Distributional Cost-effectiveness Analysis Research Articles

Estimating and Rewarding the Value of Healthcare Interventions Beyond the Healthcare Sector: A Conceptual Framework.

Evaluating healthcare interventions for their impacts beyond health outcomes may result in recognition of changes in human capital, income level, tax revenue, and government spending, which could affect economic growth and population health. In this paper, we document instances where current health technology assessment (HTA) practices fail to account for the impacts of healthcare interventions on broader society beyond the healthcare sector. We propose a novel conceptual framework, highlighting its three components (distributional cost-effectiveness analysis [DCEA], input-output model, and voting scheme) and their contributions to capturing the economic and societal ripple effects of healthcare interventions. This manuscript also outlines a case study in which the framework is applied to the reassessment of a previously evaluated digital health therapeutic for the treatment of opioid use disorder (OUD) compared with standard of care, demonstrating its practical application. The DCEA health value metric indicates that digital therapeutic is more equitable, favoring socioeconomically disadvantaged groups, while standard of care exacerbates health inequality by benefiting the already advantaged. Additionally, digital therapeutic shows potential for boosting productivity, raising income, and creating jobs, supporting its consideration by employer-sponsored health plans to optimize resource allocation for treating OUD. The conceptual framework provides insights for enhancing HTAs to incorporate the broader economic and societal impacts of healthcare interventions. By integrating DCEA, extended HTA analysis with input-output modeling, and a voting scheme, decision makers can make informed choices aligned with societal priorities, although further research and validation are necessary for practical implementation across diverse healthcare contexts.

Estimating Transition Probabilities for Modeling Major Depression in Adolescents by Sex and Race or Ethnicity Combinations in the USA.

About one-fifth of US adolescents experienced major depressive symptoms, but few studies have examined longitudinal trends of adolescents developing depression or recovering by demographic factors. We estimated new transition probability inputs, and then used them in a simulation model to project the epidemiologic burden and trajectory of depression of diverse adolescents by sex and race or ethnicity combinations. Transition probabilities were first derived using parametric survival analysis of data from the National Longitudinal Study of Adolescent to Adult Health and then calibrated to cross-sectional data from the National Survey on Drug Use and Health. We developed a cohort state-transition model to simulate age-specific depression outcomes of US adolescents. A hypothetical adolescent cohort was modeled from 12-22years with annual transitions. Model outcomes included proportions of youth experiencing depression, recovery, or depression-free casesand were reported for a US adolescent population by sex, race or ethnicity, and sex and race or ethnicity combinations. At 22 years of age, approximately 16% of adolescents had depression, 12% were in recovery, and 72% had never developed depression. Depression prevalence peaked around 16-17years-old. Adolescents of multiracial or other race or ethnicity, White, American Indian or Alaska Native, and Hispanic, Latino, or Spanish descent were more likely to experience depression than other racial or ethnic groups. Depression trajectories generated by the model matched well with historical observational studies by sex and race or ethnicity, except for individuals from American Indian or Alaska Native and multiracial or other race or ethnicity backgrounds. This study validated new transition probabilities for future use in decision models evaluating adolescentdepression policies or interventions. Different sets of transition parameters by demographic factors (sex and race or ethnicity combinations) were generated to support future health equity research, including distributional cost-effectiveness analysis. Further data disaggregated with respect to race, ethnicity, religion, income, geography, gender identity, sexual orientation, and disability would be helpful to project accurate estimates for historicallyminoritized communities.

Quantifying the Value of Reduced Health Disparities: Low-Dose Computed Tomography Lung Cancer Screening of High-Risk Individuals Within the United States

ObjectiveThis study aimed to measure the value of increasing lung cancer screening rates for high-risk individuals and its impact on health disparities. MethodsThe model estimated changes in health economic outcomes if low-dose computed tomography screening increased from current to 100% compliance, following clinical guidelines. Current low-dose computed tomography screening rates were estimated by income, education, and race, using 2017-2019 Behavioral Risk Factor Surveillance System data. The model contained a decision tree module to segment the population by screening outcomes and a Markov chain module to estimate cancer progression over time. Model parameters included information on survival, quality of life, and costs related to cancer diagnosis, treatment, and adverse events. Distributional cost-effectiveness analysis estimated the net monetary value from reduced health disparities—measured using quality-adjusted life expectancy—across income, education, and race groups. Outcomes were assessed over 30 years. ResultsLung cancer screening eligibility using US Preventive Services Task Force guidelines was higher for individuals with income <$15 000 (47.2%) and without a high-school education (46.1%) than individuals with income >$50 000 (16.6%) and with a college degree (13.5%), respectively. Increasing lung cancer screening to 100% compliance was cost-effective ($64 654 per quality-adjusted life-year) and produced economic value by up to $560 per person ($182.1 billion for United States overall). Up to 32.2% of the value was due to reductions in health disparities. ConclusionsSignificant value in increasing lung cancer screening rates derived from reducing health disparities. Policy makers and clinicians may not be appropriately prioritizing cancer screening if value from reducing health disparities is unconsidered.

EE733 Distributional Cost-Effectiveness Analysis: A Case Study on Its Potential Prospects in HTA

EE733 Distributional Cost-Effectiveness Analysis: A Case Study on Its Potential Prospects in HTA

Allotransplantation and Gene Therapy Equity for Children with Sickle Cell Disease: Distributional Cost-Effectiveness of Allotransplantation Vs Gene Therapy Vs Standard-of-Care in Pediatric Patients with Sickle Cell Disease in the United States

Introduction: Patients with sickle cell disease (SCD) have reduced quality-adjusted life expectancy due to substantial mortality risks and decreased quality-of-life for every year lived with the disease. Allotransplantation was the first treatment with curative potential, but it carries concomitant risks of transplant-related mortality and graft-versus-host disease (GVHD). Once approved, gene therapy would be the second treatment with the possibility of lifelong cure but unlike allotransplantation, it may do so without the concomitant risks. Prior research has shown that even if gene therapy is conventionally cost-ineffective versus the standard-of-care (SOC), it can be a justifiable treatment option for a historically marginalized patient population in the United States, as demonstrated by equity-weighted cost-effectiveness. For allotransplantation and gene therapy, neither conventional nor distributional (i.e., equity-weighted) cost-effectiveness is known. Accordingly, we conducted the first conventional and distributional cost-effectiveness analyses of allotransplantation therapy for pediatric patients with SCD in the United States. Methods: We built a Markov model of pediatric patients with sickle cell disease to examine the cost-effectiveness of allotransplantation vs gene therapy vs SOC and determine equity weight thresholds. Sex-, age-, and disease-severity-specific annual costs and transition probabilities across disease severities for SOC were informed by 11 years of data from US commercially insured patients with SCD (2007-2017; Optum). Disease severity was defined based on annual number of hospitalizations for vaso-occlusive crises, as defined in clinical trials. SOC included hydroxyurea, opioid therapy, antibiotics, vaccinations, and blood transfusions. Allotransplantation- and GVHD-specific transition probabilities and costs were informed by prior literature and health resource utilization data, originally reported from the Center for International Blood and Marrow Transplant Research and the Pediatric Health Information System. Quality-adjusted life-years (QALYs) were informed by published lifetime simulations of patients living with SCD and matched controls, derived initially from a polynomial fit linking Euroqual-5 Dimensions to the visual analog scale for pain in patients with SCD in several studies. Age-, sex- and disease-specific background mortality probabilities were employed. Conventional cost-effectiveness was quantified using an incremental cost-effectiveness ratio (ICER), at a willingness-to-pay threshold of $100,000/QALY. Given expert clinical experience with optimizing allotransplantation outcomes in SCD, we employed a maximum patient starting age of 12 and assumed that gene therapy cost $2.45 million and was 100% effective. To account for health inequities in SCD, we then conducted a distributional cost-effectiveness analysis (DCEA), which applies an inequality aversion parameter (i.e., how much weight one puts on reducing disparities), to the distribution of outcomes across relevant sub-groups for allotransplantation to be favored over SOC and justify funding per DCEA standards. These were compared to prior estimates for commonly used equity weights in the United States (range 0.5-3.0). Results: In the base-case, allotransplantation, gene therapy, and SOC accrued 22.1, 25.5, 15.7 discounted lifetime QALYs at costs of $1.9, $2.8, $1.0 million, respectively, with all therapies on the cost-effectiveness frontier (Table 1). The ICERs and equity weight thresholds were $138,000/QALY and 0.5, and $253,000/QALY and 4.5, for allotransplantation (vs SOC) and gene therapy (vs allotransplantation), respectively. Conclusion: Similar to findings for gene therapy versus SOC in the first published DCEA in sickle cell disease, allotransplantation also appears to be an equitable therapeutic option for pediatric patients with sickle cell disease, regardless of conventional cost-ineffectiveness and as compared to SOC. The consideration of patient-population-specific equity offers an important opportunity to derive transparent benchmarks for intertwined equity- and value-informed decision making. Quantitative consideration of equity in the context of conventional cost-ineffectiveness can be an important tipping point in justification of funding.

Cost-Effectiveness of Sutimlimab Versus Standard-of-Care in Transfusion Dependent Patients with Primary Cold Agglutinin Disease in the United States

Introduction: Primary cold agglutinin disease (CAD) is an autoimmune hemolytic anemia characterized by the presence of cold-reactive antibodies that lead to red blood cell destruction at low temperatures. Patients with CAD who are transfusion-dependent experience the burden of increased health resource utilization and reduced quality-of-life. The standard-of-care (SOC) in patients with primary CAD has included chemoimmunotherapy, transfusion support, and cold avoidance. Sutimlimab is a humanized monoclonal antibody that selectively inhibits C1-mediated hemolysis. It is the only FDA-approved therapeutic for patients with cold agglutinin disease regardless of history of transfusion, with approval based on two phase 3 clinical studies. The cost-effectiveness of this expensive therapeutic in the care of patients with either transfusion-independent or transfusion-dependent primary CAD is not known. We sought to fill this gap by determining the cost-effectiveness of sutimlimab versus standard-of-care for the transfusion-dependent patient population with primary CAD. Methods: For this independent analysis free of industry influence, we built a Markov state transition model of adult patients with transfusion-dependent primary CAD to determine the cost-effectiveness of sutimlimab versus SOC, over a lifetime time-horizon and across accepted willingness-to-pay thresholds in US dollars per quality-adjusted life-year (QALY). For transition probabilities, we employed phase 3 study data (NCT03347396) with descriptive statistics informing the documented pre- versus on-sutimlimab clinical benefit, including the decrement in hemolytic anemia severity and transfusion burden. For SOC, we assumed that all patients undergo the costs and risks of chemoimmunotherapy with bendamustine-rituximab, followed by rituximab monotherapy and subsequent treatment with mycophenolate mofetil, with all patients remaining dependent on transfusion care. Health resource utilization costs for SOC were further supplemented with country- and disease-specific real-world costs over a median of 42.8 and 33.0 months of follow-up after incident primary CAD diagnosis across two claims databases. These costs were assessed from the highest of 1) annualized cost quartile and 2) the worst ordinal category of hemolytic anemia severity (defined as hemoglobin &amp;lt;8 g/dL) across all of 1) hospitalizations, 2) emergency visits, 3) outpatient services, and 4) blood transfusions. We assumed no loss of sutimlimab efficacy and no discontinuation off sutimlimab due to adverse events. Age- and sex-adjusted, disease-specific mortality was employed. Effectiveness was calculated in QALYs and was informed directly from trial-specific utilities. The primary outcome was the incremental cost-effectiveness ratio (ICER) or the incremental net monetary benefit (iNMB), if the intervention was found to be cost-saving. The secondary outcome was a scenario analysis for patients with a body weight of &amp;lt;75 kilograms that mandate lower dosing per-kilogram sutimlimab dosing, as per the FDA package insert. We concluded by conducting deterministic and probabilistic sensitivity analyses, capturing uncertainty in all parameters simultaneously over 10,000 Monte Carlo simulations. Results: In the base-case analysis, treatment with sutimlimab versus SOC accrued 5.60 and 4.79 QALYs at costs of $9.52 and $7.67 million, respectively, with the ICER for sutimlimab treatment being $2,280,000/QALY [95% credible interval 960,000-13,900,000]. In the scenario analysis for patients with lower body weight (i.e., lower sutimlimab dosing), the ICER was $1,910,000/QALY. Model results were sensitive to sutimlimab cost. In a probabilistic sensitivity analysis, SOC was favored over sutimlimab in 100% of 10,000 iterations. An exploratory threshold analysis showed that the mortality hazard with sutimlimab would need to decrease by 201% for sutimlimab to become cost-effective. Conclusion: Sutimlimab is conventionally cost-ineffective compared to SOC in transfusion-dependent patients with CAD. Distributional cost-effectiveness analysis (DCEA) to potentially justify sutimlimab as an equitable therapeutic option despite cost-ineffectiveness in the United States context is needed.

Equitable Care for Severe Hemophilia_A: Distributional Cost-Effectiveness of Prophylactic Weekly Efanesoctocog Alfa Versus Standard-Care Factor VIII in Patients with Severe Hemophilia_A in the United States

Introduction: Prophylactic coagulation factor replacement is crucial to decrease bleeding episodes and improve quality-adjusted life expectancy (QALE) for patients with severe hemophilia A. Even with prophylactic treatment (i.e., at three to four times weekly) patients remain at risk of bleeding events and still develop chronic arthropathy across their lifetimes. The additional economic burden associated with lifelong prophylactic treatment is a major equity concern for patients with hemophilia A and their families. Efanesoctocog alfa is a recently developed extended half-life agent for hemophilia A prophylaxis and management, supporting the possibility of once-weekly prophylaxis. An open-label multicenter study (XTEND-1) showed that once-weekly dosing of efanesoctocog alfa improves the annualized bleeding rate (ABR) by 77% versus prophylaxis with standard-care factor VIII in patients with severe hemophilia A. We sought to evaluate, both for the first time, the conventional and distributional cost-effectiveness of prophylaxis with efanesoctocog alfa versus with standard-care factor VIII for patients with severe hemophilia A in the United States. Methods: For this independent analysis free of industry influence, we built a Markov model to examine the conventional cost-effectiveness of prophylaxis with efanesoctocog alfa versus standard-care factor VIII for patients with severe hemophilia A. Transition probabilities for ABRs and product utilization were informed by phase 3 study data and supplemented with FDA package insert information. Within ABRs, we accounted for treated bleeding severity and bleeding event types, inclusive of hemarthrosis, intracranial hemorrhage, in addition to assuming the prevalence of chronic arthropathy with SHL (i.e., due to silent bleed burden) is eliminated by efanesoctocog alfa. Costs for the base-case were sourced from the Centers for Medicare &amp; Medicaid Services with negotiated (i.e., lower) cost of efanesoctocog alfa supplied from the Veterans Affairs Federal Supply Schedule Service (VA-FSS). Age-, sex- and disease-specific background mortality were employed. Utilities for severe hemophilia A and disutilities for bleed events and hospitalizations were informed by extensive literature specific to severe hemophilia A. The primary outcome was the incremental cost-effectiveness ratio (ICER) in USD/QALY, at a willingness-to-pay threshold of $150,000/quality-adjusted life-year (QALY). The secondary outcome was a threshold analysis for the maximum efanesoctocog alfa price that renders efanesoctocog alfa prophylaxis cost-effective. To account for health inequities in the care of patients with hemophilia A we then conducted a distributional cost-effectiveness analysis (DCEA) to derive an equity weight threshold (i.e., inequality aversion parameter) for the decision of prophylaxis with efanesoctocog alfa versus with standard-care factor VIII. This threshold was then compared to prior estimates for commonly used equity weights in the United States (range 0.5-3.0). Results: In the base-case, prophylaxis with efanesoctocog alfa versus with standard-care factor VIII accrued 19.4 and 15.4 QALYs at mean costs of $17.3 and $9.7 million. The ICER for efanesoctocog alfa was $1,890,000/QALY (95% credible interval 1,190,000-3,330,000). Threshold analysis showed that prophylaxis with efanesoctocog alfa became cost-effective when the price of efanesoctocog alfa decreased by at least 59%. In deterministic sensitivity analysis, the model was most sensitive to the costs of efanesoctocog alfa and factor VIII. In probabilistic sensitivity analysis, standard-care factor VIII was favored over efanesoctocog alfa in 100% of 10,000 Monte Carlo simulations. The equity weight threshold for the treatment decision of prophylaxis with efanesoctocotog alfa versus with standard-care factor VIII was 7.4, higher than commonly used equity weights in the US. This implies a threshold that is higher than what has previously been used in the United States, per DCEA standards. Conclusion: As compared to standard-care factor VIII and at VA-FSS pricing, efanesoctocog alfa prophylaxis is conventionally cost-ineffective for patients with severe hemophilia A. Prophylaxis with efanesoctocog alfa does not meet commonly used distributive equity standards in the United States at this time.

1152. Quantifying the health equity related public health impact of national immunisation programmes

Abstract Background COVID-19 highlighted health inequities and the differential impact that vaccination can have on health, with respect to social advantage. While many national immunisation programmes (NIP) consider health equity as a key recommendation criterion, most lack robust frameworks and methods to quantify the impact of healthcare interventions on health equity. Equity strata can be defined by socioeconomic status, race/ethnicity, geographic location, etc… Methods The inclusion of equity in guidelines for vaccination recommendation was assessed. Vaccination policy experts and health economists were consulted on how to best capture the equity-related public health impact (PHI) of NIP. The framework of distributional cost-effectiveness analysis (DCEA) was selected to be further explored. A stepwise approach was applied to account for equity in PHI analysis i.e., the impact on health outcome distribution by equity strata, by estimating the distributional impact (step 1) and HE impact (step 2) of vaccination. A practical application of the method was illustrated through a case study on the meningococcal B (MenB) disease vaccination program in England. Results Impact of vaccination in health equity is formally captured in policy decision making in the US ACIP Evidence to Recommendation framework and the Canadian EEFA (Ethics, Equity Feasibility, Acceptability) Framework. In UK, France, Australia, Spain and Germany it is qualitatively included as part of the deliberative process, while it is not captured in the Netherlands. The DCEA framework was applied for health equity benefits of MenB infant vaccination in UK, with impact assessed across population subgroups categorised using a deprivation index. Nearly 80% of prevented cases were among the three most deprived groups. Additionally, MenB vaccination decreased inequity in the population, with positive net equity impact. Conclusion PHI of vaccination on health equity can be quantified and usefully considered in decision making. The case study demonstrates how the proposed framework could be applied to fully incorporate impact on equity in cost-effectiveness analysis. The health equity impact of vaccination can be captured in health economic evaluation although there is a need to improve the evidence base and its implementation Disclosures Eliana Biundo, PhD, GSK: employee|GSK: Stocks/Bonds Mariia Dronova, PhD, GSK: Grant/Research Support Annie Chicoye, PhD, GSK: Board Member|GSK: Grant/Research Support|GSK: Honoraria Richard Cookson, PhD, Genetech: Advisor/Consultant Nancy Devlin, PhD, GSK: Honoraria T. Mark Doherty, PhD, GSK: employee|GSK: Stocks/Bonds Antonio J Garcia-Ruiz, PhD, CHIESI: Advisor/Consultant|CHIESI: Grant/Research Support|CHIESI: Honoraria|Consumers and Users Organisations (CEACCU): Advisor/Consultant|Consumers and Users Organisations (CEACCU): Grant/Research Support|Consumers and Users Organisations (CEACCU): Honoraria|Foundation for Progress and Health (Andalusian Regional Government): Advisor/Consultant|Foundation for Progress and Health (Andalusian Regional Government): Grant/Research Support|Foundation for Progress and Health (Andalusian Regional Government): Honoraria|GSK: Honoraria|Ministry of Science and Innovation of Spain: Advisor/Consultant|Ministry of Science and Innovation of Spain: Grant/Research Support|Ministry of Science and Innovation of Spain: Honoraria|Official College of Physicians: Advisor/Consultant|Official College of Physicians: Grant/Research Support|Official College of Physicians: Honoraria|Royal Academy of Medicine and Surgery of Eastern Andalusia: Advisor/Consultant|Royal Academy of Medicine and Surgery of Eastern Andalusia: Grant/Research Support|Royal Academy of Medicine and Surgery of Eastern Andalusia: Honoraria|Sanofi Pasteur: Advisor/Consultant|Sanofi Pasteur: Grant/Research Support|Sanofi Pasteur: Honoraria|Sociedade Galega de Neuroloxía: Advisor/Consultant|Sociedade Galega de Neuroloxía: Grant/Research Support|Sociedade Galega de Neuroloxía: Honoraria|UCB: Advisor/Consultant|UCB: Grant/Research Support|UCB: Honoraria Louis P Garrison, PhD, GSK: Honoraria Terry Nolan, MD, PhD, Clover: Board Member|CSL Seqirus: Advisor/Consultant|CSL Seqirus: Grant/Research Support|Dynavax: Grant/Research Support|GSK: Advisor/Consultant|GSK: Board Member|GSK: Grant/Research Support|Iliad: Grant/Research Support|Moderna: Advisor/Consultant|Moderna: Grant/Research Support|MSD: Advisor/Consultant|MSD: Grant/Research Support|Novavax: Board Member|Pfizer: Advisor/Consultant|Sanofi: Advisor/Consultant|Sanofi: Grant/Research Support|SK Bio: Board Member Maarten Postma, Dr., Consumers and Users Organisations (CEACCU): Advisor/Consultant|Consumers and Users Organisations (CEACCU): Honoraria|Foundation for Progress and Health (Andalusian Regional Government): Advisor/Consultant|Foundation for Progress and Health (Andalusian Regional Government): Honoraria|GSK: Honoraria|Royal Academy of Medicine and Surgery of Eastern Andalusia: Advisor/Consultant|Royal Academy of Medicine and Surgery of Eastern Andalusia: Honoraria David M. Salisbury, CB FMedSci FRCP FRCPCH FFPH, Clover Pharmaceuticals: Advisor/Consultant|GSK: Advisor/Consultant|Moderna: Advisor/Consultant|Novavax Inc: Honoraria|Sanofi: Advisor/Consultant Hiral Shah, PhD, GSK: employee|GSK: Stocks/Bonds Jurgen Wasem, PhD, AstraZeneca: Advisor/Consultant|AstraZeneca: Honoraria|Clover: Advisor/Consultant|Clover: Board Member|GSK: Board Member|GSK: Honoraria|Merck: Advisor/Consultant|Merck: Honoraria|Sanofi Pasteur: Advisor/Consultant|Sanofi Pasteur: Honoraria|Seqirus: Advisor/Consultant|Seqirus: Honoraria|Serum Institute of India: Advisor/Consultant|Serum Institute of India: Board Member|Zeria: Advisor/Consultant|Zeria: Board Member Ekkehard Beck, PhD, GSK: employee|GSK: Stocks/Bonds

1780P The health inequality impact of darolutamide for non-metastatic castration-resistant prostate cancer: A distributional cost-effectiveness analysis

1780P The health inequality impact of darolutamide for non-metastatic castration-resistant prostate cancer: A distributional cost-effectiveness analysis

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

ObjectivesTo 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. MethodsWith 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. ResultsLB-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. ConclusionsLB-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.

Reconstructing the value puzzle in health technology assessment: a pragmatic review to determine which modelling methods can account for additional value elements.

Health technology assessment (HTA) has traditionally relied on cost-effectiveness analysis (CEA) as a cornerstone of evaluation of new therapies, assessing the clinical validity and utility, the efficacy, and the cost-effectiveness of new interventions. The current format of cost-effectiveness analysis, however, does not allow for inclusion of more holistic aspects of health and, therefore, value elements for new technologies such as the impact on patients and society beyond its pure clinical and economic value. This study aimed to review the recent modelling attempts to expand the traditional cost-effectiveness analysis approach by incorporating additional elements of value in health technology assessment. A pragmatic literature review was conducted for articles published between 2012 and 2022 reporting cost-effectiveness analysis including value aspects beyond the clinical and cost-effectiveness estimates; searches identified 13 articles that were eligible for inclusion. These expanded modelling approaches mainly focused on integrating the impact of societal values and health equity in cost-effectiveness analysis, both of which were championed as important aspects of health technology assessment that should be incorporated into future technology assessments. The reviewed cost-effectiveness analysis methods included modification of the current cost-effectiveness analysis methodology (distributional cost-effectiveness analysis, augmented cost-effectiveness analysis, extended cost-effectiveness analysis) or the use of multi-criteria decision analysis. Of these approaches, augmented cost-effectiveness analysis appears to have the most potential by expanding traditional aspects of value, as it uses techniques already familiar to health technology assessment agencies but also allows space for incorporation of qualitative aspects of a product's value. This review showcases that methods to unravel additional value elements for technology assessment exist, therefore, patient access to promising technologies can be improved by moving the discussion from "if" to "how" additional value elements can inform decision-making.

Estimating the US Baseline Distribution of Health Inequalities Across Race, Ethnicity, and Geography for Equity-Informative Cost-Effectiveness Analysis

ObjectivesInformation on how life expectancy, disability-free life expectancy, and quality-adjusted life expectancy varies across equity-relevant subgroups is required to conduct distributional cost-effectiveness analysis. These summary measures are not comprehensively available in the United States, given limitations in nationally representative data across racial and ethnic groups. MethodsThrough linkage of US national survey data sets and use of Bayesian models to address missing and suppressed mortality data, we estimate health outcomes across 5 racial and ethnic subgroups (non-Hispanic American Indian or Alaska Native, non-Hispanic Asian and Pacific Islander, non-Hispanic black, non-Hispanic white, and Hispanic). Mortality, disability, and social determinant of health data were combined to estimate sex- and age-based outcomes for equity-relevant subgroups based on race and ethnicity, as well as county-level social vulnerability. ResultsLife expectancy, disability-free life expectancy, and quality-adjusted life expectancy at birth declined from 79.5, 69.4, and 64.3 years, respectively, among the 20% least socially vulnerable (best-off) counties to 76.8, 63.6, and 61.1 years, respectively, among the 20% most socially vulnerable (worst-off) counties. Considering differences across racial and ethnic subgroups, as well as geography, gaps between the best-off (Asian and Pacific Islander; 20% least socially vulnerable counties) and worst-off (American Indian/Alaska Native; 20% most socially vulnerable counties) subgroups were large (17.6 life-years, 20.9 disability-free life-years, and 18.0 quality-adjusted life-years) and increased with age. ConclusionsExisting disparities in health across geographies and racial and ethnic subgroups may lead to distributional differences in the impact of health interventions. Data from this study support routine estimation of equity effects in healthcare decision making, including distributional cost-effectiveness analysis.

1006-P: Distributional Cost-Effectiveness Analysis of Glucagon-Like Peptide-1 Receptor Agonists (GLP-1 RA) and Tirzepatide Compared with Sulfonylureas (SU) in People with Inadequately Controlled Type 2 Diabetes (T2D)

We assessed the cost-effectiveness of GLP-1 RA and tirzepatide compared with SU when added to metformin in people with inadequately controlled T2D. Using data from the National Health and Nutrition Examination Survey (2013-2018), we constructed a nationally representative T2D sample poorly controlled on metformin (A1c&amp;gt; 7.0%). We used the BRAVO diabetes model, a validated microsimulation model, to predict individuals’ lifetime health and economic outcomes based on their current health profile and received treatment (GLP-1 RA, tirzepatide, or SU). We identified factors driving the cost-effectiveness and developed a scoring algorithm using these variables, with higher scores indicating more cost-effective. Under a willingness to pay threshold of $100,000/QALY, GLP-1 RA ($92,590/QALY) and tirzepatide (5 mg: $70,540/QALY, 10 mg: $60,700/QALY, 15 mg: $55,600//QALY) were all cost-effective compared with SU. The cost-effectiveness drivers included age, gender, smoking, diabetes duration, A1c, SBP, and cardiovascular disease history (Figure). Our scoring algorithm can stratify patients by the cost-effectiveness of using GLP-1 RA or tirzepatide, which can be used to design precision interventions to improve drug use targeting patients for whom the drug use can be cost-effective. Disclosure P.Li: None. A.G.Winterstein: Consultant; Bayer Inc., Genentech, Inc., Ipsen Biopharmaceuticals, Inc., Research Support; Merck Sharp &amp; Dohme Corp. J.Guo: Consultant; Pfizer Inc., Research Support; PhRMA Foundation, NIH - National Institutes of Health. H.Shao: Consultant; Lilly Diabetes. D.Guan: None. M.K.Ali: Advisory Panel; Bayer Inc., Eli Lilly and Company, Research Support; Merck &amp; Co., Inc. K.Narayan: None. V.Fonseca: Consultant; Abbott, Corcept Therapeutics, Eli Lilly and Company, Other Relationship; BRAVO4HEALTH, LLC, Research Support; Fractyl Health, Inc., Stock/Shareholder; Amgen Inc. G.Umpierrez: Research Support; Abbott, Dexcom, Inc., Baxter. L.Shi: None. J.Bian: None. Y.Guo: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK133465)

Distributional Cost-Effectiveness Analysis of Treatments for Non-Small Cell Lung Cancer: An Illustration of an Aggregate Analysis and its Key Drivers.

Distributional cost-effectiveness analysis (DCEA) facilitates quantitative assessments of how health effects and costs are distributed among population subgroups, and of potential trade-offs between health maximisation and equity. Implementation of DCEA is currently explored by the National Institute for Health and Care Excellence (NICE) in England. Recent research conducted an aggregate DCEA on a selection of NICE appraisals; however, significant questions remain regarding the impact of the characteristics of the patient population (size, distribution by the equity measure of interest) and methodologic choices on DCEA outcomes. Cancer is the indication most appraised by NICE, and the relationship between lung cancer incidence and socioeconomic status is well established. We aimed to conduct an aggregate DCEA of two non-small cell lung cancer (NSCLC) treatments recommended by NICE, and identify key drivers of the analysis. Subgroups were defined according to socioeconomic deprivation. Data on health benefits, costs, and target populations were extracted from two NICE appraisals (atezolizumab versus docetaxel [second-line treatment following chemotherapy to represent a broad NSCLC population] and alectinib versus crizotinib [targeted first-line treatment to represent a rarer mutation-positive NSCLC population]). Data on disease incidence were derived from national statistics. Distributions of population health and health opportunity costs were taken from the literature. A societal welfare analysis was conducted to assess potential trade-offs between health maximisation and equity. Sensitivity analyses were conducted, varying a range of parameters. At an opportunity cost threshold of £30,000 per quality-adjusted life-year (QALY), alectinib improved both health and equity, thereby increasing societal welfare. Second-line atezolizumab involved a trade-off between improving health equity and maximising health; it improved societal welfare at an opportunity cost threshold of £50,000/QALY. Increasing the value of the opportunity cost threshold improved the equity impact. The equity impact and societal welfare impact were small, driven by the size of the patient population and per-patient net health benefit. Other key drivers were the inequality aversion parameters and the distribution of patients by socioeconomic group; skewing the distribution to the most (least) deprived quintile improved (reduced) equity gains. Using two illustrative examples and varying model parameters to simulate alternative decision problems, this study suggests that key drivers of an aggregate DCEA are the opportunity cost threshold, the characteristics of the patient population, and the level of inequality aversion. These drivers raise important questions in terms of the implications for decision making. Further research is warranted to examine the value of the opportunity cost threshold, capture the public's views on unfair differences in health, and estimate robust distributional weights incorporating the public's preferences. Finally, guidance from health technology assessment organisations, such as NICE, is needed regarding methods for DCEA construction and how they would interpret and incorporate those results in their decision making.

EE334 Characterizing When Health Equity Should be Considered in Economic Evaluations: A Systematic Review of Extended and Distributional Cost-Effectiveness Analyses

We performed a systematic literature review to summarize patterns of applications of extended cost-effectiveness analysis (ECEA) and distributional cost-effectiveness analysis (DCEA) to gain a better understanding of when these analyses should be conducted to provide policymakers with relevant and meaningful evidence.

EE459 Racial Health Disparities in Acute Ischemic Stroke: A Targeted Literature Review for Estimates to be Used in Distributional Cost-Effectiveness Analysis

Racial health disparities in acute ischemic stroke (AIS) are documented across the patient journey, from incidence to treatment access. Equity-informative cost-effectiveness analyses, such as distributional cost-effectiveness analysis (DCEA), can allow for clear quantification of disparities and can estimate the equity consequences of treatment and access decisions. The objective of this review is to summarize existing disparities across race and ethnicity as well as social determinants of health in AIS to inform DCEA.

EE541 Health Equity Improvements Driven By Faricimab for the Treatment of Diabetic Macular Edema in an Aggregate Distributional Cost-Effectiveness Analysis from an English NHS Perspective

Distributional cost-effectiveness analysis (DCEA) is an extension of cost-effectiveness analysis that looks at the equity impact of adopting interventions, evaluating how health outcomes and costs are distributed in the population, and quantifying trade-offs between health maximisation and health equity. The National Institute of Health and Care Excellence (NICE) in the UK is exploring DCEA to support the development of its Technology Appraisals. The prevalence of diabetes and thus diabetic macular edema (DME), a complication of diabetes, is higher in more deprived areas. We aimed to conduct an aggregate DCEA from an English NHS perspective of treatments for DME recommended by NICE to capture the value of these treatments more comprehensively.

EE31 Identifying Equity-Relevant Subgroup Effects in Alzheimer’s Disease: A Literature Review to Support a Distributional Cost-Effectiveness Analysis

Given the emergence of new therapies for Alzheimer’s disease (AD), it is critical to understand the impact of access factors on health equity. Distributional cost-effectiveness analysis (DCEA) can quantify tradeoffs between overall health gains and underlying impacts on health equity. Our objective was to support development of a DCEA for AD by summarizing the existing literature across race, ethnicity, and social determinants of health (SDOH).

EE48 A Distributional Cost-Effectiveness Analysis of Cervical Cancer Screening Strategies in England

A distributional cost-effectiveness analysis (DCEA) of cervical cancer screening strategies in England was developed to: 1) evaluate distributional impacts of different strategies on health differences by socioeconomic status and sex; 2) provide an informative case study for implementation of the DCEA method in this indication.

Modelled Distributional Cost-Effectiveness Analysis of Childhood Obesity Interventions: A Demonstration.

To demonstrate how distributional cost-effectiveness analyses of childhood obesity interventions could be conducted and presented for decision makers. We conducted modelled distributional cost-effectiveness analyses of three obesity interventions in children: an infant sleep intervention (POI-Sleep), a combined infant sleep, food, activity and breastfeeding intervention (POI-Combo) and a clinician-led treatment for primary school-aged children with overweight and obesity (High Five for Kids). For each intervention, costs and socioeconomic position (SEP)-specific effect sizes were applied to an Australian child cohort (n = 4898). Using a purpose-built microsimulation model we simulated SEP-specific body mass index (BMI) trajectories, healthcare costs and quality-adjusted life years (QALYs) from age 4 to 17 years for control and intervention cohorts. We examined the distribution of each health outcome across SEP and determined the net health benefit and equity impact accounting for opportunity costs and uncertainty due to individual-level heterogeneity. Finally, we conducted scenario analyses to test the effect of assumptions about health system marginal productivity, the distribution of opportunity costs and SEP-specific effect sizes. The results of the primary analyses, uncertainty analyses and scenario analyses were presented on an efficiency-equity impact plane. Accounting for uncertainty, POI-Sleep and High Five for Kids were found to be 'win-win' interventions, with a 67% and 100% probability, respectively, of generating a net health benefit and positive equity impact compared with control. POI-Combo was found to be a 'lose-lose' intervention, with a 91% probability of producing a net health loss and a negative equity impact compared with control. Scenario analyses indicated that SEP-specific effect sizes were highly influential on equity impact estimates for POI-Combo and High Five for Kids, while health system marginal productivity and opportunity cost distribution assumptions primarily influenced the net health benefit and equity impact of POI-Combo. These analyses demonstrated that distributional cost-effectiveness analyses using a fit-for-purpose model are appropriate for differentiating and communicating the efficiency and equity impacts of childhood obesity interventions.