Mass-gathering Health Research Foundational Theory: Part 2 - Event Modeling for Mass Gatherings
1,2,3

Sheila A. Turris, NP, PhD;
3

Adam Lund, MD, MEd, FRCPC;
4

4

1,3

4

Alison Hutton, RN, PhD; Ron Bowles, EMA II,

PhD; Elizabeth Ellerson, MPH; Malinda Steenkamp, PhD; Jamie Ranse, RN, FRCNA, FACN;

5

4

Paul Arbon, RN, PhD
1. Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada
2. School of Nursing, University of Victoria, Victoria, British Columbia, Canada
3. Justice Institute of British Columbia, New Westminster, British Columbia, Canada
4. Flinders University, World Health Organization Collaborating Centre for Mass Gatherings and High
Consequence/High Visibility Events, Flinders University of South Australia, Adelaide, South Australia,
Australia
5. University of Canberra, Faculty of Health, Bruce, Australian Capital Territory, Australia
Abstract
Background: Current knowledge about mass gathering health (MGH) fails to adequately inform the understanding of
mass gatherings (MGs) because of a relative lack of theory development and adequate conceptual analysis. This
report describes the development of a series of event lenses that serve as a beginning ‘‘MG event model,’’
complimenting the ‘‘MG population model’’ reported elsewhere.
Methods: Existing descriptions of ‘‘MGs’’ were considered. Analyzing gaps in current knowledge, the authors sought
to delineate the population of events being reported. Employing a consensus approach, the authors strove to capture
the diversity, range, and scope of MG events, identifying common variables that might assist researchers in
determining when events are similar and might be compared. Through face-to-face group meetings, structured breakout
sessions, asynchronous collaboration, and virtual international meetings, a conceptual approach to classifying and
describing events evolved in an iterative fashion.
Findings: Embedded within existing literature are a variety of approaches to event classification and description. Arising
from these approaches, the authors discuss the interplay between event demographics, event dynamics, and event design.
Specifically, the report details current understandings about event types, geography, scale, temporality, crowd dynamics,
medical support, protective factors, and special hazards. A series of tables are presented to model the different analytic
lenses that might be employed in understanding the context of MG events.
Interpretation: The development of an event model addresses a gap in the current body of knowledge vis a vis
understanding and reporting the full scope of the health effects related to MGs. Consistent use of a consensus-based
event model will support more rigorous data collection. This in turn will support meta-analysis, create a foundation for
risk assessment, allow for the pooling of data for illness and injury prediction, and support methodology for evaluating
health promotion, harm reduction, and clinical response interventions at MGs.
Introduction
Less than 25 years old, mass-gathering health (MGH) is a relatively new field of research.1 As such, the science
underpinning this body of knowledge is young and developing rapidly.2 Current knowledge, however, fails to adequately
inform the understanding of mass gatherings (MGs) because of the lack of theory development and adequate
conceptual analysis.3 In December of 2013, the World Health Organization (WHO; Geneva, Switzerland) Collaborating
Center for Mass Gatherings and High Risk/Visibility Events at Flinders University, South Australia, hosted a scientific
meeting bringing together international MGH researchers. Representatives were from: Flinders

1

Scenario 1
You are the medical director for a four-day music festival held in a rural setting, annually, in summer. There are
1,200 people living in the community. The forecast is for hot, dry, windy conditions. The event will attract more than
30,000 daily attendees, many of whom will camp overnight. The nearest hospital is a 2-hour drive away.
Scenario 2
You are a physician in an urban emergency department. A qualifying marathon is going to be held in your city on
Sunday. The forecast is for rain and cool temperatures. The event will attract more than 40,000 participants and an
unknown number of spectators. There are three hospitals within a 20-minute drive. A bomb exploded at a similar
event two months earlier, which has media, police, and health officials on ‘‘high alert.’’
Table 1. Event Scenarios
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University and University of Canberra, in Australia; the University of British Columbia (BC) and the Justice Institute of BC,
in Canada; the WHO; and Public Health England (London, England, United Kingdom).
The MGH Collaborating Team, formed during this meeting, discussed various models related to MG populations and
events to support a common understanding about their human health effects. The goal of a larger, international
consensus project is the creation of a minimum data set and data dictionary for MGH research. This will support
description, measurement, comparison, evaluation, and reporting on parameters of interest, permitting international
comparisons that, to date, has been impossible.2,4-6
Mass-gathering events are complex phenomena involving the interaction between a number of elements. This
manuscript is the second of two related reports that explore key elements of MGH research. The first paper7 concerns
the various populations of people involved in MGs and proposes a MGH population model. The current manuscript
complements this work by describing and characterizing MG events, identifying specific variables of interest to MG
researchers, and addressing a gap in the current body of knowledge with regard to understanding the full scope of MGs
and their health effects.
A robust event model is increasingly important as some events attract international participants and attendees in large
numbers. In the interactions between members of the host and event populations, there exist opportunities for
injuries,8,9 the spread of infectious disease (ID),10-17 disorderly crowd behavior and movement,17-21 and the overflow of ill
and injured from the event to the surrounding community during a mass-casualty incident, perhaps overwhelming the
host infrastructure.20,22-26
Consistent data collection with regard to events will potentially:(1) permit meta-analysis and pooling of data sources
internationally;(2) further develop the methodology for evaluating health promotion and harm reduction efforts; (3)
create a solid foundation for risk assessment and illness and injury prediction modeling; and (4) provide operational
support for front-line clinicians with regard to planning for on-site medical teams.3,5,6,9
Methods
The authors critically appraised existing MG definitions and descriptions in published literature. When it became
apparent that published definitions lacked sufficient breadth and depth to support the group in defining, describing, and
explaining the health effects of MGs, a series of tables with event characteristics were developed in an iterative fashion,
capturing both academically- and operationally-relevant lenses for MGs.
Findings and Interpretation
Characterizing MG Events
An ongoing challenge for MG researchers and organizers is how to categorize events in a way that allows for grouping,
comparing, and contrasting findings. The MG literature covers a staggering range of events, each with a number of
event-related characteristics.
The cases above (Table 1) are similar in that large groups (ie, 30-40,000 people) will gather for a specific event, but their
divergent characteristics are far more intriguing (ie, one is a 1-day sporting event, the other a multi-day cultural event).
One is in a small community with limited local health care capacity, while the other is a large community with a
substantial pre-existing medical system.
How might these differences affect illness and injury rates? Can injury and illness rates be reduced? How will treatment
planning differ for individuals who present for care? A careful read of existing event descriptions27-55 reveals a multitude
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of event-related variables that are inconsistently reported and currently lack clear definitions (Table 2).
The results of the team’s iterative process are presented below. Macro-level characteristics explore the interplay
between the place, people, and the event itself, informing methodology, data collection, and meta-analysis. Meso-level
characteristics include the ‘‘demographics’’ (ie, the immutable parts of the event), the ‘‘dynamics’’ (ie, what people do;
how they feel and react), and the ‘‘design’’ (ie, what event planners/ producers do to protect attendees/participants
from illness and injury) of MGs and their relationship to risk assessment and predictive modeling. Micro-level
characteristics inform planning and mitigation efforts of frontline clinicians, medical teams, and other event personnel.
Micro-level characteristics will be detailed in a future publication regarding the consensus-based minimum data set and
data dictionary.
Below, the authors present a broader explanation of MG demographics, dynamics, and design. The elements in this
discussion represent various variables of interest to MG researchers that may be useful for risk assessment and
predictive modeling.
Demographics of an Event
In existing literature, common starting points for grouping and characterizing MG events include event type, geography,
size, and temporal considerations.
Event Type—With the exception of relatively-common events, such as marathons or cycling events, event types are
typically
Event Dimension
Demographics

Category
Event Type

Classification Examples
Citations
Races, cycling events, religious pilgrimages, cultural events, 18, 26-34
and music festivals.
Geography
Terrain, bounded or unbounded, shifting or fixed footprint, 17, 26, 28-30,
site access and egress, multi or single venue.
35
Size
Number of attendees/participants, multi-event versus
26, 36
single.
Temporality
Duration (days, hours), time of year, time of day, season, 26, 28-29, 34-35
recurrent annual event vs first-time event, peak time of
attendance.
Dynamics
Crowd Type
Gender mix, age mix, families, disabilities, special
26, 28, 29
populations.
Crowd Behavior
Mood, density, activity levels, queuing, movement,
17, 33, 36-42
behavior, predispositions, motivations, crowd movement,
and flow.
Purpose of Event
Sport, arts, religious, political, mixed; protests, riots,
20, 26-27, 29
celebrations.
Political Context
Within host community/country, police state versus
26-27, 29
democracy, civic versus private, profit versus not for profit.
Design
Protective Factors
Crowd resilience, health promotion, illness prevention,
24, 28-29, 43-46
police/ security onsite.
Special Hazards
Climate, weather conditions, motorization, obstacle
19, 20, 26, 29,
course, infectious disease exposure, alcohol and drug use, 31-36, 39, 47mosh pits, fireworks.
49
Onsite Health Services First aid only, higher level of care, location, deployment, 24, 26, 28, 31,
signage, triage.
35, 39, 41,
50-52
Host Community
Mitigation of event related increases in volume for host 24, 43, 53-54
Burden
community infrastructure.
Table 2. Dimensions of an Event Model as Reported in the Literature
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recorded without reference to a category. That is, every event is treated as unique. However, Turris et al argued that
21

an initial typology of events would include sporting, arts-related, religious/political, and miscellaneous events. This is a
macro-level model, which has limited utility for developing nuanced explanatory and predictive models. For example, it
does not distinguish between contact and noncontact sporting events, spectator vs participant sporting events, and so
on. It does, however, present an initial ‘‘high-level’’ categorization as a starting point for grouping and comparing
various types of MGs.
Strictly speaking, the event type (eg, fireworks display, boat show, and Papal visit) is only one piece of data that will help
researchers and clinicians determine the degree to which two different events may be alike. Other parameters, such as
whether or not an event is primarily spectator- or participant-based, may be as helpful in selecting events for comparison.
Geography—Every event has a geographical character and site-specific descriptors. For example, at a local music festival
held annually in Vancouver, BC (Canada), the grounds include a lake, raising the specter of accidental drowning. Similarly,
34

at the Suwa Onbashira Festival held every six years in Japan, men ride logs down steep slopes. The geography of both
events described creates a set of safety issues for event planners, attendees, and on-site medical services.
Bounded, Unbounded, or Compound Events—Milsten et al identified that an event with a mobile population would have
a different risk profile than an event for which spectators were seated throughout the event.50 This is, in part, due to the
fact that in unbounded events, there is no control with regard to the capacity of the venue. In contrast, for an event held at a
stadium with fixed seats, event planners have some control over the number of participants and spectators. A variety of event
‘‘footprints’’ that might be of interest to researchers and clinicians are presented in Table 3.
Considerations with regard to event boundaries are reported in the literature. For example, marathons tend to have a shifting
footprint. As runners continue along the course, the early parts of the course are closed. This has implications for medical
33

deployment as health care may be offered by both fixed and mobile response teams, depending on the nature of the event.
Similarly, if an event is primarily spectator-based (eg, a football match in a stadium with assigned seats), considerations related
21,56-59

to site access and egress issues become highly relevant due to the not infrequent occurrence of riots and stampedes.
Access and egress between the event and emergency services, as well as between the event and local hospitals or clinics,
should also be a consideration.
Event Size—Early work by researchers in the field of MGH began with describing and classifying MGs according to the

Event
Boundaries
Bounded

Venue/Site
Venue Permanent
Venue Temporary
Venue Mixed

Unbounded

Examples
Stadium
Stage Set Up for a
Season
National
Exhibition
Park
Community Days
Marathon
Olympics

Single Site
Multiple Sites, Single Town/City
Multiple Sites, Shifting Footprint
Compound
Multiple Sites, Multiple Towns/Cities. Event contains both sites directly involved in
event and ‘‘nonevent spaces’’ between venues.
Table 3. Classification of Mass Gatherings by Event Boundaries and Venue
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Event Size
>1,000

Common Examples in Range
Adventure obstacle race, triathlon, bike race, small venue music
performance.
1,001–10,000
Local music festival, charity fund-raiser sporting event, Ironman triathlon.
10,001–25,000
Arts festival, regional fair, rodeo, community running event.
25,001–50,000
Large marathon, out-door concert/music festival.
50,001–100,000
Community celebration around national play-off game, outdoor music
festival, national holiday.
100,001–250,000
Parade, destination international music events.
250,001–500,000
Fire-works display, protest march.
500,001–1,000,000
Religious festivals and pilgrimages, international Games.
1,000,001–5,000,000
International sport competitions, religious events (includes spectators,
participants, and staff).
.5,000,000
Religious pilgrimage (ie, Hajj).
Table 4. Classification of Mass Gatherings According to Event Size
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absolute number of attendees and/or participants. Malloy and colleagues cast a wide net and proposed that MGs be
categorized according to a variety of sizes, including small, medium, large, major, super, extreme, and mega.60 This
work contributes an understanding of the vast range of sizes, from local community events that occur on a small scale to
international events that involve movement of large numbers of people across international boundaries.
Based on existing classification schemes with regard to size, the following synthesis is presented (Table 4). The authors
acknowledge that there are challenges related to labeling a given event as small, medium, and large, as such designations
are difficult to interpret without information about the size of the host community. Instead, a range is provided.
The size of a given MG has been used to quantify risk. In Western Australia, the public health authority uses the size of a
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MG as one variable contributing to risk assessment score. this work links the absolute size of an event to risk
assessment, which is an important consideration in relation to the permitting, planning, and execution of specific events.
Ratio of Event to Host Population Size—Recently, attention has shifted toward defining MGs as a preplanned event held at a
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specific location for a defined period of time that has the potential to strain planning and response resources. This
definition is appealing as it takes into account the baseline response capacity of the host community. Understanding the size
of the event crowd relative to the size of the baseline host community population may contribute to understanding and
quantifying risk or anticipating the impact of the MG. This could be expressed as a ratio of the event population relative to the
host population (Table 5), or the event-to-host population (EHP) ratio. Essentially, this may be calculated as the number of
people in the event community (NEV) divided by the number of people in the host community (NHC).
Figure 1 draws o n the pro posed M G po pulation m o del outlined in Part 1 of this series and presents a
description and

5

Turris & 2014 Prehospital and Disaster Medicine

Figure 1. Population Model for Mass-gathering Health.

Ratio Range
<0.1
0.11–0.50
0.50–1.0
>1.0–2.0
>2.0

Example
Crowd of 500 in community of .5,000; rodeo in small town.
Crowd of 250,000 in community of 3 million; fireworks in large city.
30,000 attend mountain bike festival in a community of 70,000.
1,000,000 visitors attend football tournament in a city of 4,000,000.
10,000 attend botanical show in community of 15,000 people.
24 million attend religious pilgrimage in country of 29 million.
18,000 attend a music festival in a community of 12,000 people.
36,500 attend concert in a community of 15,000.
Turris & 2014 Prehospital and Disaster Medicine

Table 5. Examples of MGs Described by the EHP Ratio Abbreviations: EHP, event-to-host population; MG, mass
gathering.
diagrammatic representation of the relative size of events, according to the size of the population attending (Table 6).
Operationally and practically speaking, arising from the EHP ratio are considerations about response capacity. In
other words, if the absolute size of an event exceeds the size of the host community, health infrastructure may
need to be augmented. Further work is needed to delineate how response capacity might be objectively described and
determined.
Temporal Considerations—For this class of data, a myriad of time considerations apply. For example, how long is a
givenevent (eg, hours, days, or weeks)? Does the event run for several days? Is the event taking place during the daytime,
at night, or both? Outdoor seasonal events carry particular risks according to the weather conditions. Also, consider the
differences between recurring (eg, annual) and one- off events. For example, recurring events may have the benefit of a
legacy of experienced clinicians, volunteers, and participants.

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Dynamics of an Event
Crowd Mood—Political rallies, funerals of public figures, and protests are examples of event classifications that influence the
risk profile of a given MG. This is because crowd mood and behavior will be directly influenced by the tone of a given event,
perhaps making the crowd more volatile. Increased stress for individual participants may play a role in crowd volatility; also,
the risks associated with events that may be targets for deliberate acts of terror shape the relationship between the
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purpose of the event and risk to attendees. Crowd mobility and density are sometimes used as proxies for crowd mood or
indicators of risk (qualitative). Objective measures of crowd mood are not yet well developed for use in a MG context.

19

Crowd Mobility—Events have different ‘‘shapes’’ that influence how a crowd of participants and/or spectators may be
distributed through the event, ultimately shaping crowd dynamics. Understanding the event ‘‘flow’’ as it relates to crowd
mood and behavior may be valuable for both researchers and clinicians. For example, in relation to crowd mobility at a
music festival where there is a highly-active stage front, crowd surfing is more likely to occur and must be planned for.
Crowd Density—Crowd density is another important feature of event description. Crowd density is defined as the ratio
between the number of persons in the area (eg, 2.8 people/square meter is considered to be medium density; Table 7).
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Steffen et al argued that crowd density was a more important consideration than the absolute number of people present.
Measuring density at MGs is currently done in a nonstructured or anecdotal way. However, at a specific event, the
environment can be divided into different regions and the density may be measured in each region (eg, entrance to an
39

event and stage fronts). Similarly, Sun and Qin suggested dividing the environment into different regions to reflect the
63

intensity and spaces of the crowd.
Density affects people in many ways. Berk argued that density affects visibility of those within the crowd, and dense
64

crowds will automatically undercut the effect of crowd activity. For individuals in high-density crowds, it is difficult
to see more than a few neighboring individuals. Polus claimed that there is no dispute that higher crowd densities
lead to a greater interaction between individuals, and it is the impact of this interaction that is of interest, as is its
65

impact on audience members during an event and how it might impact behaviour. A vivid example of the effect of
crowd density on illness and injury rates may be found in the literature of stampedes that occur during religious
gatherings

23,66

67-69

and other types of events.

Primarily Spectator, Primarily Participant, Mixed Events—People at an event belong to one of three groups:
spectators, participants, and event crew (eg, volunteers, security, vendors,

Abbreviation
N
HC
NHC
NEv

Term
Number of People
Host Community
Number of People in the Host
Community
Event

NPP

Patient Presentations

NHCEv

Host Community and Event
Population

Definition
Integer representing the total population of interest.
The community (or communities) hosting the event.
Integer representing the total population of the host
community or communities, on a nonevent day.
Integer representing the number of people attending the
event, including attendees, spectators, talent, and
workforce.
Number of patients presenting during the event; usually
presenting to on-site health services, but may include
direct presentations to community health services.
Host community at baseline plus the number of people
attending or participating in the event.
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Table 6. Abbreviations and Definition of Terms, Population Model for MGH Abbreviation: MGH, mass-gathering health.
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Label
Extremely High Density
High Density
Medium Density
Low Density
Very Low Density

Metric
5 m2
4 m2
3 m2
2 m2
1 m2

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Table 7. Crowd Density Measurement

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Design of an Event
Special Hazards—The WHO adopts an ‘‘all hazards’’ approach to risk management and disaster planning, which may be
applied to examining and understanding event design. A hazard (ie, ‘‘any phenomenon with the potential to cause
disruption or damage to people or their environment’’) creates a degree of risk (ie, ‘‘the probability of a harmful
70

consequence’’). Adopting an all-hazards approach involves planning and developing a plan to address the range of risks and
possible emergencies that might occur during a given MG. In the context of health care in general, clinicians think about
‘‘modifiable’’ and ‘‘nonmodifiable’’ factors; however, in the context of MGs, the majority of hazards may be modifiable to some
degree, through careful event design. Consider the following examples.
Infectious Disease Exposure—The degree of risk presented by ID exposure during a MG is an active area of
inquiry for researchers. What can be accomplished to reduce risk for attendees is clear in the example of The Hajj, an
annual religious festival attended by millions in Saudi Arabia (a review of the health risks posed is detailed in Ahmed
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et al). Based on the data from this annual event, the Saudi’s have instituted a series of proactive measures to limit
morbidity and mortality, thus the government supports 24 hospitals with a capacity of roughly 5,000 beds. Medical
clinics (N 5 136) in the vicinity of the Hajj offer free medical care to all pilgrims, and there are 18 stationary and
21 mobile health care teams at the event itself. Airports have clinical examination rooms and any pilgrim suspected
15

of having a communicable disease is taken by ambulance to a dedicated 200-bed hospital.

Event Weather and Environmental Conditions—Depending on the season, temperature, and humidity, illness and
30

injuries at a given outdoor event may increase. For example, as described by Bruce, heat-related casualties can run in
72

the thousands when there is no available protection for participants. In July of 2006, during the annual Nijmegen March
in Holland, thousands of people walked 100 miles over four days, and a reported 2,725 heat-related casualties
presented for treatment during a single day when the temperature rose to 428C. Similarly, the temperature and the
humidity index may contribute to the illness rates for marathons, air shows, and other outdoor events held w h e n
temperatures are high, or even indoor events with inadequate air conditioning and/or ventilation.
In addition to reports of temperature-related illness, there have been mass-casualty incidents at MGs related to weather
73

74

conditions. For example, a lightning strike caused fatalities during a festival, a hailstorm triggered a human stampede,
75

and windy conditions caused the collapse of a large tent. Although climactic conditions can be predicted, weather
conditions on a particular day are variable and may change throughout the day. These examples point to the need to
anticipate the unexpected by considering the range of expected weather conditions in a particular region rather than
focusing on a single day or days.
Alcohol and Drug Use—The presence of drugs and alcohol, whether available through on-site vendors or brought in
concealed by patrons, is an important consideration and can be difficult to capture in a meaningful way. For example, at an
electronic dance music festival, alcohol may be sold on site; however, attendees who are underage and know that their
bags will be searched upon entry to the event may choose to imbibe right before the event (eg, while waiting in the
55

queue to enter), increasing their risk of alcohol toxicity.

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Mechanical or Other Hazards—Mechanical and other hazards are rarely reported on directly in event case reports. Instead, these
76

types of hazards (eg, small planes at airshows, amusement park rides, livestock at a rodeo, and fireworks at community
77-81

celebrations ) only appear in the literature when a mass-casualty event occurs. Collecting data on events with special
hazards, prospectively, may assist researchers in measuring the degree of risk presented by similar events, ultimately
informing event design.
Protective Factors—Illness and injury prevention is an emerging topic of discussion in the MGH literature. Several
researchers have argued that prevention efforts should be a focus during event planning. For example, Hewitt and
colleagues argued that many injuries that occurred during a rock concert were preventable if planners reduced the
distribution of items that could serve as missiles, and suggested that limiting the size of liquid containers could be
45

considered as one means of reducing overall alcohol intake.
Intrinsic hazards, present by virtue of participation in particular types (eg, participation in a marathon or cycling event
increases the risk of musculoskeletal injury), might be, to some degree, modifiable through careful event design. For
example, organizers of the New York Marathon (USA) send out education materials about the prevention of injuries and
illnesses both before and after the event (Personal Communication, Dr. Stuart Weiss, November 2012). The effect of this
intervention has not yet been tested prospectively. Embedding illness and injury prevention in the event model will help
researchers collect data and report on strategies being used to reduce the health care burden of MGs.
Health promotion vis a vis MGs has similarly been absent from the MGH literature until relatively recently. One
example of an event-related, illness-prevention strategy is the Dance Safe initiative, designed to provide information
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to attendees of electronic dance music events. At a more granular level, water stations, shelter, shade, access to
sunscreen or earplugs, and available ‘‘chill out zones’’ are all examples of event design that have yet to be
prospectively tested with regard to the effect on illness and injury rates.
Thinking more broadly about the positive health effects of events, Tewari and colleagues reported thought-provoking
findings around the longitudinal health benefits of Hindu pilgrimage event. They found that participation in the pilgrimage
increased the well-being of participants and argued that events should be reframed as not only presenting health risks,
46

but also presenting opportunities for health benefits.

On-site Health Services—Community stakeholders have a vested interest in ensuring adequate planning to prevent or
minimize the health impact of MGs in their communities. One of the ways to minimize the burden on local health care
44,67,83-85

services is to offer risk-relevant, on-site first aid and/or advanced health services throughout a special event.
The
MG literature is replete with case reports detailing event medical services. Researchers have been working on
determining the ‘‘right’’ ratio of health care providers to attendees/participants for a given type of event, required equipment
and supplies, effective communication plans, necessary transportation assets, and the appropriate infrastructure to support
25,86,87

on-site health care services at different types of events.

Host Community Burden—In order to mitigate the health care burden MGs may place on baseline community health
services, researchers would benefit from a common pool of data with which to measure community impact. Arbon et al
noted that published MG literature to date consists predominantly of observational studies and cohort studies (>58%),
3

most commonly focused on operations and Emergency Medical Services on site at the event (>48%). Case reports and
case series provide snapshots of specific events, commonly reporting patient presentations and transfers to hospital on
the day(s) the event occurred. Unfortunately, these reports will underestimate the true impact of MGs on local community
resources. Case reports rarely capture data regarding increased workload on health resources in the days or hours before
and/or after the event. As argued by Lund and colleagues, a full-operational analysis would account for both the
25

consumptive and disruptive effects of MGs on the host community’s health resources. Well-executed medical and safety
plans for events with appropriate, comprehensive risk assessments and stakeholder engagement have the best chance of
ameliorating the potential negative impact of MGs on communities.

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Overlay of the Population Model
This report has proposed a series of analytic lenses through which people might better understand and describe MGs and
their health effects. This set of lenses is drawn from existing literature and forms the beginnings of an event model. In a
companion paper, the present authors developed a population model, also central to further theory development. When
the population model overlays the event model, this may focus the attention of researchers and clinicians on the links
between the event characteristics and the event population. For example, the application of the event and population
models may focus attention on the host-to-event population ratio in a rural setting, as in the first example (Table 1). The
inverse high EHP ratio should prompt clinicians to coordinate with local health service infrastructure to temporarily
increase capacity.
Moreover, as was the case with the population model, there is also an overlay of time for the event model. Ideally,
the event model should be considered pre, during, and post event with data collection taking place during three phases
in time.
Future Implications and Limitations
None of the event lenses described operate independently. For example, considering crowd behavior and density
data together would support taking action to reduce risk for attendees, ideally before a negative health event takes
place. Aly talks about ‘‘improving’’ and ‘‘dampening factors.’’ He posited that as factors such as temperature and overall
noise levels rise, so too does the probability of aggression, making a strong case for considering the interactions
88

between event factors.
The contribution of the work in this report is in helping researchers and clinicians articulate and understand risk related to
specific events and may ultimately inform the execution of safer events. A more expanded view takes into account the
variable scale of events and considers the respective health, security, and environmental impacts of MGs. Event models
and classifications must have room for growth and adaptation as the science clarifies operational and academic validity of
categories, and so on. As the science underpinning MGH continues to develop, it will move beyond identifying important
parameters and existing conceptualizations toward the development of a robust event model.
Numerous definitions and classifications for MG events appear in the published literature. Ultimately, the purpose of event
models will be to describe events in as much detail as possible in order that researchers and others can make comparisons
between similar events and across similar event types. Also, researching the relationship between event type and risk will
require consistency of event classifications and categories.
Conclusions
Because there will always be an interaction between event type and the people who attend or participate in the
event, both event models and population models are required.
By moving towards a common understanding of population and event modeling as it pertains to MGs, the academic
community will be better able to develop methodologies to describe, evaluate, establish evidence-based minimum
standards, and study interventions aimed at medical services provision, health promotion, injury and illness
prevention, and surveillance.
Clarity in definitions and descriptions of host community, event, and patient populations in the context of MGs will permit
consistent description and reporting in the international literature. Importantly, it will also permit a better understanding
of injury and illness presentations and clarify the health service impacts on the various populations of interest. Operational
planning for emergency response, health promotion, injury and illness prevention, and surveillance will be measurable
against more precisely-defined populations.
Research in Context
Literature Review
The writing team consisted of an international collaborative group with substantial experience with clinical, research, and
policy development in the context of MGs. Review of the literature and addressing gaps in theory building identified in
published reviews of the literature prompted the consensus process.

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Interpretation
This manuscript puts forward theory-building concepts and models that may stimulate further discussion and
consensus building amongst MG researchers in the international community.
Acknowledgements
The authors would like to acknowledge: Dr. Greg Anderson, PhD, Dean, Office of Applied Research, Justice Institute of BC,
New Westminster, BC, Canada; Dr. Katherine Arbuthnott, WHO and Public Health England; and Dr. Mark Salter, Public
Health England.
Authors’ Contributions
Paul Arbon participated in the discussions at the Flinders University meeting and on team conference calls discussing
the model and the publication; he also commented on the various drafts of the document. Ron Bowles contributed
significantly to the theoretical modelling and, in conjunction with the principle author, expanded the paper into two
parts in order to clarify the population of events vs the populations of people affected by mass gatherings. Elizabeth
Ellison conducted the literature review for the two companion papers; she also commented on drafts of the
document. Alison Hutton contributed to the initial draft of this paper and wrote the section on crowd dynamics; she
was also part of the panel that gave feedback to the initial diagram concepts for this paper. Adam Lund cowrote the paper
with the principle author; he also created the initial draft and the diagram concepts and was involved in synthesizing and
editing all drafts of the manuscript. Consistent event description and modeling has been a focus of the University of
British Columbia Mass-gathering Medicine Interest Group since 2009. Jamie Ranse contributed to the development of the
event model at the Flinders University meeting and on team conference calls; he also contributed to the initial draft of
this paper. Malinda Steenkamp participated in discussions at the Flinders University meeting and on team conference
calls discussing the model and the publication; she also commented on the various drafts of the document. Sheila Turris is
the principle author on this paper; she coordinated the team process and brought the paper to completion. She is also a
member of the panel that created the original drafts of the figures and tables.

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