A Cost-Effectiveness Framework for COVID-19 Treatments for Hospitalized Patients in the United States.

INTRODUCTION: The COVID-19 pandemic is a global crisis impacting population health and the economy. We describe a cost-effectiveness framework for evaluating acute treatments for hospitalized patients with COVID-19, considering a broad spectrum of potential treatment profiles and perspectives within the US healthcare system to ensure incorporation of the most relevant clinical parameters, given evidence currently available. METHODS: A lifetime model, with a short-term acute care decision tree followed by a post-discharge three-state Markov cohort model, was developed to estimate the impact of a potential treatment relative to best supportive care (BSC) for patients hospitalized with COVID-19. The model included information on costs and resources across inpatient levels of care, use of mechanical ventilation, post-discharge morbidity from ventilation, and lifetime healthcare and societal costs. Published literature informed clinical and treatment inputs, healthcare resource use, unit costs, and utilities. The potential health impacts and cost-effectiveness outcomes were assessed from US health payer, societal, and fee-for-service (FFS) payment model perspectives. RESULTS: Viewing results in aggregate, treatments that conferred at least a mortality benefit were likely to be cost-effective, as all deterministic and sensitivity analyses results fell far below willingness-to-pay thresholds using both a US health payer and FFS payment perspective, with and without societal costs included. In the base case, incremental cost-effectiveness ratios (ICER) ranged from $22,933 from a health payer perspective using bundled payments to $8028 from a societal perspective using a FFS payment model. Even with conservative assumptions on societal impact, inclusion of societal costs consistently produced ICERs 40-60% lower than ICERs for the payer perspective. CONCLUSION: Effective COVID-19 treatments for hospitalized patients may not only reduce disease burden but also represent good value for the health system and society. Though data limitations remain, this cost-effectiveness framework expands beyond current models to include societal costs and post-discharge ventilation morbidity effects of potential COVID-19 treatments.

Estimated Impact of Public and Private Sector COVID-19 Diagnostics and Treatments on US Healthcare Resource Utilization.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) has imposed a considerable burden on the United States (US) health system, with particular concern over healthcare capacity constraints. METHODS: We modeled the impact of public and private sector contributions to developing diagnostic testing and treatments on COVID-19-related healthcare resource use. RESULTS: We estimated that public sector contributions led to at least 30% reductions in COVID-19-related healthcare resource utilization. Private sector contributions to expanded diagnostic testing and treatments led to further reductions in mortality (- 44%), intensive care unit (ICU) and non-ICU hospital beds (- 30% and - 28%, respectively), and ventilator use (- 29%). The combination of lower diagnostic test sensitivity and proportions of patients self-isolating may exacerbate case numbers, and policies that encourage self-isolating should be considered. CONCLUSION: While mechanisms exist to facilitate research, development, and patient access to diagnostic testing, future policies should focus on ensuring equitable patient access to both diagnostic testing and treatments that, in turn, will alleviate COVID-19-related resource constraints.