Department of Emergency Medicine, University of Virginia, Charlottesville, VA 22908, USA.
Am J Emerg Med. 2010 Feb;28(2):224-9. doi: 10.1016/j.ajem.2008.10.034.
Over the last 20 years, interest in medical need at mass events has increased. Many studies have been published identifying the characteristics of such events that significantly impact the number of patients who seek care. Investigators agree that weather is one of the most important variables. We performed a literature search using several biomedical databases (MEDLINE via PubMed, the Cochrane database, BMJ's Clinical Evidence compendium, and Google Scholar) for articles addressing the effect of weather on medical need at mass events. This search resulted in 8 focused articles and several other resources from the reference sections of these publications. We found that the early literature is composed of case reports and predominantly subjective observations concerning the impact of weather on medical need. Most investigators agree upon a positive relationship between heat/humidity and the frequency of patient presentation. More recent authors make attempts at quantifying the relationship and propose prediction models for patient volume and medical personnel requirements. We present an ancestral review of these studies, discuss their results collectively, and propose a simplified algorithm for predicting patient volume at mass events. This review is intended for event planners and mass event emergency medical personnel for planning future events. We also hope to stimulate further study to develop and verify prediction models.
在过去的 20 年中,人们对大型活动中的医疗需求越来越感兴趣。许多研究已经发表,确定了这些事件的特征,这些特征显著影响了寻求医疗服务的患者数量。研究人员一致认为,天气是最重要的变量之一。我们使用几个生物医学数据库(通过 PubMed 进行的 MEDLINE、Cochrane 数据库、BMJ 的临床证据摘要和 Google Scholar)进行了文献检索,以查找有关天气对大型活动中医疗需求影响的文章。该搜索产生了 8 篇重点文章和这些出版物的参考文献部分的其他几个资源。我们发现,早期文献主要由病例报告和对天气对医疗需求影响的主观观察组成。大多数研究人员同意高温/高湿度与患者就诊频率之间存在正相关关系。最近的作者试图量化这种关系,并提出了用于预测患者数量和医务人员需求的预测模型。我们对这些研究进行了回顾性分析,集体讨论了它们的结果,并提出了一种用于预测大型活动中患者数量的简化算法。本综述旨在为活动策划者和大型活动紧急医疗人员规划未来的活动提供参考。我们还希望进一步研究来开发和验证预测模型。