title: Using self-reported data to predict expenditures for the health care of older people.
abstract: OBJECTIVE
  To create and test a method for using self-reported data to predict future expenditures for the health care of older people.
  
  
  METHODS
  A two-stage regression model of the relationship between self-reported data and Medicare expenditures during the following year was constructed from a randomly selected (derivation) half of a cohort of fee-for-service Medicare beneficiaries. For the other (validation) half of the cohort, two sets of predictions of 12-month Medicare expenditures were generated, one using the new two-stage model and the other using the principal inpatient diagnostic cost group (PIP-DCG) method now used to risk-adjust capitation payments to Medicare + Choice health plans. Both sets of predictions were compared with Medicare's actual 12-month expenditures for the validation cohort.
  
  
  METHODS
  Ramsey County, Minnesota.
  
  
  METHODS
  Community-dwelling Medicare beneficiaries aged 70 and older (N = 13,682) who responded to a mailed survey.
  
  
  METHODS
  Predicted-to-observed ratio (PTOR) of Medicare expenditures.
  
  
  RESULTS
  For the validation cohort, Medicare's actual 12-month expenditures totaled $26.5 million. The two-stage model predicted Medicare expenditures of $26.4 million (PTOR = 1.00); the PIP-DCG method predicted $31.2 million (PTOR = 1.18). Within subpopulations of healthy and ill beneficiaries, the two-stage model's predictions remained considerably more accurate than the PIP-DCG predictions.
  
  
  CONCLUSIONS
  Self-reported data may predict future Medicare expenditures more accurately than administrative data about beneficiaries' demographic characteristics, and previous hospitalizations.
authors.affiliation: Department of Family Practice and Community Health, University of Minnesota Medical School, Minneapolis 55415, USA. pacal001@umn.edu
authors.forename: James T
authors.initials: JT
authors.lastname: Pacala
authors.forename: Chad
authors.initials: C
authors.lastname: Boult
authors.forename: Cristina
authors.initials: C
authors.lastname: Urdangarin
authors.forename: David
authors.initials: D
authors.lastname: McCaffrey
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006299:Health Services for the Aged / Q000191:economics*; D006801:Humans; D008297:Male; D006278:Medicare / Q000191:economics*; D008910:Minnesota; D018803:Models, Economic; D011237:Predictive Value of Tests; D012044:Regression Analysis; D012650:Self Disclosure