title: Quantifying Geographic Variation in Health Care Outcomes in the United States before and after Risk-Adjustment.
abstract: BACKGROUND
  Despite numerous studies of geographic variation in healthcare cost and utilization at the local, regional, and state levels across the U.S., a comprehensive characterization of geographic variation in outcomes has not been published. Our objective was to quantify variation in US health outcomes in an all-payer population before and after risk-adjustment.
  
  
  RESULTS
  We used information from 16 independent data sources, including 22 million all-payer inpatient admissions from the Healthcare Cost and Utilization Project (which covers regions where 50% of the U.S. population lives) to analyze 24 inpatient mortality, inpatient safety, and prevention outcomes. We compared outcome variation at state, hospital referral region, hospital service area, county, and hospital levels. Risk-adjusted outcomes were calculated after adjusting for population factors, co-morbidities, and health system factors. Even after risk-adjustment, there exists large geographical variation in outcomes. The variation in healthcare outcomes exceeds the well publicized variation in US healthcare costs. On average, we observed a 2.1-fold difference in risk-adjusted mortality outcomes between top- and bottom-decile hospitals. For example, we observed a 2.3-fold difference for risk-adjusted acute myocardial infarction inpatient mortality. On average a 10.2-fold difference in risk-adjusted patient safety outcomes exists between top and bottom-decile hospitals, including an 18.3-fold difference for risk-adjusted Central Venous Catheter Bloodstream Infection rates. A 3.0-fold difference in prevention outcomes exists between top- and bottom-decile counties on average; including a 2.2-fold difference for risk-adjusted congestive heart failure admission rates. The population, co-morbidity, and health system factors accounted for a range of R2 between 18-64% of variability in mortality outcomes, 3-39% of variability in patient safety outcomes, and 22-70% of variability in prevention outcomes.
  
  
  CONCLUSIONS
  The amount of variability in health outcomes in the U.S. is large even after accounting for differences in population, co-morbidities, and health system factors. These findings suggest that: 1) additional examination of regional and local variation in risk-adjusted outcomes should be a priority; 2) assumptions of uniform hospital quality that underpin rationale for policy choices (such as narrow insurance networks or antitrust enforcement) should be challenged; and 3) there exists substantial opportunity for outcomes improvement in the US healthcare system.
authors.affiliation: The Boston Consulting Group, Boston, Massachusetts, United States of America.
authors.forename: Barry L
authors.initials: BL
authors.lastname: Rosenberg
authors.affiliation: The Boston Consulting Group, Boston, Massachusetts, United States of America.
authors.forename: Joshua A
authors.initials: JA
authors.lastname: Kellar
authors.affiliation: The Boston Consulting Group, Boston, Massachusetts, United States of America.
authors.forename: Anna
authors.initials: A
authors.lastname: Labno
authors.affiliation: The Boston Consulting Group, Boston, Massachusetts, United States of America.
authors.forename: David H M
authors.initials: DH
authors.lastname: Matheson
authors.affiliation: The Boston Consulting Group, Boston, Massachusetts, United States of America.
authors.forename: Michael
authors.initials: M
authors.lastname: Ringel
authors.affiliation: The Boston Consulting Group, Boston, Massachusetts, United States of America.
authors.forename: Paige
authors.initials: P
authors.lastname: VonAchen
authors.affiliation: The Boston Consulting Group, Boston, Massachusetts, United States of America.
authors.forename: Richard I
authors.initials: RI
authors.lastname: Lesser
authors.affiliation: Department of Public Health Sciences, University of Rochester Medical Center, New York City, New York, United States of America.
authors.forename: Yue
authors.initials: Y
authors.lastname: Li
authors.affiliation: Center for Healthcare Outcomes and Policy, University of Michigan, Ann Arbor, Michigan, United States of America.|Department of Surgery, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Justin B
authors.initials: JB
authors.lastname: Dimick
authors.affiliation: Ariadne Labs At Brigham and Women's Hospital and Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Atul A
authors.initials: AA
authors.lastname: Gawande
authors.affiliation: The Boston Consulting Group, Boston, Massachusetts, United States of America.
authors.forename: Stefan H
authors.initials: SH
authors.lastname: Larsson
authors.affiliation: The Alerion Institute and Alerion Advisors, LLC, North Garden, Virginia, United States of America.|Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America.
authors.forename: Hamilton
authors.initials: H
authors.lastname: Moses
mesh_terms: D015897:Comorbidity; D003625:Data Collection; D004470:Economics, Medical; D005843:Geography; D017048:Health Care Costs; D006291:Health Policy; D006302:Health Services Research; D006760:Hospitalization; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D007297:Inpatients; D017063:Outcome Assessment, Health Care; D020379:Risk Adjustment; D018570:Risk Assessment; D012307:Risk Factors; D014481:United States