公共卫生部门的工作人员很少有培训材料来学习如何设计和微调系统以快速检测急性,局部化,社区获得性传染病爆发。自2014年以来,纽约市卫生和精神卫生部传染病局每天使用SaTScan分析可报告的传染病。SaTScan是一款使用扫描统计数据分析的免费软件,它可以检测到疾病活动的增加,而无需先验地说明时间周期,地理位置,或大小。传染病局的系统已经迅速发现了沙门氏菌病的爆发,军团病,志贺氏菌病,和COVID-19。本教程详细介绍了系统设计注意事项,包括地理和时间数据聚合,学习周期长度,纳入标准,是否考虑人口规模,网络位置文件设置以考虑自然边界,概率模型(例如,时空置换),星期几效应,最小和最大空间和时间集群大小,辅助群集报告标准,信令标准,并通过其他事件区分新集群与正在进行的集群。我们说明了如何通过最小化对可报告疾病患者的分析排除来支持健康公平(例如,经历无家可归的人,他们没有庇护),并考虑纯粹的空间模式,例如,对获得护理和可报告疾病检测机会较低的地区进行非参数调整。我们描述了如何微调系统时,检测到的集群太大,没有兴趣或当集群的信号被延迟,missed,太多了,或false。我们展示了通过用户界面上的内置功能自动分析和解释结果的低代码技术(例如,患者行列表,时间图,和动态地图),它在2022年7月发布的SaTScan10.1版中新推出。本教程是卫生部门工作人员使用SaTScan设计和维护可报告的传染病爆发检测系统的第一个综合资源,以促进实地调查,并开发直觉来解释结果和微调系统。虽然我们的实践经验仅限于监测某些可报告的疾病,市区,我们认为,大多数建议可推广到美国和国际上的其他司法管辖区。用于检测爆发的其他分析技术支持将使国家受益,部落,当地,以及地区公共卫生部门和他们所服务的人群。
Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using
SaTScan.
SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease\'s systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of
SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using
SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve.