Bouchouar Etran, Hetman Benjamin M, Hanley Brendan
Department of Health and Social Services, Government of Yukon, Whitehorse, Canada.
College of Public Health, University of South Florida, Tampa, FL, USA.
BMC Public Health. 2021 Jun 29;21(1):1247. doi: 10.1186/s12889-021-11132-w.
Automated Emergency Department syndromic surveillance systems (ED-SyS) are useful tools in routine surveillance activities and during mass gathering events to rapidly detect public health threats. To improve the existing surveillance infrastructure in a lower-resourced rural/remote setting and enhance monitoring during an upcoming mass gathering event, an automated low-cost and low-resources ED-SyS was developed and validated in Yukon, Canada.
Syndromes of interest were identified in consultation with the local public health authorities. For each syndrome, case definitions were developed using published resources and expert elicitation. Natural language processing algorithms were then written using Stata LP 15.1 (Texas, USA) to detect syndromic cases from three different fields (e.g., triage notes; chief complaint; discharge diagnosis), comprising of free-text and standardized codes. Validation was conducted using data from 19,082 visits between October 1, 2018 to April 30, 2019. The National Ambulatory Care Reporting System (NACRS) records were used as a reference for the inclusion of International Classification of Disease, 10th edition (ICD-10) diagnosis codes. The automatic identification of cases was then manually validated by two raters and results were used to calculate positive predicted values for each syndrome and identify improvements to the detection algorithms.
A daily secure file transfer of Yukon's Meditech ED-Tracker system data and an aberration detection plan was set up. A total of six syndromes were originally identified for the syndromic surveillance system (e.g., Gastrointestinal, Influenza-like-Illness, Mumps, Neurological Infections, Rash, Respiratory), with an additional syndrome added to assist in detecting potential cases of COVID-19. The positive predictive value for the automated detection of each syndrome ranged from 48.8-89.5% to 62.5-94.1% after implementing improvements identified during validation. As expected, no records were flagged for COVID-19 from our validation dataset.
The development and validation of automated ED-SyS in lower-resourced settings can be achieved without sophisticated platforms, intensive resources, time or costs. Validation is an important step for measuring the accuracy of syndromic surveillance, and ensuring it performs adequately in a local context. The use of three different fields and integration of both free-text and structured fields improved case detection.
急诊综合征监测自动化系统（ED-SyS）是日常监测活动以及大型活动期间快速发现公共卫生威胁的有用工具。为了改善资源较少的农村/偏远地区现有的监测基础设施，并在即将到来的大型活动期间加强监测，在加拿大育空地区开发并验证了一种低成本、低资源的自动化ED-SyS。
与当地公共卫生当局协商确定感兴趣的综合征。针对每种综合征，利用已发表的资源和专家意见制定病例定义。然后使用Stata LP 15.1（美国得克萨斯州）编写自然语言处理算法，从三个不同领域（如分诊记录、主诉、出院诊断）检测综合征病例，这些领域包括自由文本和标准化代码。使用2018年10月1日至2019年4月30日期间19082次就诊的数据进行验证。国家门诊护理报告系统（NACRS）记录用作纳入国际疾病分类第10版（ICD-10）诊断代码的参考。然后由两名评估人员对病例的自动识别进行人工验证，并将结果用于计算每种综合征的阳性预测值，以及确定检测算法的改进之处。
建立了育空地区Meditech急诊追踪系统数据的每日安全文件传输和异常检测计划。最初为综合征监测系统确定了总共六种综合征（如胃肠道、流感样疾病、腮腺炎、神经感染、皮疹、呼吸道），并增加了一种综合征以协助检测COVID-19的潜在病例。在实施验证过程中确定的改进措施后，每种综合征自动检测的阳性预测值范围从48.8%-89.5%提高到62.5%-94.1%。不出所料，我们的验证数据集中没有COVID-19的记录被标记。
在资源较少的环境中开发和验证自动化ED-SyS无需复杂的平台、大量资源、时间或成本。验证是衡量综合征监测准确性并确保其在当地环境中充分发挥作用的重要步骤。使用三个不同领域以及整合自由文本和结构化字段可改善病例检测。
