This study aimed to identify changes in the average score of countries' International Health Regulation (IHR) self-evaluation capacity (e-SPAR) in 2020 compared to 2019, and the factors associated with these changes. We collected the data from the World Health Organization (WHO) website in May 2021, then calculated the significant differences between the e-SPAR score in both years. Next, we conducted a test to identify the association between changes in member states' e-SPAR capacity scores and their COVID-19 case fatality rate (CFR), Human Development Index, Civil Liberties, and Government Effectiveness. The results showed that the average e-SPAR scores in 2020 were significantly higher than in 2019. Among the 154 countries, we included in this study, the scores of 98 countries increased in 2020, of which 37.75% were lower-middle-income countries. Meanwhile, among the 56 countries whose scores did not increase, 26 (46.42%) were high-income countries. The COVID-19 CFR was significantly associated with the changes in e-SPAR scores of 154 countries (p < 0.01), as well as the countries whose scores increased (p < 0.05). In conclusion, we consider e-SPAR to still be a useful tool to facilitate countries in monitoring their International Health Regulation (IHR) core capacity progress, especially in infectious disease control to prepare for future pandemics.
Emerging infectious diseases such as COVID-19 have become a significant health and security challenge for the world. After the Severe Acute Respiratory Syndrome (SARS) pandemic in 2003, International Health Regulation 2005 (IHR 2005) was adopted by the World Health Organization (WHO) to help countries in setting up their national preparedness for an efficient early alert and response system in handling public health events and emergencies that have the potential to cross borders[
Both self-and external-evaluation approaches have been proven to be accountable and effective in helping member countries increase their capacity to prepare for major public health threats[
In the context of the COVID-19 pandemic, a paper published in the early stage of the global pandemic claimed that countries labeled with strong operational preparedness capacities on e-SPAR (Electronic State Party Self-Assessment Annual Report) should have the capacity to respond to the COVID-19 pandemic effectively[
The 2019 and 2020 e-SPAR scores can be seen in Fig. 1. Among all 154 countries (group A), the average of 11 e-SPAR capacity scores as well as the scores for each capacity in 2020 were higher than their respective scores in 2019. From 2019 to 2020 the scores increased from 0.13 to 5.04. From the Wilcoxon signed-rank test, the average score of 11 capacities and the scores of all individual items in 2020 were significantly different from the score in 2019, except for capacities related to Zoonotic events and the human-animal interface, Food Safety, and Human Resources (p > 0.05). Next, among the 98 countries whose scores increased (group B), the Wilcoxon signed-rank test result showed that the average score of the 11 capacities increased significantly from 2019 to 2020. Risk communication, National Health Emergency Framework, and Ports of entry were the three capacities that mostly increased (p < 0.05). However, the capacity of Zoonotic events and the human-animal interface was the only capacity that was not significantly changed during the pandemic (p > 0.05). Meanwhile, among the 56 countries whose scores did not improve (group C), the Wilcoxon signed-rank test results showed the average score of the 11 capacities decreased significantly, as well as the capacity for National IHR focal point function, Food Safety, and Risk Communication (p < 0.05).
Graph: Figure 1 The countries' e-SPAR capacity scores in 2020 and 2019; asterisk: capacity with significant change (p < 0.05). Group A: 154 countries, Group B: 98 countries whose score increased, Group C: 56 countries whose score did not increase. Capacity: Legislation and financing (C1), IHR coordination and National IHR focal point function (C2), Zoonotic events and the human-animal interface (C3), Food safety (C4), Laboratory (C5), Surveillance (C6), Human Resources (C7), National Health Emergency Framework (C8), Health Service Provision (C9), Risk communication (C10), Points of entry (C11), Average of 11 capacities (AVG). This figure showed the countries' e-SPAR capacity scores in 2020 and 2019. Among all 154 countries (group A), the average of 11 e-SPAR capacity scores as well as the scores for each capacity in 2020 were higher than their respective scores in 2019. From 2019 to 2020 the scores increased from 0.13 to 5.04. From the test, it's known that within this group, capacities about Zoonotic events and the human-animal interface, Food Safety, and Human Resources (p > 0.05) were the three capacities that were significantly different. Next, among 98 countries whose scores increased (group B), the test result showed that the average score of the 11 capacities increased significantly from 2019 to 2020. Risk communication, National Health Emergency Framework, and Ports of entry were the 3 capacities that mostly increased (p < 0.05). In this group, the capacity of Zoonotic events and the human-animal interface was the only capacity that was not significantly changed during the pandemic (p > 0.05). Meanwhile, among 56 countries whose scores did not improve (group C), the test result showed the average score of the 11 capacities decreased significantly, as well as the capacity for National IHR focal point function, Food Safety, and Risk Communication (p < 0.05). Meanwhile, other capacities were not significantly changed during the first year of the COVID-19 pandemic, including capacity related to zoonotic disease control.
Description of COVID-19 CFR, countries' income level, HDI, CL, and GE among overall countries (group A), countries whose scores increased (group B) and not increased (group C) are shown in Table 1. Based on country income level, among the countries that experienced an increase in scores, the majority were lower-middle-income countries (LMICs) (37.75%), while only 18.4% of high-income countries (HICs) and low-income countries (LICs) showed an increase in scores. Meanwhile, HICs made up the majority of countries whose scores did not increase (46.43%). Similar to the distribution of countries' income levels, countries with medium development status were the group that mostly (28.57%) experienced an increase in e-SPAR scores during the pandemic. Meanwhile, countries with very high development status made up the majority of countries whose scores did not increase (55.36%) in 2020 compared to the previous year.
Table 1 Description of COVID-19 CFR, countries' income level, HDI, CL, and GE among overall countries (group A), countries whose scores increased (group B) and not increased (group C).
Overall Scores increased Scores did not increase Chi-square n = 154 (%) n = 98 (%) n = 56 (%) X2 Phi/Cramer's 0.00 1.00 0.00 Low 99 (64.29) 63 (64.29) 36 (64.29) High 55 (35.71) 35 (35.71) 20 (35.71) 16.79 0.00 0.33 LICs 24 (15.58) 18 (18.37) 6 (10.71) LMICs 46 (29.87) 37 (37.75) 9 (16.07) HMICs 40 (25.97) 25 (25.51) 15 (26.79) HICs 44 (28.57) 18 (18.37) 26 (46.43) 20.89 0.00 0.37 Low 32 (20.78) 24 (24.49) 8 (14.29) Medium 32 (20.78) 28 (28.57) 4 (7.14) High 37 (24.03) 24 (24.49) 13 (23.21) Very high 53 (34.41) 22 (22.45) 31 (55.36) 2.75 0.25 0.13 Not free 41 (26.62) 26 (26.53) 15 (26.79) Partially free 53 (34.41) 38 (38.77) 16 (34.57) Free 60 (38.96) 34 (34.69) 26 (46.43) 16.76 0.00 0.33 Weak 83 (53.90) 65 (66.33) 18 (32.14) Strong 71 (46.10) 33 (33.67) 38 (67.86)
This table describes the COVID-19 CFR, countries' income level, HDI, CL, and GE among overall countries (group A), countries whose scores increased (group B) and not increased (group C). Based on country income level, among the countries that experienced an increase in scores, the majority were lower-middle-income countries (LMICs) (37.75%), while the high-income countries (HICs) and low-income countries (LICs) were the least with 18.37% each. On the contrary, HICs were the majority among countries whose scores did not increase (46.43%). Similar to the distribution of income country level, countries with medium development status were the group that mostly (28.57%) experienced an increase in e-SPAR scores during the pandemic. Meanwhile, countries with very high development status were the majority in the group of countries whose scores did not increase (55.36%) in 2020 compared to the previous year. Next, for civil liberties status, most countries within the group of countries whose scores increased during the pandemic were "partially free" countries (38.77%), followed by "free" countries (34.69%), and the least was "not free" countries (26.53%). Meanwhile, among the countries whose scores did not increase during the pandemic, most of them were "free" countries (46.43%). Then, for government effectiveness, most of the countries whose scores increased during the pandemic had weak GE (66.33%) while most of the countries whose scores did not increase had a strong GE (67.86%). Meanwhile, for CFR of COVID-19, 64.29% of the countries in each group had low CFR.
When assessed in terms of Civil Liberties, most countries whose scores increased during the pandemic were "partially free" countries (38.77%), followed by "free" countries (34.69%), and finally "not free" countries (26.53%). Meanwhile, most of the countries whose scores did not increase during the pandemic were "free" countries (46.43%). Most of the countries whose scores increased during the pandemic had "weak" Government Effectiveness (GE) (66.33%) while most of the countries whose scores did not increase had a "strong" GE (67.86%). Meanwhile, the COVID-19 Case Fatality Rate was "low" for 64.29% of the countries in each group.
Associations between countries' changes in e-SPAR scores with HDI, CL, GE, and CFR are shown in Table 2. Models 1 and 2 were able to significantly described the changes in e-SPAR scores among 154 countries (R
Table 2 The multiple regression analysis results for group A (Model 1), group B (Model 2), and group C (Model 3).
Unstandardized coefficient Standardized coefficient t p VIF B Std. error Beta HDI −5.33 5.89 −0.14 −0.90 0.37 3.91 CL 0.03 0.02 0.16 1.51 0.13 2.03 GE −2.08 1.05 −0.34 −1.98 0.04* 5.19 CFR −0.61 0.23 −0.21 −2.64 0.01* 1.09 HDI −9.60 5.29 −0.33 −1.81 0.07 3.81 CL −0.811 1.00 −0.17 −0.81 0.42 5.24 GE 0.02 0.02 0.12 0.88 0.38 2.03 CFR −0.54 0.27 −0.19 −2.04 0.04* 1.06 HDI 8.29 8.21 0.26 1.01 0.32 3.62 CL −0.49 1.36 −0.10 −0.36 0.72 4.49 GE 0.01 0.03 0.06 0.29 0.77 2.00 CFR −0.13 0.26 −0.08 −0.52 0.61 1.22
From the table, it can be seen that Models 1 and 2 were able to significantly describe the changes in e-SPAR scores among 154 countries (constant = 5.55, F = 5.21, p < 0.01, R
The results of this study indicate that the average e-SPAR score of 154 countries in 2020 was significantly different from the score in 2019. There were 98 countries (63.63%) that experienced an increase in e-SPAR scores in 2020, while 56 countries (36.36%) experienced a decrease in e-SPAR scores. Among those whose scores increased, 63.26% were middle-income countries. Risk communication, National Health Emergency Framework, and Ports of entry were the 3 capacities that mostly increased while Zoonotic events and the human-animal interface was the only capacity that did not increase significantly. Meanwhile, among countries whose scores did not increase, most (46.43%) were high-income countries. IHR Coordination, Risk Communication, and Food Safety were the capacities that were significantly decreased in this group.
The results show that e-SPAR is an effective tool for countries to monitor the progress of their IHR core capacities[
Furthermore, our results showed that the capacity associated with zoonotic disease control was the only capacity whose scores did not change significantly in both score-increased and score-not-increased countries. This finding seems to show that the world is not ready to face a pandemic in the future, especially if it is a zoonotic disease. This is supported by many references showing that zoonotic diseases are major threats in the future[
Multiple regression analysis identifies COVID-19 CFR and GE as the two factors that are significantly associated with the changes in the e-SPAR scores of 154 countries. Several studies have also shown that countries with better government effectiveness scores, COVID-19 test numbers, and higher numbers of hospital beds[
Furthermore, the result of Model 2 showed that CFR was the only factor associated with the changes in e-SPAR scores in 2020 compared to 2019 (p < 0.05). While most of the countries with increased scores were middle-income countries, financial assistance may also have been the reason for the increase in their e-SPAR scores. Data showing the recipients of COVID-19 aid funds suggests that countries with a more severe COVID-19 burden received greater funds[
Even though the pandemic is still ongoing, this study aims to highlight these important findings. In the first year of the COVID-19 pandemic, while countries worldwide struggled to control and mitigate the pandemic, our research assessed countries' self-evaluation during a real crisis. Especially, as it is known that crisis of this scale does not occur regularly. A limitation of this study is that the changes in e-SPAR scores may have been under-scored as countries may have reported their e-SPAR scores only in the very early stage of the COVID-19 pandemic. These early e-SPAR scores may not be a true reflection of those countries' self-evaluation of their IHR capacity during the actual pandemic. Furthermore, at the outset of this study in early 2021, GNI 2020 data was not yet available. However, 2019 data was used to illustrate how pre-pandemic economic situations of countries could affect outcomes in controlling COVID-19. In addition, regarding the linear regression models, factors such as the number of donors, amount of funding received, or budget allocated by countries during the crisis to control the pandemic could also be the factors associated with the e-SPAR changes but were not considered in this study. Finally, the design of this study only allows the results to be considered as an association rather than a causal relationship.
The average of 11 e-SPAR capacity scores during the first year of the COVID-19 pandemic in 2020 was significantly different from the respective score in 2019. The lower-middle-income countries made up the majority of the countries whose e-SPAR scores increased, while high-income countries made the majority in the group whose scores did not increase. Our study results showed that CFR of COVID-19 was significantly associated with the changes in e-SPAR scores of 154 countries and 98 countries whose e-SPAR scores increased, but not countries whose e-SPAR scores did not increase. In conclusion, we consider that e-SPAR is an effective tool for countries to monitor the progress of their IHR core capacities.
The framework of this study was adopted from the Systemic Rapid Assessment (SYSRA) toolkit. We adopted SYSRA because of its coherence to the requirement for countries in implementing the IHR. In SYSRA, there are two types of assessment; horizontal and vertical. The "horizontal assessment" analyzes the health system within which the infectious disease program is embedded from a variety of perspectives. While, the second element, the "vertical assessment" is used to assess the infectious disease-specific component. Thus, both elements index the external environment (political, socio-demographic, economic) and need assessment (morbidity, mortality of the disease) as a consideration in assessing infectious diseases control programs[
To identify the changes in countries' self-evaluation capacity (e-SPAR) scores related to infectious disease control during the first year of the COVID-19 pandemic, we calculated the absolute difference between the score of IHR e-SPAR in 2019 and 2020. We collected this data from the WHO website in May 2021. There was a total of 13 items in the e-SPAR including Legislation and financing, IHR coordination and National IHR focal point function, Zoonotic events and the human-animal interface, Food Safety, Laboratory, Surveillance, Human Resources, National Health Emergency Framework, Health Service Provision, Risk communication, Points of entry, Chemical events and Radiation emergencies[
We used deaths instead of cases to reduce bias in the data we analyzed. This is because there are 3 levels used in the diagnosis of COVID-19 cases; suspected, probable, and confirmed cases. Thus, data on the number of confirmed cases of COVID-19 will fluctuate and be unstable due to changes in the diagnosis status of a patient[
Countries' income levels were determined by their Gross National Income (GNI) per capita. We collected the data for 2019 from World Development Indicator on the World Bank website. The income level of a country is determined by the country's GNI per capita[
We used HDI as the indicator to represent countries' development levels which reflect the social and environmental status of the country[
While countries' transparency was reported to be associated with their reported scores by the previous study, we also collected the CL score data from the Freedom House website for analysis[
The GE is one of the components in The Worldwide Governance Indicators (WGI). It was the indicator reflecting the quality of public services, policy formulation, and its implementation. We chose GE as one of the variables because the literature mentions that the role of government and good governance were very important in efforts to prevent and control infectious diseases[
196 countries reported their e-SPAR scores in both 2019 and 2020. Among those, only 154 countries with complete data of all indicators were used for analysis. We calculated the countries' average scores of 11 e-SPAR capacities as well as their average score for each capacity in 2019 and 2020, then calculated their absolute differences in these two consecutive years. In addition, as we found the data were not normally distributed, we conducted the Wilcoxon Sign-rank test for assessing the significance of the difference between scores.
Next, we divided the 154 countries into two groups based on their score classification, namely the group whose scores increased (n = 98), and the group whose scores did not increase (n = 56) for further analysis. A chi-square test was applied to identify the independence of countries' e-SPAR scores to their income levels, HDI, CL, GE, and COVID-19 CFR. Then we performed a multiple linear regression analysis[
This study had been presented at two conferences. The first was the 2022 International Conference on Occupational Health, Occupational Medicine, and Occupational Health Nursing in Tainan, Taiwan in April 2022. The second was the Global Health Security Conference 2022 in Singapore in June 2022. Furthermore, we are fully grateful to Taiwan's Ministry of Science and Technology for providing financial support to conduct this study, and to Taipei Medical University for providing free statistical software to analyze the data in this study.
F.B.S. and F.J.T. participated in designing the study, analyzing the data, interpreting the results, and revising the manuscript. F.B.S. and B.T. participated in doing the literature review and drafting the manuscript. All authors have read and approved the final version of the manuscript and agreed to be accountable for all aspects of the work.
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
The authors declare no competing interests.
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By Fauzi Budi Satria; Feng-Jen Tsai and Battsetseg Turbat
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