory illness with COVID-19 increased markedly during the summer months and declined thereafter (Appendix Figure 2).

All age groups had reductions in respiratory illness encounters during the early COVID-19 and COVID-19 periods, although the decline was less pronounced in the 12- to 17-year-old group (Appendix Figure 1B). Similarly, while all age groups experienced increases in encounters for respiratory illnesses during the summer months, only children in the 12- to 17-year-old group experienced increases beyond pre-COVID-19 levels. Importantly, this increase in respiratory encounters was largely driven by COVID-19 diagnoses (Appendix Figure 3). The trend in nonrespiratory illness encounters stratified by age is shown in Appendix Figure 4.

When patients were stratified by hospital setting, there were no differences between those hospitalized and those discharged from the ED (Appendix Figure 1C). Patterns in respiratory illnesses by geographic location were qualitatively similar until the beginning of the summer 2020, after which geographical variation became more evident (Appendix Figure 1D).

In this large, multicenter study evaluating ED visits and hospitalizations for respiratory and nonrespiratory illnesses at US pediatric hospitals during the 2020 COVID-19 pandemic, we found a significant and substantial decrease in healthcare encounters for respiratory illnesses. A rapid and marked decline in encounters for respiratory illness in a relatively short period of time (March 12-April 2) was observed across all hospitals and US regions. Declines were consistent across common respiratory illnesses. More modest, yet still substantial, declines were also observed for nonrespiratory illnesses.

There are likely multiple underlying reasons for the observed reductions. Social distancing measures almost certainly played an important role in interrupting respiratory infection transmission. Rapid reduction in influenza transmission during the early COVID-19 period has been attributed to social distancing measures,³ and influenza transmission in children decreases with school closures.⁹ It is also possible that some families delayed seeking care at hospitals due to COVID-19, leading to less frequent encounters but more severe illness. The similar decrease in O:E ratio for ED visits and hospitalizations, however, is inconsistent with this explanation. It is also possible that nonurgent conditions cared for in the hospital settings were diverted to other care settings. For example, during this pandemic, telehealth and telephone visits for pediatric asthma increased by 61% and 19%, respectively, while ED and outpatient visits decreased concurrently.¹⁰Similar changes in location of care may also contribute to the decline in nonrespiratory illness encounters. Decreased use of hospital resources for nonurgent care diagnoses during the pandemic would suggest that, prior to COVID-19, there was overutilization of ambulatory services at children’s hospitals. Therefore, the pandemic may be driving care to more appropriate settings.

We also found relative differences in changes in encounters for respiratory illness by age. Adolescents’ levels of respiratory healthcare use declined less and recovered at a faster rate than those of younger children, returning to pre-COVID-19 levels by the end of the study period. The reason for this age differential is likely multifaceted. Infections, such as bronchiolitis and pneumonia, are more likely to be a source of respiratory illness in younger than in older children. It is also likely that disproportionate relaxation of social distancing measures among adolescents, who are known to have a stronger pattern of social interaction, contributed to the faster rise in respiratory illness–related encounters in this age group.¹¹ Adolescents have been reported to be more susceptible to, and more likely to transmit, SARS-CoV-2 compared to younger age groups.¹² More modest, albeit similar, age-based changes were observed in encounters for nonrespiratory illnesses. It is possible that pandemic-related stressors resulted in a subsequent increase in mental health encounters among this age group.¹³ While the reason for this also is likely multifactorial, adolescent behavior, as well as transmission of infectious illness that can exacerbate nonrespiratory conditions, may be a factor.

Emerging evidence suggests that school-age children may play an important role in SARS-CoV-2 transmission in the community.^4,14 Our finding that, compared to younger children, adolescents had significantly fewer reductions in respiratory illness encounters is concerning. These findings suggest that community-based efforts to help prevent respiratory illnesses, especially COVID-19, should focus on adolescents, who are most likely to maintain social interactions and transmit respiratory infections in the school setting and their households.

This study is limited by the inclusion of only tertiary care children’s hospitals, which may not be nationally representative, and the inability to assess the precise timing of when specific public health interventions were introduced. Moreover, previous studies suggest that social distancing behaviors may have changed even before formal recommendations were enacted.¹⁵ Future studies should investigate the local impact of state- and municipality-specific mandates on the burden of COVID-19 and other respiratory illnesses.

The COVID-19 pandemic was associated with substantial reductions in encounters for respiratory diseases, and also with more modest but still sizable reductions in encounters for nonrespiratory diseases. These reductions varied by age. Encounters among adolescents declined less and returned to previous levels faster compared with those of younger children.

This publication is dedicated to the memory of our coauthor, Dr. Michael Bendel-Stenzel. Dr. Bendel-Stenzel was dedicated to bettering the lives of children and advancing our knowledge of pediatrics through his research.

Acute respiratory illnesses (ARIs), including acute exacerbations of asthma, croup, pneumonia, and bronchiolitis, are among the most common illnesses in childhood.¹ Although most ARIs can be managed in the outpatient setting, hospitalization is common with respiratory illnesses accounting for >425,000 hospitalizations annually.¹ Pneumonia, asthma, and bronchiolitis each rank among the top five reasons for pediatric hospitalization in the United States.¹ Successful efforts to prevent or mitigate the severity of ARIs could have a major impact on child health.

Exposure to secondhand smoke (SHS) is a preventable risk factor for ARI in children, particularly when there is regular exposure in the home.² Chronic exposure to SHS impacts systemic inflammation by suppressing serum interferon-gamma,³ which can lead to increased susceptibility to viral and bacterial infections,⁴ and increasing Th2 (atopic) cytokine expression, which is associated with asthma.⁵ SHS exposure in children has also been linked to diminished lung function.⁶ As a result, SHS exposure is associated with increased ARI susceptibility and severity in children.^7-10

Much research has focused on the clinical impact of SHS exposure on respiratory health in children, but little is known about the impact on patient-reported outcomes, such as health-related quality of life (HRQOL). Patient-reported outcomes help provide a comprehensive evaluation of the effectiveness of healthcare delivery systems. These outcomes are increasingly used by health service researchers to better understand patient and caregiver perspectives.¹¹ Given the known associations between SHS exposure and ARI morbidity, we postulated that regular SHS exposure would also impact HRQOL in children. In this study, we assessed the relationship between SHS exposure and HRQOL within a large, multicenter, prospective cohort of children presenting to the emergency department (ED) and/or hospital with ARI.

Study Population

This study was nested within the Pediatric Respiratory Illness Measurement System (PRIMES) study, a prospective cohort study of children with ARI in the ED and inpatient settings at five tertiary care children’s hospitals within the Pediatric Research in Inpatient Settings Network in Colorado, Pennsylvania, Tennessee, Texas, and Washington. Eligible children were two weeks to 16 years of age hospitalized after presenting to the ED with a primary diagnosis of asthma, croup, bronchiolitis, or pneumonia between July 1, 2014 and June 30, 2016. Because of an anticipated low frequency of croup hospitalizations, we also included children presenting to the ED and then discharged to home with this diagnosis. Children were assigned to a PRIMES diagnosis group based on their final discharge diagnosis. If there was a discrepancy between admission and discharge diagnoses, the discharge diagnosis was used. If a child had more than one discharge diagnosis for a PRIMES condition (eg, acute asthma and pneumonia), we chose the PRIMES condition with the lowest total enrollments overall. If the final discharge diagnosis was not a PRIMES condition, the case was excluded from further analysis. Patients with immunodeficiency, cystic fibrosis, a history of prematurity <32 weeks, chronic neuromuscular disease, cardiovascular disease, pulmonary diseases (other than asthma), and moderate to severe developmental delay were also excluded. Children admitted to intensive care were eligible only if they were transferred to an acute care ward <72 hours following admission. A survey was administered at the time of enrollment that collected information on SHS exposure, HRQOL, healthcare utilization, and demographics. All study procedures were reviewed and approved by the institutional review boards at each of the participating hospitals.

To ascertain SHS exposure, we asked caregivers, “How many persons living in the child’s home smoke?” Responses were dichotomized into non-SHS exposed (0 smokers) and SHS exposed (≥1 smokers). Children with missing data on home SHS exposure were excluded.

Health-Related Quality of Life Outcomes

We estimated HRQOL using the Pediatric Quality of Life (PedsQL^TM) 4.0 Generic Core and Infant Scales. The PedsQL instruments are validated, population HRQOL measures that evaluate the physical, mental, emotional, and social functioning of children two to 18 years old based on self- or caregiver-proxy report.^12-15 These instruments have also shown responsiveness as well as construct and predictive validity in hospitalized children.¹¹ For this study, we focused on the PedsQL physical functioning subscale, which assesses for problems with physical activities (eg, sports activity or exercise, low energy, and hurts or aches) on a five-point Likert scale (never to almost always a problem). Scores range from 0 to 100 with higher scores indicating a better HRQOL. The reported minimal clinically important difference (MCID), defined as the smallest difference in which individuals would perceive a benefit or would necessitate a change in management, for this scale is 4.5 points.^16,17

Children >8 years old were invited to complete the self-report version of the PedsQL. For children <8 years old, and for older children who were unable to complete them, surveys were completed by a parent or legal guardian. Respondents were asked to assess perceptions of their (or their child’s) HRQOL during periods of baseline health (the child’s usual state of health in the month preceding the current illness) and during the acute illness (the child’s state of health at the time of admission) as SHS exposure may influence perceptions of general health and/or contribute to worse outcomes during periods of acute illness.

Covariates collected at the time of enrollment included sociodemographics (child age, gender, race/ethnicity, and caregiver education), and healthcare utilization (caregiver-reported patient visits to a healthcare provider in the preceding six months). Insurance status and level of medical complexity (using the Pediatric Medical Complexity Algorithm)¹⁸ were obtained using the Pediatric Hospital Information System database, an administrative database containing clinical and resource utilization data from >45 children’s hospitals in the United States including all of the PRIMES study hospitals.¹³

Analysis

Descriptive statistics included frequency (%) and mean (standard deviation). Bivariate comparisons according to SHS exposure status were analyzed using chi-squared tests for categorical variables and analysis of variance for continuous variables. Multivariable linear mixed regression models were used to examine associations between home SHS exposure and HRQOL for baseline health and during admission, overall and stratified by diagnosis. Covariates in each model included age, sex, race/ethnicity, caregiver education, and healthcare visits in the preceding six months. We also included a hospital random effect to account for clustering of patients within hospitals and used robust standard errors for inference.

In a secondary analysis to explore potential dose-response effects of SHS exposure, we examined associations between an ordinal exposure variable (0 smokers, 1 smoker, ≥2 smokers) and HRQOL for baseline health and during admission for the acute illness. Because of sample size limitations, diagnosis-specific analyses examining dose-response effects were not conducted.

Study Population

Of the 2,334 children enrolled in the PRIMES study, 25 (1%) respondents did not report on home SHS exposure and were excluded, yielding a final study population of 2,309 children, of whom 728 (32%) had reported home SHS exposure. The study population included 664 children with asthma (mean age seven years [3.5]; 38% with home SHS exposure), 740 with bronchiolitis (mean age 0.7 years [0.5]; 32% with home SHS exposure), 342 with croup (mean age 1.7 [1.1]; 25% with home SHS exposure), and 563 with pneumonia (mean age 4.4 [3.8]; 27% with home SHS exposure; Table 1). Compared with non-SHS-exposed children, those with home SHS exposure tend to be slightly older (3.9 vs 3.4 years, P = .01), more likely to be non-Hispanic Black (29% vs 19%, P < .001), to have a chronic condition (52% vs 41%, P < .001), to come from a household where caregiver(s) did not graduate from college (45% vs 29%, P < .001), and to have public insurance (73% vs 49%, P < .001).

Home SHS Exposure and Health-related Quality of Life

The overall mean HRQOL score for baseline health was 83 (15), with a range across diagnoses of 82 to 87. Compared with non-SHS-exposed children, children with home SHS exposure had a lower mean HRQOL score for baseline health (adjusted mean difference –3.04 [95% CI -4.34, –1.74]). In analyses stratified by diagnosis, baseline health scores were lower for SHS-exposed children for all four conditions, but differences were statistically significant only for bronchiolitis (adjusted mean difference –2.94 [–5.0, –0.89]) and pneumonia (adjusted mean value –4.13 [–6.82, –1.44]; Table 2); none of these differences met the MCID threshold.

The overall mean HRQOL score at the time of admission was 56 (23), with a range across diagnoses of 49 to 61, with lower scores noted among SHS-exposed children compared with non-SHS-exposed children (adjusted mean difference –2.16 [–4.22, –0.10]). Similar to scores representing baseline health, admission scores were lower across all four conditions for SHS-exposed children. Only children with croup, however, had significantly lower admission scores that also met the MCID threshold (adjusted mean difference –5.71 [–10.67, –0.75]; Table 2).

To assess for potential dose-response effects of SHS exposure on HRQOL, we stratified SHS-exposed children into those with one smoker in the home (n = 513) and those with ≥2 smokers in the home (n = 215). Compared with non-SHS-exposed children, both HRQOL scores (baseline health and admission) were lower for SHS-exposed children. Consistent with a dose-response association, scores were lowest for children with ≥2 smokers in the home, both at baseline health (adjusted mean difference –3.92 [–6.03, –1.81]) and on admission (adjusted mean difference –3.67 [–6.98, –0.36]; Table 3).

Within a multicenter cohort of 2,309 children hospitalized with ARI, we noted significantly lower HRQOL scores among children exposed to SHS in the home as compared with nonexposed children. Differences were greatest for children living with ≥2 smokers in the home. In analyses stratified by diagnosis, differences in baseline health HRQOL scores were greatest for children with bronchiolitis and pneumonia. Differences in acute illness scores were greatest for children with croup.¹⁶

Our study provides evidence for acute and chronic impacts of SHS on HRQOL in children hospitalized with ARI. Although several studies have linked SHS exposure to reduced HRQOL in adults,^19,20 few similar studies have been conducted in children. Nonetheless, a wealth of studies have documented the negative impact of SHS exposure on clinical outcomes among children with ARI.^8,10,21-23 Our findings that home SHS exposure was associated with reduced HRQOL among our cohort of children with ARI are therefore consistent with related findings in adults and children. The observation that the effects of SHS exposure on HRQOL were greatest among children living with ≥2 smokers provides further evidence of a potential causal link between regular SHS exposure and HRQOL.

Although the magnitude and significance of associations between SHS exposure and HRQOL varied for each of the four diagnoses for baseline health and the acute illness, it is important to note that the point estimates for the adjusted mean differences were uniformly lower for the SHS-exposed children in each subgroup. Even so, only acute illness scores for croup exceeded the MCID threshold.¹⁶ Croup is the only included condition of the upper airway and is characterized by laryngotracheal inflammation leading to the typical cough and, in moderate to severe cases, stridor. Given that chronic SHS exposure induces a proinflammatory state,³ it is possible that SHS-exposed children with croup had more severe illness compared with nonexposed children with croup resulting in lower HRQOL scores on admission. Further, perceived differences in illness severity and HRQOL may be more readily apparent in children with croup (eg, stridor at rest vs intermittent or no stridor) as compared with children with lower respiratory tract diseases.

Of the four included diagnoses, the link between SHS exposure and asthma outcomes has been most studied. Prior work has demonstrated more frequent and severe acute exacerbations, as well as worse long-term lung function among SHS-exposed children as compared with nonexposed children.^22-24 It was, therefore, surprising that our study failed to demonstrate associations between SHS exposure and HRQOL among children with asthma. Reasons for this finding are unclear. One hypothesis is that caregivers of SHS-exposed children with asthma may be more aware of the impacts of SHS exposure on respiratory health (through prior education) and, thus, more likely to modify their smoking behaviors, or for their children to be on daily asthma controller therapy. Alternatively, caregivers of children with asthma may be more likely to underreport home SHS exposure. Thirty-eight percent of children with asthma, however, were classified as SHS-exposed. This percentage was greater than the other three conditions studied (25%-32%), suggesting that differential bias in underreporting was minimal. Given that children with asthma were older, on average, than children with the other three conditions, it may also be that these children spent more time in smoke-free environments (eg, school).

Nearly one-third of children in our study were exposed to SHS in the home. This is similar to the prevalence of exposure in other studies conducted among hospitalized children^8,10,21,25 but higher than the national prevalence of home SHS exposure among children in the United States.²⁶ Thus, hospitalized children represent a particularly vulnerable population and an important target for interventions aiming to reduce exposure to SHS. Although longitudinal interventions are likely necessary to affect long-term success, hospitalization for ARI may serve as a powerful teachable moment to begin cessation efforts. Hospitalization also offers time beyond a typical primary care outpatient encounter to focus on cessation counseling and may be the only opportunity to engage in counseling activities for some families with limited time or access. Further, prior studies have demonstrated both the feasibility and the effectiveness of smoking cessation interventions in hospitalized children.^27-30 Unfortunately, however, SHS exposure is often not documented at the time of hospitalization, and many opportunities to intervene are missed.^25,31 Thus, there is a need for improved strategies to reliably identify and intervene on SHS-exposed children in the hospital setting.

These findings should be considered in the context of several limitations. The observational nature of our study raises the potential for confounding, specifically with regard to socioeconomic status, as this is associated with both SHS exposure and lower HRQOL. Our modeling approach attempted to control for several factors associated with socioeconomic status, including caregiver education and insurance coverage, but there is potential for residual confounding. No single question is sufficient to fully assess SHS exposure as the intensity of home SHS exposure likely varies widely, and some children may be exposed to SHS outside of the home environment.³² The home, however, is often the most likely source of regular SHS exposure,^33,34 especially among young children (our cohort’s mean age was 3.6 years). Misclassification of SHS exposure is also possible due to underreporting of smoking.^35,36 As a result, some children regularly exposed to SHS may have been misclassified as nonexposed, and the observed associations between SHS exposure and HRQOL may be underestimated. Confirming our study’s findings using objective assessments of SHS exposure, such as cotinine, are warranted. Given the young age of our cohort, the PedsQL surveys were completed by the parent or legal guardian only in >90% of the enrolled subjects, and caregiver perceptions may not accurately reflect the child’s perceptions. Prior work, however, has demonstrated the validity of parent-proxy reporting of the PedsQL, including correlation with child self-report.³⁷ In our study, correlation between child and caregiver reporting (when available) was also very good (r = 0.72, 95% CI 0.64, 0.77). It is also possible that the timing of the HRQOL assessments (on admission) may have biased perceptions of baseline HRQOL, although we anticipate any bias would likely be nondifferential between SHS-exposed and nonexposed children and across diagnoses.

Nearly one-third of children in our study were exposed to SHS exposure in the home, and SHS exposure was associated with lower HRQOL for baseline health and during acute illness, providing further evidence of the dangers of SHS. Much work is needed in order to eliminate the impact of SHS on child health and families of children hospitalized for respiratory illness should be considered a priority population for smoking cessation efforts.

Acknowledgment

The authors wish to acknowledge the efforts of PRIS-PRIMES study team. The authors also wish to thank the children and families who consented to be a part of the PRIMES study.

Disclosures

The authors have no conflicts of interest relevant to this article to disclose.

Funding

This study was supported by NIH-NHLBI 1R01HL121067 to RMS.

Acute respiratory illnesses (ARIs), including acute exacerbations of asthma, croup, pneumonia, and bronchiolitis, are among the most common illnesses in childhood.¹ Although most ARIs can be managed in the outpatient setting, hospitalization is common with respiratory illnesses accounting for >425,000 hospitalizations annually.¹ Pneumonia, asthma, and bronchiolitis each rank among the top five reasons for pediatric hospitalization in the United States.¹ Successful efforts to prevent or mitigate the severity of ARIs could have a major impact on child health.

Exposure to secondhand smoke (SHS) is a preventable risk factor for ARI in children, particularly when there is regular exposure in the home.² Chronic exposure to SHS impacts systemic inflammation by suppressing serum interferon-gamma,³ which can lead to increased susceptibility to viral and bacterial infections,⁴ and increasing Th2 (atopic) cytokine expression, which is associated with asthma.⁵ SHS exposure in children has also been linked to diminished lung function.⁶ As a result, SHS exposure is associated with increased ARI susceptibility and severity in children.^7-10

Much research has focused on the clinical impact of SHS exposure on respiratory health in children, but little is known about the impact on patient-reported outcomes, such as health-related quality of life (HRQOL). Patient-reported outcomes help provide a comprehensive evaluation of the effectiveness of healthcare delivery systems. These outcomes are increasingly used by health service researchers to better understand patient and caregiver perspectives.¹¹ Given the known associations between SHS exposure and ARI morbidity, we postulated that regular SHS exposure would also impact HRQOL in children. In this study, we assessed the relationship between SHS exposure and HRQOL within a large, multicenter, prospective cohort of children presenting to the emergency department (ED) and/or hospital with ARI.

Study Population

This study was nested within the Pediatric Respiratory Illness Measurement System (PRIMES) study, a prospective cohort study of children with ARI in the ED and inpatient settings at five tertiary care children’s hospitals within the Pediatric Research in Inpatient Settings Network in Colorado, Pennsylvania, Tennessee, Texas, and Washington. Eligible children were two weeks to 16 years of age hospitalized after presenting to the ED with a primary diagnosis of asthma, croup, bronchiolitis, or pneumonia between July 1, 2014 and June 30, 2016. Because of an anticipated low frequency of croup hospitalizations, we also included children presenting to the ED and then discharged to home with this diagnosis. Children were assigned to a PRIMES diagnosis group based on their final discharge diagnosis. If there was a discrepancy between admission and discharge diagnoses, the discharge diagnosis was used. If a child had more than one discharge diagnosis for a PRIMES condition (eg, acute asthma and pneumonia), we chose the PRIMES condition with the lowest total enrollments overall. If the final discharge diagnosis was not a PRIMES condition, the case was excluded from further analysis. Patients with immunodeficiency, cystic fibrosis, a history of prematurity <32 weeks, chronic neuromuscular disease, cardiovascular disease, pulmonary diseases (other than asthma), and moderate to severe developmental delay were also excluded. Children admitted to intensive care were eligible only if they were transferred to an acute care ward <72 hours following admission. A survey was administered at the time of enrollment that collected information on SHS exposure, HRQOL, healthcare utilization, and demographics. All study procedures were reviewed and approved by the institutional review boards at each of the participating hospitals.

To ascertain SHS exposure, we asked caregivers, “How many persons living in the child’s home smoke?” Responses were dichotomized into non-SHS exposed (0 smokers) and SHS exposed (≥1 smokers). Children with missing data on home SHS exposure were excluded.

Health-Related Quality of Life Outcomes

We estimated HRQOL using the Pediatric Quality of Life (PedsQL^TM) 4.0 Generic Core and Infant Scales. The PedsQL instruments are validated, population HRQOL measures that evaluate the physical, mental, emotional, and social functioning of children two to 18 years old based on self- or caregiver-proxy report.^12-15 These instruments have also shown responsiveness as well as construct and predictive validity in hospitalized children.¹¹ For this study, we focused on the PedsQL physical functioning subscale, which assesses for problems with physical activities (eg, sports activity or exercise, low energy, and hurts or aches) on a five-point Likert scale (never to almost always a problem). Scores range from 0 to 100 with higher scores indicating a better HRQOL. The reported minimal clinically important difference (MCID), defined as the smallest difference in which individuals would perceive a benefit or would necessitate a change in management, for this scale is 4.5 points.^16,17

Children >8 years old were invited to complete the self-report version of the PedsQL. For children <8 years old, and for older children who were unable to complete them, surveys were completed by a parent or legal guardian. Respondents were asked to assess perceptions of their (or their child’s) HRQOL during periods of baseline health (the child’s usual state of health in the month preceding the current illness) and during the acute illness (the child’s state of health at the time of admission) as SHS exposure may influence perceptions of general health and/or contribute to worse outcomes during periods of acute illness.

Covariates collected at the time of enrollment included sociodemographics (child age, gender, race/ethnicity, and caregiver education), and healthcare utilization (caregiver-reported patient visits to a healthcare provider in the preceding six months). Insurance status and level of medical complexity (using the Pediatric Medical Complexity Algorithm)¹⁸ were obtained using the Pediatric Hospital Information System database, an administrative database containing clinical and resource utilization data from >45 children’s hospitals in the United States including all of the PRIMES study hospitals.¹³

Analysis

Descriptive statistics included frequency (%) and mean (standard deviation). Bivariate comparisons according to SHS exposure status were analyzed using chi-squared tests for categorical variables and analysis of variance for continuous variables. Multivariable linear mixed regression models were used to examine associations between home SHS exposure and HRQOL for baseline health and during admission, overall and stratified by diagnosis. Covariates in each model included age, sex, race/ethnicity, caregiver education, and healthcare visits in the preceding six months. We also included a hospital random effect to account for clustering of patients within hospitals and used robust standard errors for inference.

In a secondary analysis to explore potential dose-response effects of SHS exposure, we examined associations between an ordinal exposure variable (0 smokers, 1 smoker, ≥2 smokers) and HRQOL for baseline health and during admission for the acute illness. Because of sample size limitations, diagnosis-specific analyses examining dose-response effects were not conducted.

Study Population

Of the 2,334 children enrolled in the PRIMES study, 25 (1%) respondents did not report on home SHS exposure and were excluded, yielding a final study population of 2,309 children, of whom 728 (32%) had reported home SHS exposure. The study population included 664 children with asthma (mean age seven years [3.5]; 38% with home SHS exposure), 740 with bronchiolitis (mean age 0.7 years [0.5]; 32% with home SHS exposure), 342 with croup (mean age 1.7 [1.1]; 25% with home SHS exposure), and 563 with pneumonia (mean age 4.4 [3.8]; 27% with home SHS exposure; Table 1). Compared with non-SHS-exposed children, those with home SHS exposure tend to be slightly older (3.9 vs 3.4 years, P = .01), more likely to be non-Hispanic Black (29% vs 19%, P < .001), to have a chronic condition (52% vs 41%, P < .001), to come from a household where caregiver(s) did not graduate from college (45% vs 29%, P < .001), and to have public insurance (73% vs 49%, P < .001).

Home SHS Exposure and Health-related Quality of Life

The overall mean HRQOL score for baseline health was 83 (15), with a range across diagnoses of 82 to 87. Compared with non-SHS-exposed children, children with home SHS exposure had a lower mean HRQOL score for baseline health (adjusted mean difference –3.04 [95% CI -4.34, –1.74]). In analyses stratified by diagnosis, baseline health scores were lower for SHS-exposed children for all four conditions, but differences were statistically significant only for bronchiolitis (adjusted mean difference –2.94 [–5.0, –0.89]) and pneumonia (adjusted mean value –4.13 [–6.82, –1.44]; Table 2); none of these differences met the MCID threshold.

The overall mean HRQOL score at the time of admission was 56 (23), with a range across diagnoses of 49 to 61, with lower scores noted among SHS-exposed children compared with non-SHS-exposed children (adjusted mean difference –2.16 [–4.22, –0.10]). Similar to scores representing baseline health, admission scores were lower across all four conditions for SHS-exposed children. Only children with croup, however, had significantly lower admission scores that also met the MCID threshold (adjusted mean difference –5.71 [–10.67, –0.75]; Table 2).

To assess for potential dose-response effects of SHS exposure on HRQOL, we stratified SHS-exposed children into those with one smoker in the home (n = 513) and those with ≥2 smokers in the home (n = 215). Compared with non-SHS-exposed children, both HRQOL scores (baseline health and admission) were lower for SHS-exposed children. Consistent with a dose-response association, scores were lowest for children with ≥2 smokers in the home, both at baseline health (adjusted mean difference –3.92 [–6.03, –1.81]) and on admission (adjusted mean difference –3.67 [–6.98, –0.36]; Table 3).

Within a multicenter cohort of 2,309 children hospitalized with ARI, we noted significantly lower HRQOL scores among children exposed to SHS in the home as compared with nonexposed children. Differences were greatest for children living with ≥2 smokers in the home. In analyses stratified by diagnosis, differences in baseline health HRQOL scores were greatest for children with bronchiolitis and pneumonia. Differences in acute illness scores were greatest for children with croup.¹⁶

Our study provides evidence for acute and chronic impacts of SHS on HRQOL in children hospitalized with ARI. Although several studies have linked SHS exposure to reduced HRQOL in adults,^19,20 few similar studies have been conducted in children. Nonetheless, a wealth of studies have documented the negative impact of SHS exposure on clinical outcomes among children with ARI.^8,10,21-23 Our findings that home SHS exposure was associated with reduced HRQOL among our cohort of children with ARI are therefore consistent with related findings in adults and children. The observation that the effects of SHS exposure on HRQOL were greatest among children living with ≥2 smokers provides further evidence of a potential causal link between regular SHS exposure and HRQOL.

Although the magnitude and significance of associations between SHS exposure and HRQOL varied for each of the four diagnoses for baseline health and the acute illness, it is important to note that the point estimates for the adjusted mean differences were uniformly lower for the SHS-exposed children in each subgroup. Even so, only acute illness scores for croup exceeded the MCID threshold.¹⁶ Croup is the only included condition of the upper airway and is characterized by laryngotracheal inflammation leading to the typical cough and, in moderate to severe cases, stridor. Given that chronic SHS exposure induces a proinflammatory state,³ it is possible that SHS-exposed children with croup had more severe illness compared with nonexposed children with croup resulting in lower HRQOL scores on admission. Further, perceived differences in illness severity and HRQOL may be more readily apparent in children with croup (eg, stridor at rest vs intermittent or no stridor) as compared with children with lower respiratory tract diseases.

Of the four included diagnoses, the link between SHS exposure and asthma outcomes has been most studied. Prior work has demonstrated more frequent and severe acute exacerbations, as well as worse long-term lung function among SHS-exposed children as compared with nonexposed children.^22-24 It was, therefore, surprising that our study failed to demonstrate associations between SHS exposure and HRQOL among children with asthma. Reasons for this finding are unclear. One hypothesis is that caregivers of SHS-exposed children with asthma may be more aware of the impacts of SHS exposure on respiratory health (through prior education) and, thus, more likely to modify their smoking behaviors, or for their children to be on daily asthma controller therapy. Alternatively, caregivers of children with asthma may be more likely to underreport home SHS exposure. Thirty-eight percent of children with asthma, however, were classified as SHS-exposed. This percentage was greater than the other three conditions studied (25%-32%), suggesting that differential bias in underreporting was minimal. Given that children with asthma were older, on average, than children with the other three conditions, it may also be that these children spent more time in smoke-free environments (eg, school).

Nearly one-third of children in our study were exposed to SHS in the home. This is similar to the prevalence of exposure in other studies conducted among hospitalized children^8,10,21,25 but higher than the national prevalence of home SHS exposure among children in the United States.²⁶ Thus, hospitalized children represent a particularly vulnerable population and an important target for interventions aiming to reduce exposure to SHS. Although longitudinal interventions are likely necessary to affect long-term success, hospitalization for ARI may serve as a powerful teachable moment to begin cessation efforts. Hospitalization also offers time beyond a typical primary care outpatient encounter to focus on cessation counseling and may be the only opportunity to engage in counseling activities for some families with limited time or access. Further, prior studies have demonstrated both the feasibility and the effectiveness of smoking cessation interventions in hospitalized children.^27-30 Unfortunately, however, SHS exposure is often not documented at the time of hospitalization, and many opportunities to intervene are missed.^25,31 Thus, there is a need for improved strategies to reliably identify and intervene on SHS-exposed children in the hospital setting.

These findings should be considered in the context of several limitations. The observational nature of our study raises the potential for confounding, specifically with regard to socioeconomic status, as this is associated with both SHS exposure and lower HRQOL. Our modeling approach attempted to control for several factors associated with socioeconomic status, including caregiver education and insurance coverage, but there is potential for residual confounding. No single question is sufficient to fully assess SHS exposure as the intensity of home SHS exposure likely varies widely, and some children may be exposed to SHS outside of the home environment.³² The home, however, is often the most likely source of regular SHS exposure,^33,34 especially among young children (our cohort’s mean age was 3.6 years). Misclassification of SHS exposure is also possible due to underreporting of smoking.^35,36 As a result, some children regularly exposed to SHS may have been misclassified as nonexposed, and the observed associations between SHS exposure and HRQOL may be underestimated. Confirming our study’s findings using objective assessments of SHS exposure, such as cotinine, are warranted. Given the young age of our cohort, the PedsQL surveys were completed by the parent or legal guardian only in >90% of the enrolled subjects, and caregiver perceptions may not accurately reflect the child’s perceptions. Prior work, however, has demonstrated the validity of parent-proxy reporting of the PedsQL, including correlation with child self-report.³⁷ In our study, correlation between child and caregiver reporting (when available) was also very good (r = 0.72, 95% CI 0.64, 0.77). It is also possible that the timing of the HRQOL assessments (on admission) may have biased perceptions of baseline HRQOL, although we anticipate any bias would likely be nondifferential between SHS-exposed and nonexposed children and across diagnoses.

Nearly one-third of children in our study were exposed to SHS exposure in the home, and SHS exposure was associated with lower HRQOL for baseline health and during acute illness, providing further evidence of the dangers of SHS. Much work is needed in order to eliminate the impact of SHS on child health and families of children hospitalized for respiratory illness should be considered a priority population for smoking cessation efforts.

Acknowledgment

The authors wish to acknowledge the efforts of PRIS-PRIMES study team. The authors also wish to thank the children and families who consented to be a part of the PRIMES study.

Disclosures

The authors have no conflicts of interest relevant to this article to disclose.

Funding

This study was supported by NIH-NHLBI 1R01HL121067 to RMS.

Acute respiratory illnesses (ARIs), including acute exacerbations of asthma, croup, pneumonia, and bronchiolitis, are among the most common illnesses in childhood.¹ Although most ARIs can be managed in the outpatient setting, hospitalization is common with respiratory illnesses accounting for >425,000 hospitalizations annually.¹ Pneumonia, asthma, and bronchiolitis each rank among the top five reasons for pediatric hospitalization in the United States.¹ Successful efforts to prevent or mitigate the severity of ARIs could have a major impact on child health.

Exposure to secondhand smoke (SHS) is a preventable risk factor for ARI in children, particularly when there is regular exposure in the home.² Chronic exposure to SHS impacts systemic inflammation by suppressing serum interferon-gamma,³ which can lead to increased susceptibility to viral and bacterial infections,⁴ and increasing Th2 (atopic) cytokine expression, which is associated with asthma.⁵ SHS exposure in children has also been linked to diminished lung function.⁶ As a result, SHS exposure is associated with increased ARI susceptibility and severity in children.^7-10

Much research has focused on the clinical impact of SHS exposure on respiratory health in children, but little is known about the impact on patient-reported outcomes, such as health-related quality of life (HRQOL). Patient-reported outcomes help provide a comprehensive evaluation of the effectiveness of healthcare delivery systems. These outcomes are increasingly used by health service researchers to better understand patient and caregiver perspectives.¹¹ Given the known associations between SHS exposure and ARI morbidity, we postulated that regular SHS exposure would also impact HRQOL in children. In this study, we assessed the relationship between SHS exposure and HRQOL within a large, multicenter, prospective cohort of children presenting to the emergency department (ED) and/or hospital with ARI.

Study Population

This study was nested within the Pediatric Respiratory Illness Measurement System (PRIMES) study, a prospective cohort study of children with ARI in the ED and inpatient settings at five tertiary care children’s hospitals within the Pediatric Research in Inpatient Settings Network in Colorado, Pennsylvania, Tennessee, Texas, and Washington. Eligible children were two weeks to 16 years of age hospitalized after presenting to the ED with a primary diagnosis of asthma, croup, bronchiolitis, or pneumonia between July 1, 2014 and June 30, 2016. Because of an anticipated low frequency of croup hospitalizations, we also included children presenting to the ED and then discharged to home with this diagnosis. Children were assigned to a PRIMES diagnosis group based on their final discharge diagnosis. If there was a discrepancy between admission and discharge diagnoses, the discharge diagnosis was used. If a child had more than one discharge diagnosis for a PRIMES condition (eg, acute asthma and pneumonia), we chose the PRIMES condition with the lowest total enrollments overall. If the final discharge diagnosis was not a PRIMES condition, the case was excluded from further analysis. Patients with immunodeficiency, cystic fibrosis, a history of prematurity <32 weeks, chronic neuromuscular disease, cardiovascular disease, pulmonary diseases (other than asthma), and moderate to severe developmental delay were also excluded. Children admitted to intensive care were eligible only if they were transferred to an acute care ward <72 hours following admission. A survey was administered at the time of enrollment that collected information on SHS exposure, HRQOL, healthcare utilization, and demographics. All study procedures were reviewed and approved by the institutional review boards at each of the participating hospitals.

To ascertain SHS exposure, we asked caregivers, “How many persons living in the child’s home smoke?” Responses were dichotomized into non-SHS exposed (0 smokers) and SHS exposed (≥1 smokers). Children with missing data on home SHS exposure were excluded.

Health-Related Quality of Life Outcomes

We estimated HRQOL using the Pediatric Quality of Life (PedsQL^TM) 4.0 Generic Core and Infant Scales. The PedsQL instruments are validated, population HRQOL measures that evaluate the physical, mental, emotional, and social functioning of children two to 18 years old based on self- or caregiver-proxy report.^12-15 These instruments have also shown responsiveness as well as construct and predictive validity in hospitalized children.¹¹ For this study, we focused on the PedsQL physical functioning subscale, which assesses for problems with physical activities (eg, sports activity or exercise, low energy, and hurts or aches) on a five-point Likert scale (never to almost always a problem). Scores range from 0 to 100 with higher scores indicating a better HRQOL. The reported minimal clinically important difference (MCID), defined as the smallest difference in which individuals would perceive a benefit or would necessitate a change in management, for this scale is 4.5 points.^16,17

Children >8 years old were invited to complete the self-report version of the PedsQL. For children <8 years old, and for older children who were unable to complete them, surveys were completed by a parent or legal guardian. Respondents were asked to assess perceptions of their (or their child’s) HRQOL during periods of baseline health (the child’s usual state of health in the month preceding the current illness) and during the acute illness (the child’s state of health at the time of admission) as SHS exposure may influence perceptions of general health and/or contribute to worse outcomes during periods of acute illness.

Covariates collected at the time of enrollment included sociodemographics (child age, gender, race/ethnicity, and caregiver education), and healthcare utilization (caregiver-reported patient visits to a healthcare provider in the preceding six months). Insurance status and level of medical complexity (using the Pediatric Medical Complexity Algorithm)¹⁸ were obtained using the Pediatric Hospital Information System database, an administrative database containing clinical and resource utilization data from >45 children’s hospitals in the United States including all of the PRIMES study hospitals.¹³

Analysis

Descriptive statistics included frequency (%) and mean (standard deviation). Bivariate comparisons according to SHS exposure status were analyzed using chi-squared tests for categorical variables and analysis of variance for continuous variables. Multivariable linear mixed regression models were used to examine associations between home SHS exposure and HRQOL for baseline health and during admission, overall and stratified by diagnosis. Covariates in each model included age, sex, race/ethnicity, caregiver education, and healthcare visits in the preceding six months. We also included a hospital random effect to account for clustering of patients within hospitals and used robust standard errors for inference.

In a secondary analysis to explore potential dose-response effects of SHS exposure, we examined associations between an ordinal exposure variable (0 smokers, 1 smoker, ≥2 smokers) and HRQOL for baseline health and during admission for the acute illness. Because of sample size limitations, diagnosis-specific analyses examining dose-response effects were not conducted.

Study Population

Of the 2,334 children enrolled in the PRIMES study, 25 (1%) respondents did not report on home SHS exposure and were excluded, yielding a final study population of 2,309 children, of whom 728 (32%) had reported home SHS exposure. The study population included 664 children with asthma (mean age seven years [3.5]; 38% with home SHS exposure), 740 with bronchiolitis (mean age 0.7 years [0.5]; 32% with home SHS exposure), 342 with croup (mean age 1.7 [1.1]; 25% with home SHS exposure), and 563 with pneumonia (mean age 4.4 [3.8]; 27% with home SHS exposure; Table 1). Compared with non-SHS-exposed children, those with home SHS exposure tend to be slightly older (3.9 vs 3.4 years, P = .01), more likely to be non-Hispanic Black (29% vs 19%, P < .001), to have a chronic condition (52% vs 41%, P < .001), to come from a household where caregiver(s) did not graduate from college (45% vs 29%, P < .001), and to have public insurance (73% vs 49%, P < .001).

Home SHS Exposure and Health-related Quality of Life

The overall mean HRQOL score for baseline health was 83 (15), with a range across diagnoses of 82 to 87. Compared with non-SHS-exposed children, children with home SHS exposure had a lower mean HRQOL score for baseline health (adjusted mean difference –3.04 [95% CI -4.34, –1.74]). In analyses stratified by diagnosis, baseline health scores were lower for SHS-exposed children for all four conditions, but differences were statistically significant only for bronchiolitis (adjusted mean difference –2.94 [–5.0, –0.89]) and pneumonia (adjusted mean value –4.13 [–6.82, –1.44]; Table 2); none of these differences met the MCID threshold.

The overall mean HRQOL score at the time of admission was 56 (23), with a range across diagnoses of 49 to 61, with lower scores noted among SHS-exposed children compared with non-SHS-exposed children (adjusted mean difference –2.16 [–4.22, –0.10]). Similar to scores representing baseline health, admission scores were lower across all four conditions for SHS-exposed children. Only children with croup, however, had significantly lower admission scores that also met the MCID threshold (adjusted mean difference –5.71 [–10.67, –0.75]; Table 2).

To assess for potential dose-response effects of SHS exposure on HRQOL, we stratified SHS-exposed children into those with one smoker in the home (n = 513) and those with ≥2 smokers in the home (n = 215). Compared with non-SHS-exposed children, both HRQOL scores (baseline health and admission) were lower for SHS-exposed children. Consistent with a dose-response association, scores were lowest for children with ≥2 smokers in the home, both at baseline health (adjusted mean difference –3.92 [–6.03, –1.81]) and on admission (adjusted mean difference –3.67 [–6.98, –0.36]; Table 3).

Within a multicenter cohort of 2,309 children hospitalized with ARI, we noted significantly lower HRQOL scores among children exposed to SHS in the home as compared with nonexposed children. Differences were greatest for children living with ≥2 smokers in the home. In analyses stratified by diagnosis, differences in baseline health HRQOL scores were greatest for children with bronchiolitis and pneumonia. Differences in acute illness scores were greatest for children with croup.¹⁶

Our study provides evidence for acute and chronic impacts of SHS on HRQOL in children hospitalized with ARI. Although several studies have linked SHS exposure to reduced HRQOL in adults,^19,20 few similar studies have been conducted in children. Nonetheless, a wealth of studies have documented the negative impact of SHS exposure on clinical outcomes among children with ARI.^8,10,21-23 Our findings that home SHS exposure was associated with reduced HRQOL among our cohort of children with ARI are therefore consistent with related findings in adults and children. The observation that the effects of SHS exposure on HRQOL were greatest among children living with ≥2 smokers provides further evidence of a potential causal link between regular SHS exposure and HRQOL.

Although the magnitude and significance of associations between SHS exposure and HRQOL varied for each of the four diagnoses for baseline health and the acute illness, it is important to note that the point estimates for the adjusted mean differences were uniformly lower for the SHS-exposed children in each subgroup. Even so, only acute illness scores for croup exceeded the MCID threshold.¹⁶ Croup is the only included condition of the upper airway and is characterized by laryngotracheal inflammation leading to the typical cough and, in moderate to severe cases, stridor. Given that chronic SHS exposure induces a proinflammatory state,³ it is possible that SHS-exposed children with croup had more severe illness compared with nonexposed children with croup resulting in lower HRQOL scores on admission. Further, perceived differences in illness severity and HRQOL may be more readily apparent in children with croup (eg, stridor at rest vs intermittent or no stridor) as compared with children with lower respiratory tract diseases.

Of the four included diagnoses, the link between SHS exposure and asthma outcomes has been most studied. Prior work has demonstrated more frequent and severe acute exacerbations, as well as worse long-term lung function among SHS-exposed children as compared with nonexposed children.^22-24 It was, therefore, surprising that our study failed to demonstrate associations between SHS exposure and HRQOL among children with asthma. Reasons for this finding are unclear. One hypothesis is that caregivers of SHS-exposed children with asthma may be more aware of the impacts of SHS exposure on respiratory health (through prior education) and, thus, more likely to modify their smoking behaviors, or for their children to be on daily asthma controller therapy. Alternatively, caregivers of children with asthma may be more likely to underreport home SHS exposure. Thirty-eight percent of children with asthma, however, were classified as SHS-exposed. This percentage was greater than the other three conditions studied (25%-32%), suggesting that differential bias in underreporting was minimal. Given that children with asthma were older, on average, than children with the other three conditions, it may also be that these children spent more time in smoke-free environments (eg, school).

Nearly one-third of children in our study were exposed to SHS in the home. This is similar to the prevalence of exposure in other studies conducted among hospitalized children^8,10,21,25 but higher than the national prevalence of home SHS exposure among children in the United States.²⁶ Thus, hospitalized children represent a particularly vulnerable population and an important target for interventions aiming to reduce exposure to SHS. Although longitudinal interventions are likely necessary to affect long-term success, hospitalization for ARI may serve as a powerful teachable moment to begin cessation efforts. Hospitalization also offers time beyond a typical primary care outpatient encounter to focus on cessation counseling and may be the only opportunity to engage in counseling activities for some families with limited time or access. Further, prior studies have demonstrated both the feasibility and the effectiveness of smoking cessation interventions in hospitalized children.^27-30 Unfortunately, however, SHS exposure is often not documented at the time of hospitalization, and many opportunities to intervene are missed.^25,31 Thus, there is a need for improved strategies to reliably identify and intervene on SHS-exposed children in the hospital setting.

These findings should be considered in the context of several limitations. The observational nature of our study raises the potential for confounding, specifically with regard to socioeconomic status, as this is associated with both SHS exposure and lower HRQOL. Our modeling approach attempted to control for several factors associated with socioeconomic status, including caregiver education and insurance coverage, but there is potential for residual confounding. No single question is sufficient to fully assess SHS exposure as the intensity of home SHS exposure likely varies widely, and some children may be exposed to SHS outside of the home environment.³² The home, however, is often the most likely source of regular SHS exposure,^33,34 especially among young children (our cohort’s mean age was 3.6 years). Misclassification of SHS exposure is also possible due to underreporting of smoking.^35,36 As a result, some children regularly exposed to SHS may have been misclassified as nonexposed, and the observed associations between SHS exposure and HRQOL may be underestimated. Confirming our study’s findings using objective assessments of SHS exposure, such as cotinine, are warranted. Given the young age of our cohort, the PedsQL surveys were completed by the parent or legal guardian only in >90% of the enrolled subjects, and caregiver perceptions may not accurately reflect the child’s perceptions. Prior work, however, has demonstrated the validity of parent-proxy reporting of the PedsQL, including correlation with child self-report.³⁷ In our study, correlation between child and caregiver reporting (when available) was also very good (r = 0.72, 95% CI 0.64, 0.77). It is also possible that the timing of the HRQOL assessments (on admission) may have biased perceptions of baseline HRQOL, although we anticipate any bias would likely be nondifferential between SHS-exposed and nonexposed children and across diagnoses.

Nearly one-third of children in our study were exposed to SHS exposure in the home, and SHS exposure was associated with lower HRQOL for baseline health and during acute illness, providing further evidence of the dangers of SHS. Much work is needed in order to eliminate the impact of SHS on child health and families of children hospitalized for respiratory illness should be considered a priority population for smoking cessation efforts.

Acknowledgment

The authors wish to acknowledge the efforts of PRIS-PRIMES study team. The authors also wish to thank the children and families who consented to be a part of the PRIMES study.

Disclosures

The authors have no conflicts of interest relevant to this article to disclose.

Funding

This study was supported by NIH-NHLBI 1R01HL121067 to RMS.