Use of Digital Tools in Arbovirus Surveillance: Scoping Review.

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Internet Research Pub Date : 2024-11-18 DOI:10.2196/57476
Carolina Lopes Melo, Larissa Rangel Mageste, Lusiele Guaraldo, Daniela Polessa Paula, Mayumi Duarte Wakimoto
{"title":"Use of Digital Tools in Arbovirus Surveillance: Scoping Review.","authors":"Carolina Lopes Melo, Larissa Rangel Mageste, Lusiele Guaraldo, Daniela Polessa Paula, Mayumi Duarte Wakimoto","doi":"10.2196/57476","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The development of technology and information systems has led to important changes in public health surveillance.</p><p><strong>Objective: </strong>This scoping review aimed to assess the available evidence and gather information about the use of digital tools for arbovirus (dengue virus [DENV], zika virus [ZIKV], and chikungunya virus [CHIKV]) surveillance.</p><p><strong>Methods: </strong>The databases used were MEDLINE, SCIELO, LILACS, SCOPUS, Web of Science, and EMBASE. The inclusion criterion was defined as studies that described the use of digital tools in arbovirus surveillance. The exclusion criteria were defined as follows: letters, editorials, reviews, case reports, series of cases, descriptive epidemiological studies, laboratory and vaccine studies, economic evaluation studies, and studies that did not clearly describe the use of digital tools in surveillance. Results were evaluated in the following steps: monitoring of outbreaks or epidemics, tracking of cases, identification of rumors, decision-making by health agencies, communication (cases and bulletins), and dissemination of information to society).</p><p><strong>Results: </strong>Of the 2227 studies retrieved based on screening by title, abstract, and full-text reading, 68 (3%) studies were included. The most frequent digital tools used in arbovirus surveillance were apps (n=24, 35%) and Twitter, currently called X (n=22, 32%). These were mostly used to support the traditional surveillance system, strengthening aspects such as information timeliness, acceptability, flexibility, monitoring of outbreaks or epidemics, detection and tracking of cases, and simplicity. The use of apps to disseminate information to society (P=.02), communicate (cases and bulletins; P=.01), and simplicity (P=.03) and the use of Twitter to identify rumors (P=.008) were statistically relevant in evaluating scores. This scoping review had some limitations related to the choice of DENV, ZIKV, and CHIKV as arboviruses, due to their clinical and epidemiological importance.</p><p><strong>Conclusions: </strong>In the contemporary scenario, it is no longer possible to ignore the use of web data or social media as a complementary strategy to health surveillance. However, it is important that efforts be combined to develop new methods that can ensure the quality of information and the adoption of systematic measures to maintain the integrity and reliability of digital tools' data, considering ethical aspects.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"26 ","pages":"e57476"},"PeriodicalIF":5.8000,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Internet Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/57476","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

Abstract

Background: The development of technology and information systems has led to important changes in public health surveillance.

Objective: This scoping review aimed to assess the available evidence and gather information about the use of digital tools for arbovirus (dengue virus [DENV], zika virus [ZIKV], and chikungunya virus [CHIKV]) surveillance.

Methods: The databases used were MEDLINE, SCIELO, LILACS, SCOPUS, Web of Science, and EMBASE. The inclusion criterion was defined as studies that described the use of digital tools in arbovirus surveillance. The exclusion criteria were defined as follows: letters, editorials, reviews, case reports, series of cases, descriptive epidemiological studies, laboratory and vaccine studies, economic evaluation studies, and studies that did not clearly describe the use of digital tools in surveillance. Results were evaluated in the following steps: monitoring of outbreaks or epidemics, tracking of cases, identification of rumors, decision-making by health agencies, communication (cases and bulletins), and dissemination of information to society).

Results: Of the 2227 studies retrieved based on screening by title, abstract, and full-text reading, 68 (3%) studies were included. The most frequent digital tools used in arbovirus surveillance were apps (n=24, 35%) and Twitter, currently called X (n=22, 32%). These were mostly used to support the traditional surveillance system, strengthening aspects such as information timeliness, acceptability, flexibility, monitoring of outbreaks or epidemics, detection and tracking of cases, and simplicity. The use of apps to disseminate information to society (P=.02), communicate (cases and bulletins; P=.01), and simplicity (P=.03) and the use of Twitter to identify rumors (P=.008) were statistically relevant in evaluating scores. This scoping review had some limitations related to the choice of DENV, ZIKV, and CHIKV as arboviruses, due to their clinical and epidemiological importance.

Conclusions: In the contemporary scenario, it is no longer possible to ignore the use of web data or social media as a complementary strategy to health surveillance. However, it is important that efforts be combined to develop new methods that can ensure the quality of information and the adoption of systematic measures to maintain the integrity and reliability of digital tools' data, considering ethical aspects.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
在虫媒病毒监测中使用数字工具:范围审查。
背景技术和信息系统的发展给公共卫生监测工作带来了重大变化:本综述旨在评估现有证据,并收集有关在虫媒病毒(登革病毒、寨卡病毒和基孔肯雅病毒)监测中使用数字工具的信息:使用的数据库包括 MEDLINE、SCIELO、LILACS、SCOPUS、Web of Science 和 EMBASE。纳入标准是描述在虫媒病毒监测中使用数字工具的研究。排除标准定义如下:信件、社论、综述、病例报告、系列病例、描述性流行病学研究、实验室和疫苗研究、经济评估研究以及未明确描述在监测中使用数字工具的研究。对以下步骤的结果进行了评估:监测疫情或流行病、追踪病例、识别谣言、卫生机构决策、交流(病例和公告)以及向社会传播信息):在通过标题、摘要和全文阅读筛选出的 2227 项研究中,有 68 项(3%)研究被纳入。在虫媒病毒监测中使用最多的数字工具是应用程序(24 个,占 35%)和推特(目前称为 X)(22 个,占 32%)。这些工具大多用于支持传统的监测系统,加强了信息的及时性、可接受性、灵活性、对疫情或流行病的监测、病例的检测和跟踪以及简便性等方面。使用应用程序向社会传播信息(P=.02)、交流(病例和公告;P=.01)和简便性(P=.03),以及使用 Twitter 识别谣言(P=.008)在评估评分中具有统计学相关性。由于 DENV、ZIKV 和 CHIKV 在临床和流行病学上的重要性,本范围综述在选择这些虫媒病毒时存在一些局限性:在当前形势下,我们已无法忽视使用网络数据或社交媒体作为健康监测的补充策略。然而,重要的是要共同努力开发新的方法,以确保信息的质量,并采取系统措施来维护数字工具数据的完整性和可靠性,同时考虑到道德方面的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
期刊最新文献
Long-Term Efficacy of an AI-Based Health Coaching Mobile App in Slowing the Progression of Nondialysis-Dependent Chronic Kidney Disease: Retrospective Cohort Study. Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning-Based Multicohort (RIGIPREV) Study. Identification of a Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Older Adults: Machine Learning-Based Predictive Model. Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study. Development and Validation of a Machine Learning-Based Early Warning Model for Lichenoid Vulvar Disease: Prediction Model Development Study.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1