The Health Inequality Impact of a New Cancer Therapy Given Treatment and Disease Characteristics.

OBJECTIVES: This study aimed to perform a simulation study to quantify the health inequality impact of a cancer therapy given cancer and treatment characteristics using the distributional cost-effectiveness framework. METHODS: The following factors were varied in 10 000 simulations: lifetime risk of the disease, median overall survival (OS) with standard of care (SOC), difference in OS between non-Hispanic (NH)-Black and NH-White patients (prognostic effect), treatment effect of the new therapy relative to SOC, whether the treatment effect differs between NH-Black and NH-White patients (effect modification), health utility, drug costs, and preprogression and postprogression costs. Based on these characteristics, the incremental population net health benefits were calculated for the new therapy and applied to a US distribution of quality-adjusted life expectancy at birth. The health inequality impact was quantified as the difference in the degree of inequality in the "post-new therapy" versus "pre-new therapy" quality-adjusted life expectancy distributions. RESULTS: For cancer types characterized by relatively large lifetime risk, large median OS with SOC, large treatment effect, and large effect modification, the direction of the impact of the new therapy on inequality is easy to predict. When effect modification is minor or absent, which is a realistic scenario, the direction of the inequality impact is difficult to predict. Larger incremental drug costs have a worsening effect on health inequality. CONCLUSIONS: The findings provide a guide to help decision makers and other stakeholders make an initial assessment whether a new therapy with known treatment effects for a specific tumor type can have a positive or negative health inequality impact.

Expert consensus established around flexible, individualized migraine treatment utilizing a modified Delphi panel.

OBJECTIVE: To characterize treatment decision-making processes and formalize consensus regarding key factors headache specialists consider in treatment decisions for patients with migraine, considering novel therapies. BACKGROUND: Migraine therapies have long been subject to binary classification, acute versus preventive, due to limitations of available drugs. The emergence of novel therapies that can be used more flexibly creates an opportunity to rethink this binary classification. To determine the role of these novel therapies in treatment, it is critical to understand whether existing guidelines reflect clinical practice and to establish consensus around factors driving management. METHODS: A three-round modified Delphi process was conducted with migraine clinical experts. Round 1 consisted of an online questionnaire; Round 2 involved an online discussion of aggregated Round 1 results; and Round 3 allowed participants to revise Round 1 responses, incorporating Round 2 insights. Questions elicited likelihood ratings (0 = highly unlikely to 100 = highly likely), rankings, and estimates on treatment decision-making. RESULTS: Nineteen experts completed three Delphi rounds. Experts strongly agreed on definitions for "acute" (median = 100, inter-quartile range [IQR] = 5) and "preventive" treatment (median = 90, IQR = 15), but noted a need for treatment customization for patients (median = 100, IQR = 6). Experts noted certain aspects of guidelines may no longer apply based on established tolerability and efficacy of newer acute and preventive agents (median = 91, IQR = 17). Further, experts agreed on a treatment category referred to as "situational prevention" (or "short-term prevention") for patients with reliable and predictable migraine triggers (median = 100, IQR = 10) or time-limited periods when headache avoidance is important (median = 100, IQR = 12). CONCLUSIONS: Using the modified Delphi method, a panel of migraine experts identified the importance of customizing treatment for people with migraine and the utility of "situational prevention," given the ability of new treatment options to meet this need and the potential to clinically identify patients and time periods when this approach would add value.

Generalized cost-effectiveness analysis to assess treatment value in hepatitis C.

OBJECTIVES: To estimate the comprehensive value of direct-acting antivirals (DAAs) for the treatment of hepatitis C virus (HCV) compared with peginterferon alfa and ribavirin (PEG/riba) employing a generalized cost-effectiveness analysis (GCEA). STUDY DESIGN: To assess the societal-level cost-effectiveness of DAA treatment for HCV, we extended a previously published discrete-time Markov simulation model of HCV transmission and progression to include market dynamics and broader elements of value. METHODS: We followed a stepwise process to add novel value elements to a traditional CEA model for HCV treatments. For each additional element of value, we estimated incremental cost-effectiveness ratios (ICERs) of DAAs compared with PEG/riba. RESULTS: The health technology assessment (HTA)-style model yielded an ICER value of $64,512 per quality-adjusted life-year (QALY). Adding transmission dynamics resulted in an ICER value of $52,971 per QALY, whereas accounting for transmission dynamics and dynamic price and efficacy further decreased ICER values by 90% to $6406 per QALY. Incorporating genericization, productivity loss, caregiver spillover, and differential valuations of LYs vs quality of life, disease severity, and insurance value further decreased the ICER value to $4487 per QALY, a 93% reduction from the baseline HTA-style CEA to the fully realized GCEA. CONCLUSIONS: Our GCEA study results confirm that DAAs are a cost-effective treatment for HCV compared with PEG/riba even when using conventional cost-effectiveness approaches. Incorporating broader elements of value resulted in more than a 10-fold improvement in cost-effectiveness, emphasizing the substantive impact of a generalized approach and the importance of incorporating GCEAs into decision-making.