Auditing Practice Style Variation in Pediatric Inpatient Asthma Care | Asthma | JAMA Pediatrics | JAMA Network
[Skip to Navigation]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address Please contact the publisher to request reinstatement.
Friedman  B, Berdahl  T, Simpson  LA,  et al.  Annual report on health care for children and youth in the United States: focus on trends in hospital use and quality.  Acad Pediatr. 2011;11(4):263-279.PubMedGoogle ScholarCrossref
Kamble  S, Bharmal  M.  Incremental direct expenditure of treating asthma in the United States.  J Asthma. 2009;46(1):73-80.PubMedGoogle ScholarCrossref
Chen  KH, Chen  CC, Liu  HE, Tzeng  PC, Glasziou  PP.  Effectiveness of paediatric asthma clinical pathways: a narrative systematic review.  J Asthma. 2014;51(5):480-492.PubMedGoogle ScholarCrossref
Shanley  LA, Lin  H, Flores  G.  Factors associated with length of stay for pediatric asthma hospitalizations.  J Asthma. 2015;52(5):471-477.PubMedGoogle ScholarCrossref
Bratton  SL, Newth  CJ, Zuppa  AF,  et al; Eunice Kennedy Shriver National Institute of Child Health and Human Development Collaborative Pediatric Critical Care Research Network.  Critical care for pediatric asthma: wide care variability and challenges for study.  Pediatr Crit Care Med. 2012;13(4):407-414.PubMedGoogle ScholarCrossref
Parikh  K, Hall  M, Mittal  V,  et al.  Establishing benchmarks for the hospitalized care of children with asthma, bronchiolitis, and pneumonia.  Pediatrics. 2014;134(3):555-562.PubMedGoogle ScholarCrossref
Morse  RB, Hall  M, Fieldston  ES,  et al.  Hospital-level compliance with asthma care quality measures at children’s hospitals and subsequent asthma-related outcomes.  JAMA. 2011;306(13):1454-1460.PubMedGoogle ScholarCrossref
Chamberlain  JM, Teach  SJ, Hayes  KL, Badolato  G, Goyal  MK.  Practice pattern variation in the care of children with acute asthma.  Acad Emerg Med. 2016;23(2):166-170.PubMedGoogle ScholarCrossref
Silber  JH, Rosenbaum  PR, Ross  RN,  et al.  Template matching for auditing hospital cost and quality.  Health Serv Res. 2014;49(5):1446-1474.PubMedGoogle ScholarCrossref
Silber  JH, Rosenbaum  PR, Ross  RN,  et al.  A hospital-specific template for benchmarking its cost and quality.  Health Serv Res. 2014;49(5):1475-1497.PubMedGoogle ScholarCrossref
Silber  JH, Rosenbaum  PR, Trudeau  ME,  et al.  Multivariate matching and bias reduction in the surgical outcomes study.  Med Care. 2001;39(10):1048-1064.PubMedGoogle ScholarCrossref
Rosenbaum  PR.  Part II: Matching: Design of Observational Studies. New York, NY: Springer; 2010:153-253.
Silber  JH, Rosenbaum  PR, Ross  RN,  et al.  Indirect standardization matching: assessing specific advantage and risk synergy [published online February 29, 2016].  Health Serv Res.PubMedGoogle Scholar
National Heart, Lung, and Blood Institute, National Asthma Education and Prevention Program. Expert Panel Report 3: guidelines for the diagnosis and management of asthma: full report, 2007. NIH Publication No. 07-4051. Bethesda, MD: US Dept of Health and Human Services. Published August 28, 2007. Accessed July 22, 2015.
Rosenbaum  PR. Basic tools of multivariate matching. section 8.3: distance matrices. In:  Design of Observational Studies. New York, NY: Springer; 2010:168-172.
Rubin  DB.  Bias reduction using Mahalanobis metric matching.  Biometrics. 1980;36(2):293-298.Google ScholarCrossref
R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Accessed April 15, 2014.
Zubizarreta  JR.  Using mixed integer programming for matching in an observational study of kidney failure after surgery.  J Am Stat Assoc. 2012;107(500):1360-1371.Google ScholarCrossref
Zubizarreta  JR, Cerdá  M, Rosenbaum  PR.  Effect of the 2010 Chilean earthquake on posttraumatic stress: reducing sensitivity to unmeasured bias through study design.  Epidemiology. 2013;24(1):79-87.PubMedGoogle ScholarCrossref
Silber  JH, Rosenbaum  PR, McHugh  MD,  et al.  Comparing the value of better nursing work environments across different levels of patient risk [published online January 20, 2016].  JAMA Surg.PubMedGoogle Scholar
Rosenbaum  PR.  Fine Balance: Design of Observational Studies. New York, NY: Springer; 2010:197-206.
Rosenbaum  PR, Ross  RN, Silber  JH.  Minimum distance matched sampling with fine balance in an observational study of treatment for ovarian cancer.  J Am Stat Assoc. 2007;102(477):75-83.Google ScholarCrossref
Silber  JH, Rosenbaum  PR, Polsky  D,  et al.  Does ovarian cancer treatment and survival differ by the specialty providing chemotherapy?  J Clin Oncol. 2007;25(10):1169-1175.PubMedGoogle ScholarCrossref
Silber  JH, Rosenbaum  PR, Kelz  RR,  et al.  Medical and financial risks associated with surgery in the elderly obese.  Ann Surg. 2012;256(1):79-86.PubMedGoogle ScholarCrossref
Yang  D, Small  DS, Silber  JH, Rosenbaum  PR.  Optimal matching with minimal deviation from fine balance in a study of obesity and surgical outcomes.  Biometrics. 2012;68(2):628-636.PubMedGoogle ScholarCrossref
Kruskal  W, Wallis  WA.  Use of ranks in one-criterion variance analysis.  J Am Stat Assoc. 1952;47(260):583-621.Google ScholarCrossref
Rubin  DB.  The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials.  Stat Med. 2007;26(1):20-36.PubMedGoogle ScholarCrossref
Rubin  DB.  For objective causal inference, design trumps analysis.  Ann Appl Stat. 2008;2(3):808-840.Google ScholarCrossref
Simes  RJ.  An improved Bonferroni procedure for multiple tests of significance.  Biometrika. 1986;73(3):751-754.Google ScholarCrossref
Benjamini  Y, Hochberg  Y.  Controlling the false discovery rate: A practical and powerful approach to multiple testing.  J R Stat Soc B. 1995;57(1):289-300.Google Scholar
Keren  R, Luan  X, Localio  R,  et al; Pediatric Research in Inpatient Settings (PRIS) Network.  Prioritization of comparative effectiveness research topics in hospital pediatrics.  Arch Pediatr Adolesc Med. 2012;166(12):1155-1164.PubMedGoogle ScholarCrossref
Bureau of Labor Statistics. Consumer Price Index. Accessed March 10, 2014.
Rosenbaum  PR.  Reduced sensitivity to hidden bias at upper quantiles in observational studies with dilated treatment effects.  Biometrics. 1999;55(2):560-564.PubMedGoogle ScholarCrossref
Rosenbaum  PR. Models for Treatment Effects. 5.3: Dilated Effects. In:  Observational Studies. 2nd ed. New York, NY: Springer-Verlag; 2002:173-179.
Bishop  YMM, Fienberg  SE, Holland  PW. Analysis of Square Tables: Symmetry and Marginal Homogeneity. In:  Discrete Multivariate Analysis: Theory and Practice. Cambridge, MA: MIT Press; 1975:281-286.
Mantel  N.  Chi-square tests with one degree of freedom: extensions of the Mantel-Haenszel procedure.  J Am Stat Assoc. 1963;58(303):690-700.Google Scholar
Hampel  FR, Ronchett  EM, Rousseeuw  PJ,  et al. Linear models: robust estimation. In:  Robust Statistics: The Approach Based on Influence Functions. New York, NY: John Wiley & Sons; 1986:307-341.
Huber  PJ.  Robust Statistics. Hoboken, NJ: John Wiley & Sons; 1981.
Tukey  JW. Schematic summaries (pictures and numbers. In:  Eighths, Sixteenths, etc: Exploratory Data Analysis. Reading, MA: Addison-Wesley Publishing Co Inc; 1977:53-54.
Ripley  B, Bates  DM, Hornik  K,  et al. Package “MASS.” Updated April 22, 2016. Accessed March 17, 2016.
Cleveland  WS.  Robust locally weighted regression and smoothing scatterplots.  J Am Stat Assoc. 1979;74(368):829-836.Google ScholarCrossref
Original Investigation
September 2016

Auditing Practice Style Variation in Pediatric Inpatient Asthma Care

Author Affiliations
  • 1Center for Outcomes Research, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
  • 2Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
  • 3Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia
  • 4Department of Health Care Management, Wharton School, University of Pennsylvania, Philadelphia
  • 5Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
  • 6Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia
  • 7Division of General Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
  • 8Division of Emergency Medicine, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
JAMA Pediatr. 2016;170(9):878-886. doi:10.1001/jamapediatrics.2016.0911

Importance  Asthma is the most prevalent chronic illness among children, remaining a leading cause of pediatric hospitalizations and representing a major financial burden to many health care systems.

Objective  To implement a new auditing process examining whether differences in hospital practice style may be associated with potential resource savings or inefficiencies in treating pediatric asthma admissions.

Design, Setting, and Participants  A retrospective matched-cohort design study, matched for asthma severity, compared practice patterns for patients admitted to Children’s Hospital Association hospitals contributing data to the Pediatric Hospital Information System (PHIS) database. With 3 years of PHIS data on 48 887 children, an asthma template was constructed consisting of representative children hospitalized for asthma between April 1, 2011, and March 31, 2014. The template was matched with either a 1:1, 2:1, or 3:1 ratio at each of 37 tertiary care children’s hospitals, depending on available sample size.

Exposure  Treatment at each PHIS hospital.

Main Outcomess and Measures  Cost, length of stay, and intensive care unit (ICU) utilization.

Results  After matching patients (n = 9100; mean [SD] age, 7.1 [3.6] years; 3418 [37.6%] females) to the template (n = 100, mean [SD] age, 7.2 [3.7] years; 37 [37.0%] females), there was no significant difference in observable patient characteristics at the 37 hospitals meeting the matching criteria. Despite similar characteristics of the patients, we observed large and significant variation in use of the ICUs as well as in length of stay and cost. For the same template-matched populations, comparing utilization between the 12.5th percentile (lower eighth) and 87.5th percentile (upper eighth) of hospitals, median cost varied by 87% ($3157 vs $5912 per patient; P < .001); total hospital length of stay varied by 47% (1.5 vs 2.2 days; P < .001); and ICU utilization was 254% higher (6.5% vs 23.0%; P < .001). Furthermore, the patterns of resource utilization by patient risk differed significantly across hospitals. For example, as patient risk increased one hospital displayed significantly increasing costs compared with their matched controls (comparative cost difference: lowest risk, −34.21%; highest risk, 53.27%; P < .001). In contrast, another hospital displayed significantly decreasing costs relative to their matched controls as patient risk increased (comparative cost difference: lowest risk, −10.12%; highest risk, −16.85%; P = .01).

Conclusions and Relevance  For children with asthma who had similar characteristics, we observed different hospital resource utilization; some values differed greatly, with important differences by initial patient risk. Through the template matching audit, hospitals and stakeholders can better understand where this excess variation occurs and can help to pinpoint practice styles that should be emulated or avoided.