Video Abstract

Video Abstract

Close modal
OBJECTIVES:

To determine the changes in ICU admissions, ventilatory support, length of stay, and cost for patients with bronchiolitis in the United States.

METHODS:

Retrospective cross-sectional study of the Pediatric Health Information Systems database. All patients age <2 years admitted with bronchiolitis and discharged between January 1, 2010 and December 31, 2019, were included. Outcomes included proportions of annual ICU admissions, invasive mechanical ventilation (IMV), noninvasive ventilation (NIV), and cost.

RESULTS:

Of 203 859 admissions for bronchiolitis, 39 442 (19.3%) were admitted to an ICU, 6751 (3.3%) received IMV, and 9983 (4.9%) received NIV. ICU admissions for bronchiolitis doubled from 11.7% in 2010 to 24.5% in 2019 (P < .001 for trend), whereas ICU admissions for all children in Pediatric Health Information Systems <2 years of age increased from 16.0% to 21.1% during the same period (P < .001 for trend). Use of NIV increased sevenfold from 1.2% in 2010 to 9.5% in 2019 (P < .001 for trend). Use of IMV did not significantly change (3.3% in 2010 to 2.8% in 2019, P = .414 for trend). In mixed-effects multivariable logistic regression, discharge year was a significant predictor of NIV (odds ratio: 1.24; 95% confidence interval [CI]: 1.23–1.24) and ICU admission (odds ratio: 1.09; 95% CI: 1.09–1.09) but not IMV (odds ratio: 1.00; 95% CI: 1.00–1.00).

CONCLUSIONS:

The proportions of children with bronchiolitis admitted to an ICU and receiving NIV have substantially increased, whereas the proportion receiving IMV is unchanged over the past decade. Further study is needed to better understand the factors underlying these temporal patterns.

What’s Known on This Subject:

In the early part of the last decade, trends toward rising ICU admission proportion and noninvasive ventilation use were noted in patients with bronchiolitis. These trends were temporally associated with increased cost.

What This Study Adds:

In this national database analysis, the ICU admission proportion in bronchiolitis doubled between 2010 and 2019, coupled with a sevenfold increase in the use of noninvasive ventilation. These changes were not associated with changes in the proportion of invasive mechanical ventilation.

Bronchiolitis is the most common lower respiratory illness in young children, accounting for 18% of all United States hospitalizations (excluding birth) in children <2 years in 2016.14  The proportion of infants with bronchiolitis requiring ICU admission has previously been reported at ∼6% to 22%, with an increase noted between 2007 and 2013.58  Risk factors for ICU admission and invasive mechanical ventilation (IMV) include prematurity, low birth weight, age <6 months, congenital heart or lung disease, tachypnea, and apneic episodes.59 

Supportive care remains the mainstay of bronchiolitis treatment.2  There has been increasing interest in the use of noninvasive ventilation (NIV) and high-flow nasal cannula (HFNC) therapy after researchers of early studies reported improved respiratory rate and work of breathing.1013  The effects HFNC remain unclear; although the crossover design of some randomized controlled trials had suggested that HFNC might rescue some children from ICU admission,14,15  a third study without this design did not find benefit.16  Additional observational studies have variably shown decreased,5,1719  unchanged,20,21  or increased ICU use22  accompanying the use of HFNC. Care patterns for patients with bronchiolitis in the United States appear to be shifting, with falling hospitalization rates but simultaneously rising ICU admission proportion and use of both NIV and IMV in the early part of the last decade.4,8  In accordance with rising use of NIV, reports have indicated a dramatic increase in inflation-adjusted cost of hospitalization for bronchiolitis from $449 million in 2003 to $734 million in 2016.4  Debate continues regarding the appropriate use of NIV and HFNC.2325 

To build on previous reports in which researchers examined trends in bronchiolitis epidemiology, we aimed to characterize trends of the use of IMV, use of NIV, proportion admitted to the ICU, and hospitalization costs for children with bronchiolitis between 2010 and 2019 using the Pediatric Health Information System (PHIS) database.26  Additionally, we sought to examine the possible association between discharge year and the use of IMV, NIV, ICU admission, and costs after adjusting for confounders. We hypothesized that increasing use of NIV, but not decreasing use of IMV, would be present after adjustment for baseline health conditions and that this would lead to a greater proportion of children with bronchiolitis being admitted to an ICU and increased cost.

This was a retrospective cross-sectional study of PHIS. PHIS is an anonymous, quality-controlled, online administrative data warehouse of >45 US children’s hospitals across >20 states, representing the majority of large US metropolitan centers.2628  To ensure that changes seen over time were reflective of practice changes, rather than incorporation of new hospitals, the current study was limited to hospitals that have been providing data since 2010. Patients were eligible if they were discharged between January 1, 2010, and December 31, 2019, from an inpatient hospital stay with an associated primary encounter diagnosis of viral bronchiolitis (International Classification of Diseases, Ninth Revision [ICD-9] 466, or International Classification of Diseases, 10th Revision [ICD-10] J21) and were <2 years of age. There were no exclusion criteria. Because this was an observational study, there was no exposure. The outcomes of interest were IMV, NIV, ICU admission, length of stay, and cost.

Encounter-level data were extracted from PHIS (age, sex, race, ethnicity, diagnostic codes, insurance status, hospital, length of stay, ICU length of stay, complex chronic conditions,29  abstracted charges, and cost). Invasive ventilation and NIV were determined on the basis of diagnostic codes in the methods of Fujiogi et al, supplemental table 3.4  Cohort demographics were described by using summary statistics. Cost analyses were abstracted from hospital charges and determined by the cost-to-charge ratio submitted to the Centers for Medicare and Medicaid Services (CMS) annually and adjusted by the CMS wage/price index according to hospital zip code in PHIS.26  Cost-over-time analyses were adjusted for the annual gross domestic product (GDP) or national health care expenditures per capita, as stated in results, and expressed in 2010 dollars.30  To assess changes over time, the cohort was binned into groups on the basis of discharge year, and linear regression was performed, with discharge year as the independent variable. Discharge year, rather than admission year, was chosen to ensure that all patients had complete data at the time of analyses. Mixed-effects multivariable logistic regression was performed, by using a random intercept for hospital region similar to Gupta et al.8  Models were developed including age, sex, race, ethnicity, insurance status, complex chronic condition flags, and discharge year. Because age was previously known to display a nonlinear association with IMV in bronchiolitis,8  splines regression was performed. Models were simplified by using automated stepwise selection minimizing the Akaike information criterion.31  Because assessing changes in practice over time was the primary interest of the study, discharge year was forced into the model even if not significant. No other variables were forced into the models.

All statistical analyses were performed by using RStudio version 1.3.1073 (RStudio, Boston, MA) and R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria) with the following packages: caret, cowplot, dplyr, flexdashboard, forcats, ggplot2, givitiR, jtools, kableExtra, lubridate, precrec, pROC, purr, readr, stringr, tibble, tidyr, tidyverse, and tiff.32,33  An α value of .05 was set as the threshold for significance. The code used to create the article and supplement, except for single-center analyses, are publicly available at https://github.com/drjonpelly/bronchiolitis_in_phis_supplement.

We conducted 2 sensitivity analyses and 1 nested subanalysis. For sensitivity analyses, we first broadened the cohort to include encounters with any associated admission diagnoses of bronchiolitis, consistent with Fujiogi et al.4  Second, we restricted the cohort to only those patients defined within the 3M All Patient Refined Diagnosis Related Group (APR-DRG) (version 32) of “Bronchiolitis and RSV Pneumonia” (group 138, APR-DRG).34  Because bronchiolitis and influenza are increasingly diagnosed by multiplex assays,3537  we compared diagnoses of bronchiolitis with influenza (ICD-9 487, ICD-10 J09, ICD-10 J10, or ICD-10 J11). Because there was a rise in coding of “acute respiratory failure” (ICD-9518.81 or ICD-10 J96) with the transition from ICD-9 to ICD-10, we examined the patients with this diagnosis who were also diagnosed with bronchiolitis. For the subanalysis, because PHIS contains neither previously validated severity-of-illness scores3840  nor the variables necessary to derive these scores, we analyzed a nested subset of patients from the main analysis admitted to our institution with bronchiolitis. We analyzed the changes in modified electronic Pediatric Logistic Organ Dysfunction score (PELOD) and Pediatric Risk of Mortality score (PRISM) by discharge year for patients admitted to our ICU.41  The PELOD score was modified to remove points for mechanical ventilation to allow for comparison between groups stratified on level of ventilatory support.

The main analysis included 203 859 admissions with a primary encounter diagnosis of bronchiolitis among 185 658 patients across 38 centers over 10 years. The demographics for the main analysis and all sensitivity analyses are listed in Table 1. Median (interquartile range [IQR]) hospital length of stay was 3 (2–4) days, with 19.3% admitted to the ICU and 8.2% receiving charges for IMV or NIV. The median (IQR) cost per admission was $5224 ($3120–$9365). Overall survival to discharge was 99.9%. The proportion of admissions with complex chronic conditions increased over time, from 14.8% in 2010 to 18.8% in 2019 (Supplemental Information: Demographics).

TABLE 1

Cohort Demographics

VariableMain Analysis: Primary Bronchiolitis DiagnosesSensitivity: All Bronchiolitis DiagnosesSensitivity: APR-DRG v32 Bronchiolitisa
No. unique admissions, n (%) 203 859 (100) 292 390 (100) 195 233 (100) 
No. unique patients 185 658 257 167 179 339 
Age    
 Median age (IQR), mo 5 (2–11) 5 (2–12) 5 (2–11) 
Sex, n (%)    
 Male 119 393 (58.57) 171 050 (58.5) 114 183 (58.49) 
Race, n (%)    
 White 112 658 (55.26) 160 872 (55.02) 108 104 (55.37) 
 Black 40 819 (20.02) 60 172 (20.58) 38 400 (19.67) 
 Asian American 6170 (3.03) 9083 (3.11) 5973 (3.06) 
 American Indian 1554 (0.76) 2407 (0.82) 1496 (0.77) 
 Pacific islander 1690 (0.83) 2417 (0.83) 1615 (0.83) 
 Other race 34 251 (16.8) 47 047 (16.09) 33 068 (16.94) 
Ethnicity, n (%)    
 Hispanic 49 064 (24.07) 71 509 (24.46) 47 495 (24.33) 
Insurance, n (%)    
 Commercial insurance 64 361 (31.57) 91 037 (31.14) 62 523 (32.02) 
 Government insurance 132 268 (64.88) 190 514 (65.16) 125 700 (64.38) 
 Other insurance 7230 (3.55) 10 839 (3.71) 7010 (3.59) 
Complex chronic conditions, n (%)    
 Complex chronic condition 36 186 (17.75) 61 846 (21.15) 28 601 (14.65) 
 Cardiovascular condition 12 038 (5.91) 21 818 (7.46) 9801 (5.02) 
 Gastrointestinal condition 7905 (3.88) 15 093 (5.16) 6177 (3.16) 
 Hematologic or immunologic condition 2621 (1.29) 4721 (1.61) 2459 (1.26) 
 Malignancy 745 (0.37) 1688 (0.58) 657 (0.34) 
 Metabolic condition 1844 (0.9) 4000 (1.37) 1476 (0.76) 
 Neurologic condition 4174 (2.05) 8300 (2.84) 3379 (1.73) 
 Genetic condition 7884 (3.87) 13 368 (4.57) 7203 (3.69) 
 Premature or neonatal condition 7676 (3.77) 13 808 (4.72) 2151 (1.1) 
 Renal condition 1824 (0.89) 3479 (1.19) 1560 (0.8) 
 Respiratory condition 6201 (3.04) 11 644 (3.98) 4870 (2.49) 
 Technology dependent 10 263 (5.03) 18 591 (6.36) 7898 (4.05) 
 Transplant recipient 131 (0.06) 330 (0.11) 134 (0.07) 
Admission characteristics    
 Median hospital length of stay in days (IQR) 3 (2–4) 3 (2–5) 2 (2–4) 
 Admitted to ICU, n (%) 39 422 (19.34) 80 156 (27.41) 33 981 (17.41) 
 Highest support of invasive ventilation, n (%) 6751 (3.31) 19 792 (6.77) 2251 (1.15) 
 Highest support of NIV, n (%) 9983 (4.9) 19 427 (6.64) 9470 (4.85) 
 No ventilatory support, n (%) 187 125 (91.79) 253 171 (86.59) 183 512 (94) 
 Received ECMO, n (%) 113 (0.06) 537 (0.18) 1 (0) 
Cost of care    
 Median cost (IQR) 5224 (3120–9365) 6119 (3506–11 883) 5034 (3053.75–8727) 
 Median abstracted charge (IQR) 15 462 (9285–28 318) 18 384 (10 440–36 248.25) 14 943 (9082–26 269) 
 Median CMS adjusted cost (IQR) 5118 (3070–9142) 6022.5 (3455–11696) 4926 (3000–8501) 
 Median CMS adjusted abstracted charge (IQR) 15 132 (9131–27512.25) 18 072 (10 334–35 567) 14 599 (8925–25 599) 
Survival, n (%)    
 Survived to discharge 203 725 (99.93) 291 791 (99.8) 195 173 (99.97) 
VariableMain Analysis: Primary Bronchiolitis DiagnosesSensitivity: All Bronchiolitis DiagnosesSensitivity: APR-DRG v32 Bronchiolitisa
No. unique admissions, n (%) 203 859 (100) 292 390 (100) 195 233 (100) 
No. unique patients 185 658 257 167 179 339 
Age    
 Median age (IQR), mo 5 (2–11) 5 (2–12) 5 (2–11) 
Sex, n (%)    
 Male 119 393 (58.57) 171 050 (58.5) 114 183 (58.49) 
Race, n (%)    
 White 112 658 (55.26) 160 872 (55.02) 108 104 (55.37) 
 Black 40 819 (20.02) 60 172 (20.58) 38 400 (19.67) 
 Asian American 6170 (3.03) 9083 (3.11) 5973 (3.06) 
 American Indian 1554 (0.76) 2407 (0.82) 1496 (0.77) 
 Pacific islander 1690 (0.83) 2417 (0.83) 1615 (0.83) 
 Other race 34 251 (16.8) 47 047 (16.09) 33 068 (16.94) 
Ethnicity, n (%)    
 Hispanic 49 064 (24.07) 71 509 (24.46) 47 495 (24.33) 
Insurance, n (%)    
 Commercial insurance 64 361 (31.57) 91 037 (31.14) 62 523 (32.02) 
 Government insurance 132 268 (64.88) 190 514 (65.16) 125 700 (64.38) 
 Other insurance 7230 (3.55) 10 839 (3.71) 7010 (3.59) 
Complex chronic conditions, n (%)    
 Complex chronic condition 36 186 (17.75) 61 846 (21.15) 28 601 (14.65) 
 Cardiovascular condition 12 038 (5.91) 21 818 (7.46) 9801 (5.02) 
 Gastrointestinal condition 7905 (3.88) 15 093 (5.16) 6177 (3.16) 
 Hematologic or immunologic condition 2621 (1.29) 4721 (1.61) 2459 (1.26) 
 Malignancy 745 (0.37) 1688 (0.58) 657 (0.34) 
 Metabolic condition 1844 (0.9) 4000 (1.37) 1476 (0.76) 
 Neurologic condition 4174 (2.05) 8300 (2.84) 3379 (1.73) 
 Genetic condition 7884 (3.87) 13 368 (4.57) 7203 (3.69) 
 Premature or neonatal condition 7676 (3.77) 13 808 (4.72) 2151 (1.1) 
 Renal condition 1824 (0.89) 3479 (1.19) 1560 (0.8) 
 Respiratory condition 6201 (3.04) 11 644 (3.98) 4870 (2.49) 
 Technology dependent 10 263 (5.03) 18 591 (6.36) 7898 (4.05) 
 Transplant recipient 131 (0.06) 330 (0.11) 134 (0.07) 
Admission characteristics    
 Median hospital length of stay in days (IQR) 3 (2–4) 3 (2–5) 2 (2–4) 
 Admitted to ICU, n (%) 39 422 (19.34) 80 156 (27.41) 33 981 (17.41) 
 Highest support of invasive ventilation, n (%) 6751 (3.31) 19 792 (6.77) 2251 (1.15) 
 Highest support of NIV, n (%) 9983 (4.9) 19 427 (6.64) 9470 (4.85) 
 No ventilatory support, n (%) 187 125 (91.79) 253 171 (86.59) 183 512 (94) 
 Received ECMO, n (%) 113 (0.06) 537 (0.18) 1 (0) 
Cost of care    
 Median cost (IQR) 5224 (3120–9365) 6119 (3506–11 883) 5034 (3053.75–8727) 
 Median abstracted charge (IQR) 15 462 (9285–28 318) 18 384 (10 440–36 248.25) 14 943 (9082–26 269) 
 Median CMS adjusted cost (IQR) 5118 (3070–9142) 6022.5 (3455–11696) 4926 (3000–8501) 
 Median CMS adjusted abstracted charge (IQR) 15 132 (9131–27512.25) 18 072 (10 334–35 567) 14 599 (8925–25 599) 
Survival, n (%)    
 Survived to discharge 203 725 (99.93) 291 791 (99.8) 195 173 (99.97) 

ECMO, extracorporeal membrane oxygenation.

a

3M APG-DRG (version 32) “Bronchiolitis and RSV Pneumonia” (group 138).

Bronchiolitis admissions were 10.7% (19 301 of 180 272) of all PHIS hospital admissions in 2010 versus 11.3% (21 747 of 191 812) in 2019 (P = .267 for trend, Fig 1A). They comprised 7.8% (2255 of 28 901) of all PHIS ICU admissions in 2010 versus 13.2% (5334 of 40 498) in 2019 (P < .001 for trend). Overall, 2255 of 19 301 (11.7%) of patients with bronchiolitis were admitted to the ICU in 2010 versus 5334 of 21 747 (24.5%) in 2019 (average increase 1.3% per year, P < .001 for trend, Fig 2A). During the same period, 28 901 of 180 272 (16.0%) of all children <2 years of age in PHIS (any diagnosis) were admitted to the ICU in 2010 versus 40 498 of 191 812 (21.1%) in 2019 (average increase 0.5% per year, P < .001 for trend, Fig 2B).

FIGURE 1

Incidence of bronchiolitis and cost of care over time. A, the total number of inpatient admissions for the included hospitals on the left axis, including patients with a primary diagnosis of bronchiolitis (dark gray), and those without (light gray). The black line shows the percentage of admissions with bronchiolitis on the right y-axis. B, identical to (A) except that patients with any encounter diagnosis of bronchiolitis are included. C, the total annual cost of admissions, adjusted for the Center for Medicare & Medicaid services wage/price index for the hospital zip code, and the annual GDP and expressed in 2010 dollars. The color scale shows patients receiving both invasive and non-invasive ventilation (dark gray), invasive ventilation (medium gray), non-invasive ventilation (medium gray #2), and no ventilatory support (light gray). D, identical to (C) except that except that patients with any encounter diagnosis of bronchiolitis are included (as in B).

FIGURE 1

Incidence of bronchiolitis and cost of care over time. A, the total number of inpatient admissions for the included hospitals on the left axis, including patients with a primary diagnosis of bronchiolitis (dark gray), and those without (light gray). The black line shows the percentage of admissions with bronchiolitis on the right y-axis. B, identical to (A) except that patients with any encounter diagnosis of bronchiolitis are included. C, the total annual cost of admissions, adjusted for the Center for Medicare & Medicaid services wage/price index for the hospital zip code, and the annual GDP and expressed in 2010 dollars. The color scale shows patients receiving both invasive and non-invasive ventilation (dark gray), invasive ventilation (medium gray), non-invasive ventilation (medium gray #2), and no ventilatory support (light gray). D, identical to (C) except that except that patients with any encounter diagnosis of bronchiolitis are included (as in B).

Close modal
FIGURE 2

The percentage of patients with bronchiolitis (A) and all hospital admissions <2 years of age (B) admitted to the ICU over time. The y-axis shows the percent of admissions admitted to the ICU, and the x-axis shows patients grouped by discharge year. Numbers within the bars represent the percentage.

FIGURE 2

The percentage of patients with bronchiolitis (A) and all hospital admissions <2 years of age (B) admitted to the ICU over time. The y-axis shows the percent of admissions admitted to the ICU, and the x-axis shows patients grouped by discharge year. Numbers within the bars represent the percentage.

Close modal

The proportion of IMV over time did not significantly change (3.3% in 2010 versus 2.8% in 2019, P = .414 for trend, Fig 3A). However, the proportion of NIV increased more than sevenfold over the study period (1.2% in 2010 to 9.5% in 2019, P < .001 for trend, Fig 3A). During this same period, the absolute number of patients admitted to the ICU for IMV numerically decreased from 572 in 2010 to 549 in 2019 (P = .737 for trend, Fig 3B), but the absolute number of patients admitted for NIV or no ventilatory support both significantly increased (169 vs 1599, P < .001 for trend, and 1514 vs 3186, P < .001 for trend, respectively, Fig 3B).

FIGURE 3

Changes in ventilatory support over time. A, the percentage of patients with bronchiolitis receiving noninvasive (light gray) and invasive (dark gray) ventilatory support. The y-axis represents percent of admissions, and the x-axis shows patients grouped by discharge year. Numbers within the bars represent the percentage. B, absolute numbers of invasive (dark gray), noninvasive (medium gray), and no (light gray) ventilatory support among patients admitted to the ICU. The y-axis shows absolute numbers, and the x-axis shows patients grouped by discharge year.

FIGURE 3

Changes in ventilatory support over time. A, the percentage of patients with bronchiolitis receiving noninvasive (light gray) and invasive (dark gray) ventilatory support. The y-axis represents percent of admissions, and the x-axis shows patients grouped by discharge year. Numbers within the bars represent the percentage. B, absolute numbers of invasive (dark gray), noninvasive (medium gray), and no (light gray) ventilatory support among patients admitted to the ICU. The y-axis shows absolute numbers, and the x-axis shows patients grouped by discharge year.

Close modal

The mixed-effects logistic regression models to predict IMV, NIV, and ICU admission are shown in Table 2. After adjustment for age, race and ethnicity, sex, insurance status, and chronic complex conditions, discharge year was significantly associated with NIV (odds ratio: 1.24; 95% confidence interval [CI]: 1.23–1.24) and ICU admission (odds ratio: 1.09; 95% CI 1.09–1.09) but not IMV (odds ratio: 1.00; 95% CI: 1.00–1.00).

TABLE 2

Multivariable Logistic Regression

VariableInvasive Ventilation, OR (95% CI)NIV, OR (95% CI)ICU Admission, OR (95% CI)
Discharge year 1.00 (1.00–1.00) 1.24 (1.23–1.24) 1.09 (1.09–1.09) 
Sex: female 0.96 (0.93–0.98) 0.96 (0.94–0.98) 0.95 (0.94–0.96) 
Race: Black 0.83 (0.8–0.87) 1.04 (1.01–1.07) NA 
Race: other 1 (0.96–1.03) 1.09 (1.06–1.12) NA 
Ethnicity: Hispanic 0.76 (0.73–0.79) 0.81 (0.78–0.83) 0.85 (0.84–0.86) 
Ethnicity: unknown 0.9 (0.86–0.94) 1.09 (1.05–1.13) 0.79 (0.78–0.81) 
Insurance: government 1.32 (1.28–1.36) 0.97 (0.95–0.99) 1.1 (1.09–1.12) 
Insurance: other 1.06 (0.98–1.15) 0.59 (0.55–0.63) 0.95 (0.92–0.98) 
Admit age (months)    
 First-order spline 0.06 (0.05–0.06) 0.46 (0.42–0.51) 0.55 (0.52–0.58) 
 Second-order spline 0.37 (0.32–0.43) 1.18 (1.07–1.29) 1.04 (0.98–1.1) 
 Third-order spline 0.11 (0.1–0.12) 0.63 (0.59–0.68) 0.69 (0.66–0.72) 
CCC    
 Cardiovascular 4.43 (4.28–4.59) 1.76 (1.7–1.82) 2.75 (2.69–2.81) 
 Congenital or genetic defect 0.91 (0.86–0.96) 1.27 (1.21–1.33) 1.17 (1.13–1.2) 
 Gastrointestinal NA 0.82 (0.75–0.88) 1.28 (1.24–1.32) 
 Hematologic or immunologic 1.4 (1.28–1.54) 1.12 (1.04–1.2) 1.05 (1–1.1) 
 Malignancy 3.06 (2.69–3.47) 1.83 (1.62–2.07) 2.04 (1.88–2.21) 
 Metabolic 3.18 (2.94–3.43) 1.31 (1.2–1.42) 1.62 (1.53–1.7) 
 Neurologic 1.94 (1.82–2.08) 1.32 (1.24–1.4) 1.39 (1.34–1.44) 
 Prematurity or neonatal 1.6 (1.51–1.68) 1.2 (1.15–1.26) 1.18 (1.14–1.21) 
 Renal or urologic 1.35 (1.23–1.48) NA 1.21 (1.14–1.27) 
 Respiratory 3.33 (3.18–3.5) 1.21 (1.15–1.28) 1.52 (1.47–1.56) 
 Technology dependent 1.68 (1.6–1.77) 1.36 (1.27–1.47) NA 
 TPN dependent 18.65 (17.52–19.84) 4.78 (4.47–5.13) 5.77 (5.44–6.12) 
 Transplant patient 0.51 (0.34–0.76) NA NA 
VariableInvasive Ventilation, OR (95% CI)NIV, OR (95% CI)ICU Admission, OR (95% CI)
Discharge year 1.00 (1.00–1.00) 1.24 (1.23–1.24) 1.09 (1.09–1.09) 
Sex: female 0.96 (0.93–0.98) 0.96 (0.94–0.98) 0.95 (0.94–0.96) 
Race: Black 0.83 (0.8–0.87) 1.04 (1.01–1.07) NA 
Race: other 1 (0.96–1.03) 1.09 (1.06–1.12) NA 
Ethnicity: Hispanic 0.76 (0.73–0.79) 0.81 (0.78–0.83) 0.85 (0.84–0.86) 
Ethnicity: unknown 0.9 (0.86–0.94) 1.09 (1.05–1.13) 0.79 (0.78–0.81) 
Insurance: government 1.32 (1.28–1.36) 0.97 (0.95–0.99) 1.1 (1.09–1.12) 
Insurance: other 1.06 (0.98–1.15) 0.59 (0.55–0.63) 0.95 (0.92–0.98) 
Admit age (months)    
 First-order spline 0.06 (0.05–0.06) 0.46 (0.42–0.51) 0.55 (0.52–0.58) 
 Second-order spline 0.37 (0.32–0.43) 1.18 (1.07–1.29) 1.04 (0.98–1.1) 
 Third-order spline 0.11 (0.1–0.12) 0.63 (0.59–0.68) 0.69 (0.66–0.72) 
CCC    
 Cardiovascular 4.43 (4.28–4.59) 1.76 (1.7–1.82) 2.75 (2.69–2.81) 
 Congenital or genetic defect 0.91 (0.86–0.96) 1.27 (1.21–1.33) 1.17 (1.13–1.2) 
 Gastrointestinal NA 0.82 (0.75–0.88) 1.28 (1.24–1.32) 
 Hematologic or immunologic 1.4 (1.28–1.54) 1.12 (1.04–1.2) 1.05 (1–1.1) 
 Malignancy 3.06 (2.69–3.47) 1.83 (1.62–2.07) 2.04 (1.88–2.21) 
 Metabolic 3.18 (2.94–3.43) 1.31 (1.2–1.42) 1.62 (1.53–1.7) 
 Neurologic 1.94 (1.82–2.08) 1.32 (1.24–1.4) 1.39 (1.34–1.44) 
 Prematurity or neonatal 1.6 (1.51–1.68) 1.2 (1.15–1.26) 1.18 (1.14–1.21) 
 Renal or urologic 1.35 (1.23–1.48) NA 1.21 (1.14–1.27) 
 Respiratory 3.33 (3.18–3.5) 1.21 (1.15–1.28) 1.52 (1.47–1.56) 
 Technology dependent 1.68 (1.6–1.77) 1.36 (1.27–1.47) NA 
 TPN dependent 18.65 (17.52–19.84) 4.78 (4.47–5.13) 5.77 (5.44–6.12) 
 Transplant patient 0.51 (0.34–0.76) NA NA 

CCC, complex chronic condition; NA, not available; OR, odds ratio; TPN, total parenteral nutrition.

The median (IQR) hospital length of stay did not change between 2010 and 2019: 3 (2–4) days versus 3 (2–4) days, respectively. However, the median (IQR) ICU length of stay decreased from 3 (2–6) days in 2010 versus 2 (1–4) days in 2019 (P < .001 for trend). As seen in Fig 1C, the total GDP-adjusted cost of bronchiolitis diagnoses increased numerically from $145 to $157 million, but this was not statistically significant (average increase $1.1 million per year, P = .276 for trend). The cost of NIV rose from $3.8 million in 2010 to $24.6 million in 2019. The mean GDP-adjusted cost per admission decreased from $7527 to $7219 (average decrease $57 per year, P < .001 for trend, Supplemental Information: Cost). The GDP-adjusted cost per admission decreased over time for all levels of ventilatory support (Supplemental Information: Cost). Additional inflation adjustments are shown in Supplemental Information: Cost.

The complete results of all sensitivity analyses are shown in the Supplemental Information. The proportion of encounters with a primary diagnoses of bronchiolitis (used for the main analysis) did not significantly change over time (10.7% in 2010 versus 11.3% in 2019, P = .267 for trend, Fig 1A). However, there was a significant increase in the proportion of admissions with secondary diagnoses of bronchiolitis (2.9% in 2010 versus 9.7% in 2019, P < .001 for trend). Of the 88 531 patients with a secondary diagnosis of bronchiolitis, 35 140 (39.6%) had a primary diagnosis of acute respiratory failure (639 of 5193 [12.3%] in 2010 versus 11 603 of 18 650 in 2019 [62.2%], P < .001 for trend). Thus, the percentage of patients with all (primary or secondary) diagnosis of bronchiolitis increased from 13.6% in 2010 to 21.0% in 2019 (Fig 1B). Similar trends were noted for influenza (Supplemental Information: Diagnoses).

The proportion of patients admitted to the ICU and treated with IMV and NIV over time displayed similar trends to the main analysis in both the all-diagnosis and the APR-DRG groups (Supplemental Information: Incidence and Supplemental Information: Ventilation). After adjustment for confounders, discharge year remained associated with NIV and ICU admission (Supplemental Information: Model ICU and Supplemental Information: Model NIV) and was positively associated with IMV in the all-diagnosis group (odds ratio 1.02 [95% CI 1.02–1.02]) but not the APR-DRG group (Supplemental Information: Model IMV). In parallel with rising group size, the GDP-adjusted cost for the all-diagnosis group increased from $270 million in 2010 to $466 million in 2019 (average increase $22.6 million per year, P < .001 for trend, Fig 1D), whereas the APR-DRG group was similar to the main analysis ($110 million in 2010 versus $119 million in 2019, Supplemental Inforamation: Cost).

The complete results of the nested single-center subanalysis are shown in Supplemental Information: Single-Center. Mirroring national findings, the proportion of ICU admission in our center rose over the study period, outpacing overall expansion in ICU proportion (rate of change 2.4% per year versus 0.6% per year, P < .001 for both). Our ICU favors HFNC and does not code this as NIV. Thus, the number of patients with a primary diagnosis of bronchiolitis admitted to the ICU for IMV increased by 1.1 patients per year (P = .011 for trend), whereas the number of patients admitted for NIV increased by 0.2 patients per year (P = .852 for trend). However, the number of patients admitted for HFNC therapy increased by 13.5 patients per year (P < .001 for trend). Modified PELOD and PRISM scores were available for patients admitted to our ICU between 2010 and 2018.41  There was no significant change in the modified PELOD score or the PRISM score for patients admitted to our ICU over the study period.

The present analysis of >200 000 admissions for bronchiolitis over 10 years demonstrates that the ICU admission proportion for bronchiolitis has doubled, as seen in Fig 2. This augments earlier studies by now showing a full decade of steadily climbing ICU burden.8  This change outpaced overall ICU admission growth for children <2, which concomitantly rose only 33% over the study period. This is particularly striking when considering that the overall hospital admission rate for bronchiolitis has fallen over the same time period.4  Understanding the reasons for increasing ICU admission proportion in this population is clinically important. Patients with bronchiolitis admitted to the ICU have a 10% to 18% incidence of new neurologic and functional morbidities, which affect their long-term quality of life.4244  Thus, the developmental impacts of shifting patients from ward to ICU settings are unclear, and research efforts should be undertaken to predict and prevent hospital-acquired morbidities as this population grows.

During the study period, the use of NIV has increased more than sevenfold, as seen in Fig 3A. However, the rise in the use of NIV does not fully explain the rise in ICU burden. The absolute number of children admitted to the ICU without International Classification of Diseases (ICD) codes for IMV or NIV has also doubled. Because coding practices vary by hospital, it is likely that some children with HFNC therapy in this cohort were classified as receiving NIV, whereas others did not receive a diagnostic code for ventilatory support.45  Although this study replicated previous methods for identifying patients receiving NIV in health care databases,4  the absence of dedicated ICD codes for HFNC preclude more granular analyses.45  Thus, the rise in children being admitted to the ICU for “no” ventilatory support is likely partially representative of an increase in the use of HFNC.

Interestingly, after adjustment for confounders, increased rates of ICU admission and use of NIV were not coupled with decreased rates of IMV in the current study. Although epidemiological data from Australia and New Zealand18  had suggested that HFNC may reduce ICU admissions for bronchiolitis, studies in the United States22  and Canada21  have not replicated these effects. Similarly, no randomized controlled trial has directly demonstrated a reduction in ICU admission or IMV mediated by HFNC.1416  Thus, the preponderance of data (including the current study) suggests that HFNC does not rescue patients destined for IMV. It remains unclear why changes in ventilatory practices have been associated with opposite effects on ICU admission rates across the globe. This may reflect differences in underlying patient population, local pathogen strains, or variability in ward and ICU acuity. It may also be that HFNC meaningfully improves respiratory distress in patients with moderate bronchiolitis in a way that cannot be captured in an administrative database.

The current study is the first to report that rates of NIV are increasing in bronchiolitis using multivariable logistic regression accounting for age, race, ethnicity, sex, insurance status, and complex chronic conditions. Increased rates of NIV and ICU admission were not coupled with significant changes in hospital length of stay (Supplemental Information: Length of Stay). These models are limited by the available covariates in PHIS. It is possible that patients with bronchiolitis have become more ill over time in a way not captured in database analyses. In an effort to address whether severity of illness was changing over time, we conducted a nested subanalysis of patients admitted to our center and found no important differences in modified PELOD or PRISM scores over time.41  Although reassuring, this subanalysis included only 3965 of 203 859 (1.9%) of patients in the main analysis. However, if patients with bronchiolitis are becoming more ill over time, it would have to be occurring in a near-linear fashion to explain the trends seen in the current study, without regard for annual variability in pathogen virulence. This seems biologically implausible given previously published epidemiological data.4648 

In the current study, we also found that both the incidence and cost of bronchiolitis was substantially different depending on the methodology used to define the cases (Fig 1). Although percentage of admissions with a primary diagnostic code for bronchiolitis did not significantly change over the study period, there was a significant rise in coding of bronchiolitis as a secondary diagnosis (Supplemental Information: Diagnoses). This led to markedly different estimates of incidence (Supplemental Information: Incidence), and cost (Supplemental Information: Cost). The reason for the increase in coding of bronchiolitis as a secondary diagnosis remains unclear. However, similar patterns in coding for influenza and a rise in patients with acute respiratory failure being secondarily diagnosed with bronchiolitis (Supplemental Information: Diagnoses) may suggest that diagnoses are being influenced by the use of multiplex assays.3537 

This study has important limitations. Although the study encompasses >200 000 admissions for bronchiolitis over 10 years, only 38 hospitals are included in the cohort. Because the PHIS database predominantly contains data from large children’s hospitals,26  the data may not be nationally representative. Additionally, as is the case with many database analyses, granular data regarding severity of illness are not available, limiting understanding of rationale for clinician’s choices of ventilatory support. Finally, variable coding practices regarding the use of HFNC therapy preclude analysis of the change in this specific therapy. The establishment of methods to accurately identify patients treated with HFNC on the basis of ICD coding would substantially improve future studies.

The proportion of ICU admission for patients diagnosed with bronchiolitis has doubled over the past decade, outpacing overall ICU expansion. The use of NIV has increased sevenfold over the study period, and the use of invasive ventilation has not significantly changed. Further study is needed to better understand the factors underlying these temporal patterns, cost-effectiveness, and impact on children’s long-term health.

Drs Pelletier, Clark, and Horvat jointly conceptualized the study; Dr Pelletier performed database extraction and statistical coding; Dr Pelletier wrote the first draft of the manuscript; Drs Au, Fuhrman, Clark, and Horvat reviewed the initial draft of the manuscript, suggested subanalyses, and contributed substantially to the design and layout of the figures and supplemental materials; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: 5T32HD040686-20 (Dr Pelletier), 5K23NS104133 (Dr Au), 1K23HD099331-01A1 (Dr Horvat). Funded by the National Institutes of Health (NIH).

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2020-046276.

     
  • APR-DRG

    All Patient Refined Diagnosis Group

  •  
  • CI

    confidence interval

  •  
  • CMS

    Centers for Medicare and Medicaid Services

  •  
  • GDP

    gross domestic product

  •  
  • HFNC

    high-flow nasal cannula

  •  
  • ICD

    International Classification of Diseases

  •  
  • ICD-10

    International Classification of Diseases, 10th Revision

  •  
  • ICD-9

    International Classification of Diseases, Ninth Revision

  •  
  • IMV

    invasive mechanical ventilation

  •  
  • IQR

    interquartile range

  •  
  • NIV

    noninvasive ventilation

  •  
  • PELOD

    Pediatric Logistic Organ Dysfunction score

  •  
  • PHIS

    Pediatric Health Information Systems

  •  
  • PRISM

    Pediatric Risk of Mortality score

1
Lohr
MV
,
King
VJ
,
Bordley
C
, et al
.
Management of Bronchiolitis in Infants and Children: Summary
.
AHRQ Evidence Report Summaries
.
Rockville (MD)
.
Rockville, MD
:
Agency for Healthcare Research and Quality
;
2003
:
1998
2005
2
Ralston
SL
,
Lieberthal
AS
,
Meissner
HC
, et al;
American Academy of Pediatrics
.
Clinical practice guideline: the diagnosis, management, and prevention of bronchiolitis
.
Pediatrics
.
2014
;
134
(
5
).
3
American Academy of Pediatrics Subcommittee on Diagnosis and Management of Bronchiolitis
.
Diagnosis and management of bronchiolitis
.
Pediatrics
.
2006
;
118
(
4
):
1774
1793
4
Fujiogi
M
,
Goto
T
,
Yasunaga
H
, et al
.
Trends in bronchiolitis hospitalizations in the United States: 2000-2016
.
Pediatrics
.
2019
;
144
(
6
):
e20192614
5
Oakley
E
,
Chong
V
,
Borland
M
, et al
.
Intensive care unit admissions and ventilation support in infants with bronchiolitis
.
Emerg Med Australas
.
2017
;
29
(
4
):
421
428
6
Hasegawa
K
,
Pate
BM
,
Mansbach
JM
, et al
.
Risk factors for requiring intensive care among children admitted to ward with bronchiolitis
.
Acad Pediatr
.
2015
;
15
(
1
):
77
81
7
Mecklin
M
,
Heikkilä
P
,
Korppi
M
.
Risk factors for intensive care and respiratory support among infants with bronchiolitis
.
Eur Respir J
.
2016
;
48
(
suppl 60
):
PA1595
8
Gupta
P
,
Beam
BW
,
Rettiganti
M
.
Temporal trends of respiratory syncytial virus-associated hospital and ICU admissions across the United States
.
Pediatr Crit Care Med
.
2016
;
17
(
8
):
e343
e351
9
Mansbach
JM
,
Piedra
PA
,
Stevenson
MD
, et al;
MARC-30 Investigators
.
Prospective multicenter study of children with bronchiolitis requiring mechanical ventilation
.
Pediatrics
.
2012
;
130
(
3
).
10
Milani
GP
,
Plebani
AM
,
Arturi
E
, et al
.
Using a high-flow nasal cannula provided superior results to low-flow oxygen delivery in moderate to severe bronchiolitis
.
Acta Paediatr
.
2016
;
105
(
8
):
e368
e372
11
Milési
C
,
Baleine
J
,
Matecki
S
, et al
.
Is treatment with a high flow nasal cannula effective in acute viral bronchiolitis? A physiologic study [published correction appears in Intensive Care Med. 2013;39(6):1170]
.
Intensive Care Med
.
2013
;
39
(
6
):
1088
1094
12
Pham
TM
,
O’Malley
L
,
Mayfield
S
,
Martin
S
,
Schibler
A
.
The effect of high flow nasal cannula therapy on the work of breathing in infants with bronchiolitis
.
Pediatr Pulmonol
.
2015
;
50
(
7
):
713
720
13
Rubin
S
,
Ghuman
A
,
Deakers
T
,
Khemani
R
,
Ross
P
,
Newth
CJ
.
Effort of breathing in children receiving high-flow nasal cannula
.
Pediatr Crit Care Med
.
2014
;
15
(
1
):
1
6
14
Franklin
D
,
Babl
FE
,
Schlapbach
LJ
, et al
.
A randomized trial of high-flow oxygen therapy in infants with bronchiolitis
.
N Engl J Med
.
2018
;
378
(
12
):
1121
1131
15
Kepreotes
E
,
Whitehead
B
,
Attia
J
, et al
.
High-flow warm humidified oxygen versus standard low-flow nasal cannula oxygen for moderate bronchiolitis (HFWHO RCT): an open, phase 4, randomised controlled trial
.
Lancet
.
2017
;
389
(
10072
):
930
939
16
Durand
P
,
Guiddir
T
,
Kyheng
C
, et al;
Bronchopti study group
.
A randomised trial of high-flow nasal cannula in infants with moderate bronchiolitis
.
Eur Respir J
.
2020
;
56
(
1
):
1901926
17
McKiernan
C
,
Chua
LC
,
Visintainer
PF
,
Allen
H
.
High flow nasal cannulae therapy in infants with bronchiolitis
.
J Pediatr
.
2010
;
156
(
4
):
634
638
18
Schibler
A
,
Pham
TM
,
Dunster
KR
, et al
.
Reduced intubation rates for infants after introduction of high-flow nasal prong oxygen delivery
.
Intensive Care Med
.
2011
;
37
(
5
):
847
852
19
Schlapbach
LJ
,
Straney
L
,
Gelbart
B
, et al;
Australian & New Zealand Intensive Care Society (ANZICS) Centre for Outcomes & Resource Evaluation (CORE) and the Australian & New Zealand Intensive Care Society (ANZICS) Paediatric Study Group
.
Burden of disease and change in practice in critically ill infants with bronchiolitis
.
Eur Respir J
.
2017
;
49
(
6
):
1601648
20
Riese
J
,
Porter
T
,
Fierce
J
,
Riese
A
,
Richardson
T
,
Alverson
BK
.
Clinical outcomes of bronchiolitis after implementation of a general ward high flow nasal cannula guideline
.
Hosp Pediatr
.
2017
;
7
(
4
):
197
203
21
Garland
H
,
Gunz
AC
,
Miller
MR
,
Lim
RK
.
High-flow nasal cannula implementation has not reduced intubation rates for bronchiolitis in Canada
.
Paediatr Child Health
.
2020
. Available at:
22
Coon
ER
,
Stoddard
G
,
Brady
PW
.
Intensive care unit utilization after adoption of a ward-based high-flow nasal cannula protocol
.
J Hosp Med
.
2020
;
15
(
6
):
325
330
23
Ralston
SL
.
High-flow nasal cannula therapy for pediatric patients with bronchiolitis: time to put the horse back in the barn
.
JAMA Pediatr
.
2020
;
174
(
7
):
635
636
24
O’Brien
S
,
Babl
FE
,
Dalziel
SR
.
High-flow nasal cannula as rescue therapy in bronchiolitis
.
JAMA Pediatr
.
2020
;
175
(
2
):
207
208
25
Ralston
SL
.
High-flow nasal cannula as rescue therapy in bronchiolitis-reply
.
JAMA Pediatr
.
2020
;
175
(
2
):
208
27
Mongelluzzo
J
,
Mohamad
Z
,
Ten Have
TR
,
Shah
SS
.
Corticosteroids and mortality in children with bacterial meningitis
.
JAMA
.
2008
;
299
(
17
):
2048
2055
28
Narus
SP
,
Srivastava
R
,
Gouripeddi
R
, et al
.
Federating clinical data from six pediatric hospitals: process and initial results from the PHIS+ Consortium
.
AMIA Annu Symp Proc
.
2011
;
2011
:
994
1003
29
Feudtner
C
,
Feinstein
JA
,
Zhong
W
,
Hall
M
,
Dai
D
.
Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation
.
BMC Pediatr
.
2014
;
14
:
199
30
Dunn
A
,
Grosse
SD
,
Zuvekas
SH
.
Adjusting health expenditures for inflation: a review of measures for health services research in the United States
.
Health Serv Res
.
2018
;
53
(
1
):
175
196
31
Akaike
H
.
Information Theory and an Extension of the Maximum Likelihood Principle
. In:
Parzen
E
,
Tanabe
K
,
Kitagawa
G
, eds.
Selected Papers of Hirotugu Akaike
.
New York, NY
:
Springer New York
;
1998
:
199
213
32
RStudio
.
Integrated Development for R
.
Boston, MA
:
RStudio Inc
;
2016
33
R Core Team
.
R: A Language and Environment for Statistical Computing
.
Vienna, Austria
:
R Foundation for Statistical Computing
;
2018
34
3M
.
3M All Patient Refined Diagnosis Related Groups (3M APR DRG)
.
2020
.
35
Huang
HS
,
Tsai
CL
,
Chang
J
,
Hsu
TC
,
Lin
S
,
Lee
CC
.
Multiplex PCR system for the rapid diagnosis of respiratory virus infection: systematic review and meta-analysis
.
Clin Microbiol Infect
.
2018
;
24
(
10
):
1055
1063
36
Butt
SA
,
Maceira
VP
,
McCallen
ME
,
Stellrecht
KA
.
Comparison of three commercial RT-PCR systems for the detection of respiratory viruses
.
J Clin Virol
.
2014
;
61
(
3
):
406
410
37
Lin
CY
,
Hwang
D
,
Chiu
NC
, et al
.
Increased detection of viruses in children with respiratory tract infection using PCR
.
Int J Environ Res Public Health
.
2020
;
17
(
2
):
564
38
Leteurtre
S
,
Duhamel
A
,
Salleron
J
,
Grandbastien
B
,
Lacroix
J
,
Leclerc
F
;
Groupe Francophone de Réanimation et d’Urgences Pédiatriques (GFRUP)
.
PELOD-2: an update of the PEdiatric logistic organ dysfunction score
.
Crit Care Med
.
2013
;
41
(
7
):
1761
1773
39
Pollack
MM
,
Holubkov
R
,
Funai
T
, et al;
Eunice Kennedy Shriver National Institute of Child Health and Human Development Collaborative Pediatric Critical Care Research Network
.
The Pediatric Risk of Mortality Score: update 2015
.
Pediatr Crit Care Med
.
2016
;
17
(
1
):
2
9
40
Straney
L
,
Clements
A
,
Parslow
RC
, et al;
ANZICS Paediatric Study Group and the Paediatric Intensive Care Audit Network
.
Paediatric index of mortality 3: an updated model for predicting mortality in pediatric intensive care*
.
Pediatr Crit Care Med
.
2013
;
14
(
7
):
673
681
41
Horvat
CM
,
Ogoe
H
,
Kantawala
S
, et al
.
Development and performance of electronic pediatric risk of mortality and pediatric logistic organ dysfunction-2 automated acuity scores
.
Pediatr Crit Care Med
.
2019
;
20
(
8
):
e372
e379
42
Kho
N
,
Kerrigan
JF
,
Tong
T
,
Browne
R
,
Knilans
J
.
Respiratory syncytial virus infection and neurologic abnormalities: retrospective cohort study
.
J Child Neurol
.
2004
;
19
(
11
):
859
864
43
Morrison
AL
,
Gillis
J
,
O’Connell
AJ
,
Schell
DN
,
Dossetor
DR
,
Mellis
C
.
Quality of life of survivors of pediatric intensive care
.
Pediatr Crit Care Med
.
2002
;
3
(
1
):
1
5
44
Shein
SL
,
Slain
KN
,
Clayton
JA
,
McKee
B
,
Rotta
AT
,
Wilson-Costello
D
.
Neurologic and functional morbidity in critically ill children with bronchiolitis
.
Pediatr Crit Care Med
.
2017
;
18
(
12
):
1106
1113
45
Good
RJ
,
Leroue
MK
,
Czaja
AS
.
Accuracy of administrative codes for distinguishing positive pressure ventilation from high-flow nasal cannula
.
Hosp Pediatr
.
2018
;
8
(
7
):
426
429
46
Thompson
WW
,
Shay
DK
,
Weintraub
E
, et al
.
Mortality associated with influenza and respiratory syncytial virus in the United States
.
JAMA
.
2003
;
289
(
2
):
179
186
47
Estimates of deaths associated with seasonal influenza—United States, 1976–2007
.
MMWR Morb Mortal Wkly Rep
.
2010
;
59
(
33
):
1057
1062
48
Hall
CB
,
Weinberg
GA
,
Iwane
MK
, et al
.
The burden of respiratory syncytial virus infection in young children
.
N Engl J Med
.
2009
;
360
(
6
):
588
598

Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data