Values Beyond "Health" in Budget-Constrained Healthcare Systems.

OBJECTIVES: Most current methods to value healthcare treatments only incorporate measures such as quality-adjusted life-years, combining gains in health-related quality of life and life expectancy in specific ways. Failure of these methods to recognize other dimensions of value has led to calls for methods to include additional values that are associated with the healthcare treatments but not captured directly by quality-adjusted life-years. This article seeks to provide methodologically sound ways to incorporate additional health-related outcomes, focusing on budget-constrained healthcare systems, in which using standard welfare economics methods are often eschewed. METHODS: The analysis develops standard extra-welfarist approaches to maximizing aggregate health, subject to fixed-budget constraints, using Lagrange multiplier methods. Then, additional valuable health-related outcomes, eg, reduced caregiver burden, real option value, and market- and non-market productivity are introduced. The article also introduces a social welfare function approach to illuminate how disability, disease severity and other equity-related issues can be incorporated into complete welfare measures. RESULTS: Resulting analysis, fully developed in an Appendix in Supplemental Materials found at https://doi.org/10.1016/j.jval.2024.02.005 and summarized in the main text, show that understanding how average and marginal healthcare costs increase with output and how health augments "additional values" provides ways to assess willingness to pay for them in these fixed-budget situations. CONCLUSIONS: In budget-constrained healthcare systems, only from actual budget allocations can values both of health itself and "additional values" be inferred. These methods, combined with methodologically sound social welfare functions, demonstrate how to move from "health" to "welfare" in measuring the value of increased healthcare use.

Three Pillars of Support for Orphan Drug Programs: Individual Valuations, Societal Valuations, and Anonymous Altruism.

OBJECTIVES: To identify and describe potential societal and individual sources of support for orphan drug programs. METHODS: The Generalized Risk-Adjusted Cost-Effectiveness method shows that acute illness and disability severity increase individuals' willingness to pay for health gains. We develop a social welfare function (SWF) that incorporates individuals' own values, combined with politically or ethically determined weights. We introduce the concept of horizontal equity-that individuals in similar situations should be treated similarly-into the SWF. Finally, we introduce anonymous altruism into individuals' utility functions-the desire to help others, without knowing their identity. RESULTS: Combined with the empirical link between disease severity and rarity, the Generalized Risk-Adjusted Cost-Effectiveness method demonstrates heightened willingness to pay for health gains for people with rare diseases, leading rational individuals to support orphan drug programs, our first pillar of support. Adding horizontal equity to the SWF further increases societal support for orphan drug programs. Anonymous altruism, focusing most strongly on those in the most-dire circumstances, leads to altruistic support for those with severe disorders. Because innovators' economic incentives lead them to focus on larger markets, anonymous altruistic individuals will increasingly prefer public investments into rare diseases over time, as private markets systematically produce gains for common diseases. CONCLUSIONS: We identified 3 supporting pillars for orphan drug programs: (1) individuals' propensity to prefer treatments for severe diseases; (2) the preference for horizontal equity in our social welfare; (3) anonymous altruism, the desire to help strangers, coupled with market incentives that underserve strangers with rare diseases.

On the (Near) Equivalence of Welfarist and Extra-Welfarist Methods to Value Healthcare With Implications for Assessing Equity.

OBJECTIVES: While welfarist economics (WE) methods rely wholly on individuals' valuations, extra-welfarist (EW) methods seek alternative measures of value. Major reviews of the EW literature conclude that EW studies almost universally replace "utility" with "health" as the maximand. This analysis seeks to understand what conclusions are necessary and sufficient to make EW and WE methods concurrent and discusses implications for measuring social value. METHODS: Using standard WE methods, I demonstrate that EW is equivalent to WE with 2 key restrictions-individuals have constant returns to health in producing utility and health budgets are fixed. Fixing budgets removes a key WE step, determining the marginal rate of substitution between consumption and health, the willingness to pay for health gains. RESULTS: Because EW methods equate with WE with these 2 restrictions, I show how formal models to construct aggregated social welfare functions (SWFs) in WE frameworks lead directly to SWF models using EW models of value. I also show that, in fixed-budget health systems, when SWFs place different values for improving health of different subpopulations, aggregate health output fails as a SWF criterion. I demonstrate how different societal values can and should enter EW SWF models using WE criteria. I also discuss the implications when either of these key restrictions does not properly represent people's preferences. CONCLUSIONS: Once EW methods are shown to be a restricted form of WE methods, those WE methods can illuminate how best to measure SWFs in EW environments.

Reimagining Patient Data Access for Researchers.

OBJECTIVES: Widespread use of electronic health records (EHRs) now makes it feasible to expand beyond health insurance claims data to include full EHR data for health economics and outcomes research (HEOR) studies. We seek to develop ways to maximize researcher access to such data while strongly protecting patients' privacy rights. METHODS: We analyzed alternative organizational structures and intellectual property rights assignments as they now exist and compared these with structures and intellectual property rights assignments that would maximize access to data for HEOR studies and minimize transactions costs. We analyzed data protection requirements and financial incentives at 3 levels: patient decision making, patients' data aggregators, and final aggregation across patients' data. RESULTS: Creating new HEOR data systems requires new organizations and funding, while also protecting patients' data privacy rights. The Cures Act enables a new market for trusted third parties (TTPs) to aggregate patients' data. New secondary data aggregators must combine individuals' aggregated EHRs into usable HEOR databases. Maximal patient participation requires complete health insurance coverage of costs that healthcare providers charge for transmitting patients' data to TTPs. The new secondary system to aggregate data from many TTPs into usable HEOR optimally has external funding. CONCLUSIONS: Important steps remain uncompleted to achieve maximally available HEOR data while protecting patients' privacy rights. HEOR information is a public good, so private incentives to support creation and operation of this new system remain incomplete. Public and private support can expand this system to optimally improve people's health.

Methods to Adjust Willingness-to-Pay Measures for Severity of Illness.

OBJECTIVES: Both private sector organizations and governmental health agencies increasingly use illness severity measures to adjust willingness-to-pay thresholds. Three widely discussed methods-absolute shortfall (AS), proportional shortfall (PS), and fair innings (FI)-all use ad hoc adjustments to cost-effectiveness analysis methods and "stair-step" brackets to link illness severity with willingness-to-pay adjustments. We assess how these methods compare with microeconomic expected utility theory-based methods to value health gains. METHODS: We describe standard cost-effectiveness analysis methods, the basis from which AS, PS, and FI make severity adjustments. We then develop how the Generalized Risk Adjusted Cost Effectiveness (GRACE) model assesses value for differing illness and disability severity. We compare AS, PS, and FI against value as defined by GRACE. RESULTS: AS, PS, and FI have major and unresolved differences between them in how they value various medical interventions. Compared with GRACE, they fail to properly incorporate illness severity or disability. They conflate gains in health-related quality of life and life expectancy incorrectly and confuse the magnitude of treatment gains with value per quality-adjusted life-year. Stair-step methods also introduce important ethical concerns. CONCLUSIONS: AS, PS, and FI disagree with each other in major ways, demonstrating that at most, one correctly describes patients' preferences. GRACE offers a coherent alternative, based on neoclassical expected utility microeconomic theory, and can be readily implemented in future analyses. Other approaches that depend on ad hoc ethical statements have yet to be justified using sound axiomatic approaches.

Health Technology Assessment With Diminishing Returns to Health: The Generalized Risk-Adjusted Cost-Effectiveness (GRACE) Approach.

OBJECTIVES: Cost-effectiveness analysis (CEA) embeds an assumption at odds with most economic analysis-that of constant returns to health in the creation of happiness (utility). We aim to reconcile it with the bulk of economic theory. METHODS: We generalize the traditional CEA approach, allow diminishing returns to health, and align CEA with the rest of the health economics literature. RESULTS: This simple change has far-reaching implications for the practice of CEA. First, optimal cost-effectiveness thresholds should systematically rise for more severe diseases and fall for milder ones. We provide formulae for estimating how these thresholds vary with health-related quality of life (QoL) in the sick state. Practitioners can also use our approach to account for treatment outcome uncertainty. Holding average benefits fixed, risk-averse consumers value interventions more when they reduce outcome uncertainty ('insurance value') and/or when they provide a chance at positively skewed outcomes ('value of hope'). Finally, we provide a coherent way to combine improvements in QoL and life expectancy (LE) when people have diminishing returns to QoL. CONCLUSION: This new approach obviates the need for increasingly prevalent and ad hoc exceptions to CEA for end-of-life care, rare disease, and very severe disease (eg, cancer). Our methods also show that the value of improving QoL for disabled people is greater than for comparable non-disabled people, thus resolving an ongoing and mathematically legitimate objection to CEA raised by advocates for disabled people. Our Generalized Risk-Adjusted Cost-Effectiveness (GRACE) approach helps align HTA practice with realistic preferences for health and risk.

Estimating optimal willingness to pay thresholds for cost-effectiveness analysis: A generalized method.

Operationalizing cost-effectiveness analysis (CEA) requires that decisionmakers select maximum willingness to pay thresholds (K). We generalize previous methods used to estimate K using highly flexible hyperbolic absolute risk aversion (HARA) utility functions that encompass a wide range of risk behavior. For HARA utility, we calculate formulas for relative risk aversion (r*) and relative prudence (π∗ ), using literature-based estimates to calibrate our HARA model. We then assess optimal WTP thresholds (K) in absolute value and relative to income (K/M). Across the most-plausible range of risk preference parameters (r* and π∗ ), optimal K/M ratios sit (approximately) in the range of 1 to 3, although we cannot readily rule out larger K/M values. The optimal K always increases with income, while K/M falls with income if utility has increasing relative risk aversion. Results of this more-general model of economic utility are broadly consistent with previous work using more-restrictive Weibull functions. More precision in measuring the key parameters-particularly relative prudence (π∗ ) will narrow down the range of K/M estimates. The highly general HARA structure illuminates why and how optimal CEA thresholds change with income. An appendix illuminates how relative risk aversion and relative prudence relate to each other.

Vision for a systems architecture to integrate and transform population health.

Entities involved in population health often share a common mission while acting independently of one another and perhaps redundantly. Population health is in everybody's interest, but nobody is really in charge of promoting it. Across governments, corporations, and frontline operations, lack of coordination, lack of resources, and lack of reliable, current information have often impeded the development of situation-awareness models and thus a broad operational integration for population health. These deficiencies may also affect the technical, organizational, policy, and legal arrangements for information sharing, a desired practice of high potential value in population health. In this article, we articulate a vision for a next-generation modeling effort to create a systems architecture for broadly integrating and visualizing strategies for advancing population health. This multipurpose systems architecture would enable different views, alerts, and scenarios to better prepare for and respond to potential degradations in population health. We draw inspiration from systems engineering and visualization tools currently in other uses, including monitoring the state of the economy (market performance), security (classified intelligence), energy (power generation), transportation (global air traffic control), environment (weather monitoring), jobs (labor market dynamics), manufacturing and supply chain (tracking of components, parts, subassemblies, and products), and democratic processes (election analytics). We envision the basic ingredients for a population health systems architecture and its visualization dashboards to eventually support proactive planning and joint action among constituents. We intend our ambitious vision to encourage the work needed for progress that the population deserves.

The impact of gene expression profile testing on confidence in chemotherapy decisions and prognostic expectations.

PURPOSE: Little is known about whether gene expression profile (GEP) testing and specific recurrence scores (e.g., medium risk) improve women's confidence in their chemotherapy decision or perceived recurrence risk. We evaluate the relationship between these outcomes and GEP testing. METHODS: We surveyed women eligible for GEP testing (stage I or II, Gr1-2, ER+, HER2-) identified through the Surveillance, Epidemiology, and End Results (SEER) Registry of Washington or Kaiser Permanente Northern California from 2012 to 2016, approximately 0-4 years from diagnosis (N = 904, RR = 45.4%). Confidence in chemotherapy was measured as confident (Very, completely) versus Not Confident (Somewhat, A little, Not At All); perceived risk recurrence was recorded numerically (0-100%). Women reported their GEP test receipt (Yes, No, Unknown) and risk recurrence score (High, Intermediate, Low, Unknown). In our analytic sample (N = 833), we propensity score weighted the three test receipt cohorts and used propensity weighted multivariable regressions to examine associations between the outcomes and the three test receipt cohorts, with receipt stratified by score. RESULTS: 29.5% reported an unknown GEP test receipt; 86% being confident. Compared to no test receipt, an intermediate score (aOR 0.34; 95% CI 0.20-0.58), unknown score (aOR 0.09; 95% CI 0.05-0.18), and unknown test receipt (aOR 0.37; 95% CI 0.24-0.57) were less likely to report confidence. Most women greatly overestimated their recurrence risk regardless of their test receipt or score. CONCLUSIONS: GEP testing was not associated with greater confidence in chemotherapy decisions. Better communication about GEP testing and the implications for recurrence risk may improve women's decisional confidence.