We conducted a distributional cost-effectiveness analysis (DCEA) to evaluate how Medicare funding of inpatient COVID-19 treatments affected health equity in the United States.
A DCEA, based on an ...existing cost-effectiveness analysis model, was conducted from the perspective of a single US payer, Medicare. The US population was divided based on race and ethnicity (Hispanic, non-Hispanic black, and non-Hispanic white) and county-level social vulnerability index (5 quintile groups) into 15 equity-relevant subgroups. The baseline distribution of quality-adjusted life expectancy was estimated across the equity subgroups. Opportunity costs were estimated by converting total spend on COVID-19 inpatient treatments into health losses, expressed as quality-adjusted life-years (QALYs), using base-case assumptions of an opportunity cost threshold of $150 000 per QALY gained and an equal distribution of opportunity costs across equity-relevant subgroups.
More socially vulnerable populations received larger per capita health benefits due to higher COVID-19 incidence and baseline in-hospital mortality. The total direct medical cost of inpatient COVID-19 interventions in the United States in 2020 was estimated at $25.83 billion with an estimated net benefit of 735 569 QALYs after adjusting for opportunity costs. Funding inpatient COVID-19 treatment reduced the population-level burden of health inequality by 0.234%. Conclusions remained robust across scenario and sensitivity analyses.
To the best of our knowledge, this is the first DCEA to quantify the equity implications of funding COVID-19 treatments in the United States. Medicare funding of COVID-19 treatments in the United States could improve overall health while reducing existing health inequalities.
•An equity-informative distributional cost-effectiveness analysis (DCEA) examined differences in baseline health, treatment effects, and opportunity costs within the general population based on neighborhood-level social vulnerability and race and ethnicity, finding that funding of COVID-19 treatments increased overall population health and reduced inequality, with larger health gains in more socially vulnerable patients.•Key next steps for expanded DCEA use include (1) addressing critical data gaps in the baseline social distribution of health and the level of health inequality aversion in the United States and (2) determining how to best integrate the results from equity-informative cost-effectiveness analysis (like DCEAs) into current healthcare decision making.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Governments and health technology assessment agencies are putting greater focus on and efforts in understanding and addressing health inequities. Cost-effectiveness analyses are used to evaluate the ...costs and health gains of different interventions to inform the decision-making process on funding of new treatments. Distributional cost-effectiveness analysis (DCEA) is an extension of cost-effectiveness analysis that quantifies the equity impact of funding new treatments. Key challenges for the routine and consistent implementation of DCEA are the lack of clearly defined equity concerns from decision makers and endorsed measures to define equity subgroups and the availability of evidence that allows analysis of differences in data inputs associated with the equity characteristics of interest. In this article, we detail the data gaps and challenges to build robust DCEA analysis routinely in health technology assessment and suggest actions to overcome these hurdles.
•Governments are putting greater focus on tackling health inequities, and health technology assessment bodies are responding by working to formally incorporate equity considerations into the decision-making process on funding of interventions.•Distributional cost-effectiveness analysis (DCEA) is a method to assess how health effects and costs are distributed between subpopulations and any ensuing trade-offs between maximizing overall population health and equity. DCEA provides a quantitative method for incorporating equity impact assessment into decision making.•Data gaps may limit the implementation of robust DCEA routinely for products undergoing health technology assessment. Therefore, data collection, analysis, and reporting need to be improved and aligned with the equity concepts of interest to decision makers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Information 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.
Through 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.
Life 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.
Existing 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.
•Disparities in longevity, disability, and quality of life exist within and across racial and ethnic groups in the United States; however, measuring these inequalities in a representative and comprehensive manner is difficult given the limited nationally representative data on health outcomes by subgroups.•This study used Bayesian models to address suppressed mortality data across racial and ethnic subgroups and leveraged multiple linked US data sets to demonstrate that life expectancy, disability-free life expectancy, and quality-adjusted life expectancy notably vary based on race and ethnicity and geographic location. Gaps in health between racial and ethnic population subgroups in the 20% least socially vulnerable US counties and the 20% most socially vulnerable counties are large and both persist and increase with age.•Existing disparities in health across both geographies and racial and ethnic subgroups may lead to important distributional differences in the impact of health interventions. Data generated from this study can support more routine estimation of equity effects in healthcare decision making, including applications to distributional cost-effectiveness analysis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This study aimed to provide recommendations for identifying and implementing real option value (ROV) calculations in value assessment.
We identified the primary mechanisms through which ROV can be ...created based on a theoretical framework for ROV, assessed approaches for predicting future innovations and improvements in health, and described the steps for estimating ROV in a cost-effectiveness analysis framework.
The 3 primary mechanisms by which ROV can be created are when a current treatment (1) prolongs survival to increase the proportion of patients who can receive future innovations, (2) slows disease progression to increase patients’ eligibility for future innovations, and (3) directly affects the efficacy of future innovations. We provide 5 recommendations for implementing ROV in value assessment. First, the decision to quantify ROV should be based on a qualitative evaluation of whether the treatment can enable greater benefits from future innovations. Second, ROV should be quantified in the same value assessment framework (eg, cost-effectiveness analysis using quality-adjusted life-year) as the conventional value. Third, method for quantifying ROV should consider data availability, rate of innovation, and sources of future health improvements. Fourth, ROV estimate should be presented alongside the conventional value as a separate element due to its inherently large uncertainty. Finally, generalizability of ROV estimate should be evaluated, and local data should be used when available.
ROV can arise from a variety of mechanisms that should be considered before investing in an ROV analysis. Calculating ROV includes exploring different approaches for forecasting future innovations and future improvements in health.
•Rapid technological advancement calls for the consideration of real option value (ROV) in value assessment; nevertheless, methods for quantifying ROV remain exploratory and lack consensus.•This article provides guidance on when to consider ROV and how to estimate and report ROV in cost-effectiveness analysis.•This article contributes to a growing literature on augmenting conventional cost-effectiveness analysis with novel elements of value.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
This study establishes important, national benchmarks for burn centers to assess length of stay (LOS) and number of procedures across patient profiles. We examined the relationship between ...patient characteristics such as age and total body surface area (TBSA) burned and number of procedures and LOS in the United States, using the American Burn Association National Burn Repository (NBR) database version 8.0 (2002–2011). Among 21,175 surviving burn patients (TBSA > 10–60%), mean age was 33 years, and mean injury size was 19.9% TBSA. Outcomes included the number of debridement, excision, autograft procedures, and LOS. Independent variables considered were: age (linear, squared, and cubed to account for nonlinearity), TBSA, TBSAs of partial-thickness and mixed/full-thickness burns, sex, hospital-acquired infection, other infection, inhalation injury, and diabetes status. Regression methods included a mixed-effects model for LOS and ordinary least squares for number of procedures. A backward stepwise procedure (P <0.2) was used to select variables. Number of excision and autografting procedures increased with TBSA; however, this relationship did not hold for debridement. After adjusting for sex, age, and comorbidities, predicted LOS for adults (18+) was 12.1, 21.7, 32.2, 43.7, and 56.1 days for 10, 20, 30, 40, and 50% TBSA, respectively. Similarly, predicted LOS for pediatrics (age < 18) was 8.1, 18.8, 33.2, 47.6, and 56.1 days for the same TBSA groups, respectively. While average estimates for adults (1.12 days) and pediatrics (1.01) are close to the one day/TBSA rule-of-thumb, consideration of other important patient and burn features in the NBR can better refine predictions for LOS.
Abstract
Introduction
Treatment pathways in burn care are typically determined based on burn center (BC) and patient characteristics, although decisions may be influenced by anecdotal experience, ...personal preference, and hospital policies/purchasing decisions. Health economic (HE) evaluations can support improved decision-making, identifying the most cost-effective interventions for tailored care. A novel burn care model (BEACON) was developed with burn surgeons over several years and validated through numerous publications, including an assessment of the HE impacts of autologous skin cell suspension (ASCS) use for definitive burn closure. To ensure that BEACON accurately represents the current state of care, it is vital to update data that underpins model projections. This study collected real world data on practice patterns and patient outcomes for the most commonly seen burns (TBSA ≤ 20%) to update the current understanding of standard of care (SOC) costs and outcomes and to refine estimates on the impact of ASCS use in TBSA ≤ 20% patients.
Methods
Data was collected from a 10% sample of BCs, including: BC and patient characteristics, resource use, inpatient costs, and length of stay (LOS). NBR based inputs in BEACON were updated to reflect survey data for patients with TBSA ≤ 20%, with the ability to view data as a national aggregate sample and across BC characteristics. BEACON estimates patient and BC costs and outcomes across a spectrum of patient profiles (age, gender, inhalation injury, comorbidity status, burn depth, TBSA) and combines information on each patient profile to understand annual budget impact. Key outcomes were compared across the survey sample and published NBR trends. Using the updated BEACON, the BC budget impact of ASCS in burns TBSA ≤ 20% was assessed.
Results
The survey was collected from 16+ BCs, focusing on inpatient encounters in 2018. LOS was lower than NBR estimates, with some centers reporting LOS per %TBSA far below 1 d/%TBSA. Using the detailed bottom-up estimation of cost from BEACON with survey data, trends suggest total hospital costs for SOC are lower than published NBR charges given shorter LOS and updated cost and resource use assumption.
Conclusions
Compared to NBR 8.0, contemporary data suggests that fewer small TBSA burns are being treated in the inpatient setting; those treated have a LOS below NBR estimates. When using real world data, the impact of ASCS use in burns TBSA ≤ 20% was still calculated to be cost saving to a BC overall, given reductions in LOS and number of definitive closure procedures. Incorporating ASCS into appropriate TBSA ≤ 20% procedures can still result in a positive financial impact for BCs.
Applicability of Research to Practice
Provides insights into real-world practice patterns for small TBSA burns, compared to the NBR
Estimates the costs of treating small burns, and using new interventions such as ASCS
Background
Oral semaglutide was approved in 2019 for blood glucose control in patients with type 2 diabetes mellitus (T2DM) and was the first oral glucagon-like peptide 1 receptor agonist (GLP-1 RA). ...T2DM is associated with substantial healthcare expenditures in the US, so the cost of a new intervention should be weighed against clinical benefits.
Objective
This study evaluated the budget impact of a treatment pathway with oral semaglutide 14 mg daily versus oral sitagliptin 100 mg daily among patients not achieving target glycated hemoglobin (HbA1c) level despite treatment with metformin.
Methods
This study used the validated IQVIA™ CORE Diabetes Model to simulate the treatment impact of oral semaglutide 14 mg and sitagliptin 100 mg over a 5-year time horizon from a US healthcare sector (payer) perspective. Trial data (PIONEER 3) informed cohort characteristics and treatment effects, and literature sources informed event costs. Population and market share data were from the literature and data on file. The analysis evaluated the estimated budget impact of oral semaglutide 14 mg use for patients currently using sitagliptin 100 mg considering both direct medical and treatment costs to understand the impact on total cost of care, given underlying treatment performance and impact on avoidable events.
Results
In a hypothetical plan of 1 million lives, an estimated 1993 patients were treated with sitagliptin 100 mg in the target population. Following these patients over 5 years, the incremental direct medical and treatment costs of a patient using oral semaglutide 14 mg versus sitagliptin 100 mg was $US16,562, a 70.7% increase (year 2019 values). A hypothetical payer would spend an additional $US3,300,143 (7.1%) over 5 years for every 10% of market share that oral semaglutide 14 mg takes away from sitagliptin 100 mg. Univariate and scenario analyses with alternate inputs and assumptions demonstrated consistent results.
Conclusions
Use of oral semaglutide 14 mg in patients currently receiving sitagliptin 100 mg substantially increases the budget impact for patients with T2DM whose blood glucose level is not controlled with metformin over a 5-year time horizon for US healthcare payers.
Plain Language Summary
Patients with type 2 diabetes mellitus (T2DM) have many treatment options. Choices depend on factors such as cost, preference, and patient characteristics. Oral semaglutide was recently approved for the treatment of T2DM as the first oral therapy of its class. This study estimated the cost for patients treated with sitagliptin 100 mg, a commonly used T2DM treatment, versus oral semaglutide 14 mg for patients whose disease is not well controlled with metformin. Costs and effects were estimated over 5 years for each treatment strategy using predictive model equations and clinical trial data for the two treatments. These costs were considered for both a hypothetical healthcare plan of 1 million lives and the full US population. A patient treated with oral semaglutide 14 mg would expect to see 70.7% higher costs than a patient treated with sitagliptin 100 mg over 5 years. For every 10% of patients who would switch from sitagliptin 100 mg to oral semaglutide 14 mg, costs would increase by 7.1%. Changing the cost of oral semaglutide 14 mg had the greatest impact on model results. The findings from the analysis were consistent across a range of alternate model inputs. Oral semaglutide 14 mg is more costly than sitagliptin 100 mg over 5 years.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
•“Affordability”, of pharmaceutical drugs is an ill-defined, complex, multidimensional, and difficult to measure concept.•Current unidimensional affordability metrics that benefit only a few ...stakeholders, are likely inappropriate and biased.•Insurance, employment, disease severity, multiple conditions, and age inform patients’ perspectives of drug affordability.•Patient and provider perspectives on affordability deserve consideration when determining reimbursement and pricing.
The objectives of this research were to: 1) understand perspectives on affordability of pharmaceutical drugs from the point of view of stakeholders as reported in published peer-reviewed journals and conferences; 2) evaluate if (and how) perspectives on affordability overlapped across stakeholders.
The systematic literature review followed Cochrane and PRISMA guidelines. Content analysis with iterative and systematic coding of text was conducted, to identify themes.
A total of 7,372 unique citations were eligible, and 126 articles included for final synthesis. For patients, 6 core themes emerged: financial barriers, adherence, access, patient-provider communication, financial distress, and factors that impact affordability. For payers, 5 core themes: financing schemes, cost-effectiveness, budget impact, private vs. public preferences, and ethics. For providers, 3 themes: patient-provider communication, physician prescribing behavior, and finding alternatives to support patient access. For policymakers, 2 themes: measuring affordability and the role of government. Limited articles representing the manufacturer perspective were identified. Perspectives of decision makers (payers, policymakers) did not overlap with those affected by affordability (patients, providers).
This research highlights the multi-dimensionality of drug “affordability.” Multiple factors beyond cost influence patient affordability implying interventions can help alleviate affordability issues for some patients. The lack of overlap highlights potential hazards that decisions related to out-of-pocket spending, insurance coverage, reimbursement, and rationing occur without explicitly considering patient and provider perspectives.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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