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Introduction: Breast cancer treatment includes increasingly complex and expensive treatments. Accordingly, the current estimates of the cost of breast cancer treatment are out of date., Methods: The SEER-Medicare Data Link provided 142,837,978 paid Medicare claims from 398,148 female beneficiaries between the ages of 22 and 110 diagnosed with breast cancer between 2007 and 2016. These claims were compared with 153,071,044 claims from 443,952 Medicare beneficiaries without a cancer diagnosis. The total, fully adjudicated, amounts for each claim were summed to determine total treatment cost for each beneficiary. These costs were then aggregated by year after diagnosis and stage at diagnosis. The actuarial survival of beneficiaries with cancer was calculated using the Kaplan-Meier method., Results: Mean costs for the control group were $8,019 per year. The 10-year cost of cancer treatment in Medicare beneficiaries was directly related to stage at diagnosis and ranged from $103,573 for stage 0 cancers to $376,573 for stage 4 cancers. The highest cost occurred during the first 2 years after diagnosis, the time of the beneficiary's initial treatment. Following the first 2 years, healthcare costs remained elevated for at least 10 years after diagnosis., Conclusions: The 10-year treatment cost of female Medicare beneficiaries with breast cancer increases with increasing stage at diagnosis. Any effective screening technology that reduces stage at diagnosis will result in significant treatment cost savings to the Medicare program., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The priority aim of this study is to investigate the effect of carbon footprint, which is an indicator of environmental degradation, on health expenditures for the USA. In the study, cointegration analysis was performed for the period 1970-2016 by using health expenditures, carbon footprint, gross domestic product per capita and life expectancy at birth variables. According to the results of standard cointegration analysis, only cointegration relationship between health expenditures and income was found. In the models with carbon footprint, no cointegration relationship was discovered between the original values of the variables. This result was approached with suspicion, and it was thought that there might be a hidden cointegration between healthcare expenditures and carbon footprint. For this purpose, the hidden cointegration analysis and crouching error correction model proposed by Granger and Yoon [18] were employed among the positive and negative components of the variables of healthcare expenditures and carbon footprint. The results of the hidden cointegration analysis revealed that there was a hidden cointegration relationship between the positive components of healthcare expenditures and the positive components of carbon footprint. Analysis results show that a 1% increase in carbon footprint will cause a 2.04% increase in healthcare expenditures in the long term in the USA. When the positive components of the variables were considered, it was concluded that there was a one-way long-term asymmetric causality relationship between carbon footprint and healthcare expenditures. As a result of the study, it was proposed that the carbon footprint should be diminished to prevent the increasing burden of the healthcare expenditures on the budget.

This paper introduces a new graphical tool: the mean deviation concentration curve. Using a unified approach, we derive the associated dominance conditions that identify robust rankings of absolute socioeconomic health inequality for all indices obeying Bleichrodt and van Doorslaer's (J Health Econ 25:945-957, 2006) principle of income-related health transfer. We also derive dominance conditions that are compatible with other transfer principles available in the literature. To make the identification of all robust orderings implementable using survey data, we discuss statistical inference for these dominance tests. To illustrate the empirical relevance of the proposed approach, we compare joint distributions of income and health-related behavior in the United States., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

A recent integrated health care initiative in Belgium supports 12 regional pilot projects scattered across the country and representing 21% of the population. As in shared savings programs, part of the estimated savings in health spending are paid out to the projects to reinvest in new actions. Short-term savings are expected in particular from cost reductions among high-cost patients. We estimate the effect of the projects on spending using a difference-in-difference model. The sensitivity of the results to the right-skewness of spending is commonly addressed by removing or top-coding high-cost cases. However, this leads to an underestimation of realized savings at the top end of the distribution, therefore, lowering incentives for cost reduction. We show that this trade-off can be weakened by an alternative approach in which cost categories that fall out of the scope of the projects' interventions are excluded from the dependent variable. We find that this approach leads to improvements in precision and model fit that are of the same magnitude as excluding high-cost cases altogether. At the same time, it sharpens the incentives for cost reduction because the model better reflects the costs that projects can affect., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper focuses on Medicare's End-Stage Renal Disease Quality Incentive Program (QIP). QIP aims to promote high-quality services in outpatient dialysis facilities by tying their payments to their performance on pre-specified quality measures. In this paper, employing principal-agent theory, we examine the effectiveness of QIP by exploring the changes in various clinical/operational measures when they become a part of the program as a performance measure. We study five QIP quality measures; two are operational: hospitalization and readmission. And three others are clinical: blood transfusion, hypercalcemia, and dialysis adequacy. Overall, we observe a significant improvement in all QIP quality measures after being included in the program, except for readmission. We recommend adjusting the weight and redesigning the readmission measure for Medicare to incentivize providers to reduce readmission. We also discuss establishing care coordination and employing data-driven clinical decision support systems as opportunities for dialysis facilities to improve the care delivery process., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: Preference-based measures are essential for producing quality-adjusted life years (QALYs) that are widely used for economic evaluations. In the absence of such measures, mapping algorithms can be applied to estimate utilities from disease-specific measures. This paper aims to develop mapping algorithms between the MacNew Heart Disease Quality of Life Questionnaire (MacNew) instrument and the English and the US-based EQ-5D-5L value sets., Methods: Individuals with heart disease were recruited from six countries: Australia, Canada, Germany, Norway, UK and the US in 2011/12. Both parametric and non-parametric statistical techniques were applied to estimate mapping algorithms that predict utilities for MacNew scores from EQ-5D-5L value sets. The optimal algorithm for each country-specific value set was primarily selected based on root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), and r-squared. Leave-one-out cross-validation was conducted to test the generalizability of each model., Results: For both the English and the US value sets, the one-inflated beta regression model consistently performed best in terms of all criteria. Similar results were observed for the cross-validation results. The preferred model explained 59 and 60% for the English and the US value set, respectively. Linear equating provided predicted values that were equivalent to observed values., Conclusions: The preferred mapping function enables to predict utilities for MacNew data from the EQ-5D-5L value sets recently developed in England and the US with better accuracy. This allows studies, which have included the MacNew to be used in cost-utility analyses and thus, the comparison of services with interventions across the health system.

This paper investigates whether or not suffering a health shock, and becoming eligible for social security, have a joint effect on labor supply. Despite millions of people experiencing both of these events each year, no paper has focused exclusively on the joint effect that these events may have on work outcomes. This is surprising given that experiencing a health shock may impact on how a worker responds to becoming eligible for social security. With data from the Health and Retirement Study, I model weekly hours of work as a function of health shocks, social security eligibility, and their interaction. I find that this interaction leads to a 3-4 h reduction in weekly hours of work for men, but has no effect for women. The results are robust to using different work outcomes, age groups, health shock definitions, subgroups, as well as falsification and placebo tests. The results appear to be driven by men who would have had to return to work with impaired health. Policies that promote a more flexible work situation for older men may alleviate these problems in the future.

This paper analyses whether government ideology and other political- and electoral-related factors influence country-level public healthcare expenditures, focussing on the impact of the Great Recession on that relationship. We test this hypothesis for the OECD countries in 1970-2016. Our results reveal the presence of a partisan effect, left-wing governments being more likely to raise public expenditures in the health sector. We also find that coalitions increase these expenditures, whilst minority governments and those with a high presence in the lower house decrease them. Meanwhile, the opportunistic behaviour of incumbents related to the timing of elections is not supported by our results. The percentage of public expenditures over total health expenditures is also examined, obtaining similar results regarding the partisan effect. However, the onset of the Great Recession has altered these relationships, neutralizing the impact of political factors.

Background: Different countries have different preferences regarding health, and there are different value sets for popular preference-based measures across different countries. However, the cost of collecting data to generate country-specific value sets can be prohibitive for countries with smaller population size or low- and middle-income countries (LMIC). This paper explores whether existing preference weights could be modelled alongside a small own country valuation study to generate representative estimates. This is explored using a case study modelling UK data alongside smaller US samples to generate US estimates., Methods: We analyse EQ-5D valuation data derived from representative samples of the US and UK populations using time trade-off to value 42 health states. A nonparametric Bayesian model was applied to estimate a US value set using the full UK dataset and subsets of the US dataset for 10, 15, 20 and 25 health states. Estimates are compared to a US value set estimated using US values alone using mean predictions and root mean square error., Results: The results suggest that using US data elicited for 20 health states alongside the existing UK data produces similar predicted mean valuations and RMSE as the US value set, while 25 health states produce the exact features., Conclusions: The promising results suggest that existing preference data could be combined with a small valuation study in a new country to generate preference weights, making own country value sets more achievable for LMIC. Further research is encouraged.

When the patent of a brand-name, marketed drug expires, new, generic products are usually offered. Small-molecule generic and originator drug products are expected to be chemically identical. Their pharmaceutical similarity can be typically assessed by simple regulatory criteria such as the expectation that the 90% confidence interval for the ratio of geometric means of some pharmacokinetic parameters be between 0.80 and 1.25. When such criteria are satisfied, the drug products are generally considered to exhibit therapeutic equivalence. They are then usually interchanged freely within individual patients. Biological drugs are complex proteins, for instance, because of their large size, intricate structure, sensitivity to environmental conditions, difficult manufacturing procedures, and the possibility of immunogenicity. Generic and brand-name biologic products can be expected to show only similarity but not identity in their various features and clinical effects. Consequently, the determination of biosimilarity is also a complicated process which involves assessment of the totality of the evidence for the close similarity of the two products. Moreover, even when biosimilarity has been established, it may not be assumed that the two biosimilar products can be automatically substituted by pharmacists. This generally requires additional, careful considerations. Without declaring interchangeability, a new product could be prescribed, i.e. it is prescribable. However, two products can be automatically substituted only if they are interchangeable. Interchangeability is a statistical term and it means that products can be used in any order in the same patient without considering the treatment history. The concepts of interchangeability and prescribability have been widely discussed in the past but only in relation to small molecule generics. In this paper we apply these concepts to biosimilars and we discuss: definitions of prescribability and interchangeability and their statistical implementation; the relation between bioequivalence and interchangeability for small-molecule drug products; regulatory requirements and expectations of biosimilar products in various jurisdictions; possible statistical approaches to establish the similarity and interchangeability of biologic drug products; definition of other technical terms such as switchability and automatic substitution. The paper will be concluded with a discussion of the anticipated future use of interchangeability of biological drug products.