Analysis of Medical Responses in Mass Gatherings: The Commemoration Ceremonies for the 100 th Anniversary of the Battle of Gallipoli

Quantitative Metrics in Mass-Gathering Studies: A Comprehensive Systematic Review

INTRODUCTION

Mass gatherings are events where many people come together at a specific location for a specific purpose, such as concerts, sports events, or religious gatherings, within a certain period of time. In mass-gathering studies, many rates and ratios are used to assess the demand for medical resources. Understanding such metrics is crucial for effective planning and intervention efforts. Therefore, this systematic review aims to investigate the usage of rates and ratios reported in mass-gathering studies.

METHODS

In this systematic review, the PRISMA guidelines were followed. Articles published through December 2023 were searched on Web of Science, Scopus, Cochrane, and PubMed using the specified keywords. Subsequently, articles were screened based on titles, abstracts, and full texts to determine their eligibility for inclusion in the study. Finally, the articles that were related to the study's aim were evaluated.

RESULTS

Out of 745 articles screened, 55 were deemed relevant for inclusion in the study. These included 45 original research articles, three special reports, three case presentations, two brief reports, one short paper, and one field report. A total of 15 metrics were identified, which were subsequently classified into three categories: assessment of population density, assessment of in-event health services, and assessment of out-of-event health services.

CONCLUSION

The findings of this study revealed notable inconsistencies in the reporting of rates and ratios in mass-gathering studies. To address these inconsistencies and to standardize the information reported in mass-gathering studies, a Metrics and Essential Ratios for Gathering Events (MERGE) table was proposed. Future research should promote consistency in terminology and adopt standardized methods for presenting rates and ratios. This would not only enhance comparability but would also contribute to a more nuanced understanding of the dynamics associated with mass gatherings.

Measuring the Masses: The Current State of Mass-Gathering Medical Case Reporting (Paper 1)

INTRODUCTION

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.

OBJECTIVE

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

METHODS

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.

RESULTS

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.

DISCUSSION

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.

CONCLUSION

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.

Measuring the Masses: A Proposed Template for Post-Event Medical Reporting (Paper 4)

BACKGROUND

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.

METHODS

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.

FINDINGS

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.

DISCUSSION

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.

CONCLUSIONS

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.

Measuring the Masses: Domains Driving Data Collection and Analysis for the Health Outcomes of Mass Gatherings (Paper 3)

INTRODUCTION

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.

METHODS

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.

FINDINGS

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.

DISCUSSION

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.

CONCLUSION

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.

Measuring the Masses: Mass-Gathering Medical Case Reporting, Conceptual Modeling - The DREAM Model (Paper 5)

INTRODUCTION

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.

METHODS

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.

FINDINGS

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.

DISCUSSION

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.

CONCLUSION

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.