Limitations of Using Pediatric Respiratory Illness Readmissions to Compare Hospital Performance
BACKGROUND: Adult hospital readmission rates can reliably identify meaningful variation in hospital performance; however, pediatric condition-specific readmission rates are limited by low patient volumes.
OBJECTIVE: To determine if a National Quality Forum (NQF)-endorsed measure for pediatric lower respiratory illness (LRI) 30-day readmission rates can meaningfully identify high- and low-performing hospitals.
DESIGN: Observational, retrospective cohort analysis. We applied the pediatric LRI measure and several variations to evaluate their ability to detect performance differences.
SETTING: Administrative claims from all hospital admissions in California (2012-2014). PATIENTS: Children (age <18 years) with LRI (primary diagnosis: bronchiolitis, influenza, or pneumonia; or LRI as a secondary diagnosis with a primary diagnosis of respiratory failure, sepsis, bacteremia, or asthma).
MEASUREMENTS: Thirty-day hospital readmission rates and costs. Hierarchical regression models adjusted for age, gender, and chronic conditions were used.
RESULTS: Across all California hospitals admitting children (n = 239), using respiratory readmission rates, no outlier hospitals were identified with (1) the NQF-endorsed metric, (2) inclusion of primary asthma or secondary asthma exacerbation diagnoses, or (3) inclusion of 30-day emergency revisits. By including admissions for asthma, adding emergency revisits, and merging 3 years of data, we identified 9 outlier hospitals (2 high-performers, 7 low-performers). There was no association of hospital readmission rates with costs.
CONCLUSIONS: Using a nationally-endorsed quality measure of inpatient pediatric care, we were unable to identify meaningful variation in hospital performance without broadening the metric definition and merging multiple years of data. Utilizers of pediatric-quality measures should consider modifying metrics to better evaluate the quality of pediatric care at low-volume hospitals.
© 2018 Society of Hospital Medicine
Study Population
Our study included children aged ≤18 years with LRI, defined using the NQF Pediatric LRI Readmissions Measure: a primary diagnosis of bronchiolitis, influenza, or pneumonia, or a secondary diagnosis of bronchiolitis, influenza, or pneumonia, with a primary diagnosis of asthma, respiratory failure, sepsis, or bacteremia.8 International classification of Diseases, 9th edition (ICD-9) diagnostic codes used are in Appendix 1.
Per the NQF measure specifications,8 records were excluded if they were from hospitals with <80% of records complete with core elements (unique patient identifier, admission date, end-of-service date, and ICD-9 primary diagnosis code). In addition, records were excluded for the following reasons: (1) individual record missing core elements, (2) discharge disposition “death,” (3) 30-day follow-up data not available, (4) primary “newborn” or mental health diagnosis, or (5) primary ICD-9 procedure code for a planned procedure or chemotherapy.
Patient characteristics for hospital admissions with and without 30-day readmissions or 30-day emergency department (ED) revisits were summarized. For the continuous variable age, mean and standard deviation for each group were calculated. For categorical variables (sex, race, payer, and number of chronic conditions), numbers and proportions were determined. Univariate tests of comparison were carried out using the Student’s t test for age and chi-square tests for all categorical variables. Categories of payer with small values were combined for ease of description (categories combined into “other:” workers’ compensation, county indigent programs, other government, other indigent, self-pay, other payer). We identified chronic conditions using the Agency for Healthcare Research and Quality Chronic Condition Indicator (CCI) system, which classifies ICD-9-CM diagnosis codes as chronic or acute and places each code into 1 of 18 mutually exclusive categories (organ systems, disease categories, or other categories). The case-mix adjustment model incorporates a binary variable for each CCI category (0-1, 2, 3, or >4 chronic conditions) per the NQF measure specifications.8 This study was approved by the University of California, San Francisco Institutional Review Board.
Outcomes
Our primary outcome was the hospital-level rate of 30-day readmission after hospital discharge, consistent with the NQF measure.8 We identified outlier hospitals for 30-day readmission rate using the Centers for Medicare and Medicaid Services (CMS) methodology, which defines outlier hospitals as those for whom adjusted readmission rate confidence intervals do not overlap with the overall group mean rate.5, 14
We also determined the hospital-level average cost per index hospitalization (not including costs of readmissions). Since costs of care often differ substantially from charges,15 costs were calculated using cost-to-charge ratios for each hospital (annual total operating expenses/total gross patient revenue, as reported to the OSHPD).16 Costs were subdivided into categories representing $5,000 increments and a top category of >$40,000. Outlier hospitals for costs were defined as those for whom the cost random effect was either greater than the third quartile of the distribution of values by more than 1.5 times the interquartile range or less than the first quartile of the distribution of values by more than 1.5 times the interquartile range.17
