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The Development of PRIMA - A Belgian Prediction Model for Patient Encounters at Mass Gatherings

Published online by Cambridge University Press:  29 July 2020

Kris Spaepen Open the ORCID record for Kris Spaepen [Opens in a new window] ,
Winne AP Haenen  and
Ives Hubloue
Kris Spaepen*
Affiliation:
Vrije Universiteit Brussel, Research Group on Emergency and Disaster Medicine, Brussels, Belgium
Winne AP Haenen
Affiliation:
Crisis Management at Federal Public Health Service, Brussels, Belgium
Ives Hubloue
Affiliation:
Vrije Universiteit Brussel, Research Group on Emergency and Disaster Medicine, Brussels, Belgium
*
Correspondence: Kris Spaepen, RN, MSc, EMDM, Research Group on Emergency and Disaster Medicine, Vrije Universiteit Brussel, Faculty of Medicine and Pharmacy, Laarbeeklaan 103, 1090Brussels, Belgium, E-mail: [email protected]
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Abstract

Introduction:

Mass gatherings (MGs) grow in frequency around the world. With the intrinsic potential for significant health risks for all involved, MGs pose a challenge for those responsible for the provision of on-site medical care. Belgian law obliges local governments to identify and analyze the risks involving a MG. Though medical risk factors are long known, all too often, resourcing for in-event health services is based on anecdotal and previous experiences.

Problem:

Despite the fast-evolving science on MGs, the lack of reliable tools – based on empirical and analytical approaches – to predict patient presentation rates (PPRs) at MGs remains.

Methods:

A two-step method was followed to develop, update, and support a Plan Risk Manifestation (PRIMA) program. First, a continuous systematic literature review was conducted. Once developed, the model was run using data obtained from Belgian Federal Public Service (FPS; Brussels, Belgium) Health, Food Chain Safety, and Environment (HFCSE); event organizers; and municipalities.

Results:

In total, 231 studies and documents were included to form the program. With the data provided, three variables were computed to run the calculation model to predict the PPR. Three medical risk axes were defined for this model: (1) isolation risk; (2) population risk; and (3) risk at illness. A combined dataset was derived from the prediction of the PRIMA program combined with the actual data obtained after the MG. This proved a solid basis for the calculation model of the PRIMA program.

Conclusion:

Despite that validation is needed, the PRIMA program and its prediction model for PPRs at MGs carries the promise of a general, applicable prediction and risk analysis tool for a multitude of events.

Keywords

Emergency Medical Servicesmass gatheringpatient presentationspublic healthrisk assessment
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 35 , Issue 5 , October 2020 , pp. 554 - 560
Copyright
© World Association for Disaster and Emergency Medicine 2020

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