Turris Sheila A, Lund Adam, Hutton Alison, Bowles Ron, Ellerson Elizabeth, Steenkamp Malinda, Ranse Jamie, Arbon Paul
Prehosp Disaster Med. 2014 Dec;29(6):655-63. doi: 10.1017/S1049023X14001228. Epub 2014 Nov 17.
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.
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.
由于相对缺乏理论发展和充分的概念分析，目前关于群体聚集健康（MGH）的知识未能充分增进对群体聚集（MGs）的理解。本报告描述了一系列事件视角的发展情况，这些视角构成了一个初步的“MG事件模型”，对其他地方报道的“MG人群模型”起到补充作用。
考虑了现有的“MGs”描述。通过分析当前知识中的差距，作者试图界定所报道事件的范围。作者采用共识方法，努力捕捉MG事件的多样性、范围和规模，识别可能有助于研究人员确定事件何时相似以及可进行比较的共同变量。通过面对面小组会议、结构化分组会议、异步协作和虚拟国际会议，一种对事件进行分类和描述的概念方法以迭代方式得以发展。
现有文献中存在多种事件分类和描述方法。基于这些方法，作者讨论了事件人口统计学、事件动态和事件设计之间的相互作用。具体而言，报告详细阐述了当前对事件类型、地理位置、规模、时间性、人群动态、医疗支持、保护因素和特殊危害的理解。还呈现了一系列表格，以构建可用于理解MG事件背景的不同分析视角。
事件模型的发展弥补了当前知识体系在理解和报告与MGs相关的健康影响全貌方面的空白。持续使用基于共识的事件模型将支持更严格的数据收集。这反过来将支持荟萃分析，为风险评估奠定基础，允许汇集疾病和损伤预测数据，并支持评估MGs健康促进、危害减少和临床应对干预措施的方法。
