Income-Based analysis of health security in Western Asia through an integrated GHSI, MCDM, and Clustering Model

2.1. Data Acquisition: The Global Health Security Index (GHSI) as a Tool for Health Security Preparedness Assessment

The Global Health Security Index (GHSI) is a comprehensive instrument for assessing countries’ preparedness and capabilities in addressing health security threats. However, its suitability as a research tool warrants careful consideration given the discrepancies observed between predicted and actual performance during the COVID-19 pandemic (Kaiser et al., 2021). The GHSI provides a multidimensional approach to evaluating health security, encompassing various aspects such as prevention (PR), detection, reporting (DR), rapid response (RR), health system capacity (HS), commitment and adherence (CA), and risk environment (RE) (Wang and Lyu, 2023; Nasser et al., 2024a; Nasser and Alghawli, 2024). The GHSI’s publicly accessible data support extensive research opportunities and offer a holistic perspective on a country’s health security preparedness, and its structured approach facilitates robust comparisons among nations, rendering it a potentially valuable tool for our research (Global Health Security Index, 2021). The World Bank classification, which categorizes countries into four income groups based on gross national income (GNI) per capita, delineates the 17 Western Asia (WA) countries as follows: Low-income countries (LIC) comprise Syria and Yemen; lower-middle-income countries (LMC) include Jordan and Lebanon; upper-middle-income countries (UMC) encompass Armenia, Azerbaijan, Georgia, Iraq, and Turkey; and high-income countries (HIC) consist of Bahrain, Cyprus, Israel, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. For our comparative analysis, we extracted data for 17 countries in the WA region, including 2 LIC, 2 LMI, 5 UMC, and 8 HIC. To assess health security outcomes across these groups for each indicator, we examined performance metrics from 2019 and 2021 (Global Health Security Index, Global Health Security Index Data Model and Report. (2021). Available at https://ghsindex.org/report-model/). Comprehensive data can be found in Tables 1-3 of the supplementary file (Nasser, et al. 2024b) (available at: data repository https://doi.org/10.6084/m9.figshare.27992735.v3).

Furthermore, to assess the relationship between the financial resources allocated to public health preparedness and the effectiveness of resource utilization from one perspective and the health security outcomes in the region from another, this study utilizes GHSI data on two indicators: F1, a financing indicator, and F2, public healthcare spending levels per capita. A financing indicator, in the context of health security, is a metric that quantifies the financial resources dedicated to enhancing a country’s capacity to prevent, detect, and respond to disease outbreaks. This indicator provides a comprehensive representation of the financial investments made in various domains related to health security, such as preparedness funding, commitments based on Joint External Evaluation (JEE) and Performance Verification System (PVS) assessments, emergency response financing, and accountability for international commitments. The second indicator refers to the domestic general government health expenditure per capita. Comprehensive data can be found in Table 4 of the supplementary file (Nasser, et al. 2024b) (available at: data repository https://doi.org/10.6084/m9.figshare.27992735.v3).

2.2. Applying the D-CRITIC Method to Determine and Compare the Relative Importance of Health Security Indicators across Western Asia and Its Income Groups

This study employed the modified version of the CRiteria Importance Through Inter-criteria Correlation (CRITIC) method, namely the Distance Correlation-based CRITIC (D-CRITIC) method to evaluate and compare the relative importance of health security indicators across Western Asia. D-CRITIC improves upon the original CRITIC method by incorporating distance correlation, enabling objective criteria weighting based on both variance and interdependency.

This approach was introduced by Krishnan et al. in 2021; it yields more valid and stable weighting and ranking results than traditional CRITIC, thus enhancing decision-making reliability (Krishnan et al., 2021). D-CRITIC offers significant advantages for multi-criteria decision-making (MCDM). By considering both linear and nonlinear relationships between indicators through distance correlation, it enhances the accuracy of objective criteria weighting (Duo et al., 2024). Its data-driven approach minimizes subjectivity and potential bias, leading to more equitable, stable, and valid outcomes compared to the CRITIC method (Duo et al., 2024).

The procedure typically encompasses six distinct steps (Krishnan et al., 2021):

Step 1. Creating a decision matrix (DM), denoted by $U_{\mathit{ij}}$ as follows:

The score of country i on indicator j is represented by $u_{\mathit{ij}}$ . Additionally, the variables m and n indicate the total number of alternatives (countries) and criteria (health security indicators), respectively.

Step 2: Computing the normalized DM using (2):

In this scenario, $u_{\mathit{ij}}$ denotes the score assigned to alternative i for criterion j, $u_j^{+}$ represents the highest score attained for criterion j, and $u_j^{-}$ represents the lowest score recorded for criterion j.

Step 3: Calculating Standard Deviation (SD _j) for each criterion:

The symbol ( $\overline{z_j}$ ) represents the arithmetic mean (average) of the normalized values for criterion ‘j’ across all ‘m’ alternatives.

Step 4: Exploring Inter-Criterion Relationships with Distance Correlation

Equation (3) defines this calculation for any two criteria, represented as $c_j$ , and $c_{j^{\prime }}$ .

Here, $\mathit{\text{dCov}}(c_j,c_{j^{\prime }})$ represents the distance covariance between $c_j$ and $c_{j^{\prime }}$ , while $\mathit{\text{dVar}}\left(c_j\right)=\mathit{\text{dCov}}(c_j,c_j),$ represents the distance variance of $c_j$ , and $\mathit{\text{dVar}}\left(c_{j^{\prime }}\right)=\mathit{\text{dCov}}(c_{j^{\prime }},c_{j^{\prime }})$ represents the distance variance of $c_{j^{\prime }}$ .

Step 5: Computing Criterion Information Content:

The symbol $I_j$ represents a metric that quantifies the amount of information contained within the jth criterion.

Step 6: Compute the objective weights.

In this step, Equation (5) is used to calculate the objective weight of each jth criterion, reflecting its relative importance in decision-making.

In our study, this process was repeated eight times to assess the relative importance of health security indicators in the WA, LIC-LMC, UMC, and HIC groups of countries in the region for both 2019 and 2021. Detailed calculations for this step are readily available in supplementary file (Sec. 2) (Nasser, et al. 2024b) (Available at https://github.com/ProfAdelAbdulsalam/supplementary-material-and-softwares/tree/1.0.0) (https://doi.org/10.5281/zenodo.14541236).

2.3 Assessing and ranking WA countries using a combined D-CRITIC-COCOSO framework

The Combined Compromise Solution (CoCoSo) method, introduced by Yazdani et al. (2019), is a robust tool in Multi-Criteria Decision-Making (MCDM). This method effectively integrates the strengths of simple additive weighting (SAW) and the exponentially weighted product model, making it particularly adept at generating balanced and reliable rankings. Its intuitive design, adaptability, and capacity to deliver actionable insights have made it widely applicable across various decision-making contexts, enhancing its relevance to our study (Banihashemi et al., 2021; Naeem et al., 2022; Wang et al., 2023). It comprises the following implementation steps (Yazdani et al. (2019):

In this step, the initial DM ( $U_{\mathit{ij}}$ ) is constructed as described in (1) and then normalized as outlined in (6):

In this context, the weighting vector ( $w_j$ ) indicates the relative significance assigned to the j-th indicator, while ( $y_{\mathit{ij}})$ represents the normalized performance score of the i-th alternative concerning the j-th indicator.

At this stage, three distinct evaluation scores are calculated as follows:

Here, the parameter λ, ranging from 0 to 1, allows the decision-maker to fine-tune the relative importance of two aggregation techniques in determining the final compromise solution: the additive weighted sum (S _i) and the exponentially weighted product (P _i). This parameter serves as a control mechanism for balancing the influence of these two methods. A common practice is to use a value of 0.5, which gives equal weight to both S _i and P _i, ensuring a balanced consideration of the two approaches in the final outcome. This equal weighting is often employed to achieve a balanced integration of both techniques in the ultimate result.

Step 4: Calculating the composite score index and rank alternatives

Finally, Equation (12) is used to calculate the composite score.

In this investigation, the aforementioned process was iterated twice to rank Western Asian countries based on their GHSI scores for both 2019 and 2021. The alternatives are arranged in descending order according to their calculated scores. For a comparative analysis, Western Asian-ranked countries were further categorized by income group. Detailed calculations for this procedure are available in the supplementary file (Sec. 3) (Nasser, et al. 2024b) (Available at https://github.com/ProfAdelAbdulsalam/supplementary-material-and-softwares/tree/1.0.0) (https://doi.org/10.5281/zenodo.14541236)

2.4 Clustering Western Asian Countries Based on Their Composite Score Index

K-means clustering is a widely used learning algorithm for data mining and pattern recognition (Chong 2021). It partitions n observations into k clusters, where each observation belongs to the cluster with the nearest mean. The algorithm operates by iteratively assigning data points to the closest cluster center and subsequently recalculating the cluster centers based on new assignments (Cui et al., 2013).

This method is well known for its efficiency, simplicity, and adaptability across various fields. The advantages of K-means include computational efficiency and ease of implementation, rendering it suitable for large datasets (Chong, 2021). The standard K-means algorithm follows these steps (Cui et al., 2013): 1) initialize k cluster centers, often randomly; 2) assign each data point to the nearest cluster center; 3) recalculate the cluster centers based on the new assignments; 4) repeat steps 2 and 3 until convergence or a maximum number of iterations is reached.

In this study, the aforementioned process was also repeated twice to cluster Western Asian countries based on their D-CRITIC-COCOSO scores for both 2019 and 2021. The countries were clustered using a five-tiered grading system, ranging from “excellent” (Grade 1) to “poor” (Grade 5). The Western Asian-clustered countries were further divided into income tiers for comparative research. The supplementary file contains detailed calculations for this process (Sec.4) (Available at https://doi.org/10.6084/m9.figshare.27992735.v3) (Nasser, et al. 2024b).

2.5 Examining the correlation between independent financial resource allocation indicators and D-CRITIC-COCOSO Health Security Outcomes

We utilized the Spearman rank correlation method to investigate the relationship between the average performances of financial resource allocation indicators (financing (F1), and public healthcare spending levels per capita (F2)) and the mean COCOSO health security performance scores (Ci) throughout the study period. This non-parametric technique is particularly appropriate for evaluating the association between two variables without assuming specific underlying distributions (Liu, 2017). The Spearman correlation coefficient (ρ) was determined by independently ranking the sample values of both variables and then inserting the squared differences between these ranks into a formula derived from the Pearson correlation (Zimmerman, 1994). This approach is beneficial as it emphasizes the relative positions of the values rather than their absolute disparities, making it resilient to outliers and nonlinear relationships (Liu, 2017). An alternative method involves converting the values of both variables into standard scores, ranking the combined standard scores in a single sequence, and then calculating the Pearson correlation between the ranks corresponding to the original scores. This modified approach has been shown to be slightly more powerful than the conventional Spearman method for various distributions and sample sizes, ranging from 8 to 30 (Zimmerman, 1994). In summary, the Spearman rank correlation method was chosen because of its ability to assess the strength and direction of the monotonic relationship between resource allocation indicators and COCOSO scores. This technique is particularly effective in situations where the relative rankings are more important than the exact values, making it well suited for comparing performance indicators across different domains (Liu et al., 2010; Liu, 2017).

2.5 Comparative analysis

This phase consolidates and evaluates the outcomes from previous stages, examining the comparative importance of indicators, national rankings, and grouping trends across Western Asia and its economic subdivisions. The evaluation centered on uncovering regional inequalities and crucial patterns in health security performance for both 2019 and 2021. The outcomes will guide the creation of specific suggestions to improve health security readiness in each income category and the region overall. The key findings are outlined in the following sections.

2.6 Software

To determine the relative significance of health security indicators across Western Asian nations, we developed two advanced Excel-based software tools. Supplementary Software 1 - Distance Correlation-Based CRITIC Software. Source software available from (Available at https://github.com/ProfAdelAbdulsalam/supplementary-material-and-softwares/tree/1.0.0). Archived software available from (https://doi.org/10.5281/zenodo.14541236) (Nasser, et al., 2024b), License: OSI approved open license software is under GNU General Public License v3.0). This tool enables a detailed analysis by categorizing countries into income groups—Low-Income Countries (LIC), Lower-Middle-Income Countries (LMC), Upper-Middle-Income Countries (UMC), and High-Income Countries (HIC)—and provides valuable insights for the years 2019 and 2021. This tool specifically focuses on analyzing health security in low-income and lower-middle-income countries.

For the ranking analysis, we created Supplementary Software 2—An Integrated GHSI, MCDM, and Clustering Model for Health Security Analysis in Western Asia.

Source software available from: (https://github.com/ProfAdelAbdulsalam/supplementary-material-and-softwares/tree/1.0.0). Archived software available from (https://doi.org/10.5281/zenodo.14541236) (Nasser, et al., 2024b), License: OSI approved open license software is under GNU General Public License v3.0). Supplementary Software 2 evaluates the health security performance of these nations for both 2019 and 2021, integrating weighting, ranking, clustering, and Spearman rank correlation analysis into a unified Excel-based tool, offering a robust framework for health security performance assessment.

For clustering analysis, the software Cluster Analysis for Marketing (2024) was utilized, which is available for free download at Cluster Analysis for Marketing - Free Download (https://www.clusteranalysis4marketing.com/a-marketers-guide-to-cluster-analysis/free-download/).

4.1 Relative Importance of Health Security Indicators in WA region (2019 vs 2021)

Generally, in the D-CRITIC method, an indicator’s weight is directly linked to its information content (IC), with higher weights assigned to indicators that offer greater unique information, as measured by distance correlation. This adjustment ensures that indicators contributing more valuable information are weighted more heavily than those with less distinct content (Krishnan et al., 2021). Table 1 and Figures 1 and 2 present the computed information content values and corresponding weights.

Figure 1. Information Content Values.

(Source: Authors).

This Figure chart illustrates the information content values for various health security-related dimensions, comparing them by income group and region over the years 2019 and 2021. The dimensions analyzed include Prevention (PR), Detection and Reporting (DR), Rapid Response (RR), Health Systems (HS), Commitments and Adherence (CA), and Risk Environment (RE). Data is presented for Western Asia (WA), stratified into Low-Income Countries (LIC), Lower-Middle-Income Countries (LMC), Upper-Middle-Income Countries (UMC), and High-Income Countries (HIC).

Figure 2. Weights values.

(Source: Authors).

This bar chart illustrates the weight values assigned to various health security-related dimensions, comparing them by income group and region over the years 2019 and 2021. The dimensions analysed include Prevention (PR), Detection and Reporting (DR), Rapid Response (RR), Health Systems (HS), Commitments and Adherence (CA), and Risk Environment (RE). Data is presented for Western Asia (WA), stratified into Low-Income Countries (LIC), Lower-Middle-Income Countries (LMC), Upper-Middle-Income Countries (UMC), and High-Income Countries (HIC).

4.2 Health security performance ranking and clustering results

Table 2 summarizes the results of a COCOSO analysis and K-means clustering on the health security performance of WA countries for 2019 and 2021. Countries are divided into LIC, LMC, UMC and HIC groups. Key metrics include the health security score (C _i), regional rank (R _i), cluster membership (S _i), and changes in scores and ranks over the two years. Higher C _i and R _i indicate better performance, while cluster membership ranges from Cluster 1 (best) to Cluster 5 (worst).

4.3 The relationships between independent financial resource allocation indicators and D-CRITIC-COCOSO Health Security Outcomes

Our study used the Spearman rank correlation method to analyse the relationship between financial resource allocation indicators, specifically financing for health security initiatives (F1) and public healthcare spending per capita (F2), with overall health security performance scores (F3) across 17 countries. We found strong, positive correlations between both financial indicators and health security performance, with correlation coefficients of 0.98 for F1 and F3 and 0.97 for F2 and F3. These findings suggest a substantial association between financial investment in health security and the capacity to prevent, detect, and respond effectively to health threats.

4.4 Discussion

4.4.1 Health security priorities in western Asia (2019-2021)

Our analysis reveals a consistent emphasis on strengthening foundational health infrastructure, highlighted by the sustained high weight of the Health System (HS) indicator, which received weights of 0.227 in 2019 and 0.189 in 2021. This underscores the robust commitment to developing a resilient healthcare system capable of addressing diverse health challenges. These findings align with the ASPR’s National Health Security Strategy (2019–2022), which emphasizes the necessity of a strong health system to manage emerging health threats and to enhance resilience against future crises (ASPR, 2019). The significant weight assigned to the Detection and Reporting (DR) indicator further illustrates the importance of timely and accurate information in health security. With weights of 0.189 in 2019 and 0.186 in 2021, this underscores the critical role of robust disease surveillance and reporting systems in mitigating public health emergencies, resonating with the U.S. government’s Global Health Security Strategy (David, 2024). While foundational health infrastructure and detection/reporting mechanisms remain central to Western Asia’s health security strategy, a noteworthy shift towards prioritizing rapid response (RR) and risk environment (RE) management has emerged in 2021. This change reflects the dynamic nature of health security challenges, as illustrated by the COVID-19 pandemic, which necessitates adaptive policymaking (Roasio et al., 2022).

The third-ranking indicator, commitments and adherence (CA), also gained prominence, with weights of 0.167 and 0.174 in 2019 and 2021, respectively. This trend indicates that policymakers are increasingly recognizing the importance of maintaining health protocols and international commitments as foundational for effective health security. Conversely, indicators such as RE, Prevention (PR), and RR initially exhibited lower weights, suggesting that they were deprioritized in favor of foundational elements. However, the increased weights for RR (0.157) and RE (0.156) in 2021 indicate a strategic pivot towards enhancing preparedness for immediate health crises and acknowledging the impacts of environmental health risks.

This adaptive approach in health security policymaking is further supported by studies highlighting the effectiveness of Rapid Response Teams (RRTs) in reducing mortality rates and improving patient outcomes in critical situations (Alves Silva et al., 2021). For instance, a systematic review demonstrated that RRT implementation significantly decreased the incidence of cardiac arrest and overall mortality in hospitals, emphasizing the essential role of timely intervention during emergencies. Additionally, the increased emphasis on RE reflects a growing awareness of how environmental factors can exacerbate health crises, as outlined by the Asia-Pacific Health Security Action Framework (World Health Organization, 2024), which advocates for multispectral strategies to strengthen resilience against public health emergencies. Our findings suggest that resource allocation should continue to prioritize foundational health systems and robust detection/reporting capabilities while also addressing the evolving need for effective rapid response mechanisms and comprehensive environmental risk management. Moreover, this strategic shift toward a more adaptive health security strategy addresses gaps identified by the Global Health Security Index (GHSI), which highlights deficiencies in public health emergency response plans in several countries (Bellm & Nuzzo, 2021). By prioritizing foundational infrastructure, detection/reporting, rapid response, and risk environment management, Western Asia is making strides to enhance its overall health security. The dynamic nature of health threats, especially as highlighted by the COVID-19 pandemic, necessitates an adaptive approach to health security policymaking (Roasio et al., 2022). The increased weights for RR and RE indicate a dual focus on responding to immediate crises and preparing for the future challenges posed by infectious diseases and environmental hazards. This aligns with Nasser and Alghawli’s (2024) assertion that effective decision-making requires continuous evaluation and comparison. By focusing on these key indicators, policymakers can significantly enhance health security outcomes, ultimately fostering a more resilient public health landscape in Western Asia.

4.4.2 Health security priorities across income groups

Table 1 and Figures 1 and 2 showcase the changes in D-CRITIC weightings of health security indicators for different income groups (HIC, UMC, LIC/LMC) from 2019 to 2021. High-income countries (HICs) consistently prioritized detection and reporting (DR), health systems (HS), and commitment and adherence (CA) in both years. In 2019, DR led to 0.277, followed by HS at 0.212 and CA at 0.172. By 2021, DR remains the top priority at 0.258, CA increases to 0.208, and HS decreases slightly to 0.172. This minor shift indicates slight reprioritization, with CA gaining prominence. Prevention scores remained consistently low, suggesting that this was not a primary concern for HICs.

Upper-middle-income countries (UMCs) focus on health systems (HS), detection and reporting (DR), and risk environments (RE) as their main priorities. In 2019, HS had the highest weighting at 0.279, followed by DR at 0.175 and RE at 0.170. A notable shift occurred in 2021, with RE gaining importance and increasing to 0.210, surpassing DR at 0.197. This change likely reflects an increased awareness of environmental and external risks. HS remained a priority despite a slight decrease in weighting, whereas commitments and adherence continued to rank relatively low, similar to HICs.

Low- and lower-middle-income countries (LICs/LMCs) have experienced a significant shift from 2019 to 2021. In 2019, DR (0.243), HS (0.195), and CA (0.176) were the focus. However, by 2021, prevention (PR), DR, and rapid response (RR) have emerged as top-weighted indicators. PR showed a substantial increase to 0.261, with a DR of 0.248 and an RR of 0.179. This change indicates a greater emphasis on proactive health security measures, with prevention becoming the primary focus. The lower weighting for HS may suggest resource limitations or the prioritization of other immediate needs over structural healthcare improvements.

Across all income groups, DR consistently received high weights, highlighting a universal priority for early health threat identification. HS showed a general decline in weighting over time, particularly in LICs and LMCs, possibly due to resource constraints that limit structural investments. The significant increase in the prevention of LICs and LMCs by 2021 may reflect the growing focus on preventive healthcare measures. These findings indicate evolving health security priorities, with LICs and LMCs increasingly emphasizing prevention, whereas HICs and UMCs focus on detection capabilities. This shift may be influenced by region-specific challenges and capacity differences, especially in response to the post-2019 health security events.

4.4.3 Ranking and clustering results

This study’s regional analysis across Western Asia (WA) from 2019 to 2021 reveals important trends in health security, particularly as countries faced the COVID-19 pandemic’s far-reaching impacts. Results show a mixed picture: while a few countries improved or maintained their positions within their clusters, others experienced notable declines, underscoring persistent health security gaps across the region. These findings highlight the effects of shared vulnerabilities, resource constraints, and the role of international partnerships in bolstering public health systems. The implications of these results span clusters and point to strategic areas for policy intervention, targeted resource allocation, and regional collaboration.

As shown in Figure 3, countries in Cluster 1, representing the highest health security performers in WA,, display trends that reveal both resilience and vulnerability in the face of regional pressures. Armenia, for instance, experienced a substantial decline in its Composite Index (Ci) score, dropping from 16.341 in 2019 to 9.635 by 2021. This significant reduction, attributed to internal challenges and exacerbated by pandemic-related constraints, mirrors findings by Zhang et al. (2021), who noted similar declines in regional health resilience under shared resource pressures (Zhang et al., 2021). Armenia’s retention of its top rank within WA, despite this decline, suggests that other countries in the region similarly struggled with pandemic challenges and that the gap between higher and lower performers may be widening.

Figure 3. Distribution of WA countries by their clusters.

(Source: Authors).

This bar chart illustrates the distribution of Western Asia (WA) countries across clusters (S1 to S5) in 2019 and 2021. The clusters are categorized based on health security performance, where Cluster 1 (S1) represents excellent performance and Cluster 5 (S5) indicates poor performance. This visualization highlights changes in the health security status of WA countries over the specified years, offering insights into regional progress and disparities.

In contrast, Qatar’s health security investments yielded an improved regional ranking, moving from 6th to 4th between 2019 and 2021. Qatar’s progress highlights the role of high-income countries (HIC) in leveraging resources for health system reforms, as shown by Ravi et al. (2020), who documented how targeted investments enhance resilience and crisis response (Ravi et al., 2020). Georgia’s ascension to the second rank within Cluster 1 demonstrates the critical impact of sustained international partnerships, specifically its collaboration with the CDC, which has supported Georgia’s health infrastructure over decades (Biggs, 2020; Lal et al., 2022). This finding underscores the need for continuous external support to strengthen regional health security, especially for countries that lack the resources to maintain advanced health infrastructure independently.

Analysis of Cluster 2 countries shows modest but notable improvements, particularly in detection (DR) and health system capacity (HS) metrics. For instance, Jordan and Cyprus moved from the third to the second cluster, although their regional rankings remained stable at seventh and eighth, respectively. Jordan’s 19% improvement in DR and 17% increase in HS scores underscore the role of digital health investments, including telemedicine, in bolstering health capacities during the COVID-19 pandemic, as Alsabi et al. (2023) demonstrated (Alsabi et al., 2023). Similarly, Cyprus’s gains in DR and HS indicate that even modest investments in health technology and infrastructure can have significant impacts on national health security.

Despite these improvements, other high-income countries (HICs) in Cluster 2, such as Israel and Saudi Arabia, experienced slight declines in their regional rankings, falling from 2nd to 5th and 5th to 6th, respectively. This suggests that without sustained investment and regional cooperation, even high-income nations can see stagnation or regression in health security performance. Regional collaboration on health infrastructure, data sharing, and workforce development could provide a more stable foundation for health security in the region, as emphasized in studies on health resilience in high-income nations facing systemic constraints (Binagwaho et al., 2022).

The countries in Cluster 3 reveal both improvements and challenges. An increase in the number of WA countries from five (29%) in 2019 to six (35%) in 2021, as shown in Figure 3, reflects a slight increase in health security needs in this cluster, where most nations are either high-income (67%), lower-middle-income (LMC) (17%), or upper-middle-income (UMC) (17%). Oman’s improvement from the 10th to the 9th regional rank demonstrates incremental progress, likely supported by targeted investments in health capacity and detection infrastructure. Conversely, Bahrain and Kuwait experienced minor declines in their rankings, highlighting the need for continued investment in preventive and rapid response capabilities to sustain health security gains.

Lebanon and Azerbaijan’s advancement in regional and cluster rankings suggests that even under economic constraints, countries can improve health security by prioritizing critical areas, such as prevention (PR) and detection (DR). Research indicates that optimizing resource allocation towards these core functions, especially in times of crisis, can significantly improve a country’s health system resilience (Kruk et al., 2017). The trends in Cluster 3 suggest that WA countries could benefit from a model that emphasizes foundational health security investments even in the absence of extensive resources to ensure sustainability and incremental progress.

The most concerning findings relate to Clusters 4 and 5, where low-income and conflict-affected countries, such as Iraq, Yemen, and Syria, have faced regressions or stagnation in health security capabilities. Despite modest increases in certain health scores, Iraq’s decline from the fourth to the fifth cluster indicates insufficient systemic resilience to maintain previous gains. Iraq’s lower detection (DR) and prevention (PR) scores indicate gaps in disease surveillance and public health infrastructure; areas that are crucial for crisis response are often neglected in low-resource settings (Hamalaw et al., 2021).

Syria and Yemen’s consistent positioning in the fifth cluster reveals systemic public health weaknesses exacerbated by ongoing political and economic instability. Yemen’s PR score of just 0.8 in 2021 underscores the severe limitations in preventive health capabilities, leaving the country highly vulnerable to disease outbreaks. Similarly, Syria’s low DR and HS scores illustrate critical deficiencies in early detection and health-system capacity, which is consistent with studies noting that crisis-affected regions require a comprehensive approach that combines infrastructure development, workforce training, community engagement, and policy reform to achieve long-term health security (Alhaffar & Janos, 2021).

4.4.3 Spearman rank correlation results

The near-perfect correlations between financial resource allocation indicators, specifically financing for health security initiatives (F1) and public healthcare spending per capita (F2), with overall health security performance scores (Ci) across WA countries, underscore that financial investment plays a critical role in bolstering health security. Higher levels of both targeted health security financing (F1) (0.98) and general public healthcare spending (F2) (0.97) were consistently associated with higher health security performance (F3). This relationship is particularly relevant for countries with substantial financial inputs, such as Armenia and Georgia, which show high health security scores, reflecting the effectiveness of financial investments in achieving improved preparedness and response capabilities. Conversely, countries with minimal or no spending in these categories, such as Syria and Yemen, exhibit some of the lowest health security scores, highlighting a likely resource gap in public health infrastructure that limits their resilience against health threats.

4.5 Implications and recommendations

The observed trends indicate shifting priorities in health security strategies tailored to resource levels and specific risks faced by different income groups. High-income and upper-middle-income countries continue to prioritize detection and reporting, emphasizing the early identification of health threats to support rapid responses. By 2021, low- and lower-middle-income countries are increasingly focusing on prevention as a top priority, reflecting a proactive approach in contexts where reactive capacities may be limited. The lower weighting for health systems across all groups suggests either resource limitations or strategic reallocation toward other critical health security areas, possibly in response to post-2019 health events. These evolving priorities underscore diverse approaches to health security, reflecting each income group’s unique challenges, resources, and public health goals.

Furthermore, this study highlights considerable disparities in health resilience among countries shaped by income levels, resource availability, and political stability. High-income countries (HICs), such as Qatar and Georgia, improved their health security rankings by leveraging financial investments and strategic international partnerships, while conflict-affected, low-resource nations, namely Iraq, Yemen, and Syria, experienced stagnation or decline. These findings underscore the urgent need for a regional framework to address health security inequities, bridge the gap between high- and low-income nations, and foster sustainable public health resilience across the WA. The observed disparities align with the broader global patterns seen in low-income and politically unstable regions, where limitations in funding and infrastructure often constrain health security progress. Iraq’s mixed performance—with some improvements in detection and reporting (DR) but an overall cluster decline—illustrates the challenge of sustaining health system gains in volatile settings without consistent, comprehensive investment and policy commitment. Similarly, Yemen’s persistently low scores in prevention (PR) mirror challenges observed in sub-Saharan Africa, where under-resourced health systems struggle to prioritize preventive health measures, leading to high public health burdens and limited security capabilities (Nasser & Alghawli, 2024).

The results indicated that the challenges faced by Iraq, Yemen, and Syria in health security are intricately linked to substantial resource allocation issues. For Iraq, the decline from the fourth to the fifth cluster, despite some advances in specific areas, such as detection, suggests that targeted investments without a holistic, integrated approach may be insufficient. Improvements in specific functions such as detection capabilities offer limited, temporary benefits if not supported by robust health infrastructure, workforce training, and preventive care, which are necessary for long-term resilience (Emami et al., 2024). The persistently low PR and DR scores for Yemen and Syria emphasize the urgent need for foundational health infrastructure investments, particularly in crisis-prone regions where preventive measures are essential to mitigate disease spread. Research in crisis settings highlights that strengthening early detection and preventive health measures reduces the spread of communicable diseases and alleviates public health burden over time (Meckawy et al., 2022). Allocating resources to these core health security areas, such as early intervention, surveillance, and effective disease management, can enhance health resilience and reduce vulnerability to future crises.

The findings of the correlation analysis have important policy implications. First, they suggested that investments in health security financing (F1) and general healthcare spending (F2) are likely critical for achieving higher health security performance. Governments and international organizations aiming to enhance global health security might consider these financial indicators as key benchmarks for strengthening the health system. By strategically increasing F1 and F2, countries may improve their ability to meet international health security standards and enhance their response capabilities in line with the Joint External Evaluation (JEE) and Performance Verification System (PVS) recommendations. To mitigate the identified challenges, policy interventions that prioritize preventive care, detection, and overall health system resilience are crucial for countries facing stagnation or decline in health security. For Iraq, policy adjustments focused on integrating resources across crisis responses, infrastructure, and workforce development could promote a balanced health system and ensure sustainable improvement. For Yemen and Syria, persistently low PR and DR scores underscore the need for both regional and international support to establish a basic public health infrastructure. Regional cooperation can also standardize protocols, enhance healthcare workforce training, and align public health policies with international standards, thus enabling lower-resource countries to manage public health threats more effectively and reduce their vulnerability to crises.

In summary, the findings of this study highlight the essential need for a unified regional approach to health security in WA, encouraging equitable resource allocation, coordinated health strategies, and comprehensive policy interventions that collectively enhance public health resilience across various economic and political landscapes.

Glossary

CoCoSo (The Combined Compromise Solution): A MCDM ranking model.

D-CRITIC (Distance Correlation-based Criteria Importance through Inter-criteria Correlation): A method for determining the relative importance of criteria.

Global Health Security Index (GHSI): A comprehensive assessment tool that measures health security capabilities across countries, focusing on various indicators that contribute to national and global health security.

Multi-Criteria Decision Making (MCDM): A set of methods and techniques used to evaluate and prioritize multiple conflicting criteria in decision-making processes.

WA (Western Asia) A geographical region in Asia.

Ethics and consent

No Ethical approval or consent needed.