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.

Risk preferences over health: Empirical estimates and implications for medical decision-making.

Mainstream health economic theory implies that an expected gain in health-related quality of life (HRQoL) produces the same value for consumers, regardless of baseline health. Several strands of recent research call this implication into question. Generalized Risk-Adjusted Cost-Effectiveness (GRACE) demonstrates theoretically that baseline health status influences value, so long as consumers are not risk-neutral over health. Prior empirical literature casts doubt on risk-neutral expected utility-maximization in the health domain. We estimate utility over HRQoL in a nationally representative U.S. population and use our estimates to measure risk preferences over health. We find that individuals are risk-seeking at low levels of health, become risk-averse at health equal to 0.485 (measured on a 0-1 scale), and are most risk-averse at perfect health (coefficient of relative risk aversion = 4.51). We develop the resulting implications for medical decision making, cost-effectiveness analyses, and the proper theory of health-related decision making under uncertainty.

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.

A guide to extending and implementing generalized risk-adjusted cost-effectiveness (GRACE).

The generalized risk-adjusted cost-effectiveness (GRACE) model generalizes conventional cost-effectiveness analysis (CEA) by introducing diminishing returns to Health-Related Quality of Life (QoL). This changes CEA practice in three ways: (1) Willingness to pay (WTP) increases exponentially with untreated illness severity or pre-existing permanent disability, and WTP ends up lower for mild diseases but higher for severe diseases compared with conventional CEA; (2) Average treatment effectiveness should be adjusted for uncertainty in outcomes; and (3) The marginal rate of substitution between life expectancy and QoL varies with health state. Implementing GRACE requires new parameters describing risk preferences over QoL, the marginal rate of substitution between life expectancy (LE) and QoL, and the variance and skewness of treatment outcomes distributions. In this paper, we provide: (1) a generalized WTP threshold incorporating the possibility of permanent disability; (2) a simpler method to estimate the tradeoff rate between QoL and LE, eliminating the need to carry out treatment-by-treatment estimates; (3) a more-general method to adjust WTP for illness severity that permits non-constant relative risk-aversion in QoL; (4) a new approach to estimating risk-preferences over QoL, leveraging established empirical methods from "happiness" economics; and (5) a step-by-step guide for practitioners wishing to implement multi-period GRACE analyses.

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.

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.

Is the Choice of Cost-Effectiveness Threshold in Cost-Utility Analysis Endogenous to the Resulting Value of Technology? A Systematic Review.

BACKGROUND: Cost-utility analysis (CUA) is widely used for health technology assessment; however, concerns exist that cost-utility analysts may suggest higher cost-effectiveness thresholds (CETs) to compensate for technologies of relatively lower value. OBJECTIVE: We explored whether selection of a CUA study's CET was endogenous to estimated incremental cost-effectiveness ratios (ICERs). METHODS: We systematically reviewed the US cost-effectiveness literature between 2000 and 2017 where studies with explicit CET and ICERs were included. We classified the ratio of studies hypothesized to analyze cost-effective technologies at low CETs (i.e., less than $100,000/quality-adjusted life-year [QALY]) vs higher CETs (i.e., $100,000-$150,000/QALY) relative to their ICER, using a Chi square test to examine whether technologies that were cost effective at high CETs would still be cost effective at lower thresholds. We also performed fixed-effects linear regression exploring the associations between ICERs and reported CETs over time. RESULTS: Among 317 ICERs reviewed: (A) 185 had an ICER < $50,000/QALY; (B) 53 had $50,000 ≤ ICER, < $100,000; (C) 20 had $100,000 ≤ ICER < $150,000; and (D) 59 had an ICER ≥ $150,000. Chi square testing showed a strong association (p < 0.001) between estimated ICER values and chosen CET, illustrating a lack of independence between the two. The regression analysis indicated that CETs have a baseline value of $52,000 and grow by $0.37 for each dollar increase in the estimated ICER. CONCLUSIONS: Cost-effectiveness thresholds represent the hypothesis tests of typical CUAs. Our analysis highlights that most CUAs that cite high CETs also result in greater ICERs for the novel interventions that they investigate; thus, these interventions would otherwise not have been cost effective at lower CETs. Selection of a CET may come after the ICER is calculated to infer value that suits a hypothesis.

Health technology assessment with risk aversion in health.

Standard cost-effectiveness models compare incremental cost increases to incremental average gains in health, commonly expressed in Quality-Adjusted Life Years (QALYs). Our research generalizes earlier models in several ways. We introduce risk aversion in Quality of Life (QoL), which leads to "willingness-to-pay" thresholds that rise with illness severity, potentially by an order of magnitude. Unlike traditional CEA analyses, which discriminate against persons with disabilities, our analysis implies that the marginal value of improving QoL rises for disabled individuals. Our model can also value the uncertain benefits of medical interventions by employing well-established analytic methods from finance. Finally, we show that traditional QALYs no longer serve as a single index of health, when consumers are risk-averse. To address this problem, we derive a generalized single-index of health outcomes-the Generalized Risk-Adjusted QALY (GRA-QALY). Earlier models of CEA that abstract from risk-aversion nest as special cases of our more general model.

Feasibility of Measuring Preferences for Chemotherapy Among Early-Stage Breast Cancer Survivors Using a Direct Rank Ordering Multicriteria Decision Analysis Versus a Time Trade-Off.

OBJECTIVES: Chemotherapy is increasingly a preference-based choice among women diagnosed with early-stage breast cancer. Multicriteria decision analysis (MCDA) is a promising but underutilized method to facilitate shared decision making. We explored the feasibility of conducting an MCDA using direct rank ordering versus a time trade-off (TTO) to assess chemotherapy choice in a large population-based sample. METHODS: We surveyed 904 early-stage breast cancer survivors who were within 5 years of diagnosis and reported to the Western Washington State Cancer System and Kaiser Permanente Northern California registries. Direct rank ordering of 11 criteria and TTO surveys were conducted from September 2015 to July 2016; clinical data were obtained from registries or medical records. Multivariable regressions estimated post hoc associations between the MCDA, TTO, and self-reported chemotherapy receipt, considering covariates. RESULTS: Survivors ranged in age from 25 to 74 years and 73.9% had stage I tumors. The response rate for the rank ordering was 81.0%; TTO score was 94.2%. A one-standard deviation increase in the difference between the chemotherapy and no chemotherapy MCDA scores was associated with a 75.1% (95% confidence interval 43.9-109.7%; p < 0.001) increase in the adjusted odds of having received chemotherapy; no association was found between the TTO score and chemotherapy receipt. CONCLUSIONS: A rank-order-based MCDA was feasible and was associated with chemotherapy choice. Future research should consider developing and testing this MCDA for use in clinical encounters. Additional research is required to develop a TTO-based model and test its properties against a pragmatic MCDA to inform future shared decision-making tools.

A New Method to Determine the Optimal Willingness to Pay in Cost-Effectiveness Analysis.

OBJECTIVE: To provide a new approach to estimate optimal willingness to pay (WTP) for health technology assessment (HTA). STUDY DESIGN: This analysis specified utility as a function of income and calibrated it using estimates of relative risk aversion, from which the optimal WTP (K) can be determined using Garber and Phelps' results (1997). METHODS: This analysis used the highly flexible Weibull utility function, calibrated with estimates of relative risk aversion (r*) derived from multiple data sources. The analysis centered on r* = 1 and conducted sensitivity analysis on r* and key Weibull parameters. For a range of income (M), graphs demonstrated how K/M and K vary with M. Results were compared with estimates of K and K/M from alternative models. Extrapolation from a representative individual to population-wide health plans was discussed. RESULTS: Using r* = 1 and central values of other key parameters, K/M (at average income for developed nations) was approximately 2× annual income. Both K and K/M rose with income. Sensitivity analysis showed that results depend moderately on the chosen value of r* and specific Weibull utility function parameters. At average income, the optimal K/M ratio (2×) was modestly lower than many standard recommendations (typically 3× average income) and substantially lower than estimates using value-of-statistical-life approaches. CONCLUSIONS: The new model, although not yet perfected, provides a different way to identify the WTP cutoff for HTA. Extrapolation to more than twice the calibration income ($50 000) is advised against. Analysis of other approaches to estimate the optimal K reveal potential upward biases.

Valuing Health: Evolution, Revolution, Resistance, and Reform.

A number of methods have sought to determine the value of interventions and services that promote health, even when no agreement exists on the proper way to determine and define "value." Previous valuation efforts began simply by counting deaths or measuring life expectancy, slowly evolving to the widespread use of cost-effectiveness analysis (CEA) as the de facto normative standard for medical interventions. Users of CEA recognize that the method is incomplete. Further, no meaningful agreement exists on how best to apply CEA in decision settings because of either inadequacies in the CEA framework or lack of consensus on how to use it in a setting with budget constraints. Yet efforts to value health still predominantly use (and continue to recommend) this limited framework. Is this owing to a lack of new ideas and motivation, resistance to change, or an aversion to embrace more comprehensive systems approaches? We argue that tools of systems engineering can advance our capabilities, but they have had only limited use in health policy. We identify some reasons and specifically highlight the promise of systems-analytic platforms-such as multicriteria decision support systems-and the need to make them more accessible for different uses in real situations with real consequences. We also explore the need for comparative testing of different multicriteria approaches (including direct comparisons with CEA) to learn when and by how much the recommendations differ and what the consequences might be.

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.

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.

Toward Alignment in the Reporting of Economic Evaluations of Diagnostic Tests and Biomarkers: The AGREEDT Checklist.

OBJECTIVES: General frameworks for conducting and reporting health economic evaluations are available but not specific enough to cover the intricacies of the evaluation of diagnostic tests and biomarkers. Such evaluations are typically complex and model-based because tests primarily affect health outcomes indirectly and real-world data on health outcomes are often lacking. Moreover, not all aspects relevant to the evaluation of a diagnostic test may be known and explicitly considered for inclusion in the evaluation, leading to a loss of transparency and replicability. To address this challenge, this study aims to develop a comprehensive reporting checklist. METHODS: This study consisted of 3 main steps: 1) the development of an initial checklist based on a scoping review, 2) review and critical appraisal of the initial checklist by 4 independent experts, and 3) development of a final checklist. Each item from the checklist is illustrated using an example from previous research. RESULTS: The scoping review followed by critical review by the 4 experts resulted in a checklist containing 44 items, which ideally should be considered for inclusion in a model-based health economic evaluation. The extent to which these items were included or discussed in the studies identified in the scoping review varied substantially, with 14 items not being mentioned in ≥47 (75%) of the included studies. CONCLUSIONS: The reporting checklist developed in this study may contribute to improved transparency and completeness of model-based health economic evaluations of diagnostic tests and biomarkers. Use of this checklist is therefore encouraged to enhance the interpretation, comparability, and-indirectly-the validity of the results of such evaluations.