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Value-Based Insurance Design and Medication Adherence: Opportunities and Challenges
Kevin A. Look, PharmD, PhD
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Value-Based Insurance Design and Medication Adherence: Opportunities and Challenges

Kevin A. Look, PharmD, PhD
Improving the quality of studies evaluating the impact of value-based insurance design programs on medication adherence will serve to better inform healthcare system change.
The primary author reported employment by the study firm or study sponsor in 6 studies,22,25,26,28,32,37 and in 6 additional studies, the study sponsor was also the study firm or involved as a research collaborator.10,29,33-35,38 Mean effect sizes in these studies were nearly twice as large as in studies that did not report such relationships. The highest effect sizes (>6.5%) were seen in those studies in which the primary author reported employment by the study firm or study sponsor.


High-quality evidence demonstrating a meaningful impact of V-BID programs on outcomes such as medication adherence is of particular importance, as recent evidence suggests V-BID programs may not result in cost savings, or may even result in increased costs.35,40 In accordance with prior research, V-BID programs were consistently associated with improved adherence, with a mean increase in adherence of 3.4 percentage points over 1 year.15,40 However, concerns about the quality of evidence available to support the V-BID concept are valid, and several study design and reporting elements were consistently identified as being in need of improvement.

Methods: Lacking Detail and Transparency

Many of the studies evaluating V-BID programs suffered from a lack of clarity when describing the methods used, or a lack of transparency when reporting results. Baseline and/or final adherence rates could not be accurately determined for nearly half of the included studies, which are particularly important when evaluating the magnitude of a change in medication adherence. This is concerning because several studies showing large effect sizes had low baseline adherence levels (<55%),22,29,31,32 which may be evidence of “regression to the mean” instead of a clinically meaningful improvement in adherence.41 Authors should clearly disclose baseline and/or final adherence values in addition to effect sizes.

Also of concern, several studies have utilized treatment and control groups with differing plan characteristics that are not accounted for. For example, 1 study compared treatment members enrolled in small employer group plans with control group members enrolled in large employer group plans.37 Similarly, 3 studies used a treatment group consisting of 32,032 underwritten employers and 51 self-insured employers, of which 49% had fewer than 50 subscribers; the control group consisted of 176 self-insured employers, with 84% having more than 1000 subscribers.33-35 If the 2 groups differed in their health characteristics or medication-taking behavior (ie, adherence), these nonequivalent control groups would result in biased treatment effects.42 When reasonable, authors should utilize treatment and control groups with plan characteristics as similar as possible, and report the size and direction of any bias detected between the 2 groups.

Other problems related to the reporting of results were identified in V-BID program evaluations. Final treatment and control group sample sizes could not be accurately determined in several studies.33,34,37 Additionally, several studies did not report the statistical significance of their findings,28,32,38 or drew positive conclusions from limited study findings.36-38 Finally, several studies22,33-36 used different measures of baseline adherence and effect size, which can cause misleading results. For example, 1 study reported the V-BID effect size as the percentage change in adherence; actual adherence changes were approximately half as large.36 This approach could potentially mislead readers to believe that the V-BID program studied was more effective at improving medication adherence than it actually was. Study authors should take care to ensure that consistent terminology and definitions are used throughout a study, that methods are thoroughly described, and that results (positive, neutral, and negative) are fully disclosed.

Disease Management and Other Confounders

Although nearly two-thirds of all V-BID programs studied have included a DM program, few studies have evaluated how these DM programs impact medication adherence. One such study found a significant increase in adherence in the DM group, but nonsignificant increases in the non-DM group.30 A related study found that V-BID in combination with DM was significantly better than DM alone.31 In contrast, a recent study of 76 V-BID plans found that plans which did not offer DM programs had a significantly greater impact on adherence than plans with DM programs when adjusting for other plan design characteristics, although the reverse was true for benefits offering wellness programs.40

Characteristics of the DM programs may affect how they impact medication adherence within a V-BID initiative. Factors including the intensity of DM program interactions (eg, nurse phone call vs targeted mailing), the timing of DM implementation (eg, existing vs newly implemented), and enrollment requirements (eg, mandatory vs voluntary) may impact the adherence estimates seen in V-BID studies. One study found that members receiving nurse counseling had more adherence improvements than members receiving health education mailings.32 Additionally, research has shown that programs with barriers to enrollment (eg, laboratory testing and survey completion) may have significant implications for the overall effectiveness of V-BID initiatives.43 This review found that V-BID programs with newly implemented DM programs had effect sizes nearly twice those of ones with existing programs, potentially due to the mandatory nature of these newly implemented DM programs. Further research is warranted into the impact of DM programs and how their characteristics impact medication adherence when used in combination with V-BID initiatives.

The impact of other potential confounding factors seen in studies of V-BID programs is still unclear. For example, 2 studies evaluated a V-BID program offered by Novartis Pharmaceuticals, which did not require cost-sharing for Novartis products.28,29 Although the authors reported that about one-fifth of the medications dispensed prior to program implementation were Novartis products, neither study evaluated how inclusion of these no-cost medications affected their results. Another VBID program included member incentives valued at over $500 annually per enrollee.38

Mail order and 90-day medication supplies may also impact medication adherence changes in V-BID evaluations. Maciejewski et al found that adherence rates were 17 to 22 percentage points higher in every therapeutic class analyzed among enrollees who had filled at least 1 prescription for a 90-day supply of medication.33 This is particularly concerning in that 1 study found an approximately 20% increase in mail order utilization in response to V-BID co-pay reductions.36 Other potential issues include pharmacy auto-refill and medication synchronization programs. The majority of studies do not assess the impact of such factors and do not control for their presence, which may artificially inflate the effects of V-BID programs on medication adherence when using prescription drug claims.

Conflicts of Interest

Of particular concern, much of the evidence for the V-BID approach comes from studies in which the primary author is employed by the study firm or study sponsor, or in which the study sponsor was involved as the study firm or as a research collaborator. Although the extent of the influence study firms or sponsors have on the results of such research is unknown, this review found that these relationships may impact reported effect sizes. Authors need to ensure the transparency and independence of their research, and ensure thorough and accurate disclosure of potential conflicts of interest.

Further Research and Expansion of the V-BID Concept

Based upon the characteristics of the studies included in this review, several opportunities exist for further research on and expansion of the V-BID concept. This review found little published research in settings such as managed care organizations (MCOs), PPOs, and health maintenance organizations (HMOs), or among small employer groups.27,36-37 The impact of a V-BID program may be greater in tightly managed settings (eg, MCOs or HMOs), and insurers that are responsible for both prescription drug and medical spending may realize a higher return on investment from a V-BID program than an insurer responsible only for prescription drug spending. Additionally, although several simulation studies exist,44-46 no empirical studies have been performed in Medicare or Medicaid populations. Piloting and evaluating V-BID programs in different health systems may help identify where V-BID initiatives are most effective.

Previous V-BID programs in the literature have focused on treatments for a small number of diseases that have multiple low-cost generic drug options available. No published studies have included medications used in the treatment of conditions such as cancer, organ transplant, human immunodeficiency virus/AIDS, and end-stage renal disease. Patients with these conditions may greatly benefit from improved adherence and realize greater medical cost savings; however, these conditions may be less desirable from a payer perspective, as they tend to have very high drug costs and few generic alternatives. Furthermore, few studies have evaluated outcomes such as lab values or patient-centered outcomes such as member satisfaction, disease control, and affordability.26,38 Finally, more longer-term evaluations are needed to determine the sustainability of V-BID programs on medication adherence and costs, as recent evidence suggests V-BID programs may not result in short-term cost savings or may even result in increased costs.35,40


Many of the studies evaluating V-BID programs suffered from a lack of clarity when describing the methods used or a lack of transparency when reporting results, and much of the evidence comes from studies where potential conflicts of interest exist. Authors should ensure that baseline and/or final adherence values are reported in addition to effect sizes, that consistent terminology is used throughout a study, that results are assessed for potential bias due to confounding factors, and that due diligence has been performed to eliminate alternative explanations. Improving the quality and expanding the scope of the V-BID evidence base will serve to better inform healthcare system change.


The author would like to acknowledge the helpful comments of Michelle Chui, PharmD, PhD, and David Mott, PhD, on earlier versions of this manuscript.

Author Affiliations: Social and Administrative Sciences Division, University of Wisconsin School of Pharmacy (KAL), Madison, WI.

Source of Funding: None.

Author Disclosures: The author reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design, acquisition of data, analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript for important intellectual content, statistical analysis, administrative, technical, or logistic support, and supervision (KAL).

Address correspondence to: Kevin A. Look, PharmD, PhD, Assistant Professor, Social and Administrative Sciences Division, University of Wisconsin School of Pharmacy, 777 Highland Ave, Madison, WI 53705-2222. E-mail:
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