Cost-benefit analysis (CBA) provides a clear decision rule: undertake an intervention if the monetary value of its benefits exceed its costs. However, due to a reluctance to characterize health benefits in monetary terms, users of cost-utility and cost-effectiveness analyses must rely on arbitrary standards (e.g., < $50,000 per QALY) to deem a program "cost-effective." Moreover, there is no consensus regarding the appropriate dollar value per QALY gained upon which to base resource allocation decisions. To address this, the authors determined the value of a QALY as implied by the value-of-life literature and compared this value with arbitrary thresholds for cost-effectiveness that have come into common use. A literature search identified 42 estimates of the value of life that were appropriate for inclusion. These estimates were classified by method: human capital (HK), contingent valuation (CV), revealed preference/job risk (RP-JR) and revealed preference/non-occupational safety (RP-S), and by U.S. or non-U.S. origin. After converting these value-of-life estimates to 1997 U.S. dollars, the life expectancy of the study population, age-specific QALY weights, and a 3% real discount rate were used to calculate the implied value of a QALY. An ordinary least-squares regression of the value of a QALY on study type and national origin explained 28.4% of the variance across studies. Most of the explained variance was attributable to study type; national origin did not significantly affect the values. Median values by study type were $24,777 (HK estimates), $93,402 (RP-S estimates), $161,305 (CV estimates), and $428,286 (RP-JR estimates). With the exception of HK, these far exceed the "rules of thumb" that are frequently used to determine whether an intervention produces an acceptable increase in health benefits in exchange for incremental expenditures.

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BACKGROUND In the Medicare Shared Savings Program (MSSP), accountable care organizations (ACOs) have financial incentives to lower spending and improve quality. We used quasi-experimental methods to assess the early performance of MSSP ACOs. METHODS Using Medicare claims from 2009 through 2013 and a difference-in-differences design, we compared changes in spending and in performance on quality measures from before the start of ACO contracts to after the start of the contracts between beneficiaries served by the 220 ACOs entering the MSSP in mid-2012 (2012 ACO cohort) or January 2013 (2013 ACO cohort) and those served by non-ACO providers (control group), with adjustment for geographic area and beneficiary characteristics. We analyzed the 2012 and 2013 ACO cohorts separately because entry time could reflect the capacity of an ACO to achieve savings. We compared ACO savings according to organizational structure, baseline spending, and concurrent ACO contracting with commercial insurers. RESULTS Adjusted Medicare spending and spending trends were similar in the ACO cohorts and the control group during the precontract period. In 2013, the differential change (i.e., the between-group difference in the change from the precontract period) in total adjusted annual spending was −$144 per beneficiary in the 2012 ACO cohort as compared with the control group (P = 0.02), consistent with a 1.4% savings, but only −$3 per beneficiary in the 2013 ACO cohort as compared with the control group (P = 0.96). Estimated savings were consistently greater in independent primary care groups than in hospital-integrated groups among 2012 and 2013 MSSP entrants (P = 0.005 for interaction). MSSP contracts were associated with improved performance on some quality measures and unchanged performance on others. CONCLUSIONS The first full year of MSSP contracts was associated with early reductions in Medicare spending among 2012 entrants but not among 2013 entrants. Savings were greater in independent primary care groups than in hospital-integrated groups.

Difference-in-difference (DD) methods are a common strategy for evaluating the effects of policies or programs that are instituted at a particular point in time, such as the implementation of a new law. The DD method compares changes over time in a group unaffected by the policy intervention to the changes over time in a group affected by the policy intervention, and attributes the “difference-in-differences” to the effect of the policy. DD methods provide unbiased effect estimates if the trend over time would have been the same between the intervention and comparison groups in the absence of the intervention. However, a concern with DD models is that the program and intervention groups may differ in ways that would affect their trends over time, or their compositions may change over time. Propensity score methods are commonly used to handle this type of confounding in other non-experimental studies, but the particular considerations when using them in the context of a DD model have not been well investigated. In this paper, we describe the use of propensity scores in conjunction with DD models, in particular investigating a propensity score weighting strategy that weights the four groups (defined by time and intervention status) to be balanced on a set of characteristics. We discuss the conceptual issues associated with this approach, including the need for caution when selecting variables to include in the propensity score model, particularly given the multiple time point nature of the analysis. We illustrate the ideas and method with an application estimating the effects of a new payment and delivery system innovation (an accountable care organization model called the “Alternative Quality Contract” (AQC) implemented by Blue Cross Blue Shield of Massachusetts) on health plan enrollee out-of-pocket mental health service expenditures. We find no evidence that the AQC affected out-of-pocket mental health service expenditures of enrollees.

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.

After 3 years of the MSSP, participation in shared-savings contracts by physician groups was associated with savings for Medicare that grew over the study period, whereas hospital-integrated ACOs did not produce savings (on average) during the same period. (Funded by the National Institute on Aging.).

By abandoning the archaic principle that all services must cost the same for all patients, we can move to a high-value health system.by Michael E. Chernew, Allison B. Rosen, and A. Mark Fendrick ABSTRACT: When everyone is required to pay the same out-of-pocket amount for health care services whose benefits depend on patient characteristics, there is enormous potential for both under-and overuse. Unlike most current health plan designs, Value-Based Insurance Design (VBID) explicitly acknowledges and responds to patient heterogeneity. It encourages the use of services when the clinical benefits exceed the cost and likewise discourages the use of services when the benefits do not justify the cost. This paper makes the case for VBID and outlines current VBID initiatives in the private sector as well as barriers to further adoption. [Health Affairs 26, no. 2 (2007): w195-w203 (published online 30