System related to operating hospitals, shorter blockages, saved lives


As of June 16, Austin’s Staged Alert System. Credit: City of Austin

According to new research published in the journal, a staged alert system, designed by scientists and public health officials to guide local policies, helped a city prevent hospital surges and long closures. Communications on Nature.

In a new study led by the University of Texas at Austin COVID-19 Modeling Consortium in collaboration with Northwestern University, researchers describe the system that has guided COVID-19 policies in Austin, Texas, for more than a year. helping to safeguard the and avoid costly measures. It tracks the number of new daily admissions at COVID-19 Hospital and causes changes in guidance when admissions cross specific threshold values. While using this alert system, Austin has kept the COVID-19 per capita mortality rate lowest among all major Texas cities.

“Austin’s alert system has been optimized to balance the city and socioeconomic goals, “said Lauren Ancel Meyers, a professor of integrative biology and director of the University of Texas COVID-19 Modeling Consortium.” For more than a year, it has helped our community adapt to rapidly changing risks, protecting the integrity of our hospital systems, and limited the . “

Throughout the COVID-19 pandemic, policymakers fought to combat COVID-19 while minimizing the social and economic consequences. Governments around the world enacted a variety of alert systems that trigger blockages when cases or hospitalizations reach critical levels. According to the newspaper, the Austin system was better at preventing hospital surges than the ICU-based triggers used in France and better at avoiding blockages than Harvard Global Health’s widely cited recommendations.

“Our flexible method can design adaptive policies to combat COVID-19 worldwide and prepare for future pandemic threats,” Meyers said. “When we compared Austin’s optimized triggers to other similar alert systems, we found that it does a much better job of balancing the competition audience. and economic goals “.

David Morton of Northwestern University designed the study with Meyers and Haoxiang Yang, an associate postdoctoral researcher at the Center for Nonlinear Studies (CNLS) at the Los Alamos National Laboratory.

“The success of the Austin system stems in part from its reliance on hospital admission data, which provides a more reliable signal for COVID-19 transmission than reported cases, and in part from our rigorous optimization of alert triggers, ”Morton said. The researchers obtained thresholds that provided a 95% guarantee that hospitals would not be exceeded.

The three hospital systems in the Austin area, Ascension Seton, St. David’s HealthCare and Baylor Scott & White Health, provided key data that was not available in most other U.S. cities during the early months of the pandemic, including ICU estimates and hospital capacity. and daily reports of new hospital admissions COVID-19.

“The pandemic motivated a level of cooperation among the various health workers across the community in a very special and effective way,” said Clay Johnston, dean of Dell Medical School in UT Austin. “Together, we created a trigger system based on the latest local data, which was critical to a coordinated response that helped prevent ICUs from exceeding capacity and ultimately saving lives.”

“The staged alert system was developed working with hospital systems and members of the UT COVID-19 modeling consortium in Austin,” said Dr. Desmar Walkes of the Austin-Travis County Health Authority. “It resulted from a unique partnership between the three city leaders systems and academics. This is proof that communicating behavior change is most effective when it is based on science and data. ”

The COVID-19 risk model uses hospital data to guide decisions about social distancing

More information:
Communications on Nature (2021). DOI: 10.1038 / s41467-021-23989-x

Citation: System linked to operating hospitals, shorter blockages, saved lives (2021, June 18) retrieved June 18, 2021 at html

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