Mass gathering medicine: a predictive model for patient presentation and transport rates

INTRODUCTION: This paper reports on research into the influence of environmental factors (including crowd size, temperature, humidity, and venue type) on the number of patients and the patient problems presenting to first-aid services at large, public events in Australia. Regression models were developed to predict rates of patient presentation and of transportation-to-a-hospital for future mass gatherings. OBJECTIVE: To develop a data set and predictive model that can be applied across venues and types of mass gathering events that is not venue or event specific. Data collected will allow informed event planning for future mass gatherings for which health care services are required. METHODS: Mass gatherings were defined as public events attended by in excess of 25,000 people. Over a period of 12 months, 201 mass gatherings attended by a combined audience in excess of 12 million people were surveyed throughout Australia. The survey was undertaken by St. John Ambulance Australia personnel. The researchers collected data on the incidence and type of patients presenting for treatment and on the environmental factors that may influence these presentations. A standard reporting format and definition of event geography was employed to overcome the event-specific nature of many previous surveys. RESULTS: There are 11,956 patients in the sample. The patient presentation rate across all event types was 0.992/1,000 attendees, and the transportation-to-hospital rate was 0.027/1,000 persons in attendance. The rates of patient presentations declined slightly as crowd sizes increased. The weather (particularly the relative humidity) was related positively to an increase in the rates of presentations. Other factors that influenced the number and type of patients presenting were the mobility of the crowd, the availability of alcohol, the event being enclosed by a boundary, and the number of patient-care personnel on duty. Three regression models were developed to predict presentation rates at future events. CONCLUSIONS: Several features of the event environment influence patient presentation rates, and that the prediction of patient load at these events is complex and multifactorial. The use of regression modeling and close attention to existing historical data for an event can improve planning and the provision of health care services at mass gatherings.