How to ration scarce resources equitably
Among the many failures of America’s first COVID-19 disaster response, unprepared federal authorities mismanaged the allocation of emergency medical equipment as the pandemic grew. The decisions of the Federal Emergency Management Agency “were inconsistent and lacked transparency, which frustrated state officials,” according to Yale’s Vahideh Manshadi, Chicago Booth’s Rad Niazadeh and Yale doctoral student Scott Rodilitz.
To be fair, the government’s stockpile of emergency medical and personal protective equipment was designed to help manage localized emergencies, not a pandemic. But rationing – during a pandemic or in other disaster response settings – can be done fairly and efficiently, the researchers say.
A tough time to keep things fair
Researchers turned to April 2020, as COVID-19 infections spread across the United States, to test their model. This series of maps illustrates when each state’s demand for emergency medical equipment was expected to peak, forcing authorities to calculate the amount of the nation’s stock to deploy and hold from week to week. ‘other.
When states were expected to peak demand for medical equipment in April 2020
Five States April 1-7
Seven more states plus Washington, DC, April 8-14
17 other states from April 15 to 21
20 states remaining after April 21
Manshadi et al., 2021
They have developed a method that they call Proportionate Projected Allocation that makes a good faith effort to deploy public resources not only to alleviate current suffering, but also to care for future victims who would be left behind in a first-come system. , first served.
As the coronavirus began to spread from the first hot spots, government planners realized the disease would spread rapidly nationwide and had to decide how to allocate resources between communities already in pain and those who will subsequently be infected. . Faced with global equipment shortages, FEMA has struggled to keep up, prioritizing deliveries to medical facilities that risk running out within 72 hours, according to testimony to Congress from Administrator Peter Gaynor. This left some communities on their own as a stockpile of FEMA protective medical equipment that would normally have lasted a year ran out within weeks.
The researchers base their proportional allocation alternative on the theory of justice proposed by the late philosopher John Rawls, which defines fairness from the perspective of a neutral observer. As a result, they aim to maximize the well-being of the most disadvantaged communities. The aim is to balance fairness and efficiency, as well as being simple and transparent, two qualities that are particularly important for public policy, they say. To achieve this, their model takes into account the complicated correlation structure of future demands when making allocation recommendations for a community in need. It is then based on a simple statistical analysis of needs and likely outcomes, which tend to be difficult to predict with high accuracy in various communities during a pandemic such as COVID-19.
Using the April 1, 2020 projections from the University of Washington’s Institute for Health Metrics and Evaluation, researchers divided U.S. states into four groups based on when they were expected to reach peak demand for beds. intensive care. They then performed 10,000 simulations using their mathematical model of medical and protective equipment distribution. The result was a fill rate (FR) which determines what fraction of a community’s needs can be met while maintaining enough emergency stock for future needs.
The researchers calculate that their model, which focuses on maximizing RF in all communities, outperforms other rationing policies by up to 33% when evaluated using their efficiency and equity metric. . A limited supply of essential goods is a common problem in disasters such as natural disasters, and their approach can be used in these contexts as well. “Our framework lends itself to extensions such as taking into account generalized objectives and rationing several types of resources,” they write. “More broadly, it serves as a basic model for theoretically studying sequential allocation problems with a goal beyond utility maximization.”