title: Predictors and outcomes of unplanned readmission to a different hospital.
abstract: OBJECTIVE
  To examine patient, hospital and market factors and outcomes associated with readmission to a different hospital compared with the same hospital.
  
  
  METHODS
  A population-based, secondary analysis using multilevel causal modeling.
  
  
  METHODS
  Acute care hospitals in California in the USA.
  
  
  METHODS
  In total, 509 775 patients aged 50 or older who were discharged alive from acute care hospitals (index hospitalizations), and 59 566 who had a rehospitalization within 30 days following their index discharge.
  
  
  METHODS
  No intervention.
  
  
  METHODS
  Thirty-day unplanned readmissions to a different hospital compared with the same hospital and also the costs and health outcomes of the readmissions.
  
  
  RESULTS
  Twenty-one percent of patients with a rehospitalization had a different-hospital readmission. Compared with the same-hospital readmission group, the different-hospital readmission group was more likely to be younger, male and have a lower income. The index hospitals of the different-hospital readmission group were more likely to be smaller, for-profit hospitals, which were also more likely to be located in counties with higher competition. The different-hospital readmission group had higher odds for in-hospital death (8.1 vs. 6.7%; P < 0.0001) and greater readmission hospital costs ($15 671.8 vs. $14 286.4; P < 0.001) than the same-hospital readmission group.
  
  
  CONCLUSIONS
  Patient, hospital and market characteristics predicted different-hospital readmissions compared with same-hospital readmissions. Mortality and cost outcomes were worse among patients with different-hospital readmissions. Strategies for better care coordination targeting people at risk for different-hospital readmissions are necessary.
authors.affiliation: Graduate School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea.
authors.forename: Hongsoo
authors.initials: H
authors.lastname: Kim
authors.affiliation: Icahn School of Medicine at Mount Sinai, New York, NY, USA James J Peters VA Medical Center, Geriatrics Research, Education and Clinical Center, Bronx, NY, USA.
authors.forename: William W
authors.initials: WW
authors.lastname: Hung
authors.affiliation: Department of Statistics, College of Natural Sciences, Seoul National University, Seoul, South Korea.
authors.forename: Myunghee Cho
authors.initials: MC
authors.lastname: Paik
authors.affiliation: General Internal Medicine, Yale University School of Medicine, New Haven, CT, USA.
authors.forename: Joseph S
authors.initials: JS
authors.lastname: Ross
authors.affiliation: Independent Statistical Consultant, Washington, DC, USA.
authors.forename: Zhonglin
authors.initials: Z
authors.lastname: Zhao
authors.affiliation: Department of Statistics, College of Natural Sciences, Seoul National University, Seoul, South Korea.
authors.forename: Gi-Soo
authors.initials: GS
authors.lastname: Kim
authors.affiliation: Icahn School of Medicine at Mount Sinai, New York, NY, USA James J Peters VA Medical Center, Geriatrics Research, Education and Clinical Center, Bronx, NY, USA Jewish Home Lifecare, Research Institute on Aging, New York, NY, USA.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Boockvar
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002140:California; D003627:Data Interpretation, Statistical; D066264:Datasets as Topic; D005260:Female; D005544:Forecasting; D006761:Hospitals; D006801:Humans; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D010351:Patient Discharge; D010359:Patient Readmission / Q000639:trends*
keywords: health policy; hospital care; patient outcomes; readmissions