title: Hospice Enrollment, Local Hospice Utilization Patterns, and Rehospitalization in Medicare Patients.
abstract: BACKGROUND
  Rehospitalizations are prevalent and associated with decreased quality of life. Although hospice has been advocated to reduce rehospitalizations, it is not known how area-level hospice utilization patterns affect rehospitalization risk.
  
  
  OBJECTIVE
  The study objective was to examine the association between hospice enrollment, local hospice utilization patterns, and 30-day rehospitalization in Medicare patients.
  
  
  METHODS
  With a retrospective cohort design, 1,997,506 hospitalizations were assessed between 2005 and 2009 from a 5% national sample of Medicare beneficiaries. Local hospice utilization was defined using tertiles representing the percentage of all deaths occurring in hospice within each Hospital Service Area (HSA). Cox proportional hazard models were used to assess the relationship between 30-day rehospitalization, hospice enrollment, and local hospice utilization, adjusting for patient sociodemographics, medical history, and hospital characteristics.
  
  
  RESULTS
  Rates of patients dying in hospice were 27% in the lowest hospice utilization tertile, 41% in the middle tertile, and 53% in the highest tertile. Patients enrolled in hospice had lower rates of 30-day rehospitalization than those not enrolled (2.2% versus 18.8%; adjusted hazard ratio [HR], 0.12; 95% confidence interval [CI], 0.118-0.131). Patients residing in areas of low hospice utilization were at greater rehospitalization risk than those residing in areas of high utilization (19.1% versus 17.5%; HR, 1.05; 95% CI, 1.04-1.06), which persisted beyond that accounted for by individual hospice enrollment.
  
  
  CONCLUSIONS
  Area-level hospice utilization is inversely proportional to rehospitalization rates. This relationship is not fully explained by direct hospice enrollment, and may reflect a spillover effect of the benefits of hospice extending to nonenrollees.
authors.affiliation: 1 Department of Medicine, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin.
authors.forename: Timothy R
authors.initials: TR
authors.lastname: Holden
authors.affiliation: 2 Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin.|3 Department of Family Medicine, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin.|4 Department of Surgery, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin.
authors.forename: Maureen A
authors.initials: MA
authors.lastname: Smith
authors.affiliation: 5 Department of Medicine, Rheumatology Division, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin.
authors.forename: Christie M
authors.initials: CM
authors.lastname: Bartels
authors.affiliation: 6 Department of Medicine, Hematology, Oncology, and Palliative Care Medicine Division, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin.
authors.forename: Toby C
authors.initials: TC
authors.lastname: Campbell
authors.affiliation: 7 Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin.
authors.forename: Menggang
authors.initials: M
authors.lastname: Yu
authors.affiliation: 8 Department of Medicine, Geriatrics Division, University of Wisconsin School of Medicine and Public Health , Madison, Wisconsin.|9 Geriatric Research Education and Clinical Center, William S. Middleton Hospital , U.S. Department of Veterans Affairs, Madison, Wisconsin.
authors.forename: Amy J H
authors.initials: AJ
authors.lastname: Kind
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006738:Hospices / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D006278:Medicare; D010359:Patient Readmission / Q000639:trends*; D012189:Retrospective Studies; D014481:United States