Joseph P. Newhouse, PhD

 Banner Health, a large delivery system in Maricopa County, Arizona, entered into both Medicare and commercial insurance contracts that varied the amount of financial risk that Banner assumed. Rates of utilization and spending under these various contracts were investigated.

 Prior to 2012, Banner held Medicare Advantage (MA) contracts, and in 2012 it began as a Medicare Pioneer accountable care organization (ACO). Banner also introduced a commercial ACO contract in that year. We compared risk-adjusted healthcare utilization and spending in the MA plan, the ACO, and a local traditional Medicare (TM) comparison group. We also compared risk-adjusted utilization and spending in Banner’s commercial ACO with that of a comparison group drawn from the same employment groups who were not attributed to Banner providers.

 We used claims and encounter data to measure utilization and spending. We risk adjusted using CMS and HHS Hierarchical Condition Categories.

 Within Medicare, MA enrollees had lower risk-adjusted utilization and total spending than either the Pioneer ACO participants or a local TM comparison group. Participation in the Pioneer ACO program was associated with a greater reduction in hospitalization rates for ACO patients relative to local TM patients served by non-ACO providers, but the effect on total medical spending was ambiguous. Risk-adjusted differences between the commercial ACO group and the fee-for-service comparison group were generally small.

 The results are consistent with CMS’ efforts to shift reimbursement away from pure fee-for-service reimbursement.

We assessed the performance of a large delivery system, Banner Health, that takes risk under a Medicare Advantage (MA) plan, a Medicare accountable care organization (ACO), and a commercial ACO.

Within Medicare, risk-adjusted healthcare utilization was less in Banner’s MA plan than in its Pioneer ACO and in a traditional Medicare comparison group.

Its ACO program had a larger fall in hospitalization rates than a traditional Medicare comparison group.

Spending effects in its commercial ACO were modest, perhaps because of churn.

These results support CMS’ efforts to shift reimbursement away from traditional fee-for-service.

Value-based purchasing has emphasized moving away from pure fee-for-service reimbursement by shifting some financial risk from insurers to healthcare delivery systems and provider groups. One of the highest-profile efforts has been accountable care organizations (ACOs), which share financial risk with payers for a defined population of patients rather than being paid solely on a fee-for-service basis for an undefined population. Successful ACOs could contemplate assuming full financial risk—for example, by becoming Medicare Advantage (MA) plans or entering into capitation (percent of premium or delegated risk) contracts with existing MA or commercial insurance plans.

Evaluation of ACO performance to date has largely focused on individuals in Medicare ACOs, comparing their healthcare utilization with that of individuals in traditional Medicare (TM) who are not in ACOs.

 These evaluations have found modestly lower spending and unchanged or modestly higher quality at ACOs, with savings growing over time and with effects concentrated in physician- rather than hospital-based entities. Evaluation of a commercial ACO-like contract found a similar result.

In this paper, we broaden the focus by comparing a Medicare ACO not only with a TM comparison group but also with an MA plan within the same delivery system. In addition, we compare utilization and cost in the same organization’s commercial ACO with a commercially insured comparison group. Because the organization shared risk in its MA plan over our entire period of observation, we expected that utilization and spending in the MA plan would initially be below that of the ACO group, in which accepting risk began during the observation period. After the establishment of the Medicare and commercial ACOs, we expected their utilization to decrease more rapidly than that of comparison groups.

Banner Health and Its Insurance Contracts

Our data came from 1 large delivery system, Banner Health, which is headquartered in Phoenix, Arizona (Maricopa County). Banner operates in several sites in the western United States, but we limited our sample to residents of Maricopa County, where the great majority of Banner users live. Not only was Banner one of the original participants in the Medicare Pioneer ACO program that began in 2012, but for several years before 2012, it partnered with Blue Cross Blue Shield of Arizona (BCBS Arizona) to offer an MA plan. Banner’s contractual incentives in the MA plan were complex, but risk was shared approximately equally between BCBS Arizona and Banner. In its Pioneer ACO, Banner chose a Core Option B contract, which meant it accepted 70% 2-sided risk in year 1 and 75% 2-sided risk in years 2 and 3, with both upside and downside risk capped at 10% of total spending.

Also starting in 2012, Banner partnered with Aetna to offer a commercial ACO product to larger self-insured employers (those with >50 employees) that had an existing preferred provider organization (PPO) contract. Similar to Medicare’s ACO attribution rules, employees of the participating firms and their dependents were prospectively attributed to a Banner primary care physician (PCP) if they used a Banner PCP for the plurality of evaluation and management (E&M) services in the prior year. Providers of those not attributed were reimbursed at negotiated fee-for-service rates. Similar to the Medicare program, Banner shared financial risk for the attributed participants for all medical services against a benchmark. Employee benefits were the same for all employees and dependents in the PPO contract. In addition to its Pioneer ACO and Medicare Advantage plan, Banner had other risk-based arrangements, such that about 30% of its revenue was risk-based.

Banner’s performance in the Pioneer ACO program depended on the method of assessment. CMS’ formal evaluation for years 1 and 2 used a difference-in-differences (DID) model with 2 TM control groups: 1 from the local (“near”) market and 1 from a nonlocal (“far”) market—with the latter group to account for potential spillovers in the local market. The question that the CMS evaluation sought to answer was whether the ACO’s spending growth was less than either comparison group’s. On this criterion, Banner did not save money in years 1 and 2.

CMS’ method for rewarding Pioneer ACOs, however, differed from its evaluation method and was based on a benchmark, which was a function of the historical spending of attributed beneficiaries at the ACO trended forward at a national trend rate. Using this method of assessment, Banner performed well (

The data for the ACO and TM comparison groups come from the 100% Medicare files for Maricopa County for 2010 to 2014. All parts A and B spending are included; drug spending was omitted, other than injected or infused drugs covered under Part B. MA data come from BCBS Arizona. The MA covered services analyzed here are the same as the TM services. The MA dollar figures use allowed charges, which are based on contracted unit prices that are confidential. These unit prices are not identical to TM prices, so some of the difference in spending between the MA group and the other 2 groups arises from unit price differences.

An alternative to using the contracted charges is to impute Medicare unit prices based on procedure and site-of-service codes. Although this would hold unit price constant in spending comparisons, it is a laborious and potentially error-prone procedure; we thus rejected it because BCBS Arizona asserted that its prices closely approximated TM prices, consistent with findings nationally and consistent with having a competitive MA product.

 Because of the close approximation between the contracted unit prices and TM prices, the proportion of spending differences between the MA group and the other 2 groups that is attributable to unit price differences should be small.

 Although we compared various measures of utilization and total spending among the MA, ACO, and TM comparison groups, we could not obtain comparable spending values for specific types of services for the MA group because of differing aggregations of services in our data. For example, we could not determine MA emergency department (ED) spending because it was included with inpatient spending if the patient was admitted. Therefore, we instead made 3-way comparisons among MA, the ACO, and a TM comparison group for total medical spending and for various utilization measures but only a 2-way comparison of ACO and TM spending on specific medical services.

We faced 2 other issues in comparing the MA plan’s performance with that of the ACO and TM plans. About a quarter of hospital admissions in the MA plan were covered by a capitated contract, and for those admissions the paid claims files show a zero dollar amount. To obtain comparable spending figures, we imputed the mean payment for the relevant diagnosis-related group among the MA hospital claims with positive dollars. Second, all home health services in the MA plan were covered by a capitated contract and, consequently, show no individual-level spending. Therefore, we imputed spending for all home health claims using the estimated equation for risk-adjusted home health spending at the patient level in the ACO contract. Because home health services account for only 3% to 6% of total spending in the ACO, depending on the year, this approximation should induce little error.

The commercial data come from Aetna for Maricopa County residents for 2010 to 2014. All medical and physician services are included, but drugs were excluded because they are sometimes covered under a separate contract.

Our study was approved by the Harvard Medical School Institutional Review Board.

Although the Pioneer program’s actual attribution of beneficiaries to ACOs was prospective and based on use in the prior 3 years, we used retrospective assignment to assign beneficiaries to providers in each study year. We could not apply the Pioneer program’s prospective assignment method consistently because we lacked data for 3 years prior to the study period; however, as a result, we avoided the problem of regression-to-the-mean effects that prospective assignment potentially introduces when applied to an initial cohort that is fixed.

To avoid assignment to a time-varying panel of physicians, we kept the list of ACO physicians constant over time using National Physician Identifiers (NPIs) to isolate within-provider effects of the program. We used NPIs rather than Tax Identification Numbers (TINs) to identify physicians because Pioneer ACOs were not required to include all providers with the same TIN in the ACO. To define the set of physicians in our main analyses, we used the physicians in the Banner ACO as of 2012, although we also tested the sensitivity of using those in the ACO in 2014 instead. In short, we evaluated the performance of the same group of physicians before and after the ACO contracts began. The TM comparison group comprised TM beneficiaries in Maricopa County who were not attributed to the Banner ACO.

To maintain comparability with the ACO-attributed group, we excluded those beneficiaries in both the TM group and in the MA plan with no use of qualifying E&M services in the calendar year, because that group could not be attributed. This zero-use group constituted 10.2% to 10.5% of the TM group depending on the year; we cannot know what proportion of this group would have been attributed to Banner if they had used E&M services. The MA group had 3.3% to 4.9% of nonusers, depending on the year.

Although we have unique identification numbers for individual MA providers, they are idiosyncratic, not NPIs or TINs. We therefore analyzed the MA data using a constant set of providers, namely those providing services to MA beneficiaries in 2012. We tested the sensitivity of the results to those providing services in 2014 and to those providing services in the calendar year being analyzed (a nonconstant set of providers).

To increase comparability and in the spirit of doubly robust regression, we balanced the ACO, TM, and MA groups using inverse probability weights based on cells defined by age group (65-74, 75-84, and ≥85 years) and gender. Matching only on time-invariant factors, such as age and gender, avoids bias that can arise from matching on time-varying variables, such as pre-ACO period outcome measures.

For all comparisons, we show annual risk-adjusted utilization rates, as well as total annual risk-adjusted spending per person for each year from 2010 to 2014 for the ACO, TM, and MA groups. To risk adjust, we used CMS Hierarchical Condition Categories (HCCs) version 12 and diagnoses from 2009.

We used standard linear regression methods for each group separately with the individual’s HCC risk score on the right-hand side. The predicted rates that we show set the risk score to 1.0. In equation form, we estimated the following equation for each of the 3 groups:

 is an outcome measure (spending or utilization) for individual 

Because the trend in the 3-year post-ACO period is informative, we present our main results in the text using figures that show predicted annual utilization rates and spending from the equation above. The absolute values shown are centered at the mean risk score. In addition to calculating annual results, we carried out a standard DID analysis that compared the 2 years of the pre-ACO period (2010-2011) with the 3 years of the post-ACO period (2012-2014) for the ACO group relative to the TM or MA groups. Regression equations from the DID analysis are available in the 

. Although the trend lines appear reasonably parallel in the pre-ACO

period, we cannot conduct a formal test with only 2 years of data.

Actual attribution was prospective and similar to Medicare, but we used retrospective attribution to analyze the data for the same reasons as with the Medicare sample. We risk adjusted commercial spending using HHS-HCCs, V0314.127.L1,

 and estimated equations for the ACO and comparison groups similar to the Medicare equation shown previously. The HHS-HCC model uses concurrent diagnoses with a separate model for each metal level in the exchange. We used the model for the Gold plan because its actuarial value is close to that of the actual plan and, as in the Medicare case, centered the predicted values at the mean risk score. We did not have firm identifiers, so we could not include firm fixed effects. Thus, there may be some modest bias to the degree that the penetration of Banner differs by firm.

We disaggregated total spending and use into inpatient, E&M, ED, and other outpatient spending. Like the Medicare analysis, we do not have data on drug spending other than drugs covered by the medical benefit. Because our data set included a flag from the plan for attribution, which was based on the past year’s use, we compared stability of attribution in using prospective and retrospective attribution.

After inverse probability weighting, the age—sex groups were well balanced (

 [A-D] shows risk-adjusted utilization rates of various medical services in the MA, ACO, and TM comparison group among those with positive use. Although the percentage of users in MA was greater than in the 2 TM groups, as noted previously, MA hospitalization rates were below those of the ACO and the TM comparison groups in all years (Figure 1 [A]). The differences between the hospitalization rate in the MA group versus the ACO and TM groups steadily narrowed over time, but the MA rate remained about 10% below the rates of the other 2 groups in 2014, the final year of observation.

In the 2-year pre-ACO period, the hospitalization rate in the ACO and TM groups had parallel trends, but after the establishment of the ACO, the rate in the ACO group fell at a more rapid rate (Figure 1 [A]). In 2010, the rate of skilled nursing facility (SNF) days in both the ACO and TM groups was about twice that of the MA plan rate, but the MA rate rose steadily, whereas rates in the other 2 groups fell (Figure 1 [B]). The ACO—TM comparison is difficult to interpret because pre-ACO period trends differ. Neither E&M office visit nor ED visit rates exhibited any notable trend (Figure 1 [C and D]).

Consistent with its lower use of acute and postacute services, the MA group had the lowest total risk-adjusted spending in all years (

). Nevertheless, its spending rose consistently through the 5-year period, whereas spending in the TM and ACO groups did not vary nearly as much. By 2014, spending in the MA group had converged toward that of the other 2 groups; however, it remained 10% below that of the 2 groups, and the difference was larger in the first 2 years of the ACO.

Spending in the Medicare ACO cohort was slightly higher in the pre-ACO period than in the TM comparison group, and in 2012—the first year of the ACO&mdash;it ticked marginally up. It then fell to the same level as the comparison group in 2013 and 2014. 

 show corresponding spending data on specific services for the ACO and TM comparison groups.

DID results that compare averages for the 2 pre-ACO years with the 3 post-ACO years are shown in 

. These results add no new insights to the results just described. Unadjusted rates are shown in 

The risk-adjusted data show that total cost in the ACO group rose at the same rate as in the comparison group in the pre-ACO period. However, in 2012—the first year of the ACO&mdash;costs rose in the commercial ACO relative to the comparison group but thereafter fell at a faster rate than in the comparison group, such that by 2014&mdash;the third year of the ACO&mdash;costs were approximately equal (

 [A]). This result is mainly driven by the experience with inpatient costs and, to a much lesser degree, by outpatient non-E&M costs (Figure 3 [B-E]). Differences in other types of costs are small. Unadjusted commercial rates of utilization and spending on these services are shown in 

. DID results for the commercial group are shown in 

. Like the Medicare DID results, these shed no new light.

We also assessed the proportion of commercially insured individuals assigned to the ACO using retrospective attribution who would also have been assigned using prospective attribution. For Banner, these values were a little more than 40% in 2013 and 2014; for non-Banner physicians, the values were a little more than 60%. The non-Banner values are higher in part because individuals attributed to a given non-Banner physician in 2013 and a physician in another non-Banner group in 2014 both count as being attributed to a non-Banner physician, whereas an individual had to remain within Banner in both years to be attributed to Banner. Both these values are well below the 80% value for Medicare beneficiaries because of the churn among employers in commercial insurance that does not occur among the Medicare population.

Within the Medicare program, we expected the MA group to exhibit the lowest spending over the years we observed because Banner faced financial risk throughout the period, whereas the ACO did not begin until 2012. We also expected the ACO group to exhibit slower growth in use and cost than the TM comparison group after the ACO was established. In fact, the MA group did have the lowest spending of the 3 groups, driven by the lowest use of hospital and postacute services.

Also as expected, hospitalization rates in the Pioneer ACO group declined more rapidly than in the TM comparison group (with similar pre-ACO period trends for the 2 groups). Comparison of SNF rates was difficult because pre-ACO period trends differed. Counter to expectation, ACO spending rose relative to the comparison group in the first year of the ACO but then fell faster in the next 2 years. The subsequent entry of Medicare Shared Savings Program plans in the local market may have biased our comparison against the ACO.

Articles

One delivery system’s healthcare utilization in its Medicare Advantage product was notably less than in its Pioneer accountable care organization or in a traditional Medicare comparison group.

Delivery System Performance as Financial Risk Varies

 The introduction of generic second-generation antipsychotics (SGAs), starting with risperidone in July 2008, could reduce antipsychotic spending and cost-related use barriers. This study examines associations between generic risperidone use and spending and adherence after introduction among Medicare Advantage (MA) beneficiaries.

 The study included MA beneficiaries receiving SGA treatment prior to July 2008. We examined antipsychotic spending using linear models, adherence (proportion of days covered ≥80%) using logistic models, and nonpersistence (time to first gap in antipsychotic use >30 days) in 2009 using Cox proportional hazard models, comparing beneficiaries with versus without generic use, adjusting for individual and plan characteristics.

Between July 2008 and December 2009, 22.8% of beneficiaries had ≥1 fill of generic risperidone: 73% of those previously using branded risperidone and 6.7% of those previously using other SGAs. Beneficiaries in private fee-for-service (FFS) versus health maintenance organization (HMO) plans had lower rates of generic use (hazard ratio [HR], 0.73 [95% CI, 0.56-0.96]); however, cost-sharing levels were not associated with generic use. Compared with beneficiaries who continued using other SGAs, those who switched from other SGAs to generic risperidone in 2008 had lower out-of-pocket spending (—$214; 95% CI, –$314 to –$115), higher adherence (odds ratio, 2.34; 95% CI, 1.62-3.40) and lower rates of nonpersistence (HR, 0.56; 95% CI, 0.46-0.69) in 2009.

Generic use was concentrated among patients previously using branded risperidone. HMO plans appeared to be more effective at encouraging generic use than unmanaged private FFS plans; however, patient financial incentives had limited influence on switching. Additional opportunity remains to encourage greater generic SGA use, reduce spending, and potentially improve treatment adherence and outcomes.

	Risperidone was the first widely used second-generation antipsychotic (SGA) available as a generic. We examined patterns of use in the first 18-months of introduction and found the following: 

		Use of generic risperidone was concentrated among those previously using branded risperidone (ie, bioequivalent substitution); there was limited therapeutic (non-bioequivalent) substitution from other brand SGAs. 

		Rates of generic risperidone take-up were greater in Medicare Advantage HMO plans than in private fee-for-service plans; however, patient financial incentives had limited influence on generic use. 

		Beneficiaries who switched to generics had lower out-of-pocket spending and higher levels of drug adherence compared with beneficiaries who remained on brand SGA therapy.

Antipsychotics are among the top selling classes of drugs in the United States, largely driven by spending on second-generation antipsychotics (SGA). Antipsychotics are one of 6 protected drug classes within the Medicare Part D prescription drug program, meaning that plans are required to include all or substantially all drugs within the class on plan formularies. Medicare spending on antipsychotics was $5.9 billion in 2009, second only to antihyperlipidemics.

Several commonly used SGAs have recently lost patent protection, starting with Risperdal (risperidone) in July 2008, and then Zyprexa (olanzapine), Geodon (ziprasidone) and Seroquel (quetiapine) in 2011 and 2012. The entry of new generic SGAs could result in substantial cost savings for Medicare and other payers. The Congressional Budget Office estimated that the overall Part D savings attributable to generic substitution in 2007 were approximately $33 billion.

However, realizing savings associated with the availability of generic antipsychotics could be more challenging than in other therapeutic classes. Physicians and patients are often hesitant to change psychotropic drug regimens for stable patients due to concerns about lack of tolerability or effectiveness of new regimens.

 Market reports suggest that the entry of generic risperidone did not decrease the market share of other SGAs, suggesting limited generic substitution across molecules (ie, nonbioequivalent substitution).

 Even for bioequivalent substitution of the same molecule, some studies note concerns about potential reductions in adherence due to patients’ anxieties regarding changes in the name, packaging, or appearance of the drug.

 Conversely, greater generic substitution could improve adherence by reducing the out-of-pocket costs associated with treatment

; however, little is known about the effects of switching to generic SGAs on spending or adherence.

We examined changes in SGA treatment choices among Medicare Advantage (MA) beneficiaries in the first 18-months after generic risperidone introduction (July 2008—December 2009) and Part D plan characteristics associated with generic use. We also examined the associations between generic risperidone use and antipsychotic spending and adherence.

This study included noninstitutionalized beneficiaries enrolled in MA prescription drug plans offered by a national carrier, including health maintenance organizations (HMOs), preferred provider organizations (PPOs), and private fee-for-service (FFS) plans. Identifying information on the plans included in the study has been removed, including markets served and exact details on cost sharing and plan structures.

MA-HMO plans have the most closed physician networks, whereas PPOs have more open networks. Until 2011, private FFS plans were not required to have formal provider networks and included the same physicians as in traditional Medicare (96% of eligible physicians, nationally).

 Because plans with tighter network structures have a greater range of tools with which to influence physician behavior, we hypothesized that generic use would be greater in HMO versus private FFS plans. Examples of commonly used tools include providing feedback to physicians on their relative rates of generic use and drug spending, and the use of physician financial incentives to encourage generic drug use.

Antipsychotics are a protected class under Part D; thus, plans have to include all drugs within the class in their formularies, but could vary tier placement and/or cost-sharing amounts for drugs within the class. We hypothesized that greater cost sharing for brand relative to generic drugs would be associated with higher rates of generic risperidone use.

All study plans had a similar formulary for antipsychotics in terms of tier placement, but co-payment levels for these tiers varied across plans. In 2007, most SGAs were preferred brand drugs (tier 2). Following the introduction of generic risperidone in 2008, risperidone was on tier 1 and Risperdal (risperidone) became nonformulary, or a nonpreferred brand, with a try/fail requirement for generic risperidone. Most of the other commonly used SGAs remained preferred brands without utilization management requirements, while some SGAs became nonpreferred brands.

We focused on beneficiaries with at least 1 SGA fill between January and June 2008, and examined SGA use patterns after the introduction of generic risperidone in July 2008. Beneficiaries were censored upon disenrollment or death. Because we focused, in part, on the relationship between cost-sharing levels and drug choices, we excluded beneficiaries receiving Part D low income subsidies (LIS) who had minimal cost sharing. In sensitivity analyses among LIS beneficiaries, findings were consistent.

We linked Part D Event files, medical claims, plan information, and beneficiary characteristics for all subjects. To identify the type of SGA use, we used the Medi-Span Electronic Drug File (version 2)

Generic Risperidone Use and Part D Plan Characteristics

We examined the cumulative proportion of beneficiaries with any generic risperidone use between July 2008 and December 2009. We used Cox proportional hazard models to examine the plan characteristics associated with time to first generic risperidone use. We stratified both analyses by whether beneficiaries were previously using brand risperidone or other SGAs before generic introduction.

We classified beneficiaries’ plan types as HMO, PPO or private FFS, and beneficiaries’ benefit designs as enhanced or basic Part D—enhanced plans included coverage for generics during the standard coverage gap (eg, after $2510 in total drug spending in 2008) and basic plans had no gap coverage. We also examined the relative cost of brand versus generic drugs during the initial coverage period prior to the gap, and focused on the preferred brand (tier 2) versus generic (tier 1) co-payment differential because the most commonly used SGAs were tier 2 drugs throughout the study period.

These models adjusted for beneficiary age, gender, race/ethnicity, Part D comorbidity risk score (RxHCC), mental health diagnoses, reason for Medicare entitlement (disabled vs aged), and prior drug spending. As a measure of beneficiaries’ stability on their SGA regimens, we adjusted for the length of time beneficiaries were receiving the last SGA used prior to July 2008. Among non-risperidone users, we also adjusted for the generic name of the last SGA used prior to July 2008.

Generic Substitution, Spending, and Adherence

We examined the association between SGA drug use patterns and antipsychotic spending (total and out of pocket) and treatment adherence and persistence. Our primary analyses examine variations in these outcomes in 2009 based on 2008 switching patterns. We focused on 4 SGA use patterns, as defined by the last SGA used prior to July 2008 and SGA use between July and December 2008: 1) bioequivalent generic substitution (brand risperidone to generic risperidone), 2) nonbioequivalent generic substitution (non-risperidone SGA to generic risperidone), 3) nonbioequivalent brand switching (non-risperidone SGA to other SGA brand), and 4) no change (same non-risperidone SGA brand throughout 2008). Beneficiaries with no SGA fills between July and December 2008 were excluded from these analyses (19.9% and 23% of brand risperidone and non-risperidone users, respectively). Because Part D Event files do not capture data on uncovered drugs, we reliably cannot assess brand risperidone use after July 2008.

Antipsychotic Spending, Adherence, and Nonpersistence

We examined total and out-of-pocket antipsychotic drug spending in 2009. We examined adherence monthly (January 2008-December 2009) and annually (2009) using the proportion of days covered (PDC). The PDC was calculated as the sum of available days’ supply of all antipsychotics divided by the total days in each time period. We defined nonpersistence as having a gap in antipsychotic supply >30 days.

To examine the associations between patterns of SGA use (eg, generic substitution) in 2008 and annual 2009 drug spending and adherence (ie, PDC >80%), we use linear and logistic regression models, respectively, adjusting for plan characteristics and covariates described above, as well as antipsychotic PDC levels in 2008. To examine the association between patterns of SGA use and nonpersistence in 2009, we used Cox proportional hazard models, adjusting for the same covariates.

To examine changes in monthly adherence levels by SGA use patterns, we used linear fixed effects (within-person) regression models to estimate changes in monthly PDC relative to the month prior to generic introduction (June 2008). These models are robust to potential measured and unmeasured time-stable confounders; we adjusted for time-varying covariates, including calendar month, beneficiaries’ Part D risk scores, and tier 2 versus tier 1 co-payment differentials.

Among all subjects, 24.3% used brand risperidone prior to generic introduction in July 2008. Overall, 41.3% had schizophrenia or bipolar disorder, 46.1% were disabled, and about 35.4% and 57.1% were in HMO and private FFS plans, respectively (

). Mean cost-sharing levels were approximately $3 (tier 1) and $25 (tier 2).

 presents the cumulative percent of beneficiaries who used generic risperidone between July 2008 and December 2009. Among those using brand risperidone prior to July 2008, 67.7% used generic risperidone by December 2008 and 73% by December 2009. Among those using other brand SGAs prior to July 2008, 6.7% used generic risperidone by December 2009.

In multivariate analyses, greater cost-sharing differentials for generic versus brand drugs and enhanced Part D coverage were not significantly associated with generic use rates (

). In contrast, enrollment in private FFS versus HMO plans was associated with lower rates of nonbioequivalent generic substitution (hazard ratio [HR], 0.73; 95% CI, 0.56-0.96); there were no significant differences in bioequivalent generic substitution rates.

Compared with beneficiaries who continued using the same non-risperidone SGA before and after generic introduction, those who switched to risperidone in 2008 (nonbioequivalent generic substitution) had similar 2009 total spending, but lower out-of-pocket antipsychotic spending (difference = —$214; 95% CI, –$314 to –$115). In contrast, those who switched to a different brand SGA in 2008 (nonbioequivalent brand switching) had higher total and out-of-pocket spending in 2009. Beneficiaries who switched from brand to generic risperidone (bioequivalent substitution) had lower total antipsychotic spending (difference = –$943; 95% CI, –$1,042 to –$843) and out-of-pocket spending (difference = –$445; 95% CI, –$485 to –$404).

Antipsychotic Adherence and Nonpersistence

Beneficiaries who switched antipsychotics, either to generic risperidone or other brand SGAs, had higher odds of adherence in 2009 versus those who remained on the same brand SGA: odds ratio (OR), 1.72 (95% CI, 1.48-2.00) for bioequivalent generic substitution; OR, 2.34 (95% CI, 1.62-3.40) for nonbioequivalent generic substitution; OR, 1.43 (95% CI, 1.04-1.96) for nonbioequivalent brand switching (

). These beneficiaries also had lower rates of nonpersistence in 2009, defined as the time to first gap in SGA use >30 days, compared with those who continued using the same brand SGA in 2008.

 presents within-person changes in the PDC by antipsychotics in each month in 2008 and 2009 relative to June 2008—the month prior to generic introduction. Declines in use toward the end of each calendar year are consistent with the pattern of an increasing number of beneficiaries reaching the coverage gap and losing drug coverage. Those with nonbioequivalent generic substitution had the smallest relative reductions in SGA use by the end of 2009 compared with other beneficiaries (—2.6 percentage points in PDC; 95% CI, –8.3 to 3.0), including those who switched to a different brand SGA (–15.7 percentage points; 95% CI, –20.4 to –10.9); those who stayed on the same brand SGA throughout 2008 had the largest reductions in use by the end of 2009 (–27.5 percentage points; 95% CI, –28.9 to –26.1).

This study focused on the effects of the introduction of the first widely used SGA, risperidone, in Medicare Part D. We examined the levels of generic risperidone use in the first 18 months of introduction among beneficiaries receiving ongoing SGA drug therapy. Fewer than 1 in 4 beneficiaries used generic risperidone by the end of the study period, and the vast majority of use was among those previously using brand risperidone. Generic risperidone use was associated with lower total and out-of-pocket antipsychotic spending, as well as higher levels of adherence and persistence to SGA therapy.

This study examines the plan characteristics associated with generic risperidone use and spending and adherence outcomes associated with use among Medicare Advantage Part D beneficiaries.

The Introduction of Generic Risperidone in Medicare Part D

Prior analyses of Medicare health plans have exam-ined either utilization of services or quality of care, but not both jointly. Our objective was to compare utilization and quality for Medicare Advantage (MA) enrollees with diabetes or cardiovascular disease to that for similarly defined traditional Medicare (TM) beneficiaries.

Cross-sectional matched observational study using data for 2007.

 We obtained individual-level Healthcare Effectiveness Data and Information Set (HEDIS) relative resource use (RRU) and quality data for patients enrolled in MA, and then developed comparable claims-based measures for matched samples of TM beneficiaries. Main outcome measures: utilization levels for inpatient care, evaluation and management services, and surgery; number of emergency department (ED) and inpatient visits; and quality of ambulatory care measures.

We studied approximately 680,000 MA health maintenance organization (HMO) enrollees with diabetes and 270,000 HMO enrollees with cardiovascular conditions. For both conditions and almost all major strata, the RRU was lower for those enrolled in MA than for those in TM. Spending for those with diabetes was $5223 for MA HMO enrollees compared with $6413 for those in TM (cost ratio, 0.81; 

 <.001). ED utilization rates were consistently lower in MA than TM (567 vs 719 visits/1000 enrollees; rate ratio, 0.79; 

 <.001). Health plans that are more established, nonprofit, and/or larger generally had lower resource use and better relative quality than did smaller, newer, for-profit HMOs or preferred provider organizations.

RRU for those with diabetes or cardiovascular disease is lower in MA, while quality of care is higher. Better MA plans may add value to the care of these major chronic medical conditions.

We compared price-standardized utilization and quality of care for Medicare Advan-tage (MA) enrollees with diabetes or cardiovascular disease with matched beneficiaries from traditional Medicare in geographic areas. We found that:

		For both conditions, relative resource use was lower for those enrolled in MA than for those in traditional Medicare.

		Quality of care for diabetes and cardiovascular disease measures was higher in MA for the 4 measures examined, although plans varied greatly in their performance.

		Health plans that are more established, nonprofit, and/or larger generally had lower relative resource use and better relative quality than did HMOs or PPOs that were smaller, newer, and/or for-profit.

Under managed competition, health plans are expected to compete to provide health services of high quality while also controlling the overall costs of care. A key element of a health plan’s ability to contain cost is its ability to control utilization. Although some transparency initiatives are now publicizing the costs that health plans pay for specific services,

such approaches have not assessed differences in the full range of medical services used by patients with particular health conditions. Moreover, many have concerns that health plans that are more successful at controlling utilization might also deliver care of lower quality, though existing data from commercial health plans show no clear relationship between spending and quality.

Medicare’s managed care program, Medicare Advantage (MA), currently provides care to 15.7 million Americans, representing 30% of beneficiaries.

 Relative to traditional Medicare (TM), MA plans may be able to treat patients with particular diagnoses with greater efficiency while attaining equal or superior quality through their flexibility in enrollee benefits, network contracting, and coordination of care, but whether they do so is not known.

 Since 1997, health plans participating in Medicare have been required to report annually on measures of the quality of preventive care and of the management of chronic diseases such as diabetes.

 In 2006 and 2007, CMS also required MA plans to report a set of relative resource use (RRU) measures focused on the care of patients with diabetes (2006 and 2007) and cardiovascular disease (2007 only).

 Such measures of price-adjusted utilization allow for direct comparisons of utilization among health plans as well as between MA and TM.

In this study, we evaluated both the utilization of services and the quality of ambulatory care provided by MA health plans by comparing annual standardized spending and quality of care for 2 specific medical conditions for MA health plan enrollees with corresponding measures among TM beneficiaries.

From CMS and the National Committee for Quality As-surance (NCQA), we obtained health-plan-level Healthcare Effectiveness Data and Information Set (HEDIS) measures of RRU and individual-level HEDIS data on quality of ambulatory care for patients with diabetes and cardiovascular disease enrolled in MA health plans. We focused on care delivered in 2007, the most recent year in which MA plans were required to report these measures before reporting became voluntary and much less consistent.

 We constructed similar measures within TM from Medicare claims data for a random 20% sample of beneficiaries. Given the well-known geographic variation in Medicare services, we created matched samples for each health plan based on its geographic region and enrollee demographic characteristics.

. The RRU measures use standardized pricing applied uniformly to services delivered within MA and TM, thereby accounting for variation in prices due to geography and negotiated prices in MA.

 Spending on all services for all patients with qualifying diagnoses is calculated over the entire year. The RRUs are risk-stratified by age (categorized into 5-year intervals for patients aged 65-85 years), sex, type of diabetes (type 1 or type 2) or cardiovascular disease (acute myocardial infarction, congestive heart failure, angina, or coronary artery disease) and the presence or absence of 1 or more major comorbidities (ie, cardiovascular conditions, chronic obstructive pulmonary disease [COPD], depression, hypertension, or chronic kidney disease for diabetes, and asthma, COPD, diabetes, and hypertension for cardiovascular disease). We then aggregated these strata using nationally determined weights to create a standardized measure of overall resource use. Spending categories include inpatient care, surgery and other procedures, and evaluation and management services, and we also sum spending across these categories. Rates of emergency department (ED) visits and inpatient admissions are also reported. Beneficiaries with concomitant specified dominant medical conditions including active cancer, end-stage renal disease, human immunodeficiency virus/AIDS, and organ transplants are excluded.

HEDIS quality data are collected from administrative billing or encounter records, or by using a hybrid approach in which medical records are also reviewed for services that may not be recorded in administrative data.

CMS has audited HEDIS quality measures reported by Medicare health maintenance organizations (HMOs) and found them to be highly accurate.

 In order to compare MA and TM, we focused on measures that can be constructed from Medicare claims for TM enrollees, including low-density lipoprotein (LDL) cholesterol testing in the current year for enrollees aged 65 to 75 years with cardiovascular disease, and 3 services for enrollees aged 65 to 75 years with diabetes: glycated hemoglobin (A1C) testing in the current year; LDL cholesterol testing in the current year; and a diabetic retinal exam in the current or prior year.

We defined health plans as CMS contracts, meaning a health plan unit operating in a single state, or in a few cases, up to 3 adjoining states, and we included both HMOs and preferred provider organizations (PPOs). We focused on beneficiaries 65 years or older who were enrolled for the entire calendar year. We excluded beneficiaries in legacy health plans that were reimbursed on a cost basis rather than by capitation. In addition, we excluded beneficiaries in private fee-for-service plans because these plans are not required to report HEDIS data to CMS, as well as those enrolled in special needs plans because such plans serve nonrepresentative beneficiaries. Finally, we excluded HMOs with fewer than 500 enrollees (accounting for <0.2% of enrollees).

To create a comparison sample for each health plan for both the RRU and the diabetes quality analyses, we used the TM enrollment file and Part A and Part B claims files for a random 20% of beneficiaries to identify all persons who were continuously enrolled in Medicare Part A and Part B for the entire reporting year and were 65 years or older as of January 1, 2007.

We excluded residents of long-stay nursing homes—identified using a validated algorithm&mdash;because these beneficiaries rarely enroll in MA; however, we had no similar method to exclude these beneficiaries from the MA data.

We applied NCQA specifications to identify the eligible populations for the measures, assigned a standardized priceobtained from NCQA to each delivered service identified in the claims, and aggregated spending across groups using the exact specifications from NCQA.

Medicare Beneficiary Summary files provided demographic data (age, sex, race/ethnicity, zip code, county, and state of residence), vital status, and health plan and Medicare enrollment information for each beneficiary.

We categorized health plans as large (>25,000 enrollees) versus small, and identified health plans new to MA since 2006. CMS provided the tax status.

We compared RRUs and quality of care in each MA plan with a TM sample matched by geographic distribution (RRUs and quality measures) and demographic characteristics (quality measures only) and then aggregated these results to obtain national estimates. For the RRU measures, the control TM population was weighted to match the exact distribution of health plan enrollees across all zip codes in which it operated. Matching on geography controlled for variation in practice patterns within Medicare across regions.

By matching at the zip code level where possible, we also controlled for unmeasured socioeconomic characteristics associated with residence at this level of geography.

To provide nationally representative estimates of over-all HMO and PPO performance relative to traditional Medicare, we averaged HEDIS RRUs and quality mea-sures, respectively, over MA plans and matched cohorts of traditional Medicare enrollees, weighted by MA enrollment. To assess variation in performance of health plans based on particular characteristics relative to matched traditional Medicare in local areas, we used hierarchical regression models with correlated bivariate random effects for each health plan and its matched traditional Medicare sample and fixed effects for the health plan characteristics noted above, including an indicator vari-able for PPOs, with separate coefficients for the MA and traditional Medicare samples. Because more than 80% of PPOs were small, new, and for-profit, the PPO measure pertains to just this category of PPOs (the few other PPOs were not included in these models).

Finally, for each HMO plan, we created a composite of the 3 diabetes quality measures by taking the mean across the measures, and similarly created an aggregate measure of spending by summing over the 3 spending categories: inpatient care, surgery and other procedures, and evaluation and management services. We constructed similar summary measures of quality and spending for the matched TM cohort for each health plan. We then plotted mean quality relative to the local TM comparison group against mean spending relative to the same comparison group to provide a visual representation of the marginal contribution versus TM for each health plan. We also created a similar plot for the cardiovascular cohort using the single quality measure available for this group.

Analyses were conducted with SAS version 9.2 (SAS Institute, Cary, North Carolina). Two-tailed P values are reported for statistical tests. Our study protocol was approved by the Human Studies Committee of Harvard Medical School and the CMS Privacy Board.

Characteristics of the Medicare HMOs and PPOs in our study appear in 

. We studied data from 190 HMOs and 67 PPOs that included 4,207,433, and 318,293 enrollees, respectively, in 2007. About two-thirds of enrollees were in for-profit health plans. Although 75% of the health plans were small (<25,000 members), these HMOs represented only about 25% of enrollment. Most HMOs had participated in Medicare prior to 2006, but only 11 PPOs had done so.

The MA HMOs in our study enrolled approximately 680,000 beneficiaries with diabetes (and the PPOs approximately 50,000) and approximately 268,000 enrollees with 1 of the 4 cardiovascular conditions (and approximately 12,000 PPO enrollees; sample sizes vary slightly by measure). For the diabetes cohort, just under half were male (48.8%) and more than 80% were white. The largest proportion was from the south and more than 85% had at least 1 comorbid condition (

). After weighting the TM sample to match the MA distribution, the 2 samples had identical demographic characteristics.

Utilization in MA and TM by Types of Diabetes and Cardiovascular Disease

With 1 exception, total standardized spending, as well as each of the 3 categories of spending, was markedly lower for MA HMO enrollees than for matched TM enrollees (

). For instance, total spending was 19% less for diabetics enrolled in MA than for those enrolled in TM ($5223 vs $6413; 

 <.001). The single exception was for diabetics without comorbidity (eg, inpatient spending of $1309 for MA vs $925 for TM; 

 <.001). Visits to the ED were consistently lower in MA, as were rates of hospital inpatient discharges. Similar results were observed for those enrolled in PPOs (

Spending and Utilization by Type of Health Plan 

Patterns of comparative price-standardized utilization by type of plan are presented in

 shows the detailed results). For large, established, nonprofit HMOs, all 3 categories of spending were lower than for the matched TM sample, with differences ranging from 16% (evaluation and man-agement services for diabetics) to 70% (surgery rates for those with cardiovascular disease). In contrast, results were mixed for new, for-profit, small HMOs, where aggregate spending was higher in some categories in MA (eg, inpatient spending for cardiovascular disease, 16% higher), and lower for others (eg, surgery and procedures 43% lower for diabetics in MA). Results for PPO health plans were similar to those for new, for-profit, small HMOs.

Spending and Quality of Care by Health Plan Characteristics 

The mean plan-weighted rates of A1C testing, LDL cholesterol testing, and diabetic retinal exams were 89.7%, 87.6%, and 65.2%, respectively. 

 presents scatter plots of HMO health plan spending and the composite measure of quality for the diabetes cohort by health plan age (established prior to 2006 or not), size (>25,000 members), and tax status. Several findings are apparent: first, while members of most health plans experienced higher quality than the matched TM population in their area and therefore are plotted above the solid horizontal axis, the MA-TM difference varied substantially and for some plans was negative (MA worse than TM). Similarly, MA spending was lower than that in TM for most health plans, but the magnitude of the difference varied and was sometimes reversed (plotted to right of solid vertical axis). Second, there was little association between the spending and quality effects (Pearson correlation coefficient, 0.16), as manifested by the nearly equal distribution of plans across the 4 quadrants formed by median splits (dashed lines) on the 2 variables. Finally, although HMO health plans of each type are present in all 4 quadrants of each plot, the upper left hand quadrant (higher quality/lower spending) contains the most established, large, nonprofit HMO plans and the lower right hand quadrant (lower quality/higher spending) contains a higher proportion of new, small, for-profit HMO plans. A similar pattern was seen for the cardiovascular measures (

This study provides the first compre-hensive comparison between MA and TM of price-standardized utilization and quality of care for those with diabetes and cardiovascular disease, 2 prevalent and costly chronic medical conditions. We found several notable results: first, for both cohorts, RRU—which is a measure of total utilization using a standardized set of prices&mdash;was lower in MA health plans than in TM in each of the main categories of spending examined. Moreover, MA plans achieved higher performance on measures of ambulatory quality.

 Second, marked heterogeneity was evident among MA plans. Most older, larger, nonprofit health plans were able to achieve substantial reductions in service utilization while delivering care of high quality, whereas many newer, smaller, for-profit plans had similar or greater utilization when compared with TM. Finally, utilization among PPOs—an alternative arrangement to HMOs that is generally less managed and coordinated&mdash;showed patterns that were similar to new, smaller HMOs and to TM.

Our findings have important implications for policy. With the 2010 passage of the Affordable Care Act (ACA), the attention of policy makers has now shifted to controlling the seemingly inexorable growth in healthcare costs.

Delivering high-value care requires decreasing utilization of services of low value while simultaneously maintaining or increasing the delivery of services of high value. We show that substantial numbers of larger, nonprofit HMO MA plans appear to be delivering care of high quality, while doing so with substantially fewer resources. Whether this is more due to their actions, as payers perhaps linked to their size and ability to influence provider behavior, or to use of more limited and selected networks of providers, or both, is unknown. Although there has been much focus on payment reform in TM—such as the launching of accountable care organizations (ACOs)&mdash;30% of Medicare enrollees are in MA health plans, a far larger proportion than are currently in ACOs.

MA plans are currently paid more than TM on average.

Because of Medicare regulations and competitionamong plans, many of the savings from these extra payments and the reduced utilization we documented in this study are passed through to beneficiaries in the form of lower premiums, less cost sharing, and benefits for noncovered services.

 However, providers may also profit from these excess payments, as they may be able to negotiate higher prices from MA plans. To finance its expansion of health insurance, the ACA reduced reimbursement for MA plans; how these reductions will impact plan and beneficiary participation and the future growth of the MA program remains an open question.

Compared with traditional Medicare, relative resource use for those with diabetes or cardiovascular disease is lower in Medicare Advantage, while quality of care is higher.

A Comparison of Relative Resource Use and Quality in Medicare Advantage Health Plans Versus Traditional Medicare

Medicare Advantage prescription drug plans (MA-PDs) and standalone prescription drug plans (PDPs) face different incentives for plan design resulting from the scope of covered benefits (only outpatient drugs for PDPs versus all drug and nondrug services for Medicare Advantage [MA]/MA-PDs). The objective is to begin to explore how MA-PDs and PDPs may be responding to their different incentives related to benefit design.

We compared 2012 PDP and MA-PD average formulary coverage, prior authorization (PA) or step therapy use, and copayment requirements for drugs in 6 classes used commonly among Medicare beneficiaries.

We primarily used 2012 Prescription Drug Plan Formulary and Pharmacy Network Files and MA enrollment data. 2011 Truven Health MarketScan claims were used to estimate drug prices and to compute drug market share. Average coverage and PA/step rates, and average copayment requirements, were weighted by plan enrollment and drug market share.

MA-PDs are generally more likely to cover and less likely to require PA/step for brand name drugs with generic alternatives than PDPs, and MA-PDs often have lower copayment requirements for these drugs. For brands without generics, we generally found no differences in average rates of coverage or PA/step, but MA-PDs were more likely to cover all brands without generics in a class.

We found modest, confirmatory evidence suggesting that PDPs and MA-PDs respond to different incentives for plan design. Future research is needed to understand the factors that influence Medicare drug plan design decisions.

We found modest, confirmatory evidence suggesting that stand-alone prescription drug plans (PDPs) and Medicare Advantage prescription drug plans (MA-PDs) respond to different incentives for plan design. MA-PDs are generally more likely to cover and less likely to require prior authorization or step therapy for brand name drugs with generic alternatives than PDPs, and MA-PDs often have lower copayment requirements for these drugs.

This paper provides suggestive evidence that a stand-alone Part D plan has drawbacks resulting from lack of integration with the medical benefit.

This information may inform efforts to regulate Medicare prescription drug plan generosity.

There are 2 types of Medicare Part D prescription drug plans. In 2010, approximately 17.7 million Medicare beneficiaries enrolled in a stand-alone prescription drug plan (PDP), continuing to receive Parts A and B benefits through the traditional Medicare program.

 Approximately 9.9 million beneficiaries enrolled in a Medicare Advantage-Prescription Drug plan (MA-PD), receiving Parts A and B benefits through the qualified Medicare Advantage (MA) plan that sponsored their MA-PD. Due to differences in the scope of benefits covered under PDPs versus MA/MA-PDs, these 2 types of plans face different incentives for drug plan design. Because of the integration of drug and nondrug coverage in MA plans and MA-PDs, MA-PDs have an incentive to provide relatively generous coverage for medications for which the incremental costs of doing so are offset by decreased medical spending over the period of expected enrollment.

 In contrast, PDPs, which are responsible only for drug expenditures, do not reap any efficiency gains with respect to Parts A and B services that may result from more generous drug coverage, and thus have lower incentives relative to MA-PDs to encourage use of drugs that may result in offsets for nondrug expenditures.

Both MA-PDs and PDPs have a choice of offering a plan with the defined standard benefit, a benefit that is actuarially equivalent to the defined standard benefit and consistent with CMS regulations governing coverage, or enhanced benefits. The defined standard benefit for 2013 has a deductible of $325. For spending between $326 and $2970, the plan pays 75% and the enrollee pays 25%. For expenditures between $2970 and $6733.75—known as the coverage gap&mdash;enrollees are responsible for a larger share, with enrollees paying 79% of generic drug expenditures and 47.5% of brand drug expenditures in the gap. For expenditures exceeding the $6733.75 catastrophic coverage limit, Medicare pays 80%, the plan pays 15%, and the enrollee pays 5%.

Previous studies have documented average aggregate (ie, aggregated across all drugs) differences in use of cost sharing and utilization management for PDPs relative to MA-PDs, with MAPDs typically offering more generous coverage.

 For example, in 2012, PDP enrollees faced some form of utilization management (ie, prior authorization [PA] requirements, step therapy requirements, or quantity limits) for 36% of drugs listed on the plan’s formulary while MA-PD enrollees faced utilization management for 31% of formulary drugs.

Also, MA-PDs had a lower average premium, were more likely to have a zero deductible, and were more likely to provide additional benefits in the coverage gap than PDPs.

Little is known about how MA-PDs and PDPs make decisions about coverage and utilization management requirements for specific medications, and data on aggregate differences in coverage levels for the 2 types of plans like the data described above do not shed much light on this complex decision-making process. To begin to explore how MA-PDs and PDPs may be responding to their different incentives around drug benefit design, we compared 2012 PDP and MA-PD average coverage, utilization management, and copayment requirements for specific drugs in several classes of medications used commonly among Medicare beneficiaries.

To identify MA-PDs and PDPs, we used the January 2012 CMS Prescription Drug Plan Formulary and Pharmacy Network Files (hereafter, “CMS formulary files”). These files contain information on plan characteristics as well as National Drug Code (NDC)-level information on formulary coverage, PA and step therapy requirements, and copayment requirements for all MA-PDs and PDPs operating in January 2012, with the exception of employersponsored MA-PDs. We excluded 125 private fee-for-service MA plans, as well as 506 special needs plans, 47 cost plans (ie, plans that Medicare contracts with a cost-reimbursement basis under Section 1876 of the Social Security Act), 61 plans serving US territories, 28 plans with no enrollment as of January 2012, and 1 plan for which formulary information was not available. After these exclusions, there were 1035 PDPs and 1512 MA-PDs in our sample.

Next, we linked the CMS formulary files for these plans with January 2012 plan enrollment information from the MA/Part D Contract and Enrollment Data available on CMS’ website.

To characterize the generosity of drug coverage in MA-PDs versus PDPs, we focused on 6 medication classes used frequently by Medicare beneficiaries for chronic medical conditions and for which at least one generic alternative was available in the class. Because drug plans generally cover all or almost all generic medications generously due to their relatively low cost, we focused analyses on brand name medications. Two of the 6 classes studied (antidepressants and antipsychotics) are Part D “protected” classes, meaning that plans must cover at least 1 formulation of every drug. The other 4 (Alzheimer’s disease medications, angiotensin receptor blockers [ARBs], bisphosphonates, and statins) are nonprotected classes, meaning that plans must cover at least two drugs in the class. Plans are permitted to use utilization management requirements like PA or step therapy to influence medication use in all classes, both protected and non-protected.

We examined 3 primary measures of coverage generosity: 1) formulary coverage; 2) use of PA and/or step therapy, conditional on formulary coverage; and 3) average copayment required for a 30-day prescription. To assess average formulary coverage, we first calculated coverage percentages for each formulation of each drug, collapsing over different NDCs for the same formulation. We expected plan coverage decisions for brand drugs to differ based on whether a generic equivalent of the brand drug was available. We also thought decisions about drugs might differ for brand combination medications (ie, medications that combine two or more molecular entities). Thus, we divided drugs into 3 categories for each class: brand drugs with a generic equivalent available, brand drugs with no generic equivalent available, and combination drugs (of the 19 combination products studied, all but 1 were brands without generic equivalents).

Within each drug class and category, we weighted the coverage percentages by plan enrollment and drug market share within the class, and then calculated the mean coverage percentage for each class and category for PDPs versus MA-PDs. We used 2011 Truven Health MarketScan data, which includes prescription drug claims for over 50 million individuals with employer-sponsored private insurance, to calculate drug market share. By weighting on the basis of drug market share, we “down-weight” observations for rarely-used drugs.

To test for a statistically-significant difference in average coverage between PDPs and MA-PDs, we calculated 

tests of the difference in the mean number of drugs covered for drug categories with more than 1 drug and 

 values from t tests of the difference in proportion covered for categories with a single drug, as well as an F-test of the null hypothesis that all effects are zero. We followed the same process for use of PA/ step therapy.

To calculate the average co-payment requirements for a 30-day prescription, we used plan-level information on the copayment required for each specific NDC from the CMS formulary files. For plans that required coinsurance rather than copayments for a given medication, we instead applied the plan’s coinsurance rate for the initial coverage period (ie, covering expenditures after the deductible is met and before the coverage gap is entered) for a specific NDC from the CMS formulary files to NDC level data on average total price (ie, average of plan plus patient payments per 30-day prescription, not accounting for any rebates negotiated between the plan and the pharmaceutical manufacturer) from the 2011 Truven Health MarketScan data. As for the coverage measures described above, we weighted observations by plan enrollment and drug market share. We calculated 

 values from t tests of the difference in weighted mean copayment requirements for each drug class and category, as well as an F-test of the null hypothesis that all effects are zero.

Finally, we calculated the percentage of MA-PD plans and the percentage of PDPs that covered all brand drugs with no generic equivalents in each class, weighting by plan enrollment but not drug market share, to ensure that we captured differences in covering all formulations, including those that are rarely used.

Brand name drugs with generic equivalents

. Generics accounted for the vast majority of prescriptions filled for drugs with generic equivalents. The average generic market share for these medications ranged from 90.8% for Alzheimer’s medications to 99.7% for statins. On average, MA-PDs were significantly more likely than PDPs to cover brand drugs with generic equivalents in all 4 non-protected classes (Alzheimer’s disease drugs, ARBs, bisphosphonates, and statins) (Table 1).

On average, PDPs covered only 2.5% to 4.6% of brands with generic equivalents for statins, bisphosphonates, and ARBs, and covered 53.5% of brand Alzheimer’s drugs with generic equivalents. MA-PDs covered significantly more of these drugs on average in all classes (12.5% more for Alzheimer’s drugs to 25.6% more for bisphosphonates, all 

 <.0001). For the 2 protected classes, coverage of antipsychotic brands with generics tended to be more generous for MA-PDs vs PDPs, although this did not reach statistical significance (

= .052). There were no statistically significant differences between PDPs and MA-PDs for antidepressants.

Conditional on formulary coverage, MA-PDs were significantly less likely than PDPs to require PA/step therapy for drugs in 3 of 6 classes (Alzheimer’s medications, bisphosphonates, and antidepressants), while MA-PDs were more likely to require PA/step therapy for ARBs than PDPs (Table 1, right columns). There were no statistically significant differences in formulary coverage for MA-PDs vs PDPs for the other 2 classes.

Average copayment requirements per 30-day prescription were lower, on average, for MA-PDs relative to PDPs for 4 classes (ARBs, bisphosphonates, statins, and antipsychotics). Average copayment requirements were higher for MA-PDs relative to PDPs for Alzheimer’s medications, and there was no statistically significant difference between the 2 plan types for antidepressants (

Medicare-Advantage Prescription drug plans (MA-PDs) and standalone PDPs appear to respond to different incentives for plan design.

Joseph P. Newhouse, PhD

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