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Article Excerpt
A spending cap is an insurance design feature that changes out-of-pocket prices faced by the insured after she exceeds a spending limit. Often the cap is a coverage limit, so that plan enrollees must pay full price after their spending has reached a specified level. Studies examining the effects of exceeding prescription drug spending caps on utilization and health have found that exceeding a cap results in greater out-of-pocket costs and self-reported financial burden (Tseng et al. 2003, 2004), reduction in drug utilization (Soumerai et al. 1991; Martin and McMillan 1996; Hsu et al. 2006, 2008; Joyce et al. 2007), and higher use of emergency room, hospital, and nursing home services (Soumerai et al. 1991; Hsu et al. 2006).
Survey responses from enrollees provide most of what is known about the impact of exceeding a spending cap on enrollee prescription drug use. Prior studies have seldom been able to examine the prescription drug utilization of the same individuals before and after they reach an expenditure cap. When insurers pay nothing above an expenditure cap, enrollees are less likely to file claims for drugs purchased after the cap is exceeded. (1) Thus, several previous cap studies have not observed actual levels of utilization and out-of-pocket costs after a cap was exceeded but instead have extrapolated past spending to estimate burden on beneficiaries (Tseng et al. 2003, 2004). (2) An exception is a study of cost sharing of Medicare+Choice beneficiaries enrolled in a large prepaid integrated delivery system (Hsu et al. 2006). Hsu and colleagues found significantly lower drug utilization and greater nonadherence for beneficiaries subject to a spending cap in comparison with similar enrollees in a plan with no limit; to allay concern that postcap drug use might not be observed, they cite a concurrent survey of their study population reporting that beneficiaries did not buy drugs out of plan even after consumers faced the full drug price. (3)
For this study, we were able to observe the monthly prescription drug utilization of enrollees in a state pharmacy assistance plan with a soft cap that required enrollees to pay higher copayments (but less than full price) after they incurred total spending (both state coverage and out of pocket) greater than a threshold amount. This allowed us to address the following research questions:
* Do enrollees reduce prescription drug utilization and expenses when they face higher copayments after exceeding a soft cap?
* Do enrollees increase the proportion of drugs that are generic, as opposed to brand name, when they face higher copayments after exceeding a soft cap?
THE ILLINOIS PHARMACY ASSISTANCE PLAN
Before the implementation of Medicare Part D coverage for prescription drugs, a number of states funded pharmacy assistance programs for low-income seniors not eligible for Medicaid (Safran et al. 2002). Beginning in 2001, states were allowed to seek federal cost sharing through Medicaid 1115 waivers for such programs. In 2002, Illinois was among the first states to obtain a waiver to gain the federal Medicaid match for prescription drugs provided under the program to persons aged 65 and older with incomes up to 200 percent of the Federal Poverty Level (FPL) who do not qualify for Medicaid. Its program, called SeniorCare, was launched in June 2002. At the beginning of the program year, the plan charged most enrollees a copayment of $4 for each brand prescription filled and $1 per generic prescription. However, if the total prescription drug expenditures for an enrollee reached $1,750, copayments increased to 20 percent of drug cost, plus the original $1 or $4. (4)
METHODS AND DATA
Econometric Specification
Our objectives were to estimate the effect of exceeding the soft cap on prescription drug utilization, measured as the number of prescriptions filled, and to assess effects on total drug spending and the mix of brand and generic drugs. Using individual data observed before and after the cap was exceeded, our econometric approach accounted for unobserved individual heterogeneity while avoiding bias that can arise in panel specifications when an independent variable (exceeding the cap) is a function of lagged values of the dependent variable (use, spending, or proportion generic use) (Bond, 2002).
We estimated models for each of the dependent variables (monthly number of prescriptions, spending, and proportion of prescriptions that were for generic rather than brand-name drugs) among only those beneficiaries who exceeded the cap. The observations were aggregated into two periods for each beneficiary: the average for the months before and for the months after the cap was exceeded. This averaging smoothed out monthly fluctuations, increasing the stability of the observations. This approach also precluded the cap indicator from varying between individuals on the time dimension, which would give rise to endogeneity.
The month that the cap was exceeded was identified by determining whether the cumulative drug claims paid by the program and the beneficiary through each month exceeded $1,750. Because enrollees face first precap and then postcap out-of-pocket prices in the month they exceed the cap, this month was excluded from the analysis. We also excluded a 1-month adjustment period after the cap was exceeded. We classified the study group of cap exceeders by their precap monthly drug expenditures to investigate systematic differences in effects of the soft cap over types of cap exceeders.
The following equation was estimated for each of the three dependent variables (drug use, spending, and proportion generic):
[[gamma].sub.i]t = [[beta].sub.0] + [[beta].sub.1] Spending [cohort.sub.i] + [[beta].sub.2] Disease [cohort.sub.i] + [[beta].sub.3] [Characteristics.sub.i] + [[delta].sub.1][Over.sub.t] + [[delta].sub.2][Over.sub.t] x Spending [cohort.sub.i] + [[delta].sub.3][Over.sub.t] x Disease [cohort.sub.i] + [[delta].sub.4][Over.sub.t] x [Characteristics.sub.i] + [e.sub.it] (1)
where Spending [cohort.sub.i] is a vector of three indicator variables for spending quartiles based on precap drug spending, Disease [cohort.sub.i] is a vector of dummy variables for five chronic conditions (diabetes, coronary artery disease, cerebrovascular disease, chronic obstructive pulmonary disease [COPD], and arthritis), [Characteristics.sub.i] are sociodemographic indicators for age, race, sex, and income, [Over.sub.t]...
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