The Effect of the Medicare Part D Prescription Benefit on Drug Utilization and Expenditures

Context In 2006, Medicare inaugurated a drug benefit for older adults. Contribution Using data from a random sample of pharmacy customers who were beneficiaries of the program after the enrollment deadline, the authors estimate that the drug benefit saved people about $9 a month and gave them an extra 14 days of pills, on average. Caution The authors did not examine effects of the doughnut hole and did not look at specific drugs or drug classes. Implication The Medicare drug benefit has led to modest decreases in expense and increases in drug use for older adults. The Editors The Medicare Modernization Act Prescription Drug Benefit (Part D) established a prescription drug benefit for all 43 million Medicare beneficiaries in the United States. This is the largest change to Medicare since the program began several decades ago (1). The prescription benefit has increased the proportion of Medicare enrollees with prescription drug coverage (2), and the Centers for Medicaid & Medicare Services report that among seniors eligible for Medicare, Part D enrollees save on out-of-pocket expenses compared with those not enrolled in Part D (3, 4). Previous reports also have examined enrollment and used surveys to examine beneficiaries' beliefs and experiences (57). Although these reports and others (8, 9) point to positive effects of the Part D benefit, several questions remain unanswered about its overall effect on prescription utilization and expenditures. This is because studies have tended to focus only on beneficiaries who have enrolled in a Medicare Part D plan. Changes in drug utilization or expenditures measured in these studies could represent Part D coverage, trends in prescription drug use unrelated to Part D, or differences between beneficiaries who enrolled in Part D and those who did not. A 2004 projection suggested that the Medicare drug benefit would reduce average out-of-pocket expenditures by about 14% among elderly persons with Medicare coverage and by about 47% among elderly persons without preexisting drug coverage, resulting in an overall increase in total drug spending of about 6% (10). This prediction was mainly driven by the fact that almost three fourths of elderly persons in the United States already had some kind of drug coverage and that Medicare Part D would have a small effect on their expenditures and utilization. Yet with the exception of 1 recent study (11), little work has been done to empirically estimate the effect of the Part D benefit on prescription drug utilization and expenditures. We sought to address these limitations by analyzing pharmacy claims from a national pharmacy chain accounting for approximately one eighth of the market share of prescription medicines in the United States. Although this sample may not be nationally representative, it offers an opportunity to study the effect of Part D on a broad and heterogeneous population that accounts for a substantial portion of the entire Medicare population. Our analyses compare the effect of Part D on prescription drug utilization and expenditures among persons eligible for the benefit who enrolled in a Part D plan, persons eligible for the benefit who did not enroll, and noneligible persons. We also distinguish between the overall effect of Part D during the penalty-free enrollment period and the postpenalty-free period, when enrollment was stable, to determine the steady-state effect of Part D, independent of enrollment dynamics. Methods Sample and Measurements We selected a 5% random sample of unique pharmacy customers who filled at least 1 prescription during both the 2005 and the 2006 calendar years through the Walgreens pharmacy chain, whether at a retail store or by mail order. For each person in our sample, we obtained claims data for every prescription filled between 1 September 2004 and 31 April 2007. For each prescription claim, we obtained data on the claimant's demographic characteristics (age, sex, language preference, ZIP code of residence), insurance characteristics (whether the claim was paid through a prescription drug plan, method of payment), pharmacy characteristics (ZIP code location), prescription characteristics (National Drug Code, therapeutic class, drug dose, number of treatment days, date dispensed, number of refills), and expenditures (amount paid out of pocket, amount paid by third party). We used data on claimants' ZIP code of residence (the residence recorded at the person's first pharmacy claim in 2005) to link the pharmacy claims data to data from the 2000 U.S. Census, including information on the total population, median household income, income per capita, proportion of urban residents, proportion of African-American persons, unemployment rate, and poverty rate within the ZIP code of residence (12, 13). We also used data from the Behavioral Risk Factor Surveillance System (BRFSS) to compare the characteristics of our sample with those of all Medicare beneficiaries. We used simple descriptive statistics to compare the age and sex distribution of our sample with those of persons age 60 to 63 years and 66 to 79 years in the BRFSS. We also linked BRFSS data to 2000 U.S. Census data to examine whether the ZIP codelevel demographic characteristics of our pharmacy sample were similar to those of the nationally representative BRFSS sample in the same age groups, and whether the characteristics of our sample were similar to those of only BRFSS respondents who lived in the same geographic areas as our pharmacy sample. We excluded persons 80 years of age or older because the proportion of persons in nursing homes is higher among this age group and because changes in the Medicare Modernization Act regarding persons receiving nursing home care and long-term care (14) do not extend to the majority of the Medicare population. Outcomes Our 2 outcomes were monthly average out-of-pocket prescription costs and prescription utilization, as measured by the quantity of a prescription medicine sufficient for 1 day of therapy (pill-day). Each person in our sample contributed 32 observations corresponding to 32 months of data. We divided the 32 months into 3 periods: the prePart D period (September 2004 to December 2005); the ramp-up postPart D period, during which seniors could enroll in Part D plans without penalty (January 2006 to May 2006); and the stable postPart D period, after the deadline for penalty-free Part D enrollment (June 2006 to April 2007). Because we aimed to estimate the effect of Part D, we sought to compare observed trends in out-of-pocket costs and utilization among seniors eligible for the benefit (the Part Deligible group, consisting of persons age 66 to 79 years as of 1 January 2006) with predicted counterfactual trends for the same group of seniors had Part D not been enacted. Thus, we sought to estimate the effect of Part D on expenditures and utilization netting out contemporaneous changes to the outcome variable due to factors unrelated to Part D (15, 16). To estimate counterfactual trends, we used the contemporaneous time profile of the corresponding outcomes in a control group of persons age 60 to 63 years who were not yet eligible for the benefit (the Part Dineligible group). We dropped persons ages 64 and 65 years as of 1 January 2006 from the sample because they had partial-year Medicare eligibility during the study period. We first confirmed that the trends in outcomes in persons 60 to 63 years of age provided an adequate control for persons in the eligible group during the prePart D era (September 2004 to December 2005) by examining both the statistical significance and magnitude of the coefficients in our regression model that represented the differential trends between the Part Deligible and Part Dineligible groups during the prePart D era. We then used the counterfactual control trends in the ramp-up period and in the stable enrollment period after the Part D enrollment deadline in May 2006 to represent the counterfactual trends in outcomes for the Part Deligible group assuming Part D had not been enacted. Statistical Analysis We estimated trends and generated predictions by using generalized estimating equations (GEEs). We adjusted for sample demographic characteristics (sex, English-speaking status, Medicaid coverage, age), ZIP codelevel characteristics (total population, median household income, per-capita income, proportion of urban residents, proportion of African-American persons, unemployment rate, poverty rate), fixed effects for calendar months (January to December), study period (prePart D, ramp-up postPart D, and stable postPart D), and Part Deligible group. Moreover, we explored a variety of splines for representing differential time trends between periods; we ultimately used cubic splines because they produced the best fit for both the prePart D and the stable postPart D periods. A linear spline was used for the ramp-up postPart D period because this period was short. We added up to 3-way interactions between study period and Part Deligible group fixed effects and the splines over time. We assessed overall model fit by using a variety of goodness-of-test criteria. We used gamma distribution to model out-of-pocket expenditures and negative binomial distribution to model pill-days. All estimators used log-link models. In addition, we explored the correlation structure between monthly observations within participants and used a first-order autoregressive correlation structure for out-of-pocket costs and an unstructured correlation matrix for pill-days. We had 2 main goals. First, we sought to estimate the policy effect of Part D coverage during both the ramp-up and stable postPart D periods. These effects represent the mean differences in the monthly outcomes between the observed factual trends and predicted counterfactual trends if Part D had not been enacted over these 2 periods. The Appendix Figure provides details about the specifications of these models and how the c