This article has been cited by other articles in PMC. Abstract MCBS data are used to analyze the predictability of drug expenditures by Medicare beneficiaries.
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.
Introduction 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. 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, Two studies in the early s Stuart et al. 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 More recently, an unpublished study, Hogan, used MCBS data to estimate the predictability of drug spending using several risk adjusters designed for medical and hospital services.
He found R2 measures of 0. As in the prior study by Coulson and Stuart , adding previous year prescription spending significantly increased the R2. This existing research suggests that drug expenditures are predictable and persistent relative to the expenditures currently covered by Medicare. It analyzed the Medicare population as a whole then separately as individuals with and without drug coverage.
Separate models are appropriate because forecasting or risk adjustment on behalf of the Medicare Program ultimately pertain to an insured population, albeit one that may contain individuals who are currently uninsured, and one would expect the marginal impact of drug coverage to vary by condition.
The emphasis was on attaining a basic finding regarding predictability; the authors did not seek to refine HCCs, conduct a detailed analysis of individual predictors, perform specification tests, or assess multiple measures of fit.
Beginning in fall , the MCBS is a longitudinal panel survey of a representative national sample of the Medicare population conducted under the auspices of CMS. Over 12, Medicare beneficiaries, both aged and disabled, living in the community or in institutions are sampled from Medicare enrollment files, and surveyed three times a year using computer-assisted personal interviewing.
MCBS interviewers collect extensive information on individuals' use and expenditures for health services including source of payment, as well as information on health insurance, health and functional status, socioeconomic status, and demographic characteristics. The MCBS Files link Medicare claims to survey-reported events, and provide complete expenditure, and source of payment data on all health care services, including those not covered by Medicare, notably prescription drugs and long-term care.
Prescription drug utilization data in the MCBS are based on self-reports of each prescription filled and refilled during the year.
To assure accurate recall, respondents are asked to keep bill records, and prescription containers to show interviewers during the yearly interviews. During return visits, MCBS interviewers provide print-outs of the last recorded prescription use and ask respondents to correct entries, state whether these prescriptions are still being taken, and report new medications added since the last interview.
Despite these precautions, there are concerns about underreporting. A recent comparison of MCBS self-reported medication use, and pharmacy claims found under reporting rates of The current study drew on both the survey data for drug expenditures and individual characteristics , and the inpatient, outpatient, and physician claims for claims-based measures of health status.
In addition, to be in the sample, beneficiaries were required to have completed three MCBS survey rounds in each year since persons with missed interviews have incomplete prescription records.
Individuals were deemed to have drug coverage if they stated they had drug coverage in response to survey questions or if there was indication of third party payment in the drug claims data. Study Variables The dependent variable in all models was annual drug expenditures for the year including expenditures for drugs currently covered by Medicare measured in terms of the AWP for each prescription filled during the year. The study used AWP rather than the imputed transaction prices listed in the MCBS to create a standardized measure of individual drug expenditures.
Using AWP preserved variation due to differences in beneficiary utilization patterns, and characteristics of individual prescriptions brand or generic, strength, and days supply. In contrast, transaction prices vary by drug coverage status because of differences in the discounts and rebates negotiated by various payers.
While AWP is an inflated measure of drug prices, it is preferable in this context because the emphasis is on the variability of drug expenditures and on relative, rather than absolute, levels of drug expenditure. In addition, there is no alternative approach to drug pricing that is widely accepted. These included heart disease, cancer, arthritis, lung disease, mental illness, Alzheimer's, diabetes, hypertension, osteoporosis, stroke, benign prostatic hypertrophy, paralysis, Parkinson's, and hip fracture.
In the majority of models, predictor variables were based on data. In the concurrent model, however, the HCC condition indicators were derived using diagnoses measured in Drug Expenditure Models The study used ordinary least squares OLS regression models with unweighted observations in order to maximize the efficiency of parameter estimates. Linear models were chosen because they are often the basis of risk-adjustment methodologies. The basic model was: We estimated six variants of this model.
Model 1 omitted the health status measure, and provided a baseline for subsequent results. Model 2 measured health status via the 14 indicators for self-reported conditions. Model 3 replaced the self-reported conditions with indicators for individual conditions derived from Medicare claims using the HCC methodology.
This was the model of greatest interest because it was a prospective model based on Medicare claims data; this is the information that would be appropriate, and available for forecasting and risk-adjustment. In addition, Medicare's existing risk adjustment methodology is the natural point of departure for work in this area. Model 4 essentially constrained the relative importance of individual conditions in predicting drug expenditure to be the same as their relative importance in predicting the physician, and inpatient expenditures currently covered by Medicare.
Note that these plans generally do not offer full drug coverage so the measure is far from exact. Models 5 and 6 shed some light on the persistence of drug expenditures. Model 5 used the concurrent, rather than prospective, condition indicators, i. Comparing Models 3 and 5 gives a sense of the relative importance of chronic conditions, which persist from year to year, in driving drug expenditure.
Model 6 is a variant of Model 3, which includes an additional regressor, lagged drug expenditures. While lagged drug expenditures may not be available for forecasting, and are typically not appropriate for payment applications because they blunt incentives for cost containment , this model offers direct insight into the persistence of drug expenditures and, by extension, into the potential for adverse selection on the part of purchasers, and risk selection on the part of insurers in the market for drug insurance.
Adverse selection is the tendency of those who are particularly likely to have above average covered expenses to also have an above average tendency to purchase insurance.
Similarly, risk selection is the tendency of insurers to design their products, direct their marketing, and otherwise act to attract individuals likely to have below average covered expenditures into their pool. Results Table 1 presents descriptive characteristics of the sample in More than one-half the beneficiaries were female 56 percent.
About 17 percent were recipients of Social Security disability insurance under age Another 6 percent were beneficiaries age 65 or over who had previously been entitled to Medicare through Social Security disability insurance. Just over one-quarter of the sample was age 80 or over, and about two-thirds of the beneficiaries lived in urban areas.
Relative to the population with drug coverage, the population without drug coverage was more likely to be female, 80 years of age or over, and lives in a rural area. The population without drug coverage was less likely to be or have been entitled to Medicare because of disability, perhaps because many of the disabled currently have drug coverage through the Medicaid Program.