Assessments of the Value of New Interventions Should Include Health Equity Impact

Next-generation sequencing in oncology: challenges in economic evaluations

INTRODUCTION

Next-generation sequencing (NGS) identifies genetic variants to inform personalized treatment plans. Insufficient evidence of cost-effectiveness impedes the integration of NGS into routine cancer care. The complexity of personalized treatment challenges conventional economic evaluation. Clearly delineating challenges informs future cost-effectiveness analyses to better value and contextualize health, preference-, and equity-based outcomes.

AREAS COVERED

We conducted a scoping review to characterize the applied methods and outcomes of economic evaluations of NGS in oncology and identify existing challenges. We included 27 articles published since 2016 from a search of PubMed, Embase, and Web of Science. Identified challenges included defining the evaluative scope, managing evidentiary limitations including lack of causal evidence, incorporating preference-based utility, and assessing distributional and equity-based impacts. These challenges reflect the difficulty of generating high-quality clinical effectiveness and real-world evidence (RWE) for NGS-guided interventions.

EXPERT OPINION

Adapting methodological approaches and developing life-cycle health technology assessment (HTA) guidance using RWE is crucial for implementing NGS in oncology. Healthcare systems, decision-makers, and HTA organizations are facing a pivotal opportunity to adapt to an evolving clinical paradigm and create innovative regulatory and reimbursement processes that will enable more sustainable, equitable, and patient-oriented healthcare.

New International Pharmaco-Economic Collaboration on Alzheimer's Disease (IPECAD) Open-Source Model Framework for the Health Technology Assessment of Early Alzheimer's Disease Treatment: Development and Use Cases

OBJECTIVES

Reimbursement decisions for new Alzheimer's disease (AD) treatments are informed by economic evaluations. An open-source model with intuitive structure for model cross-validation can support the transparency and credibility of such evaluations. We describe the new International Pharmaco-Economic Collaboration on Alzheimer's Disease (IPECAD) open-source model framework (version 2) for the health-economic evaluation of early AD treatment and use it for cross-validation and addressing uncertainty.

METHODS

A cohort state-transition model using a categorized composite domain (cognition and function) was developed by replicating an existing reference model and testing it for internal validity. Then, features of existing Institute for Clinical and Economic Review (ICER) and Alzheimer's Disease Archimedes Condition-Event Simulator (AD-ACE) models assessing lecanemab treatment were implemented for model cross-validation. Additional uncertainty scenarios were performed on choice of efficacy outcome from trial, natural disease progression, treatment effect waning and stopping rules, and other methodological choices. The model is available open-source as R code, spreadsheet, and web-based version via https://github.com/ronhandels/IPECAD.

RESULTS

In the IPECAD model incremental life-years, quality-adjusted life-years (QALY) gains and cost savings were 21% to 31% smaller compared with the ICER model and 36% to 56% smaller compared with the AD-ACE model. IPECAD model results were particularly sensitive to assumptions on treatment effect waning and stopping rules and choice of efficacy outcome from trial.

CONCLUSIONS

We demonstrated the ability of a new IPECAD open-source model framework for researchers and decision makers to cross-validate other (Health Technology Assessment submission) models and perform additional uncertainty analyses, setting an example for open science in AD decision modeling and supporting important reimbursement decisions.

A Research Framework to Improve Health Disparity Evidence Gaps in Value Assessments

A value assessment is intended as a tool for evaluating healthcare treatments to gauge value and inform decisions. Economic value assessments typically incorporate a cost-effectiveness analysis, focusing on costs and health outcomes important to payers, missing important information to ensure existing markets optimize resource allocation. Despite frequent calls for more explicit consideration of health equity impacts in value assessments, health economists continue to develop models informed by traditional cost and quality-of-life data that do not capture differences experienced by health disparity populations. This conceptual paper proposes a research framework to enhance data collection and analysis to address these gaps and better quantify the value of a health innovation, and better assess how a new intervention impacts health disparities. The framework comprises three distinct phases that build on one another: (1) contextualization of lived experiences for disadvantaged communities; (2) individual-level quantification of health disparities for cost and quality-of-life measures; and (3) quantifying community-level impacts.

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.

The Health Inequality Impact of Liquid Biopsy to Inform First-Line Treatment of Advanced Non-Small Cell Lung Cancer: A Distributional Cost-Effectiveness Analysis

OBJECTIVES

To perform a distributional cost-effectiveness analysis of liquid biopsy (LB) followed by, if needed, tissue biopsy (TB) (LB-first strategy) relative to a TB-only strategy to inform first-line treatment of advanced non-small cell lung cancer (aNSCLC) from a US payer perspective by which we quantify the impact of LB-first on population health inequality according to race and ethnicity.

METHODS

With a health economic model, quality-adjusted life-years (QALYs) and costs per patient were estimated for each subgroup. Given the lifetime risk of aNSCLC, and assuming equally distributed opportunity costs, the incremental net health benefits of LB-first were calculated, which were used to estimate general population quality-adjusted life expectancy at birth (QALE) by race and ethnicity with and without LB-first. The degree of QALYs and QALE differences with the strategies was expressed with inequality indices. Their differences were defined as the inequality impact of LB-first.

RESULTS

LB-first resulted in an additional 0.21 (95% uncertainty interval: 0.07-0.39) QALYs among treated patients, with the greatest gain observed among Asian patients (0.31 QALYs [0.09-0.61]). LB-first resulted in an increase in relative inequality in QALYs among patients, but a minor decrease in relative inequality in QALE.

CONCLUSIONS

LB-first to inform first-line aNSCLC therapy can improve health outcomes. With current diagnostic performance, the benefit is the greatest among Asian patients, thereby potentially widening racial and ethnic differences in survival among patients with aNSCLC. Assuming equally distributed opportunity costs and access, LB-first does not worsen and, in fact, may reduce inequality in general population health according to race and ethnicity.

Estimating the prevalence of diagnosed Alzheimer disease in England across deprivation groups using electronic health records: a clinical practice research datalink study

OBJECTIVE

Estimate the prevalence of diagnosed Alzheimer's disease (AD) and early Alzheimer's disease (eAD) overall and stratified by age, sex and deprivation and combinations thereof in England on 1 January 2020.

DESIGN

Cross-sectional.

SETTING

Primary care electronic health record data, the Clinical Practice Research database linked with secondary care data, Hospital Episode Statistics (HES) and patient-level deprivation data, Index of Multiple Deprivation (IMD).

OUTCOME MEASURES

The prevalence per 100 000 of the population and corresponding 95% CIs for both diagnosed AD and eAD overall and stratified by covariates. Sensitivity analyses were conducted to assess the sensitivity of the population definition and look-back period.

RESULTS

There were 448 797 patients identified in the Clinical Practice Research Datalink that satisfied the study inclusion criteria and were eligible for HES and IMD linkage. For the main analysis of AD and eAD, 379 763 patients are eligible for inclusion in the denominator. This resulted in an estimated prevalence of diagnosed AD of 378.39 (95% CI, 359.36 to 398.44) per 100 000 and eAD of 292.81 (95% CI, 276.12 to 310.52) per 100 000. Prevalence estimates across main and sensitivity analyses for the entire AD study population were found to vary widely with estimates ranging from 137.48 (95% CI, 127.05 to 148.76) to 796.55 (95% CI, 768.77 to 825.33). There was significant variation in prevalence of diagnosed eAD when assessing the sensitivity with the look-back periods, as low as 120.54 (95% CI, 110.80 to 131.14) per 100 000, and as high as 519.01 (95% CI, 496.64 to 542.37) per 100 000.

CONCLUSIONS

The study found relatively consistent patterns of prevalence across both AD and eAD populations. Generally, the prevalence of diagnosed AD increased with age and increased with deprivation for each age category. Women had a higher prevalence than men. More granular levels of stratification reduced patient numbers and increased the uncertainty of point prevalence estimates. Despite this, the study found a relationship between deprivation and prevalence of AD.

Evidence Clearinghouses as Tools to Advance Health Equity: What We Know from a Systematic Scan

Evidence clearinghouses evaluate and summarize literature to help decision-makers prioritize and invest in evidence-informed interventions. Clearinghouses and related practice-oriented tools are continuously evolving; however, it is unclear the extent to which these tools assess and summarize evidence describing an intervention's impact on health equity. We conducted a systematic scan to explore how clearinghouses communicated an intervention's equity impact and reviewed their underlying methods and how they defined and operationalized health equity. In 2021, we identified 18 clearinghouses that were US-focused, web-based registries of interventions that assigned an intervention effectiveness rating for improving community health and the social determinants of health. We reviewed each clearinghouse's website and collected publicly available information about their health equity impact review, review methods, and health equity definitions and values. We conducted a comparative analysis among select clearinghouses using qualitative methods. Among the 18 clearinghouses, fewer than half (only seven) summarized an intervention's potential impact on health equity. Overall, those seven clearinghouses defined and operationalized equity differently, and most lacked transparency in their review methods. Clearinghouses used one or more approaches to communicate findings from their review: summarize study findings on differential impact for subpopulations, curate interventions that reduce health disparities, and/or assign a disparity/equity rating to each intervention. Evidence clearinghouses can enhance equity-focused methods and be transparent in their underlying values to better support the uptake and implementation of evidence-informed interventions to advance health equity. However, clearinghouses are unable to do so without underlying equity-focused empirical evidence.