Currently Viewing:
The American Journal of Managed Care February 2013
Are Benefits From Diabetes Self-Management Education Sustained?
JoAnn Sperl-Hillen, MD; Sarah Beaton, PhD; Omar Fernandes, MPH; Ann Von Worley, RN, BSHS, CCRP; Gabriela Vazquez-Benitez, PhD, MSc; Ann Hanson, BS; Jodi Lavin-Tompkins, RN, CNP, CDE, BC-ADM; William Parsons, MS; Kenneth Adams, PhD; and C. Victor Spain, DVM, PhD
Impact of Oral Nutritional Supplementation on Hospital Outcomes
Tomas J. Philipson, PhD; Julia Thornton Snider, PhD; Darius N. Lakdawalla, PhD; Benoit Stryckman, MA; and Dana P. Goldman, PhD
Comparative Effectiveness Research and Formulary Placement: The Case of Diabetes
Michael E. Chernew, PhD; Rick McKellar, BS; Wade Aubry, MD; Roy Beck, MD, PhD; Joshua Benner, PharmD, ScD; Jan E. Berger, MD, MJ; A. Mark Fendrick, MD; Felicia Forma, BSc; Dana Goldman, PhD; Anne Peters, MD; Rebecca Killion, MA; Darius Lakdawalla, PhD; Douglas K. Owens, MD; and Joe Stahl, MA
Oral Nutritional Supplementation
Gordon L. Jensen, MD, PhD
Medical Homes Require More Than an EMR and Aligned Incentives
Samantha L. Solimeo, PhD, MPH; Michael Hein, MD, MS; Monica Paez, BA; Sarah Ono, PhD; Michelle Lampman, MA; and Greg L. Stewart, PhD
Do Electronic Medical Records Improve Diabetes Quality in Physician Practices?
Jeffrey S. McCullough, PhD; Jon Christianson, PhD; and Borwornsom Leerapan, MD, PhD
Short-Term Costs Associated With Primary Prophylactic G-CSF Use During Chemotherapy
Suja S. Rajan, MHA, MS, PhD; William R. Carpenter, MHA, PhD; Sally C. Stearns, PhD; and Gary H. Lyman, MD, MPH
The Cost of Implementing Inpatient Bar Code Medication Administration
Julie Ann Sakowski, PhD; and Alana Ketchel, MPP, MPH
Spending and Mortality in US Acute Care Hospitals
John A. Romley, PhD; Anupam B. Jena, MD, PhD; June F. O'Leary, PhD; and Dana P. Goldman, PhD
Currently Reading
Cost-Effectiveness of Medicare Drug Plans in Schizophrenia and Bipolar Disorder
Kenneth J. Smith, MD, MS; Seo Hyon Baik, PhD; Charles F. Reynolds III, MD; Bruce L. Rollman, MD, MPH; and Yuting Zhang, PhD
Guidance for Structuring Team-Based Incentives in Healthcare
Daniel M. Blumenthal, MD, MBA; Zirui Song, PhD; Anupam B. Jena, MD, PhD; and Timothy G. Ferris, MD, MPH

Cost-Effectiveness of Medicare Drug Plans in Schizophrenia and Bipolar Disorder

Kenneth J. Smith, MD, MS; Seo Hyon Baik, PhD; Charles F. Reynolds III, MD; Bruce L. Rollman, MD, MPH; and Yuting Zhang, PhD
In Medicare Part D, generic drug coverage was cost saving compared with no coverage in bipolar disorder and schizophrenia while improving health outcomes.
Background: Medicare Part D has a drug coverage gap, which imposes risks for discontinuing medications, particularly in mental health disorders where drug costs are high. However, some beneficiaries have generic drug coverage in the gap.

Objectives: To examine the health outcomes and cost-effectiveness of generic-drug coverage compared with no gap coverage in patients with bipolar disorder and schizophrenia.

Study Design: Markov model–based cost-effectiveness analysis using identical hypothetical cohorts to examine drug coverage strategies.

Methods: The incremental cost-effectiveness of Part D coverage strategies was estimated, using differences in medical costs and quality-adjusted life years between plans. Coverage strategy–specific costs and hospitalization rates were obtained from 2007 Medicare data, adjusted for age, sex, race, and health status.

Results: When comparing generic-only coverage with no gap coverage, generic-only coverage cost less and was more effective than no gap coverage, due mainly to lower hospitalization rates. In sensitivity analyses, generic-only coverage continued to be favored over no gap coverage unless generic coverage costs increased >3% in bipolar disorder and >5% in schizophrenia; generic coverage in the gap was also favored in probabilistic sensitivity analyses.

Conclusions: In Medicare Part D, generic drug coverage was cost saving compared with no coverage in bipolar disorder and schizophrenia while improving health outcomes. Policy makers and insurers might consider generic-only coverage, rather than no gap coverage, to both conserve healthcare resources and improve health.

(Am J Manag Care. 2013;19(2):e55-e63)
Optimal drug benefit design can decrease drug and nondrug spending while also improving health outcomes.

  • The Medicare Part D prescription drug benefit has a coverage gap, which has a greater impact on patients with bipolar disorder and schizophrenia.

  • A cost-effectiveness analysis of Part D plans is unconventional but can inform managed care decision making.

  • Generic drug coverage in the gap is cost saving compared with no gap coverage (the standard Part D design) and improves health outcomes.
Medicare Part D offers prescription drug coverage for Medicare beneficiaries. Since the program’s inception in 2006, many enrollees have benefited from improved drug coverage and increased medication use.1-3 However, a major concern is the large coverage gap in the standard Part D design, where beneficiaries pay 100% of medication costs out-of-pocket. About one-third of all Medicare beneficiaries enter this coverage gap each year,4,5 and once there, they often reduce medication use,4,6-8 which may lead to increases in hospitalization and medical spending.

The coverage gap is an even larger concern for Medicare beneficiaries with severe mental disorders such as bipolar disorder and schizophrenia. First, they are much more likely to enter the gap: 62% of aged beneficiaries with bipolar disorder and 56% of those with schizophrenia entered the gap in 2007.9 Second, if they discontinue psychotropic medications, they may relapse to more severe episodes and require psychiatric hospitalization.10 Third, they experience high rates of comorbid chronic physical conditions such as heart disease and diabetes, which can be exacerbated by untreated mental illness and increase morbidity, medical spending, and mortality.11

The standard Part D benefit in 2007 included 4 phases: (1) an initial $265 deductible; (2) a period in which beneficiaries paid 25% of drug costs between $265 and $2400; (3) a coverage gap in which they paid 100% of costs between $2401 and $3850, where they reached their total out-of-pocket spending catastrophic limit; and (4) a catastrophic coverage period where they paid 5% of costs.12 Although the standard Part D benefit includes these 4 phases, some companies offering Part D drug plans modified the design and offered either “actuarially equivalent” or enhanced plans. In 2007, for example, 72% of stand-alone Part D plans had the standard coverage gap, 27% offered coverage for generic drugs used in the gap, and fewer than 1% offered coverage for both brandname and generic drugs.

In addition, the Medicare Part D drug benefit includes substantial premium and cost-sharing subsidies for Medicare beneficiaries with low incomes, which come directly from the federal government. Each year, roughly 50% of beneficiaries enrolled in Part D plans receive a low-income subsidy (LIS).13 Due to more generous benefits, those with LIS are not exposed to the coverage gap even when their pharmacy spending reached the coverage gap threshold.14 Enrollment in LIS is not due to self-selection, because only beneficiaries who qualify due to low income can choose LIS and most eligible beneficiaries do enroll in LIS. For this reason, this group is very different from non-LIS beneficiaries.

Our objective in this analysis was to examine differences in health outcomes and costs between coverage groups in patients with bipolar disorder and schizophrenia. We were most interested in differences between the no-gap-coverage and the generic-coverage groups, because plan self-selection could be an issue and policies governing the plans’ availability and affordability could affect health costs and outcomes for beneficiaries entering the coverage gap. For example, sicker patients could more often select the more generous generic coverage, while patients in the less generous no-gap-coverage plans might defer care more often and have worse health outcomes as a result. Beneficiaries in those plans are also likely to be more comparable to each other than to beneficiaries in LIS plans, where prominent differences in income and education level occur. In the primary analysis, we evaluated the costeffectiveness of generic coverage compared with no coverage. In a secondary analysis, we also included the LIS plan to complete the comparisons.


Data Source

We obtained demographic and enrollment information, plan benefits, prescription drug events, and medical claims for Medicare beneficiaries with schizophrenia and/ or bipolar disorder (100% of non-dual eligibles and a 20% random sample of dual eligibles) who were continuously enrolled in Part D plans in 2007. We identified patients with each condition based on the existence of 2 or more inpatient or outpatient claims for schizophrenia (International Classification of Diseases, Ninth Revision, code 295) or bipolar disorder (codes 296.0, 296.1, 296.4-2.96.8, 301.11, 301.13) between January 1, 2007, and December 31, 2007. Because their treatment patterns might differ, we separately evaluated those who qualified for Medicare due to disability (denoted hereafter as disabled) or due to age (denoted as aged). The study design was approved by the institutional review board at the University of Pittsburgh.


Health outcomes included mortality and all-cause hospitalization rates. Cost outcomes were drug costs and total medical costs. The drug costs included Part D plan payment before rebates, beneficiary out-of-pocket spending, and subsidy amount. The total medical costs included inpatient, outpatient, and physician services as well as drug costs.

Study Groups and Adjustments

To control for differences in beneficiary characteristics between groups, all outcomes (hospitalization, mortality, and costs) were adjusted for age, sex, race, number of Elixhauser comorbidities, and the Centers for Medicare & Medicaid Services Hierarchical Condition Category (CMS-HCC) scores; for pharmaceutical spending, we used the analogous prescription drug hierarchical condition category (CMS-RxHCC) scores.15 CMS-HCC and RxHCC scores are the beneficiary risk adjusters used by CMS to adjust payment to plans for medical and pharmacy costs, respectively.

The adjustment was conducted in 2 steps. First, we ran separate regressions for each outcome, controlling for drug coverage study group (eg, no coverage vs generic coverage) and the covariates listed above. Second, we calculated the predicted values based on beneficiaries’ drug coverage group and the averages for the covariates across coverage groups. More precisely, in the first stage, we ran a logistic regression for binary outcomes such as mortality; for costs, we ran an ordinary least squares regression to estimate the predicted value for each drug group. Some outcomes were approximately normally distributed, but inpatient spending had a large concentration of beneficiaries with no expenditures and some beneficiaries with very large expenditures. If our goal had been to obtain individual-level adjusted costs conditional on covariates, it would have been necessary to use a mixed-model approach (eg, a 2-part model or zeroinflated gamma model) to address the problem of skewed data with a large number of zero expenditures. However,  our goal here was to estimate the predicted value for each group using the national average of the covariates; ordinary least squares regression gave us efficient estimators of the conditional expectation function. In addition, Buntin and Zaslavsky suggested that all these estimators give very similar results empirically.16 We ran this 2-stage adjustment for each outcome, separately for disabled and aged beneficiaries.

Self-selection could be a factor in the makeup of the no-coverage-gap and generic-only-coverage groups; patients who anticipate entering the coverage gap may chose the more generous generic- coverage plan. Because of this choice, we postulated more illness and healthcare expenditures in patients with generic coverage, which could bias the analysis against generic coverage compared with no gap coverage.

Markov Model

To estimate the incremental cost-effectiveness of drug coverage strategies, we constructed a 3-state Markov model comprised of outpatient therapy, hospitalization, and death. We used TreeAge Pro Suite 2009 software (TreeAge Software, Williamstown, Massachusetts) to build the model. Over the model’s 1-year time horizon and 12 monthly Markov cycles, hospitalization and death rates were calculated using Medicare utilization data. In this population, all patients were taking medications and few switched plans. In the Markov model, these characteristics were reflected in the assumptions that all cohort members were on at least 1 medication and that all cohort members remained in the same coverage group throughout the model time horizon. Beneficiaries could be hospitalized 1 or more times per year, based on the hospitalization probability.

We analyzed separate models for bipolar disorder and schizophrenia, as well as for disabled and aged beneficiaries. In the primary analyses, we used identical hypothetical condition–specific cohorts to test differences in costs and outcomes between no gap coverage and generic-only coverage for all cohort members; the secondary analysis examined all 3 coverage strategies (ie, no gap coverage, generic-only coverage, and LIS).

Costs were total drug and nondrug costs from the Medicare perspective, incorporated into the model as constant monthly costs. Hospitalization costs were not considered separately, but instead as a component of nondrug medical costs. Because it was a 1-year model, discounting was not performed.17

The effectiveness term was quality-adjusted life years (QALYs), accounting for quality-of-life differences between coverage groups using utility weights. QALYs are the product of a health state’s utility and time spent in that state. Utilities measure preference for health states on a scale where 0 equals death and 1 equals perfect health. Utility weights for bipolar disorder were derived from SF-36 values18; schizophrenia utilities were Euroqol utility values19 (Table 1). In the base case analysis, we assumed no differences in outpatient utility between coverage plans; thus, differences in effectiveness between plans were based on differences in hospitalization rates.

Utilities for hospitalized patients with bipolar disorder were calculated as a weighted average, based on the relative likelihood (as observed in the referenced study) of uncontrolled mania or depression and their respective utility weights.18 Utilities for hospitalized schizophrenia patients were based on average pretreatment utilities.19 Hospitalized patients were assumed to have decreased utility throughout the month of their hospitalization to account for decreased utility prehospitalization and posthospitalization, as well as during their hospital stay. To account for the possibility of hospitalizations of differing duration or condition severity, we performed sensitivity analyses where hospitalization utility was varied widely. In the base case, we assumed that uncontrolled illness had the same utility, hospitalized or not, possibly biasing against strategies that result in fewer hospitalizations.

Sensitivity Analysis

Copyright AJMC 2006-2019 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up