Going forward, the federal response to the COVID-19 pandemic will require centralized decision-making around how to equitably allocate, and reallocate, limited supplies of ventilators to states in need. Projections from the Institute for Health Metrics and Evaluation at the University of Washington, which assume that all states will institute strict social distancing practices and maintain them until after infections peak, show states will hit their peak demand at different time points throughout the months of April and May. Many states are predicted to experience a significant gap in ICU capacity, and similar, if not greater, gaps in ventilator capacity, with the time point at which needs will begin to exceed current capacity varying by state [8].
We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency is to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse condition. The model is applied to study the allocation of ventilator inventory in the COVID-19 pandemic by FEMA to different US states. Findings suggest that if less than 60% of the ventilator inventory is available for non-COVID-19 patients, FEMA's stockpile of 20,000 ventilators (as of 03/23/2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non-COVID-19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top-most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (till 05/31/20) is about 232,000 ventilator days, with a peak shortfall of 17,200 ventilators on
Background and Objectives: Reducing discard is important for the US transplantation system, as nearly 20% of the deceased donor kidneys are discarded. One cause for the discards is the avoidance of protracted cold ischemia times. Extended cold ischemia times at transplant is associated with additional risk of graft failure and patient mortality. A preference for local (within the same donor service area) or low-kidney donor risk index organs, the endogeneity of cold ischemia time during organ allocation, and the use of provisional offers all complicate the analysis of cold ischemia times' influence on kidney acceptance decision making. Design, Setting, Participants, and Methods: Using 01/2018-06/2019 Organ Procurement and Transplantation Network data, we modeled the probability of accepting an offer for a kidney after provisional acceptance. We use logistic regression that includes cold ischemia time, KDRI, and other covariates selected from literature. Endogeneity of cold ischemia time is treated by a two-stage instrumental variables approach. Results: Logistic regression results for 3.33 million provisional acceptances from 12,369 donors and 108,313 candidates quantify tradeoffs between cold ischemia time at the time of offer acceptance and donor-recipient characteristics. Overall, each additional 2 hours of cold ischemia time impacts acceptance for nonlocal and local recipients (OR=0.75 (0.73,0.77), OR=0.88 (0.86,0.91); p < 0.001). For kidney donor risk index >1.75 (kidney donor profile index>85) kidneys, an additional 2 hours of cold ischemia time for nonlocal and local recipients impacts acceptance with OR=0.58 (0.54,0.63) (nonlocal) and OR=0.65 (0.6,0.7) (local); p<0.001. The impact of an additional 2 hours of cold ischemia time on acceptance of kidneys with kidney donor risk index 1.75 (kidney donor profile index 85) is less pronounced for nonlocal offers (OR= ≤ ≤ 0.82 (0.80,0.85); p < 0.001) and not significant for local offers. Conclusions: The acceptability of marginal organs rises when placements are nearer to the donor and when cold ischemia time is reduced.
M.B. developed research design, conducted data analysis, and drafted the article. S.M. reviewed research design, supervised data analysis, and edited the article.
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