The econometric analysis of monthly prescription and expenditure data for enrollees in a state pharmacy assistance program indicates that near-poor elders responded to an increase in out-of-pocket price by significantly reducing the number of prescriptions they filled. This was accompanied by significantly lower monthly drug expenditures (sum of program and out-of-pocket spending). The percentage decrease in total spending was greater than the percentage decrease in number of prescriptions, suggesting that elders were continuing to meet some prescription needs by shifting to lower-cost medicines. This observation is reinforced by the finding that the proportion of prescriptions that were generic rather than brand increased significantly. It is also noteworthy that these effects were greatest for enrollees in the highest quartile for precap drug spending. On average, these high spenders reduced the number of prescriptions filled by 21 percent and reduced spending by 27 percent.
Although the estimated declines in number of drugs purchased and drug expenditure associated with exceeding the cap were substantial, these declines occurred in response to percentage increases in out-of-pocket prices that were much larger. The out-of-pocket price to enrollees in the months before the cap was exceeded was estimated to average $2.82 per prescription filled; in months after the cap was hit the out-of-pocket price per prescription for the same drugs is computed to average $13.04, an increase of 363 percent. This is consistent with inelastic demand, as indicated by the computed (arc) elasticity of demand of −0.12.
The findings may also be put into perspective by considering the impact of the insurance design on enrollee out-of-pocket costs in relation to income. The estimated increase in out-of-pocket price per prescription was not large in dollar terms, and it remained a relatively small portion of the program expenditure per prescription used by SeniorCare enrollees. However, for elders on fixed incomes below 200 percent of FPL, these out-of-pocket price increases were likely very meaningful. If these enrollees were to maintain their rates of use of generic and brand drugs after hitting the cap, they would have experienced total monthly out-of-pocket expenditures that loom large in relation to income. At the out-of-pocket prices computed above, the monthly out-of-pocket expense for the average precap prescriptions would increase from 1.5 to 6.4 percent of a monthly income of $1,100, the estimated mean income for our population. Estimated average precap utilization for enrollees in the high-spending quartile would require 2.1 percent of an $1,100 per month income to be spent out of pocket, rising to 9.5 percent of average monthly income out of pocket after the cap was exceeded. It is no surprise that enrollees faced with these increased out of pocket costs respond by cutting back on utilization, and, to some extent, switching toward generic drugs.
Conceptually, a cap effect is different from an exogenous copayment effect in that beneficiaries subject to a cap have the ability to anticipate exceeding the cap and thus to modify their utilization behavior before exceeding the cap. Auxiliary regression analyses of monthly spending for cohorts who hit the cap in different months (not shown) uncovered mixed evidence of a slight down turn in spending before the cap for some cohorts. This phenomenon should be further explored using models that can account for month-to-month revisions of expected future prices. In addition, a second year of data could reveal whether enrollees who exceeded the cap in their first program year exhibited different behavior when their copayments reverted to the precap level as a new insurance year began. Enrollees who learn from past experience with the cap may maintain lower spending in subsequent years in order to make their limited coverage last longer, or they may respond to a reinstatement of the lower copayment by spending at previous rates.
Because our ultimate concern is the health impacts of prescription drug plan design features, of greatest interest is further research detailing drug utilization changes made by enrollees who exceed the soft cap, and the link between these and any health effects. Such an investigation could supplement survey-based studies, which have demonstrated poor health outcomes for elders who report skipping doses, splitting pills, or failing to fill prescriptions due to cost (Heisler et al. 2004
; Piette et al. 2004
). In-depth investigation could detail whether enrollees who reduced their expenditures on prescription drugs in response to exceeding the cap did so by stretching certain prescriptions over a longer time period, by omitting certain drugs altogether, or by seeking generic substitutes for certain drugs. Follow-up analysis of linked Medicare claims could assess whether any clinical effects resulting from specific patterns of underuse by disease cohort can be observed in future health services utilization.
It is both a strength and a limitation of the current analysis that it relied on data derived from outpatient drug claims filed under an ongoing insurance program. Because out-of-pocket costs to enrollees were substantially below full pharmacy prices both before and after an enrollee exceeded the cap, we can be reasonably sure that we have captured most prescription drug utilization. However, data were missing for some enrollees, and claims were incomplete for one of the 12 program months. Over-the-counter drugs that might substitute for prescription drugs are not observable because they were not covered by SeniorCare.
This paper represents a contribution to the growing literature on the impact of caps and copayments on prescription drug utilization with respect to both data and methods. A previous study that compared Medicare+Choice enrollees with and without a hard cap of $1,000 (Hsu et al. 2006
) found increasing differences between the two groups in the months after the spending limit was exceeded but focused on overall differences due to plan design rather than the temporal differences before and after the cap. Other studies of enrollees' response to the cap as a plan design feature have queried enrollees about their responses to a cap, but they have not made use of actual utilization for enrollees before and after they exceed a cap.