Mass gatherings (MGs) often bring together professionals and organizations that collaborate irregularly or have never engaged in joint working. They involve interaction and communication among multiple and diverse services, which can often prove challenging. Planning such an event is of paramount importance for its success, and interorganizational communication ranks among its most important aspects. Nonetheless, there is limited empirical evidence to support interagency communication in MGs.

This study used the 2017 Athens Marathon (Athens, Greece) as the empirical setting to examine how interorganizational communication was perceived among the multiple public health and safety professionals during the planning and implementation phase of the event.

Data comprised 15 semi-structured in-depth interviews with key informants, direct observations of meetings and the event itself, and documentary analysis. Open coding and thematic analysis were used to analyze the data.

Findings indicated three key components of interorganizational communication in such an event: (1) shared situational awareness; (2) interorganizational understanding; and (3) implementing liaison officers.

This study outlined the factors that influenced interorganizational communication before and during a MG. Practical implications arising from this study may inform the way organizers of marathons and other mass sporting events can engage in effective interorganizational communication.

Increasing numbers of marathon running events are taking place around the world. The difficulty encountered in the management of mass gatherings, especially running in marathons, is how emergency services can deliver treatment in a timely manner. Therefore, for this kind of situation, preparation is the key to success in terms of patient management.

The aim of this study was to describe the presentation of cases at a start-finish medical post in an international marathon race set in a rural area.

All medical record forms were collected from the start-finish medical post of the Khon Kaen International Marathon (KKIM) 2020. The race took place on January 26, 2020. The data were coded by two authors, and in the case of different codes, the final codes were determined by discussion.

The total number of participants in this event was 16,489. Participants who used the start-finish medical post numbered 74 (44.8 people per 10,000). More than one-half of patients were male (41; 56.9%), while 31 (34.0%) were female. The age range of the casualties was from 17 to 88 years old. The rate of incidence for those who used this post was 44.8 per 10,000 participants. The greatest density of users was at 3.40 hours after the marathon had started. The common symptoms which were found consisted of 17 soft tissue injuries (23.0%), 15 instances of cramps (20.3%), and 11 musculoskeletal (MSK) injuries (14.9%). Almost all patients were discharged, and only two of the cases were actually admitted to the hospital. No statistical significance between males and females was found (OR = 0.81; 95% CI, 0.51-1.3). However, marathon and half-marathon runners had a higher risk of being casualties (OR = 3.49; 95% CI, 1.71-7.15 and OR = 3.51; 95% CI, 1.79-6.88).

The injuries of most of the patients who used the medical post at a start-finish point were mild. Distances which are longer than 20km increase the risk for getting injured. However, a prospective study and multi-session interpretation is recommended.

To validate the Belgian Plan Risk Manifestations (PRIMA) model, actual patient presentation rates (PPRs) from Belgium’s largest football stadium were compared with predictions provided by existing models and the Belgian PRIMA model.

Actual patient presentations gathered from 41 football games (2010-2019) played at the King Baudouin Stadium (Brussels, Belgium) were compared with predictions by existing models and the PRIMA model. All attendees who sought medical help from in-event health services (IEHS) in the stadium or called 1-1-2 within the closed perimeter around the stadium were included. Data were analyzed by ANOVA, Pearson correlation tests, and Wilcoxon singed-rank test.

A total of 1,630,549 people attended the matches, with 626 people needing first aid. Both the PRIMA and the Hartman model over-estimated the number of patient encounters for each occasion. The Arbon model under-estimated patient encounters for 9.75% (95% CI, 0.49-19.01) of the events. When comparing deviations in predictions between the PRIMA model to the other models, there was a significant difference in the mean deviation (Arbon: Z = −5.566, P <.001, r = −.61; Hartman: Z = −4.245, P <.001, r = .47).

When comparing the predicted patient encounters, only the Arbon model under-predicted patient presentations, but the Hartman and the PRIMA models consistently over-predicted. Because of continuous over-prediction, the PRIMA model showed significant differences in mean deviation of predicted PPR. The results of this study suggest that the PRIMA model can be used during planning for domestic and international football matches played at the King Baudouin Stadium, but more data and further research are needed.

This study aims at exploring the dynamics of health-care provision during recent unplanned public mass gatherings in Beirut, and how the health-care system adapts to mass movements in protests.

A qualitative study was conducted using semi-structured interviews with 12 health-care providers who volunteered at medical tents set during protests in Beirut, Lebanon. Responses were transcribed and coded.

Three themes were noted: preparedness and logistics, encountered cases, and participants’ proposed recommendations. In terms of preparedness and logistics, participants lacked knowledge of field medicine protocols and an organizational structure. They faced difficulties in securing equipment and advertising their services. Most encountered cases were physical injuries rather than mental health problems. The participants proposed both short-term recommendations, including advice on how to boost care provided, and long-term recommendations on structuring the health-care system to be better prepared.

On-site health-care provision during unplanned mass gatherings is a vital need. We recommend forming a task force of health-care workers from various fields led by the Ministry of Public Health in every respective country to plan protocols, train personnel, and secure resources beforehand.

Mass-gathering events (MGEs) occur regularly throughout the world. As people congregate at MGEs, there is an increased risk of transmission of communicable diseases. Novel respiratory viruses, such as Severe Acute Respiratory Syndrome Coronavirus-1 (SARS-CoV-1), Influenza A Virus Subtype H1N1 Strain 2009 (H1N1pdm09), Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or Coronavirus Disease 2019 (COVID-19), may require specific infection prevention and control strategies to minimize the risk of transmission when planning MGEs. This literature review aimed to identify and analyze papers relating to novel respiratory viruses with pandemic potential and to inform MGE planning.

This paper used a systematic literature review method. Various health care databases were searched using keywords relating to MGEs and novel respiratory viruses. Information was extracted from identified papers into various tables for analysis. The analysis identified infection prevention and control strategies used at MGEs to inform planning before, during, and following events.

In total, 27 papers met the criteria for inclusion. No papers were identified regarding SARS-CoV-1, while the remainder reported on H1N1pdm09 (n = 9), MERS-CoV (n = 15), and SARS-CoV-2 (n = 3). Various before, during, and after event mitigation strategies were identified that can be implemented for future events.

This literature review provided an overview of the novel respiratory virus epidemiology at MGEs alongside related public health mitigation strategies that have been implemented at these events. This paper also discusses the health security of event participants and host communities in the context of cancelling, postponing, and modifying events due to a novel respiratory virus. In particular, ways to recommence events incorporating various mitigation strategies are outlined.

Respiratory transmission, especially in mass gatherings, is considered one of the main ways of influenza transmission. The Hajj ceremony, as one of the largest gatherings worldwide, can increase the distribution of influenza infection. Thus, the present study aimed to evaluate the incidence of influenza among Hajj pilgrims.

In this present systematic review and meta-analysis, all English studies published by 2019 were extracted from several databases such as the Web of Science, PubMed, Scopus, Cochrane Library, Science Direct, and Google Scholar. Finally, the data were extracted using a pre-prepared checklist and then analyzed by fixed and random effects model tests in the meta-analysis, Cochran, meta-regression, and Begg’s test.

Eighteen studies with a sample size of 62 431 were entered into the meta-analysis process. The overall prevalence of influenza, in addition to the prevalence of types A, B, and C influenza, was estimated at 5.9 (95% CI: 4.3-8.0), 3.6 (95% CI: 2.6-4.9), 2.9 (95% CI: 2.8-3.1), and 0.9% (95% CI: 0.5-1.5), respectively.

In general, influenza remains widespread regardless of vaccinating pilgrims and following health protocols. Therefore, it is recommended that comprehensive management and educational approaches be used to reduce the prevalence of influenza and its adverse consequences among the pilgrims.

Without a robust evidence base to support recommendations for medical services at mass gatherings (MGs), levels of care will continue to vary and preventable morbidity and mortality will exist. Accordingly, researchers and clinicians publish case reports and case series to capture and explain some of the health interventions, health outcomes, and host community impacts of MGs. Streamlining and standardizing post-event reporting for MG medical services and associated health outcomes could improve inter-event comparability, thereby supporting and promoting growth of the evidence base for this discipline. The present paper is focused on theory building, proposing a set of domains for data that may support increasingly comprehensive, yet lean, reporting on the health outcomes of MGs. This paper is paired with another presenting a proposal for a post-event reporting template.

The conceptual categories of data presented are based on a textual analysis of 54 published post-event medical case reports and a comparison of the features of published data models for MG health outcomes.

A comparison of existing data models illustrates that none of the models are explicitly informed by a conceptual lens. Based on an analysis of the literature reviewed, four data domains emerged. These included: (i) the Event Domain, (ii) the Hazard and Risk Domain, (iii) the Capacity Domain, and (iv) the Clinical Domain. These domains mapped to 16 sub-domains.

Data modelling for the health outcomes related to MGs is currently in its infancy. The proposed illustration is a set of operationally relevant data domains that apply equally to small, medium, and large-sized events. Further development of these domains could move the MG community forward and shift post-event health outcomes reporting in the direction of increasing consistency and comprehensiveness.

Currently, data collection and analysis related to understanding health outcomes arising from MGs is not informed by robust conceptual models. This paper is part of a series of nested papers focused on the future state of post-event medical reporting.

Without a robust evidence base to support recommendations for first aid, health, and medical services at mass gatherings (MGs), levels of care will continue to vary. Streamlining and standardizing post-event reporting for MG medical services could improve inter-event comparability, and prospectively influence event safety and planning through the application of a research template, thereby supporting and promoting growth of the evidence base and the operational safety of this discipline. Understanding the relationships between categories of variables is key. The present paper is focused on theory building, providing an evolving conceptual model, laying the groundwork for exploring the relationships between categories of variables pertaining the health outcomes of MGs.

A content analysis of 54 published post-event medical case reports, including a comparison of the features of published data models for MG health outcomes.

A layered model of essential conceptual components for post-event medical reporting is presented as the Data Reporting, Evaluation, & Analysis for Mass-Gathering Medicine (DREAM) model. This model is relational and embeds data domains, organized operationally, into “inputs,” “modifiers,” “actuals,” and “outputs” and organized temporally into pre-, during, post-event, and reporting phases.

Situating the DREAM model in relation to existing models for data collection vis a vis health outcomes, the authors provide a detailed discussion on similarities and points of difference.

Currently, data collection and analysis related to understanding health outcomes arising from MGs is not informed by robust conceptual models. This paper is part of a series of nested papers focused on the future state of post-event medical reporting.

Case reports are commonly used to report the health outcomes of mass gatherings (MGs), and many published reports of MGs demonstrate substantial heterogeneity of included descriptors. As such, it is challenging to perform rigorous comparisons of health services and outcomes between similar and dissimilar events. The degree of variation in published reports has not yet been investigated.

Examine patterns of post-event medical reporting in the existing literature and identify inconsistencies in reporting.

A systematic review of case reports was conducted. Included were English studies, published between January 2009 and December 2018, in Prehospital and Disaster Medicine (PDM) or Current Sports Medicine Reports (CSMR). Analysis of each paper was used to develop a list of 27 categories of data.

Seventy-five studies were initially reviewed with 54 publications meeting the inclusion criteria. Forty-two were full case reports (78%) and 12 were conference proceedings (22%). Of the 27 categories of data studied, only 13 were consistently reported in more than 50% of publications. Reporting patterns included inconsistent use of terminology/language and variable retrievability of reports. Reporting on event descriptors, hazard and risk analysis, and clinical outcomes were also inconsistent.

Case reports are essential tools for researchers and event team members such as medical directors and event producers. The authors found that current case reports, in addition to being inconsistent in content, were generally descriptive rather than explanatory; that is, focused on describing the outcomes as opposed to exploring possible connections between context and health outcomes.

This paper quantifies and demonstrates the current state of heterogeneity in MG event reporting. This heterogeneity is a significant impediment to the functional use of published reports to further the science of MG planning and to improve health outcomes. Future work based on the insights gained from this analysis will aim to align and standardize reporting to improve the quality and value of event reporting.

Standardizing and systematizing the reporting of health outcomes from mass gatherings (MGs) will improve the quality of data being reported. Setting minimum standards for case reporting is an important strategy for improving data quality. This paper is one of a series of papers focused on understanding the current state, and shaping the future state, of post-event case reporting.

Multiple data sources were used in creating a lean, yet comprehensive list of essential reporting fields, including a: (1) literature synthesis drawn from analysis of 54 post-event case reports; (2) comparison of existing data models for MGs; (3) qualitative analysis of gaps in current case reports; and (4) set of data domains developed based on the preceding sources.

Existing literature fails to consistently report variables that may be essential for not only describing the health outcomes of a given event, but also for explaining those outcomes. In the context of current and future state reporting, 25 essential variables were identified. The essential variables were organized according to four domains, including: (i) Event Domain; (ii) Hazard and Risk Domain; (iii) Capacity Domain; and (iv) Clinical Domain.

The authors propose a first-generation template for post-event medical reporting. This template standardizes the reporting of 25 essential variables. An accompanying data dictionary provides background and standardization for each of the essential variables. Of note, this template is lean and will develop over time, with input from the international MG community. In the future, additional groups of variables may be helpful as “overlays,” depending on the event category and type.

This paper presents a template for post-event medical reporting. It is hoped that consistent reporting of essential variables will improve both data collection and the ability to make comparisons between events so that the science underpinning MG health can continue to advance.

Shopping centers (SCs) are social areas with a group of commercial establishments which attract customers of numerous people every day. However, analysis of urgent health conditions and provided health care in SCs has not been performed so far.

The aim of the study was to perform a comparative analysis of clients visiting SCs and demographics, complaints, and health care of patients admitted to Emergency Medical Intervention Units (EMIU) located in grand SCs in Ankara, Turkey.

Customer and health care records of nine grand SCs in Ankara from January 1, 2018 through December 31, 2018 were evaluated retrospectively. Health care services in EMIUs of SCs were provided by employed medical staff. Data including demographic characteristics, complaints, treatment protocols, discharge, and referral to hospital of the patients were retrospectively analyzed from medical registration forms.

Medical records of nine grand SCs were analyzed. Number of customers could not be obtained in three SCs due to privacy issues and were not included in patient presentation rate (PPR) and transport-to-hospital rate (TTHR) calculation. Total number of customers in the remaining six SCs were 53,277,239. The total number of patients seeking medical care was 6,749. The number of patients seeking health care in six SCs with known number of customers was 4,498 and PPR ranged from 0.018 to 0.381 patients per 1,000 attendants. The median age of the recorded 4,065 patients (60.2%) was 28 (interquartile range [IQR]: 38-21), and 3,611 (53.5%) of the patients admitted to EMIUs were female. The number of patients treated in the SC was 4,634 (68.6%) and 189 patients (2.8%) were transferred-to-hospital by ambulance for further evaluation and treatment. Transportation to hospital was required in 125 patients who sought medical care in six SCs which provided total number of customers, and TTHR ranged from 0.000 to 0.005 patients per 1,000 attendants. No sudden cardiac death was seen. Medical conditions were the primary reasons for seeking health care. The most frequent causes of presentation were laceration and abrasions (639 patients, 9.4%).

The PPR and TTHR in SCs are low. The most common causes of presentation are minor conditions and injuries. Majority of urgent medical conditions in SCs can be managed by health care providers in EMIUs.

A Belgian predictive medical resource tool, Plan Risk Manifestations (PRIMA), for the prediction of the number of patient encounters at mass gatherings (MGs) has recently been developed, in addition to the existing models of Arbon and Hartman. This study presents the results of the validation process for the PRIMA model for music MGs.

A retrospective study was conducted using data gathered from music MGs in the province of Antwerp (Belgium) during the period of 2012-2016. Data from 87 music MGs were used for the study. The forecast of medical resources for these events was determined by entering the characteristics of individual events into the Arbon, Hartman, and PRIMA models. In order to determine if the PRIMA model is under- or over-predictive, the data gathered were retrospectively compared to the predicted number of resources needed using the aforementioned models. Statistical analysis included means, medians, and interquartile ranges (IQRs). Nonparametric related samples test (Wilcoxon Samples Signed Rank Test) for comparison of the median in deviations in predictions of patient presentation rates (PPRs) was performed using SPSS version 23 (IBM Corp.; Armonk, New York USA). Confidence interval levels were set at 95% and results were deemed statistically significant at P <.05. This triple comparison was used to determine the overall performance of all three models.

All three models had an acceptable rate of over-prediction of number of patient encounters ([Arbon 25.29%; 95% CI, 30.91-43.74]; [Hartman 29.89%; 95% CI, 57.10-68.90]; and [PRIMA 19.54%; 95% CI, 57.80-76.20]). But all models also had a high rate of under-prediction of number of patient encounters ([Arbon 74.71%; 95% CI, 453.31-752.52]; [Hartman 70.11%; 95% CI, 546.90-873.77]; and [PRIMA 78.16%; 95% CI, 288.91-464.89]). Only the PRIMA model succeeded in the correct prediction of the number of patient encounters on two occasions (2.3%).

Results of this study are in-line with existing literature. When comparing the predicted patient encounters, all three models had high rates of under-prediction and moderate rates of over-prediction. When comparing mean deviations, the PRIMA model had the lowest mean deviation of all predicted PPRs. Belgian events of the types included in the presented data may use the PRIMA model with confidence to predict PPRs and estimate the in-event health services (IEHS) requirements.

Mass gatherings (MGs) grow in frequency around the world. With the intrinsic potential for significant health risks for all involved, MGs pose a challenge for those responsible for the provision of on-site medical care. Belgian law obliges local governments to identify and analyze the risks involving a MG. Though medical risk factors are long known, all too often, resourcing for in-event health services is based on anecdotal and previous experiences.

Despite the fast-evolving science on MGs, the lack of reliable tools – based on empirical and analytical approaches – to predict patient presentation rates (PPRs) at MGs remains.

A two-step method was followed to develop, update, and support a Plan Risk Manifestation (PRIMA) program. First, a continuous systematic literature review was conducted. Once developed, the model was run using data obtained from Belgian Federal Public Service (FPS; Brussels, Belgium) Health, Food Chain Safety, and Environment (HFCSE); event organizers; and municipalities.

In total, 231 studies and documents were included to form the program. With the data provided, three variables were computed to run the calculation model to predict the PPR. Three medical risk axes were defined for this model: (1) isolation risk; (2) population risk; and (3) risk at illness. A combined dataset was derived from the prediction of the PRIMA program combined with the actual data obtained after the MG. This proved a solid basis for the calculation model of the PRIMA program.

Despite that validation is needed, the PRIMA program and its prediction model for PPRs at MGs carries the promise of a general, applicable prediction and risk analysis tool for a multitude of events.