Research on the Compilation of a Composite Index from the Perspective of Public Value—The Case of the Global Health Security Index

Abstract

The Global Health Security Index (GHSI), the first report on the world’s defensive capabilities against major infectious diseases, released in 2019, deviated from the actual performance of countries globally during COVID-19. Principal component analysis is used to deconstruct the multiple dimensions of public value on the GHSI index; reasons are explored for the deviation between the GHSI scoring results of countries worldwide and their performance in the COVID-19 pandemic, and the logical principles of composite index compilation are analyzed. The results show that the dimensions selected for inclusion in the GHSI are relatively isometric, and omissions of important values are the fundamental reason for the deviation. The composite index is the quantification of qualitative values, and public value affects the process of compiling the composite index in at least four respects: dimension selection, specific indicators, weight-setting, and evaluation-scoring. Therefore, public value should become the theoretical basis for compiling a composite index. This study effectively combines qualitative and quantitative research, provides theoretical explanations and practical guidance for further iterative updates of the GHSI and the optimization of world health and security governance tools, and provides a broader research perspective for the development of composite indices.

1. Introduction

On 23 January 2020, the novel coronavirus pandemic broke out, and Corona Virus Disease 2019 (hereinafter referred to as COVID-19) cases were reported worldwide. On 31 January 2020, the World Health Organization announced that the COVID-19 pandemic had become a public health emergency of international concern. As of April 2023, the number of confirmed cases globally has exceeded 680 million. The cumulative death toll of COVID-19 worldwide has exceeded 6 million, including more than 500,000 deaths in Brazil, India, Mexico, the United States and other countries [1]. Public health safety has become an important aspect of national and global governance [2,3].

The outbreak and spread of the COVID-19 pandemic is not the only threat and challenge to the health security of countries worldwide. In the past decade, global infectious diseases such as avian flu, SARS, bubonic plague and Ebola have had a great impact on the development of global politics and the economy, creating more serious global economic challenges than the two economic crises in 1988 and 2008 [4,5,6,7], causing serious casualties and property losses and threatening human health and safety. Therefore, there is a great demand in both theory and practice to accurately describe the current public health and safety situation of a country and scientifically evaluate that country’s ability to respond to sudden large-scale pandemics of infectious diseases.

Scholars have conducted a long series of studies on public health safety-evaluation, such as research on the outbreak, management, and aftermath of specific infectious disease pandemics [8,9,10]. At the microscale, they have explored the construction and evaluation of public health safety emergency systems in regions, cities, communities, and other areas [11,12]; at the macroscale, research has focused on evaluation methods, governance capabilities, and deficiencies in public health security at the national and global levels [13,14,15]. The above research focuses on the specific perspective of public health safety. The academic community still lacks a comprehensive evaluation index to evaluate the ability of countries worldwide to prevent sudden large-scale outbreaks of infectious disease. There is a public call for more in-depth research and an evaluation of the threat of pandemic diseases worldwide to provide early warning and preventive measures for potential public health events [16].

At this point, the Global Health Security Index (hereinafter referred to as GHSI) emerged. The proposal of the GHSI has a positive significance for countries seeking to improve national responses to public health and safety incidents, and scholars have conducted multiple studies using the GHSI. For example, Boyd et al., 2020, used the GHSI to assess the health and security vulnerabilities of their own country (and its neighboring countries) to ensure that their country is prepared to respond to global crises [17]. Through the GHSI, Lal et al., 2020, analyzed how the health system can withstand the impact of persistent large-scale outbreaks of infectious diseases from the perspective of universal health coverage, and proposed increasing investment in fragmented health resources owned at the grassroots level [18]. Wilson et al., 2020, matched known global large-scale outcomes of infectious disease, such as SARS, Middle East Respiratory Syndrome, and Ebola, with their country’s GHSI scores and established a linear regression model. They believe that the GHSI has the potential to become an effective tool for guiding the preparedness of biosafety and health governance systems [19].

Shortly after the release of the GHSI, the COVID-19 pandemic broke out. This pandemic is not only a test and challenge to the health and security systems of various countries, but also an observation and test of the efficacy of the GHSI. Geometrically increasing infection and mortality rates, as well as the global spread, have confirmed some of the conclusions in the GHSI report—namely, that no country was fully prepared for an upcoming global pandemic. At the same time, however, the scores and rankings of the most critical countries in the report deviate from the actual performance of countries during COVID-19, creating many controversies.

Some countries that ranked and scored lower in the GHSI, such as Vietnam (ranked 50th with a score of 49.1), New Zealand (ranked 35th with a score of 54.0), and China (ranked 51st, with a score of 48.2), effectively controlled the spread of the pandemic within a relatively short period, achieving good governance results. New Zealand even achieved zero cases domestically as early as August 2020. The United States ranks first globally in terms of scores and rankings among all countries evaluated by the GHSI. However, during the COVID-19 pandemic, according to the standard of death toll per 100,000 people, the US ranked only 166th among 184 countries, and only 30th among the 35 “developed economies” defined by the International Monetary Fund (IMF) [20]. This deviation from reality has led to much criticism of the GHSI. Aitken (2020) measured the disease burden of 100 countries during the COVID-19 pandemic, and found that the actual situation is contrary to the results predicted by the GHSI [21]. Dalglish (2020) believes that the GHSI has drawn some inaccurate conclusions in the field of world health and safety, and that COVID-19 is exposing this problem [22]. Razavi (2020) believes that the GHSI has not added any new value to today’s global health and security governance [23]. Some scholars pointed out that even the most casual observers know that the two countries with the highest scores on the GHSI (the United States and the United Kingdom) have the highest mortality rates of COVID-19, and their handling of this first-class disease has been quite poor [16,24].

Why does the GHSI’s evaluation and ranking of countries in the world deviate so much from their actual performance in the COVID-19 pandemic? The innovation of this article lies in the deconstruction of GHSI through public value, while expanding this approach to the composite index, and applying the results to a more general context. This article attempts to raise and answer the following three questions: First, what value dimensions are included in the GHSI, and what are the positions and substantive relationships of these values in space? Second, will there be a gap between the score ranking and the actual situation only in the GHSI? Can more accurate data and finer indicators reduce the differences between reality and research? If not, what can a different composite index achieve? Third, what is the logical basis and theoretical basis behind the development of composite indices, especially weighted average indices?

The purpose of this research is to use principal component analysis to deconstruct a complex index system, exploring the internal relationships between the constituent dimensions of the index and striving to make contributions in the following areas: First, it analyzes the preparation methods and specific evaluation rules of the GHSI, studies the recent status and characteristics of global health and security guarantee capabilities, and gains a more comprehensive understanding of the recent levels, trends and forefront of global health and security development. Second, extracting the core public value of the GHSI, and expanding it to an argument about the construction of composite indices, requires consideration of a richer set of dimensions of public value to improve its objectivity and explanatory power. Third, the principles and theoretical basis of the compilation of composite indices are traced and the connection between the compilation of composite indices and public value is explored.

This article has the following structure: Section 2 introduces the theoretical basis and data sources of the GHSI, and the PCA as a methodology is explained. Section 3 presents the results of the PCA. Section 4 presents a discussion, expanding the results to the connection between public value and composite indices. Finally, in Section 5 we conclude the paper and discuss potential future research directions.

2. Materials and Methods

2.1. Data Source

There are currently two internationally recognized and highly credible public health safety evaluation indices. One is the Joint External Evaluation (JEE) designed by the World Health Organization (WHO) in accordance with International Health Regulations. The second is the Global Health Security Index (GHSI), jointly developed and designed by the Nuclear Threat Initiative and the Johns Hopkins Center for Health Security in collaboration with the Economist Intelligence Unit (EIU). The JEE, as a workflow and tool, regularly evaluates the health and safety levels and capabilities of various countries worldwide [25]. It is suitable for countries to use to integrate resources from multiple departments and institutions, and to monitor and evaluate their own ability to prevent and detect various public health risks [13,26,27,28]. The GHSI absorbs the JEE evaluation indicators and improves on the JEE [29], attempting to use a unified institutional framework to conduct a comprehensive evaluation and horizontal comparison of the safety and health security capabilities of the 195 member parties that signed up to the International Health Regulations (2005).

On the basis of the JEE, the GHSI project team expanded the value connotations of public health safety assurance, including six dimensional indicators, as shown in

Table 1. GHSI Six Dimensional Indicators and Thirty-Four Microindicators.

	Prevention	Detection and Reporting	Rapid Response	Health System	Compliance with International Norms	Risk Environment
1	Antimicrobial resistance	Laboratory system	Emergency preparedness and response planning	Health capacity in clinics, hospitals and community care centers	IHR reporting comliance and disaster risk reduction	Political and security risks
2	Zoonotic disease	Surveillance and reporting	Exercising response plans	Medical countermeasures and personnel deployment	Cross-border agreements on public and animal health emergency response	Socioeconomic resilience
3	Biosecurity	Epidemiology workforce	Emergency response operation	Healthcare access	International commitments	Infrastructure adequacy
4	Biosafety	Data integration berween human/animal/ environmental health sectors	Linking public health and security authorities	Communications with healthcare workers during a public health emergency	JEE and PVS	Entironmental risks
5	Dual-use research and culture of responsible science		Risk communication	Infection control practices and availability of equipment	Financing	Public health vulnerabilities
6			Access to communications infrastructure	Capacity to test and approve new medical countermeasures	Commitment to sharing of genetic & biological data & specimens
7			Trade and travel restrictions

: ① Prevention (PRE): preventing the occurrence or release of pathogens; ② Detection and reporting (DAR): early monitoring and reporting of potential international outbreaks of concern; ③ Rapid response (RR): quickly responding to and reducing the spread of the pandemic; ④ Health system (HS): a solid and comprehensive health system supporting the treatment of patients and the protection of healthcare workers; ⑤ Compliance with international norms (CWIN): commitment to improving national capabilities, bridging gaps, raising funds, and complying with global norms; ⑥ Risk environment (RE): the overall risk environment and national vulnerability to biological threats. The GHSI includes 34 micro indicators and 85 specific implementation indicators. The GHSI project team collected and organized official public data from 195 countries and, for the first time, established a globally unified health and safety assurance system framed as an index. The aim is to measure the ability of each country to respond to infectious disease outbreaks and ensure health and safety, and to better evaluate the ability of each country to respond to major infectious disease outbreaks. It is currently the most comprehensive national public health evaluation index in the world.

In this indicator system, all data are sourced from official public information from the World Health Organization and various countries, and data from different units and measurement methods are normalized so that they are dimensionless. The score range for all dimensions is 0–100, with 100 being the best possible score. Subsequently, final scores for the GHSI are obtained through weighted averaging. At the same time, the GHSI report provides 33 specific recommendations, including the recommendation that each country’s public health and safety capacity should follow the principles of openness and transparency and be regularly evaluated. Priority should be given to developing national biosafety monitoring capabilities and a global biosafety monitoring system, and each country should develop large-scale pandemic prevention and response strategies to incorporate into its national security plans. The GHSI prioritizes the emergency-response and governance capabilities of various countries in complex and comprehensive situations such as biological events, deliberate attacks, laboratory leaks, major disease outbreaks, sudden pandemics of infectious diseases, and daily health and hygiene guarantees, with a focus on whether these capabilities operate normally, whether they are rehearsed during periods of normalcy, and whether they have been verified during specific events.

2.2. Principal Component Analysis

Previous research mainly focuses on how to use the GHSI to improve national capacity for the public health security guarantee [17,18,19]. To discuss whether the evaluation results of the GHSI are comprehensive and why they deviated from reality, it is necessary to further understand what logic the GHSI followed at the beginning of its construction, whether this logic is reasonable, and whether accurate evaluation results can be obtained.

To further analyze the construction of the GHSI and explore the possible reasons for empirical deviations, it is necessary to reduce information redundancy, extract the main variables, and explore the core value dimensions that influence the rankings and scores of various countries. The GHSI is a composite index with many indicators, multiple measurement dimensions, and a strong correlation between indicators, suitable for principal component analysis (hereinafter referred to as PCA). PCA integrates and reduces the initial variables through linear transformation, and after dimensionality reduction, the principal components are rendered independent of each other [30]. The use of PCA can mainly achieve the following three goals: First, PCA can reduce the number of indicator variables to simplify analysis, and retain the vast majority of information in measurement dimensions. Especially when the data has more than three variables, high-dimensional raw data will make visualization difficult. After PCA dimensionality reduction, complex raw data can be visualized more clearly [31]. This is conducive to identifying the core public values of GHSI measurement. Second, through PCA, a large amount of multidimensional information can be merged and reduced in dimensionality; what is more, the positions and relationships of multiple variables can also be discovered spatially. This will help us identify the correlations between different indicators in the GHSI, and identify missing public value dimensions. Finally, PCA can detect outlier samples, which will help us identify countries with special circumstances in the GHSI for further analysis and research [30]. Therefore, this article uses principal component analysis to analyze the GHSI.

3. Result

3.1. Correlation Matrix of Dimensions

As shown in Figure 1, in the correlation coefficient matrix of the GHSI’s six variables, there is a strong correlation between prevention (PRE), monitoring and reporting (DAR), rapid response (RR), and the health system (HS). The correlation between compliance with international conventions (CWIN) and risk environment (RE) is at a moderate level, and there is a strong correlation between the six variables measured by the GHSI and their values, making it suitable for principal component analysis.

3.2. Principal Components Calculation

According to the principal component variance contribution rate formula,

$$G\left(m\right)=\sum _{i=1}^m{\lambda }_i/\sum _{k=1}^p{\lambda }_k$$

According to the calculation, as shown in Figure 2, Principal component one (DIM1) explains 70% of the information in the six variables. Among the six variables, PRE, HS, PR, and DAR contribute more to DIM1 than the expected average contribution. Principal component two (DIM2) explains 12.3% of the information in six variables, with RE and CWIN contributing more to DIM2 than the expected average contribution among the six variables. Principal component three (DIM3) explains 6.7% of the information in six variables, and the variable with a higher contribution to DIM3 than the expected average contribution is CWIN. After screening and comparison, the first three principal components were ultimately selected for research. The decomposition degree of other main components is relatively low, so it was temporarily set aside. At the same time, we focused on analyzing the principal components DIM1 and DIM2 with a cumulative GM greater than 80%, and a variance contribution rate greater than 10%. DIM1 and DIM2 collectively explain 82.3% of the information in the GHSI, with the following expressions:

Principal component one:

$$PC1=0.17\times PR+0.32\times DAR+0.23\times RR+0.09\times HS+0.49\times CWIN-0.39\times RE$$

Principal component two:

$$PC2=0.14\times PR-0.11\times DAR+0.03\times RR+0.26\times HS-0.41\times CWIN+0.86\times RE$$

3.3. Explanation of the GHSI Principal Component Connotation

Understanding and interpreting the connotations of principal components requires a comprehensive consideration of multiple aspects. As shown in

Table 2. Component load, p-value, and correlation between variables and the principal components of each variable.

Dimensions	Correlation with DIM1	p-Value	Correlation with DIM2	p-Value	PC1 Loadings (Rotate = Varimax)	PC2 Loadings (Rotate = Varimax)
PRE	0.9216861	0.00	——	0.00	0.76	0.53
DAR	0.8623781	0.00	−0.2030356	0.00	0.83	0.31
RR	0.8705059	0.00	——	0.00	0.77	0.42
HS	0.9206799	0.00	——	0.00	0.70	0.61
CWIN	0.7501491	0.00	−0.4513629	0.00	0.87	0.04
RE	0.6640934	0.00	0.6870192	0.00	0.17	0.94

, component loadings refer to the correlation coefficient between the observed variable and the principal component, representing the impact of the original variable on the principal component. The observed variable p value is used for correlation-testing. Correlation shows the correlation between the original variable and the principal component. Meanwhile, the loadings are rotated; the rotation method is varimax. As shown in Figure 3 and Figure 4, the contributions of variables to principal components represent the relative importance of the variables in the variability of a given principal component. The higher the contribution is, the greater the contribution of the variable to the principal component. Variables that are not related to any principal component or are related only to dimensions with low overall explanatory rates can be removed to simplify the overall analysis. The combination of the four can be used to explain the actual meaning of the principal components.

According to the formula,

$$Contrib=\left[\left(C1\ast Eig1\right)+\left(C2\ast Eig2\right)\right]∕\left(Eig1+Eig2\right)$$

C1 and C2 are the contributions of variables on PC1 and PC2, respectively, while Eig1 and Eig2 are the eigenvalues of PC1 and PC2, respectively.

We calculate the total contribution of the given principal components, DIM1 and DIM2, as shown in Figure 4. The variables risk environment, health system, and prevention exceed the average expected contribution. Therefore, when explaining the connotation of DIM1 and DIM2, the above three dimensions are mainly considered. Figure 3 shows that the explanation of DIM1 requires a focus on the first four variables, which have load scores of over 70% in prevention, detection-and-reporting, rapid response and health systems, and compliance with international standards. The p-value passes the correlation test. In summary, health systems and prevention have the highest contribution to the given principal component DIM1. Combined with the remaining variables, the secondary indicators mostly focus on the prevention, control, and monitoring of daily infectious diseases, as well as the response and implementation of plans after the declaration of health emergencies. Therefore, DIM1 can be interpreted as a public health emergency response system; DIM2 has a load score of 95% in the risk environment dimension, and the p-value passes the correlation test. Among the variables, risk environment has the most prominent contribution to DIM2, which is negatively correlated or unrelated to the remaining other dimensions. The secondary indicators include political and security risks, socioeconomic resilience, infrastructure health, environmental risks, and public health vulnerability. Therefore, DIM2 can be interpreted as environmental risk. The variable with the highest contribution in DIM3 is compliance with international conventions, and comprehensive secondary indicators should be interpreted as the degree of informational openness and transparency.

3.4. Clustering of Principal Component Variables

The GHSI has a total of 6 primary indicators, 34 secondary indicators, and 85 specific evaluation criteria [32]. Massive quantities of data can easily blur the focus of an evaluation. As shown in Figure 5, the relationship between variables can be seen. Positively correlated variables are grouped together, while negatively correlated variables are located on the opposite side of the graph. The distance between variables and their origin represents the correlation between variables and principal components, and the smaller the circumference is around these variables, the greater the correlation. By using PCA to group all variables, it can be seen that, in space, the positions of the six variables are very concentrated, and the angle between HS, PRE, RR, DAR, and CWIN is very small. This proves that the similarity and correlation between dimensions is high and belongs to the same type of grouping. RE is set at a large angle from other variables and belongs to another group. This is consistent with interpretation results of the principal component meanings. We find that, in the GHSI, the choice of public value dimensions is isometric, without examining health and safety capabilities from multiple independent perspectives. Although the GHSI has evaluated and measured six dimensions, it focuses on measuring the value of only two dimensions. Different variables have highly homogeneous information in the same dimension, which can flatten the multidimensional elements of a social health response and affect the ability of indicators to reflect reality.

4. Discussion

From the factual results, it is difficult to adapt the GHSI so as to evaluate the different manifestations of countries’ responses to pandemic diseases. Many scholars have analyzed the reasons for the deviation between the GHSI measurement results and the actual situation. Baum (2021) proposed that the GHSI lacks consideration of the contributions of regional organizations, geographical location, and globalization in the spread and control of the pandemic [24]. Chang et al., 2020, found defects in the weight-setting of the GHSI by ranking the numerical scores and order of the six categories and conducting regression tests using the least squares method [33]. Razavi et al., 2020, argue that the GHSI tends to prioritize high-income countries [23]. This article summarizes these findings and finds that the above analyses attempt to pursue more accurate data collection and more refined measurement techniques, but have not yet deviated from the original public value paradigm of the GHSI. Under the existing paradigm of public value, the conclusions drawn by the GHSI are accurate, but the existing paradigm has limitations. Without analyzing, reconstructing, and improving the index from the perspective of public value, it is difficult to eliminate deviations at the root.

The GHSI is a composite evaluation index. From the perspective of public value, this article analyzes the deviation between the predicted results of the GHSI and practical reality. The reason is that the value dimension measured by the GHSI is relatively singular, lacks a tolerance mechanism to accommodate national heterogeneity, and does not fully consider political and cultural values. The process of compiling composite indices requires coupling and calculating things that have different functions, mechanisms, perspectives, and evaluation standards. The logical basis and process of its compilation are deeply influenced by public value. The governance of public health safety is the process by which the government creates public value, and refers to more than the public value of health technology [34]. The entire system should include political value, economic value, cultural value, and other aspects. Some important value dimensions were not considered in the GHSI, which is the fundamental reason for the deviation between the GHSI’s evaluation results and practical experience.

4.1. The Composite Index Is a Quantification of Qualitative Values

The GHSI, as a governance technology or tool, attempts to measure a series of characteristics of large-scale pandemic infectious disease prevention and preparedness work, and integrate them into a common indicator, to quantify and compare the performance of different countries on the same highly complex issue. The GHSI is a natural prior index that, at a superficial level, is an objective, neutral, and purely accurate numerical calculation. In fact, it reflects the paradigm that its creators believed the world should follow and the rules by which it should be measured. The entire process is deeply imprinted with values. Similarly, the posterior composite index is also strongly influenced by values due to differences in public value weights. The limitation of composite indicators is that they attempt to restructure and blend different types of public value through objective measurement methods but cannot avoid the profound impact of the creators and executors’ own values on composite indicators [35,36], which inevitably leads to deviations between the evaluation index and the real world.

The impact of values on indicators is reflected in at least the following four aspects: First, values influence dimensional choices. Composite indicators are usually used to measure a complex thing, and values determine which fields to choose to measure this complex thing in, such as politics, economy, culture, society, or ecology [37]. Second, values influence the selection of specific indicators. A dimension can be described by multiple indicators, and how to choose between multiple indicators depends on the judgment of the depth or importance of the impact of the indicators on the dimension, which depends on values [38]. Once again, values influence weight-setting. A composite index with weighted averages in each category has flexible and variable weights for the combination of primary and secondary indicators, evaluation rules, and total scores. However, different values can lead to differences in the weight-evaluation of the same public value, which directly affects the calculation results [39]. Finally, values influence specific evaluation scores. Although GHSI-type composite indicators have made every effort to use standardized quantifications and objective data, instead of subjective scoring indicators, to avoid errors caused by subjective factors determined by scoring personnel, there are still many indicators that are difficult to maintain as simple binaries or scales, and that rely on people to make judgments and evaluations. As a result, scoring is inevitably influenced by the worldview and values of the staff [40].

4.2. Lack of a Public Value Tolerance Mechanism

Different countries and people have different preferences and priority choices for the same public value. Different value preferences and priority choices can lead to governments and the public adopting differentiated governance and response methods in health and safety incidents. This difference does not necessarily have advantages or disadvantages. National governments will choose the “optimal solution” that best suits their national conditions and reality, especially in sudden large-scale outbreaks of infectious disease. The optimal solution for different countries is likely to be different. In the GHSI evaluation system, there is no tolerance mechanism that can accommodate differences in value preferences and country choices, and flexibly adjust the resultant score. Such a “one size fits all” unified template can easily erase the reasonable differences that cannot be ignored between different regions by stressing completely unified evaluation standards.

For example, for the public value of informational openness and transparency, the GHSI has a total of 34 indicators, of which 13 involve examining whether a country has reported and submitted plans to international institutions such as the World Health Organization. These reports include detailed evaluation requirements that emphasize open-source information such as “public access”, “public evidence”, and “reporting to international organizations”. Information transparency is indeed an important public value, but the GHSI overly emphasizes and relies on publicly available information for scoring, which means that there must be publicly available data or official evidence indicating that a certain kind of work has been or is being implemented. Otherwise, even if there are de facto operations on the ground, it will be considered invalid in the rating. This requirement has certain limitations. If the subject has the corresponding capabilities but does not choose to publicly record relevant evidence through official channels, or if some low- and middle-income countries can invest only limited resources into more urgent choices, such as expanding the coverage of primary health infrastructure, thereby limiting the human and financial resources to process key documents for public provision or access, it will result in non-objectively low scores. Understanding the reasons for the success of, or obstacles encountered by, different countries requires an analytical framework that can accommodate differences and nuanced interpretations. The GHSI’s overly rigid universal template cannot accommodate different public value choices, nor can it determine the feasibility and effectiveness of different public choices, making it difficult to respond to the deviation between the reality of the pandemic response and the evaluation results.

4.3. A Composite Index Needs to Measure Multiple Dimensions of Public Value and Broaden the Connotations of Rankings

Ranking is the acquisition and integration of public value data [41], and the evaluation index system is the comprehensive superposition of multiple rankings. Therefore, the selection and superposition method of determining value is crucial. For composite evaluation indices, the selected values need to be kept as independent as possible, and highly relevant indicators that share a certain direction or space should not be selected simultaneously to avoid ineffective or inefficient measurement [42]. For example, although the measurement framework of the GHSI is detailed and objective, its measurement dimensions are relatively concentrated, and the main reference quantities are the prevention and monitoring of biosafety systems and emergency responses in the event of a pandemic. The value differences in the political environment, and some value dimensions that have been proven to affect the national health security guarantee in the practice of the COVID-19 pandemic, such as the differences in collective action choices caused by cultural background differences, administrative organizational structure, social mobilization mechanisms and other public values, are seriously under-represented in the GHSI’s investigation. Therefore, the single-dimension results in the GHSI reflect more on the values of the governance technologies and tools of the main health and safety system in the evaluation ranking system, rather than deeply measuring the functionality and effectiveness of the entire health system.

4.3.1. Insufficient Consideration of Political Public Values and Lack of Reflection of Differences in Administrative Organizational Structure

Different countries have different administrative organizational structures, and different modes of interaction can be formed between countries and provinces, provinces and regions, regions and communities, and communities and the public. Public health is not a simple technical problem. The GHSI does not fully emphasize, and even occasionally ignores, complex political factors. In this COVID-19 pandemic, political factors have obviously become a more basic driving force, and even anchored a country’s public health policy. Its administrative organizational structure was an important factor in China’s ability to quickly control the spread of infections during this pandemic [43]. Similar to the way that the human body secretes a large amount of adrenaline during external crises and traumas, which improves various bodily functions, China’s administrative organizational structure has a corresponding capacity for heightened demand. At the beginning of the COVID-19 pandemic, medical resources in key pandemic cities were extremely scarce. The government mobilized resources from all sides, established two shelter hospitals within three days, increased the number of beds by more than a thousand, and dispatched tens of thousands of medical staff for support, which greatly improved the ability of medical personnel to generate diagnoses, admit patients and create isolation wards in a short time [44,45]. An administrative organizational structure differing from that of many countries has created an extremely important political and public value, allowing for flexibility in health and safety governance, with strong resource- and personnel-mobilization capabilities. In the short term, various resources that were originally insufficient per capita, geographically dispersed, and unevenly distributed are transferred to lower-level administrative units where they are urgently needed, units that can quickly adjust work methods according to instructions from superiors, significantly improving local pandemic response capabilities. Political public values, similar to national capabilities, government at all levels, and leadership capabilities, have demonstrated an irreplaceable and powerful role in this pandemic. Weakening political public value or using a single indicator to measure political public value will inevitably affect the degree of coupling between the GHSI results and reality.

4.3.2. Neglecting the Importance of Cultural Public Values

The COVID-19 pandemic vividly illustrated the different impacts of cultural backgrounds on health and safety governance. First, a country’s previous history of pandemics can become a common cultural background for both the country and its people [43]. Whether there has been any experience of large-scale pandemics of infectious diseases before, whether it was severe, whether the losses were significant, and whether the lessons learned were institutionalized and fixed will greatly affect the management of other pandemic diseases that are occurring or about to occur. This history will enable professional medical personnel to have more corresponding health knowledge; government agencies can respond faster based on past experience; and the public is more willing to accept guidance from public health policies [46]. Research shows that countries that experienced SARS could better cope with COVID-19 [47]. Second, the level of trust that the public has in the government is important. At the onset of a major infectious disease pandemic, people trust the government’s judgment, recognize the professional knowledge of medical staff, and are willing to change their daily behaviors according to health policies. These interactions and behaviors are part of the cultural values formed by the long-term and complex cultural evolution of each country, and have greatly affected the promotion of public health governance policies during the COVID-19 pandemic [48]. Finally, in the context of large-scale pandemics, difficult choices and trade-offs are often involved, and the priority judgments and value choices of each country are also closely related to cultural values [49]. However, the GHSI underestimated the role of cultural values in health and safety, and lacked the capacity to evaluate and investigate this dimension, making it difficult to explain the social reality of the COVID-19 pandemic in addition to governance technology.

4.4. For the Future Development of Composite Indices, Public Value Should Be the Foundation for the Compilation of Composite Indices

The word “public” in public value emphasizes the commonality and universality of this value. Public value theory is considered to encompass or surpass the theories of new public management, new public services, and governance. It is an extremely important theoretical development in the field of public management in the past two decades, and it is leading to a transformation of public sector research and practical paradigms, especially in performance measurement [50,51,52,53]. The GHSI can measure an “accurate” result, but this “accuracy” represents only the measurement results under the original value system of the indicator. The deviation between the actual experience of this pandemic and the predicted results of the GHSI vividly tells us that there are still some deep-rooted cognitive and practical habits that affect the process of compiling composite indices. Therefore, when compiling composite indices, we should not merely silo a certain “public value” as a certain dimension or indicator in the original evaluation system. This would be only a mild improvement on the original indicator system, and the overall situation remains within the original paradigm. In the process of compiling composite indices, public value should not be cosmetic. It should become the theoretical basis for the compilation of composite indices, and serve as the underlying logic to be followed when constructing an indicator system. At the same time, it is not possible to strengthen one or two values while ignoring and weakening other values, especially when examining the complex social, cultural, and political values embodied in public value. Public managers need to balance competing values, [54] and cannot simply attribute their topics of examination to “goals”, “performance”, or “benefits” [55]. Therefore, in the process of index compilation, we should not only identify, select, and measure value, but also consider creating value as the future development direction of composite indices.

4.5. As Evaluators and Users, We Should Comprehensively View the Rankings and Scores Provided by the GHSI and Other Composite Indices and Look for National Development Neighbors

National development neighbors refer to entities in the evaluation system that are similar to a country’s own development status [56]. Scoring and ranking are intuitive methods for measuring existing conditions, motivating developmental willingness, and monitoring operational effectiveness. The composite index provides the possibility of horizontal comparison and vertical research among multiple agents in complex systems, but individuals cannot and should not judge the action policies and results of agents based on scores and rankings. Ranking and scores should not become a single criterion for one-sided evaluation of the “strengths and weaknesses” between countries [57]. Evaluated nations and users need to have a rational view of rankings and scores. First, the index evaluation results can be seen as a tool for diagnosing and positioning the country itself, prompting decision-makers to shift their governance focus and investment willingness toward health and safety, and to expect positive changes [32]. Second, a more in-depth and detailed analysis of outliers or their own outlier scores should be conducted. If they are outliers in a “strong” direction, these need to be maintained and consolidated. Conversely, if they are outliers in a “weak” direction, the subject must be held to have shortcomings in creating a certain type of public value, and targeted optimization and improvement should be carried out. For example, Thailand and Uganda belong to typical “strong” outliers in the GHSI. Thailand ranks sixth in the GHSI total score and is the only country in the top 10 that does not belong to a high-income, developed country. Uganda is a low-income country, but in total score, it ranks 63rd, indicating that it has achieved more-than-expected results with fewer resources and funds [32]. Individuals from similar “strong” outliers can use the GHSI to determine their own advantages, leverage strengths and avoid weaknesses, and create greater public value. Finally, it is crucial to identify national development neighbors, expand advantages, fill gaps, and promote the overall development of national strength.

5. Conclusions

The GHSI has conducted a detailed assessment of the overall health and safety assurance capabilities of various countries worldwide. However, the single choice of public value dimension, and the lack of a public value tolerance mechanism, limit its objective explanatory power and accuracy. In order to ensure the sustainable development of the GHSI, multiple public values should be taken as the logical basis for the compilation and improvement of the GHSI. Specifically, the GHSI should broaden the selection dimensions of public values, strengthen the construction of tolerance mechanisms for political public values, and consider the diverse administrative organizational structures of different countries. Culture and history are one of the important indicators that affect a country’s ability to ensure health and safety. In the future development of the GHSI, it is also important to reflect cultural public values.

Finally, national and social governance remains a challenging field, and composite indices are important governance tools in the governance process. Compilers and users not only need to improve the professionalism and precision of governance tools and technologies, but also need to understand that national and social governance is a comprehensive and complex system. The compilation of composite indices needs to be based on a nuanced understanding of public value to achieve a better coupling between governance tools and objective results in society.