Evidence map›Paper›PMID 40969320›Full record

ArticleF1000Research2025

Revealing Principal Components, Patterns, and Structural Gaps in Health Security among High-Income Countries: A Comparative Analysis Using PCA and a Multi-Scenario Clustering Approach.

Adel A Nasser, Mijahed Nasser Aljober, Abed Saif Ahmed Alghawli, Amani A K Essayed

Abstract readComparative Study
In one paragraph

Article in F1000Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Adel A NasserInformation Systems and Computer Science, Sa'adah University, Sa'adah, Yemen.ORCID https://orcid.org/0000-0001-5456-1303
Mijahed Nasser AljoberArtificial Intelligence, Modern Specialized University, Sana'a, Yemen.
Abed Saif Ahmed AlghawliComputer Science, Prince Sattam bin Abdulaziz University, Al Kharj, Riyadh Province, Saudi Arabia.ORCID https://orcid.org/0000-0002-6586-2141
Amani A K EssayedComputer Science, Prince Sattam bin Abdulaziz University, Al Kharj, Riyadh Province, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The COVID-19 pandemic highlighted significant weaknesses in health security systems, even in high-income countries (HICs), underscoring the necessity for a more nuanced understanding of their distinct strengths and vulnerabilities. Existing research often offers broad evaluations and fails to capture the complex internal dynamics of health-security performance. This study seeks to fill this gap by identifying the latent factors that define health security capacities in HICs and clustering countries based on these factors. Methods: A multistage analytical framework was employed based on the 2017-2021 Global Health Security Index (GHSI) dataset. Initially, Principal Component Analysis (PCA) with varimax rotation was applied to the 37 GHSI indicators to reduce dimensionality and reveal latent structures within the data. This process identified nine principal components for the subsequent analysis. Subsequently, K-means clustering was utilized under three methodological scenarios: using countries' average scores across the nine extracted components, based on 13 high-loading indicators from the first principal component, and using aggregated scores across the six original GHSI categories. This design facilitated a comprehensive comparison of the clustering outcomes across different data representations. Results: Analysis found nine components that together explained 74.50% of the total differences, with the first component-"Foundational Capacity, Regulations, Resilience, and Prevention-Detection Systems"-making up 37.62% of that total. Together, the first three components explained 51.81% of the total variance. Clustering across all three scenarios categorized high-income countries into four levels, revealing significant disparities. Nauru, the Cook Islands, and Palau consistently ranked lowest, highlighting critical gaps in foundational capacities and systemic readiness despite their high-income status. This study shows that wealth alone does not ensure preparedness, revealing distinct performance patterns and weaknesses across countries. Conclusion: The findings underscore the need for tailored policies, multi-method evaluations, and sustained global cooperation to enhance resilience and guide investments in national and global health security.

Indexed as

COVID-19Developed CountriesGlobal HealthCluster AnalysisHumansPandemicsPrincipal Component AnalysisSARS-CoV-2health security; high-income countries; Principal Component Analysis clustering; K-means; influential indicators

Identifiers

PMID40969320
PMCPMC12441671

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.