Archive for December, 2010

Value-Based Insurance Design: A Bargain at $10 Per Day?

A recent edition of Health Affairs included two new studies of copay reductions for prescription medications, commonly referred to as a type of value-based insurance design (VBID).  These studies are a much-needed addition to the literature given the paucity of empirical studies.  The idea behind lowering copays for prescription medication is to increase medication adherence by removing cost as a potential barrier and to hopefully improve health outcomes, in the short or long-term.

The results of the two new evaluations are generally consistent with an earlier study.  When copays are partially or entirely waived, medication adherence, commonly measured as the medication possession ratio (MPR), increases by 2 to 4 percentage points, depending on the drug class.

Key Findings From Studies of Prescription Copay Waivers
Study Waiver Absolute Change in MPR
ACEIs Diabetes Statins
Choudry (2010)* Zero copay     ~ 2.3
Maciejewski (2010) Zero copay for generics; Brands moved from Tier 3 to Tier 2 2.9 3.8 1.8
Chernew (2008) Zero copay for generics; 50% copay reduction for brands 2.6 4.0 3.4

How meaningful is this change in adherence?

  • From a clinical perspective, the change represents 7 to 14 additional days of medication over a course of one year, the clinical benefit of which is simply unknown.
  • From a cost perspective, these copay waivers cost between $7 and $10 per additional day of medication use (using Chernew as the case study).
  • To put this figure in context, at the time of the Chernew study, daily drug cost ranged from $1.14 to $2.68 per day* for these medications—which means the copay waiver resulted in an incremental daily cost of treating these patients that was 3 to 6 times higher than the daily ingredient cost.

Does this represent good value?  Likely not.  Of course, plans can raise copays for low value therapy classes to offset these costs, but so far, the market has shown reluctance to do so.  Advocates of copay waivers might argue that this is siloed thinking because increased drug costs will be offset by reductions in medical spend.  Certainly sounds appealing, doesn’t it?  To learn more, look for Part 2 of this discussion in the near future.

Source for cost calculations: Fairman and Curtiss, JMCP 2008.

*Choudry used a similar measure to MPR-proportion of days covered

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Is Health Dialog the Best Buy in Disease Management?

The disease management industry appeared to receive some good news in September when Health Dialogue published a randomized controlled trial of its care management program in The New England Journal of Medicine, an unprecedented achievement for an industry that has been plagued by questions of value.   The study has been widely touted at health conferences in the last few years, but its recent publication represents the first chance to better understand their care model and evaluation approach.

The study was a randomized trial that compared usual care to enhanced care support, the key differences being the number and type of patients targeted as well as the number of contact attempts.  Patients with heart failure, CAD, COPD, diabetes, or asthma represented less than twenty-five percent of the targeted population.   The other 75% of targeted patients included those at high risk for hospitalization for preference-sensitive conditions (defined as a condition for which at least two valid, alternative treatments strategies are available, such as hip replacement) and patients with other high-risk conditions (e.g., back and neck pain).

How Much Did the Program Save?

  • Overall savings were 3.6% ($8 PMPM) or 2.7% ($6 PMPM) after subtracting out program fees of $2 PMPM.
  • Savings were driven by reductions in hospitalizations. After one year, the hospital admission rate was 10.1% lower for the enhanced-support group, a reduction which was almost entirely accounted for by a reduction in admissions for preference-sensitive and high variation medical conditions (never defined in paper).
  • Hospital admissions and medical expenditures actually increased for the cohort of patients with chronic conditions and gaps in care.
  • No difference was found between the enhanced and usual care groups for lab tests or pharmaceuticals.

How Did The Program Achieve These Medical Savings?

These findings show that Health Dialog’s care management model is generating at least 20% of its savings through its unique feature of shared decision-making for preference-sensitive conditions and that the program is having minimal, if any, impact on traditional quality of care measures.  Accordingly, the label “disease management” may be a misnomer—note that the authors never referred to the program as such.

How might the program be delivering the savings for patients without preference-sensitive conditions?  The authors indicate they had access to real-time discharge notifications.  This feature, in and of itself, could explain Health Dialog’s success for traditional DM patients in the absence of notable quality improvements.  Research has shown that timely intervention at discharge and beyond (a.k.a., transitional care model), particularly for patients with heart failure, can provide real short-term savings.  Over the years, the DM industry has struggled to identify these hospitalized patients in a timely fashion.  One clue that this rapid discharge notification may be an explanation is the large reduction in hospitalizations for patients with heart failure.  Heart failure is the condition for which transitional care has the strongest evidence base and in this study, heart failure patients experienced a reduction in hospitalizations that was more than three times any of the other chronic conditions.  However, this hypothesis is mere speculation—the authors acknowledge that their purpose was not to determine which specific components accounted for the savings; and given the program’s commercial focus, these details are not likely to be revealed.

What Are the Implications for Disease Management Purchasing?

Certainly Health Dialog’s research has notable limitations, perhaps the biggest being how will the program perform over time–did patients who decided against surgery in the first year stick to that decision in the following year?  Despite the unknowns related to potential quality improvements and how the program generates saving, definitive answers to these questions may not be a necessary requirement to purchase.  In the near term, the study’s methodology is stronger than what we have seen in the commercial market to date, its theoretical basis for savings for preference-sensitive conditions is strong, and the reach rate exceeded 50 percent for its traditional disease management patients, better than the market norm.  While this study may not be a great advertisement for traditional disease management, it provides compelling support for Health Dialog’s potentially unique model.

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Pharmaceutical Outcomes Unveiled

The study and application of health outcomes research to the management of pharmaceuticals is a messy business.  Research tools range from the large randomized controlled trial to the small, self-reported health outcomes study.  Considerable uncertainty still exists about the best methodology for many areas of inquiry, and commercial interests and publication bias runs rampant.   While pharmaceutical manufacturers are the most studied offenders, health care vendors are all potential violators; and of course, bias is not limited to those with commercial interests.

A study published earlier this year in JAMA once again brought awareness to the extent of the bias problem, with headlines reporting “Science for Sale.” Considering top medical journals, scientists reviewed over 600 studies from 2006 that had reported statistically non-significant primary outcomes.  They subsequently conducted a detailed analysis of 72 of those they considered to be of the highest quality—all randomized controlled trials.  Upon detailed review, they found that 50 percent of the articles misrepresented the data in the study conclusions, leaving the impression that the treatments were effective even though the primary results indicated otherwise.  This “spin” —which the study authors define as specific reporting strategies, whatever their motive, to highlight that the experimental treatment is beneficial, despite a statistically nonsignificant difference for the primary outcome, or to distract the reader from statistically nonsignificant results—also appeared in nearly 60% of the conclusions found in the study abstracts.

If the conclusions in 50 percent of the studies published in top medical journals are being spun toward independent interests, what is the magnitude of distortion in health outcomes research related to pharmaceuticals, where the methodological choices are greater, the standards less well-defined, the chance of study registration prior to initiation far less likely, and the quality of peer-review often suspect?  The issue of bias only serves to compound decision-makers’ challenge in reviewing and applying the rapidly growing body of health outcomes research to make a well-informed decision about their pharmacy benefits.  Recognizing this challenge, in the months ahead, our plan is to laud those who use the right evaluation methods for health outcomes assessment and call out those who do not and to provide simple tools for decision-makers to increase their knowledge and ability to see through the rhetoric.  Finally, by adding another voice on the side of rigorous analysis, the truth about what works and what doesn’t will continue to crowd out studies that are merely repackaged marketing.

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