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


Abstract

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.

Keywords: COVID 19; Coronavirus; Cost-effectiveness; Economic evaluation; Inpatient treatment; Productivity.

Figures

Fig. 1
Fig. 1
Model structure. Patients in the “Alive (no ventilation during inpatient stay)” state comprise patients discharged alive from the “no oxygen support” and the “oxygen support without ventilation” states. Patients in the “Alive (ventilation during inpatient stay)” state represent patients discharged alive from the “oxygen support with ventilation” state. Ventilation in the model refers to invasive mechanical ventilation. BSC?=?best supportive care; w/o?=?without
Fig. 2
Fig. 2
Base case one-way sensitivity analyses presenting change in cost per QALY gained on the x-axis and most influential variables on the y-axis. FFS = fee-for-service; ICER = incremental cost-effectiveness ratio; BSC = best supportive care; LOS = length of stay; OWSA = one-way sensitivity analysis. a OWSA—base case payer perspective (Bundled Payment); b OWSA—base case payer perspective (FFS Payment); c OWSA—base case societal perspective (Bundled Payment); d OWSA—base case societal perspective (FFS Payment). The mean age among patients discharged alive at discharge was assumed to equal the mean age at admission among all patients admitted
Fig. 3
Fig. 3
Base case probabilistic sensitivity analysis. Dashed lines represent willingness to pay thresholds at $150K (top), $100K (middle), and $50K (bottom) per QALY, respectively. Per the legend to the right of the figure, the base case and scenario clinical profiles are all plotted to show the overlap of outcomes given changes in efficacy elements
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