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Medicaid Managed Care Penetration and Drug Utilization for Patients With Serious Mental Illness

Aaron L. Schwartz, PhD; Jacqueline Pesa, PhD, MPH; Dilesh Doshi, PharmD; John Fastenau, PhD, MPH; Seth A. Seabury, PhD; Eric T. Roberts, PhD; and David C. Grabowski, PhD
This study examines the relationship between Medicaid managed care penetration within a state and spending on pharmaceuticals for patients with serious mental illnesses.
ABSTRACT

Objectives: State Medicaid programs are under increasing pressure to contain pharmaceutical spending. Many states have attempted to limit spending through greater Medicaid managed care penetration, which rose nationally from 54.5% in 1999 to 74.9% in 2011. It is not clear how this expansion has affected beneficiaries with serious mental illness (SMI)—a vulnerable population that often has their drug spending “carved out” from their managed care benefit. We sought to assess the association between managed care penetration and pharmaceutical spending on drugs for SMIs in these states.

Study Design: Retrospective cohort study.

Methods: State-year observations were constructed to study the relationship between managed care penetration and pharmaceutical spending on drugs for SMIs over the period 1999 to 2011. We analyzed the relationship using both cross-sectional and panel-data methods.

Results: Our cross-sectional analyses suggested that carve-out states with greater managed care penetration spend significantly less per enrollee on pharmaceuticals for the treatment of mental disorders: our panel data analyses did not generate statistically meaningful results.

Conclusions: Future studies should address whether any effects of managed care on mental health prescription utilization and spending reflect improved care coordination or worsening access to valuable care for the population with SMI.

Am J Manag Care. 2016;22(5):346-353
Take-Away Points
 
Our findings suggest that states with higher Medicaid managed care penetration have lower spending on serious mental illness (SMI) drugs. We cannot conclude that the relationship is causal, but the finding does raise a number of interesting issues for health policy. 
  • If managed care is associated with less use of ineffective drugs, then this lower utilization can be viewed as cost-effective, with state Medicaid programs achieving similar outcomes at a lower cost. 
  • However, if managed care is also associated with less use of effective drugs, then this decreased utilization may not be cost-effective due to the increased likelihood of negative health outcomes, such emergency department visits, hospitalizations, homelessness, incarcerations, and death. 
  • Most states carve out some or all behavioral health services from managed care benefits. A potential downside of such arrangements is fragmentation of care, particularly for vulnerable beneficiaries with SMI, who are likely consumers of other medical services.
Medicaid is the nation’s largest source of financing for behavioral health services.1 It serves many beneficiaries with serious mental illness (SMI), including schizophrenia, bipolar disorder, and major depressive disorder.2 Although many dually eligible beneficiaries with SMI receive their drug coverage through Medicare under the Part D program, drugs treating SMI still account for almost 20% of Medicaid pharmacy spending.3 In 2009, approximately 5 million individuals (11.9% of all Medicaid beneficiaries) received antipsychotics or antidepressant medications through Medicaid.4

One of the major provisions of the Affordable Care Act (ACA) is a sweeping increase in Medicaid eligibility for those states choosing to opt in. The Congressional Budget Office estimates that Medicaid expansion will increase the number of nonelderly enrollees in Medicaid and the Children’s Health Insurance Program (CHIP) by 10 million in 2016.5 The costs of this expansion initially will be fully financed by the federal government and, by 2020, the federal share of Medicaid expansion funding will decline to 90%, which will require states to pay for the balance of new enrollees’ costs.6 Increased enrollment in Medicaid, coupled with states’ growing obligation to finance the program’s costs, will likely impose new pressures on states to curb Medicaid spending—for both newly and previously eligible populations. 

Historically, states have attempted to control Medicaid fee-for-service (FFS) prescription drug spending with tools such as formulary restrictions, step therapies, and prior authorization; however, these have sometimes produced unintended consequences in the FFS setting. In the case of drug treatments for SMI, prior research has demonstrated that the use of formulary restrictions may have significant adverse effects for patients—especially those who may benefit from atypical antipsychotics or other novel therapies.7-9 Furthermore, in many cases, these poor outcomes were significant enough to eliminate any potential cost savings from the policies.10 Similarly, the use of prior authorization for atypical antipsychotics by the Maine Medicaid program was found to increase medication discontinuations without lowering spending.11

Increasingly, states have turned to contracts with Medicaid managed care plans in order to better control costs and reduce budgetary uncertainty. However, in many states, prescription drug spending is “carved out” (ie, not included) in the managed care benefit. Under a carve-out arrangement, prescription drug benefits are managed on an FFS basis, which excludes them from the set of services for which a managed care plan has oversight and direct financial liability. Conceptually, this suggests that carve-out reduces both the ability of and incentives for managed care plans to coordinate pharmaceutical use with spending on other health services, potentially leading to “cost spillovers” elsewhere in the system.12

This issue is particularly relevant in the treatment of SMI, where poor adherence and discontinuation may have serious consequences, including emergency department (ED) visits, inpatient hospitalization, homelessness, incarceration, or even suicide.13-15 Indeed, a simple change in drug coverage may result in unintended consequences. For example, following the implementation of Medicare Part D, many dually eligible beneficiaries had difficulty accessing psychiatric medications, leading to increased psychiatric ED visits.16,17 On the other hand, in light of growing evidence that prescription drug adherence might result in lower spending on services that are carved into most Medicaid managed care programs (eg, nondrug inpatient and urgent care), these plans might still have an incentive to promote pharmaceutical adherence among carved-out beneficiaries with SMI.18

New research is needed to understand the implications of Medicaid managed care for the treatment of SMI. In this study, we examined the relationship between the expansion of managed care in Medicaid programs and Medicaid spending on pharmaceuticals for the SMI population in carve-out states.

METHODS
We collected historical Medicaid managed care penetration rates, SMI prescription utilization data, and additional state-level information for all states and the District of Columbia from 1999 to 2011 (inclusive). These data were used to analyze whether states’ levels of Medicaid managed care penetration were associated with their level of prescription use for the SMI population.

Data

Medicaid managed care penetration rates were derived from annual enrollment reports published by CMS and its predecessor, the Health Care Financing Administration.19 These reports document enrollment in both comprehensive and limited benefit managed care plans and include the proportion of each state’s Medicaid beneficiaries enrolled in managed care plans (ie, the penetration rate). Several states administered Medicaid programs on an exclusively FFS basis: New Hampshire in 2004, 2010, and 2011; Mississippi in 2002, 2003, and 2007; and Alaska and Wyoming from 1999 to 2011. We retained these observations in our analyses, coding these states as having no Medicaid managed care penetration in the applicable years.

Drug utilization data were collected from CMS annual Medicaid State Drug Utilization Data files. Each file contains information for covered outpatient drugs that were paid for by a state Medicaid agency. Specifically, the files include information on the number of prescriptions and the amount of reimbursement for those prescriptions by National Drug Code (NDC). Notably, the files do not include prescriptions paid by Medicare Part D plans on behalf of dual eligible beneficiaries. The CMS database of State Drug Utilization files is relatively complete, missing files for only 6 of the 323 (1.9%) state-year observations in which states were eligible to report prescription drug spending data (ie, states that carved out the managed care prescription drug benefit, or for which the managed care penetration rate was 0, in a particular year).

Because the CMS annual Medicaid State Drug Utilization Data only record prescriptions that were paid for via FFS (either due to a carve-out of the drug benefit or through a traditional FFS model), we used the Medicaid Analytic Extract (MAX) Prescription Drug Tables, published by CMS for the years 1999 to 2011, to identify each state’s carve-out status by year.20 We treated a state as operating under a carve-out arrangement in a given year if it carved out the prescription drug benefit from all of its managed care plans—or prepaid health plans—for at least three-fourths of that year. The MAX Prescription Drug Tables did not provide information about carve-out arrangements for 2000 and 2010, so we imputed data for these years by conservatively coding a state as carving out the drug benefit only if it did so in both adjacent years (ie, 1999 and 2001 for the missing 2000 data, and 2009 and 2011 for the missing 2010 data). States whose carve-out status could not be determined (Louisiana and Vermont) were dropped from our analyses, as were the 2 states (Maryland and Minnesota) that did not carve out the prescription drug benefit from their Medicaid managed care plans in any year of our study period.

Other state-year–level data were derived from 2 main sources. First, various resident sociodemographic characteristics and insurance coverage rates were calculated from the Annual Social and Economic Supplement to the Current Population Survey (CPS), a large national survey of households by the Census Bureau.21 Second, information on states’ healthcare provider counts were collected from the Area Health Resource File (AHRF) Access System, a compendium of geocoded data compiled by the Health Resources and Services Administration. Rates of providers per capita were calculated using AHRF state provider totals and CPS state population estimates. Because provider counts are not available for every year, missing data were imputed by modeling state-specific quadratic time trends.

Drug Utilization Outcomes

Our primary outcomes were annual rates of SMI prescription use and spending for the Medicaid SMI population in states and years in which the Medicaid prescription drug benefit was carved out of managed care or that ran a traditional FFS Medicaid program. We designated drugs as SMI prescriptions on the basis of their pharmacologic class (eg, atypical antipsychotics)22 or specific SMI indications. In order to check the robustness of our results, we chose to construct 2 alternate definitions of SMI prescriptions: a narrow definition including only antipsychotic medications and a broader definition including additional drugs with primary psychiatric indications, like antidepressants. Although all our psychiatric prescriptions consist of psychiatric drugs without major nonpsychiatric uses, these 2 measures allow us to isolate our findings for antipsychotics relative to the broader class of SMI drugs. A full list of classes included in each definition is provided in the eAppendix (available at www.ajmc.com).

The construction of the outcome variables involved the following steps. For each state and year, we tabulated the total annual Medicaid spending and number of prescriptions for each NDC falling within our SMI definitions. We excluded a small number of drug utilization outliers (0.1% of annual drug observations) because their prescription totals were larger than 100 times the state median for that drug. We then calculated the total state SMI prescription utilization and spending among all drugs within our SMI definitions. We further excluded 7 state-year observations that exhibited very high SMI prescription spending relative to the state trend: Washington (2006), South Dakota (2007), and Tennessee (2003-2007). After applying this restriction, we obtained a final analytic sample of 310 observations from 46 managed care carve-out and Medicaid FFS states that had valid SMI prescription spending and utilization data (Figure 1). Total spending and utilization amounts were divided by the size of each state’s Medicaid SMI population, which was estimated using the annual count of Medicaid beneficiaries in the CPS and 2011-2012 survey estimates of the rates of adult SMI by state.23

Analyses

We employed 2 alternate modeling approaches to assess the association between managed care penetration and SMI prescription outcomes. The first, a pooled cross-sectional approach, tested for whether the levels of a state’s prescription outcomes in a given year were associated with the level of its managed care penetration rate, controlling for time-varying state characteristics and shared time trends in prescription outcomes across all states. This analysis consisted of linear regression models of state prescription outcomes on the state Medicaid managed care penetration rate (the variable of interest), year indicators, and state characteristics.

 
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