Academic journal article Health Care Financing Review

Predictability of Prescription Drug Expenditures for Medicare Beneficiaries

Academic journal article Health Care Financing Review

Predictability of Prescription Drug Expenditures for Medicare Beneficiaries

Article excerpt

MCBS data are used to analyze the predictability of drug expenditures by Medicare beneficiaries. Predictors include demographic characteristics and measures of health status, the majority derived using CMS' diagnosis cost group/hierarchical condition category (DCG/HCC) risk-adjustment methodology. In prospective models, demographic variables explained 5 percent of the variation in drug expenditures. Adding health status measures raised this figure between 10 and 24 percent of the variation depending on the model configuration. Adding lagged drug expenditures more than doubled predictive power to 55 percent. These results are discussed in the context of forecasting, and risk adjustment for the proposed new Medicare drug benefit.


Background and Aims

There are two reasons why researchers and policymakers should care about the predictability of prescription drug spending in the Medicare population. First, is the need to incorporate prescription drug expenditures into Medicare spending forecasts in light of the new Medicare drug benefit. The most challenging forecast will be the first one, which must be made without access to actual drug spending data. Instead, the initial predictions will be drawn from simulated scenarios, undoubtedly using data from the MCBS, in much the same manner as that the U.S. Congressional Budget Office and CMS estimated the costs of previous drug benefit proposals. Second, this topic is also important because payments to private plans for administering the benefit must incorporate a reasonable assessment of risk.

There are surprisingly few studies that directly address the issue of predictability of drug spending. This may be explained in part by the facts that private insurers rarely offer free standing drug benefits, and that the public programs that offer these benefits (primarily State pharmaceutical assistance programs) have not sought to develop private risk-based contracts. In general, pharmacy benefits managers do not assume the majority of risk in contracts with either public or private insurers, and there has been a shift away from capitation in this market (Booz Allen Hamilton, 2003). Two studies in the early 1990s (Stuart et al., 1991; Coulson and Stuart, 1992) examined the persistence of drug spending in Pennsylvania's PACE. The authors were able to explain between 2 and 4 percent of the individual variance in spending with only limited demographic characteristics available from PACE enrollment files. However, prior year spending explained nearly 70 percent of the total variance in current year expenditures. This finding leads to the conclusion that drug spending is highly persistent.

More recently, an unpublished study, (Hogan, 2000) used 1992-1997 MCBS data to estimate the predictability of drug spending using several risk adjusters designed for medical and hospital services. He found [R.sup.2] measures of 0.15 for both prospective and concurrent versions of the disability payment system (designed for Medicaid), 0.07 for the prospective principal inpatient diagnostic cost group (PIP-DCG) model, and 0.21 for a prospective model containing claims-based condition indicators. As in the prior study by Coulson and Stuart (1992), adding previous year prescription spending significantly increased the [R.sup.2].

This existing research suggests that drug expenditures are predictable and persistent relative to the expenditures currently covered by Medicare. As a comparison, the demographic and health status measures in the prospective DCG/HCC model (CMS's current methodology for predicting Medicare expenditures) explain roughly 9 percent of the variation in Medicare-covered physician, and hospital expenditures for the Medicare population (Ash, Ellis, and Pope, 2000). In the context of Medicare risk adjustment, Newhouse, Buntin, and Chapman (1997), remarked that, "It appears that anyone observing the past spending of a given person could explain about 20-25 percent of the variance in actual annual spending. …

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