Posts Tagged Cost-Effectiveness
While there has been no shortage of press about the recent FDA approval of MakenaTM, made by K-V Pharmaceutical Company, I think the recent recently published letter in the New England Journal of Medicine about the pharmacoeconomics of the drug is quite useful.
Separate from the business and ethical questions of the $30,000 plus price tag for Makena (a drug that previously cost around $300 as a generic), one could argue that the pharmacoeconomics of the drug at the new price may still render the medication a cost-effective therapy. A physician at Aetna examined just this question, evaluating the pharmacoeconomics of Makena based on the likely rate of treatment, the efficacy of the drug versus placebo, and the subsequent costs avoided due to treatment.
Based on published literature, Aetna estimated that about 139,000 women are candidates for Makena, of which about 22% (30,500) are likely to have a recurrent preterm birth absent medication. With treatment, 33% (10,000) preterm births could be prevented, saving $334 million in direct medical costs and $519 million in indirect medical costs. The indirect medical costs include maternal care, special education, early intervention costs, etc. The cost of treating the 139,900 women, at $29,000 per course of treatment (price before distribution mark-ups) would be $4.0 billion. Accordingly, including both direct and indirect medical costs, Makena will cost $8 for every $1 saved, a strongly negative ROI.
However, one could argue that demonstration of net cost-savings should not be the criteria for coverage but rather payers should examine the drug’s cost-effectiveness, measured as cost per life-year saved (LYS). To meet the threshold of $100,000 per LYS (inflation adjusted for the more commonly used $50,000 per quality-adjusted life year), the drug would need to prevent nearly 35,000 life years or 435 deaths, assuming an average lifespan of 80 years. Previous studies suggest this is unlikely as the drug has shown a small (and not statistically significant) effect on reducing mortality.
Another coverage option is to identify opportunities to further target use of the drug to patients at the greatest risk for preterm birth. However, even with an ability to target only patients with a 100% risk of preterm birth (highly implausible), the drug would still cost nearly double the amount it saves in direct and indirect medical costs given its effectiveness. Of course, these figures represent back-of-the-envelope pharmacoeconomic calculations for Makena; but absent very significant unmeasured benefits, the analysis highlights the difficulty this drug will have in demonstrating a favorable pharmacoeconomic profile.
In a previous post, I commented on a study that examined the correlation between medication compliance and use of other healthcare services over three years for a large population of commercially insured members with a diabetes or cardiovascular-related diagnosis. The study found a strong association between greater medication adherence (defined as a medication possession ratio of 0.80 or higher) and lower utilization of other medical services, primarily hospitalizations. Considering all healthcare costs, not just disease-related, they reported an ROI of 3:1 for medication adherence in dyslipidemia and 10:1 for hypertension.
While my previous post highlighted a key methodological concern about the study, one of the most powerful tools for quickly stress-testing a study’s findings is to conduct a plausibility test to see if the results match up with what other rigorous research would suggest. For this plausibility comparison, I selected a meta-analysis of data from 90,056 individuals in 14 randomized trials of statins. There are other meta-analyses and randomized controlled trials that I could have chosen, which would have led to similar conclusions.
The adherence study included all patients with dyslipidemia as evidenced by a diagnosis in the medical claims. To be conservative, I selected patients from the meta-analysis who were taking statins for secondary prevention and looked at 5-year effectiveness, as shown in the table below. Given the absolute risk reductions observed for hospitalizations for MI, revascularization, and strokes, the estimated number of hospital days avoided across all the patients was 0.33. In contrast, the adherence study reported an average of 1.18 fewer hospital days for adherent patients versus non-adherent patients with dyslipidemia.
It is not plausible that the nearly 4-fold greater hospital reduction reported in the adherence study (1.18 versus 0.33 days) was due to greater medication compliance. The implausibility is compounded by the fact that I included more severe patients, followed them for a much longer period of time, and examined the full effect of statins versus placebo rather than the effect of differences in adherence, all of which inflated our hospital days avoided.
|Patients with Previous MI or CAD|
|Event||Statin||No Statin||Absolute Reduction||Length of Stay||Hospital days avoided|
|Total hospital days avoided||0.33|
Plausibility tests are quick and powerful and can be used to test the ROI claims from disease management vendors, medication compliance programs, and many other healthcare services. Recognizing the need for such tools and plan sponsors’ limited time to examine vendors’ savings claims, we designed plausibility calculators specific to disease management and value-based insurance to help plan sponsors discern fact from fiction. They are free so the next time you are listening to a vendor’s sales pitch, you can do a quick reality check.
The honeymoon for disease management (DM) has clearly passed. Health plans and employers are frustrated with the lack of value provided by their DM vendors but they often struggle to understand why their DM program isn’t working as intended or what to do to correct it. The combination of frustration and confusion has resulted in a wave of market experimentation fueled by vendors who claim to have discovered the allusive secret ingredient that makes DM work.
Published today in the American Journal of Managed Care is a paper I wrote to help plan sponsors better understand what is really known about savings from DM, why telephone-based DM does not save money and what it takes to generate real savings. This work is based on my market experiences, an extensive review of the literature, both in DM and other related disciplines, and a detailed assessment of what is known about cost-saving healthcare services and treatments.
The answer to why DM does not provide short-term savings lies partly in the myriad of cost-effectiveness assessments that have been conducted on the treatment for chronic disease over the last 30 years. Cohen and Neumann reported that less than 20 percent of preventive measures or treatments for chronic conditions are cost-saving, even for a 30-year time horizon. Kahn found that aggressive implementation of nationally recommended medical activities would increase costs over a 30-year period for all activities except smoking cessation. Looking more specifically at the individual components of DM programs, the evidence is equally compelling. At the level of the individual program activity, cost-savings has not been shown for treatment of these chronic conditions, the exception being heart failure. Accordingly, one should not necessarily expect cost savings for the program as a whole, absent a belief that DM can independently improve the outcomes of patients with chronic disease without affecting the key clinical goals for each disease.
Cost-Effectiveness of Common Disease Management Activities/Goals
|Diabetes||A1C < 7%||No|
|LDL Cholesterol < 100 mg/dl||No|
|Blood pressure < 130/80 mmHG||No|
|CAD||Antihyperlipidemics (LDL < 100 mg/dl)||No|
|Asthma||Inhaled anti-inflammatory use||No|
|Asthma education on symptom monitoring and/or trigger avoidance||Unknown|
|HF||ACE inhibitor use||Yes|
|Beta blocker use||Yes|
|Structured remote monitoring (weight, blood pressure, etc.)||Yes|
While the cost-effectiveness literature does not bode well for the future of telephone-based DM as currently designed, cost-effectiveness research suggests that better targeting of treatment activities and patients may provide opportunity for cost-savings for some disease states. Although a targeted approach is both intuitive and supported by the evidence, it has two practical problems that will prevent it from being widely adopted in the marketplace.
- First, a more targeted approach is not a revenue-optimizing model for DM vendors. For example, only about 5% of asthmatics would be targeted for intervention if the criteria were real cost-savings, hardly a desirable approach for DM vendors whose revenue model is based on volume due to their large fixed cost structure.
- A second barrier to adoption is the marketability of the more realistic return-on-investment (ROI). An employer is unlikely to select a vendor that is offering a 1.85 ROI over a vendor with an inflated ROI (that also includes a much larger group of patients since it is not targeted). The same problem holds true for health plans—even though they often understand the methodological problems of the vendors’ ROIs, ultimately they too must market their program to the employers who sometimes believe all ROIs are created equal.
To learn more about proven strategies for short-term savings, take a look at the full published article.
Econometrics has finally discovered the hidden truth about pharmaceuticals—they save money, a lot of money. A recent study found that compliance with pharmaceuticals demonstrated a 8.4:1 ROI for congestive heart failure, 10.1:1 for hypertension, 6.7:1 for diabetes, and 3.1:1 for dyslipidemia. This is certainly a surprising result given the contradictory findings from other credible research on the cost-effectiveness of pharmaceuticals. However, it’s likely to capture some headlines given its intuitive appeal to many so I took a closer look.
This study, published by researchers at one of the large PBMs, examined the correlation between medication compliance and use of other healthcare services over several years for a large population of commercially insured members with a diabetes or cardiovascular-related diagnosis. It is well-known in the published literature that making causal conclusions from the cross-sectional examination of medication compliance against total medical spend is highly problematic due to the Healthy Adherer Effect, which is the tendency of people who are adherent to their medications to also engage in other healthy behaviors, such as exercising regularly and eating a healthy diet. Reason being, it is difficult, if not impossible, to control for differences in patient behavior that one cannot measure in the claims data. In this particular study, the Healthy Adherer Effect was likely compounded by assigning a compliance score of zero to patients having a diagnosis but no prescription claim for the condition.
The study authors stated that they overcame the systematic selection bias created by the Healthy Adherer Effect through the use of an econometrics technique called fixed-effect regression—basically an alternative form of the more commonly used ordinary least squares regression (OLS). As is often the case with econometric models, to believe in the superiority of fixed-effect modeling, you have to believe a key underlying assumption that the model makes, which is that confounders, such as eating healthy and exercise, do not vary over time in conjunction with medication compliance. As the authors acknowledge in the references, “fixed-effects modeling does not allow for the control of confounders that vary over time. Thus, for example, if patients who become adherent simultaneously start exercising regularly (assuming that both of these behavioral changes reduce health services use and spending), the estimated impact of adherence would remain biased.” The authors make no case for why this assumption would hold true and it hardly seems plausible given what is already known on the subject.
While such fixed-effects estimators may be an improvement on basic cross-sectional methods, they are still quite limited when it comes to uncovering a true causal effect when the confounder(s) varies over time; and like OLS, will tend to overestimate the causal effect of pharmaceuticals on medical spending in the presence of the Healthy Adherer Effect. Some evidence to that effect lies in the manuscript’s appendix—for most of the conditions, the difference between the estimate for the OLS regression, which the authors acknowledge does not address the selection bias problem, and the fixed-effect model was minimal (e.g., marginal effects estimate for inpatient days for heart failure of 5.731 for OLS vs. 5.715 for fixed-effects).
However, setting aside the more technical discussion, one of the most informative and practical ways to test the strength of these results is to conduct a plausibility test of the ROI, which basically means to compare these findings against credible randomized controlled trials of medication efficacy. I’ll show you those results in an upcoming post. Of course, none of this discussion is meant to minimize the importance of improved medication compliance. It continues to be a critically important gap to address but employers and other plan sponsors should be provided realistic expectations about the economic value of improved compliance.