Application of the Policy Regime Framework to understand COVID-19 policy response in the Southeast U.S.: How RAPID research can provide lessons learned after a public health crisis.

Quick-response research during a time of crisis is important because time-sensitive findings can inform urgent decision-making, even with limited research budgets. This research, a National Science Foundation-funded Rapid Response Research (RAPID), explores the United States (U.S.) government's messaging on science in response to the COVID-19 pandemic, and how this messaging informed policy. Using rapidly emerging secondary data (e.g., policy documents taken from government websites and others), much of which has since been removed or changed, we examined the interactions between governing bodies, non-governmental organizations, and civilian populations in the Southeastern U.S. during the first 2 years of the pandemic. This research helps to better understand how decision-makers at the federal, state, and local levels responded to the pandemic in three states with the lowest vaccine rates and highest levels of poverty, income inequality, and disproportionate impacts borne by people of color in the nation: Alabama, Louisiana, and Mississippi. This study incorporates the Policy Regime Framework to discuss how two foundational concepts (ideas and institutions) helped govern policy implementation during the COVID-19 pandemic. This research fills a significant information gap by providing a better understanding of how policy regimes emerge across multiple levels of government and impact vulnerable populations during times of a public health crisis. We use automated text analysis to make sense of a large quantity of textual data from policy-making agencies. Our case study is the first to use the Policy Regime Framework in conjunction with empirical data, as it emerged, from federal, state, and local governments to analyze the U.S. policy response to COVID-19. We found the U.S. policy response included two distinct messaging periods in the U.S. during the COVID-19 pandemic: pre and post-vaccine. Many messaging data sources (agency websites, public service announcements, etc). have since been changed since we collected them, thus our real-time RAPID research enabled an accurate snapshot of a policy response in a crisis. We also found that there were significant differences in the ways that federal, state, and local governments approached communicating complex ideas to the public in each period. Thus, our RAPID research demonstrates how significant policy regimes are enacted and how messaging from these regimes can impact vulnerable populations.

Advance Care Planning and Treatment Intensity Before Death Among Black, Hispanic, and White Patients Hospitalized with COVID-19.

BACKGROUND: Black and Hispanic people are more likely to contract COVID-19, require hospitalization, and die than White people due to differences in exposures, comorbidity risk, and healthcare access. OBJECTIVE: To examine the association of race and ethnicity with treatment decisions and intensity for patients hospitalized for COVID-19. DESIGN: Retrospective cohort analysis of manually abstracted electronic medical records. PATIENTS: 7,997 patients (62% non-Hispanic White, 16% non-Black Hispanic, and 23% Black) hospitalized for COVID-19 at 135 community hospitals between March and June 2020 MAIN MEASURES: Advance care planning (ACP), do not resuscitate (DNR) orders, intensive care unit (ICU) admission, mechanical ventilation (MV), and in-hospital mortality. Among decedents, we classified the mode of death based on treatment intensity and code status as treatment limitation (no MV/DNR), treatment withdrawal (MV/DNR), maximal life support (MV/no DNR), or other (no MV/no DNR). KEY RESULTS: Adjusted in-hospital mortality was similar between White (8%) and Black patients (9%, OR=1.1, 95% CI=0.9-1.4, p=0.254), and lower among Hispanic patients (6%, OR=0.7, 95% CI=0.6-1.0, p=0.032). Black and Hispanic patients were significantly more likely to be treated in the ICU (White 23%, Hispanic 27%, Black 28%) and to receive mechanical ventilation (White 12%, Hispanic 17%, Black 16%). The groups had similar rates of ACP (White 12%, Hispanic 12%, Black 11%), but Black and Hispanic patients were less likely to have a DNR order (White 13%, Hispanic 8%, Black 7%). Among decedents, there were significant differences in mode of death by race/ethnicity (treatment limitation: White 39%, Hispanic 17% (p=0.001), Black 18% (p<0.0001); treatment withdrawal: White 26%, Hispanic 43% (p=0.002), Black 28% (p=0.542); and maximal life support: White 21%, Hispanic 26% (p=0.308), Black 36% (p<0.0001)). CONCLUSIONS: Hospitalized Black and Hispanic COVID-19 patients received greater treatment intensity than White patients. This may have simultaneously mitigated disparities in in-hospital mortality while increasing burdensome treatment near death.

Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence-powered analysis of 12-lead intake electrocardiogram.

BACKGROUND: Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that artificial intelligence (AI) can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications. OBJECTIVE: Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE). METHODS: We studied intake ECGs from 1448 COVID-19 patients (60.5% male, aged 63.4 ± 16.9 years). Records were labeled by mortality (death vs discharge) or MACE (no events vs arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data. RESULTS: A total of 245 (17.7%) patients died (67.3% male, aged 74.5 ± 14.4 years); 352 (24.4%) experienced at least 1 MACE (119 arrhythmic, 107 HF, 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60 ± 0.05 and 0.55 ± 0.07, respectively; these were comparable to AUC values for conventional models (0.73 ± 0.07 and 0.65 ± 0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance. CONCLUSION: Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients' risk of mortality or MACE. Our models' accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.