Traditional cost-effectiveness analysis (TCEA) overlooks nonlinear patient health preferences, potentially under-valuing health gains for severe diseases and overvaluing health gains for less severe diseases. Generalized and risk-adjusted cost effectiveness (GRACE) can address this limitation by incorporating patient risk preferences and disease severity, but its implication for valuing treatments for neurodegenerative diseases is unclear. A five-state Markov model was developed to estimate the health economic value of a hypothetical treatment which delayed dementia onset by 30 % versus the standard of care (SoC) for 70-year-old patients diagnosed with mild cognitive impairment. GRACE was implemented using relative risk aversion (RRA) estimates from a U.S. general population survey. Treatment value was measured as risk-and severity adjusted net monetary benefit (RASA-NMB). Risk-neutral TCEA results were computed for comparison. Under TCEA, the hypothetical treatment improved health outcomes by 0.49 quality-adjusted life-years (QALYs) (3.72 vs 3.23 QALYs). Using GRACE, Generalized and Risk-Adjusted QALYs (GRA-QALY) were 3.69 times larger under GRACE than TCEA (2.15 ΔGRA-QALY; 4.91 GRA-QALYs vs 2.76 GRA-QALYs), reflecting additional utility from reduced variance in the distribution of future health and a higher value placed on additional survival relative to health improvement. However, willingness to pay for these gains was 76.4 % lower under GRACE due to high mortality before patients progressed to severe health states ($23,622 per GRA-QALY vs $100,000 per QALY). On net, GRACE increased the net monetary benefit by 3 % compared to TCEA (RASA-NMB = $51,430 vs TCEA NMB = $49,914). In summary, despite large increases in patient utility gains, GRACE had a modest impact on treatment value for neurological conditions causing cognitive impairments.
This paper develops a theoretical model to show how changes in the default system of organ donor registration, along with a Priority Rule, affect society's cadaveric organ donation rate. The developed model is on the lines of Kessler and Roth (2012) and seeks to inspect the hypotheses put forward by Li et al (2013), solely based on their laboratory experiment. The model shows that some of these results can change under specific assumptions regarding the population of potential donors. Among the hypotheses examined in this paper, special emphasis has been given to hypothesis 4, which evaluates whether a nation benefits more from the introduction of the priority rule compared to a change in the default system of organ donation to presumed consent, when the baseline system is explicit consent without the priority rule. The findings suggest that the outcome of such an analysis, in an economy like the United States, will critically depend on how one chooses to quantify the monetary value of Quality Adjusted Life Years (QALYs) gained from organ transplantation and the transaction cost of donor registration. The paper highlights that in the year 2016, the US would have benefited relatively more, in terms of donor registrations for deceased kidney donation, from a change in the default system of organ donation to presumed consent rather than an introduction of the priority rule. This result holds as long as the monetary value of a QALY gained from Deceased Kidney Transplantation is less than $2,068,717.04, at 2016-level prices. The robustness of this result has been confirmed through Monte Carlo Uncertainty Analysis, with statistical significance at the 99 % level.
Chronic obstructive pulmonary disease (COPD) is a group of progressive lung diseases, leading to increased healthcare use, expenditures, and risk of mortality. In June 2024, the Institute for Clinical and Economic Review (ICER) released a traditional cost-effectiveness analysis (CEA) of ensifentrine + background maintenance therapy (BMT) for the treatment of moderate-to-severe COPD. This study's objective is to analyze the sensitivity of ICER's traditional CEA methods to various adjustments to their model, including Generalized Cost-Effectiveness Analysis (GCEA) elements such as generalized risk-adjusted cost-effectiveness (GRACE), dynamic pricing, and stacked cohorts. A Markov model simulated the value of ensifentrine + BMT relative to BMT only for treating moderate-to-severe COPD, with patients transitioning across health states defined by levels of lung functionality. We estimated the annual value-based prices (VBPs) and incremental cost-effectiveness ratios of ensifentrine + BMT relative to BMT using traditional CEA and GCEA methods, including multiple scenario analyses. The VBP of ensifentrine + BMT is estimated at $61,008 when all GCEA elements are included. Relative to the traditional CEA static pricing scenario, GRACE increases VBPs by roughly 10 %, while dynamic pricing increases VBPs by 7-11 %. Stacked cohorts with dynamic pricing increases VBPs by 86-170 %. When including GCEA elements and adjustments to better reflect real-world COPD clinical pathways, ensifentrine + BMT is cost-effective below an annual price of $61,000 at a willingness-to-pay of $150,000 per quality-adjusted life-year.
Neurological conditions adversely impact patients and society due to both quality-of-life decrements and high financial burden. Traditional cost effectiveness methods, however, may undervalue neurological treatments by assuming patients are risk neutral. This study seeks first to quantify insurance value for hypothetical treatments that delay the (i) cognitive and (ii) physical impairments of neurological conditions. Moreover, this study also measures risk preferences over neurological health states to inform parameterization of generalized risk-adjusted cost effectiveness (GRACE) analyses. Two national surveys – one evaluating cognitive impairments and the other mobility impairments – were administered to U.S. residents aged ≥21 years between July 2023 to November 2023. First, a multiple random staircase design was used to elicit respondents’ willingness-to-pay (WTP) for coverage of a hypothetical, new treatment that delayed the progression of cognitive or mobility impairments relative to the standard of care. Insurance value was calculated as the share of the stated preference estimated WTP that exceeded the expected quality-adjusted life year (QALY)-based value assuming risk neutrality. Second, to measure risk aversion, respondents were asked to (i) estimate health-related quality of life (HRQoL) for cognitive and mobility impairment health states using a visual analog scale, and (ii) choose between two hypothetical treatments with probabilistically varying across outcomes following the Holt and Laury (Holt, C. A., and S. K. Laury. 2002. “Risk Aversion and Incentive Effects.” American Economic Review 92 (5): 1644–55). Respondents’ indifference points were inferred from survey responses and used to estimate relative risk aversion (RRA) assuming a constant relative risk aversion utility function. Among n = 295 respondents meeting inclusion criteria for the cognitive survey, 64.9 % were female and the average age was 51 years (SD = 16). WTP for generous insurance coverage of a new treatment delaying cognitive impairment was $646.88 per year compared to $260.80 calculated under traditional (i.e. risk neutral) cost-effectiveness approaches, implying a risk-adjusted cost effectiveness threshold of $248,037 per QALY. Respondents were risk averse over cognitive impairment outcomes, with mean RRA of 1.49 (95 % CI: [1.29, 1.68]). Among the 259 respondents meeting the inclusion requirement for the mobility survey 51.0 % were female and the average age was 49 years (SD = 16 years). WTP for insurance coverage of a new treatment that would prevent progression of mobility impairments was $671.35 per year compared to $133.23 calculated under traditional cost-effectiveness, implying a risk-adjusted cost effectiveness threshold of $502,193 per QALY. Respondents were risk averse over mobility outcomes with mean RRA of 0.68 (95 % CI: [0.51, 0.86]). Due to insurance value, respondents exhibited high willingness to pay for treatments that reduced cognitive and mobility impairments caused by neurological conditions. Individuals were risk averse over both cognitive- and mobility-related neurology health states.
Individuals of Middle Eastern and North African (MENA) ancestry in the US have been the targets of anti-immigrant policies, counterterrorism operations, and vitriolic political rhetoric. Yet, lack of data identifying MENA individuals has prevented systematic evaluation of the impact of these policies and rhetoric on MENA communities' wellbeing, including investment in health capital. We begin to address this gap in knowledge by focusing on the travel ban from majority Muslim countries implemented at the start of the first Trump administration. Using a large, longitudinal medical records database we evaluate the impact of this policy on preventive care use among MENA children in the US, finding decreased well-visits, and associated vaccinations among MENA children. Documenting MENA health outcomes following changes in official US policy is paramount for understanding the full consequences of policies that target underrepresented groups.
The rapid emergence of vaccines and therapeutics in response to the onset of the coronavirus (COVID-19) pandemic demonstrated the value of medical innovation. These advances not only led to enhanced patient welfare by reducing the disease’s mortality and morbidity but also reduced the need for costly prevention measures, such as cuts in economic activity. This paper offers the first estimate of the portion of economic value generated by these medical innovations that was appropriated as earnings by the innovating companies, measured by the ratio of company earnings to the overall societal value generated by the innovations. To estimate the value and appropriation of COVID-19 innovations, one must necessarily make assumptions about what disease-specific and preventive activity would have been in the absence of these new innovations. To obtain robustness in our findings across such scenarios, we estimate industry appropriation across a wide range of counterfactual scenarios that would occur under no innovation. These scenarios include previous assessments of the contributing subparts of the value generated by the innovations. Our primary finding is that, within the large range of these counterfactual scenarios, upper-bound measures of the proportion of value appropriated by the industry ranged from 0.2 % to 4.6 % of the value generated by the vaccine and treatment innovations. Even though these are upper bound appropriation rates, they are significantly lower than those documented for other significant health sciences innovations. This suggests that COVID-19 vaccines and treatments were remarkable, not only in their swift development but also in the considerable societal value they provided, which extended far beyond the rewards to the innovating companies.
This study argues that value assessment conducted from a societal perspective should rely on the Generalized Cost-Effectiveness Analysis (GCEA) framework proposed herein. Recently developed value assessment inventories - such as the Second Panel on Cost-Effectiveness's "impact inventory" and International Society of Pharmacoeconomics Outcomes Research (ISPOR) "value flower" - aimed to more comprehensively capture the benefits and costs of new health technologies from a societal perspective. Nevertheless, application of broader value elements in practice has been limited in part because quantifying these elements can be complex, but also because there have been numerous methodological advances since these value inventories have been released (e.g. generalized and risk-adjusted cost effectiveness). To facilitate estimation of treatment value from a societal perspective, this paper provides an updated value inventory - called the GCEA value flower - and a user guide for implementing GCEA for health economics researchers and practitioners. GCEA considers 15 broader value elements across four categories: (i) uncertainty, (ii) dynamics, (iii) beneficiary, and (iv) additional value components. The uncertainty category incorporates patient risk preferences into value assessment. The dynamics category petals account for the evolution of real-world treatment value (e.g. option value) and includes drug pricing trends (e.g. future genericization). The beneficiary category accounts for the fact health technologies can benefit others (e.g. caregivers) and also that society may care to whom health benefits accrue (e.g. equity). Finally, GCEA incorporates additional broader sources of value (e.g. community spillovers, productivity losses). This GCEA user guide aims to facilitate both the estimation of each of these value elements and the incorporation of these values into health technology assessment when conducted from a societal perspective.
Despite the implementation of significant measures by European countries in recent years, smoking rates in Europe remain persistently high. The European Commission is currently undertaking a comprehensive review of its tobacco regulations. This article aims to address critical inquiries that arise during the amendment of the regulatory framework. We evaluate the effectiveness of existing tobacco control methods and observe a diminishing impact on promoting smoking cessation. Additionally, we explore how individuals of varying genders respond to the regulatory environment. We propose a comprehensive and evidence-based framework for implementing a taxation system in response to the proliferation of emerging products, including e-cigarettes and heated tobacco. This system is designed to align effectively with health policy objectives, providing a strategic approach to curbing tobacco use and promoting public health.
As the tax base for traditional tobacco excise taxes continues to erode, policymakers have growing interest to expand taxation to novel and reduced-risk tobacco products. Chief among the latter are electronic nicotine delivery systems (ENDS; commonly known as e-cigarettes), although other reduced-risk tobacco products such as heated tobacco and smokeless tobacco products are also being considered for taxation. There are many possible rationales for taxing such products: to raise revenue, to correct for health externalities, to improve public health, to correct for internalities caused by irrationality or misinformation, and to redistribute income. Although each rationale leads to a different objective function, the conclusions regarding relative tax rates are largely the same. The relatively higher price elasticity of demand for e-cigarettes (compared to cigarettes) and the lower marginal harms from use imply in each case that taxes on e-cigarettes and other harm-reduced products should be relatively lower, and likely much lower, than those on cigarettes. Additional considerations concerning the policy goal of discouraging use of any tobacco product by youth are discussed as well.
Abstract This research examines the mental health inequalities between employed and unemployed individuals among the fluctuations over the business cycle. To analyze whether a recession affects self-evaluated mental health and consequently increases the demand for mental health care, I exploit the sudden increase of the unemployment rate in Spain during the period 2007–2009. First, I analyze the impairment of self-evaluated mental health as a consequence of the Great Recession and if it prevails during the economic recovery. In addition, I estimate if the effect on self-reported mental health is reflected in demand for mental health care. The results from an event study design show that the economic downturn increases the differences between employed and unemployed individuals in self-evaluated mental health. However, and despite the continuous improvement in unemployment, the mental health gap remained unchanged between 2014 and 2017, which could imply the persistence of some lasting impacts of the Great Recession on mental health. Nonetheless, I find a reduction in the differences of using drugs related to mental health during the period 2011–2012, when I estimate the largest inequalities in self-evaluated mental health.
Abstract In recent years, Medicare margins of U.S. short-term acute care hospitals participating in the inpatient prospective payment system (IPPS) have declined nationally by over 10 percentage points, from 2.2% in 2002 to −8.7% in 2019. This trend conceals critical regional variations, with recent studies documenting particularly low and negative margins in metropolitan areas with higher labor costs despite geographic adjustments by the Centers for Medicare & Medicaid Services (CMS). In this article, we describe recent trends in California hospitals’ traditional fee-for-service Medicare operating margins compared to hospital operating margins across payers and changes in the CMS hospital wage index (HWI) used to adjust Medicare payments. We conduct an observational study of audited financial reports of IPPS-participating California hospitals using California Department of Health Care Access and Information and CMS data for years 2005–2020 (n = 4429 reports included in the analysis). We describe trends in financial measures by payer and investigate associations between HWI and traditional Medicare margins, focusing on the pre-COVID period of 2005 through 2019. During that period, California hospitals’ statewide traditional Medicare operating margin declined from −27 to −40%, and financial shortfalls in caring for fee-for-service Medicare patients more than doubled ($4.1 billion in 2005 to $8.5 billion in 2019, both values in 2019 dollars). Meanwhile, operating margins from commercial managed care patients increased from 21% in 2005 to 38% in 2019. There was a stable negative association between HWI and traditional Medicare operating margins throughout the period (p = 0.000 in 2005; p < 0.0001 in 2006–2020), indicating that areas of California with higher health care wages had persistently worse traditional Medicare operating margins than areas with lower wages.
Abstract This study seeks to analyze the overall impact that biopharmaceutical innovation had on disability, Social Security recipiency, and the use of medical services of U.S. community residents during the period 1998–2015. We test the hypothesis that the probability of disability, Social Security recipiency, and medical care utilization associated with a medical condition is inversely related to the number of drug classes previously approved for that condition. We use data from the 1998–2015 waves of the Medical Expenditure Panel Survey and other sources to estimate probit models of an individual’s probability of disability, Social Security recipiency, and medical care utilization. The effect of biopharmaceutical innovation is identified by differences across over 200 medical conditions in the growth in the lagged number of drug classes ever approved. 18 years of previous biopharmaceutical innovation is estimated to have reduced: the number of people who were completely unable to work at a job, do housework, or go to school in 2015 by 4.5%; the number of people with cognitive limitations by 3.2%; the number of people receiving SSI in 2015 by 247 thousand (3.1%); and the number of people receiving Social Security by 984 thousand (2.0%). Previous innovation is also estimated to have caused reductions in home health visits (9.2%), inpatient events (5.7%), missed school days (5.1%), and outpatient events (4.1%). The estimated value in 2015 of some of the reductions in disability, Social Security recipiency, and use of medical care attributable to previous biopharmaceutical innovation ($115 billion) is fairly close to 2015 expenditure on drug classes that were first approved by the FDA during 1989–2006 ($127 billion). However, for a number of reasons, the costs are likely to be lower, and the benefits are likely to be larger, than these figures.
Abstract In this paper we revisit the relationship between health outcomes, income, and income inequality by applying alternative panel methodologies to a dataset of high-income countries spanning the time period 1980–2017. In this direction, we adopt alternative methodological frameworks in order to provide a) meaningful results by taking into account standard errors that alleviate problems of cross-sectional (spatial) and temporal dependence, and b) insights into the underlying relationships at several points of the conditional distribution of the health outcomes dependent variables. The evidence strongly supports the significant role that income plays in determining health outcomes. The findings relating to income inequality and nonlinear terms are more fragmented in that their significance and sign-direction depend on the functional form and the respective quantiles of the distribution the relationships are evaluated.
Medicare Part D has significantly enhanced access to prescription drugs among Medicare beneficiaries. However, the recent rapid rise of utilization management policies in the Medicare Part D program may have adversely affected access to prescription drugs. I study the effects of expected and observed exposure to utilization management in prescription drug utilization using Medicare Part D claims data from 2009 to 2016 and an instrumental variables strategy based on the interaction of lagged health status and the set of plans available to each beneficiary. I find that the expected share of spending subject to utilization management increases the observed share, with the smallest effect for prior authorization. Increases in the expected share of drug spending subject to prior authorization increases Part D spending by $122.27 per percentage point, with almost three-quarters of this increase being paid by the Medicare program, rather than beneficiaries or plans. Comparable increases in step therapy and quantity limit exposure increase spending by $46 and decrease spending by $31, respectively. Interestingly, increased exposure to prior authorization and quantity limits increases the average price per 30-day prescription.
The aim of this paper is to evaluate the utility of the Health and Retirement Study (HRS) for studying the impact of working conditions on individuals' health, well-being and labor supply decisions at older ages. I provide a brief overview of the information on working conditions that is currently available in the HRS and discuss implications for studies on the effects of working conditions on the individual life course. I conclude with a discussion of how recent and projected trends in the U.S. workforce are reflected in the current HRS survey content.
Abstract Selection bias is an ongoing concern in large-scale panel surveys where the cumulative effects of unit nonresponse increase at each subsequent wave of data collection. A second source of selection bias in panel studies is the inability to link respondents to supplementary administrative records, either because respondents do not consent to link or the matching algorithm fails to locate their administrative records. Both sources of selection bias can affect the validity of conclusions drawn from these data sources. In this article, I discuss recently proposed methods of reducing both sources of selection bias in panel studies, with a special emphasis on reducing selection bias in the US Health and Retirement Study.
This study aims to investigate the association between gross domestic product (GDP), mortality rate (MR) and current healthcare expenditure (CHE) in 31 high-income countries. We used panel data from 2000 to 2017 collected from WHO and OECD databases. The association between CHE, GDP and MR was investigated through a random-effects model. To control for reverse causality, we adopted a test of Granger causality. The model shows that the MR has a statistically significant and negative effect on CHE and that an increase in GDP is associated with an increase of CHE (p < 0.001). The Granger causality analysis shows that all the variables exhibit a bidirectional causality. We found a two-way relationship between GDP and CHE. Our analysis highlights the economic multiplier effect of CHE. In the debate on the optimal allocation of resources, this evidence should be taken into due consideration.
Abstract The Health and Retirement Study is an amazing resource for those studying aging in the United States, and a fantastic model for other countries who have created similar longitudinal studies. The raw amount of information, from data on income, wealth, and use of health services to employment, retirement, and family connections on to the collection of clinical biomarkers can be both empowering and overwhelming to a researcher. Luckily through the process of engagement with the research community and constant improvement, these reams of data are not only consistently growing in a thoughtful and focused direction, they are also explained and summarized to increase the ease of use for all. One of the very useful areas of the HRS is the Contextual Data File (CDF), which is the focus of this review. The CDF provides access to easy-to-use helpful community-level data in a secure environment that has allowed researchers to answer questions that would have otherwise been difficult or impossible to tackle. The current CDF includes data in six categories (University of Michigan Institute for Social Research. 2017. HRS Data Book: The Health and Retirement Study: Aging in the 21st Century, Challenges and Opportunities for Americans. Ann Arbor: University of Michigan. Also available at https://hrs.isr.umich.edu/about/data-book, 17): 1. Socio-economic Status and Demographic Structure 2. Psychosocial Stressors 3. Health Care 4. Physical Hazards 5. Amenities 6. Land Use and the Built Environment. Each of these areas have allowed researchers to answer interesting questions such as what is the impact of air pollution on cognition in older adults (Ailshire, J., and K. M. Walsemann. 2021. “Education Differences in the Adverse Impact of PM 2.5 on Incident Cognitive Impairment Among U.S. Older Adults.” Journal of Alzheimer’s Disease 79 (2): 615–25), the impact of neighborhood characteristics on obesity in older adults (Grafova, I. B., V. A. Freedman, R. Kumar, and J. Rogowski. 2008. “Neighborhoods and Obesity in Later Life.” American Journal of Public Health 98: 2065–71), or even what do we gain from introducing contextual data to a survey analysis (Wilkinson, L. R., K. F. Ferraro, and B. R. Kemp. 2017. “Contextualization of Survey Data: What Do We Gain and Does it Matter?” Research in Human Development 14 (3): 234–52)? My review focuses on the potential to expand contextual data in a few of these areas. From new data sets developed and released by the U.S. Census Bureau, to improved measurements of climate and environmental risk, there are numerous new data sources that would be a boon to the research community if they were joined together with the HRS. The following section begins by breaking down the opportunity provided by community or place-based data before moving on to specific recommendations for new data that could be included in the HRS contextual data file.
