Alisa B. Busch, MD, MS

 To determine the impact of high-deductible health plans (HDHPs) on health care use among individuals with bipolar disorder.

 Interrupted time series with propensity score—matched control group design, using

a national health insurer’s claims data set with medical, pharmacy, and enrollment data.

 The intervention group was composed of 2862 members with bipolar disorder who were enrolled for 1 year in a low-deductible (≤$500) plan and then 1 year in an HDHP (≥$1000) after an employer-mandated switch. HDHP members were propensity score matched 1:3 to contemporaneous controls in low-deductible plans. The main outcomes included out-of-pocket spending per health care service, mental health—related outpatient visits (subclassified as visits to nonpsychiatrist mental health providers and to psychiatrists), emergency department (ED) visits, and hospitalizations.

 Mean pre— to post–index date out-of-pocket spending per visit on all mental health office visits, nonpsychiatrist mental health provider visits, and psychiatrist visits increased by 21.9% (95% CI, 15.1%-28.6%), 33.8% (95% CI, 2.0%-65.5%), and 17.8% (95% CI, 12.2%-23.4%), respectively, among HDHP vs control members. The HDHP group experienced a –4.6% (95% CI, –11.7% to 2.5%) pre- to post change in mental health outpatient visits relative to controls, a –10.9% (95% CI, –20.6% to –1.3%) reduction in nonpsychiatrist mental health provider visits, and unchanged psychiatrist visits. ED visits and hospitalizations were also unchanged.

After a mandated switch to HDHPs, members with bipolar disorder experienced an 11% decline in visits to nonpsychiatrist mental health providers but unchanged psychiatrist visits, ED visits, and hospitalizations. HDHPs do not appear to have a “blunt instrument” effect on health care use in bipolar disorder; rather, patients might make trade-offs to preserve important care.

High-deductible health plan (HDHP) members with bipolar disorder experienced a moderate decrease in nonpsychiatrist mental health outpatient visits, but rates of psychiatrist visits, medication use, emergency department visits, and hospitalizations did not change.

HDHP members with bipolar disorder might elect to pay more out of pocket to maintain psychiatrist care and associated medication use but not nonpsychiatrist mental health provider care.

HDHPs do not appear to have a “blunt instrument” effect on health care use in bipolar disorder; rather, patients might make trade-offs to preserve important care.

Policy makers, employers, and health plans could use these findings to construct highly efficient value-based or tailored health insurance designs that optimize health care use and spending; for example, plans might reduce out-of-pocket costs for nonpsychiatrist mental health provider visits to enhance use.

Bipolar disorder is a serious mental illness characterized by acute episodes of mania, hypomania, and depression. Bipolar spectrum disorders include subtypes bipolar I, bipolar II, and cyclothymia. These often have an early age of onset, high risk of suicide, and high rates of co-occurring psychiatric conditions such as substance use disorders.

 In the United States, the 12-month prevalence of bipolar disorder is 2.8% and the lifetime prevalence is 4.4%.

 Although persons with bipolar disorder can have asymptomatic periods, episodes of clinical instability and impairment are common.

 In addition to managing acute mood episodes, guideline-recommended care for bipolar disorder requires a chronic care model of evidence-based medications, ongoing follow-up, and often evidence-based psychotherapies during nonmanic phases of care, such as individual and group psychoeducation, cognitive behavior therapy, interpersonal social rhythm therapy, and family-focused therapy, to reduce depressive symptoms or prevent future manic or depressive episodes.

In an effort to control rising health care costs, payers and employers are increasingly adopting high-deductible health plans (HDHPs)

 that require high out-of-pocket payments.

 HDHP advocates believe that providing patients with information about the quality of medical services while exposing them to greater costs will create “activated healthcare consumers”

 who will seek higher-value health care, adopt healthy behaviors, and reduce future costs.

HDHPs have proliferated over the past decade. In 2018, 58% of covered workers had deductibles of $1000 or more and 26% had deductibles of $2000 or more.

 Based on findings going back to the RAND Health Insurance Experiment (HIE)

 of the 1970s and 1980s, high cost sharing is generally considered a “blunt instrument” that reduces all health care utilization among all types of patients. However, more recent studies have found that such reductions do not occur in all clinical situations

 HDHP effects on patients with serious mental illness including bipolar disorder are unknown.

We chose to study patients with bipolar disorder because it is a serious mental illness that is common enough among commercially insured persons to study robustly. Bipolar disorder is also a chronic condition and requires consistent access to medications and expensive specialist care. This population could be “tipped over the edge” by even small reductions in access given that suboptimal adherence to bipolar medications can result in debilitating episodes of mania or depression. We hypothesized that the higher out-of-pocket spending for specialist care under HDHPs would reduce mental health outpatient visits to both psychiatrists and nonpsychiatrist mental health providers and that reduced psychiatrist visits would reduce bipolar medication fills. The expected direction of emergency department (ED) visits and hospitalizations was uncertain.

We drew our commercially insured study population from a large national claims database with members enrolled between January 2003 and December 2012. Data included medical, pharmacy, and outpatient claims from members of a large national health plan.

We used an interrupted time series with propensity-matched comparison series research design.

 We included individuals with bipolar disorder who were enrolled in low-deductible plans during a baseline year. Some experienced an employer-mandated switch to HDHPs and were followed for a subsequent year. Others continued in low-deductible plans because their employers offered only these arrangements, and we followed these members as the control group. Thus, our study groups were not offered a choice of deductible level, minimizing self-selection.

Employer selection and study group identification.

 Our process for identifying study groups involved first identifying eligible employers and then eligible patients with bipolar disorder at those employers. In a given benefit year (the 12 months when an employer provides certain health insurance benefits), we classified employers as offering low- or high-deductible coverage based on offering exclusively annual deductibles of $0 to $500 or at least $1000, respectively (

]). We then defined the pool of potential HDHP group employers as those with at least 1 year of low-deductible coverage followed by at least 1 year of mandated HDHP coverage.

We defined the index date for employers that switched to HDHPs as the first day of the month when the switch occurred. We defined the index date for employers that did not switch plans as the first day of the month when their yearly account was renewed. Members had index dates as early as January 1, 2004, and as late as January 1, 2012. For each person, we defined the beginning of the baseline period as 12 months before the employer’s index date.

 Study cohort selection began with 53 million overall members up to age 64 years enrolled during 2000-2012 (

 we used a hierarchical algorithm and evidence of outpatient and inpatient bipolar disorder diagnoses captured in 

International Classification of Diseases, Ninth Revision, Clinical Modification

) codes to classify patients as having bipolar I, bipolar II, or uncertain type (eAppendix section I[B]).

After applying this algorithm, the bipolar cohort included 333,780 members. After restricting to members who were continuously enrolled for at least 2 years in the previously defined employers, who were aged 12 to 64 years during the calendar year of the index date, who had a most recent bipolar diagnosis that occurred from 5 years to 4 months prior to the index date, and whose employers did not carve out mental health benefits, we had a prematch pool of 2950 HDHP members and 26,911 controls with bipolar disorder.

Primary measures included mental health outpatient visits, overall and subclassified into (1) visits to psychiatrists and (2) visits to nonpsychiatrist mental health providers (eg, psychologists, counselors, mental health social workers). We chose this categorization because among mental health specialists, psychiatrists typically prescribe medications for bipolar disorder, whereas nonpsychiatrist mental health specialists commonly provide psychotherapy. In secondary analyses, we also examined the remaining small subset of mental health visits to (3) non—mental health specialist clinicians such as primary care providers. To better understand changes in use of health care that was not specifically related to mental health, we also measured non–mental health–related office visits, overall ED visits, and overall hospitalizations. Non–mental health–related visits comprised all office visits (including hospital outpatient visits) not classified as mental health related using the approach described earlier. That is, they included visits to non–mental health specialists that had evaluation and management codes for office visits (except those that were mental health specific) and a primary or secondary diagnosis that was not related to mental health.

As a secondary measure, we assessed bipolar medication use to determine whether this key component of outpatient bipolar disorder therapy was affected. Our measure was the number of days per study year that the member had at least 1 medication that is recommended for the treatment of bipolar disorder (lithium, atypical antipsychotics, and select anticonvulsants) on hand. We also assessed out-of-pocket spending on these health services per member and per visit, event, or prescription. Details about measure construction are included in the eAppendix.

Using 2000 US Census block group data and validated methods,

 we classified members according to the income and education levels of their neighborhood (

 We applied the Johns Hopkins ACG algorithm, a validated measure that predicts mortality,

 to members’ baseline year to estimate general morbidity, including both mental and physical conditions. We matched study groups on morbidity level because patients with differing levels of morbidity will have differing cost exposure and contact with the health system. We first used geocoding to classify patients as being from white, black, Hispanic, or mixed neighborhoods and then, when available, overwrote a participant’s classification as Hispanic or Asian using the E-Tech (Ethnic Technologies) system,

 which analyzes full names and geographic locations of individuals. This validated approach of combining surname analysis and Census data has positive and negative predictive values of approximately 80% and 90%, respectively.

 Other covariates included age category (12-18, 19-29, and 30-64 years) and US region of residence (West, Midwest, South, Northeast).

Member-level clinical covariates included bipolar disorder type (type I, type II, or uncertain type) (eAppendix section I[B]), number of baseline outpatient psychiatrist and nonpsychiatrist mental health provider visits per quarter, substance use disorder diagnosis (

 codes 291.x-292.x, 303.xx-305.xx), number of mental health ED visits per quarter, number of baseline mental illness (bipolar disorder or depression) inpatient days per quarter, number of baseline 30-day equivalent bipolar medication fills per quarter, and number of months from first observed bipolar diagnosis to the index date.

Derived employer-level characteristics included employer size (1-49, 50-99, 100-249, 250-499, 500-999, or ≥1000 enrollees); index month/year; percentage of women; percentage of enrollees in each of 4 US regions and in income, education, age, and race/ethnicity categories; employer baseline cost level and trend derived from a standardized cost variable; and mean employer ACG score.

To further minimize potential selection effects, we used a 2-level (employer- and member- level) propensity score matching approach

 and estimated propensity scores predicting the likelihood of a mandated HDHP switch based on the employer-level covariates defined above. Within these quartiles, we performed the member-level match between contemporaneous HDHP and control group members in the match pool with index dates between January 1, 2004, and January 1, 2012 (matching details in eAppendix section I[D]). After propensity score matching, the final study sample included 2862 HDHP members with bipolar disorder and 7705 matched controls.

We compared baseline characteristics of our study groups using a standardized differences approach.

 To determine whether HDHP members might be dropping out of their employers’ health plans at different rates compared with control members, we compared characteristics of the groups that had at least 12, 13, 18, and 24 months of enrollment, counting from the beginning of the baseline period.

After analyzing interrupted time series data (eAppendix section I[E]) and finding that all primary outcome measures had parallel baseline trends (data not shown), we used generalized estimating equations in a difference-in-differences analytic framework to compare changes in outcomes among members in the year before and after the mandated HDHP switch vs controls. Regression models for outpatient visits and corresponding out-of-pocket spending used a negative binomial distribution and were adjusted for employer size, calendar year of index date, gender, age category, race, ethnicity, and neighborhood income level and education level. The term of interest was the 2-way interaction between an indicator of HDHP vs control group and an indicator of the year before vs after the index date. Using terms from the regression model, we then used marginal effects methods

to calculate mean adjusted baseline and follow-up outpatient visit rates and out-of-pocket spending, as well as absolute and relative changes.

 We used a similar approach to model ED visits, hospitalizations, and corresponding out-of-pocket spending, but we used a zero-inflated negative binomial distribution to account for patients with zero utilization and spending in these areas.

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After matching, all standardized differences between HDHP and control group characteristics were well below 0.2, indicating minimal differences (Table 1).

 The average age of HDHP and control members was approximately 37 years, and 62% in each group were women. Approximately 26% lived in low-income neighborhoods, 16% lived in low-education neighborhoods, 4% were Hispanic, and the mean (SD) ACG morbidity score was 2.1 (3.0). About half of members (HDHP, 45.4%; control, 48.0%) were enrolled through midsize employers with 100 to 999 enrollees. Differential dropout was minimal and thus should not bias our results (

In adjusted difference-in-differences analyses, out-of-pocket spending from before to after the index date for all medical services increased by 20.9% (95% CI, 15.7%-26-3%) among HDHP members relative to controls (

). HDHP members experienced a 21.9% (95% CI, 15.1%-28.6%) increase in mental health visit prices (spending per visit) in the follow-up year compared with baseline relative to control group members (

) but no detectable changes in overall mental health outpatient visits (relative change, —4.6% [95% CI, –11.7% to 2.5%]) (

). The HDHP group experienced a 13.1% (95% CI, 4.1%-22.2%) relative pre- to post increase in out-of-pocket spending per member for outpatient mental health visits (

Analyses by visit type revealed that, in the context of a 33.8% (95% CI, 4.3%-63.3%) relative increase in visit price, HDHP members reduced nonpsychiatrist mental health provider visits by 10.9% (95% CI, —20.6% to –1.3%) (Table 2 and Figure). In contrast, psychiatrist visits were unchanged (0.6% [95% CI, –9.3% to 10.6%]) despite a 17.8% (95% CI, 12.2%-23.4%) increase in visit price. Per-member out-of-pocket spending changes were 4.9% (95% CI, –9.0% to 18.9%) for nonpsychiatrist mental health provider visits and 15.5% (95% CI, 4.7%-26.4%) for psychiatrist visits (eAppendix Figure 3). Non–mental health provider visits for mental health reasons and corresponding out-of-pocket spending were unchanged (

). HDHP members experienced a —9.0% (95% CI, –23.5% to 5.5%) nonsignificant relative change in non–mental health visits (Table 2).

HDHP members experienced no detectable changes in the proportion of days with any bipolar medication on hand after the HDHP switch relative to controls (Table 2 and 

) despite an increase in 30-day fill price of 16.8% (95% CI, 10.3%-23.3%) at follow-up vs baseline. Similarly, ED visits and hospitalizations were unchanged (

), whereas per-event prices increased by 40.1% (95% CI, 20.5%-59.8%) and 38.7% (95% CI, 3.7%-73.8%), respectively.

After an employer-mandated transition to HDHPs, commercially insured members with bipolar disorder maintained stable overall mental health outpatient visit rates relative to similar patients who remained in low-deductible health plans. However, nonpsychiatrist mental health provider visits declined by 11% among HDHP members, whereas psychiatrist visits were unchanged despite increased prices per visit. We did not find statistically significant changes in use of bipolar medications, ED visits, or hospitalizations.

A potential interpretation is that HDHP members with bipolar disorder elected to pay more out of pocket to maintain psychiatrist care and associated medication use but not nonpsychiatrist mental health provider care. HDHP members with bipolar disorder might be trying to preserve visits that they needed for medication refills, changes, or dose adjustments. Or, given that visits to nonpsychiatrist mental health providers generally occur more frequently, patients might view reducing the frequency of such visits as a necessary trade-off to manage their out-of-pocket expenses. It is difficult to interpret the lack of ED visit changes, in part because visits could simultaneously decrease because of HDHP financial barriers and increase because of inappropriately deferred care, leaving overall rates unchanged. In addition, HDHP members might not be able to reduce this key source of care given that research has demonstrated that individuals with bipolar disorder use the ED to obtain urgent mental health care.

 Previous research has not addressed effects of HDHPs on outpatient care among patients with mental health conditions. Earlier studies examined the broad impact of cost sharing on mental health care utilization. For example, the RAND HIE of the 1970s and 1980s found that higher cost sharing led to lower mental health care utilization compared with the demand for general medical services.

 In contrast, a more recent study found that patients’ price sensitivity for mental health and general medical care was comparable.

 Similar to our results, that study found significant variation in price sensitivity within types of mental health care (eg, visits, medications). However, these older studies did not examine populations with serious mental illness and were unable to distinguish cost-sharing effects on patient visits to different provider types. We are also not aware of research regarding the association between HDHP enrollment and changes in nonpsychiatrist mental health care visits, emergency care, and hospitalizations. In a context of reduced nonpsychiatrist mental health care visits, we found that rates of ED visits and hospitalizations did not change for HDHP members.

Substantially more is known about the impact of cost sharing on non—mental health care use and outcomes. The RAND HIE

 found that high out-of-pocket costs reduced almost all utilization to a similar degree. However, similar to our results among individuals with bipolar disorder, more recent studies have found that such reductions do not occur consistently in all clinical situations

 Recent research has generally found that when services are subject to the deductible, utilization decreases more among vulnerable members (eg, low-income, high-morbidity members) than less vulnerable members.

The RAND study also predicted that utilization decreases would not worsen health outcomes except among vulnerable members. Few modern studies have assessed health outcomes, but 2 detected evidence of increased adverse outcomes among low-income and high-morbidity members with diabetes.

Our study thus adds the novel insight that commercially insured patients with a serious mental illness might not experience cost sharing in HDHPs as a “blunt instrument” but rather make trade-offs to preserve certain types of mental health care. Policy makers, employers, and health plans could therefore use our findings to construct highly efficient value-based

 health insurance designs that optimize health care use and spending; for example, plans might reduce out-of-pocket costs for nonpsychiatrist mental health provider visits to enhance use while maintaining cost sharing for psychiatrist visits. Findings could also help clinicians recognize high-deductible insurance as a risk factor for patients with serious mental illness to reduce their use of certain mental health—related services.

Further research should investigate whether the reduced visits to nonpsychiatrist mental health providers that we detected cause adverse mental health or clinical events and should examine detailed medication adherence measures. Future studies should also address effects of HDHP transitions on patients with bipolar disorder who have lower incomes, higher morbidity, and higher levels of cost sharing under HDHPs with health savings accounts.

Our study has several limitations. We studied enrollees with employer-sponsored health insurance enrolled through a single national claims database. In addition, this study did not focus on the most vulnerable subset of members with bipolar disorder who might have public insurance or be uninsured. Nevertheless, given that HDHPs are almost exclusively a feature of commercial health insurance, our results should be generalizable to many individuals with bipolar disorder in employer-sponsored plans. Although we measured changes in ED visits and hospitalizations, these are imperfect health outcomes measures and our study was unable to assess important outcomes such as mania, depression,

 or suicide attempts. Our data did not allow us to classify members as the beneficiary, spouse, or dependent. Observational studies are subject to bias from unmeasured confounders. However, the controlled interrupted time series design that we used guards against most threats to validity.

 Finally, the patients included in our study were in different phases of their condition with regard to duration of recognized disease and disease control but unlikely to be imbalanced by study group.

HDHP members with bipolar disorder who faced substantial increases in cost sharing preserved psychiatrist visits while reducing nonpsychiatrist mental health provider visits. Further research should determine how such reduced care affects mental health and clinical outcomes such as suicide, mania, and depression.

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High-deductible health plan members with bipolar disorder experienced a reduction in nonpsychiatrist mental health provider visits but no changes in other utilization.

Effect of High-Deductible Insurance on Health Care Use in Bipolar Disorder

 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.

Alisa B. Busch, MD, MS

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