title: Cost trajectories from the final life year reveal intensity of end-of-life care and can help to guide palliative care interventions.
abstract: OBJECTIVE
  Exploration of healthcare utilisation patterns in the final life year to assess palliative care potential.
  
  
  METHODS
  Retrospective cluster analyses (k-means) of anonymised healthcare expenditure (HCE) trajectories, derived from health insurance claims of a representative sample of Swiss decedents who died between 2008 and 2010 (2 age classes: 4818 <66 years, 22 691 elderly).
  
  
  RESULTS
  3 (<66 years) and 5 (elderly) trajectory groups were identified, whose shapes were dominated by HCE from inpatient care in hospitals and at nursing homes. In each age class, the most expensive group (average cumulative HCE for <66 years: SFr 84 295; elderly: SFr 84 941) also had the largest abundance of cancers (<66 years: 55%; elderly: 32%) and showed signs of continued treatment intensification until shortly before death. Although sizes of these high-cost groups were comparatively small (26% in younger; 6% in elderly), they contributed substantially to the end-of-life HCE in each age class (62% and 18%, respectively).As age increased, these potential target groups for palliative care gained in share among <66-year olds (from 9% in children to 28% in 60-65-year olds), but decreased from 17% (66-70-year olds) to 1% (>90-year olds) among elderly.
  
  
  CONCLUSIONS
  Cost trajectory clustering is well suited for first-pass population screenings of groups that warrant closer inspection to improve end-of-life healthcare allocation. The Swiss data suggest that many decedents undergo intensive medical treatment until shortly before death. Investigations into the clinical circumstances and motives of patients and physicians may help to guide palliative care.
authors.affiliation: Institute for Epidemiology, Biostatistics & Prevention, University of Zürich, Zürich, Switzerland.|CSS-Institute for Empirical Health Economics, Luzern, Switzerland.
authors.forename: Viktor
authors.initials: V
authors.lastname: von Wyl
authors.affiliation: Polynomics AG, Olten, Switzerland.
authors.forename: Harry
authors.initials: H
authors.lastname: Telser
authors.affiliation: Palliative Care Unit, Department of Internal Medicine, GZO Hospital Wetzikon, Wetzikon, Switzerland.
authors.forename: Andreas
authors.initials: A
authors.lastname: Weber
authors.affiliation: Polynomics AG, Olten, Switzerland.|Department of Business Administration, University of Zürich, Zürich, Switzerland.
authors.forename: Barbara
authors.initials: B
authors.lastname: Fischer
authors.affiliation: CSS-Institute for Empirical Health Economics, Luzern, Switzerland.|Department of Economics, University of Zürich, Zürich, Switzerland.
authors.forename: Konstantin
authors.initials: K
authors.lastname: Beck
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D016000:Cluster Analysis; D005260:Female; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000191:economics*; D006761:Hospitals / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000191:economics* / Q000628:therapy; D009735:Nursing Homes / Q000191:economics; D010166:Palliative Care / Q000191:economics*; D012189:Retrospective Studies; D013557:Switzerland; D013727:Terminal Care / Q000191:economics*
keywords: Clinical decisions; Methodological research; Service evaluation; Terminal care