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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 Accordingly, the current estimates of the cost of breast cancer treatment are out of date., Methods: The SEER-Medicare Data Link provided 142,837,978 paid Medicare claims from 398,148 female beneficiaries between the ages of 22 and 110 diagnosed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.)

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.)