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首页> 外文期刊>Frontiers in Public Health >Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map
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Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map

机译：Covid-19大流行的风险和保护因素：快速证据地图

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Background: Given the worldwide spread of the 2019 Novel Coronavirus (COVID-19), there is an urgent need to identify risk and protective factors and expose areas of insufficient understanding. Emerging tools, such as the Rapid Evidence Map (rEM), are being developed to systematically characterize large collections of scientific literature. We sought to generate an rEM of risk and protective factors to comprehensively inform areas that impact COVID-19 outcomes for different sub-populations in order to better protect the public. Methods: We developed a protocol that includes a study goal, study questions, a PECO statement, and a process for screening literature by combining semi-automated machine learning with the expertise of our review team. We applied this protocol to reports within the COVID-19 Open Research Dataset (CORD-19) that were published in early 2020. SWIFT-Active Screener was used to prioritize records according to pre-defined inclusion criteria. Relevant studies were categorized by risk and protective status; susceptibility category (Behavioral, Physiological, Demographic, and Environmental); and affected sub-populations. Using tagged studies, we created an rEM for COVID-19 susceptibility that reveals: (1) current lines of evidence; (2) knowledge gaps; and (3) areas that may benefit from systematic review. Results: We imported 4,330 titles and abstracts from CORD-19. After screening 3,521 of these to achieve 99% estimated recall, 217 relevant studies were identified. Most included studies concerned the impact of underlying comorbidities (Physiological); age and gender (Demographic); and social factors (Environmental) on COVID-19 outcomes. Among the relevant studies, older males with comorbidities were commonly reported to have the poorest outcomes. We noted a paucity of COVID-19 studies among children and susceptible sub-groups, including pregnant women, racial minorities, refugees/migrants, and healthcare workers, with few studies examining protective factors. Conclusion: Using rEM analysis, we synthesized the recent body of evidence related to COVID-19 risk and protective factors. The results provide a comprehensive tool for rapidly elucidating COVID-19 susceptibility patterns and identifying resource-rich/resource-poor areas of research that may benefit from future investigation as the pandemic evolves.
机译：背景：鉴于2019年新型冠状病毒（Covid-19）的全球传播，迫切需要识别风险和保护因素，并暴露不足的理解区域。正在开发出新的工具，例如快速证据地图（REM），以系统地表征大量的科学文献。我们寻求产生风险和保护因素，以全面地通知影响不同子人群的Covid-19结果的区域，以便更好地保护公众。方法：我们开发了一种协议，包括研究目标，研究问题，PECO声明以及通过将半自动机器学习与我们的审核团队的专业知识结合起来，筛选文献的过程。我们将此协议应用于在2020年初发布的Covid-19开放研究数据集（CORD-19）中的报告。Swift-Active Screener根据预定义的纳入标准优先考虑记录。通过风险和保护地位对相关研究进行分类;易感性类别（行为，生理，人口统计和环境）;和受影响的子人群。使用标记研究，我们为Covid-19易感性创建了一个REM，揭示了：（1）当前的证据; （2）知识差距; （3）可能受益于系统审查的区域。结果：我们从CIRD-19导入了4,330个标题和摘要。在筛选3,521的这些以达到99％的估计召回后，确定了217项相关研究。最多的研究涉及潜在的合并症（生理）的影响;年龄和性别（人口统计）;和社会因素（环境）关于Covid-19结果。在相关研究中，常常报道较老的雄性具有最贫困的成果。我们注意到儿童和易感子团体的Covid-19研究，包括孕妇，种族少数群体，难民/移民和医疗工作者，以及少数研究检查保护因素。结论：使用REM分析，我们综合了最近与Covid-19风险和保护因素相关的证据。结果为快速阐明了Covid-19易感模式，并识别资源丰富/资源差的研究领域，可以从未来调查中受益，以便迅速阐明Covid-19易感模式，并随着大流行的发展中受益。

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