INJURY PATTERNS AND CROWD BEHAVIOUR AT MASS GATHERING EVENTS

Jamie RanseAssistant Professor

University of Canberra

www.jamieranse.com

twitter.com/jamieranse

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• Definition

• Conceptual model

• Workload characteristics

• Workload prediction

• Event design

• Event managers

• Case study

overview

definition

Class Subclass Persons Resources Example

Mass gathering

Small 200 – 1,500 Local area Local fair

Medium 1,500 – 10,000 Local area Local sports game

Large 10,000 – 100,000 Local +/- State Concert

Major mass gathering 100,000 – 250,000 State +/- Interstate Music festivalAgricultural show

Super mass gathering 250,000 – 500,000 State and Interstate Motor sports event

Extreme mass gathering

500,000 – 1,000,00National +/- international

Religious festival

Mega mass gathering 1,000,000+National and International

Olympics

conceptual model

workload characteristics

• Other extreme events

• Can we predict workload?

temperature

bounded or not

ETOH / drug availability

humidity

participant numbers

day / nightindoors / outdoors

crowd behaviour

• What factors influence workload / presentations?

level of onsite care

age of participants

duration

geography

workload characteristics and conceptual model

workload prediction

• Why predict workload?• What to predict?

– Patient presentation rate (PPR)

0.14 to 90/1,000 (majority 0.5 and 2.6/1,000)– Transport to hospital rate (TTHR)

0.01 to 0.55/1,000– Referral to hospital rate

5% - 10% of PPR

• How?– Predictive models– Historical prediction

event design

• Crowd behaviour– Music festivals

– Spaces

• Patient access

event managers

• Health service– What level of service do you need?

– Health risk assessment

• Event managers– Overall risk management

– Patient information: confidentiality v duty of care

event managers

implications

• Pre-ambulance (PPR)

• Pre-hospital (TTHR)

implications

• Pre-ambulance (PPR)

• Pre-hospital (TTHR)

• Emergency Department

(RTHR)

implications

• Pre-ambulance

• Pre-hospital

• Emergency Department

• Hospital– surgery

case study

To understand the characteristics of people who present as patients to on-site health care at outdoor music festivals in Australia

aim

• Retrospective review of patient report forms • 26 outdoor music festivals • Four different states of Australia• Minimum data set

– Illness– Injury – Environmental– Mental Health

design and setting

• 4950 presentations

• Almost two thirds were female (n=3087, 62.4%)

• The mean age of all patient presentations was 21.3 (5.8) years

• The majority of patients (n=3875, 78.3%) were ≤25 years of age

demographics

• The majority of patient presentations (n=2766, 55.9%) presented with illness related concerns

• The risk of illness was 1.7 times (OR=1.71; 95% CI 1.51-1.94; p<0.001) higher for females than males in the ≤25 year age group

• Most common presenting problem was headache (n=1389, 52.9%).

• Pain (n=264 10%), asthma (n=216 8.2%), and nausea and vomiting (n=211 8%)

illness

• The risk of a female sustaining an injury was almost half (OR=0.54; 95% CI 0.47-0.62; p<0.001) that of males

• The main types of injury presentations were superficial lacerations (n=281; 20.4%); sprain or strains (n=268; 19.2%), and head injuries (concussion) (n=168; 11.9%)

• Crushing injuries; blisters and foreign bodies; external to eye was significantly higher for females than males

injury

• Alcohol related presentations most common (n=250 32.8%)

• Substance related (n=135 17.7%), • Combined alcohol and substance use (n=125 16.4%)• Heat exhaustion more prevalent for females (p=<0.001),

while substance related presentations more prevalent for males (p=<0.001)

environment

• Gender didn’t demonstrate a significant risk for mental health related presentations

• 29 cases overall: average of one per event

mental health

• Most patients returned to the event

• Environmental-related category highest TTHR

outcome

INJURY PATTERNS AND CROWD BEHAVIOUR AT MASS GATHERING EVENTS

Jamie RanseAssistant Professor

University of Canberra

www.jamieranse.com

twitter.com/jamieranse

youtube.com/jamieranse

linkedin.com/in/jamieranse