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.