Geographic Data Uncovers Trends in COVID-19 Mortality, Spread

To further discover trends in COVID-19 mortality and the spread of the virus, researchers are increasingly leveraging geographic and population data for new insights on how the disease operates.

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A team from the University of Minnesota and the University of Washington recently used state-level data to find a statistical relationship between the number of hospital beds occupied by COVID-19 patients in a state and reported mortality.

The results, published in the Journal of General Internal Medicine, showed that COVID-19 patients occupied nearly 20 percent of all ICU-bed use in all 23 states examined, as well as about five percent of non-ICU bed capacity.

Additionally, a one percent increase in ICU-bed use, or 17 beds on average, was associated with 2.84 more COVID-19 deaths over the next seven days, while a one percent increase in non-ICU bed use was associated with 17.84 more COVID-19 deaths.

“These estimates provide a better understanding of the projections of the COVID-19 pandemic in the US especially when states are monitoring economic activities, and provide important practice insights for hospitals in terms of assessment of hospital bed and ICU bed capacity and preparedness,” said Pinar Karaca-Mandic, professor and academic director of the Medical Industry Leadership Institute (MILI) in the U of M’s Carlson School of Management. 

While some states may have been able to expand the number of hospital beds, doing so for ICU beds was likely difficult due to infrastructure constraints, the researchers noted.

“Most policy interventions to address the COVID-19 outbreak in the United States have focused on ‘flattening the curve’ – an approach to spread out hospitalizations over a longer duration to avoid overwhelming the healthcare system,” said Soumya Sen, associate professor and academic director of the Management Information Systems Research Center (MISRC) in the Carlson School.

“The forecasting models informing these policy decisions make assumptions about the relation between the number of COVID-19 cases, hospitalizations, intensive care unit (ICU) demand, and subsequent impact on mortality.” 

A separate team of researchers from the University of Alabama also recently leveraged geographic data to discover new trends in COVID-19 mortality. The group used county-level data on COVID-19 deaths as well as estimates of morbid obesity rates for each US county.

The study examined adults aged 18 to 64 and found that morbid obesity rates are positively correlated with COVID-19 case and death rates, and that morbid obesity rates can explain nine percent of the variation in COVID-19 death rates.

Additionally, by overlapping the data geographically, researchers found that spatial clusters of high rates of morbid obesity are associated with spatial clusters of high rates of COVID-19 deaths.

This study is the first repeatable quantitative analysis that addresses the relationship between COVID-19 deaths and morbid obesity. The short-term implications of these findings could impact policy and treatment. In the long-term, the results highlight the need to strengthen public health efforts that aim to reduce obesity.

“Health practitioners and policymakers need to understand the influence that morbid obesity has on negative COVID‐19 outcomes in order to respond to this and similar emerging infectious diseases in the future,” said Dr. Kevin Curtin, UA professor of geography.

“As a matter of practical importance, with the complex interactions that are likely to produce negative COVID‐19 outcomes, any single variable that can explain more than 9 percent of the variation is worth examining further.”

At the University of Utah, a team used innovative space-time statistics to detect geographic areas where the population had an elevated risk of contracting the virus. The group ran the analysis every day using daily COVID-19 case counts from January 22 to June 5, 2020 to establish regional clusters, defined as a collection of disease cases closely grouped in time and space.

For the first month, the clusters were very large, especially in the Midwest. Starting on April 25, the clusters become smaller and more numerous, a trend that persists until the end of the study.

Researchers created a web application of the clusters that the public can check daily. However, the group noted that state officials would need to do smaller scale analysis to identify specific locations for intervention.

“We applied a clustering method that identifies areas of concern, and also tracks characteristics of the clusters—are they growing or shrinking, what is the population density like, is relative risk increasing or not?” said Alexander Hohl, lead author and assistant professor at the Department of Geography at the U.

“We hope this can offer insights into the best strategies for controlling the spread of COVID-19, and to potentially predict future hotspots.”