Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study.
Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study.
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DOI：
10.1136/bmjopen-2021-051278
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发表时间：
2021-07-02
期刊：
影响因子：
2.9
通讯作者：
Chaccour C
中科院分区：
文献类型：
--
作者：
Gabaldon-Figueira JC;Brew J;Doré DH;Umashankar N;Chaccour J;Orrillo V;Tsang LY;Blavia I;Fernández-Montero A;Bartolomé J;Grandjean Lapierre S;Chaccour C
咳嗽是COVID-19和其他呼吸道疾病的常见症状。然而，由于缺乏可靠和可扩展的监测系统，客观地测量其频率和演变受到阻碍。这可以通过新开发的利用智能手机便携性的人工智能模型来克服。在持续的COVID-19大流行的背景下，用于呼吸道疾病综合征监测的咳嗽检测是早期爆发检测和疾病监测的简单方法。在本方案中，我们评估了基于人群的数字咳嗽监测预测西班牙纳瓦拉人群呼吸系统疾病发病率的能力，同时评估了这些平台的个体决定因素。Cendea de Cizur，Zizur Mayor或参加当地纳瓦拉大学（潘普洛纳）的参与者将被邀请使用智能手机应用程序Hyfe Cough Tracker监测他们的夜间咳嗽。检测到的咳嗽将在时间和空间上聚合。将从当地卫生机构收集参与者队列中COVID-19和其他诊断呼吸道疾病的发病率，以及研究区域和人口，并用于对这些独立时间序列进行自回归移动平均分析。在混合方法设计中，我们将通过评估参与模式和社会人口学特征来探索连续数字咳嗽监测的障碍和促进因素。参与者将填写一份可接受性问卷，一个小组将参加焦点小组讨论。获得加拿大蒙特利尔大学中心医院伦理委员会和西班牙纳瓦拉医学研究伦理委员会的伦理批准。初步调查结果将与民政和卫生当局分享，并报告给个别参与者。研究结果将提交同行评审的科学期刊和国际会议发表。NCT 04762693。
Cough is a common symptom of COVID-19 and other respiratory illnesses. However, objectively measuring its frequency and evolution is hindered by the lack of reliable and scalable monitoring systems. This can be overcome by newly developed artificial intelligence models that exploit the portability of smartphones. In the context of the ongoing COVID-19 pandemic, cough detection for respiratory disease syndromic surveillance represents a simple means for early outbreak detection and disease surveillance. In this protocol, we evaluate the ability of population-based digital cough surveillance to predict the incidence of respiratory diseases at population level in Navarra, Spain, while assessing individual determinants of uptake of these platforms. Participants in the Cendea de Cizur, Zizur Mayor or attending the local University of Navarra (Pamplona) will be invited to monitor their night-time cough using the smartphone app Hyfe Cough Tracker. Detected coughs will be aggregated in time and space. Incidence of COVID-19 and other diagnosed respiratory diseases within the participants cohort, and the study area and population will be collected from local health facilities and used to carry out an autoregressive moving average analysis on those independent time series. In a mixed-methods design, we will explore barriers and facilitators of continuous digital cough monitoring by evaluating participation patterns and sociodemographic characteristics. Participants will fill an acceptability questionnaire and a subgroup will participate in focus group discussions. Ethics approval was obtained from the ethics committee of the Centre Hospitalier de l’Université de Montréal, Canada and the Medical Research Ethics Committee of Navarre, Spain. Preliminary findings will be shared with civil and health authorities and reported to individual participants. Results will be submitted for publication in peer-reviewed scientific journals and international conferences. NCT04762693.
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