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
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".