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Validation of a Belgian Prediction Model for Patient Encounters at Music 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 ,
Leonard Kaufman ,
Kevin Beens ,
Philippe Vandekerckhove  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
Leonard Kaufman
Affiliation:
Vrije Universiteit Brussel, Research Group on Emergency and Disaster Medicine, Brussels, Belgium
Kevin Beens
Affiliation:
The Flemish Cross, Antwerp, Belgium
Philippe Vandekerckhove
Affiliation:
Belgian Red Cross Flanders, Mechelen, 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: kris.spaepen@vub.be
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Abstract

Introduction:

A Belgian predictive medical resource tool, Plan Risk Manifestations (PRIMA), for the prediction of the number of patient encounters at mass gatherings (MGs) has recently been developed, in addition to the existing models of Arbon and Hartman. This study presents the results of the validation process for the PRIMA model for music MGs.

Methods:

A retrospective study was conducted using data gathered from music MGs in the province of Antwerp (Belgium) during the period of 2012-2016. Data from 87 music MGs were used for the study. The forecast of medical resources for these events was determined by entering the characteristics of individual events into the Arbon, Hartman, and PRIMA models. In order to determine if the PRIMA model is under- or over-predictive, the data gathered were retrospectively compared to the predicted number of resources needed using the aforementioned models. Statistical analysis included means, medians, and interquartile ranges (IQRs). Nonparametric related samples test (Wilcoxon Samples Signed Rank Test) for comparison of the median in deviations in predictions of patient presentation rates (PPRs) was performed using SPSS version 23 (IBM Corp.; Armonk, New York USA). Confidence interval levels were set at 95% and results were deemed statistically significant at P <.05. This triple comparison was used to determine the overall performance of all three models.

Results:

All three models had an acceptable rate of over-prediction of number of patient encounters ([Arbon 25.29%; 95% CI, 30.91-43.74]; [Hartman 29.89%; 95% CI, 57.10-68.90]; and [PRIMA 19.54%; 95% CI, 57.80-76.20]). But all models also had a high rate of under-prediction of number of patient encounters ([Arbon 74.71%; 95% CI, 453.31-752.52]; [Hartman 70.11%; 95% CI, 546.90-873.77]; and [PRIMA 78.16%; 95% CI, 288.91-464.89]). Only the PRIMA model succeeded in the correct prediction of the number of patient encounters on two occasions (2.3%).

Conclusion:

Results of this study are in-line with existing literature. When comparing the predicted patient encounters, all three models had high rates of under-prediction and moderate rates of over-prediction. When comparing mean deviations, the PRIMA model had the lowest mean deviation of all predicted PPRs. Belgian events of the types included in the presented data may use the PRIMA model with confidence to predict PPRs and estimate the in-event health services (IEHS) requirements.

Keywords

Emergency Medical Servicesmass gatheringoutdoor music festivalspatient presentationsprediction
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 35 , Issue 5 , October 2020 , pp. 561 - 566
Copyright
© World Association for Disaster and Emergency Medicine 2020

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