TY - JOUR AU - Silenou, Bernard C AU - Nyirenda, John L Z AU - Zaghloul, Ahmed AU - Lange, Berit AU - Doerrbecker, Juliane AU - Schenkel, Karl AU - Krause, Gérard PY - 2021 DA - 201/12/23 TI -非洲用于大流行控制的数字卫生工具的可用性和适用性:范围审查和聚类分析乔- JMIR公共卫生Surveill SP - e30106六世- 7 - 12 KW -移动应用KW - mHealth KW -流行病学监测KW -传染病KW -疫情应对KW -健康信息管理KW -公共卫生KW -审查KW -传输网络AB -背景:获得监管越来越多的移动健康监测或疫情管理工具在非洲已成为一个挑战。目的:本研究的目的是绘制用于非洲传染病监测或爆发管理的移动卫生工具的功能组合。方法:我们结合文献系统综述和专家电话调查的数据进行了范围综述。我们通过搜索2010年1月至2020年12月之间发表的文章，应用了PRISMA(系统评价和元分析首选报告项目)指南。此外，我们使用了受访者驱动的抽样方法，并于2019年10月至2020年2月在所有非洲国家国家公共卫生机构的代表中进行了电话调查。我们结合这些发现，并使用层次聚类方法根据工具的功能(属性)对它们进行分组。结果:我们从1914年的出版物中确定了30个工具和来自52%(28/54)非洲国家的45个答复。大约13%的工具(4/30;监视爆发响应管理和分析系统，运行。Data, CommCare, and District Health Information Software 2) covered 93% (14/15) of the identified attributes. Of the 30 tools, 17 (59%) tools managed health event data, 20 (67%) managed case-based data, and 28 (97%) offered a dashboard. Clustering identified 2 exceptional attributes for outbreak management, namely contact follow-up (offered by 8/30, 27%, of the tools) and transmission network visualization (offered by Surveillance Outbreak Response Management and Analysis System and Go.Data). Conclusions: There is a large range of tools in use; however, most of them do not offer a comprehensive set of attributes, resulting in the need for public health workers having to use multiple tools in parallel. Only 13% (4/30) of the tools cover most of the attributes, including those most relevant for response to the COVID-19 pandemic, such as laboratory interface, contact follow-up, and transmission network visualization. SN - 2369-2960 UR - https://publichealth.www.mybigtv.com/2021/12/e30106 UR - https://doi.org/10.2196/30106 UR - http://www.ncbi.nlm.nih.gov/pubmed/34941551 DO - 10.2196/30106 ID - info:doi/10.2196/30106 ER -