Universal health coverage mitigated COVID-19 health-related consequences in Asia Oceania

The COVID-19 pandemic has been a continual challenge since 2020, and it continues to impact people and industries as a disaster caused by a biological hazard. This study examined universal health coverage (UHC) scores in relation to the performance in combating COVID-19 in the Southeast Asian region (SEAR) and the Western Pacific region (WPR), along with the State Party Self-Assessment Annual Reporting (SPAR) index under the international health regulations (IHC). The numbers of infections and deaths per million population from December 2019 to June 2022 were used as primary outcomes to measure countries’ performance. Countries with UHC scores of 63 or higher had a significantly lower number of infected patients and deaths. In addition, several inter-capacity correlations within the SPAR capacities, including with C8 (the National Health Emergency Framework), as well as a very strong correlation to C4 (Food Safety), C5 (Laboratory), and C7 (Human Resources). Furthermore, C9 (Health Service Provisions) has a very strong correlation to C1 (Legislation and Financing), C2 (International Health Regulation Coordination and a National IHR Focal Point function), and C4 (Food Safety), suggesting that the capability to manage an emerging infectious disease form blocks of capacities. In conclusion, UHC clearly mitigated the health-related consequences of COVID-19 in South-East Asia Region (SEAR) and Western Pacific Region (WPR). Investigating the correlation between the SPAR capacities and UHC is a promising approach for future research, including the importance of the provision of health services, points of entry, and, most importantly, risk communications as critical factors for managing pandemic. This study constitutes a good opportunity to apply the SPAR index to define which capacities correlate with the outcome of the pandemic in terms of infections and deaths.


Introduction
During the initial stages of the COVID-19 pandemic, several countries and organizations put policies and regulations into effect to control COVID-19. The pandemic's impact on the world was felt in multiple respects, such as economic, social, and health impacts. During the initial phases of the COVID-19 pandemic, before the introduction of the vaccine, many people were infected, and they rapidly spread the disease to those in close contact with them. At the end of 2020, according to the data from the Worldmeter database [1], there had been 83 [2,3]. In addition, these variants are still developing, and novel versions are being substituted for the older ones. Due to the rapidly changing nature of the virus, people follow regulatory controls and seek medical treatment to prevent and protect themselves from the threat of COVID-19. A range of regulations are in place for the prevention of COVID-19, such as lockdown, travel restrictions, and social distancing policies. All of these policies can be efficacious when used at the appropriate time and place. For instance, New Zealand launched a policy of lockdown following their initial bout of COVID-19 cases [4,5]. Substantial research is being done to improve the progress of vaccines to combat the impact of COVID-19. Machingaidze et al. [6] reported that as of June 2021, more than 121 candidate vaccines had been developed, and approximately 18 vaccines had been approved for use in least one country.
A pandemic is among the most severe disasters. Although pandemics with similar viruses had been experienced previously (i.e., the severe acute respiratory syndrome (SARS) coronavirus (CoV) pandemic in 2002 [7] and the Middle East respiratory syndrome (MERS) CoV pandemic in 2012 [8]), the current SARS-CoV-2 virus is rapidly changing, which reduces the effect of the vaccine. Therefore, nonpharmacological interventions (NPI) are being used as classic but effective strategies, and it is recommended for the public to adapt their lifestyles to cope with the impact of the pandemic before the arrival of treatment methods (e.g., vaccines and medication [9]). Government policy for coping with the pandemic is critical for mitigating its impact. Anttiroiko et al. [10] noted that different strategies could yield different outcomes for mitigating the impacts of COVID-19. Another study noted that Asian countries are more proactive than European ones and found that the success of Asian countries in 2020-2021 should be considered the benchmark. Still, they also showed that this success depends upon the involvement of the people in the country. Four main strategies were suggested for the leaders to deal with the pandemic, namely, leadership, government transparency, legitimacy (public trust), and planning and preparedness [11]. In addition, risk criteria levels are among the main factors that can differentiate whether a country can effectively deal with COVID-19 [12]. It is also suggested that the government should take rapid action (e.g., lockdown, to deal with this point of risk). Lockdown and quarantine policies are still considered the main policies for controlling COVID-19 and have been widely implemented in multiple countries [12]. In particular, these policies can be effective for controlling the impact of COVID-19 during the early phase of the pandemic [13,14]. The pandemic requires the collaboration of academic and nonacademic healthcare-related stakeholders as well as a whole-community approach for dealing with COVID-19 [15][16][17].
Counts of cases of COVID-19 infection and death have often been used in research to measure countries' performance in dealing with the pandemic [18]. Therefore, the effectiveness of NPI in controlling the COVID-19 situation has been a central main research object for multiple academic studies and international health organizations. One study focused on the two indices of prevention and resilience to assess the effectiveness of controlling COVID-19 [19]. Regarding resilience, the outcomes may be the number of cases of infection and death. By contrast, for prevention, that study set the outcome as the amount of COVID-19 vaccines provided to the people by country [19].
Multiple studies have examined the factors that can reduce the risk of outbreaks of COVID-19. One study concluded that greater expenditures in the healthcare system could be one of the main factors for mitigating the impact of COVID-19 [20]. A review article found that ethnicity has some effects on the COVID-19 mortality rate, suggesting differences in susceptibility according to ethnicity [21]. The severity of COVID-19 may have a seasonal impact, leading to lower impacts in warmer seasons [22]. Similarly, population density, maximum temperature, and humidity can have some effects on the duration of the COVID-19 waves [23]. Another study explained that high levels of air pollutants, together with low wind speeds, can increase the probability of people getting infected by COVID-19 [24].
To implement policy for dealing with this kind of pandemic, the decision-making procedures should be rational [25] and ensure feasibility. For feasibility, application of policies should consider prioritize minimizing the impact of COVID-19, in particular, reducing deaths and preventing overloading of healthcare workers [26].
In March 2020, the World Health Organization (WHO) declared COVID-19 a pandemic [27] and proposed policies and regulations to support healthcare systems across the world. At present, 196 countries, including the 194 member states of WHO, have adopted the 2005 International Health Regulation (IHR) [28]. The IHR is intended to prevent and protect public health from the spread of disease [29]. The IHR is the primary regulation, and numerous frameworks and regulations have been created for it to comply with the IHR. The IHR also provides an index for countries to self-assess whether they can fully apply the IHR, termed the IHR State Party Self-Assessment Annual Report (SPAR) [30]. The SPAR index describes how well a given country can handle the risk of infectious disease disasters. Razavi et al. used the SPAR index as a primary key in defining the core capacities to deal with the COVID-19 situation [31]. Some research, such as the study of Satria et al. [32], also mentioned that the capacity of SPAR could potentially mitigate the impact of COVID-19 and even the future pandemic.
WHO also created an index for assessing the fundamentals of health service delivery for each country called the universal health coverage (UHC) score. The UHC was created to support WHO in its provision of equal health access for all people [33] and as indicator 3.8.1 "Coverage of essential health services" for the Sustainable Development Goals (SDGs) [34], Several studies have sought to prove that a country with high-value UHC scores will have a superior performance in dealing with the COVID-19 situation [35,36].
On the other hand, beginning in 2005, two disaster management frameworks for preventing and reducing the impacts of the COVID-19 disaster have been in operation. First, the Hyogo Framework for Action (HFA) was used from 2005 to 2015 [37]; following this, the Sendai Framework for Disaster Risk Reduction (Sendai Framework) has replaced the HFA and will remain valid until 2030 [38,39]. Furthermore, the Sendai Framework was developed to prevent and curb the damage inflicted by various types of disasters by enhancing disaster-related cooperation among all stakeholders [17]. In its first phase (2019), COVID-19 appeared in Asia. Evidence suggests that the pandemic struck the Asian continent without any warning. Following this, COVID-19 rapidly spread across the world.
Therefore, the combination of social indices might create a valid, relevant proxy for the evaluation of the vulnerability and coping capacity of countries against COVID-19 and other potential pandemics. This study compares the performance of pandemic management between countries according to the UHC score and the SPAR index to determine which aspects of IHR affects outcomes using the data from WHO's Southeast Asian region and the Western Pacific Region (WPR). It then examines the Asian and Oceania continents.

Related indices and frameworks
• UHC score Several studies have used the UHC index to analyze pandemic response. Thapa et al. discovered that higher UHC values are positively correlated with protection from the COVID-19, particularly for countries with high levels of social capital [40]. The attempts that various countries have made to reach the UHC goals indicate the significant use of UHC scores for identifying the effectiveness of each country's response to the COVID-19 pandemic. WHO has provided the data for various indicators of UHC coverage on its official website, WHO Global Health Observatory (WHO GHO) [41,42]. It may be possible to hypothesize that the service coverage index of the UHC (SDG 3.8.1) is the most representative index for public health by country, and the UHC score has been chosen as an independent variable by multiple research. For example, significant novel research focusing on the using of the UHC index to analyze the capabilities of the country to cope with health-related problems such as noncommunicable, and communicable disease [43]. Regarding this, the UHC index with a total score of 100, some novel studies such as [43,44] indicate that UHC score of 80 can indicate a good performance for the health coverage. In contrast, the study of Khan et al. [45] used the world average of the UHC index to be the main criteria to separate the country based on the high and low levels of the UHC index. Thapa et al. [40] performed an analysis to identify correlations between the UHC index and the state of the COVID-19 pandemic. The results of this study can be summarized in the observation that the UHC index shows a strong correlation with the state of the COVID-19 pandemic, indicating that higher values on the UHC index can yield better performance for the COVID-19 pandemic.

• SPAR
The SPAR features 13 capacity categories and 24 indicators to measure the progress of fully adopting the IHR. The scores for each capacity are measured on a scale of 1-100 [46]. The 13 capacities are given in Table 1. In addition, each country self-reports its capacity status, and the results are available from https://extranet.who.int/e-spar. Furthermore, SPAR measures the level of preparedness for public health emergencies and other events [31]. Gilbert et al. [47], adopted this index to measure the level of preparedness among African countries in dealing with the COVID-19 pandemic and Wang et al. [48] analyzed the Global Health Security index together with the SPAR index for multiple countries, including Iran, Japan, and South Korea, based on COVID-19. Kandel et al. [30] noted that C1-C11 measure the capacities for supporting government in coping with the COVID-19 situation. It can be proposed from this that the SPAR capacities will likely have high effectiveness for mitigating the COVID-19. Regarding the previous analysis of novel research, there were some research performed the analysis to indicate which SPAR capacity has a relationship with the COVID-19 pandemic, such as infections and deaths [49]. Capacity to respond to radiation emergency events such as nuclear radiation Note. Data for the State Party Self-Assessment Annual Reporting (SPAR) capacities definitions were adopted from the World Health Organization (2020), Electronic State Parties Self-Assessment Annual Reporting Tool (e-SPAR), 2019 [45].
• HEALTH-EDRM The HEALTH-EDRM framework is the most recent disaster health management framework from the WHO against health emergencies and disasters, created in accordance with the Sendai Framework. It was launched in October 2019 in collaboration with but not limited to 27 member countries worldwide and WHO experts [18]. The main objective of the HEALTH-EDRM framework is to enhance health-related functions to protect people's lives [50]. These functions include prevention, preparedness, and readiness [50]. The HEALTH-EDRM framework concept seeks to improve some on the aspects present in the previous frameworks and regulations, including the Sendai Framework, the World Health Assembly, and the SDGs [50]. Means of improving disaster management based on the HEALTH-EDRM framework are shown in Table 2.
The six main stakeholders in the healthcare and disaster management systems must work together to reach the goal of the HEALTH-EDRM framework. A summary of each stakeholder's role is shown in Table 3.
Substantial research has been undertaken to describe the HEALTH-EDRM framework as a tool for creating partnerships and promoting collaborative research based on the health-related aspects [51,52]. Numerous studies provide information about the HEALTH-EDRM framework and its ease of implementation owing to the low cost of the implementation process [52].

COVID-19 situation and policies in Asia and Oceania
• COVID-19 in Asia Asia was the first continent affected by COVID-19. The virus originated in China and then spread to other countries in the different regions of Asian. South Asia, in particular, was significantly impacted by COVID-19. Low-income countries were especially vulnerable due to their insufficient healthcare systems, which lacked the capacity [9] to support patients in a pandemic crisis of this magnitude. In the initial stages of the COVID-19 in the South Asia region, the number of cases was limited [53]. However, response to and management of the disease nevertheless sufficiently spread news and information on the disease [53]. In addition, this region is the most impoverished in Asia, with 21% of the total population of Asia [54]. The regional healthcare systems were not able to match those of other regions in supporting COVID-19 patients. During the early stages of the pandemic, most South Asian countries adopted social distancing measures and the lockdown policies [55]. At present in Asia, COVID-19 cases continue to climb. Lee et al. [56] predicted that the ongoing increase in COVID-19 cases is due to migration of workers to their hometown from Bangladesh, India, and Pakistan. Countries in other regions, such as Japan, have also experienced significant effects from COVID-19. Using the results of a questionnaire survey, Suppasri et al. [57] found that most Japanese people were afraid of the COVID-19 pandemic, drawing on the evidence that they sought to protect themselves by wearing a mask. The most impactful effects were the decreased domestic demand for goods and services, entailing a dramatic reduction in the tourism business and deleterious effects on health [58]. Ultimately, for the overall policies that were implemented and applied in the Asia continent, this continent shows outstanding the other regions based on more past experiences related to the pandemic crisis such as SARS and MERS [59,60]. Another key success factor that caused Asian countries to perform better for coping with the pandemic was the ability to announce COVID-19 control policies such as travel restrictions and social distancing for people within a country. However, some other areas, such as Hong Kong and Singapore, have had some problems in implementing the policies due to political issues [60]. Finally, as the South Asia region has high population concentrations of populations and poverty, it was difficult to control COVID-19 in every region [61].
• COVID-19 in Oceania COVID-19 had a smaller impact on Oceania due to its geography, being made up of island nations. To control the influx of COVID-19, governments closed the borders [62]. At the beginning of the pandemic, 70% of the small island countries in Oceania had zero COVID-19 affected cases [63]. However, even though Oceania countries thus dealt with COVID-19, some COVID-19 clusters did arise in this region. An example of this can be found in the Ruby Princess cruise ship cluster, which arrived in Australia on March 19, 2020 [64]. Describing the COVID-19 control policies in the Oceania counties, Zhao [65] explained that governments in Oceania focused on preventing COVID-19 from entering their countries. The policies that countries used mainly focused on the point of entry and the reactive lockdown policies for preventing the spread of COVID-19. The rapid response of countries in Oceania, in particular that of Australia and New Zealand, exhibited high effectiveness in controlling and preventing a significantly negative situations for COVID-19 regarding these countries [63]. However, some countries, particularly Papua New Guinea, encountered problems in managing COVID-19 due to their limited capacity of their health care systems, including low numbers of health care workers [66], as witnessed in their low UHC score [41].

Research designs
In response to the current state of the COVID-19 pandemic, multiple risk management frameworks and assessment indices are proposed.
• Potential methodologies for UHC analysis Two main methods have been adopted to extract the key contents of a UHC score key contents. The first is difference-in-difference analysis. Using this, Kim et al. [44] analyzed the impact of COVID-19 on childhood immunization coverage among the WHO countries. Their study separated UHC score into two groups, one with a UHC score of greater than or equal to 80 and the other, with a UHC score less than 80. The second method of analysis is a regression analysis. Recently, researchers have been focusing on the use of the UHC score as an independent variable and defining whether the UHC score has any influence on the infectious and death cases not [35,36]. Furthermore, some studies, including Coccia's [67], have analyzed the differences in the COVID-19 situation between countries with longer and shorter l lockdown periods. However, there are some limitations to the use of regression analysis, due to restrictions based on the regression's assumptions. To perform the analysis to define the difference between the two groups of data, one approach is to use statistical methods such as a t-test to examine hypothesis. Because the data focused on in this research are related to infections and deaths per million people, the methods used in a given case should match time series data in the form of accumulated monthly data. Coccia [67] performed an analysis to define the different performances in controlling COVID-19 between country groups with longer and shorter lockdown periods in relation to COVID-19 management performance, measured by the fatality rate and other factors. Huang et al. [68] performed a similar analysis to identify the difference in the number of confirmed cases between the two groups of city areas, namely, rural and urban areas by using the Mann-Whitney test to determine differences. In this research, the authors aimed to perform an analysis to determine whether a higher UHC score could be effectively mitigate the impact of the COVID-19, based on the number of infectious and death cases.
• Potential methodologies for SPAR analysis Many researchers have used clustering analysis methods with SPAR to identify the potential of countries for managing the COVID- 19 situation. To conduct clustering analysis, studies have used other variables to create a clustering model, investigating it together with analyses of JEE data and the various infectious and death rates [47]. In addition, Kandel et al. [30] performed an analysis by separating the type of country based on the predefined performance categories according to the SPAR index. This study elaborates a function to group the 13 capacities into five main categories: prevent, detect, respond, enabling function, and operational readiness.
Here, while we can classify a country into a group based on its COVID-19 performance, it is still necessary to specify the capacity for managing COVID-19 effectively. As stated Satria et al. [32] noted, it is possible that improved SPARs scores could improve the fatality rate from COVID-19 in a country. In this study, our analysis investigates correlations between SPAR index and also COVID-19 in a country, as defined based on the number of cases and number of deaths. By this means, the SPAR index can be extracted to help the country mitigate the impact of COVID-19.

Samples and data
• Relationship between UHC and the outcome Two WHO regions, South-East Asia region (SEAR) and the WPR, were selected as the focal area of this research due, as they cover the majority of Asia and Oceania and are in close proximity with the initial outbreak in China. We have excluded North Korea and Brunei due to data limitations, and the data from the remaining 30 countries were extracted to perform the analysis. Fig. 1 shows the countries included in the analysis.
Infections and deaths from COVID-19 in the South-East Asia Region (SEAR) and Western Pacific Region (WPR) (between January 31, 2020, and November 30, 2021) were used as the dependent variable. The 2019 UHC scores (UHC service coverage index [SDG 3.8.1]) as of December 2022 were retrieved from the official website of WHO, drawing on the reference of the WHO GHO [41,42]. Using these data, countries with a lower than average UHC score will be grouped as "L," and those with a higher than average score will be grouped as "H." The summary data are presented in Table 4. The average value for the UHC score from 183 countries was 64.04, and the average value for the UHC score in the SEAR along with WPR was 62.8. We therefore took a UHC score of 63 as the threshold to separate the "H" and "L" groups.
The first objective of this research was to assess the infections and deaths per million people for the H and L countries to illustrate the performance for dealing with COVID-19. The data were retrieved from Our World in Data (OWID) [69], a main source of information related to the COVID-19 worldwide. Both infections and deaths per million people are presented in Figs. 2 and 3. Note. H groups member countries with a higher UHC score than the UHC average score. L stands for the member countries with a lower UHC score than the UHC average score.
In Figs. 2 and 3, the accumulated infections and deaths are retrieved based on the last day of each month from January 2020 to November 2022.

• Relationship between SPAR and the outcome
In the second section of the analysis, the SPAR index in each category was selected as the independent variable. Each SPAR capacity score for a total of 13 capacities in the countries in the SEAR and WPR is presented in Table 5.
In Table 5, the SPAR index is presented for each country in the SEAR and WPR, along with the average SPAR index score from the countries that sent their assessment scores to WHO. On this basis, a correlation analysis has been performed using the 13 measured capacities from 30 countries in the SEAR and WPR.

Statistical analysis
The data for both accumulated infectious and deaths due to COVID-19 are set as time series data, collected at the end of each month. The objective of this research is to define the different performances between H and L countries in terms of their UHC score. The Mann-Whitney test was the main statistical tool used to perform the analysis, due to its nature as the nonparametric test [70]. The Mann-Whitney test assesses the equality of the means between the two groups of populations or samples [70]. The equation to calculate the Mann-Whitney statistics can be illustrated seen in Equation (1), where the variable U indicates the number of times that a data point in the first population or sample exceeds a corresponding data point from the second population. Next, the terms m and n indicate the number of data points in both time series data. Finally, term T can indicates the sum of rank attributes according to the preliminarily selected hypothesis. According to the concept proposed in Ref. [71], there are three main types of hypotheses, including identifying a difference between the two populations, proving whether the a population is greater than another population, and proving whether a population is less than another population. The first hypothesis asserts that the performance of the H countries is better than that of the L countries in terms of COVID-19 infections and deaths per million people. To assess this using the Mann-Whitney, we assert that the value of both infections and deaths per million people for the H groups is significantly lower than the L group.
The number of COVID-19 infections and deaths per million people has been used a central factor for defining the correlation among the SPAR capacities. Therefore, the analytical procedure has been performed with the changing rate of COVID-19 infections and deaths per million people on a half-year basis, using the formula in Equation (2).

Rate of change =
The amount of (infectious or death)cases per million people in the considered point The number of period until the considered point (Month) Six-month cycles were used for the analysis in this study. Thus, the number of COVID-19 infections and deaths at the end of June 2020, December 2020, and June 2021 was used to conduct the correlation analysis. Correlation analysis is a method for finding the strength and weakness in pairs of variables in a linear relationship [72]. The output of the correlation analysis can be measured on the basis of values between − 1 and 1, where the value of the correlation indicates the strong positive linear relationship between the two variables. 0 indicates that there is no linear relationship between the two variables, while − 1 indicates the strong negative linear relationship between the two variables. Therefore, the value of the correlation result can be computed based on Equation (3). Thus, the value of r indicates the correlation result between the comparison of the variables x and y.

Results
First, the analysis to determine whether the performance between the group of countries with a high UHC index differs from that with a low UHC index. Second, the analysis to investigate the correlation among the capacities of the SPAR index based on the focused countries along with the rate of change for COVID-19 infections and deaths.
The results are shown in Table 6, and p-values indicate significant differences between the group of H and L countries. In addition to the data for infections and death cases per million people in Fig. 3, it can be seen that the capability for H dominates the capability for the L group.

Statistical interpretation of the SPAR index
Two primary analyses are conducted based on the SPAR index. The first represents the performance of the correlation analysis among the 13 capacities of the SPAR index. The second analysis represents the performance of the correlation analysis to determine which capacity is correlated with the rate of change based on the value of infections and deaths for each country. Thus, the results of the analyses are represented together in Table 7.
Regarding the Pearson correlation analysis for the data in Tables 7 and in the first analysis, we decided to consider only capacities with a strong correlation. Here, a strong correlation analysis is defined with Pearson correlation coefficient higher than 0.6 [72,74]. The results of the SPAR index, which show a strong inter-capacity correlation, are shown in Table 8.
The correlation analysis, conducted to find the correlations for the SPAR capacity with the rate of infections and deaths per million people, did not reveal any strong correlation with the rate for all time periods (data not shown). Only C10 (Risk Communication) was weakly correlated with both the rate of the infections and deaths per million people for the 18-month period, based on the value of the correlation coefficient between 0.3 and 0.5 [72].

Discussion
A UHC score, as represented by the UHC service coverage index (SDG 3.8.1), indicates a positive influence on a country's ability to manage the pandemic. The results of the analyses indicate that countries with UHC scores higher than the threshold of 63 exhibited better performance based on its significantly lower infections and deaths per million people. This proves that the UHC score fulfills the objective of UHC, based on the coverage in the field of infectious diseases. Furthermore, the result of this study is in line with other studies [35,36,44], such that the higher the UHC score, the better the country can deal with the COVID-19 situation than countries with lower UHC scores.
However, this study found that the SPAR categories for capacities are mutually inter-related but do not directly lead to the capacity to manage the COVID-19 outcomes, which contrasts with Wijesekara et al. [49] and Satria et al. [32] who concluded that the SPAR score has a high potential for dealing with the COVID-19 situation. However, it is in line with the aspect that only the SPAR index cannot reflect the performance of the country in dealing with the COVID-19. Nevertheless, there are some the capacities that can lead the good performance in the COVID-19. From these results of this research, C8 (the National Health Emergency Framework) is seen to have a very strong correlation with C4 (Food Safety), C5 (Laboratory), and C7 (Human Resources). Furthermore, C9 (the Health Service Provision) has a very strong correlation with C1 (Legislation and Financing), C2 (IHR Coordination and NFP function), and C4 (Food Safety). Moreover, C11 (Points of Entry) has a very strong correlation with C1 (Legislation and Financing) and C13 (Radiation Emergencies). Finally, C12 (Chemical Events) has a very strong correlation with C7 (Human Resources), and C13 (Radiation Emergencies) has a very strong correlation with C7 (Human Resources) and C11 (Points of Entry).
First, for the group correlated with C8 (C4, C5, and C7) are considered essential for countries in managing the risks associated with unexpected disasters. This is because emergency disaster management forms the country's plan for managing emergencies. The HEALTH-EDRM framework is the latest emergency disaster management framework created by WHO. Countries are encouraged to adopt the HEALTH-EDRM framework to increase their capacity based on the C8 capacity. This capacity has a strong correlation with C4 (Food and Safety) and C5 (Laboratory). Furthermore, the HEALTH-EDRM framework indicated that avoidance of possible risk should be ensured to prevent damage due to disasters; based on this, C4 and C5 form the capacity related to the issue of risk entering. In addition, C7 (Human Resources) is a main key to ensuring success in the application of the concept of emergency disaster management. This pertains in particular to the HEALTH-EDRM framework, as the concept of the HEALTH-EDRM requires the cooperation of all stakeholders related to disaster management. Next, the results related to the correlation of the group of correlated with C9 (C1, C2, and C4), and correlated with C11 (C1), are considered to be closely related to the pandemic. For the group correlated with C9 (C1, C2, and C4), C9 represents the capacity to measure the speed of detection, response, and recovery, based on health emergency events, which require budget, planning, and the supervision of the IHR officers to deal with the health emergency events. For groups C11 and C1, the points of entry are considered to be essential aspects of the SPAR index and to be the main keys for preventing the health emergencies. Table 6 Comparison results based on COVID-19 infections cases per million people between H and L countries.

p-value
Infections p-value <0.01 Deaths p-value <0.01 Note. H represents WHO member countries with a higher UHC score than the average, and L represents countries with a lower one. This is because the majority of the health emergency events originate with people as carriers and transmitters of the disease (especially for COVID-19) [75]. In addition, surveillance measures at entry points require substantial budgets to fully monitor potential risk. However, the results of the correlation between the capacity of the SPAR index do not match the correlation between the SPAR index and the rate of COVID-19 infections or deaths per million people. In this study, the rate of infections and deaths per million people for the period of 6, 12, and 18 months from January 2020 to June 2021 was selected as the focus of this research. The result of the analysis indicated that a weak negative correlation [72] between C10 and the infection and death rates per million people over the 18 month period. Furthermore, the results of the correlation analysis did not indicate a strong correlation between any capacity measured by the SPAR index and the rate of infections and deaths per million people. However, it is a positive sign for WHO to show that the IHR can provide resilience, based on the current phase of the pandemic. In addition, C10 is only the capacity that features some correlation with COVID-19 or a strong correlation with C5, C6, C7, C8, C9, C12, and C13, which are related to the primary function of managing the pandemic. This can be explained based on the perspective of prevention from the entry points (C5 and C6) healthcare system and the disaster management framework (C7, C8, and C9). For C10 (risk communication), this indicates that the communication process for connecting all of the information systems between both of the IHR side, and the hazard-related organizations can reduce the impact of COVID-19 in the countries with a high C10 score. Risk communication is a primary key for the a disaster risk management (DRM) framework, relating to the phase of preparedness and response, which are the main phases of the DRM [76]. Risk communication is also an essential component and function of the HEALTH-EDRM framework. This framework promotes the use of real-time sharing of information between the public and government to reduce the impact from multiple disasters, including the pandemic [50]. In addition, this study exhibits a further analysis based on risk communication performed by the countries that are considered to have been less impacted by the pandemic. In addition, these countries have several experts who created the HEALTH-EDRM framework: New Zealand and Vietnam. In conclusion, the result for this objective is in line with the findings of Leask and Hooker [77], Abrams and   Greenhawt [78], and Huynh [79], who found that risk communication is the main function that can be used to reduce the impact of the COVID-19.
Vietnam is a developing country located in the Southeast Asia region that recorded its first COVID-19 infection on January 23, 2020, at the beginning of the pandemic. To prevent the influx of COVID-19, they stopped all flights from Wuhan because of the severe COVID-19 situation there [79]. Later, Vietnam sought to monitor COVID-19 cases around the world. Next, it tried to prevent COVID-19 from entering its borders by stopping all incoming flights from countries with severe COVID-19 epidemics [79]. Then, Vietnam also developed an application and technology to track COVID-19 infections in their country and apply self-quarantine and other intense COVID-19 preventative policies [79,80]. Furthermore, following a strategy from the HEALTH-EDRM framework, Vietnam also proposed guidelines for sharing information among multiple organizations, such as the healthcare industry, media, and transportation [81]. The government's actions were another factor that helped prevent the spread of COVID-19 [82].
New Zealand has among the lowest rates of COVID-19 infection and deaths in Oceania. Our findings show that developed countries dealt better with COVID-19. However, many developed countries have suffered grave consequences from COVID-19. New Zealand had little effects from COVID-19 due to its geographic isolation [62]. It has no direct connection to other countries. New Zealand had its first COVID-19 infection on February 26, 2020. Its policies for dealing with it included preparing hospital rooms and preventing infected people from entering the country's borders [62]. New Zealand took many actions to prevent COVID-19 from entering. The New Zealand government immediately announced a lockdown policy to prevent spread of COVID-19 [4,5]. In addition, because of its speedy response to the COVID-19 infections, New Zealand developed a COVID-19 rapid test kit to test for infection [37]. Because of its quick response and measures and policies, Taiwan also showed similar success to New Zealand [83]. For its recovery, New Zealand was forward-thinking and created policies to support the collaboration of businesses with the government [84].
From the analysis conducted in this research (including the main analysis), this study implies two main policies for reducing the impact of COVID-19. First, communication is essential for mitigating the impact of the pandemic. Using the case of Vietnam, the speed of sharing information among stakeholders related to risk organizations can allow the government and the related organizations to act effectively and efficiently. In addition, risk communication capacity, assessed using the SPAR index indicates a negative correlation the changing rate of COVID-19. Second, a quick decision -making is essential in reducing the impact of COVID-19. The government of New Zealand acted first by avoiding risking exposure to COVID-19. The main result of our analysis also indicates the case of New Zealand because C9 (the health service provision capacity) also indicates the speed of detection of health risk emergency. This was also strongly correlated with the majority of the important SPAR capacities.

Conclusion
The two core analytical result of this research leads to two injunctions. First, the member countries of the IHR should put greater effort into increasing the UHC score to reduce the impact of COVID-19. To improve UHC scores, member countries can follow the tracer indicators provided in the UHC manual [85], in particular that on infectious diseases, which is the most crucial tracer indicator for the pandemic related. Second, countries should put more effort into risk communication to reduce the impact of COVID-19. Hence, the health-related organizations should focus more on communicating processing going forward among health-related organizations, healthcare workers, and the public. This creates an effective way of providing more information related to COVID-19 to the public, which can prevent problems such as hoarding and excess anxiety [78].
This study also had some limitations. In every country, numbers of deaths are widely used and are among the most representative values showing the impact of COVID-19. However, coverage of COVID-19 testing and the number of people who receive vaccinations are also critical parameters to describe the impact of COVID-19. This study did not include such values, as the capacity for performing the COVID-19 testing and vaccine coverage were not equal for all countries [86,87], in particular for low-income countries Furthermore, our research results are not ideally accurate to establish how effectively COVID-19 was managed in each country due to the differences in measures taken and the quickness of the responses [88].
Another limitation and research gap identified is the specific functions or protocols for effectively managing COVID-19. There is room for improvement in terms of the analytical result for this research by focusing on the index in each SPAR capacity for scope to determine the direct function or protocol to deal with COVID-19 and future pandemics. The interaction between SPAR capacities and UHC scores is a very interesting future research question.
Finally, although the data for the WHO assessment including UHC and the SPAR index are from the latest figures, these latest figures were assessed before the COVID-19 pandemic, which could affect some of the results of this analysis. For further research, factors such as numbers of people who received the COVID-19 vaccine, the level of rule and regulations applied to dealing with the COVID-19 situation and COVID-19 variants can be used as alternative and extended factors for conducting the analysis. Thus, the COVID-19 variants B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), B.1.617.2 (Delta), and B.1.1.529 (Omicron) [89] may also be the triggering factors that indicate the phase of the COVID-19 pandemic. However, these factors require more proof to be applicable for performing further analysis. In the meantime, COVID-19 is continuing to spread its impact over many countries. While some countries were fully able to control COVID-19, others are still trying hard to solve the crisis. This analysis measures the impact of COVID-19 from its initial phase to its current state. When separating out its phases to use a time series analysis. In addition, WHO assessment indexes, such as JEE, can also be used as a factor in performing the analysis. This study can also be extended by increasing the number of countries examined in the analysis, as well as applying advanced analytical methods such as principal component analysis which requires larger sample sizes.