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Nonlinear Modelling for Predicting Patient Presentation Rates for Mass Gatherings

Published online by Cambridge University Press:  02 July 2018

Paul Arbon ,
Murk Bottema ,
Kathryn Zeitz ,
Adam Lund ,
Sheila Turris ,
Olga Anikeeva  and
Malinda Steenkamp
Paul Arbon*
Affiliation:
Torrens Resilience Institute, Flinders University, Adelaide, Australia
Murk Bottema
Affiliation:
Torrens Resilience Institute, Flinders University, Adelaide, Australia
Kathryn Zeitz
Affiliation:
Torrens Resilience Institute, Flinders University, Adelaide, Australia
Adam Lund
Affiliation:
Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada
Sheila Turris
Affiliation:
Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada
Olga Anikeeva
Affiliation:
Torrens Resilience Institute, Flinders University, Adelaide, Australia
Malinda Steenkamp
Affiliation:
Torrens Resilience Institute, Flinders University, Adelaide, Australia
*
Correspondence: Professor Paul Arbon, PhD Torrens Resilience Institute Flinders University GPO Box 2100 Adelaide, South Australia 5001, Australia E-mail: [email protected]
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Abstract

Introduction

Mass gatherings are common in Australia. The interplay of variables, including crowd density and behavior, weather, and the consumption of alcohol and other drugs, can pose a unique set of challenges to attendees’ well-being. On-site health services are available at most mass gatherings and reduce the strain on community health facilities. In order to efficiently plan and manage these services, it is important to be able to predict the number and type of presenting problems at mass gatherings.

Problem

There is a lack of reliable tools to predict patient presentations at mass gatherings. While a number of factors have been identified as having an influence on attendees’ health, the exact contribution of these variables to patient load is poorly understood. Furthermore, predicting patient load at mass gatherings is an inherently nonlinear problem, due to the nonlinear relationships previously observed between patient presentations and many event characteristics.

Methods

Data were collected at 216 Australian mass gatherings and included event type, crowd demographics, and weather. Nonlinear models were constructed using regression trees. The full data set was used to construct each model and the model was then used to predict the response variable for each event. Nine-fold cross validation was used to estimate the error that may be expected when applying the model in practice.

Results

The mean training errors for total patient presentations were very high; however, the distribution of errors per event was highly skewed, with small errors for the majority of events and a few large errors for a small number of events with a high number of presentations. The error was five or less for 40% of events and 15 or less for 85% of events. The median error was 6.9 presentations per event.

Conclusion:

This study built on previous research by undertaking nonlinear modeling, which provides a more realistic representation of the interactions between event variables. The developed models were less useful for predicting patient presentation numbers for very large events; however, they were generally useful for more typical, smaller scale community events. Further research is required to confirm this conclusion and develop models suitable for very large international events.

Arbon P, Bottema M, Zeitz K, Lund A, Turris S, Anikeeva O, Steenkamp M. Nonlinear modelling for predicting patient presentation rates for mass gatherings. Prehosp Disaster Med. 2018;33(4):362–367

Keywords

environmentevent planningmass gatheringmathematical model
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 33 , Issue 4 , August 2018 , pp. 362 - 367
Copyright
© World Association for Disaster and Emergency Medicine 2018 

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Footnotes

Conflicts of interest/funding: This study was funded by the Australian Research Council (ARC; Canberra, Australia) Discovery Projects scheme (project number: DP140101448). The authors have no conflicts of interest to declare.

References

1. Arbon, PA. The development of conceptual models for mass-gathering health. Prehosp Disaster Med. 2004;19(3):208-212.Google Scholar
2. Arbon, PA, Cusack, L, Verdonk, NR. Mass gathering public health and emergency medicine literature review: levels of evidence. Australasian Journal of Paramedicine. 2013;10(1):5.Google Scholar
3. Zeitz, K, Zeitz, C, Arbon, PA. Forecasting medical work at mass-gathering events: predictive model versus retrospective review. Prehosp Disaster Med. 2005;20(3):164-168.Google Scholar
4. Hnatow, DA, Gordon, DJ. Medical planning for mass gatherings: a retrospective review of the San Antonio Papal Mass. Prehosp Disaster Med. 1991;6(4):443-450.Google Scholar
5. De Lorenzo, RA. Mass-gathering medicine: a review. Prehosp Disaster Med. 1997;12(1):68-72.Google Scholar
6. Michael, JA, Barbera, JA. Mass-gathering medical care: a twenty-five-year review. Prehosp Disaster Med. 1997;12(4):305-312.Google Scholar
7. Milsten, AM, Maguire, BJ, Bissell, RA, Seaman, KG. Mass-gathering medical care: a review of the literature. Prehosp Disaster Med. 2002;17(3):151-162.Google Scholar
8. Arbon, PA, Bridgewater, FH, Smith, C. Mass-gathering medicine: a predictive model for patient presentations and transport rates. Prehosp Disaster Med. 2001;16(3):150-158.Google Scholar
9. 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.Google Scholar
10. Baird, MB, O’Connor, RE, Williamson, AL, Sojka, B, Alibertis, K, Brady, WJ. The impact of warm weather on mass event medical need: a review of literature. Am J Emerg Med. 2010;28(2):224-229.Google Scholar
11. Breiman, L, Friedman, J, Olshen, R, Stone, C. Classification and Regression Trees. Boca Raton, Florida USA: CRC Press; 1984.Google Scholar
12. Hastie, Tibshirani, Friedman, J. The Elements of Statistical Learning. New York USA: Springer; 2009.Google Scholar
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