Forecasting medical work at mass-gathering events: predictive model versus retrospective review

Prehosp Disaster Med. May-Jun 2005;20(3):164-8. doi: 10.1017/s1049023x00002399.


Introduction: Mass-gathering events are dynamic and challenge traditional medical management systems. To improve the system for the provision of first aid at mass-gathering events, an evaluation of two models that assist in forecasting the number of patients presenting for first-aid services was conducted.

Method: A prospective evaluation of a recurrent, mass-gathering event was undertaken comparing predicted patient presentations and ambulance transfers generated by a predictive model developed by Arbon et al and a retrospective review of seven years of historical, event data as described by Zeitz et al.

Results: Patient presentation rate (per 1,000 patrons) for this event was 1.6 and the transport to hospital rate (per 1,000 patrons) was 0.07. The retrospective review closely predicted the actual overall attendance. Both methods forecast the number of patients presenting on a daily basis. The prediction proved to be more accurate, on a day-by-day basis, using the Zeitz method.

Conclusion: The Arbon method is particularly useful for events where there is no or limited information about previous medical work. Retrospective review of data generated from specific events (Zeitz method) considers the unique and individual variability that can occur from event to event and is more accurate at predicting patient presentations when the data are available. Both methods have the potential to be used more frequently to adequately and efficiently plan for the resources required for specific events.

Publication types

  • Comparative Study
  • Evaluation Study

MeSH terms

  • Ambulances / statistics & numerical data
  • Disaster Planning / methods*
  • Emergency Medical Services / methods*
  • Emergency Medical Services / statistics & numerical data
  • Humans
  • Mass Behavior*
  • Models, Theoretical*
  • Retrospective Studies*
  • South Australia / epidemiology
  • Wounds and Injuries / epidemiology