Jonathan D. Ketcham, PhD; and Kosali I. Simon, PhD
After Medicare Part D was implemented in January 2006, it covered 53% of the elderly by mid-2006 (just after the end of open enrollment for 2006)1
and 54% of the elderly by January 2007 (just after the end of open enrollment for 2007).2
Despite substantial interest from policymakers and researchers alike, few large-scale studies of its effects exist. In this article we utilize a nationwide sample of prescriptions filled from December 2004 through December 2007 to consider how prescription drug utilization and out-of-pocket (OOP) costs changed for elderly patients compared with near-elderly patients during the first and second years of Medicare Part D. By using all elderly patients as the treatment group, we studied the aggregate impact of Part D: (1) direct effects experienced by those who added Part D as new coverage, (2) substitution effects for those for whom Part D replaced other coverage, and (3) indirect effects for those not covered by Part D but whose coverage was affected by Part D’s existence.
Part D can affect elderly patients’ net prescription drug utilization in several ways. First, Part D enrolled some elderly persons who previously lacked drug coverage; if demand is not perfectly inelastic, new coverage would increase the quantity of drugs consumed through lower OOP costs. To examine this extensive margin, we analyzed changes in the number of elderly filling any prescription before and after January 2006. Utilization also could have increased at the intensive margin for those previously covered if their coverage under Part D was more generous than their previous coverage (eg, because of lower patient OOP costs or fewer supply-side restrictions such as prior authorization requirements.) Conversely, net utilization could have decreased if prior coverage was more generous than Part D coverage or if employers responded to Part D’s introduction by reducing the generosity of retiree coverage.
Two existing peer-reviewed studies also examined this aggregate effect. Both prior studies used data from only Walgreens pharmacies. Yin et al used data covering September 2004-April 2007 for those age 66-79 years, with the experience of those age 60-63 years as a control group.3
They found that Part D increased use of medications by 1.1% and OOP costs fell by 8.8% during the initial open enrollment period (January-May 2006). During the following 11 months (June 2006-April 2007), they found that utilization increased 5.9% and OO P costs decreased by 13.1%. Lichtenberg and Sun also used Walgreens data to compare drug use among those age >65 years with those age <65 years from September 2004 to December 2006; they found an increase of 12.8% in quantity and an 18.4% reduction in OOP costs.4
In this article we reconcile the seemingly disparate findings from these 2 studies and compare them with our own results, paying attention to the various strengths and weaknesses of each data source and method.
Studies on the effect of Part D on access to medications also have been conducted with survey data. Using the Health and Retirement Study, Levy and Weir found that although there was substantial take-up of Part D by the uninsured, prescription consumption did not appear to increase.5
Neuman et al conducted a survey of Medicare beneficiaries in fall 2006 and reported that, compared with employer coverage, OOP costs were higher under Part D.6
Before implementation of Part D, Pauly forecast that Part D would reduce elderly patients’ OOP costs by 14% overall, by halving costs for the 25% who were uninsured.7
Using a 0.4 price elasticity of demand measure, he estimated a 6% increase in utilization as a result of Part D.
We analyzed a large dataset of prescriptions covering the period from December 2004 through December 2007 from Wolters Kluwer Health’s Source Lx database.8
Our dataset comprises 1,417,366,769 prescriptions filled by 34,198,008 unique patients, whose age as of 2007 was >58 years. McKesson Corporation (formerly Per-Se Technologies, and NDCHealth before that) collects these data primarily from their switches that transmit electronic claims between pharmacies and third parties (eg, insurance companies and pharmacy benefit managers) that help pay for prescriptions. Source Lx covers all 50 states and includes approximately 31% of the nation’s prescriptions, filled at approximately 32,000 pharmacies (not restricted to any particular pharmacy chains), including retail, mail-order, and hospital pharmacies, as well as other institutions that fill prescriptions such as long-term care facilities.8
The dataset includes prescription-fill level information on patient age, OOP costs versus third-party payment amounts, number of days’ supply, the pharmacy’s ZIP code, pharmacy type, and an encrypted patient identifier that tracks usage over time. Descriptive statistics are provided in the eAppendix (available at www.ajmc.com
We compared 2 consistent cohorts: a group always age eligible for Medicare since January 2006 (age >66 years as of 2007) versus a group always age ineligible for Medicare (age 58-64 years as of 2007). (Our data included the patients’ birth year but not month, so we excluded those who turned 65 during either 2006 or 2007. Because a small fraction of elderly persons are not eligible for Medicare and some persons under age 65 years are eligible, our treatment-control group definitions may cause small underestimates of the effects of Part D. See Lichtenberg and Sun.4
) Using these groups, we made nationwide difference-in-difference comparisons of the aggregate change from January 2005 to December 2007. Use of a control group was important to separate Part D’s effects from those of other factors that changed over time. For example, several widely used products such as Zoloft lost patent protection in 2006. To the extent that near-elderly patients also experienced the effects of these other changes, our method identified Part D’s effects better than a simple comparison of 2005 data with 2006 data for elderly patients.
In addition to showing the aggregate effects of Part D for the first and second years of the program, we also report monthly estimates to consider how Medicare’s effects varied during the initial open enrollment and transition period in early 2006, early in the calendar year before patients had met their deductibles, and later in the year when some entered the donut hole. To justify use of our control group, we used data prior to the implementation of Part D to validate the underlying assumption that the pre–part D time trend did not significantly differ between the 2 age groups.Outcome Measures
We present results for 3 main outcomes: days’ supply, number of patients filling prescriptions, and patient OOP costs per day’s supply. We also analyzed total prescriptions filled, but we did not report these results separately because they were virtually identical to those for days’ supply. Because our 2 utilization outcomes (days’ supply and number of patients with prescriptions filled) were compared in terms of totals for the 2 cohorts over time, we had to adjust for different rates of mortality for the 2 cohorts, or we could have found relative decreases in total use occurring for the older cohort simply because of more deaths. To do this adjustment, we normalized our totals by the size of the national population estimated or projected by the US Census Bureau for each cohort at each point in time and report per capita changes.9,10
We report the percent changes over time in 3 outcomes to calculate the difference-in-difference impact of Medicare Part D.Correction for Biases
Certain features of our dataset pose hurdles to estimating the impact of Part D on use of prescription drugs by the elderly and need to be explained up front. First, our data largely but not exclusively tracked the same pharmacies over time. This unbalanced panel could bias our results if the pharmacies that entered or left the data catered to a single age cohort disproportionately. To address this concern, we tested the robustness of our results on a panel of retail pharmacies that consistently reported data during our study period. These included 77.4% of the total days’ supply in the full dataset.
Three sources of drug procurement are under-reported or absent in these data (“pure cash,” mail order, and free samples). These may have systematically changed as a result of Part D, causing us to overestimate or underestimate the true impact of Part D on the actual quantity of medications used by seniors.
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