Factors Associated With Employee Participation in a Value-Based Insurance Design Initiative
Published Online: December 13, 2013
Daniel J. Elliott, MD, MSCE; Seema S. Sonnad, PhD; Xin Xu, MS; Karen Anthony, MS; Edmondo J. Robinson, MD, MBA; Ruth T. Aguiar, BA; John Barron, PharmD; Paula Stillman, MD, MBA; and A. Mark Fendrick, MD
Value-based insurance design (VBID) programs are designed to reduce or eliminate cost barriers to evidence-based healthcare services. Accumulating evidence suggests that VBID initiatives are associated with modest increases in medication adherence with targeted therapies.1-7 The ultimate impact on cost and clinical outcomes is less clear, but decision-analytic models and preliminary evidence from VBID implementations suggest that reductions in subsequent complications and comorbidity may lead to overall cost-effectiveness and possibly cost savings.3,8-12
VBID initiatives are featured in the Affordable Care Act of 2010 and are generating signifi cant interest among large employers. Despite becoming increasingly common, the optimal design of VBID initiatives has not been determined. In particular, it is not clear whether these programs should be made universally available to all beneficiaries or whether more selective criteria should be used, such as requiring patients to actively enroll in order to qualify for the benefits. On one hand, requiring patients to actively participate (ie, by requiring simultaneous participation in a disease management program) may increase awareness and benefit at the individual level. However, this may have unintended consequences if the requirement to participate provides a barrier to enrollment and decreases participation in the VBID program. This may beparticularly harmful if the individuals who could potentially derive the most benefit are excluded from the program. In 2009 Christiana Care Health System (Christiana Care) implemented a VBID program that eliminated copayments for medications and supplies for glycemic control for benefi ciaries with diabetes. To receive the benefit, participants were required to complete a brief baseline evaluation and provide consent for review of administrative claims data. Our objective in this paper is to determine the participation rate and describe the baseline characteristics of eligible beneficiaries who did and did not choose to enroll in Christiana Care’s voluntary VBID initiative. Our findings provide the first empiric evidence to understand how participation requirements may impact the uptake of a VBID program and will have implications for implementing VBID more broadly.
We conducted a retrospective cohort study of all benefi ciaries who were eligible to participate in a voluntary VBID program for employees and dependents of Christiana Care Health System in Wilmington, Delaware. We compared demographic and medical characteristics and healthcare utilization patterns in the year before the program started between eligible beneficiaries who chose to participate and those who chose not to participate.
Study Setting, Program Description, and Requirements for Participation
Christiana Care is the largest healthcare provider and largest private employer in the state of Delaware. Christiana Care is self-insured with approximately 17,000 beneficiaries. Blue Cross Blue Shield of Delaware provided administrative management for Christiana Care benefits.
In March of 2009 Christiana Care implemented the Copayment Elimination Program (“the program”), an 18-month-long pilot program eliminating copayments for all diabetes medications and supplies. Details of the program have been published previously.13 Eligible beneficiaries were identifi ed using claims data and received invitation letters mailed out from Blue Cross Blue Shield of Delaware. Advertisements also appeared on the Christiana Care intranet portal and in key locations throughout Christiana Care facilities. Interested benefi ciaries completed an online registration followed by an onsite meeting with program staff. During that meeting benefi ciaries completed a baseline survey of diabetes history and health status; received standardized measurements of blood pressure, weight, and height; and had baseline laboratory tests including a fasting lipid panel and glycated hemoglobin (A1C). Participants were required to provide consent for release of administrative claims data for the purposes of evaluation. Once these steps were complete eligible copayments for participants were eliminated for 12 months. After 12 months, contingent on the participant returning for laboratory testing and survey completion, the copayment elimination extended an additional 6 months.
Identification of Study Population: This study used claims data to retrospectively identify benefi ciaries who would have been eligible for participation in the program at the time it was initiated. Eligible beneficiaries included all employees and dependents with at least 1 inpatient or outpatient visit with a diagnosis of diabetes mellitus (International Classification of Diseases, Ninth Revision, Clinical Modification codes 250.x) in the year prior to the start of the program. Because our goal was to compare health services utilization and medication adherence among eligible beneficiaries in the year preceding program start, we excluded patients not continuously enrolled in the employer-sponsored insurance plan during the entire study period. We also excluded beneficiaries who would turn 65 years of age prior to the end of the fi rst year of the program because the nearness of Medicare eligibility may have impacted the decision to participate.
Study Variables: We extracted demographic and clinical characteristics and utilization patterns for the study cohort from March 1, 2008, to February 28, 2009, the year prior to program initiation. All data were from administrative claims data deidentifi ed by a third-party organization (Healthcore, Wilmington, Delaware).
Disease and comborbidity variables included diabetes type, comorbid diagnoses, healthcare utilization, laboratory testing, and costs. We classified diabetes as type 1 if there was at least 1 claim for type 1 diabetes mellitus (T1DM) (250.1, 250.3). We classified comorbidities using all primary and secondary diagnosis codes from inpatient or outpatient settings in the 2 years prior to program initiation using the composite of Elixhauser and Charlson-Deyo classifications as described by Gagne et al.14 We measured hospitalizations and visits to an emergency department and ambulatory providers. We classified ambulatory providers as primary care, cardiology, endocrinology, nephrology, and ophthalmology based on primary designated specialty in claims files. We used billing codes to determine the frequency of testing of A1C and lipids. We generated total paid claims and total out-of-pocket costs for medical care and pharmacy claims.
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