Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-26T03:27:59.636Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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

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

Arbon, P. Mass-gathering medicine: a review of the evidence and future directions for research. Prehosp Disaster Med. 2007;22(2):131–135.CrossRefGoogle ScholarPubMed
World Health Organization. Communicable disease alert and response for mass gatherings. Epidemic and Pandemic Alert and Response. Technical Workshop; Geneva, Switzerland: 2008. http://www.who.int/csr/resources/publications/WHO_HSE_EPR_2008_8c.pdf. Accessed January 26, 2014.Google Scholar
Scholliers, A, Gogaert, S, Vande Veegaete, A, Gillebeert, J, Vandekerckhove, P. The most prevalent injuries at different types of mass gathering events: an analysis of more than 150,000 patient encounters. Prehosp Disaster Med. 2017;32(S1):S136–S136.CrossRefGoogle Scholar
Turris, SA, Lund, A, Bowles, RR. An Analysis of mass casualty incidents in the setting of mass gatherings and special events. Disaster Med Public Health Prep. 2014;8(2):143–149.CrossRefGoogle Scholar
Turris, SA, Camporese, M, Gutman, SJ, Lund, A. Mass-gathering medicine: risks and patient presentations at a 2-day electronic dance music event - year two. Prehosp Disaster Med. 2016;31(6):687–688.Google Scholar
FitzGibbon, KM, Nable, JV, Ayd, B, et al. Mass-gathering medical care in electronic dance music festivals. Prehosp Disaster Med. 2017;32(5):1–5.CrossRefGoogle ScholarPubMed
Arnold, JL. Risk and risk assessment in health emergency management. Prehosp Disaster Med. 2005;20(3):143–154.CrossRefGoogle ScholarPubMed
Scholliers, A, Gogaert, S. Can the patient influx at mass gatherings be predicted? A first attempt to crunch the numbers. Prehosp Disaster Med. 2017;32(S1):S135–S135.Google Scholar
Arbon, P, Bridgewater, FHG, Smith, C. Mass gathering medicine: a predictive model for patient presentation and transport rates. Prehosp Disaster Med. 2001;16(3):150–158.CrossRefGoogle ScholarPubMed
Hartman, N, Williamson, A, Sojka, B, et al. Predicting resource use at mass gatherings using a simplified stratification scoring model. Am J Emerg Med. 2009;27(3):337–343.CrossRefGoogle ScholarPubMed
Milsten, AM, Tennyson, J, Weisberg, S. Retrospective analysis of mosh-pit-related injuries. Prehosp Disaster Med. 2017;32(6):1–6 CrossRefGoogle ScholarPubMed
Friedman, MS, Plocki, A, Likourezos, A, et al. A prospective analysis of patients presenting for medical attention at a large electronic dance music festival. Prehosp Disaster Med. 2017;32(1):78–82.CrossRefGoogle Scholar
Grange, JT, Green, SM, Downs, W. Concert medicine: spectrum of medical problems encountered at 405 major concerts. Acad Emerg Med. 1999;6(3):202–207.CrossRefGoogle ScholarPubMed
Van Sassenbroeck, DK, Calle, PA, Rousseau, FM, et al. Medical problems related to recreational drug use at nocturnal dance parties. Eur J Emerg Med. 2003;10(4):302–308.CrossRefGoogle ScholarPubMed
Spaepen, K, Haenen, WAP, Hubloue, I. The development of PRIMA - a Belgian prediction model for patient encounters at mass gatherings. Prehosp Disaster Med. 2020. In Press.Google Scholar
Gogaert, S, Vande Veegaete, A, Scholliers, A, Vandekerckhove, P. “MedTRIS” (Medical Triage and Registration Informatics System): a web-based client server system for the registration of patients being treated in first aid posts at public events and mass gatherings. Prehosp Disaster Med. 2016;31(05):557–562.CrossRefGoogle ScholarPubMed
Nable, JV, Margolis, AM, Lawner, BJ, et al. Comparison of prediction models for use of medical resources at urban auto-racing events. Prehosp Disaster Med. 2014;29(6):608–613.CrossRefGoogle ScholarPubMed
Hutton, A, Savage, C, Ranse, J, Finnel, D, Kub, J. The use of Haddon’s Matrix to plan for injury and illness prevention at outdoor music festivals. Prehosp Disaster Med. 2015;30(2):175–183 CrossRefGoogle ScholarPubMed
Rosiers, J, Van Damme, J, Hublet, A, et al. In hogere sferen? Volume 3: een onderzoek naar het middelengebruik bij Vlaamse studenten. Brussel, Belgium: Vereniging voor Alcohol- en andere Drugproblemen. https://www.vad.be/assets/in-hogere-sferen-volume-3-een-onderzoek-naar-het-middelengebruik-bij-vlaamse-studenten. Accessed February 22, 2015.Google Scholar
Zeitz, KM, Zeitz, CJ, Arbon, P. Forecasting medical work at mass-gathering events: predictive model versus retrospective review. Prehosp Disaster Med. 2005;20(3):164–168.CrossRefGoogle ScholarPubMed
Ranse, J, Hutton, A, Turris, SA, Lund, A. Enhancing the minimum data set for mass-gathering research and evaluation: an integrative literature review. Prehosp Disaster Med. 2014;29(3):280–289.CrossRefGoogle Scholar