When a US hospital system is overwhelmed by disaster, Crisis Standards of Care guide the triage teams forced to choose which patients receive scarce life support treatments. Analogous to an organ allocation system, these algorithms convert ethical principles into a concrete rank ordering of candidates for treatment with life support allocation scores. Disasters that produce scarcity tend to fall hardest on disadvantaged communities, especially racial and ethnic minority groups. In Chicago, 63% of the initial victims of the COVID-19 pandemic were Black people living predominantly in socially vulnerable neighborhoods. When designing algorithms for the allocation of scarce life support, public health officials should take this context into account. However, exactly how to address structural inequity in scarce resource allocation is controversial.

Place-based disadvantage indices, such as the Area Deprivation Index (ADI) and the Social Vulnerability Index, offer a potential solution. Using these validated geographical measures of neighborhood deprivation to allocate scarce healthcare resources counteracts the risk-increasing effects of social disadvantage, including disadvantage produced by racialized residential segregation, and is more likely to survive legal challenges. Current proposals would increase the priority score of people from disadvantaged neighborhoods by an arbitrary amount, or set aside a small reserve (e.g., 20%) for vulnerable areas, but the consequences of these protocols are unknown as they have not been rigorously simulated.

The overall objective of this project is to develop a life support allocation algorithm that accurately and equitably allocates scarce ICU treatments in a crisis. Students will have the opportunity to contribute to ongoing work in the following specific aims:

1. Develop novel life support allocation scores that rely on place-based disadvantage indices.

2. Ethically incorporate age into life support allocation

3. Simulate existing life support allocation scores using a ICU Crisis Simulation Model

4. Externally validate novel allocation scores in N3C, the National COVID Cohort Collaborative Data Enclave.

Projects will use the University of Chicago's CRI COVID-19 data mart and the data from collaborating centers via a previously developed multi-centered approach to decentralized data collaboration ( https://github.com/08wparker/Common-Long-ICU-Format). Projects may also use National COVID Cohort Collaborative Data Enclave, which currently contains geocoded records from 14 million patients from 74 sites.

Specific methods include structural equation modeling and machine learning to design novel allocation scores and discrete event simulation modeling to evaluate the potential impact of novel allocation policies. In parallel to these empirical techniques, the lab relies on a deep philosophical understanding of the ethical principles that guide the allocation of scarce healthcare resources.

Most projects use the open-source version of R with the RStudio IDE. Some machine learning applications will use python and we also occasionally use Stata to estimate specific biostatistical models. All software is either free or provided by the NIH-funded Healthcare Ethics and Allocation Lab (HEAL) in the MacLean Center for Clinical Medical Ethics (directed by Dr. William F Parker, MD, MSCP, Ph.D)

American Thoracic Society

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