精准公共卫生,未来疫情管理的关键:范围审查。

IF 2.9 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES DIGITAL HEALTH Pub Date : 2024-08-12 eCollection Date: 2024-01-01 DOI:10.1177/20552076241256877
Ellappa Ghanthan Rajendran, Farizah Mohd Hairi, Rama Krishna Supramaniam, Tengku Amatullah Madeehah T Mohd
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引用次数: 0

摘要

背景:精准公共卫生(PPH)是公共卫生医学的一个新兴领域。通过应用各类数据,精准公共卫生能够在特定时间范围内为特定人群提供更有针对性的干预措施。然而,PPH 的应用存在一些挑战和局限性,需要加以解决:我们的目的是提供证据,说明 PPH 在疫情管理中的各种应用、PPH 应用中可使用的数据类型以及 PPH 应用中的局限性和障碍:在 PubMed、Web of Science 和 Science Direct 中检索了相关文章。我们根据《系统综述和荟萃分析首选报告项目》(PRISMA)的范围界定综述指南选择文章。证据评估结果以叙述形式而非定量形式呈现:共有 27 篇文章被纳入范围界定审查。大多数文章(74.1%)侧重于 PPH 在疾病监测和信号检测中的应用。此外,研究中使用最多的数据类型是监测数据(51.9%)、环境数据(44.4%)和互联网查询数据。大多数文章强调数据质量和可用性(81.5%)是 PPH 应用的主要障碍,其次是数据集成和互操作性(29.6%):疫情管理中的 PPH 应用利用广泛的数据源和分析技术来加强疾病监测、调查、建模和预测。通过利用这些工具和方法,PPH 有助于提高疫情管理的效力和效率,最终减轻传染病对人口造成的负担。在疫情管理中应用 PPH 方法的局限性和挑战突出表明,有必要加强监测系统,促进相关利益攸关方之间的数据共享与合作,并在维护隐私和道德原则的同时实现数据收集方法的标准化。
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Precision public health, the key for future outbreak management: A scoping review.

Background: Precision Public Health (PPH) is a newly emerging field in public health medicine. The application of various types of data allows PPH to deliver more tailored interventions to a specific population within a specific timeframe. However, the application of PPH possesses several challenges and limitations that need to be addressed.

Objective: We aim to provide evidence of the various use of PPH in outbreak management, the types of data that could be used in PPH application, and the limitations and barriers in the application of the PPH approach.

Methods and analysis: Articles were searched in PubMed, Web of Science, and Science Direct. Our selection of articles was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Scoping Review guidelines. The outcome of the evidence assessment was presented in narrative format instead of quantitative.

Results: A total of 27 articles were included in the scoping review. Most of the articles (74.1%) focused on PPH applications in performing disease surveillance and signal detection. Furthermore, the data type mostly used in the studies was surveillance (51.9%), environment (44.4), and Internet query data. Most of the articles emphasized data quality and availability (81.5%) as the main barriers in PPH applications followed by data integration and interoperability (29.6%).

Conclusions: PPH applications in outbreak management utilize a wide range of data sources and analytical techniques to enhance disease surveillance, investigation, modeling, and prediction. By leveraging these tools and approaches, PPH contributes to more effective and efficient outbreak management, ultimately reducing the burden of infectious diseases on populations. The limitation and challenges in the application of PPH approaches in outbreak management emphasize the need to strengthen the surveillance systems, promote data sharing and collaboration among relevant stakeholders, and standardize data collection methods while upholding privacy and ethical principles.

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来源期刊
DIGITAL HEALTH
DIGITAL HEALTH Multiple-
CiteScore
2.90
自引率
7.70%
发文量
302
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