COVID-19 and Latinx Disparities: Highlighting the Need for Medical Schools to Consider Accepting DACA Recipients.

COVID-19 revealed and magnified the preexisting health inequities faced by many vulnerable groups. The Latinx community is one of these groups and has borne the brunt of disparate rates of infection, hospitalization, and mortality associated with COVID-19. These disparities are rooted in social inequities, such as poverty and lack of access to health care, as well as health inequities associated with disparate disease and condition burdens. Moreover, the lack of an adequate Latinx physician workforce contributes to and exacerbates these inequities. The COVID-19 pandemic has intersected with the U.S. Supreme Court's decision in the Department of Homeland Security v. Regents of the University of California case. The court's decision in this case struck down the attempted ending of the Deferred Action for Childhood Arrivals (DACA) program, although it was settled that the government could end the program if it was done lawfully. Even though this constitutes a win for DACA recipients, the decision is a stopgap as the future of DACA recipients remains vulnerable and subject to other legal challenges and political vagaries. In a time when the need to ameliorate health inequities for the Latinx community is so pronounced, DACA recipient medical trainees could provide much-needed relief. Since the implementation of DACA, some medical schools have decided to accept DACA recipient students, but many do not. This access-limiting practice stymies a group of potential trainees who could help to increase the Latinx physician workforce, as the majority of DACA recipients are Latinx. This article argues that all medical schools should take steps to consider accepting DACA recipient applicants in line with the principles of health equity and suggests 5 recommendations for medical school admissions, support, and advocacy practices.

COVID-19 and Stock Market Volatility: A Clustering Approach for S&P 500 Industry Indices

We study how the COVID-19 pandemic affected some of the conditional volatilities of S&P 500 industries, using a new model feature-based clustering method on a fitted TGARCH model. Rather than using the estimated model parameters to compute a distance matrix for the stock indices, we suggest using a distance based on the autocorrelations of the estimated conditional volatilities. Both hierarchical and non-hierarchical algorithms are used to assign the set of industries into clusters. The results show a clear change in the composition of each cluster between the period before the first US COVID-19 case and the period during the pandemic.