Resource Allocation and Crisis Standards of Care

This perspective by White & Lo responds to concerns that existing ICU triage protocols for allocation of scarce critical care resources may compound existing inequities that disadvantage patients from Black, Latinx, Indigenous, and lower-SES communities. During the pandemic, Black, Latinx, and Indigenous persons have been significantly more likely to suffer infection, hospitalization, and death due to COVID-19 compared to white persons. The authors recommend three strategies to mitigate health inequities in triage protocols:

1. introducing a correction factor based on area deprivation index or similar measures into triage scores to reduce the impact of baseline structural inequities;
2. giving heightened priority to individuals in essential, high-risk occupations, including both healthcare-related and other types of work, such as food service, that are at high risk of infection due to frequent workplace exposures; and
3. rejecting use of long-term life expectancy and categorical exclusions as allocation criteria.

These recommendations are part of an extended debate over the practical and ethical challenges of scarce resource allocation that has been galvanized by the COVID-19 pandemic, including prior framework proposals by the authors, among many others. Most proposals have emphasized utilitarian goals of maximizing the number of lives saved and, in some frameworks, the number of life-years saved, but these approaches can unfairly disadvantage members of groups who are subject to existing structural inequities that contribute to worse baseline health status and may therefore negatively affect resource priority. In addition, the authors note that allocation strategies at the state and federal level are also crucial to advancing equity, including ensuring that safety net hospitals receive allocations from the strategic national stockpile and other sources and that establishing interhospital transfer mechanisms.

This article by Rubin et al. assesses how several existing crisis standards of care (CSC) protocols would have distinguished between COVID-19 patients requiring intensive care and mechanical ventilation using a retrospective cohort of 120 patients. The study found that the vast majority of patients in the cohort would have been in the highest priority group using protocols focused on Sequential Organ Failure Assessment (SOFA) score alone, while priority based on SOFA score and 1-year life expectancy would have differed only slightly. Using SOFA score and 5-year life expectancy would have added significant differentiation depending on definitions of priority groups. These approaches represent the real-world allocation protocols of New York, Maryland, Pennsylvania (based on work by White & Lo, authors of the perspective article above), and Massachusetts (prior to recent revisions). The study results challenge the utility of the various protocols for accomplishing their intended objectives, insofar as most models placed the vast majority of patients in the highest priority group and SOFA scores at time of ICU admission did not appear to be reliability associated with short-term survival. If priority models do not adequately differentiate between patients based on relevant outcomes, they may result in a default allocation on a the first-come, first-served basis that the models attempt to avoid due to potential inequities.

Data Collection and Equity

This article by Cruz & Smith is a qualitative study focused on health care workers’ understanding of Race, Ethnicity, and Language (REAL) data in electronic health records (EHRs). Such data has been crucial in identifying and responding to racial and ethnic disparities associated with COVID-19, among other uses in tracking disparities in health care access, treatment, and outcomes, and various state and federal programs mandate collection of such data. However, the study found that providers, staff, and administrators expressed apprehension over REAL data collection based on:

• disagreement over the data’s significance, including the expected purpose of collection;
• perceived barriers to data retrieval, such as lack of standardization across providers and tensions over race and immigration nationally;
• uncertainty regarding uses and dissemination of the data, including whether it would be used for clinical decision-making or system research and with whom data would be shared (e.g., public agencies, other providers, insurers).

The study site was a large integrated delivery system in an urban area that serves as the county’s medical safety-net. Data collection along these lines is essential to addressing structural inequities, and the article notes that future training and policy efforts may focus on reinforcing the specific significance of data collection, developing standardized procedures, and clarifying process for data use and dissemination