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政策简报:通过数据改善国家疫苗接种决策

Policy brief: Improving national vaccination decision-making through data.

作者信息

Evans Sandra, Schmitt Joe, Kalra Dipak, Sokol Tomislav, Holt Daphne

机构信息

Sandra Evans Health Policy, Liverpool, United Kingdom.

Global Health Press, Singapore, Singapore.

出版信息

Front Public Health. 2024 Dec 17;12:1407841. doi: 10.3389/fpubh.2024.1407841. eCollection 2024.

Abstract

Life course immunisation looks at the broad value of vaccination across multiple generations, calling for more data power, collaboration, and multi-disciplinary work. Rapid strides in artificial intelligence, such as machine learning and natural language processing, can enhance data analysis, conceptual modelling, and real-time surveillance. The GRADE process is a valuable tool in informing public health decisions. It must be enhanced by real-world data which can span and capture immediate needs in diverse populations and vaccination administration scenarios. Analysis of data from multiple study designs is required to understand the nuances of health behaviors and interventions, address gaps, and mitigate the risk of bias or confounding presented by any single data collection methodology. Secure and responsible health data sharing across European countries can contribute to a deeper understanding of vaccines.

摘要

生命历程免疫着眼于多代人接种疫苗的广泛价值,呼吁拥有更多的数据力量、协作和多学科工作。人工智能领域的快速进步,如机器学习和自然语言处理,可以加强数据分析、概念建模和实时监测。GRADE 流程是为公共卫生决策提供信息的宝贵工具。它必须通过真实世界的数据加以强化,这些数据能够涵盖并捕捉不同人群和疫苗接种管理场景中的即时需求。需要对来自多种研究设计的数据进行分析,以了解健康行为和干预措施的细微差别,填补空白,并降低任何单一数据收集方法所呈现的偏差或混杂风险。欧洲国家之间安全且负责任的健康数据共享有助于更深入地了解疫苗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bd7/11685149/94272dbdcf43/fpubh-12-1407841-g001.jpg

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