Suppr超能文献

新加坡数字接触者追踪工具在 COVID-19 应对中的表现:横断面研究。

Performance of Digital Contact Tracing Tools for COVID-19 Response in Singapore: Cross-Sectional Study.

机构信息

Department of Clinical Epidemiology, Office of Clinical Epidemiology, Analytics, and Knowledge, Tan Tock Seng Hospital, Singapore, Singapore.

Department of Urology, Tan Tock Seng Hospital, Singapore, Singapore.

出版信息

JMIR Mhealth Uhealth. 2020 Oct 29;8(10):e23148. doi: 10.2196/23148.

Abstract

BACKGROUND

Effective contact tracing is labor intensive and time sensitive during the COVID-19 pandemic, but also essential in the absence of effective treatment and vaccines. Singapore launched the first Bluetooth-based contact tracing app-TraceTogether-in March 2020 to augment Singapore's contact tracing capabilities.

OBJECTIVE

This study aims to compare the performance of the contact tracing app-TraceTogether-with that of a wearable tag-based real-time locating system (RTLS) and to validate them against the electronic medical records at the National Centre for Infectious Diseases (NCID), the national referral center for COVID-19 screening.

METHODS

All patients and physicians in the NCID screening center were issued RTLS tags (CADI Scientific) for contact tracing. In total, 18 physicians were deployed to the NCID screening center from May 10 to May 20, 2020. The physicians activated the TraceTogether app (version 1.6; GovTech) on their smartphones during shifts and urged their patients to use the app. We compared patient contacts identified by TraceTogether and those identified by RTLS tags within the NCID vicinity during physicians' 10-day posting. We also validated both digital contact tracing tools by verifying the physician-patient contacts with the electronic medical records of 156 patients who attended the NCID screening center over a 24-hour time frame within the study period.

RESULTS

RTLS tags had a high sensitivity of 95.3% for detecting patient contacts identified either by the system or TraceTogether while TraceTogether had an overall sensitivity of 6.5% and performed significantly better on Android phones than iPhones (Android: 9.7%, iPhone: 2.7%; P<.001). When validated against the electronic medical records, RTLS tags had a sensitivity of 96.9% and specificity of 83.1%, while TraceTogether only detected 2 patient contacts with physicians who did not attend to them.

CONCLUSIONS

TraceTogether had a much lower sensitivity than RTLS tags for identifying patient contacts in a clinical setting. Although the tag-based RTLS performed well for contact tracing in a clinical setting, its implementation in the community would be more challenging than TraceTogether. Given the uncertainty of the adoption and capabilities of contact tracing apps, policy makers should be cautioned against overreliance on such apps for contact tracing. Nonetheless, leveraging technology to augment conventional manual contact tracing is a necessary move for returning some normalcy to life during the long haul of the COVID-19 pandemic.

摘要

背景

在 COVID-19 大流行期间,有效的接触者追踪工作既耗费人力又时间紧迫,但在缺乏有效治疗和疫苗的情况下,接触者追踪也是必不可少的。新加坡于 2020 年 3 月推出了首个基于蓝牙的接触者追踪应用程序 TraceTogether,以增强新加坡的接触者追踪能力。

目的

本研究旨在比较接触者追踪应用程序 TraceTogether 与基于可穿戴标签的实时定位系统 (RTLS) 的性能,并将其与国家传染病中心 (NCID) 的电子病历进行验证,NCID 是 COVID-19 筛查的国家转诊中心。

方法

NCID 筛查中心的所有患者和医生都被发放了 RTLS 标签(CADI Scientific)用于接触者追踪。共有 18 名医生于 2020 年 5 月 10 日至 5 月 20 日被部署到 NCID 筛查中心。医生在轮班期间在智能手机上激活 TraceTogether 应用程序(版本 1.6;GovTech),并敦促他们的患者使用该应用程序。我们比较了在医生驻点的 10 天期间,在 NCID 附近,通过 TraceTogether 和 RTLS 标签识别的患者接触者。我们还通过在研究期间内的 24 小时时间范围内验证了 156 名在 NCID 筛查中心就诊的患者的医生-患者接触情况,对这两种数字接触追踪工具进行了验证。

结果

RTLS 标签对通过系统或 TraceTogether 识别的患者接触者具有 95.3%的高灵敏度,而 TraceTogether 的总体灵敏度为 6.5%,在 Android 手机上的性能明显优于 iPhone(Android:9.7%,iPhone:2.7%;P<.001)。与电子病历进行验证时,RTLS 标签的灵敏度为 96.9%,特异性为 83.1%,而 TraceTogether 仅检测到 2 名与未就诊的医生有接触的患者。

结论

TraceTogether 识别临床环境中的患者接触者的灵敏度远低于 RTLS 标签。虽然基于标签的 RTLS 在临床环境中进行接触者追踪的效果良好,但在社区中实施比 TraceTogether 更具挑战性。鉴于接触者追踪应用程序的采用和功能存在不确定性,政策制定者应避免过度依赖此类应用程序进行接触者追踪。尽管如此,利用技术增强传统的手动接触者追踪是在 COVID-19 大流行的漫长过程中恢复生活常态的必要举措。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60cc/7599064/e743ee56ac78/mhealth_v8i10e23148_fig1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验