非欧盟欧洲国家的健康安全不平等:使用综合mcdm -机器学习方法的跨国比较评估。

Q2 Pharmacology, Toxicology and Pharmaceutics
F1000Research Pub Date : 2025-08-19 eCollection Date: 2025-01-01 DOI:10.12688/f1000research.163662.2
Mijahed Nasser Aljober, Adel A Nasser, Abed Saif Ahmed Alghawli, Amani A K Elsayed
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引用次数: 0

摘要

目标:在一个日益相互关联的世界中,卫生安全的有效性对于制定知情的卫生政策和加强公共卫生成果至关重要。本研究旨在分析27个非欧盟欧洲国家的卫生保健服务,确定关键优先事项和趋势,针对非洲和东地中海地区(EMR)制定基准,并对卫生安全绩效进行排名和分组,以便为有针对性的干预措施提供信息。方法:利用2019年、2021年和2017-2021年六个全球健康安全指数指标的汇总数据,本研究采用了一个集成的熵-可可素- k -均值框架。采用熵值法确定非欧盟国家的卫生安全优先事项和趋势,使其能够与非洲和EMR区域进行跨区域比较,以突出优先事项的变化和差距。Entropy-CoCoSo(综合妥协方案)模型生成动态排名,而K-means聚类将国家分为五个风险集群(高到危险)。这种整合促进了跨国动态排名和聚类分析,为非欧盟国家的针对性干预提供了信息。结果:熵分析显示,检测和报告是最关键的指标(权重:0.388),反映了监测的差异。风险环境的影响仍然很小(0.067),突出了对外部威胁的一贯脆弱性。对规范的遵守情况急剧上升(0.091→0.123),表明2019年后出现了监管空白或对卫生标准的遵守情况不平衡。跨区域比较突出了非欧盟国家对检测和报告的关注,而非洲则强调预防和EMR中医疗基础设施的优先次序。排名和聚类显示出明显的差异:亚美尼亚、挪威和英国一直被评为“高”,相反,安道尔、摩纳哥、圣马力诺和塔吉克斯坦(聚类5:“危险”)表现出系统性的弱点。结论:本研究强调需要制定量身定制的政策,以应对非欧盟欧洲不断演变的医疗卫生挑战。协调监测系统、扩大预防措施和弥合合规差距至关重要。区域合作和向低绩效国家重新分配资源对于缩小差距至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Health Security inequalities in Non-EU European Countries: A Cross-National Comparative Assessment Using an Integrated MCDM-Machine Learning Approach.

Health Security inequalities in Non-EU European Countries: A Cross-National Comparative Assessment Using an Integrated MCDM-Machine Learning Approach.

Health Security inequalities in Non-EU European Countries: A Cross-National Comparative Assessment Using an Integrated MCDM-Machine Learning Approach.

Health Security inequalities in Non-EU European Countries: A Cross-National Comparative Assessment Using an Integrated MCDM-Machine Learning Approach.

Objectives: In an increasingly interconnected world, the effectiveness of health security (HeS) is pivotal in shaping informed health policies and enhancing public health outcomes. This study aims to analyses HeS in 27 non-EU European countries, identifying key priorities and trends, benchmarking against African and Eastern Mediterranean regions (EMR), and ranking and clustering health security performance to inform targeted interventions.

Methods: Utilizing 2019, 2021, and aggregated 2017-2021 data from six Global Health Security Index indicators, this study applied an integrated Entropy-CoCoSo-K-means framework. The Entropy method was employed to identify health security (HeS) priorities and trends in Non-EU countries, enabling cross-regional comparisons with African and EMR regions to highlight priority shifts and disparities. The Entropy-CoCoSo (Combined Compromise Solution) model generated dynamic rankings, while K-means clustering categorized countries into five risk clusters (high to dangerous). This integration facilitated cross-national dynamic rankings and cluster analyses, informing targeted interventions across Non-EU countries.

Results: Entropy analysis reveals that detection and reporting emerged as the most critical indicator (weight: 0.388), reflecting disparities in surveillance. The risk environment remains minimally influential (0.067), highlighting consistent vulnerabilities to external threats. Compliance with norms shows a sharp rise (0.091 → 0.123), indicating emerging regulatory gaps or uneven adherence to health standards post-2019. Cross-regional comparisons highlighted a focus on detection and reporting in non-EU countries versus an emphasis on prevention in Africa and healthcare infrastructure prioritization in the EMR. Ranking and clustering revealed stark disparities: Armenia, Norway, and the UK consistently ranked "High," In contrast, Andorra, Monaco, San Marino, and Tajikistan (Cluster 5: "Dangerous") exhibited systemic weaknesses.

Conclusion: This study underscores the need for tailored policies to address non-EU Europe's evolving HeS challenges. Harmonizing surveillance systems, scaling preventive measures, and bridging compliance gaps are critical. Regional collaboration and resource reallocation to low-performing nations are essential to mitigate disparities.

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来源期刊
F1000Research
F1000Research Pharmacology, Toxicology and Pharmaceutics-Pharmacology, Toxicology and Pharmaceutics (all)
CiteScore
5.00
自引率
0.00%
发文量
1646
审稿时长
1 weeks
期刊介绍: F1000Research publishes articles and other research outputs reporting basic scientific, scholarly, translational and clinical research across the physical and life sciences, engineering, medicine, social sciences and humanities. F1000Research is a scholarly publication platform set up for the scientific, scholarly and medical research community; each article has at least one author who is a qualified researcher, scholar or clinician actively working in their speciality and who has made a key contribution to the article. Articles must be original (not duplications). All research is suitable irrespective of the perceived level of interest or novelty; we welcome confirmatory and negative results, as well as null studies. F1000Research publishes different type of research, including clinical trials, systematic reviews, software tools, method articles, and many others. Reviews and Opinion articles providing a balanced and comprehensive overview of the latest discoveries in a particular field, or presenting a personal perspective on recent developments, are also welcome. See the full list of article types we accept for more information.
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