Value-Based Insurance Design Defined as Copayment Reduction:

Where Have All the Studies Gone? Part 2

In Part 1 of this posting, I explained the recent shift in the meaning of the term “value-based,” hinted at missing research evidence, and made a passing reference to an award-winning song included on one of the few folk albums in U.S. history to attain #1 status for a full month—Peter, Paul, and Mary, released in 1962 by the artists of the same name. Today, on the fifty-first anniversary of John F. Kennedy’s commencement address at Yale, also in 1962, I draw on his key theme and ask: when it comes to VBID, have we “enjoyed the comfort of opinion without the discomfort of thought”?

A characterization that no “discomfort” associated with hard work has been experienced in VBID research would be unfair because extensive analysis has been done—much (but not the majority) of high quality.[1] However, some key information has gone unreported, as shown in the table below:

Table 1. Copayment Reduction Studies

First Author (Publication Year)—Description	Current Status
Chernew (2008). Quasi-experimental (difference-in-difference times series analysis with nonequivalent comparison group)[2]   Copayment of $0 for generic, 50% reduction for brand   Outcome measure was MPR change.	Reported. Copayment reductions were associated with a 2.6 to 4.0 MPR point difference (i.e., an added 7 to 14 days of therapy per year).   Key information missing from publication included: *Pre-intervention cost-sharing amounts in the comparison plan *Comparability of relevant benefit features in the study plans *Industry sectors of employees in study plans
Original analytic design of planned follow-up to Chernew 2008 (study described in previous table row). Outcomes included hospitalizations and ER visits, which had been predicted by the investigators to decline as a result of the medication adherence improvements observed.[2]	Results not reported. Instead, after initial analysis found “considerable uncertainty surrounding estimates of the impact of the [VBID] intervention on aggregate spending,” the authors developed a pharmacoeconomic model to estimate outcomes, describing it as “evidence that value-based insurance can be effective.”[3]
Spaulding (2009). MHealthy intervention for University of Michigan employees and dependents with diabetes; prospective trial with nonrandomized comparison group. Primary outcomes were medication utilization and adherence. Secondary outcomes were health care costs and utilization rates for hospitalizations, ER visits, and outpatient visits.[4]	Results not reported, although the study was initiated in 2006 and follow-up was scheduled to end in January 2009. Only exception was a poster described in a review article that reported “a 7 percent increase in adherence to blood pressure lowering medication and a nonsignificant 4 percent increase in adherence to statins.”[5]
Observational studies of copayment reductions conducted between 2010 and 2012[1]	Reported. Methodologically strongest of these studies indicated average MPR effect sizes of 1.4 to 4.0 percentage points, or about 5 to 15 added days of therapy per year.   Common problems identified in an editorial by Fairman and Curtiss included:   *No information about generic drug utilization *No information about net payer cost *Incomplete reporting of key study details (e.g., sample composition method) *Failure to control for or report mail order utilization rates or 90-day fill policies in all but one study
Choudhry (2011). Post-MI FREEE study of providing evidence-based medications to heart attack survivors free of charge after hospital discharge; block-randomized controlled trial (health plans, rather than patients, were randomized).[6] Nonequivalent communications to study groups (see below).   Primary outcome was occurrence of revascularization procedure or major vascular event (heart attack, stroke, unstable angina, CHF, or in-hospital death from CVD)	Primary outcome reported. *No significant difference between the study groups. *No pre-intervention values reported for either group.
Secondary outcome: Rate of major events as defined above	One secondary outcome reported. *Slightly and significantly lower for free medication group – 21.5 versus 23.3 events per 100 person-years   *No pre-intervention values reported for either group
Secondary outcome: Published analysis plan indicated that utilization outcomes would include annual rates of doctor visits, ER visits, and hospital admissions.	*Secondary outcome results not reported, although follow-up ended in November 2010
Secondary outcome: Published analysis plan indicated that investigators would report the outcome including out-of-hospital death data from the CDC.	*Secondary outcome results not yet reported; out-of-hospital death data are subject to a 1.5 to 2-year time lag and therefore should have been available for all patients by November 2012
Results for originally planned minimum follow-up time of one year	*Results not reported; results reflect minimum three-month follow-up.

CHF=congestive heart failure; CVD=cardiovascular disease; MPR=medication possession ratio, a commonly used measure of medication adherence, defined as days supply of dispensed medication divided by calendar time in days.

As Fred Curtiss and I pointed out in an editorial in April 2012, the most important of these studies is Post-MI FREEE, which, as its name implies, was an assessment of the effect of providing free medications to patients after hospital discharge for a heart attack. Despite Post-MI FREEE’s unique status as the first randomized study of copayment reductions, its value was greatly diminished by three critically important limitations.[7]

First was that the utilization outcomes pre-specified in the analysis plan have never been reported, although patient follow-up ended about two and one-half years ago. Nonreporting of pre-specified outcomes is always a cause for concern but was especially problematic in Post-MI FREEE because of the block-randomized design that assigned entire plans (rather than individual members) to free medication or usual coverage (copayments).  Specifically, because there was no control on medical reimbursement levels in the block randomization process, it is possible that the study groups did not have equivalent payment rates for the same medical services at baseline. Thus, it is possible that the post-intervention cost outcomes give an incomplete or even misleading picture of the effect of the intervention on service utilization. Compounding the problem, no baseline (pre-intervention) costs were reported for either study group.

Second patients in only one group—the group that did not receive free medications—were told that they would be on medications for a long time and would have to give up a lot of fun stuff. For both groups, letters advising patients about the study contained a list of medications that are recommended “to keep your heart strong,” but the usual coverage (control group) letters advised patients that “You may be on some medications for many years …” The usual coverage group letters also, unlike the free coverage letter, advised patients of American Heart Association recommendations for diet, exercise, and limitations on alcohol intake.

Third was a change in the study analysis plan to reduce the minimum length of follow-up from twelve months to three months because of lower-than-expected participation. A typical and sensible approach to handling this unavoidable development would have been a sensitivity analysis limited to study patients who had the originally intended twelve month minimum follow-up. However, although 65% of the study sample met that criterion, meaning that plenty of cases were available to perform a sensitivity analysis, none was performed.[7] As a result, we don’t know if the intervention’s effects would have persisted beyond the short follow-up period. Previous research suggests cause for concern on this point because of the large amount of noncompliance that takes place after the first three months of therapy but before the end of the first year. This decision was especially unfortunate because previous research in a similar patient population suggests that the survival benefits of statins are not observed until about 24 months of therapy.[8]

And—even with all of those potential biases toward finding optimal results for the free-medication group—the reported findings showed that only 12% of patients given free drugs, compared with 9% of those who paid copayments, were fully adherent to their drugs.[6]

Key Takeaway Points for Health Care Payers:

What To Do When the Studies Have “Gone to Flowers, Every One …” 

 

The history of VBID (copayment reduction) promotion is a notable but certainly not isolated example of noise outstripping scientific evidence in modern culture.  Much of the promised data have never been reported, and those that have been published suggest that the effects of copayment reductions are modest and may not be worth the added cost. It’s been said before, but it bears repeating: the strongest voices do not necessarily have the most important or valuable message.

So, when you read or hear about a proposed policy change in health care, whether it is VBID or a different proposal, consider the following questions:

• What was the follow-up time for the study being cited as evidence for the policy change? Does it represent a time frame during which one can reasonably expect meaningful benefits to occur?  If not, the results may represent confounding or the effects of another factor not measured in the study and/or presented in the report.
      
• Does the study follow-up period represent the time frame during which you will have to pay for the recommended intervention? (The investigators may have “lived with” this intervention for just a few short months; if you implement it, you may have its costs with you for a long time—perhaps long after intervention benefits stop accruing).
      
• To what degree do the characteristics reported by the authors match to those of the group to which you might apply the proposed intervention? For example, an intervention tested in a group of university professors may not have the same effect if implemented in an auto-workers’ union.
      
• Do the outcome measures represent your cost? Some copayment reduction studies report only total cost, which does not reflect the actual cost of the program to the payer. It is essential to report the net cost after taking loss of copayment revenues (offsets) from patients into account.
      
• If a pre-intervention versus post-intervention study was performed, did the investigators report the pre-intervention values for the outcome measures? Without this information, it’s hard for a decision maker to understand the actual effects of an intervention relative to the baseline status of the study sample.
      
• How was the intervention—such as “value-based”—defined? For example, the results of a “value-based” intervention that promoted a high-efficiency provider network provide little or no information about a “value-based” program that uses copayment reduction to incentivize medication use.
      
• Does the program described in the report include multiple components that you might or might not want to implement simultaneously? For example, if the program combined copayment reductions with other strategies, such as gym club memberships or educational interventions, it is hard to tell which program components were responsible for the outcomes. (And, if you cannot tell from reading the reporting what features were included in the program, it is reasonable to be skeptical about its findings.)
      
• Have relevant details for both study groups been presented in the study report? For example, for an intervention targeted to improve drug adherence, have the authors reported whether formularies, copayment levels, and utilization management policies (e.g., step therapy and prior authorization) were reasonably equivalent at baseline or otherwise explained the comparability of the groups (e.g., both groups were drawn from the same health plan or employer group with a uniform benefit design)?
      
• Have all the pre-specified outcomes been reported? (Note: This one takes a little detective work but is extremely helpful.) Use PubMed or, for randomized trials, www.clinicaltrials.gov to find the investigators’ pre-specified analysis plan.[9] If an analysis plan is available, compare it to the study report. If outcomes are missing from the report, query the investigators. If the outcomes are still not reported a considerable length of time after the end of follow-up, it is reasonable to consider the possibility of publication bias—that is, the outcomes were not reported because they did not fit the predispositions of the investigators, study sponsors, journal peer reviewers, or editors.
      
• Have previous studies of the same topic gone unreported, as has been the case in VBID? If so, it is reasonable to be skeptical about even the most promising research findings. For those of you familiar with probability theory—the underlying logic of statistical significance testing is that a certain number of results within a sampling distribution (a hypothetical sequence of repeated samples) will be statistically significant based on chance (sampling error) alone. If the results for actual samples have gone unreported, information necessary to interpret the results of statistical tests for any one sample is missing.

For health care payers, who are routinely bombarded today with assertions about "evidence-based" policies or services, these considerations should be viewed as critically important factors in decision making about value-based designs—or any proposed intervention, for that matter. When making decisions that affect not only pocketbooks, but also patient well-being, the best policy is circumspection: review the evidence that is reported, take into account the absence of evidence that has gone unreported, carefully examine the applicability of the available information for your population and setting, and don’t be afraid to ask questions.

 

[1] Fairman KA, Curtiss FR. What do we really know about VBID? Quality of the evidence and ethical considerations for plan sponsors. J Manag Care Pharm. 2011;17(2):156-174.

[2] Chernew ME, Shah MR, Wegh A, et al. Impact of decreasing copayments

on medication adherence within a disease management environment. Health

Aff (Millwood). 2008;27(1):103-12; Fairman KA, Curtiss FR. Making the world safe for evidence-based  policy: let’s slay the biases in research on value-based insurance design. J Manag Care Pharm. 2008;14(2):198-204.

[3] Chernew ME, Juster IA, Shah M, et al. Evidence that value-based insurance can be effective. Health Aff (Millwood). 2010;29(3):530-36.

[4] Spaulding A, Fendrick AM, Herman WH, et al. A controlled trial of value-based insurance design - the MHealthy: Focus on Diabetes (FOD) trial. Implement Sci. April 2009.

[5] Choudhry NK, Rosenthal MB, Milstein A. Assessing the evidence for

value-based insurance design. Health Aff (Millwood). 2010;29(11):1988-94.

[6] Choudhry NK, Avorn J, Glynn RJ, et al.; Post-Myocardial Infarction Free Rx Event and Economic Evaluation (MI FREEE) trial. Full coverage for preventive medications after myocardial infarction. N Engl J Med. 2011;365(22):2088-2097; Choudhry NK, Brennan T, Toscano M, et al. Rationale and design of the Post-MI FREEE trial: a randomized evaluation of first-dollar drug coverage for post-myocardial infarction secondary preventive therapies. Am Heart J. 2008;156(1):31-37.

[7] Fairman KA, Curtiss FR. VBID, the PPACA, and FREEE medications: Did politics trump the evidence about cost sharing? J Manag Care Pharm. 2012;18(2):146-156.

[8] Bavry AA, Mood GR, Kumbhani DJ, Borek PP, Askari AT, Bhatt DL.

Long-term benefit of statin therapy initiated during hospitalization for an

acute coronary syndrome: a systematic review of randomized trials. Am J

Cardiovasc Drugs. 2007;7(2):135-41.

[9] At www.clinicaltrials.gov, you can search by name of a medical condition, drug, or investigator.