Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-28T05:28:41.818Z Has data issue: false hasContentIssue false
  • English
  • Français

Logistic Red Flags in Mass-Casualty Incidents and Disasters: A Problem-Based Approach

Published online by Cambridge University Press:  03 February 2022

Lorenzo Gamberini ,
Guglielmo Imbriaco Open the ORCID record for Guglielmo Imbriaco [Opens in a new window] ,
Pier Luigi Ingrassia ,
Carlo Alberto Mazzoli ,
Stefano Badiali ,
Davide Colombo ,
Luca Carenzo Open the ORCID record for Luca Carenzo [Opens in a new window] ,
Alfonso Flauto ,
Marco Tengattini  and
Federico Merlo
...Show all authors
Lorenzo Gamberini
Affiliation:
Department of Anesthesia, Intensive Care and Prehospital Emergency, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy
Guglielmo Imbriaco
Affiliation:
Emilia Est Emergency Dispatch Center – Helicopter Emergency Medical Service, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy Critical Care Nursing Master course, University of Bologna, Italy
Pier Luigi Ingrassia
Affiliation:
Centro di Simulazione (CeSi), Centro Professionale Sociosanitario, Lugano, Switzerland
Carlo Alberto Mazzoli
Affiliation:
Department of Anesthesia, Intensive Care and Prehospital Emergency, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy
Stefano Badiali
Affiliation:
Department of Anesthesia, Intensive Care and Prehospital Emergency, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy
Davide Colombo
Affiliation:
CRIMEDIM Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health, Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Novara, Italy
Luca Carenzo*
Affiliation:
Department of Anesthesia and Intensive Care Medicine, IRCCS Humanitas Research Hospital, Rozzano, Italy
Alfonso Flauto
Affiliation:
Emilia Est Emergency Dispatch Center – Helicopter Emergency Medical Service, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy
Marco Tengattini
Affiliation:
CRIMEDIM Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health, Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Novara, Italy
Federico Merlo
Affiliation:
CRIMEDIM Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health, Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Novara, Italy
Massimo Azzaretto
Affiliation:
CRIMEDIM Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health, Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Novara, Italy
Alessandro Monesi
Affiliation:
Critical Care Nursing Master course, University of Bologna, Italy Intensive Care Unit, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy
Fernando Candido
Affiliation:
Intensive Care Unit, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy
Carlo Coniglio
Affiliation:
Department of Anesthesia, Intensive Care and Prehospital Emergency, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy
Giovanni Gordini
Affiliation:
Department of Anesthesia, Intensive Care and Prehospital Emergency, Maggiore Hospital Carlo Alberto Pizzardi, Bologna, Italy
Francesco Della Corte
Affiliation:
CRIMEDIM Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health, Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Novara, Italy
*
Correspondence: Luca Carenzo, MD, MSc, EDIC Department of Anesthesia and Intensive Care Medicine, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background:

Mass-casualty incidents (MCIs) and disasters are characterized by a high heterogeneity of effects and may pose important logistic challenges that could hamper the emergency rescue operations.

The main objective of this study was to establish the most frequent logistic challenges (red flags) observed in a series of Italian disasters with a problem-based approach and to verify if the 80-20 rule of the Pareto principle is respected.

Methods:

A series of 138 major events from 1944 through 2020 with a Disaster Severity Score (DSS) ≥ four and five or more victims were analyzed for the presence of twelve pre-determined red flags.

A Pareto graph was built considering the most frequently observed red flags, and eventual correlations between the number of red flags and the components of the DSS were investigated.

Results:

Eight out of twelve red flags covered 80% of the events, therefore not respecting the 80-20 rule; the number of red flags showed a low positive correlation with most of the components of the DSS score. The Pareto analysis showed that potential hazards, casualty nest area > 2.5km2, number of victims over 50, evacuation noria over 20km, number of nests > five, need for extrication, complex access to victims, and complex nest development were the most frequently observed red flags.

Conclusions:

Logistic problems observed in MCIs and disaster scenarios do not follow the 80-20 Pareto rule; this demands for careful and early evaluation of different logistic red flags to appropriately tailor the rescue response.

Keywords

disaster medicinemajor incidentmass-casualty incidentstandardized datauniform reporting
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 37 , Issue 2 , April 2022 , pp. 197 - 204
Check for updates
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the World Association for Disaster and Emergency Medicine

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

CRED. Human Cost of Disasters (2000-2019). Hum Cost Disasters. 2020;1-2.Google Scholar
Sendai Framework for Disaster Risk Reduction 2015-2030. UNDRR. https://www.undrr.org/publication/sendai-framework-disaster-risk-reduction-2015-2030. Accessed July 2, 2021.Google Scholar
Tintinalli, JE, (ed). Tintinalli’s Emergency Medicine. A Comprehensive Study Guide. Eighth edition. New York USA: McGraw-Hill Education/Medical; 2015.Google Scholar
Bayram, JD, Zuabi, S, McCord, CM, Sherak, RAG, Hsu, EB, Kelen, GD. Disaster metrics: evaluation of de Boer’s Disaster Severity Scale (DSS) applied to earthquakes. Prehosp Disaster Med. 2015;30(1):22–27.10.1017/S1049023X14001393CrossRefGoogle ScholarPubMed
de Boer, J. Definition and classification of disasters: introduction of a disaster severity scale. J Emerg Med. 1990;8(5):591–595.10.1016/0736-4679(90)90456-6CrossRefGoogle ScholarPubMed
Caldera, HJ, Wirasinghe, SC, Zanzotto, L. An approach to classification of natural disasters by severity. Can Soc Civ Eng. 2016;3:1804–1814.Google Scholar
Othman, SH, Beydoun, G, Sugumaran, V. Development and validation of a Disaster Management Metamodel (DMM). Inf Process Manag. 2014;50:235–271.CrossRefGoogle Scholar
Kelly, C. Simplifying disasters: developing a model for complex non-linear events. Aust J Emerg Manag. 1999;14:25–27.Google Scholar
Asghar, S, Alahakoon, D, Churilov, L. A Comprehensive Conceptual Model for Disaster Management. Clayton School of Information Technology, Monash University, Australia.Google Scholar
Hardy, S, Fattah, S, Wisborg, T, Raatiniemi, L, Staff, T, Rehn, M. Systematic reporting to improve the emergency medical response to major incidents: a pilot study. BMC Emerg Med. 2018;18(1):4.10.1186/s12873-018-0153-xCrossRefGoogle ScholarPubMed
Schumpeter, JA. Vilfredo Pareto (1848–1923). Q J Econ. 1949;63:147–173.Google Scholar
Mollah, AH, Long, M, Baseman, HS. Risk Management Applications in Pharmaceutical and Biopharmaceutical Manufacturing. Hoboken, New Jersey USA: John Wiley and Sons; 2013.CrossRefGoogle Scholar
Ferro, G. Assessment of major and minor events that occurred in Italy during the last century using a disaster severity scale score. Prehosp Disaster Med. 2005;20(3):316–323.10.1017/S1049023X00002776CrossRefGoogle ScholarPubMed
Legge 24 Febbraio 1992, N. 225. Gazz Uff Ser Gen n 64. 1992; Suppl. Ord.CrossRefGoogle Scholar
Décret n°2005-1157 du 13 septembre 2005 relatif au plan ORSEC et pris pour application des articles L741-1 à L741-5 du code de la sécurité intérieure. Légifrance. https://www.legifrance.gouv.fr/loda/id/JORFTEXT000000421070/. Accessed June 16, 2021.Google Scholar
Box, GEP, Meyer, RD. An analysis for un-replicated fractional factorials. Technometrics. 1986;28:11–18.CrossRefGoogle Scholar
Jaffe, E, Dadon, Z, Alpert, EA. Wisdom of the crowd in saving lives: the Life Guardians app. Prehosp Disaster Med. 2018;33(5):550–552.10.1017/S1049023X18000754CrossRefGoogle ScholarPubMed
Bazyar, J, Farrokhi, M, Khankeh, H. Triage systems in mass casualty incidents and disasters: a review study with a worldwide approach. Maced J Med Sci. 2019;7(3):482–494.10.3889/oamjms.2019.119CrossRefGoogle ScholarPubMed
Jet Hits a School in Italy, Killing 12. The New York Times. https://www.nytimes.com/1990/12/07/world/jet-hits-a-school-in-italy-killing-12.html. Accessed June 29, 2021.Google Scholar
David, JS, Bouzat, P, Raux, M. Evolution and organization of trauma systems. Anaesth Crit Care Pain Med. 2019;38(2):161–167.10.1016/j.accpm.2018.01.006CrossRefGoogle ScholarPubMed
Dean, MD, Nair, SK. Mass-casualty triage: distribution of victims to multiple hospitals using the SAVE model. Eur J Oper Res. 2014;238:363–373.10.1016/j.ejor.2014.03.028CrossRefGoogle Scholar
Amram, O, Schuurman, N, Hedley, N, Hameed, SM. A web-based model to support patient-to-hospital allocation in mass casualty incidents. J Trauma Acute Care Surg. 2012;72(5):1323–1328.CrossRefGoogle ScholarPubMed
Kamali, B, Bish, D, Glick, R. Optimal service order for mass-casualty incident response. Eur J Oper Res. 2017;261:355–367.10.1016/j.ejor.2017.01.047CrossRefGoogle Scholar
Marres, GMH, Taal, L, Bemelman, M, Bouman, J, Leenen, LPH. Online victim tracking and tracing system (ViTTS) for major incident casualties. Prehosp Disaster Med. 2013;28(4):445–453.10.1017/S1049023X13003567CrossRefGoogle ScholarPubMed
Haverkort, JJM, Bouman, JH, Wind, JDD, Leenen, LPH. Continuous development of a major incident in-hospital victim tracking and tracing system, withstanding the challenges of time. Disaster Med Public Health Prep. 2017;11(2):244–250.10.1017/dmp.2016.122CrossRefGoogle Scholar
Gamberini, L, Imbriaco, G, Flauto, A, et al. Mass casualty management after a boiling liquid expanding vapor explosion in an urban area. J Emerg Med. 2021;60(4):471–477.10.1016/j.jemermed.2020.11.029CrossRefGoogle Scholar
Shultz, JM, Garcia-Vera, MP, Santos, CG, et al. Disaster complexity and the Santiago de Compostela train derailment. Disaster Health. 2016;3(1):11–31.10.1080/21665044.2015.1129889CrossRefGoogle Scholar
Tin, D, Granholm, F, Hart, A, Ciottone, GR. Terrorism-related chemical, biological, radiation, and nuclear attacks: a historical global comparison influencing the emergence of counter-terrorism medicine. Prehosp Disaster Med. 2021;36(4):399–402.10.1017/S1049023X21000625CrossRefGoogle ScholarPubMed
Konert, A, Smereka, J, Szarpak, L. The use of drones in emergency medicine: practical and legal aspects. Emerg Med Int. 2019;2019:3589792.10.1155/2019/3589792CrossRefGoogle Scholar
Álvarez-García, C, Cámara-Anguita, S, López-Hens, JM, et al. Development of the aerial remote triage system using drones in mass casualty scenarios: a survey of international experts. PLoS One. 2021;16(5):e0242947.10.1371/journal.pone.0242947CrossRefGoogle ScholarPubMed
Drone Helps Firefighters Rescue Two from River in Maine. ABC News. https://abcnews.go.com/US/drone-helps-firefighters-rescue-river-maine/story?id=32163196. Accessed June 29, 2021.Google Scholar
Lowes, AJ, Cosgrove, JF. Prehospital organization and management of a mass casualty incident. BJA Educ. 2016;16(10):323–328.CrossRefGoogle Scholar
Evans, AW. Fatal train accidents on Europe’s railways: an update to 2019. Accid Anal Prev. 2021;158:106182.10.1016/j.aap.2021.106182CrossRefGoogle ScholarPubMed
Supplementary material: File

Gamberini et al. supplementary material

Gamberini et al. supplementary material

Download Gamberini et al. supplementary material(File)
File 36.1 KB