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Use of Predictive Modeling to Plan for Special Event Medical Care During Mass Gathering Events

Published online by Cambridge University Press:  06 June 2019

Rachel L. Allgaier ,
Nina Shaafi-Kabiri ,
Carla A. Romney ,
Lee A. Wallis ,
John Joseph Burke ,
Jaspreet Bhangu  and
Kevin C. Thomas
Rachel L. Allgaier
Affiliation:
Division of Emergency Medicine, Department of Family and Emergency Medicine, Stellenbosch University, Cape Town, South Africa
Nina Shaafi-Kabiri
Affiliation:
Laboratory for Human Neurobiology, Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA
Carla A. Romney
Affiliation:
Department of Medical Sciences & Education, Boston University School of Medicine, Boston, MA
Lee A. Wallis
Affiliation:
Division of Emergency Medicine, Department of Family and Emergency Medicine, Stellenbosch University, Cape Town, South Africa
John Joseph Burke
Affiliation:
Laboratory for Human Neurobiology, Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA
Jaspreet Bhangu*
Affiliation:
Laboratory for Human Neurobiology, Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA
Kevin C. Thomas*
Affiliation:
Laboratory for Human Neurobiology, Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA
*
Correspondence and reprint requests to Kevin C. Thomas, Boston University School of Medicine, 650 Albany Street, X140, Boston, MA 02118 (e-mail: [email protected]).
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Abstract

Objectives:

In 2010, South Africa (SA) hosted the Fédération Internationale de Football Association (FIFA) World Cup (soccer). Emergency Medical Services (EMS) used the SA mass gathering medicine (MGM) resource model to predict resource allocation. This study analyzed data from the World Cup and compared them with the resource allocation predicted by the SA mass gathering model.

Methods:

Prospectively, data were collected from patient contacts at 9 venues across the Western Cape province of South Africa. Required resources were based on the number of patients seeking basic life support (BLS), intermediate life support (ILS), and advanced life support (ALS). Overall patient presentation rates (PPRs) and transport to hospital rates (TTHRs) were also calculated.

Results:

BLS services were required for 78.4% (n = 1279) of patients and were consistently overestimated using the SA mass gathering model. ILS services were required for 14.0% (n = 228), and ALS services were required for 3.1% (n = 51) of patients. Both ILS and ALS services, and TTHR were underestimated at smaller venues.

Conclusions:

The MGM predictive model overestimated BLS requirements and inconsistently predicted ILS and ALS requirements. MGM resource models, which are heavily based on predicted attendance levels, have inherent limitations, which may be improved by using research-based outcomes.

Keywords

mass gathering medicinepatient transfertriage
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 13 , Issue 5-6 , December 2019 , pp. 874 - 879
Copyright
Copyright © 2019 Society for Disaster Medicine and Public Health, Inc. 

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