Incremental Health Care Costs of Anxiety and Depression Among Medicare Beneficiaries With Cancer.

PURPOSE: Mental health comorbidities are commonplace among patients with cancer and have been associated with adverse health outcomes and elevated health care costs. Given the rapidly evolving cancer care landscape, an updated understanding of the prevalence and costs of mental health conditions among patients with cancer is needed. This study assessed the incremental costs of anxiety and depression among Medicare beneficiaries with cancer. METHODS: This retrospective cohort study used the SEER-Medicare database. Patients diagnosed with melanoma, breast, lung, prostate, or colorectal cancer between July 2013 and December 2017 were followed for at least 12 months and up to 36 months after cancer diagnosis. Patients were categorized on the basis of anxiety/depression (AD) diagnosis: (1) predating cancer, (2) onset after cancer, or (3) no AD. Multivariable regression was used to estimate differences in all-cause incremental costs (before v after cancer) between the three groups. RESULTS: Of 230,626 patients, 10% had AD before their cancer diagnosis and 22% were diagnosed after cancer. In the first year after cancer diagnosis, average monthly health care costs were $5,750 in US dollars (USD) for patients with newly onset, $5,208 (USD) for patients with preexisting, and $3,919 (USD) for patients without a diagnosis of AD. The incremental cost of cancer was the greatest among patients with newly onset AD-$1,458 (USD) per month greater than those with no AD. Similar patterns were observed across cancer types and stages. CONCLUSION: One in three Medicare beneficiaries with cancer in this study had a diagnosis of anxiety or depression. Newly onset AD is associated with an increase in health care costs of $17,496 (USD) per year. Screening and management of mental health conditions for patients with cancer should be part of coordinated oncology care.

Quantifying spillover benefits in value assessment: a case study of increased graft survival on the US kidney transplant waitlist.

AIM: To quantify the wider impacts of increased graft survival on the size of the kidney transplant waitlist and health and economic outcomes. MATERIALS AND METHODS: The analysis employed known steady-state solutions to a double-queueing system as well as simulations of this system. Baseline input parameters were sourced from the Organ Procurement and Transplant Network and the United States Renal Data System. Three increased graft survival scenarios were modeled: decreases in repeat transplant candidates joining the waitlist of 25%, 50%, and 100%. RESULTS: Under the three scenarios, we estimated that the US waitlist size would decrease from 91,822 to 85,461 (6.9% decrease), 80,073 (12.8% decrease), and 69,340 (24.4% decrease), respectively. Patient outcomes improved, with lifetime quality-adjusted life years (QALYs) for a 1-year cohort of transplant recipients increasing by 10,010, 16,888, and 43,345 over the three scenarios. Discounted lifetime costs for the cohort in the new steady state were lower by $1.6 billion, $2.3 billion, and $9.0 billion for each scenario, respectively. Spillover impacts (i.e. benefits that accrued beyond the patients who directly experienced increased graft survival) accounted for 41-48% of the QALY gains and ranged from cost increases of 3.3% to decreases of 5.5%. LIMITATIONS: The model is a simplification of reality and does not account for the full degree of patient heterogeneity occurring in the real world. Health economic outcomes are extrapolated based on the assumption that the median patient is representative of the overall population. CONCLUSIONS: Increasing graft survival reduces demand from repeat transplants candidates, allowing additional candidates to receive transplants. These spillover impacts decrease waitlist size and shorten wait times, leading to improvements in graft and patient survival as well as quality-of-life. Cost-effectiveness analyses of treatments that increase kidney graft survival should incorporate spillover benefits that accrue beyond the direct recipient of an intervention.

For which diseases do broader value elements matter most? An evaluation across 20 ICER evidence reports.

BACKGROUND: U.S. value framework developers such as the Institute for Clinical and Economic Review (ICER) use cost-effectiveness analysis to value new health care technologies. Often, these value assessment frameworks use a health system perspective without fully accounting for societal and broader benefits and costs of an intervention. Although there is ongoing debate about the most appropriate methods for including broader value elements in value assessment, it remains unclear whether the inclusion of these value elements is likely to affect the quantitative estimates of treatment value. OBJECTIVE: To assess variations in the relevance of broader value elements to cost-effectiveness analysis across diseases. METHODS: Thirty-two broader value elements (e.g., caregiver burden, health equity, real option value, productivity) not traditionally included in health technology assessments were identified through a targeted literature review. Evidence reports published by ICER between July 2017 and January 2020 were evaluated to identify which broader value elements were discussed as relevant to each disease in the report text. The study examined whether there were associations among ICER's discussion of broader value elements, rare disease status, treatment cost, estimated treatment cost-effectiveness, and ICER committee voting results for contextual considerations and additional benefits/disadvantages. RESULTS: The most commonly cited broader value element category in the ICER evidence reports was household and leisure (e.g., absenteeism from normal activities and caregiver burden). More value elements were cited for inherited retinal disease (19 elements) and sickle cell disease (18 elements) than for other diseases. Cardiovascular disease and diabetes had the fewest number of value elements cited (7 elements). Rare diseases were more likely to have broader value elements cited compared with nonrare diseases (15.9 vs. 11.5, P < 0.001). Treatments with higher (i.e., less favorable) incremental cost-effectiveness ratios were more likely to have a greater number of broader value elements cited (ρ = 0.625, P < 0.001). CONCLUSIONS: The presence of broader value elements varied across diseases, with less cost-effective treatments more likely to have a higher number of relevant broader value elements. Inclusion of all relevant value elements in value assessments will more appropriately incentivize innovation and improve allocation of research funding. DISCLOSURES: This study was sponsored by Novartis Pharmaceutical Corporation. At the time of this study, Shafrin was employed by PRECISIONheor, a consultancy to the life sciences industry that received financial support from Novartis to conduct this study. Dennen, Pednekar, and Birch are employed by PRECISIONheor. Bhor was an employee of Novartis Pharmaceutical Corporation at the time this research was conducted and manuscript was developed and reports grants from Novartis, unrelated to this work. Kanter has served on scientific advisory boards and steering committees for and reports receiving consulting fees from Novartis Pharmaceutical Corporation and is a site principal investigator on studies funded by Novartis Pharmaceutical Corporation. Kantar also reports support from Sickle Cell Disease Association of America Inc. and National Heart, Lung, and Blood Institute, unrelated to this work. Neumann reports advisory boards or consulting fees from Novartis Pharmaceutical Corporation and PRECISIONheor, as well as advisory boards or consulting fees unrelated to this study from AbbVie, Amgen, Avexis, Bayer, Congressional Budget Office, Janssen, Merck, Novartis, Novo Nordisk, Precision Health Economics, Veritech, Vertex; funding from The CEA Registry Sponsors by various pharmaceutical and medical device companies; and grants from Amgen, Lundbeck, Bill and Melinda Gates Foundation, National Pharmaceutical Council, Alzheimer's Association, and the National Institutes for Health.