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Figure.  Prevalence, Cost, and Variation in Cost for the 25 Most Costly Conditions
Prevalence, Cost, and Variation in Cost for the 25 Most Costly Conditions

Data are derived from Pediatric Health Information System database spanning from January 1, 2016, to December 31, 2019. Bubble size indicates the interhospital variation in cost per encounter per condition (ie, larger bubble size means greater variation). B, orange bubbles indicate surgical conditions; grey bubbles indicate medical and surgical conditions

Table 1.  Patient, Encounter, and Hospital Characteristics for Children With Inpatient Hospital Encounters at 45 US Children’s Hospitals, 2016 to 2019
Patient, Encounter, and Hospital Characteristics for Children With Inpatient Hospital Encounters at 45 US Children’s Hospitals, 2016 to 2019
Table 2.  Prevalence, Cost, and Variation in Cost for the 50 Most Prevalent and 50 Most Costly Inpatient Hospital Conditions at 45 US Children’s Hospitals From 2016 to 2019a
Prevalence, Cost, and Variation in Cost for the 50 Most Prevalent and 50 Most Costly Inpatient Hospital Conditions at 45 US Children’s Hospitals From 2016 to 2019a
Table 3.  Comparison of 10 Most Prevalent and Costly Conditions in Children With and Without a Complex Chronic Condition at 45 US Children’s Hospitals, 2016 to 2019
Comparison of 10 Most Prevalent and Costly Conditions in Children With and Without a Complex Chronic Condition at 45 US Children’s Hospitals, 2016 to 2019
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    1 Comment for this article
    Unexamined comorbidity
    Jeoffry Gordon, MD, MPH | Essentials for Children, California DPH
    The large prevalence of hospitalized pediatric patients with "Major Depressive Disorder" may be linked to child abuse trauma, which may not be captured by the US disease coding system.
    CONFLICT OF INTEREST: None Reported
    Original Investigation
    Pediatrics
    July 26, 2021

    Identifying Conditions With High Prevalence, Cost, and Variation in Cost in US Children’s Hospitals

    Author Affiliations
    • 1Department of Pediatrics, Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
    • 2Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, Canada
    • 3Children’s Hospital Association, Lenexa, Kansas
    • 4Department of Pediatrics and Department of Epidemiology and Biostatistics, University of California, San Francisco
    • 5Philip R. Lee Institute for Health Policy Studies, San Francisco, California
    • 6Department of Pediatrics, University of Utah, Primary Children’s Hospital, Salt Lake City
    • 7Healthcare Delivery Institute, Intermountain Healthcare, Salt Lake City, Utah
    • 8Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
    JAMA Netw Open. 2021;4(7):e2117816. doi:10.1001/jamanetworkopen.2021.17816
    Key Points

    Question  What conditions have the highest prevalence, cost, and variation in cost in hospital pediatrics?

    Findings  This cohort study included 2 882 490 inpatient hospital encounters of children from US children’s hospitals to identify conditions with high prevalence, cost, and interhospital variation in cost. Examples of conditions that were identified as having high prevalence, cost, and variation in cost included major depressive disorder, scoliosis, acute appendicitis with peritonitis, asthma, and dehydration.

    Meaning  The findings from this cohort study could inform funders and researchers of areas at which research in hospital pediatrics should be targeted to improve the evidence base and outcomes of hospitalized children.

    Abstract

    Importance  Identifying high priority pediatric conditions is important for setting a research agenda in hospital pediatrics that will benefit families, clinicians, and the health care system. However, the last such prioritization study was conducted more than a decade ago and used International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes.

    Objectives  To identify conditions that should be prioritized for comparative effectiveness research based on prevalence, cost, and variation in cost of hospitalizations using contemporary data at US children’s hospitals.

    Design, Setting, and Participants  This retrospective cohort study of children with hospital encounters used data from the Pediatric Health Information System database. Children younger than 18 years with inpatient hospital encounters at 45 tertiary care US children’s hospitals between January 1, 2016, and December 31, 2019, were included. Data were analyzed from March 2020 to April 2021.

    Main Outcomes and Measures  The condition-specific prevalence and total standardized cost, the corresponding prevalence and cost ranks, and the variation in standardized cost per encounter across hospitals were analyzed. The variation in cost was assessed using the number of outlier hospitals and intraclass correlation coefficient.

    Results  There were 2 882 490 inpatient hospital encounters (median [interquartile range] age, 4 [1-12] years; 1 554 024 [53.9%] boys) included. Among the 50 most prevalent and 50 most costly conditions (total, 74 conditions), 49 (66.2%) were medical, 15 (20.3%) were surgical, and 10 (13.5%) were medical/surgical. The top 10 conditions by cost accounted for $12.4 billion of $33.4 billion total costs (37.4%) and 592 815 encounters (33.8% of all encounters). Of 74 conditions, 4 conditions had an intraclass correlation coefficient (ICC) of 0.30 or higher (ie, major depressive disorder: ICC, 0.49; type 1 diabetes with complications: ICC, 0.36; diabetic ketoacidosis: ICC, 0.33; acute appendicitis without peritonitis: ICC, 0.30), and 9 conditions had an ICC higher than 0.20 (scoliosis: ICC, 0.27; hypertrophy of tonsils and adenoids: ICC, 0.26; supracondylar fracture of humerus: ICC, 0.25; cleft lip and palate: ICC, 0.24; acute appendicitis with peritonitis: ICC, 0.21). Examples of conditions high in prevalence, cost, and variation in cost included major depressive disorder (cost rank, 19; prevalence rank, 10; ICC, 0.49), scoliosis (cost rank, 6; prevalence rank, 38; ICC, 0.27), acute appendicitis with peritonitis (cost rank, 13; prevalence rank, 11; ICC, 0.21), asthma (cost rank, 10; prevalence rank, 2; ICC, 0.17), and dehydration (cost rank, 24; prevalence rank, 8; ICC, 0.18).

    Conclusions and Relevance  This cohort study found that major depressive disorder, scoliosis, acute appendicitis with peritonitis, asthma, and dehydration were high in prevalence, costs, and variation in cost. These results could help identify where future comparative effectiveness research in hospital pediatrics should be targeted to improve the care and outcomes of hospitalized children.

    Introduction

    The hospital is a high-cost, resource-intensive setting where there is increasing pressure to provide safe and high-quality care efficiently for children.1,2 Despite the high cost of hospital care, there are still many areas in pediatric hospital care that lack high-quality evidence, including the treatment of children with common conditions and those with complex health care needs.3,4 Comparative effectiveness research, which aims to determine which clinical and health care delivery strategies are most effective in real-word settings, is important to inform practice, reduce unnecessary practice variation, and improve health outcomes.5

    Prioritizing topics for comparative effectiveness research in hospital pediatrics is an important step to develop a research agenda that will benefit children and families, clinicians, and the health care system. A 2012 analysis by Keren et al6 identified high-priority pediatric conditions for comparative effectiveness research using data on prevalence, cost, and variation in cost of hospitalizations in US children’s hospitals. However, the study by Keren et al6 included data from 2004 to 2009, which are now more than a decade old. The study also used the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)7 codes to identify the primary discharge diagnosis, but in 2015, the US transitioned to International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM),8,9 which has improved specificity and increased granularity.8,10 The Institute of Medicine recommends setting the prioritization criteria every 5 years and having the priority-setting cycle (ie, producing a rank-order list of conditions to be prioritized) every 3 years.11 A 2011 review by Dubois and Graff,12 which developed a framework for setting priorities for research, also suggested updating research prioritization using the same frequency. Over time, improvements in health care delivery, technologies, and procedures may affect costs, variation in care, and treatment choices.12 Therefore, it is important to update the prioritization regularly.12

    In this study, we updated the research prioritization agenda in hospital pediatrics using a similar approach to Keren et al,6 using the ICD-10-CM system applied to contemporary data. We aimed to identify conditions that should be prioritized for comparative effectiveness research in hospital pediatrics. The specific objectives were to describe the condition-specific prevalence, cost, and variation in cost of pediatric hospitalizations and rank order conditions according to prevalence and cumulative cost, and identify conditions with high prevalence, cost, and variation in cost as targets for prioritization for research in hospitalized children.

    Methods

    This cohort study was approved by the research ethics board of the Hospital for Sick Children, and the requirement for informed consent was waived because patient-level data were deidentified. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

    Design and Data Source

    We conducted a retrospective cohort study using data from the Pediatric Health Information System (PHIS), an administrative database containing hospitalization data from 50 tertiary care children’s hospitals developed by the Children’s Hospital Association, located in Lenexa, Kansas. The PHIS database includes detailed data on demographics, diagnosis codes, service locations, procedures, and charges. The hospital billing data are mapped to a common set of clinical transaction codes, which are further categorized into imaging studies, clinical services, laboratory tests, pharmacy, supplies, and room charges. Data are subjected to several checks of reliability and validity and processed into data quality reports.

    Study Population

    The study population included children younger than 18 years with an inpatient hospital encounter (ie, inpatient and observation encounters in the PHIS database) between January 1, 2016, and December 31, 2019. We excluded hospitals that had incomplete billing data for the study period. We also excluded encounters for children with an ICD-10-CM primary discharge diagnosis code for normal newborn births, with external cause codes, with invalid diagnosis codes, with missing billing or cost data, and those from ambulatory surgery. We also excluded extreme cost outliers (defined as the top 1% of standardized cost within each condition) to minimize potential data errors and unusual clinical encounters, similar to the study by Keren et al.6

    Patient, Encounter, and Hospital Characteristics

    Patient characteristics included age (<30 days, ≥30 days to <1 year, 1-4 years, 5-12 years, and 13-17 years), sex, race/ethnicity (categorized as non-Hispanic White, non-Hispanic Black, Hispanic, and other [including American Indian, Alaska Native, Asian, multiracial, Native Hawaiian, Pacific Islander, missing data, and other]), and primary payer (ie, government, private, or other). Race/ethnicity was self-identified by parents and families using each hospital’s classification system and was included as a characteristic to describe children with encounters. Median zip code household income as a percentage of the federal poverty level13 was determined for each encounter to understand the socioeconomic distribution of the cohort. We used Rural-Urban Commuting Area codes to determine the rural-urban classification of each patient’s residence into metropolitan, micropolitan, small town, and rural areas.14-16 We determined the number of complex chronic conditions (CCCs)17 present (0, 1, 2, or ≥3)18 based on a 1-year lookback or until birth if younger than 1 year from each hospital encounter date. We also identified the patient type based on the encounter location (ie, inpatient or observation unit) and determined the length of stay (in days). For the hospital characteristics, we identified the census region (Midwest, Northwest, South, or West), and the median volume of inpatient encounters per year.

    Pediatric Clinical Classification System

    We classified the primary discharge diagnosis code for all encounters using the Pediatric Clinical Classification System (PECCS).19 The PECCS (developed using the Healthcare Cost and Utilization Project Clinical Classifications Software20 and the pediatric diagnosis code grouper used by Keren et al6) classifies all 72 446 ICD-10-CM diagnosis codes into 834 clinically meaningful categories to help identify specific pediatric conditions, including treatments (eg, chemotherapy). Conditions were further divided into medical, surgical, or medical/surgical based on the percentage of encounters with a surgical ICD-10-CM Procedure Coding System procedure or a Current Procedural Terminology code. Conditions with less than 30% of encounters with a surgical procedure code were classified as medical, more than 70% as surgical, and between 30% and 70% as medical/surgical.

    Calculation of Standardized Cost

    Since cost of individual items (eg, laboratory tests, imaging, room charges) varied between hospitals, we used standard costs of those items across hospitals. The Cost Master Index, calculated yearly and maintained by the Children’s Hospital Association, provides the standard unit costs for all individual items. For each item billed in a given year, the item’s cost is determined using the item’s charge, the hospital- and department-specific ratio of cost to charges, and the number of billed units for the item. Then, the within-hospital median of costs for the specific item is calculated. Finally, the across-hospital median of the within-hospital median cost for the item provides the standardized unit cost for the specific item during a specific year.6,21

    Hospitalization costs were used as a surrogate measure of the volume of resources used for the encounters.6 These costs were standardized to eliminate the high interhospital variation in item costs.6 For each condition, we calculated the cost of an encounter by multiplying the number of units for each clinical transaction code item by the item’s standardized cost. We then summed the standardized costs of each line item for that encounter. We defined each clinical transaction code item’s standardized cost by the Cost Master Index,6 and adjusted costs for inflation to 2019 US dollars using the Consumer Price Index for hospital services.22 When we use the term cost, we are referring to the calculated standardized cost.

    Outcome Measures

    We determined the condition-specific prevalence rank for each hospital condition based on the number of encounters over the study period. For each condition, we determined the condition-specific cost rank based on the cumulative cost of hospital encounters over the study period. The condition-specific variation in cost per encounter across hospitals was also determined over the study period.

    Statistical Analysis

    We determined the mean cost per inpatient hospital encounter for each hospital condition. We then determined the variation in cost of hospitalization by condition for the 50 most prevalent and 50 most costly conditions, focusing on their cost per encounter, across hospitals. The condition-specific variation in cost across hospitals was adjusted for known drivers of variation in cost to minimize confounding from other factors that may bias the magnitude of variation in cost per encounter across hospitals.6,23-25 These included age, sex, race/ethnicity, patient type, and number of CCCs present (0, 1, 2, or ≥3). Rural-Urban Commuting Area, primary payer, and income were not included owing to high multicollinearity. The variation in cost per encounter was assessed using 2 methods presented in the study by Keren et al.6 First, for number of outlier hospitals, we counted the number of hospitals with more than 30% of their encounters for each condition in either the highest or lowest quintile of cost per encounter. Second, for intraclass correlation coefficient (ICC), the amount of variation in costs (cost per encounter) for each condition across hospitals was divided by the total variation in the cost per encounter (ie, sum of the within- and across hospital variation of costs). ICC was calculated using a mixed-effects model, with hospital as a random intercept, and patient characteristics as fixed effects.6

    Additional analyses were performed to determine the 25 most prevalent and 25 most costly conditions for children with CCCs17 vs children without. These analyses were conducted because children with medical complexity have a low prevalence but high total health care costs26 and have unique disease management and health care needs. Analyses were conducted using SAS statistical software version 9.4 (SAS Institute). Data were analyzed from March 2020 to April 2021.

    Results

    There were 5 555 810 hospital encounters in children’s hospitals between January 1, 2016, to December 31, 2019. After applying the exclusion criteria, 2 882 490 inpatient hospital encounters across 45 children’s hospitals were included (eFigure in Supplement 1).

    Patient, Encounter, and Hospital Characteristics

    Of the 2 882 490 inpatient encounters, 2 188 278 (75.9%) were children aged 1 year or older, the median (interquartile range [IQR]) age was 4 (1-12) years, and 1 554 024 (53.9%) were boys (Table 1). Children with 1 or more CCC accounted for 1 132 532 encounters (39.3%). Over half of the encounters (1 551 117 encounters [53.8%]) were of children with a median household income less than 200% of the US federal poverty level, and 1 623 655 encounters (56.3%) were in children covered by government insurance. A total of 1 852 308 encounters (64.3%) were owing to medical conditions, 578 230 encounters (20.1%) were owing to surgical conditions, and 451 952 encounters (15.7%) were owing to medical/surgical conditions. The median (IQR) length of stay was 3 (2-5) days, and the median hospital volume of inpatient encounters per year was 15 067 (9510-19 514) encounters.

    Prevalence and Cost

    Table 2 shows the 50 most prevalent and 50 most costly hospital conditions, with a total of 74 different conditions, sorted by total cost over the 4-year period. Of 74 conditions, 49 (66.2%) were medical, 15 (20.3%) were surgical, and 10 (13.5%) were medical/surgical. The top 10 conditions by cost accounted for $12.4 billion of $33.0 billion total costs (37.4%) and 592 815 encounters (33.8% of all encounters). Extreme immaturity conditions (ie, birth weight 500-749 g) had the highest cost per encounter, at $382 910 (95% CI, $368 084-$397 736). There were also 2 mental health conditions observed in the top 50 most prevalent and 50 most costly hospital conditions: major depressive disorder (cost rank, 19; prevalence rank, 10; ICC, 0.49) and suicide and intentional self-inflicted injury (cost rank, 57; prevalence rank, 20; ICC, 0.19).

    From the 74 most prevalent and/or costly conditions, major depressive disorder (ICC, 0.49), type 1 diabetes with complications (ICC, 0.36), diabetic ketoacidosis (ICC, 0.33), and acute appendicitis without peritonitis (ICC, 0.30) were 4 conditions with the highest degree of interhospital variability in cost per encounter using ICC. In total, there were 9 conditions that had an ICC higher than 0.20 (the additional 5 conditions were scoliosis: ICC, 0.27; hypertrophy of tonsils and adenoids: ICC, 0.26; supracondylar fracture of humerus: ICC, 0.25; cleft lip and palate: ICC, 0.24; and acute appendicitis with peritonitis: ICC, 0.21). When evaluating interhospital variation in cost using the outlier hospital analysis, more than half of the hospitals had a high proportion of high- or low-cost hospitalizations for 9 conditions (Table 2). Major depressive disorder had the highest number of outlier hospitals (33 cost outlier hospitals).

    Conditions that were high in prevalence, cost, and variation in cost included, for example, major depressive disorder (cost rank, 19; prevalence rank, 10; ICC, 0.49), scoliosis (cost rank, 6; prevalence rank, 38; ICC, 0.27), acute appendicitis with peritonitis (cost rank, 13; prevalence rank, 11; ICC, 0.21), asthma (cost rank, 10; prevalence rank, 2; ICC, 0.17), and dehydration (cost rank, 24; prevalence rank, 8; ICC, 0.18). The Figure illustrates the top 25 costly conditions. Major depressive disorder (Figure, A) was highly prevalent, costly, and had the highest interhospital variability in cost per encounter of all medical conditions. Figure, B, represents 3 surgical and 4 medical/surgical conditions. Scoliosis and acute appendicitis with peritonitis were surgical conditions that were highly prevalent, costly, and with high interhospital variability.

    Prevalence and Cost by Presence of Pediatric Complex Chronic Condition

    Table 3 presents the volume of the 10 most prevalent conditions and cost of the 10 most costly (based on cumulative cost) conditions in children with a CCC vs those without. The 25 most prevalent and most costly conditions are reported in eTable 1 and eTable 2 in Supplement 1. The rank-order of the conditions differed between the 2 groups. In children with a CCC, the most prevalent and most costly conditions were chemotherapy and respiratory failure. However, in children without a CCC, bronchiolitis was the most prevalent and most costly condition. In children with a CCC, the 25 most costly conditions cost $15.8 billion, while in children without CCC they cost $8.3 billion. Furthermore, the cost per encounter for some of the top 25 costly conditions (eg, respiratory failure, pneumonia) that were present in both groups were 2- to 3-fold greater in children with a CCC.

    Discussion

    In this cohort study using a newly developed ICD-10-CM–based pediatric grouper and administrative and billing data from 45 tertiary care US children’s hospitals, including more than 2 million inpatient hospital encounters, we provide an updated prioritization of topics for comparative effectiveness research in hospital pediatrics. Much has changed since the initial prioritization study,6 including the transition to ICD-10-CM, new evidence and treatment protocols, population size and demographics, and costs associated with inpatient stays.27 These updated results on prevalence, cost, and variation in cost could be used by funders and the research community as one input to inform comparative effectiveness research prioritization. For example, this data combined with patient, family, and clinician priorities can be used to establish a research agenda in hospital pediatrics.28,29 Furthermore, for conditions for which high-quality evidence exists, these data on prevalence and cost can also be used by clinicians and health care administrators to prioritize quality improvement initiatives.

    An important finding in our study is the inclusion of 2 mental health conditions among the 50 most costly and prevalent conditions from inpatient encounters, compared with no mental health conditions reported previously.6 Major depressive disorder was the 19th most costly and 10th most prevalent condition, while suicide and intentional self-inflicted injury was the 57th most costly and 20th most prevalent. These findings are consistent with other reports on the substantial increase in mental health disorder hospitalizations and costs in children.30-32 Furthermore, both conditions had high variation in standardized cost, with major depressive disorder having the highest ICC for cost and 33 cost outlier hospitals. The high rank in prevalence and cost of the mental health conditions may also reflect the shortage of inpatient psychiatric facilities. Children who require inpatient mental health treatment are often admitted to the medical unit until a psychiatric inpatient bed becomes available, referred to as mental health boarding.33 Mental health boarding may result in delays obtaining access to psychiatric inpatient services and lead to long inpatient stays with high encounter costs.34 Another contributing factor may be the shortage of child psychiatrists in both outpatient facilities and hospitals in several US regions,35,36 with declining ratios of child psychiatrists to children over time.35 Poor access to outpatient psychiatric care may result in higher mental health–related hospitalizations. These high costs and variations signal the need for increased research on effective diagnostics and therapeutics for children hospitalized with mental health conditions, increased infrastructure for providing mental health services, greater care standardization and care quality monitoring, and increased availability of inpatient psychiatric services for children.

    While a direct comparison between this study and the study by Keren et al6 is difficult owing to differences in patient type used to identify priorities and coding (ICD-9-CM vs ICD-10-CM), there were notable changes in our updated prioritization ranking. Conditions that were ranked higher in cumulative cost in our study included septicemia and respiratory failure in newborns, while conditions that were ranked lower included necrotizing enterocolitis, cellulitis, and cystic fibrosis. We also observed an increase in the interhospital cost variation among some conditions in our study including asthma, respiratory distress syndrome in newborns, dehydration, and acute appendicitis without peritonitis.

    We identified the top 25 most prevalent and 25 most costly conditions in children with a CCC vs children without. Children with a CCC accounted for 39.3% of the inpatient encounters and were responsible for substantial costs: the 25 most costly conditions costed $15.8 billion in children with a CCC vs $8.3 billion in children without CCC. Similar findings of high hospital costs in children with medical complexity have been reported previously.26,37,38 In some of the most costly conditions found in both groups (eg, respiratory failure, pneumonia), the cost per encounter in children with CCC was 2- to 3-fold higher than in children without CCC. Comparative effectiveness research is needed to inform how to best manage conditions in children with medical complexity, as they are often excluded from clinical trials for common conditions, such as pneumonia and bronchiolitis.39,40 Researchers can include children with medical complexity in future studies by including additional safety measures and subgroup analyses. Further, complex care programs that bridge inpatient and outpatient care can reduce hospitalizations, hospital days, and hospital costs in medically complex children.41-43

    Limitations

    This study has some limitations. First is the possible misclassification of conditions owing to coding errors with administrative data or varying coding practices across hospitals, which may be one source of variation in costs. Second, standardized costs using Cost Master Index6 do not reflect the true costs of providing care but rather interprets the volume of resources consumed during the encounter. Standardized costs may also make costs at hospitals with lower internal costs incorrectly appear higher than their actual cost, and vice versa.21 Nevertheless, standardized cost, which uses the same unit prices across hospitals, is a valuable approach for understanding variation in resource use. Future research could use time-driven activity-based costing, which estimates the cost of resources consumed as a patient moves along a care process to more accurately estimate cost.44,45 Third, it is possible that unmeasured factors (eg, unmeasured comorbidities) account for some of the interhospital variation in costs. Our analyses serve to identify conditions that require further research to understand the sources of variation (eg, clinical management) and drivers of interhospital differences in resource use (eg, lack of evidence or lack of care standardization despite high-quality evidence). Future condition-specific research could drill down using secondary diagnosis codes to understand variation in cost across hospitals. Fourth, the 30% quintile-based approach used to identify outlier hospitals may seem arbitrary; however, there is currently no criterion standard or standard threshold. The approach used in this study was based on a previous study by Keren et al.6 Fifth, the PHIS database does not include data from community hospitals, and it will be important to conduct similar analyses using data from community hospitals. Sixth, there are variations across hospitals in disease severity, operative complexity, and availability of resources for conditions, and this may affect the variation in costs. Seventh, this study also did not include data from during the COVID-19 pandemic, which has been associated with significantly reduced pediatric hospitalization volume.46 Eighth, burden of illness (ie, cost, prevalence) was used to identify conditions that should be prioritized for research in hospital pediatrics. There are other important inputs, such as clinician and patient priorities,47-49 and other research-related criteria (eg, cost and time required to complete the research) that are critical for identifying research priorities.12

    Conclusion

    In this cohort study, we provide an updated prioritization list of conditions for comparative effectiveness research in hospital pediatrics using information on prevalence, cost, and variation in cost of hospitalizations at 45 US children’s hospitals. Comparative effectiveness research is important for determining which clinical interventions, such as diagnosis and treatment protocols, and health care delivery models are most effective in improving health outcomes in the real-world setting. The results of our study could assist funders and researchers to develop and refine a research agenda in hospital pediatrics and assist clinicians and health care administrators to prioritize quality improvement initiatives.

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    Article Information

    Accepted for Publication: May 12, 2021.

    Published: July 26, 2021. doi:10.1001/jamanetworkopen.2021.17816

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

    Corresponding Author: Sanjay Mahant, MD, MSc, Department of Pediatrics, Institute for Health Policy, Management and Evaluation, University of Toronto, Hospital for Sick Children, 555 University Ave, Toronto, ON M5G1X8, Canada (sanjay.mahant@sickkids.ca).

    Author Contributions: Dr Hall and Mr Rodean had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Gill, Anwar, Thavam, Hall, Srivastava, Keren, Mahant.

    Acquisition, analysis, or interpretation of data: Gill, Anwar, Thavam, Hall, Rodean, Kaiser, Mahant.

    Drafting of the manuscript: Gill, Anwar, Thavam, Mahant.

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

    Statistical analysis: Gill, Rodean.

    Administrative, technical, or material support: Gill, Thavam, Keren, Mahant.

    Supervision: Gill, Kaiser, Mahant.

    Conflict of Interest Disclosures: Dr Gill reported receiving grants from Physicians’ Services Incorporated Foundation, Canadian Institutes of Health Research (CIHR) and expense reimbursement from EBMLive, and CIHR Institute of Human Development, Child and Youth Health outside the submitted work. Dr Srivastava reported serving as founder for I-PASS Patient Safety Institute and receiving grants from the Agency for Healthcare Research and Quality, Patient-Centered Outcomes Research Institute, and National Institutes of Health and personal fees from Children’s Hospitals. No other disclosures were reported.

    Group Members: The Pediatric Research in Inpatient Setting (PRIS) Network members who contributed to this study appear in Supplement 2.

    Additional Contributions: Mitch Harris, PhD (Children’s Hospital Association), provided comments on the analysis and interpretation of the data. He was not compensated for this work.

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