• Kathryn Zeitz, Pari Delir Haghighi, Frada Burstein, and Jeffrey Williams.
• St John Ambulance, Manuka, Australia. kmzeitz@onaustralia.com.au
• Aust Health Rev. 2013 Jun 1;37(3):402-6.

ObjectiveThe present study was designed to further understand the psychosocial drivers of crowds impacting on the demand for healthcare. This involved analysing different spectator crowds for medical usage at mass gatherings; more specifically, did different football team spectators (of the Australian Football League) generate different medical usage rates.MethodsIn total, 317 games were analysed from 10 venues over 2 years. Data were analysed by the ANOVA and Pearson correlation tests. RESULTS; Spectators who supported different football teams generated statistically significant differences in patient presentation rates (PPR) (F15, 618=1.998, P=0.014). The present study confirmed previous findings that there is a positive correlation between the crowd size and PPR at mass gatherings but found a negative correlation between density and PPR (r = -0.206, n=317, P<0.0005).ConclusionsThe present study has attempted to scientifically explore psychosocial elements of crowd behaviour as a driver of demand for emergency medical care. In measuring demand for emergency medical services there is a need to develop a more sophisticated understanding of a variety of drivers in addition to traditional metrics such as temperature, crowd size and other physical elements. In this study we saw that spectators who supported different football teams generated statistically significant differences in PPR. What is known about this topic? Understanding the drivers of emergency medical care is most important in the mass gathering setting. There has been minimal analysis of psychological 'crowd' variables. What does this paper add? This study explores the psychosocial impact of supporting a different team on the PPR of spectators at Australian Football League matches. The value of collecting and analysing these types of data sets is to support more balanced planning, better decision support and knowledge management, and more effective emergency medical demand management. What are the implications for practitioners? This information further expands the body of evidence being created to understand the drivers of emergency medical demand and usage. In addition, it supports the planning and management of emergency medical and health-related requirements by increasing our understanding of the effect of elements of 'crowd' that impact on medical usage and emergency healthcare.

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