Use of Predictive Modeling to Plan for Special Event Medical Care During Mass Gathering Events. (December 2019)

• Record Type: Journal Article
• Authors: Allgaier, Rachel L.; Shaafi-Kabiri, Nina; Romney, Carla A.; Wallis, Lee A.; Burke, John Joseph; Bhangu, Jaspreet; Thomas, Kevin C.
• Is Part Of: Disaster medicine and public health preparedness. Volume 13:Number 5/6(2019)
• Page Start: 874
• Page End: 879
• Publication Date: 2019-12
• Subjects: mass gathering medicine, -- patient transfer, -- triage; Disaster medicine -- Periodicals; Emergency management -- Planning -- Periodicals; Public health -- Periodicals; 363.34
• DOI: 10.1017/dmp.2019.1
• Languages: English
• ISSNs: 1935-7893

Abstract

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.