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INJURY PATTERNS AND CROWD BEHAVIOUR
AT MASS GATHERING EVENTS
Jamie Ranse
Assistant Professor
University of Canberra
www.jamieranse.com
twitter.com/jamieranse
youtube.com/jamieranse
linkedin.com/in/jamieranse
• 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 festival
Agricultural show
Super mass gathering 250,000 – 500,000 State and Interstate Motor sports event
Extreme mass
gathering
500,000 – 1,000,00
National +/-
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 Ranse
Assistant Professor
University of Canberra
www.jamieranse.com
twitter.com/jamieranse
youtube.com/jamieranse
linkedin.com/in/jamieranse

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INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 

Injury patterns and crowd behaviour at mass gathering events

  • 1. INJURY PATTERNS AND CROWD BEHAVIOUR AT MASS GATHERING EVENTS Jamie Ranse Assistant Professor University of Canberra www.jamieranse.com twitter.com/jamieranse youtube.com/jamieranse linkedin.com/in/jamieranse
  • 2. • Definition • Conceptual model • Workload characteristics • Workload prediction • Event design • Event managers • Case study overview
  • 3. 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 festival Agricultural show Super mass gathering 250,000 – 500,000 State and Interstate Motor sports event Extreme mass gathering 500,000 – 1,000,00 National +/- international Religious festival Mega mass gathering 1,000,000+ National and International Olympics
  • 5. 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
  • 6. workload characteristics and conceptual model
  • 7. 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
  • 8. event design • Crowd behaviour – Music festivals – Spaces • Patient access
  • 9. event managers • Health service – What level of service do you need? – Health risk assessment
  • 10. • Event managers – Overall risk management – Patient information: confidentiality v duty of care event managers
  • 12. implications • Pre-ambulance (PPR) • Pre-hospital (TTHR) • Emergency Department (RTHR)
  • 13. implications • Pre-ambulance • Pre-hospital • Emergency Department • Hospital – surgery
  • 15. To understand the characteristics of people who present as patients to on-site health care at outdoor music festivals in Australia aim
  • 16. • 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
  • 17. • 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
  • 18. • 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
  • 19. • 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
  • 20. • 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
  • 21. • Gender didn’t demonstrate a significant risk for mental health related presentations • 29 cases overall: average of one per event mental health
  • 22. • Most patients returned to the event • Environmental-related category highest TTHR outcome
  • 23. INJURY PATTERNS AND CROWD BEHAVIOUR AT MASS GATHERING EVENTS Jamie Ranse Assistant Professor University of Canberra www.jamieranse.com twitter.com/jamieranse youtube.com/jamieranse linkedin.com/in/jamieranse