This paper estimates the effects of a large employer's value-based insurance initiative designed to improve adherence to recommended treatment regimens. The intervention reduced copayments for five chronic medication classes in the context of a disease management (DM) program. Compared to a control employer that used the same DM program, adherence to medications in the value-based intervention increased for four of five medication classes, reducing nonadherence by 7-14 percent. The results demonstrate the potential for copayment reductions for highly valued services to increase medication adherence above the effects of existing DM programs.
Value-based insurance design reduces patient copayments to encourage the use of health care services of high clinical value. As employers face constant pressure to control health care costs, this type of coverage has received much attention as a cost-savings device. This paper's examination of one value-based insurance design program found that the program led to reduced use of nondrug health care services, offsetting the costs associated with additional use of drugs encouraged by the program. The findings suggest that value-based insurance design programs do not increase total systemwide medical spending.
Disease management (DM) programs claim to achieve cost savings by reducing clinical adverse events. While measuring changes in adverse events is straightforward, plausibly demonstrating savings has been contentious, especially absent an external comparison population. In this situation, a single-population methodology is often used, in which the cost trend for those with no program conditions ("non-chronics"--NC) forms the expected trend for those who have at least 1 program condition ("chronics"--C). The methodology's fundamental assumption is that--absent DM--C and NC trends would be identical (or bear a constant relationship over time). We assessed this assumption by altering the values of key variables used to identify C and NC, and to calculate trend. We compared C and NC baseline trends for a 23-condition telephonic DM multiemployer program representing nearly 300,000 members. Trends were calculated for 16 combinations of values for 4 key variables: identification look-back frame (12 vs. 24 months); identification threshold (high vs. lower specificity); claims runout (3 vs. 6 months); and minimum required insurance eligibility (any 6 months vs. 12 months continuous eligibility in the measurement year). Identification was performed by annual qualification. Changes in values for the 4 key variables markedly impacted baseline C and NC trends. C trends varied between 10.1% and 13.1%; NC trends between 5.2% and 12.8%. C-NC trend differences ranged between -1.9% and +7.0%. The combination of 24 months identification look-back, high identification threshold, 6 months runout, and any-6-months eligibility gave the most convergent C and NC trends (10.4% and 10.7%). Seemingly minor changes in key variables impact C and NC trends in single-population pre-post DM savings methodologies. When a suitable comparison population is not available, at least 1 year of baseline C and NC trends should be reported-as recommended by the DMAA--and values of key variables used should be specified. Plausibility metrics (eg, hospitalizations) should be reported.
* affiliation when research was conducted DISCLOSURESMichael Chernew and Mark Fendrick provide consulting services to Hewitt Associates LLC and ActiveHealth Management related to value-based insurance design, and accept consulting fees and speaking honoraria related to value-based insurance design. Allison Rosen is employed by the University of Michigan's Center for Value-Based Insurance Design. Wegh, Rosenberg, and Juster are employed by ActiveHealth Management, which provides consulting services to employers, health plans, and pharmacy benefit managers. Shah is no longer affiliated with ActiveHealth Management. The study referred to in the JMCP editorial and this letter was supported by GlaxoSmithKline and Pfizer, and the Center for Value-Based Insurance Design lists 6 pharmaceutical manufacturers as supporters.All authors were responsible for the study concept and design of this response to an editorial. All authors shared equally the data analysis, data interpretation, writing, and revision.
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