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The American Journal of Managed Care October 2015
Scalable Hospital at Home With Virtual Physician Visits: Pilot Study
Wm. Thomas Summerfelt, PhD; Suela Sulo, PhD; Adriane Robinson, RN; David Chess, MD; and Kate Catanzano, ACNP-BC
Health Coaching by Medical Assistants Improves Patients' Chronic Care Experience
David H. Thom, MD, PhD, MPH; Danielle Hessler, PhD; Rachel Willard-Grace, MPH; Denise DeVore, BA; Camille Prado, BA; Thomas Bodenheimer, MD, MPH; and Ellen H. Chen, MD
The Path to Value Through the Use of Holistic Care
Roy A. Beveridge, MD, Chief Medical Officer, Humana
Delivering Value by Focusing on Patient Experience
Paula Chatterjee, MD, MPH; Thomas C. Tsai, MD, MPH; and Ashish K. Jha, MD, MPH
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Medication Adherence and Healthcare Disparities: Impact of Statin Co-Payment Reduction
Jennifer Lewey, MD; William H. Shrank, MD, MSHS; Jerry Avorn, MD; Jun Liu, MD, MPH; and Niteesh K. Choudhry, MD, PhD
Solutions for Filling Gaps in Accountable Care Measure Sets
Tom Valuck, MD, JD, MHSA; Donna Dugan, PhD, MS; Robert W. Dubois, MD, PhD; Kimberly Westrich, MA; Jerry Penso, MD, MBA; and Mark McClellan, MD, PhD
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Fenfang Li, PhD; Jing Guo, PhD; Audrey Suga-Nakagawa, MPH; Ludvina K. Takahashi, BA; and June Renaud, BEd
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Donna M. Wilson, PhD, RN; Corrine D. Truman, PhD, RN; Jessica A. Hewitt, BScKin; and Charl Els, MBChB, FCPsych, MMedPsych, ABAM, MROCC
Request of Acute Phase Markers in Primary Care in Spain
Maria Salinas, PhD; Maite López-Garrigós, MD; Emilio Flores, PhD; Joaquin Uris, PhD; and Carlos Leiva-Salinas, MD
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Mark H. Ebell, MD, MS; and Taylor Radke, MPH
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"Meaningful" Clinical Quality Measures for Primary Care Physicians
Cara B. Litvin, MD, MS; Steven M. Ornstein, MD; Andrea M. Wessell, PharmD; and Lynne S. Nemeth, RN, PhD

Medication Adherence and Healthcare Disparities: Impact of Statin Co-Payment Reduction

Jennifer Lewey, MD; William H. Shrank, MD, MSHS; Jerry Avorn, MD; Jun Liu, MD, MPH; and Niteesh K. Choudhry, MD, PhD
This study examined patterns of medication adherence after a reduction in medication co-payment amount among privately insured patients living in racially diverse neighborhoods.
ABSTRACT
 
Objectives: Minority patients have lower rates of cardiovascular medication adherence, which may be amenable to co-payment reductions. Our objective was to evaluate the effect of race on adherence changes following a statin co-payment reduction intervention.
 
Study Design: Retrospective analysis.
 
Methods: The intervention was implemented by a large self-insured employer. Eligible individuals in the intervention cohort (n = 1961) were compared with a control group of employees of other companies without such a policy (n = 37,320). As a proxy for race, we categorized patients into tertiles based on the proportion of black residents living in their zip code of residence. Analyses were performed using difference-in-differences design with generalized estimating equations. 

Results: Prior to the new co-payment policy, adherence rates were higher for individuals living in areas with fewer black residents. In multivariable models adjusting for demographic factors, clinical covariates and baseline trends, the co-payment reduction increased adherence by 2.0% (P = .14), 2.1% (P = .15) and 6% (P <.0001) for intervention patients living in areas with the bottom, middle and top tertiles of the proportion of black residents. These results persisted after adjusting for income. 

Conclusions: Co-payment reduction for statins preferentially improved adherence among patients living in communities with a higher proportion of black residents. Further research is needed on the impact of value-based insurance design programs on reducing racial disparities in cardiovascular care.
 
Am J Manag Care. 2015;21(10):696-704
Take-Away Points
 
Patients living in predominately minority neighborhoods are less likely to be adherent to important medications that prevent heart disease compared with patients living in largely white neighborhoods. Eliminating co-payments for evidence-based medications improves adherence preferentially for patients living in minority neighborhoods. 
  • Disparities in cardiovascular outcomes are prevalent in the United States and may be improved by programs that focus on improving medication adherence. 
  • Quality improvement interventions may have differential impact based on the racial/ethnic composition of communities. 
  • Interventions provided by the employer or health insurer can have a meaningful clinical impact.
Differences in the use of evidence-based cardiovascular (CV) therapies, such as percutaneous coronary intervention and bypass surgery, are believed to contribute to the persistent racial and ethnic disparities in patients with coronary artery disease.1-6 Lower rates of long-term adherence to evidence-based medications, including statins, that are likely to be clinically significant, have been documented among racial/ethnic minorities.7-9 This has been found even when controlling for income, and may be amenable to intervention.10-12
 
While many factors contribute to nonadherence, patient out-of-pocket costs (ie, co-payments, coinsurance, and deductibles) appear to be a central issue, even for patients with prescription drug coverage.7,13,14 Cost-related medication nonadherence may affect some patients more than others. Among patients with at least partial prescription drug coverage, nonadherence, secondary to cost, is highest among those with lower income, more medical conditions, and worse self-reported health status.15 In some studies, black patients are more likely to report cost-related barriers to adherence compared with whites, even when socioeconomic factors are taken into account.15-17
 
Some employers and insurers have attempted to address cost-related medication nonadherence with reduced patient cost sharing for evidence-based medications through value-based insurance design programs. This has led to improvements in adherence, resource utilization, and clinical outcomes for patients with CV disease.18-20 These programs may also be particularly effective for racial and ethnic minorities, as has recently been reported among high-risk patients discharged after acute myocardial infarction.21 It is unclear whether these findings are generalizable to a more heterogeneous and lower-risk population with CV disease. Accordingly, we sought to evaluate whether statin co-payment reductions for patients with diabetes or vascular disease19,22 differentially affected medication adherence for patients living in neighborhoods with different racial compositions.
 
METHODS
Setting and Design
A large national employer eliminated co-payments for statins for patients with diabetes or vascular disease on January 1, 2007. We evaluated whether the impact of this co-payment reduction differed by race, as measured by the racial composition of a patient’s neighborhood. We compared the impact of this benefit change on medication adherence, stratified by zip code–level race data, with a control population of comparable patients drawn from Horizon Blue Cross Blue Shield of New Jersey (BCBSNJ), the largest insurer in New Jersey. The national employer and BCBSNJ use the same pharmacy benefit manager.
 
Data Sources
We combined complete paid pharmacy and medical services claims data from both the intervention and control employers to create a relational database consisting of all filled prescriptions, procedures, inpatient and outpatient physician encounters, hospitalizations, long-term care admissions, and deaths for all patients studied. All traceable person-specific identifying factors were transformed into anonymous, coded study numbers to protect subjects’ privacy. The Institutional Review Board of Brigham and Women’s Hospital approved this study.
 
Cohort Eligibility

A detailed description of the study cohort has been published previously.19 In brief, eligible patients were those with diabetes or vascular disease—based on claims for diabetes medications or supplies, antiplatelet medications, or beta-blockers—who filled a prescription for a statin between January 1, 2006, and December 31, 2007. Patients entered the cohort on the date of their first medication fill after January 1, 2006, and were followed until they died, lost insurance eligibility, or the study period ended.
 
Medication Adherence
We measured medication adherence by estimating the number of days of medication available or the “proportion of days covered” (PDC) in each month between January 2006 and December 2007, based on prescriptions actually filled. To do this, we created a “supply diary” for each patient-day by aggregating consecutive fills of each medication class being studied based on dispensing dates, quantity dispensed, and reported days’ supply. All statins were considered to be interchangeable. When a dispensing occurred before the previous dispensing should have run out, utilization of the new medication was assumed to begin the day after the end of the old dispensing. If a patient accumulated more than 180 days’ supply on a given day, the accumulated supply was truncated at 180 days. The PDC was calculated by dividing the number of days of medication available to each patient within a given month by the number of calendar days in that month. We subtracted from the denominator any days a patient spent in the hospital or a nursing home.
 
Race

The predictor of interest was the percentage of black residents living in each patient’s zip code, determined by linking the patient’s zip code to 2000 US Census data. In the primary analysis, we divided patients into tertiles based on the percentage of black residents in each patient’s zip code. The bottom, middle, and top tertiles corresponded to a mean percentage of 1%, 5%, and 30% black residents, respectively. This method has been used in previous research to assess the impact of racial23,24 and socioeconomic25 composition of neighborhoods on health outcomes.
 
Covariates
Patient demographics were assessed as of the date of cohort entry. Individual level characteristics included age and gender; zip code–level characteristics were median income, percentage of residents who were low-income, and percentage of residents who had completed high school. Comorbidities were assessed based on medical service and pharmacy claims from the 12-month period before cohort entry and included coronary artery disease (International Classification of Diseases, Ninth Revision [ICD-9] codes 410.x-414.x, 429.2, V45.81), congestive heart failure (ICD-9 code 428.x), diabetes (ICD-9 code 250.x), hypertension (ICD-9 code 401.x-404.x), Charlson comorbidity score, number of hospitalizations, and number of prescription medications.26
 


 
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