Can Comorbidity Data Explain Cross-State and Cross-National Difference in COVID-19 Death Rates?

Many efforts to predict the impact of COVID-19 on hospitalization, intensive care unit (ICU) utilization, and mortality rely on age and comorbidities. These predictions are foundational to learning, policymaking, and planning for the pandemic, and therefore understanding the relationship between age, comorbidities, and health outcomes is critical to assessing and managing public health risks. From a US government database of 1.4 million patient records collected in May 2020, we extracted the relationships between age and number of comorbidities at the individual level to predict the likelihood of hospitalization, admission to intensive care, and death. We then applied the relationships to each US state and a selection of different countries in order to see whether they predicted observed outcome rates. We found that age and comorbidity data within these geographical regions do not explain much of the international or within-country variation in hospitalization, ICU admission, or death. Identifying alternative explanations for the limited predictive power of comorbidities and age at the population level should be considered for future research.

A systems approach for resources management during the COVID-19 pandemic: Multi-agency perspectives from New England.

The emergence of COVID-19 in the United States has overwhelmed local hospitals, produced shortages in critical protective supplies for medical staff, and created backlogs in burials and cremations. Because systemic disruptions occur most acutely at a local scale, facilitating resource coordination across a broad region can assist local responses to COVID-19 surges. This article describes a structured systems approach for coordinating COVID-19 resource distribution across the six New England states of the United States. The framework combines modeling tools to anticipate resource shortages in medical supplies, personnel needs, and fatality management for individual states. The approach allows decision makers to understand the magnitude of local outbreaks and equitably allocate resources within a region based on the present and future needs. This model contributed to determining material distribution in New England as the 2020 COVID-19 surges unfolded in the spring and fall seasons. Using a systems analysis, the model demonstrates the translation of anticipated COVID-19 cases into resource demands to enable regional coordination of scarce resources.

An Analytical Perspective on Pandemic Recovery.

After implementing restrictions to curb the spread of coronavirus, governments in the United States and around the world are trying to identify the path to social and economic recovery. The White House and the Centers for Disease Control and Prevention have published guidelines to assist US states, counties, and territories in planning these efforts. As the impact of the coronavirus pandemic has not been uniform, these central guidelines need to be translated into practice in ways that recognize variation among jurisdictions. We present a core methodology to assist governments in this task, presenting a case for appropriate actions at each stage of recovery based on scientific data and analysis. Specifically, 3 types of data are needed: data on the spread of disease should be analyzed alongside data on the overall health of the population and data on infrastructure-for example, the capacity of health systems. Local circumstances will produce different needs and present different setbacks, and governments may need to reinstate as well as relax restrictions. Transparent, defensible analysis can assist in making these decisions and communicating them to the public. In the absence of a widely administered vaccine, analysis remains one of our most important tools in addressing the coronavirus pandemic.