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The Bayesian Network Model Revealed the Hospital Decision-making Rationale for Flood Evacuation Planning

Published online by Cambridge University Press:  25 June 2025

Takashi Nagata Open the ORCID record for Takashi Nagata [Opens in a new window] ,
Takeru Abe ,
Koji Nishiyama ,
Mototaka Inaba ,
Ayako Ohashi ,
Takashi Shiga  and
Akihito Hagihara
Takashi Nagata
Affiliation:
Department of Emergency Medicine, https://ror.org/05jr18655Japan Self Defense Forces Central Hospital, Setagaya, Tokyo, Japan
Takeru Abe
Affiliation:
Center for Integrated Science and Humanities, https://ror.org/012eh0r35Fukushima Medical University, Fukushima, Japan
Koji Nishiyama
Affiliation:
Department of Urban and Environmental Engineering, Graduate School of Engineering, https://ror.org/00p4k0j84Kyushu University, Fukuoka city, Fukuoka, Japan
Mototaka Inaba
Affiliation:
Department of Emergency, Critical Care, and Disaster Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, https://ror.org/02pc6pc55Okayama University, Okayama city, Okayama, Japan
Ayako Ohashi
Affiliation:
Department of Neuropsychiatry, Graduate School of Medical Sciences, https://ror.org/00p4k0j84Kyushu University, Fukuoka city, Fukuoka, Japan
Takashi Shiga
Affiliation:
Department of Emergency Medicine, https://ror.org/053d3tv41International University of Health and Welfare Tokyo, Tokyo, Japan
Akihito Hagihara*
Affiliation:
Department of Preventive Medicine and Epidemiology, https://ror.org/01v55qb38National Cerebral and Cardiovascular Center, Suita city, Osaka, Japan
*
Corresponding author: Akihito Hagihara; Email: hagihara.akihito.878@m.kyushu-u.ac.jp
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Abstract

Backgrounds

Heavy rain and flood frequently occur in recent years and hospitals’ preparedness for flood is important. To secure patient safety, hospital evacuation planning and drills due to flooding by heavy rain is inevitable. In the study the relation of factors with hospitals’ preparedness for flood by heavy rain was analyzed.

Methods

Subjects of the study were disaster base hospitals in Japan (n = 765). Internet survey conducted in 2022. Bayesian network was used to analyze the interrelation of factors.

Results

430 hospitals (56.2%) were used for analysis. 42.1% of the hospital were located in designated flooded area and 33.7% of the hospitals have planning of hospital evacuation due to flooding. Display of area where flooding is expected in case of heavy rain and landslide warning area leads to a hospital evacuation planning and evacuation drills.

Conclusion

Display of flooded area by heavy rain or landslide warning zone by governments is effective in advancing hospital preparedness for flood. Hospitals’ recent experience of flood or landslide did not lead to evacuation planning or evacuation drills due to flood. These findings are useful in advancing hospitals’ preparedness for flood and heavy rain.

Keywords

hospital evacuationBayesian networkfloodlandslide evacuation plan

Information

Type
Brief Report
Information
Disaster Medicine and Public Health Preparedness , Volume 19 , 2025 , e163
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc

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References

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