The potential effectiveness of the WHO International Health Regulations capacity requirements on control of the COVID-19 pandemic: a cross-sectional study of 114 countries

Abstract

Objectives

We examined if the WHO International Health Regulations (IHR) capacities were associated with better COVID-19 pandemic control.

Design

Observational study.

Setting

Population-based study of 114 countries.

Participants

General population.

Main outcome measures

For each country, we extracted: (1) the maximum rate of COVID-19 incidence increase per 100,000 population over any 5-day moving average period since the first 100 confirmed cases; (2) the maximum 14-day cumulative incidence rate since the first case; (3) the incidence and mortality within 30 days since the first case and first COVID-19-related death, respectively. We retrieved the 13 country-specific International Health Regulations capacities and constructed linear regression models to examine whether these capacities were associated with COVID-19 incidence and mortality, controlling for the Human Development Index, Gross Domestic Product, the population density, the Global Health Security index, prior exposure to SARS/MERS and Stringency Index.

Results

Countries with higher International Health Regulations score were significantly more likely to have lower incidence (β coefficient −24, 95% CI −35 to −13) and mortality (β coefficient −1.7, 95% CI −2.5 to −1.0) per 100,000 population within 30 days since the first COVID-19 diagnosis. A similar association was found for the other incidence outcomes. Analysis using different regression models controlling for various confounders showed a similarly significant association.

Conclusions

The International Health Regulations score was significantly associated with reduction in rate of incidence and mortality of COVID-19. These findings inform design of pandemic control strategies, and validated the International Health Regulations capacities as important metrics for countries that warrant evaluation and improvement of their health security capabilities.

Keywords

Clinical epidemiologic studies epidemiology health policy infectious diseases non-clinical

Introduction

Since the novel coronavirus disease 2019 (COVID-19) outbreak in the Hubei Province of China,¹ its transmission to other nations has resulted in a substantial global health burden.^2,3 COVID-19 can transmit from human to human in all communities in many regions.³ The World Health Organization has declared this emerging infectious disease as a Public Health Emergency of International Concern. On 11 March 2020, the World Health Organization announced it as a global pandemic event due to its rapid increase in the incidence and mortality in many countries.⁴ The disease has already led to more than 3 million confirmed cases and more than 217,000 deaths among patients with COVID-19, affecting 213 nations or regions.⁵

In response to the public health emergency of international concern, the World Health Organization has provided guidelines for state parties to control the pandemic of COVID-19.⁶ The guidelines consist of several major domains, including overall coordination, community engagement and risk communication, measurements of public health, health services and case management, prevention and control of pandemic, as well as surveillance mechanism. The United States Centers for Disease Control and Prevention has promoted mitigation strategies in communities with the COVID-19 epidemic by devising emergency framework, identification of cases and tracing close contacts.⁷ Different strategies have also been suggested for individuals, including self-quarantine, social distancing, hand washing, home cleaning and other hygienic measures.⁸ In addition to the continuity of containment, the World Health Organization Scientific and Technical Advisory Group for Infectious Hazards further proposed many other public health measures to eliminate the disease.⁹

Nevertheless, few studies have evaluated the effectiveness of country-specific public health capacities on controlling COVID-19 transmission. This study aimed to investigate the association between the capacity score of the International Health Regulations (IHRs) and the control of COVID-19 transmission. The study hypothesised that nations with a higher capacity score of IHRs were significantly associated with better control of COVID-19 transmission.

Methods

Data source

We extracted the capacity score of the Electronic State Parties Self-Assessment Annual Reporting Tool in 2019 from the World Health Organization website.¹⁰ The State Parties of the IHRs report to the World Health Assembly yearly on their implementation of capacity requirements under certain regulations as part of their obligations to devise and sustain minimum core capacities for regular surveillance and public health response. These regulations are legal instruments developed to improve the capacities of all State Parties for the prevention, detection, assessment, notification and response to public health events of international concern. The objective of the scoring system is to enhance transparency and mutual accountabilities among State Parties for global public health security. These initiatives are under the World Health Organization IHR Monitoring and Evaluation Framework.¹⁰ The e-SPAR score consists of 13 IHR capacities measured by 24 indicators (Supplementary Table 1), which are adopted to evaluate the performance of each capacity. Maintenance of these capacities in each country is crucial to international health protection, as their complete implementation and adherence could safeguard global health security. These 13 IHR capacities include: (1) Legislation and Financing; (2) IHR coordination and national IHR focal point functions; (3) Zoonotic events and the human-animal interface; (4) Food safety; (5) Laboratory; (6) Surveillance; (7) Human Resources; (8) National Health Emergency Framework; (9) Health Service Provision; (10) Risk communication; (11) Points of entry; (12) Chemical events; and (13) Radiation emergencies. A total of 166 countries have submitted their capacity scores via e-SPAR.

Furthermore, we collected data on potential covariates that could influence the incidence and mortality of COVID-19 in each country. These include: (1) the Global Health Security (GHS) index,¹¹ which is a comprehensive evaluation of the global health security capacities in 195 countries. GHS is different from IHR as it was devised to assess country-specific capability to mitigate and prevent epidemics while IHR focus on regular surveillance and public health response; (2) the Human Development Index (HDI)¹² in 2019, based on the United Nations Development Programme; (3) the Gross Domestic Product (GDP) in 2019 for each country, as extracted from the World Bank, the Central Intelligence Agency (CIA) World Factbook and the Economist Intelligence Unit¹³; (4) the population density of each nation retrieved by data from the World Population Review¹⁴; (5) previous exposure to Severe Acute Respiratory Syndrome (SARS) in 2003 and/or Mediterranean East Respiratory Syndrome (MERS) in 2012, as extracted from the World Health Organization^15,16; and (6) the COVID-19 Government Response Stringency Index, which is a composite measure based on nine response indicators including testing policies, school closures, workplace closures and travel bans.¹⁷

Outcome variables

A panel of experts consisting of physicians, public health professionals and epidemiologists discussed the most clinically meaningful outcome measures that could represent control of COVID-19, based on the timeframe used by the European Centre for Disease Prevention and Control and recently published literature.^18–20 We included the following primary outcome variables: (1) the maximum rate of incidence increase per 100,000 population over any moving average for overlapping 5-day time windows since the 100 confirmed COVID-19 cases; (2) the maximum 14-day cumulative incidence increase rate per 100,000 population since the first confirmed COVID-19 case; (3) the incidence per 100,000 population within 30 days since the first COVID-19 diagnosis; (4) the mortality per 100,000 population within 30 days since the first COVID-19-related death, for each nation from the Johns Hopkins Centre for Systems Science and Engineering (CSSE)²¹; (5) the maximum weekly excess all-cause mortality during the COVID-19 pandemic, defined as the maximum percentage difference between the number of weekly deaths in 2020 and the average number of deaths in the same week over the previous five years.²² The timeframe used for outcomes (1) and (2) was 22 January to 31 March 2020, while for outcomes (3) and (4), they were country-specific. The starting date ranged from 22 January to 2 March 2020 (median: 11 February 2020) while the ending date ranged from 20 February to 31 March 2020 (median: 11 March 2020).

Statistical analysis

The Stata version 14.0 (College Station, TX, USA) was used for all data entry and statistical analysis. We examined whether the overall e-SPAR score and its 13 IHR capacities were associated with outcomes (1) to (4). Four linear regression analyses were performed with (1) Model 1 consisting of GDP, HDI and population density; (2) Model 2 with the 2019 GHS index of each country added to model 1; (3) Model 3 with both GHS index and previous exposure to SARS and/or MERS of each country incorporated to model 1; and (4) Model 4 with GHS index, previous exposure to SARS and/or MERS, and Stringency Index of each country incorporated to model 1. We also evaluated the interaction and multicollinearity among these covariates. To evaluate the influence of outliers on the fit of the regression model, we conducted the leave-one-out cross validation. All p values < 0.05 were considered statistically significant. In terms of excess all-cause mortality, we conducted the comparative qualitative analysis by generating a scatter plot.

Results

The distribution of the IHR capacity score and covariates

The average of capacities for all World Health Organization regions was 63% in 2019, and the highest average scores were recorded for Europe (75%) while the lowest scores were observed in the African Region (44%) (Supplementary Figures 1 and 2). Supplementary Table 2 shows the distribution of the e-SPAR score in all available countries. For the overall e-SPAR index, Canada (score 99 out of 100), Russian Federation (99), Luxembourg (97), South Korea (97) and the United Arab Emirates (96) were assigned the highest scores. Supplementary Table 3 summarised the incidence and mortality outcomes for each country. Among the 114 countries included for analysis, the GDP ranged from 2 to 21,374 (median: 125) billion United States Dollar; HDI ranged from 0.434 to 0.956 (median: 0.798); population density ranged from 2 to 19,427 (median: 99) per km²; GHS score ranged from 23.6 to 83.5 (median: 46.2); Stringency Index ranged from 7.7 to 52.8 (median: 20.3). There were 29 and 27 countries with previous exposure to SARS and MERS, respectively.

The association between IHR capacities and COVID-19 incidence/mortality

Tables 1 to 4 show the association between the overall e-SPAR score and the various outcome variables while controlling for GDP, HDI, population density, GHS index and prior exposure to SARS and/or MERS. Countries with higher e-SPAR score were significantly more likely to have lower incidence per 100,000 population within 30 days since the first COVID-19 patient was diagnosed (β coefficient −24.1, 95% CI −35.2, −13.0, p < 0.001) (Table 3). A similar association was observed for the maximum rate of incidence increase per 100,000 population over any 5-day moving average period since the 100 confirmed cases have been reported in the nation (β coefficient −0.19, 95% CI −0.31, −0.08, p = 0.001) (Table 1); the maximum 14-day cumulative incidence rate per 100,000 population since the first confirmed case (β coefficient −2.08, 95% CI −3.27, −0.88, p = 0.001) (Table 2); and the mortality per 100,000 population within 30 days since the first COVID-19-related death was reported (β coefficient −1.74, 95% CI −2.52, −0.95, p < 0.001) (Table 4). All four models show that the e-SPAR score was associated with the incidence/mortality outcomes (Table 5). Country-specific HDI and the GHS index, but not GDP, population density, previous exposure to SARS and/or MERS, and Stringency Index were significantly associated with the four incidence and mortality outcomes. Among the 13 IHD capacities, ‘Legislation and Financing’, ‘IHR Coordination and National IHR Focal Point Functions’, ‘Zoonotic Events and the Human-animal Interface’, ‘Surveillance’, ‘National Health Emergency Framework’, ‘Points of Entry’, and ‘Chemical Events’ were consistently associated with lower incidence and mortality for all four outcomes (Table 6). There were no interactions or multicollinearity detected between the e-SPAR score, GHS index, previous exposure to SARS or MERS, Stringency Index and other potential covariates. The cross-validation did not observe any model error driven by outliers for all four outcomes: (1) root mean squared errors (MSE): 6.8 < 7.3; (2) root MSE: 72.7 < 78.0; (3) root MSE: 754.7 < 812.6; (4) Root MSE: 54.9 < 61.4. Figure 1 shows the distribution of maximum excess all-cause mortality and IHR capacity score for individual countries. There was a total of 33 countries included in the analysis, with Spain (132.4%), Italy (97.6%), the Netherlands (46.5%), Belgium (43.1%) and France (39.0%) having the highest excess all-cause mortality rates and moderate IHR capacity score (80–90). The lowest excess all-cause mortality rates were observed in Latvia (−2.5%), Poland (−0.5%) and Bulgaria (1.4%), although they also had lower IHR capacity score (60–80).

Figure 1.

The distribution of maximum excess all-cause mortality and IHR capacity score.

Table 1.

The association between the e-SPAR score and the maximum rate of incidence increase per 100,000 population over any 5-day moving average period since the 100 confirmed cases have been reported (n = 93).

	Model 1				Model 2				Model 3
	β coefficients	95% CI		p	β coefficients	95% CI		p	β coefficients	95% CI		p
e-SPAR score	−0.19	−0.31	−0.08	0.001	−0.15	−0.27	−0.04	0.009	−0.15	−0.27	−0.03	0.013
GDP	0.00	0.00	0.00	0.970	0.00	−0.00	0.00	0.263	0.00	−0.00	0.00	0.262
HDI	41.3	24.7	57.9	<0.001	49.8	32.1	67.5	<0.001	49.7	31.7	67.6	<0.001
Population density	−0.00	−0.00	0.00	0.634	−0.00	−0.00	0.00	0.529	−0.00	−0.00	0.00	0.542
GHS index					−0.20	−0.36	−0.03	0.021	−0.19	−0.36	−0.03	0.024
Previous exposure to SARS/MERS									−0.22	−3.33	2.90	0.889

e-SPAR: electronic State Parties Self-Assessment Annual Reporting Tool; GDP: Gross Domestic Product; GHS: Global Health Security; HDI: Human Development Index; MERS: Middle East Respiratory Syndrome; SARS: Severe Acute Respiratory Syndrome. The figures in bold indicate statistical significance (p < 0.05).

Table 2.

Factors associated with the maximum 14-day cumulative incidence rate per 100,000 population since the first case (n = 93).

	Model 1				Model 2				Model 3
	β coefficients	95% CI		p	β coefficients	95% CI		p	β coefficients	95% CI		p
e-SPAR score	−2.08	−3.27	−0.88	0.001	−1.64	−2.86	−0.42	0.009	−1.63	−2.90	−0.36	0.012
GDP	−0.00	−0.01	0.01	0.828	0.000	−0.00	0.01	0.341	0.00	−0.00	0.01	0.343
HDI	443.2	265.1	621.2	<0.001	533.3	343.4	723.1	<0.001	532.5	340.5	724.5	<0.001
Population density	−0.00	−0.02	0.01	0.652	−0.01	−0.02	0.01	0.547	−0.01	−0.02	0.01	0.557
GHS index					−2.08	−3.85	−0.31	0.022	−2.07	−3.87	−0.28	0.024
Previous exposure to SARS/MERS									−1.28	−34.7	32.2	0.940

Table 3.

Factors associated with the mortality per 100,000 population within 30 days since the first COVID-19-related death (n = 114).

	Model 1				Model 2				Model 3
	β coefficients	95% CI		p	β coefficients	95% CI		p	β coefficients	95% CI		p
e-SPAR score	−24.1	−35.2	−13.0	<0.001	−19.5	−30.6	−8.5	0.001	−18.4	−29.8	−7.10	0.002
GDP	−0.03	−0.09	0.04	0.368	0.03	−0.04	0.10	0.444	0.03	−0.04	0.10	0.409
HDI	3934.2	2430.0	5438.7	<0.001	5081.7	3477.4	6686.0	<0.001	5045.3	3436.2	6654.4	<0.001
Population density	0.01	−0.06	0.09	0.763	−0.02	−0.09	0.06	0.625	−0.02	−0.09	0.06	0.618
GHS index					−27.4	−44.2	−10.7	0.002	−26.2	−43.3	−9.2	0.003
Previous exposure to SARS/MERS									−139.7	−472.5	193.0	0.407

Table 4.

Factors associated with the mortality per 100,000 population within 30 days since the first case reported (n = 114).

	Model 1				Model 2				Model 3
	β coefficients	95% CI		p	β coefficients	95% CI		p	β coefficients	95% CI		p
e-SPAR score	−1.74	−2.52	−0.95	<0.001	−1.52	−2.32	−0.72	<0.001	−1.54	−2.37	−0.72	<0.00
GDP	−0.00	−0.00	0.00	0.943	0.00	−0.00	0.01	0.326	0.00	−0.00	0.01	0.340
HDI	201.6	95.2	308.0	<0.001	256.6	140.2	372.9	<0.001	257.4	140.4	374.5	<0.001
Population density	−0.00	−0.01	0.00	0.995	−0.00	−0.01	0.00	0.596	−0.00	−0.01	0.00	0.599
GHS index					−1.31	−2.53	−0.10	0.034	−1.34	−2.58	−0.10	0.034
Previous exposure to SARS/MERS									3.39	−20.8	27.6	0.782

Table 5.

Regression model incorporating Stringency Index as a covariate for the four outcomes (n = 114).

	A				B				C				D
	β	95% CI		p	β	95% CI		p	β	95% CI		p	β	95% CI		p
e-SPAR score	−0.16	−0.29	−0.04	0.010	−1.75	−3.08	−0.43	0.010	−21.70	−34.75	−8.65	0.001	−1.87	−2.83	−0.91	<0.001
GDP	0.00	0.00	0.00	0.303	0.00	0.00	0.01	0.386	0.03	−0.05	0.11	0.407	0.00	0.00	0.01	0.243
HDI	56.9	36.7	77.1	<0.001	609.4	392.4	826.3	<0.001	5746.8	3813.9	7679.7	<0.001	307.1	164.7	449.5	<0.001
Population density	0.00	0.00	0.00	0.686	0.00	−0.02	0.01	0.688	−0.01	−0.20	0.18	0.903	0.00	−0.01	0.02	0.805
GHS index	−0.22	−0.40	−0.05	0.014	−2.37	−4.27	−0.48	0.015	−30.65	−49.93	−11.37	0.002	−1.66	−3.08	−0.24	0.022
SARS/MERS	−0.01	−3.41	3.38	0.994	0.92	−35.52	37.35	0.960	−111.81	−491.73	268.11	0.560	−0.47	−28.46	27.52	0.973
Stringency Index	−0.07	−0.30	0.16	0.544	−0.68	−3.12	1.76	0.580	−8.54	−31.22	14.13	0.456	0.36	−1.31	2.03	0.666

A: the maximum rate of incidence increase per 100,000 population over any 5-day moving average period since the 100 confirmed cases; B: the maximum 14-day cumulative incidence rate per 100,000 population since the first case (n = 93); C: the mortality per 100,000 population within 30 days since the first COVID-19-related death (n = 114); D: the mortality per 100,000 population within 30 days since the first case reported (n = 114). e-SPAR: electronic State Parties Self-Assessment Annual Reporting Tool: GDP: Gross Domestic Product; GHS: Global Health Security. HDI: Human Development Index. MERS: Middle East Respiratory Syndrome; SARS: Severe Acute Respiratory Syndrome. The figures in bold indicate statistical significance (p < 0.05).

Table 6.

The association between the components of the e-SPAR score and the incidence and mortality rates of COVID-19.

	Incidence Outcome (A)*				Incidence Outcome (B)*				Incidence Outcome (C)*				Mortality*
International Health Regulations (IHR) Capacity	β	95% CI		P	β	95% CI		P	β	95% CI		P	β	95% CI		P
Average score	−0.194	−0.306	−0.083	0.001	−2.078	−3.273	−0.884	0.001	−24.125	−35.264	−12.985	<0.001	−1.739	−2.527	−0.952	<0.001
1. Legislation and Financing	−0.152	−0.235	−0.069	<0.001	−1.605	−2.497	−0.712	0.001	−15.344	−23.719	−6.969	<0.001	−1.250	−1.832	−0.668	<0.001
2. IHR Coordination and Focal Point Functions	−0.122	−0.215	−0.03	0.010	−1.276	−.2.268	−0.283	−0.012	−11.638	−19.985	−3.290	0.007	−0.879	−1.468	−0.290	0.004
3. Zoonotic Events and the Human–animal Interface	−0.08	−0.158	−0.001	0.047	−0.846	−1.688	−0.004	0.049	−9.943	−17.679	−2.207	0.012	−0.910	−1.448	−0.373	0.001
4. Food Safety	−0.089	−0.183	0.005	0.063	−1.041	−2.044	−0.039	0.042	−11.564	−20.967	−2.161	0.016	−0.942	−1.603	−0.281	0.006
5. Laboratory	0.031	−0.075	0.138	0.561	0.277	−0.864	1.419	0.63	−1.181	−11.866	9.504	0.827	−0.386	−1.140	0.368	0.312
6. Surveillance	−0.081	−0.159	−0.004	0.040	−0.891	−1.721	−0.062	0.036	−12.578	−20.850	−4.307	0.003	−1.072	−1.647	−0.496	<0.001
7. Human Resources	−0.074	−0.148	−0.001	0.048	−0.743	−1.534	0.047	0.065	−7.057	−14.745	0.632	0.072	−0.366	−0.915	0.183	0.189
8. National Health Emergency Framework	−0.118	−0.194	−0.042	0.003	−1.247	−2.063	−0.431	0.003	−16.695	−24.611	−8.780	<0.001	−1.007	−1.581	−0.434	0.001
9. Health Service Provision	−0.089	−0.182	0.003	0.057	−0.98	−1.967	0.008	0.052	−12.572	−21.786	−3.358	0.008	−0.740	−1.400	−0.080	0.028
10. Risk Communication	−0.021	−0.089	0.046	0.532	−0.238	−0.96	0.485	0.515	−3.924	−10.705	2.858	0.254	−0.359	−0.838	0.119	0.140
11. Points of Entry	−0.062	−0.114	−0.009	0.022	−0.637	−1.202	−0.072	0.028	−8.228	−13.697	−2.760	0.004	−0.562	−0.951	−0.173	0.005
12. Chemical Events	−0.058	−0.113	−0.004	0.035	−0.694	−1.292	−0.095	0.024	−7.442	−13.269	−1.614	0.013	−0.599	−1.063	−0.134	0.012
13. Radiation Emergencies	−0.034	−0.085	0.017	0.19	−0.393	−0.955	0.17	0.169	−6.798	−12.469	−1.126	0.019	−0.594	−1.044	−0.144	0.010

e-SPAR: electronic State Parties Self-Assessment Annual Reporting Tool. *The incidence and mortality outcomes include: (A) the maximum rate of incidence increase per 100,000 population over any 5-day moving average period since the 100 confirmed cases have been reported in the nation; (B) maximum 14-day cumulative incidence rate per 100,000 population since the first case; (C) the incidence per 100,000 population within 30 days since the first COVID-19 diagnosis; and (D) the incidence per 100,000 population within 30 days since the first COVID-19-related death. The figures in bold indicate statistical significance (p < 0.05).

Discussion

Statement of principal findings

This analysis included 114 countries globally and reported that a higher IHR capacity score was significantly associated with more optimal control of transmission of COVID-19. In particular, ‘Legislation and Financing’, ‘IHR Coordination and National IHR Focal Point Functions’, ‘Zoonotic Events and the Human-animal Interface’, ‘Surveillance’, ‘National Health Emergency Framework’, ‘Points of Entry’ and ‘Chemical Events’ were consistently associated with lower increasing rate of incidence and mortality. Regions with higher HDI were found to have a greater increase rate of incidence and mortality.

Meaning of the study and relationship with other studies

Previous studies have estimated the effect of various public health control strategies on mitigation of the COVID-19 pandemic.^23,24 Prem et al. have examined the effect of physical distancing measures and changes in population mixing on the progression of the COVID-19 epidemic in Wuhan.²³ In addition, another study explored the impact of behavioural changes and public health measures on COVID-19 and influenza transmission in the community based on laboratory-confirmed cases, influenza surveillance data and influenza hospitalisations in children.²⁴ Yet another study based on the municipal Notifiable Disease Report System of Wuhan evaluated the potential impact of non-pharmaceutical public health interventions on rates of laboratory-confirmed COVID-19 infections.²⁵ The researchers reported that these non-pharmaceutical public health interventions were associated with improved COVID-19 control in terms of the daily confirmed case rate and the effective reproduction number of SARS-CoV-2.

Besides, non-pharmaceutical public health interventions are more cost-effective than pharmacological intervention during pandemics. For example, a study investigating the cost-effectiveness of different pandemic measures²⁶ showed that non-pharmaceutical public health interventions were significantly more cost-effective than vaccination and antiviral treatment in the influenza pandemic. However, there is a research gap in evaluating the effectiveness of specific non-pharmaceutical public health interventions on controlling global pandemics. There is no evidence at a high-quality level which demonstrated the impact of the implementation of non-pharmaceutical public health interventions during the pandemic.²⁷ Moreover, recent studies on non-pharmaceutical public health interventions such as social distancing measures on controlling the COVID-19 transmission were mainly reviews or based on modelling research.^28,29

This is the first study indicating that higher IHR capacity score and related capabilities in a region, as evaluated by e-SPAR, have the potential to mitigate and control COVID-19 transmission. The implication is that countries that are dedicated to combatting COVID-19 transmission may formulate strategies according to the IHR guidelines and capacities. The reason for the positive association observed between IHR capacity score and better control of the COVID-19 pandemic includes the fact that IHR has already prioritised the public health capacity that is contextualised to the nation’s health system, enabling real-life changes in COVID-19 cases to be realised. Furthermore, the existence of tested, proven and functional constructs for controlling pandemic outbreaks at the source was also taken into consideration by the e-SPAR score. The questionnaires used to calculate the score include those that evaluate if the nation’s capacity is regularly checked and functional in real-world settings, therefore indicating the reliability, robustness and generalisability of the capacity score in predicting pandemic control. The current results highlight that the capacities and sub-indicators of the e-SPAR score can be used as a framework for policy-making by informing concrete steps to finance and filling policy gaps in nations where public health strategies are inadequate.

We observed that many capacities of IHR were consistently associated with better control of the COVID-19 pandemic. Indeed, whether legislation, laws, regulations, administrative requirements, policies or other government instruments in place are sufficient for implementation of IHR is important. A functional mechanism is also needed for the coordination of relevant sectors in the implementation of IHR. To combat the pandemic, mechanisms for detecting and responding to infectious sources and potential zoonoses are required. Surveillance is also important and this includes an early warning function for early detection of a public health event. It is also useful to develop and implement multi-hazard national public health emergency preparedness and response plan. As for points of entry, general obligations should be fulfilled, including coordination and communication. Lastly, mechanisms for detection, alertness and response to chemical emergencies that may constitute a public health event of international concern should be established and implemented.

HDI was associated with a higher increasing rate of the transmission of COVID-19. It is a statistic composite indicator of education level, population lifespan and per capita income level measurements.¹³ Concrete evidence shows that people with higher income and educational level tend to have more social networking and human contacts.³⁰ Individuals with lower incomes may have a barrier in travelling and the reciprocal exchange of different resources in social networks. The stigma of lower income and educational level may also be a contributing factor of exclusion from social networking. In addition, the lower incidence and mortality rates of COVID-19 in countries with a lower HDI (e.g. African countries) may be related to a combination of lower rates of testing and higher proportion of young population. However, the current analysis did not account for the heterogeneity of income level for different populations within a country. The burden of COVID-19 could be higher among the poor population in high-income countries.

The results found countries with moderate IHR capacity score tend to have higher excess all-cause mortality rates. However, the qualitative analysis also identified lower excess all-cause mortality rates in some countries with lower IHR capacity score, such as Latvia, Poland and Bulgaria. A potential explanation is that IHR is measuring the ability to take action and not the intention to do so. The less-prepared country could do better if the government is competent and responsive. Nevertheless, we did not perform a quantitative analysis due to the limited number of countries with available data on excess all-cause mortality rates.

Strengths and weaknesses of the study

This study reported the application of the e-SPAR score, which is the first comprehensive evaluation of public health and its related capabilities globally that brings about the State Parties to the IHRs by the World Health Organization. The different capacities were rigorously developed referring to a framework that prioritises the assessment of public health measures by an international advisory panel. The IHR capacities had a strong relationship with the national healthcare system and other risk factors at a national level for a specific country. We have also considered other potential modifying variables such as HDI, GDP, population densities and previous exposure of the countries to SARS or MERS, which are objective factors collected from recognised public sources. However, several limitations should be mentioned. First, incidence/mortality data can vary by the reporting infrastructure and mechanism which are different for each nation. The availability of confirmatory COVID-19 tests, their uptake rate among cases with relevant symptoms and health-seeking behaviours of suspected cases can also influence the data reported for each country. Furthermore, mortality may be affected by some patient characteristics such as age distribution and concomitant chronic diseases. The sociodemographic characteristics and past medical history of infected COVID-19 patients might vary across nations with different e-SPAR score. Also, we have adopted timeframes of 5, 14 and 30 days as the observation time for incidence and mortality in each nation as the outcomes of the COVID-19 pandemic. Although we made references to published literature,^16–19 these periods are arbitrary, and future research should examine the most optimal time points of observation.

Unanswered questions and future research

To conclude, we examined the association between e-SPAR score and the control of COVID-19 transmission. The score was observed to be significantly associated with reduction in rate of incidence and mortality of COVID-19, which raised the potential impact of IHR capabilities on COVID-19 pandemic control. These results support the application of the e-SPAR score as a framework for regions where their public health capabilities might need further development. Our results may be useful for policy-makers to develop strategies based on IHR to mitigate the COVID-19 pandemics. However, the findings could be biased by variations in case/mortality detection and reporting, confounding effects of underlying health policies and demographic differences for different countries.

Supplemental Material

sj-pdf-1-jrs-10.1177_0141076821992453 - Supplemental material for The potential effectiveness of the WHO International Health Regulations capacity requirements on control of the COVID-19 pandemic: a cross-sectional study of 114 countries

Supplemental material, sj-pdf-1-jrs-10.1177_0141076821992453 for The potential effectiveness of the WHO International Health Regulations capacity requirements on control of the COVID-19 pandemic: a cross-sectional study of 114 countries by Martin CS Wong, Junjie Huang, Sunny H Wong and Jeremy Yuen-Chun Teoh in Journal of the Royal Society of Medicine

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Supplemental material, sj-pdf-2-jrs-10.1177_0141076821992453 for The potential effectiveness of the WHO International Health Regulations capacity requirements on control of the COVID-19 pandemic: a cross-sectional study of 114 countries by Martin CS Wong, Junjie Huang, Sunny H Wong and Jeremy Yuen-Chun Teoh in Journal of the Royal Society of Medicine

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Supplemental material, sj-pdf-3-jrs-10.1177_0141076821992453 for The potential effectiveness of the WHO International Health Regulations capacity requirements on control of the COVID-19 pandemic: a cross-sectional study of 114 countries by Martin CS Wong, Junjie Huang, Sunny H Wong and Jeremy Yuen-Chun Teoh in Journal of the Royal Society of Medicine

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Supplemental material, sj-pdf-4-jrs-10.1177_0141076821992453 for The potential effectiveness of the WHO International Health Regulations capacity requirements on control of the COVID-19 pandemic: a cross-sectional study of 114 countries by Martin CS Wong, Junjie Huang, Sunny H Wong and Jeremy Yuen-Chun Teoh in Journal of the Royal Society of Medicine

Supplemental Material

sj-pdf-5-jrs-10.1177_0141076821992453 - Supplemental material for The potential effectiveness of the WHO International Health Regulations capacity requirements on control of the COVID-19 pandemic: a cross-sectional study of 114 countries

Supplemental material, sj-pdf-5-jrs-10.1177_0141076821992453 for The potential effectiveness of the WHO International Health Regulations capacity requirements on control of the COVID-19 pandemic: a cross-sectional study of 114 countries by Martin CS Wong, Junjie Huang, Sunny H Wong and Jeremy Yuen-Chun Teoh in Journal of the Royal Society of Medicine

Footnotes

Declarations

Competing interests: None declared.

Funding

None declared.

Ethical approval

This study has been approved by the Survey and Behavioural Research Ethics Committee of the Chinese University of Hong Kong (SBRE-19-583).

Guarantor

JH.

Contributorship

MCSW participated in conception and design of the study, and drafted the first version of the manuscript. JH collected and analysed the data. All other authors participated in refinement of study design, data interpretation and provide critical comments on the manuscript.

Acknowledgements

We wish to express our gratitude to Mr. Peter Choi of the JC School of Public Health and Primary Care, The Chinese University of Hong Kong for his technical assistance.

Provenance

Not commissioned; peer reviewed by Frank Ryan, Martin Mckee and Julie Morris.

ORCID iD

Martin CS Wong

Supplemental material

Supplemental material for this article is available online.

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