Using Self‐Reported Data to Predict Expenditures for the Health Care of Older People

OBJECTIVES: To create and test a method for using self‐reported data to predict future expenditures for the health care of older people. DESIGN: 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. SETTING: Ramsey County, Minnesota. PARTICIPANTS: Community‐dwelling Medicare beneficiaries aged 70 and older (N = 13,682) who responded to a mailed survey. MEASUREMENTS: 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. CONCLUSION: Self‐reported data may predict future Medicare expenditures more accurately than administrative data about beneficiaries' demographic characteristics, and previous hospitalizations.