Soumya Sen PhD McKnight Presidential Fellow Mary & Jim Lawrence Fellow of Carlson School Director of Research, MIS Research Center Associate Professor, Information & Decision Sciences Carlson School of Management  University of Minnesota, Minneapolis, MN

Study Finds Stay-at-Home Orders Reduced Hospitalizations

MedicalResearch.com Interview with:

Soumya Sen PhD McKnight Presidential Fellow Mary & Jim Lawrence Fellow of Carlson School Director of Research, MIS Research Center Associate Professor, Information & Decision Sciences Carlson School of Management  University of Minnesota, Minneapolis, MN

Dr. Soumya Sen

Soumya Sen PhD
McKnight Presidential Fellow
Mary & Jim Lawrence Fellow of Carlson School
Director of Research, MIS Research Center
Associate Professor, Information & Decision Sciences
Carlson School of Management
University of Minnesota, Minneapolis, MN

MedicalResearch.com: What is the background for this study?

Response: As the Covid-19 pandemic unfolded across the United States, one of the greatest barriers to understanding the extent of the problem was the lack of reliable and consistent data. Some of the metrics being reported, such as case count and death, are insufficient for hospital capacity planning. Case count is a conservative estimate of the actual number of infected individuals in the absence of community-wide serologic testing, while death count is a lagging metric and insufficient for proactive hospital capacity planning.

A more valuable metric for assessing the effects of public health interventions on the health care infrastructure is hospitalizations. Therefore, the Medical Industry Leadership Institute (MILI) and the Management Information Systems Research Center (MISRC) at the Carlson School of Management launched the COVID-19 hospitalization tracking project in March to consistently track and report daily hospitalizations from all reporting states. Tracking daily hospitalization data is a major step forward in quantifying the current impact on local hospital systems, modeling and  forecasting future utilization needs, and tracking the rate of change in the disease severity. It is also useful for understanding the role of health policy interventions in slowing or reducing the impact of the pandemic.

MedicalResearch.com: What are the main findings?

Response: This research paper assessed the association between the issuance of stay-at-home orders and trends in hospitalization across four states – CO, MN, OH, VA. These were the only four states that implemented a statewide stay-at-home order and provided at least 7 consecutive days of cumulative hospitalization data for COVID-19 before the stay-at-home order date and at least 17 days following the order date. These 17 days include a 12-day incubation period – which is the median time it takes for someone from getting infected to needing hospitalization – and hence, the time it takes for stay-at-home orders to become effective in impacting hospitalizations.

We observe that initially the hospitalization numbers were growing exponentially in each of the states but after the 12-day median effectiveness period following the issuance of stay-at-home orders, there was a slowdown in the hospitalizations in all the four states. Our findings suggest that stay-at-home orders, alongside other factors like increased awareness and preventive habits, likely slowed down hospitalization needs by 50-60%. 

MedicalResearch.com: What should readers take away from your report?

Response: Our results suggest that stay-at-home orders helped reduce the hospitalization needs by a significant amount in each state. For example, 5 days after the order had its initial impact the difference between projections based on the initial trend and actual hospitalizations after the median effectiveness date of stay-at-home order are as follows:

-Minnesota: projected: 988; actual 361. (63% lower)
-Colorado: projected 8,637; actual 1,632 (81% lower)
-Ohio: projected 4,353; actual 1,612 (63% lower)
-Virginia: projected 2,335; actual 1,048 (55% lower)

So social distancing enforcement is a public health policy intervention that states need to consider to reduce the impact of its healthcare infrastructure.

MedicalResearch.com: What recommendations do you have for future research as a result of this work?

Response: As states consider relaxing these measures, they need to be very cautious and gradual with their approach so that the growth rates do not go back to the exponential rates that were observed in the initial period from the beginning of the outbreak up to and including the median effectiveness date of the stay-at-home orders.

MedicalResearch.com: Is there anything else you would like to add? 

Response: Our study shows an association between stay-at-home orders and hospitalization trends, but there are other factors such as general awareness, practice of preventive measures, self-imposed social distancing etc that too may have helped in reducing the hospitalization numbers. Additionally, the outbreak of the disease in minority communities with lack of access to health insurance and information may have also impacted the observed hospitalization numbers. Understanding the role of demographic factors in the hospitalization trends is an important direction for future research from a public health perspective.

Citation:

Sen S, Karaca-Mandic P, Georgiou A. Association of Stay-at-Home Orders With COVID-19 Hospitalizations in 4 States. JAMA. Published online May 27, 2020. doi:10.1001/jama.2020.9176

https://jamanetwork.com/journals/jama/fullarticle/2766673

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May 28, 2020 @ 8:48 pm

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