Introduction
A mass gathering is defined as an event where a group of people come together for a common purpose within a particular space or venue. Examples of mass gathering include agricultural shows, music festivals, and sporting events. In a health context, a mass gathering is an event “where there is the potential for a delayed response to health emergencies because of limited access to patients or other features of the environment and location.” 1 A number of challenges in providing adequate health care exist at a mass gathering, primarily related to the environment and to patient access and egress. Reference Ranse and Zeitz2 For many years, it has been widely argued that three domains will influence the presentations of patients at mass-gathering events (MGEs): environmental, psychosocial, and biomedical. Reference Arbon3 This discussion has begun within the context of biomedical aspects of care. Reference Ranse and Hutton4 The present paper examines the environmental aspects of mass-gathering research and evaluation.
Background
The development of the proximity model for mass-gathering health in 2004 by Arbon proposed three domains that inter-related with each other to provide a picture of mass-gathering health. Reference Arbon5 Arbon stated the environmental domain incorporates the environmental features, including weather and terrain. This paper proposes that this definition needs to grow to include aspects such as the environmental surroundings, conditions, and influences that affect attendees at these events. For the purpose of this paper, the physical elements of the crowd such as density and type will be included, but will exclude crowd behavior such as motivations for attendance, mood, and activities.
As in Arbon’s model, Reference Arbon5 the “availability of drugs and alcohol” is still there – but is seen more of an influencer than a sub-domain. Environmental factors that influence patient presentations at mass gatherings, and are reported in the literature, include crowd attendance and density, venue characteristics (unbound versus bound; extended or focused; and local terrain), type of event, outdoor or indoor, weather (temperature and humidity), availability of alcohol or drugs, and whether attendees are predominantly seated or mobile. Reference Arbon5 There are many different ways of reporting environmental variables within the literature; these include wind speed, Reference Anikeeva, Arbon and Zeitz6 dew point, Reference Baird, O’Connor, Williamson, Sojka, Alibertis and Brady7–Reference Westrol, Koneru, McIntyre, Caruso, Arshad and Merlin10 time of day and day of week, Reference Baird, O’Connor, Williamson, Sojka, Alibertis and Brady7,Reference Zeitz, Zeitz and Arbon11 air quality, Reference Feldman, Gao, Zhu, Simatovic, Licskai and To12–Reference Steffen, Bouchama and Johansson14 precipitation, Reference Selig, Hastings, Cannon, Allin, Klaus and Diaz9,Reference Westrol, Koneru, McIntyre, Caruso, Arshad and Merlin10,Reference Steffen, Bouchama and Johansson14–Reference Moore, Williamson, Sochor and Brady17 Wet-Bulb Globe Temperature index (WBGT), Reference Kakamu, Wada, Smith, Endo and Fukushima18 historical data, Reference Baird, O’Connor, Williamson, Sojka, Alibertis and Brady7,Reference Locoh-Donou, Yan and Berry8,Reference Zeitz, Zeitz and Arbon11,Reference Feldman, Gao, Zhu, Simatovic, Licskai and To12,Reference Moore, Williamson, Sochor and Brady17,Reference Friedman, Plocki and Likourezos19–Reference Smith, Wessels, Naicker, Leuenberger, Fuhri and Wallis22 cold weather, Reference Baird, O’Connor, Williamson, Sojka, Alibertis and Brady7,Reference Steffen, Bouchama and Johansson14,Reference Burdick15,Reference Moore, Williamson, Sochor and Brady17,Reference Alquthami and Pines23–Reference Yazawa, Kamijo, Sakai, Ohashi and Owa26 allergic reactions, Reference Grant, Nacca, Prince and Scott27 availability of free water, Reference Locoh-Donou, Yan and Berry8,Reference Friedman, Plocki and Likourezos19,Reference Enock, Jacobs and Olympic24,Reference Johansson, Batty, Hayashi, Al Bar, Marcozzi and Memish28 altitude, Reference Burdick15 and the presence of ethanol. Reference Baird, O’Connor, Williamson, Sojka, Alibertis and Brady7,Reference Moore, Williamson, Sochor and Brady17,Reference Hartman, Williamson and Sojka20,Reference Nable, Margolis and Lawner21
Aim
This paper aims to: (1) identify consistent reporting data standard for the environmental domain, and (2) begin a discussion on developing a framework of variables to collect environmental data for research and evaluation for patient presentations at MGEs.
Methods
Design
This research used an integrated literature review design to identify environmental factors that are reported in the mass-gathering literature.
Search Strategy
The search strategy included different combinations of Medical Subject Headings (MeSH) terms and keywords that are relevant to environmental factors and MGEs. All terms and keywords are outlined in Table 1. Terms and keywords in the columns were combined using the OR search strategy, while terms and keywords in the rows were combined using AND combinations.
Table 1. MeSH Terms and Keywords
Abbreviation: MeSH, Medical Subject Heading.
Inclusion and Exclusion Criteria
All identified papers were assessed by three authors, KG, AH, and JR against the following inclusion and exclusion criteria. Inclusion criteria included:
Event-level data;
Written in English language;
Peer-reviewed journal; and
2003-current.
Exclusion criteria included:
Editorials;
Discussion and theoretical papers; and
Studies that focused on infectious diseases.
Search Results
The search returned 462 potentially relevant papers from MEDLINE (US National Library of Medicine, National Institutes of Health; Bethesda, Maryland USA; Figure 1 Reference Liberati, Altman and Tetzlaff29 ). Of the 462 articles retrieved, 330 papers were excluded based on title, 81 papers were excluded based on abstract review, with the remaining 51 papers undergoing a detailed full-text examination. After full examination, 12 papers were excluded because they did not meet the inclusion criteria.
Figure 1. PRISMA Flow Diagram. Reference Liberati, Altman and Tetzlaff29
Data Analysis
In order to determine environmental factors that influence patient presentation rates (PPRs) at mass gatherings, articles were grouped in a pragmatic manner.
Findings
Arbon’s Reference Arbon5 mass-gathering model was used as a starting point for this review. Table 2 Reference Arbon3,Reference Arbon5–Reference Johansson, Batty, Hayashi, Al Bar, Marcozzi and Memish28,Reference Lund, Turris, Amiri, Lewis and Carson30–Reference Krul, Sanou, Swart and Girbes43 (available online only) outlines environmental variables reflected in this model, as well as other variables identified in the literature.
Discussion
It is important to “initiate international discussion for consistency in the reporting of data from mass gatherings, whilst acknowledging the meaningful data collection and reporting across societies and mass gatherings needs to be flexible.” Reference Ranse and Hutton4 Accordingly, in the discussion below, the authors argue that there are several sub-domains within the domain of “environment” as originally conceptualized by Arbon, which include: event type, built environment, crowd, and meteorological.
Crowd Factors
Crowd variables collected in the reviewed papers included attendance, density, mobility, and demographics. Within these variables, there are conflicting findings and different methods used in regards to the correlation of crowd attendance to presentation rates. In order to capture the range of participants in attendance, a variety of strategies were used, including grouping expected crowd into categorizes by <10,000 or 10,000<20,000 or 20,000<30,000 or 30,000<40,000 or ≥40,000, Reference Anikeeva, Arbon and Zeitz6 and Smith Reference Smith, Wessels, Naicker, Leuenberger, Fuhri and Wallis22 extended the groupings of expected attendance numbers to cater for <50,000, <70,000, and <90,000 persons attending. Hartman Reference Hartman, Williamson and Sojka20 used a simplified scoring model suggested to capture attendance, categorizing into groups of >15,000 or 10,000-15,000 or <1,000. Reference Hartman, Williamson and Sojka20 While Lund Reference Lund, Turris, Amiri, Lewis and Carson30 used an online registry software package to recorded attendance, Zeitz Reference Zeitz, Zeitz and Arbon11 used a pre-existing database application. Nable’s Reference Nable, Margolis and Lawner21 prediction model estimated the crowd attendance through event managers, based on event ticket sales, and compared to crowd estimates from the Baltimore City Police Department (Baltimore, Maryland USA).
The measurement of density (number of people per m²) was used by Anikeeva, Reference Anikeeva, Arbon and Zeitz6 Khademipur, Reference Khademipour, Nakhaee, Anari, Sadeghi, Ebrahimnejad and Sheikhbardsiri31 and Johansson Reference Johansson, Batty, Hayashi, Al Bar, Marcozzi and Memish28 to monitor crowd safety. Anikeeva Reference Anikeeva, Arbon and Zeitz6 categorized crowd density into four levels: very low, low, medium, and high (Table 2). Whereas Khademipur Reference Khademipour, Nakhaee, Anari, Sadeghi, Ebrahimnejad and Sheikhbardsiri31 suggested an equation that uses height and weight to calculate the outer body surfaces of individuals. And Johansson Reference Johansson, Batty, Hayashi, Al Bar, Marcozzi and Memish28 recommended using global positioning system technology to produce fine spatial scales. There was little discussion around collection of demographics.
Built Environment Factors
Whether attendees are predominantly seated or mobile has a huge influence on the management and control of crowds at mass gatherings. When attendees are seated, the risk of injury is reduced in comparison to when attendees are mobile. Reference Friedman, Plocki and Likourezos19 Audience attendance can be collected through ticket sales, with the amount of “seated” at the event determined by the percentage of filled seating. Reference Locoh-Donou, Yan and Berry8 Further variables collected were bounded/unbounded, shade, and indoor/outdoor. As shown in DeMott’s Reference DeMott, Hebert, Novak, Mahmood and Peksa32 review, outdoor events have a higher PPR than indoor events.
Event Type
Next to weather, event type is the most important predictor of medical usage rate. Reference Moore, Williamson, Sochor and Brady17 Table 2 (available online only) outlines how research in the past has identified categories for their studies and reviews. The type of event is a multi-layered variable that plays a major role in determining PPRs at mass gatherings. Concerts, compared to sporting games, festivals, and entertainment events, generated the highest PPRs. Reference Goldberg, Maggin and Molloy16 The availability of alcohol and other drugs is correlated to this finding, considering that almost twice as many patients presented for medical attention for intoxication at concerts compared with the other events. Reference Goldberg, Maggin and Molloy16
Meteorological Factors
Weather was measured as an over-arching category in 33 of the reviewed papers included, but it was also measured in a variety of ways, such as humidity, precipitation, dew point, air quality, WBGT, altitude, cold weather, and outdoor.
Even though weather was the most commonly collected variable, it was often collected as a combination of data points, for example temperature and humidity. Weather was also used in a causal relationship for patient presentations. For example, Perron Reference Perron, Brady, Custalow and Johnson33 determined that with every 10°C increase in the heat index, patients presenting for care increased by three (per 10,000 patrons). Although weather, particularly heat index, is one of the most researched factors for influencing patient presentation trends, it should be considered in context with other variables.
Although temperature, humidity, and heat index were variables collected in 29 of the reviewed papers, only 14 documented the method of data collection. Of those 14, ten used either the National Weather Service (Silver Spring, Maryland USA), Reference Baird, O’Connor, Williamson, Sojka, Alibertis and Brady7,Reference Nable, Margolis and Lawner21 National Oceanic and Atmospheric Administration (Silver Spring, Maryland USA), Reference Westrol, Koneru, McIntyre, Caruso, Arshad and Merlin10,Reference Tang, Kraus and Brill25 Ministry of the Environment and Climate Change (Toronto, Ontario, Canada), Reference Feldman, Gao, Zhu, Simatovic, Licskai and To12 National Environmental Agency of Singapore, Reference Perron, Brady, Custalow and Johnson33 Australia’s Bureau of Meteorology (Melbourne, Australia), and the World Weather Online website, Reference Kakamu, Wada, Smith, Endo and Fukushima18 or the university’s weather station. Reference Locoh-Donou, Yan and Berry8,Reference Perron, Brady, Custalow and Johnson33
Selig Reference Selig, Hastings, Cannon, Allin, Klaus and Diaz9 collected daily high temperature, temperature, mean humidity, barometric pressure, and dew point from the website Weather Underground (San Francisco, California USA). Smith Reference Smith, Wessels, Naicker, Leuenberger, Fuhri and Wallis22 categorized weather into season and used a classification system for a risk score based on geographical setting. Zeitz Reference Zeitz, Zeitz and Arbon11 collected temperature values from the maximal daily forecast from the previous 24 hours, rather than the actual temperature on the day of the event, and mean values for the previous seven years’ daily humidity. One study had electronic weather stations positioned at opposite outer boundaries of each event. In addition to collecting temperature and humidity, the weather stations also collected wind speed and brightness. This occurred automatically at 30-second intervals, allowing changes in the weather to be monitored over time. Reference Anikeeva, Arbon and Zeitz6 Besides collecting temperature and humidity to determine heat index, Westrol’s Reference Westrol, Koneru, McIntyre, Caruso, Arshad and Merlin10 study also obtained precipitation data from the National Oceanic and Atmospheric Administration Climate Data Online, as well as event logs. Westrol, Reference Westrol, Koneru, McIntyre, Caruso, Arshad and Merlin10 Yazawa, Reference Yazawa, Kamijo, Sakai, Ohashi and Owa26 and Lund Reference Lund, Turris, Amiri, Lewis and Carson30 collected descriptive sky conditions data, such as “rainy, sunny, snowy, or cloudy,” recorded by medical personnel or researchers at the event.
Additional variables such as presence of ethanol, time of day/week, availability of free water, and historical data are not as frequent, but for specific mass gatherings, are an important aspect of data collection.
Study Limitations
Despite the use of a rigorously designed search strategy, there is potential that the search outcomes and the subsequent findings of this literature review are at-risk of selection bias. The search strategy was restricted to studies published in English, and as such, may have not identified relevant studies written in other languages. Overall, the quality of the current evidence is low. Twenty-two of the studies reviewed were retrospective reviews or reports. Seven of the studies reviewed were single case studies, two longitudinal, and one prospective and cross-sectional. Five other papers consisted of two literature reviews, an expert opinion, a systematic review, and a commentary. A further limitation of this review is that only articles from the past 15 years were included.
Future Opportunities
As outlined by Arbon, Reference Arbon5 there are three categories which influence the preparedness of health services at mass gatherings: biomedical, environmental, and psychosocial. A biomedical minimum data set (MDS) and reporting methods have been proposed by Ranse, et al. Reference Ranse and Hutton4 This paper has provided a framework for collecting data in regards to environmental variables that influence PPRs at MGEs. The authors have found that in the last ten years from Arbon’s Reference Arbon5 model, that many other environmental variables have been considered by other authors and should be considered when researching and evaluating mass gatherings.
Conclusion
This paper has presented a range of variables that are currently collected and reported for mass-gathering research and evaluation. From the literature review analysis, this paper proposes three sub-domains; these being weather, the crowd, and the built environment. It is not expected that all variables documented in this manuscript could be collected at all mass gatherings; however, it is expected that each researcher will determine data collected upon their own needs, resources, and feasibility to collect data variables. As mass-gathering science develops, a set of minimum environmental variables is needed to collect data for the purpose of making comparisons across societies for MGEs.
Conflicts of interest
none
Supplementary Material
To view supplementary material for this article, please visit https://doi.org/10.1017/S1049023X19004813
