Public health officials and elected leaders around the country have continued to relax and reinstate restrictions in response to swells of COVID-19 cases in their areas. 

This past week, researchers at Northwestern University and the University of Texas at Austin released a paper in the Proceedings of the National Academy of Sciences describing a new framework to help make those decisions. 

The model outlines specific hospital admission thresholds at which short-term shelter-in-place orders should be enacted, according to a press release from UT Austin.

“While many cities have implemented alert levels and new policies, our research may be the first to provide clear guidance for exactly what to track (hospital admissions data) and exactly when to act (strict thresholds),” said David Morton, paper coauthor, who is a chair and professor of industrial engineering and management sciences at Northwestern, in a statement.

WHY IT MATTERS

As the world waits for advancements in the COVID-19 vaccine, it has become necessary to try and stymie the coronavirus' spread through social distancing measures. Several regions have reopened indoor bars and dining, gyms, movie theaters, and other venues with higher rates of transmission, only to move to shut them down again as virus rates have swelled. 

In Austin, Texas, city leaders used the model described in the PNAS paper to help in decision-making regarding when to escalate alert levels.

"A lock-down is triggered when a seven-day moving average of daily hospital admissions grows to exceed a lock-down threshold, which we specify for each day," said researchers. In Austin's case, given local hospital capacity, that number was 80 before September 30 and 215 after that date.

"Relaxation of a lock-down is triggered when: (a) the same moving average drops below the lock-down threshold and (b) the total hospitalizations are under a safety threshold," researchers continued. 

Using this method, the research teams predicted that hospitals will be able to stay safely under capacity, though mortality rates will still be higher than in an indefinite lockdown situation. 

The method also relies on relatively high adherence to "cocooning." The researchers note that it will not be as successful in scenarios where the populations do not conform to social distancing guidelines.

"Whereas policy makers and public dashboards primarily track confirmed COVID-19 cases and deaths, we intentionally use COVID-19 hospitalization data to fit our model parameters and guide policy," wrote the researchers in the paper. 

"Hospitalization admissions are less subject to bias than confirmed case counts, given the heterogeneous and rapidly changing test availability and priorities across the United States, and they are less time-lagged than reported COVID-19 deaths," they continued.

“Communities need to act long before hospital surges become dangerous. Hospital admissions data give an early indication of rapid pandemic growth, and tracking that data will ensure that hospitals maintain sufficient capacity," said Morton in the statement.

THE LARGER TREND

Hospital admission rates may be less subject to bias than confirmed case counts, but the available data around them remains murky, on a nationwide basis. The move by the Trump administration to dramatically shift the system for hospital utilization reporting in July triggered a wave of concerns from public health experts, some of whom accused the U.S. Department of Health and Human Services of needlessly politicizing the data.

Other state hospital associations said the change had prevented them, at least in the near future, from accessing their own data.

"The core issue for us is that it's left the state of Missouri basically in the dark for local data," said Dave Dillon, vice president of media and public relations at the Missouri Hospital Association, last month.

"Moving from a known platform that all of the individuals could easily manipulate … has harmed our ability to have that situational awareness, certainly in the short term," Dillon continued.

ON THE RECORD

"We developed this framework to ensure that COVID-19 never overwhelms local health care capacity while minimizing the economic and societal costs of strict social-distancing measures,” said Lauren Ancel Meyers, a coauthor of the paper and director of the University of Texas COVID-19 Modeling Consortium, in a statement.

Kat Jercich is senior editor of Healthcare IT News.
Twitter: @kjercich
Healthcare IT News is a HIMSS Media publication.