Journal of Infection and Public Health 16 (2023) 196–205
Contents lists available at ScienceDirect
Journal of Infection and Public Health
journal homepage: www.elsevier.com/locate/jiph
Global Health Security Index not a proven surrogate for health systems 
capacity to respond to pandemics: The case of COVID-19 ]]]]]]]] ]]
Robert Kaba Alhassan a,⁎, Edward Nketiah-Amponsah b, Agani Afaya c, 
Solomon Mohammed Salia c, Aaron Asibi Abuosi d, Jerry John Nutor e
a Centre for Health Policy and Implementation Research, Institute of Health Research, University of Health and Allied Sciences, Ghana 
b Department of Economics, University of Ghana, Legon, Ghana 
c Department of Nursing, School of Nursing and Midwifery, University of Health and Allied Sciences, Ho, Ghana 
d Department of Public Administration and Health Services Management, University of Ghana, Legon, Ghana 
e School of Nursing, University of California, San Francisco, USA 
a r t i c l e  i n f o a b s t r a c t
Article history: Introduction: Global Health Security borders on prevention, detection and response to public health threats 
Received 13 September 2022 like the novel coronavirus disease 2019 (COVID-19). Global Health Security Index (GHSI) of 2019 and 2021 
Received in revised form 7 December 2022 revealed the world remains ill-prepared to deal with future pandemics, evident in the historic impact of 
Accepted 18 December 2022 COVID-19 on countries. As at 7th December 2022, COVID-19 has infected over 600 million people and 
claimed over six million lives, mostly in countries with higher GHSI scores.
Keywords: 
Objective: Determine whether the GHSI scores of countries have a correlation with COVID-19 cases, deaths 
COVID-19 
Global Health Security Index and vaccination coverage, while adjusting for country level dynamics.
Universal Health Coverage Methods: This paper utilizes GHSI database of 195 countries. Data consists of 171 questions grouped into 37 
Primary Health Care indicators across six overarching categories on health security and COVID-19. Multivariate multiple re-
Pandemics gression analysis with robust standard errors was conducted to test the hypothesis that high GHSI ratings 
Preparedness do not guarantee better COVID-19 outcomes like cases, deaths and vaccination coverage. Also, avplots STATA 
Health systems command was used to check outliers with potential negative effect on outcome and predictor variables.
Results: Global average GHSI score for all 195 countries was 38.9. United States of America recorded the 
highest GHSI score of 75.9 but also recorded one of the highest COVID-19 cases and deaths; Somalia re-
corded the worst GHSI score of 16.0 and one of the lowest COVID-19 cases and deaths. High GHSI scores did 
not associate positively with reduction in COVID-19 cases (Coef=157133.4, p-value=0.009, [95%CI 39728.64 
274538.15]) and deaths (Coef=1405.804, p-value=0.047, [95%CI 18.1 2793.508]). However, high GHSI ratings 
associated with increases in persons fully vaccinated per 100 population (Coef=0.572, p-value=0.000, 
[95%CI.272.873]).
Conclusion: It appears the world might still not be adequately prepared for the next major pandemic, if the 
narrative remains unchanged. Countries that recorded higher GHSI scores, counter-intuitively, recorded 
higher COVID-19 cases and deaths. Countries need to invest more in interventions towards attaining 
Universal Health Coverage (UHC) including integrated health systems and formidable primary health care to 
enhance preparedness and response to pandemics.
© 2022 Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is 
an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction
Universal health coverage (UHC) is defined by the World Health 
Organization (WHO) as all individuals and communities receiving 
⁎ Correspondence to: Centre for Health Policy and Implementation Research, health services that they need without suffering financial hardship 
Institute of Health Research, University of Health and Allied Sciences, Ho. PMB 31, 
Volta Region, Ho, Ghana. [1]. According to the WHO, UHC includes the full spectrum of es-
E-mail addresses: ralhassan@uhas.edu.gh, arkabason@gmail.com (R.K. Alhassan),  sential, quality health services, from health promotion to prevention, 
enamponsah@ug.edu.gh (E. Nketiah-Amponsah), aagani@uhas.edu.gh (A. Afaya),  treatment, rehabilitation, and palliative care across the life course. 
ssmohammed@uhas.edu.gh (S.M. Salia), AAbuosi@ug.edu.gh (A.A. Abuosi),  Additionally, health workforce must have optimal skills-mix in 
Jerry.Nutor@ucsf.edu (J.J. Nutor).
https://doi.org/10.1016/j.jiph.2022.12.011 
1876-0341/© 2022 Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/by-nc-nd/4.0/). 
R.K. Alhassan, E. Nketiah-Amponsah, A. Afaya et al. Journal of Infection and Public Health 16 (2023) 196–205
health care facilities to achieve UHC [1]. Attaining UHC however representing a fully vaccinated coverage of 17.4% [13]. Although the 
remains elusive if Global Health Security (GHS) is challenged and number of infected persons in Africa has been relatively low, the 
countries are not pandemic ready. In effect, GHS can be said to be a impact of COVID-19 on economies remains phenomenal, especially, 
panacea to attaining UHC and vice-versa because precarious GHS in countries with already fragile economies and weaker health 
situation endangers efforts towards attainment of UHC, particularly systems.
in already fragile health systems. On the other hand, countries need Available literature shows countries responded differently to the 
to have robust structures for UHC so they can attain and consolidate COVID-19 pandemic largely based on the existing structure of their 
GHS gains. UHC emphasises on access to comprehensive, appro- health systems. In some cases, COVID-19 reversed gains made in 
priate, timely, and quality health services, without financial burden UHC and public health interventions at the PHC level. Even though 
[2]. GHS on the other hand is centered on preventing, detecting, and literature abounds on the effect of COVID-19 on health systems 
responding to public health threats, particularly by protecting across the globe, there is still dearth of empirical evidence on the 
people and societies worldwide from infectious disease threats [3]. correlation between health security performance scores of countries 
Erondu et al. [4] however argued that GHS guides development for and their response to COVID-19 in terms of record of case counts, 
the core capacities of public health but quick to add that GHS in deaths and vaccination coverage.
isolation does not address primary health-care (PHC) functions such Current outlook on the GHSI performance is worrying and also 
as curative services, patient management and capacity for clinical begs the critical question of whether humanity is ready for a future 
surges. pandemic like COVID-19 or one that is worst. Would the world’s 
GHS and UHC are therefore complimentary concepts for resilient response to COVID-19 be better if the GHSI scores were better? 
health systems and must be effectively managed in a balanced These questions and more informed this scientific paper. The paper 
manner. Erondu et al. [4] and Wenham et al. [2] observed that al- analysed the latest 2021 GHSI data on 195 countries vis-à-vis the 
though UHC has the capacity to empower Primary Health Care (PHC) case count of COVID-19, deaths and vaccination coverage in these 
systems and improve access to health care services, there is a pro- pertinent countries. Authors sought to test the hypothesis that high 
pensity for mainstream UHC interventions to relegate infectious GHSI scores of countries have no significant correlation with better 
disease threats to the background in favour of health insurance and COVID-19 response outcomes in terms of cases, deaths and vacci-
individual health services, thus compromising GHS gains in health nation coverage.
systems.
Outbreak of the novel coronavirus disease 2019 (COVID-19) has Methods
taught the world a critical lesson of prioritizing harmonized health 
systems over fragmented systems that do not integrate GHS and Study design
UHC interventions within countries. For instance, albeit the United 
States of America (USA) is among the top-rated countries in pan- This an empirical descriptive correlation population-based study. 
demic preparedness in the 2019 and 2021 Global Health Security The paper was written based on analysed secondary data on global 
Index (GHSI) ratings [5], the country also ironically recorded one of health security and COVID-19 outcome measures. Data was accessed 
the world’s highest numbers of COVID-19 cases and deaths [6–8]. from the 2021 GHSI database which is publicly available at https:// 
This scenario demonstrates the importance of aligning health prio- www.ghsindex.org/report-model/.
rities in a wholistic manner through an oriented PHC system that is 
fully integrated with the pillars of UHC and health security in Data territorial coverage
countries. This agenda can be achieved via multisectoral policy and 
action, and empowerment of people and communities for health [9]. Secondary data coverage was 195 countries/territories across the 
Total of 195 countries participated in the 2019 and 2021 Global globe. In terms of the regional coverage, data was accessed from 
Health Security Index (GHSI) assessments based on six (6) bench- countries in Africa (n = 54), Central Asia (n = 5), Eastern Asia (n = 5), 
marked parameters namely: prevention, detection and reporting, Europe (n = 43), Latin America/Caribbean (n = 33), Northern America 
rapid response, health system, norms and risk environment [5]. It (n = 2), Oceania (n = 16), South-eastern Asia (n = 11), Southern Asia 
emerged from these global assessments that the world remains (n = 9) and Western Asia (n = 17). The population size coverage 
dangerously unprepared to deal with future epidemic and pandemic ranged from countries with less than 1 million population (n = 34) to 
threats. Particularly, the latest 2021 GHSI data shows a global 1–10 million (n = 81), 10–50 million (n = 600) and over 100 million 
average score of 38.9% for the 195 countries captured [5]. According (n = 13). Data on countries distribution by income levels included 
to the 2021 GHSI report, none of the 195 countries placed in the top low-income countries (LICs) (n = 34), lower middle-income coun-
tier of the ranking. This performance signals that significant gaps tries (LMICs) (n = 45), upper middle income (UMICs) (n = 56) and 
exist for all countries and across all six (6) GHSI categories of as- higher income countries (IHCs) (n = 60) (see Supplementary File 1).
sessment on global health security [5].
Since the WHO declared COVID-19 in March 2020 as a pandemic Eligibility and exclusion criteria
of public health concern, the virus has infected over 600 million 
people and claimed more than six million lives across the globe as at Major criteria for inclusion in the data curation was all 195 
December, 2022 [8]. In terms of the vaccination coverage, the WHO countries captured in terms of completeness of data on COVID-19 
reports that over 12 billion vaccines have so far been administered cases, deaths and vaccination coverage as at 2021 and the Global 
worldwide [8]; out of this number, more than66% have received at Health Security Index (GHSI) scores for 2019 and 2021. Countries 
least one dose of the vaccine and approximately 8.79 billion doses that did not meet these criteria were dropped from the final analysis.
are administered each day. Unfortunately, barely 17.8% of people in 
developing countries have received at least one dose of the vaccine GHSI methodology
[10] due to varied reasons [11,12].
Within the WHO African region, for instance, an estimated 11.7 GHSI data was first published in October 2019 among 195 
million cases of COVID-19 have been recorded with over 200,000 countries across the globe [14]. These countries composed of the 
deaths as at August, 2022. Moreover, 818 million doses of the COVID- States Parties to the International Health Regulations [15]. GHSI is an 
19 vaccine have been received out of which 71.4% doses have been initiative of the Nuclear Threat Initiative (NTI) and the Center for 
utilized and 583.7 million doses administered to target populations, Health Security at the Johns Hopkins Bloomberg School of Public 
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R.K. Alhassan, E. Nketiah-Amponsah, A. Afaya et al. Journal of Infection and Public Health 16 (2023) 196–205
Health, with Economist Impact. The index was based on existing TX, USA) for further analysis. Background information on the data set 
knowledge and understanding on individual countries preparedness was analysed descriptively highlighting the means, standard devia-
to prevent, detect, and respond to infectious disease threats. GHSI is tions for continuous variables, and frequencies and percentages for 
also based on research on the 195 countries from August, 2020 the categorical variables.
through June, 2021. Data was collected through qualitative and Multivariate multiple regression analysis with robust standard 
quantitative approaches based on publicly available country level errors was employed to test the main hypothesis that GHSI perfor-
information. Details are reported in the GHSI Methodology Re- mance of countries has no significant association with better COVID- 
port [14]. 19 outcome measures (i.e., COVID-19 case counts, deaths and vac-
cination coverage), holding covariates constant. All statistical tests 
GHSI Indicators were conducted at 95% confidence level.
GHSI indicators are benchmarked against external factors with a Outcome variables of interest
potential influence on global health security. The benchmarked Main outcome variables of interest were: Cumulative COVID-19 
factors include gross domestic product (GDP) per capita, and the cases (numeric); Cumulative COVID-19 deaths (numeric); Persons 
United Nations Development Programme’s (UNDP) Human fully vaccinated (numeric); Persons fully vaccinated per 100 popu-
Development Index [14]. GHSI indicators are embedded in an in- lation (numeric).
teractive model publicly available as an Excel workbook at 
www.GHSIndex.org. Predictor variables of interest
Six (6) category of indicators used to measure GHSI scores are: Predictor variables were cumulative GHSI score for 2021 (nu-
prevention (Prevention of the emergence or release of pathogens); meric) and change in GHSI score between 2019 and 2021 (proxy for 
detection and reporting (Early detection and reporting for epidemics progress made) (numeric).
of potential international concern); rapid response (Rapid response 
to and mitigation of the spread of an epidemic); health system Co-variates controlled
(Sufficient and robust health system to treat the sick and protect Covariates controlled for in the regression models were: WHO 
health workers); compliance with international norms regions/territories (categorical), population estimates of country 
(Commitments to improving national capacity, financing plans to (categorical), and income levels of country (categorical).
address gaps, and adhering to global norms); risk environment Multicollinearity diagnostics were conducted on the predictor 
(Overall risk environment and country vulnerability to biological variables and co-variates through pairwise correlation test and a 
threats) [14] (see Supplementary File 2). post-estimation test for variance inflation factors (VIFs). It was ob-
served all variables were below the 10.0 rule thumb for exclusion as 
GHSI tools and scoring criteria collinear predictor variables. The average VIF was 2.20. Additionally, 
to check for outliers we used the post-estimation avplots STATA 
GHSI consists of 171 questions grouped into 37 indicators across command (added-variable plots) to check extreme values that could 
six (6) overarching categories, stated supra. Overall score (0−100) for have a negative effect on our outcome and predictor estimators. It 
each country is a weighted sum of the six categories; each category was found that all data points were in range and no outliers ob-
is scored on a scale of 0–100, where 100 represents the most desired served (see Fig. 1 and 2).
health security conditions and 0 represents the least desired health 
security conditions. A score of 100 does not however indicate that a Findings
country has perfect national health security conditions; likewise, a 
score of 0 does not mean a country has no capacity. Instead, the Background information
scores of 100 and 0 represent the highest or lowest possible score, 
respectively, as measured by the GHSI criteria [14]. Each category is Analysis of the secondary data show that most (28%) of the 
normalized based on the sum of its underlying indicators and sub- countries captured in the GHSI data were African countries, followed 
indicators, and an identical weight is then applied. Default weights by Europe (22%) and Latin America/Caribbean (17%). Barely 1% of the 
used in the ranking are based on neutral (or identical) weights which countries were in the Northern American region. In terms of the 
are dynamic and can be adapted by users [14]. population coverage, 42% of the countries were within the popula-
tion range of 1–10million and 7% had a population of 100million+ ; 
Ethical considerations most of the countries were classified as high-income countries with 
17% classified as low-income countries. Tables 1 and 2 show details 
Secondary data was mainly used for the analysis and there was of the background information on the 195 countries from the 
not contact with human subjects. As reported in the GHSI GHSI data.
Methodology Report [14], all ethical considerations were met during 
the primary data collection processes by Global Health Security GHSI and COVID-19 performance scores
Index Team. Moreover, request for permission to use the secondary 
data was granted by the Global Health Security Index Team (see Average 2021 GHSI score for all 195 countries was 38.9 out of the 
Supplementary File 3). expected optimum score of 100. United States of America (USA) 
recorded the highest GHSI score of 75.9 while Somalia recorded the 
Data analysis worst GHSI score of 16; on the average, the world witnessed a 
marginal positive change of 0.20 between 2019 and 2021 in health 
Data was accessed from the GHSI online data repository (https:// security. Some specific countries nonetheless recorded negative 
www.ghsindex.org/report-model/) after receiving written permis- change or retrogression (see Supplementary File 1). Data on the 
sion to use the data. Some variable names and codes were however GHSI and COVID-19 performance scores further show countries from 
recoded, re-labeled and merged with corresponding secondary on the Northern American region recorded the highest GHSI score of 
COVID-19 outcome measures (i.e. cases, deaths and vaccination 72.9 followed by European region (mean=46.1), South-eastern Asia 
coverage) per country. Excel data was later exported to STATA sta- (mean=45.1), Central Asia (mean=37.7) and Latin America/Caribbean 
tistical analysis software (version 12.0) (StataCorp, College Station, (mean=37.7). The least GHSI score was recorded in the African region 
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R.K. Alhassan, E. Nketiah-Amponsah, A. Afaya et al. Journal of Infection and Public Health 16 (2023) 196–205
Fig. 1. Correlation between COVID-19 cumulative case count and deaths and predicter variables, 
Source: Global Health Security Index Database (2021).
Fig. 2. Correlation between cumulative vaccination coverage per 100 population and predicter variables, 
Source: Global Health Security Index Database (2021).
Table 1 
Background information. 
Indicators Obs. GHSI_2021 (Mean (SD) GHSI_2019 Total COVID-19 cases Total COVID-19 deaths
(Mean (SD)
Region
Africa 54 29.1 (5.8) 29.4 (6.2) 11,239,867 253,772
Central Asia 5 37.7 (7.0) 37.7 (6.3) 1854,147 23,771
Eastern Asia 5 46.1 (19.4) 46.7 (18.2) 32,556,922 88,075
Europe 43 52.4 (10.1) 52.4 (11.0) 189,506,543 1713,031
Latin America/Caribbean 33 37.7 (10.3) 37.4 (9.7) 72,649,195 1735,725
Northern America 2 72.9 (4.3) 71.9 (6.1) 89,571,299 1046,388
Oceania 16 29.7 (14.9) 28.6 (14.8) 10,604,984 50,117
South-eastern Asia 11 45.1 (12.2) 43.0 (13.0) 33,973,191 392,082
Southern Asia 9 34.9 (4.4) 35.3 (5.2) 61,798,796 742,375
Western Asia 17 39.2 (12.1) 39.9 (11.3) 32,976,613 258,716
Population
< million 41 31.4 (7.7) 30.2 (7.7) 6178,203 49,474
1–10million 81 42.7 (14.0) 42.8 (14.1) 213,378,853 1778,323
10–50million 60 36.9 (14.0) 37.4 (13.6) 103,695,057 1209,163
100million+ 13 47.8 (12.0) 47.3 (11.8) 213,479,444 3267,092
Income levels
Low income 34 27.2 (6.1) 28.0 (5.9) 11,345,152 99,619
Lower middle income 45 33.7 (7.1) 33.5 (7.3) 82,240,410 1164,574
Upper middle income 56 37.3 (12.2) 37.0 (12.0) 135,567,092 2588,930
High income 60 50.9 (13.0) 50.5 (13.6) 307,578,903 2450,929
Source: Secondary Data of Global Health Security Index 2021
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Table 2 response, the proxy indicator of vaccination coverage shows countries 
Variable definitions. with population of 100million+ ; countries within UMICs bracket and 
Variables Statistics those in Eastern Asia recorded higher absolute numbers of vaccina-
Outcome variables Mean Std. Dev. tions (see Fig. 3). Vaccination coverage per 100 population similarly 
Cumulative COVID-19 cases (n = 191) 2810112.9 8349878.2 show countries with population size of 100milion+ ; those within 
Cumulative COVID-19 deaths (n = 191) 33005.508 105171.5 high income economies and Eastern Asia region recorded higher 
Persons fully vaccinated (n = 190) 24898579 1.152e+ 08 vaccination rate per 100 (see Fig. 4).
Persons fully vaccinated per 100 (n = 190) 51.439 26.932
Explanatory variables Mean Std. Dev.
GHSI_2021 score (n = 195) 38.908 13.642 Correlation between GHSI and COVID-19 outcome measures
GHSI progress proxy* (n = 195) 0.202 2.507
Covariates Freq. (f) Percent (%) It was discovered that (counter intuitively) an increase in GHSI 
WHO region scores did not translate into a reduction in the incidence of COVID- 
Africa 54 27.69
Central Asia 5 2.56 19 cases, holding other variables constant (Coef=157133.4, p- 
Eastern Asia 5 2.56 value=0.009, [95%CI 39728.64 274538.15]) (see Table 3). In effect, 
Europe 43 22.05 there appeared to be an unfavourable association between GHSI 
Latin America/Caribbean 33 16.92 performance and incidence of COVID-19 cases in the 195 countries. 
Northern America 2 1.03 Consequently, even though high GHSI scores are (intuitively) ex-
Oceania 16 8.21
South-eastern Asia 11 5.64 pected to translate into reduced COVID-19 cases (negative correla-
Southern Asia 9 4.62 tion), the data found the opposite (counter-intuitive) results where 
Western Asia 17 8.72 high GHSI scores did not translate into reduction in COVID-19 cases 
Population as expected.
< million 41 21.03 Likewise, it was observed that an increase in GHSI score did not 
1–10million 81 41.54
10–50million 60 30.77 translate into a reduction in COVID-19 deaths in the 195 countries 
100million+ 13 6.67 rather increased same, holding other factors constant 
Income levels (Coef=1405.804, p-value=0.047, [95%CI 18.1 2793.508]) (see Table 3).
Low income 34 17.44 As shown in Table 4, GHSI scores had a positive association with 
Lower middle income 45 23.08
Upper middle income 56 28.72 COVID-19 vaccination coverage and persons vaccinated per 100 
High income 60 30.77 population. For instance, an increase in GHSI score by one point 
Source: Secondary Data of Global Health Security Index 2021 increases the percentage of persons fully vaccinated per 100 popu-
*Percentage change in 2021 and 2019 GHSI scores lation by approximately 0.6 (Coef=0.572, p-value=0.000, 
[95%CI.272.873]), holding other covariates constant (see Table 4). 
Thus, countries that demonstrated better performance in GHSI were 
(mean=29.1). Countries with population of 100million+ also re- also more likely to witness an improvement in their response to 
corded the highest GHSI mean score of 47.8; the least GHSI mean COVID-19 in terms of persons fully vaccinated per 100 population.
score of 31.4 was recorded in countries with a population size less Tables 5 and 6 show a decomposition of multivariate regression 
than one million. Countries that fell within low-income levels also output in terms of the six (6) categories of global health security in-
recorded the lowest average GHSI score of 27.2 compared to coun- dices and COVID-19 outcome measures. We also found that “preven-
tries within the high-income bracket recording an average of 50.9. tion of emergence or release of pathogens” has significant association 
In terms of the COVID-19 performance measures, 35% of the global with vaccination coverage per 100 population (Coef = 0.307, p-value = 
COVID-19 cases in 2021 were recorded in Europe while Central Asia 0.022, [95%CI 0.044 0.569]). GHSI category on “rapid response” cor-
region recorded barely 0.35% of the total global COVID-19 cases. Latin related negatively with number of persons fully vaccinated (Coef = 
America/Caribbean region recorded 27.5% of the global COVID-19 −2110977.1, p-value = 0.022, [95%CI −3919988.3 −301965.87]); simi-
cases followed by Europe (27.2%); the least morbidity cases were re- larly, improving “national financing” capacity also had a negative as-
corded in Central Asia (0.38%) and Oceania (0.79%). Countries with sociation with persons fully vaccinated per 100 population (Coef = 
high population sizes also recorded high COVID-19 cases and deaths, −0.365, p-value = 0.003, [95%CI −.6 −.129]). However, a positive asso-
and likewise HICs and UMICs (see Table 1). In respect of COVID-19 ciation was observed between overall “risk environment” and persons 
Fig. 3. COVID-19 vaccinations: persons fully vaccinated, 
Source: Data of Global Health Security Index database (2021).
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R.K. Alhassan, E. Nketiah-Amponsah, A. Afaya et al. Journal of Infection and Public Health 16 (2023) 196–205
Fig. 4. COVID-19 vaccinations: persons fully vaccinated per 100, 
Source: Data of Global Health Security Index database (2021).
Table 3 
Linear regression on correlation between COVID-19 cases and deaths and GHSI score. 
Independent variables Model 1: COVID-19 Cases Model 2: COVID-19 Deaths
Coef. p-value [95% Conf Interval] Sig Coef. p-value [95% Conf Interval] Sig
GHSI_2021 score 157133.4 0.009 39728.64 274538.15 *** 1405.804 0.047 18.1 2793.508 **
GHSI progress proxy* -221634.38 0.254 -603660.95 160392.21 -1254.942 0.584 -5770.431 3260.546
Africa Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref.
Central Asia -829710.16 0.784 -6788797 5129376.7 -7614.816 0.831 -78050.196 62820.565
Eastern Asia -509440.39 0.872 -6748370.2 5729489.4 -81988.785 0.030 -155731.86 -8245.709 **
Europe 639595.98 0.739 -3150135.6 4429327.6 17078.562 0.453 -27715.413 61872.537
Latin America/Caribbean 677069.4 0.697 -2746601.6 4100740.4 36565.156 0.076 -3902.046 77032.357 *
Northern America 33088885 0.000 22973340 43204429 *** 380807.92 0.000 261243.93 500371.91 ***
Oceania 1576835.9 0.472 -2741791.7 5895463.5 16770.038 0.518 -34275.396 67815.473
South-eastern Asia -539668.53 0.817 -5145355.6 4066018.6 -13644.948 0.621 -68083.376 40793.48
Southern Asia 2824337.4 0.243 -1936228.5 7584903.4 10957.378 0.701 -45311.691 67226.448
Western Asia 194308.13 0.923 -3767774.3 4156390.5 6905.32 0.771 -39925.812 53736.451
< million Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref.
1–10million 1021358.7 0.528 -2168808.6 4211525.9 12941.79 0.499 -24765.437 50649.017
10–50million 301054.91 0.860 -3053973.4 3656083.2 8624.61 0.668 -31031.246 48280.467
100million+ 11385899 0.000 6352211.5 16419587 *** 217082.06 0.000 157584.74 276579.39 ***
Low income Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref.
Lower middle income -904832.61 0.577 -4097607.8 2287942.6 -9949.774 0.603 -47687.826 27788.278
Upper middle income -261156.01 0.886 -3851811.8 3329499.8 7589.963 0.725 -34850.97 50030.897
High income -395676.86 0.860 -4832892.1 4041538.4 -20781.056 0.435 -73228.176 31666.063
Constant -4987651.2 0.012 -8877826.9 -1097475.4 ** -50906.817 0.030 -96888.024 -4925.61 **
Models output Model 1 Model 2 
Mean dependent var2810112.864 Mean dependent var33005.508 
SD dependent var 8349878.210 SD dependent var 105171.496 
R-squared 0.478 R-squared 0.540 
Number of obs 191.000 Number of obs 191.000 
F-test 9.302 F-test 11.942 
Prob  >  F 0.000 Prob  >  F 0.000 
Akaike crit. (AIC)6541.256 Akaike crit. (AIC)4845.949 
Bayesian crit. (BIC)6599.797 Bayesian crit. (BIC)4904.489
Source: Secondary Data of Global Health Security Index 2021
* ** p  <  .01, * * p  <  .05, * p  <  .1
fully vaccinated per 100 population (Coef=0.835, p-value=0.000, suggesting the world literally failed in the health security and pre-
[95%CI.538 1.132]), holding other co-variates constant (see Table 6). paredness test.
As part of efforts towards guaranteeing preparedness for future 
Discussion pandemics by countries, the Global Health Security Agenda (GHSA) 
group was set up and the framework published in 2018. According to 
Health security of the world remains a critical concern given the the GHSA 2024 Framework [16], more countries by the year 2024 
devastating impact of COVID-19 making it one of the worst pan- would have completed an evaluation of health security capacity si-
demics in the history of mankind. Moreover, the low performance of tuation in their settings [16]. The report further intimates that 
countries in the GHSI assessments is a palpable manifestation of the countries assessed with the GHSI parameters would have strength-
vulnerability of the world to these unexpected pandemics. Per ened their capacities and demonstrated improvements in at least 
analysed GHSI data the global average score was 38.9 out of 100 [5], five technical areas to a level of ‘demonstrated capacity’ in line with 
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Table 4 
Linear regression on correlation between COVID-19 vaccination coverage and GHSI score. 
Independent variables Model 1: Persons fully vaccinated Model 2: Persons fully vaccinated per 100
Coef. p-value [95% Conf Interval] Sig Coef. p-value [95% Conf Interval] Sig
GHSI_2021 score 89238.458 0.920 -1660727.8 1839204.7 .572 0.000 .272 .873 * **
GHSI progress proxy* -2880148 0.310 -8464850.8 2704554.8 .119 0.806 -.84 1.079
Africa Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref.
Central Asia 2858382.2 0.949 -84647899 90364663 12.175 0.112 -2.856 27.205
Eastern Asia 2.541e+ 08 0.000 1.488e+ 08 3.595e+ 08 * ** 24.984 0.007 6.893 43.075 * **
Europe 5633530.7 0.844 -50927679 62194741 .09 0.985 -9.626 9.805
Latin America/Caribbean -5195678.9 0.840 -55760181 45368824 12.848 0.004 4.163 21.534 * **
Northern America 26787339 0.724 -1.227e+ 08 1.763e+ 08 -4.245 0.745 -29.919 21.429
Oceania 4512196.5 0.887 -58147061 67171454 21.203 0.000 10.44 31.966 * **
South-eastern Asia 10805452 0.755 -57352901 78963805 30.586 0.000 18.879 42.294 * **
Southern Asia 71454430 0.046 1441427.8 1.415e+ 08 * * 32.683 0.000 20.657 44.709 * **
Western Asia 2478141.9 0.933 -55886282 60842565 5.803 0.255 -4.222 15.828
< million Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref.
1–10million 2624328 0.912 -44255877 49504533 -8.239 0.045 -16.291 -.186 * *
10–50million 11864216 0.636 -37506564 61234997 -5.937 0.169 -14.418 2.543
100million+ 2.006e+ 08 0.000 1.267e+ 08 2.745e+ 08 * ** -9.39 0.146 -22.084 3.303
Low income Ref. Ref. Ref. Ref. Ref. Ref. Ref. Ref.
Lower middle income 6897813 0.775 -40567359 54362985 10.574 0.011 2.421 18.727 * *
Upper middle income 26039964 0.335 -27078183 79158110 20.025 0.000 10.901 29.149 * **
High income 7012143.9 0.833 -58399705 72423993 38.328 0.000 27.092 49.563 * **
Constant -18475710 0.533 -76787588 39836169 6.364 0.212 -3.652 16.38
Models output Model 1 Model 2 
Mean dependent var24898578.563 Mean dependent var51.439 
SD dependent var 115218529.283 SD dependent var 26.932 
R-squared 0.410 R-squared 0.682 
Number of obs 190.000 Number of obs 190.000 
F-test 7.037 F-test 21.651 
Prob  >  F 0.000 Prob  >  F 0.000 
Akaike crit. (AIC)7527.564 Akaike crit. (AIC)1608.256 
Bayesian crit. (BIC)7586.010 Bayesian crit. (BIC)1666.703
Source: Secondary Data of Global Health Security Index 2021
* ** p  <  .01, * * p  <  .05, * p  <  .1
the World Health Organization (WHO) International Health Reg- countries [25–30], especially at the primary health care level. 
ulations (IHR) Framework [15]. Moreover, before the outbreak of COVID-19, studies [2,3] argued on 
Indeed, the GHSA framework (2018) and GHSI assessments have the need to prioritise integrated health systems over fragmented 
been applauded as laudable steps towards independent determina- ones. This approach, according to health system experts, ensures 
tion of the global health security situation. Barely a few months after seamless integration of global security, UHC and primary health care 
the first GHSI assessment, COVID-19 was declared a pandemic by the building blocks within countries.
WHO with deleterious consequences. COVID-19 impact on countries In lieu of the global public health paradox discovered from the 
vindicated the GHSI scores that the world was never ready for major GHSI data, the hypothesis has been proven that optimum GHSI 
pandemics. Even western countries with more robust health systems performance on its own does not translate into assured prepared-
that scored higher GHSI scores did not translate into positive COVID- ness of health systems to respond effectively to pandemics. Indeed, 
19 response outcomes [9,17–19]. even though United States of America scored the highest 2021 GHSI 
Analysis of the latest GHSI data, reported in this paper, particu- score of 75.9, it also recorded the highest number of COVID-19 
larly demonstrates the unfavourable correlation between GHSI confirmed cases and deaths. On the contrary many countries in 
scores and COVID-19 outcome measures. One could glean from Africa which recorded GHSI scores below the global average of 38.9 
findings of the GHSI assessments that more needs to be done to recorded low COVID-19 cases and deaths, corroborating findings by 
enhance resilience of health systems for future pandemics. Empirical similar studies on this subject [31,32].
evidence attests that even before the first case of COVID-19 was Nonetheless, higher GHSI scores corresponded positively with 
recorded in December, 2019 the world’s performance in the GHSI did the percentage of persons fully vaccinated against COVID-19. This 
not point to a world that is prepared to respond to a major pandemic observation contradicts conclusions by Aitken et al. [20] who found 
like COVID-19 [20]. This observation is corroborated by findings in that the 2019 GHSI scores had an inverse correlation with vaccina-
this current paper where higher GHSI scores did not necessarily tion coverage. Perhaps, this could be attributed to the relatively early 
translate into a reduction in the cumulative number of COVID-19 days of the pandemic when progress in the development and de-
cases and deaths. Earlier studies [9,17–19] arrived at similar con- ployment of the COVID-19 vaccine were still minimal.
clusions when they analysed the 2019 GHSI data sets against similar Evidence from the GHSI data strongly suggests a compelling need 
parameters on COVID-19. to revisit the GHSI assessment criteria to adequately account for the 
Previous studies had alluded to the fact that a lack of integrated contextual and environmental factors that impinge on accurate de-
health systems within countries compromises resilience of health termination of health system preparedness and health security si-
systems to respond to pandemics including COVID-19 [21,22]. Evi- tuation in countries. Chang and McAleer [19] made similar 
dence from this study shows that even though many western recommendations having acknowledged this potential lapse in the 
countries scored higher on the GHSI than their Asian and African GHSI methodology. If the GHSI is not serving its purpose as a sur-
countries, the later recorded better COVID-19 outcomes in terms of rogate for health system preparedness for pandemics (evident in the 
case counts and deaths. Some experts attributed these dynamics to current GHSI data) then same must be revisited and possibly revised. 
better integrated health systems in some Asian [21–24] and African It is also important to reiterate the caveat that issues of data quality, 
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R.K. Alhassan, E. Nketiah-Amponsah, A. Afaya et al. Journal of Infection and Public Health 16 (2023) 196–205
Table 5 
Linear regression on categories of global health security and COVID-19 outcome measures. 
Independent variables Model 1: COVID-19 Cases Model 2: COVID-19 Deaths
Coef. p-value [95% Conf Interval] Coef. p-value [95% Conf Interval]
GHSI score category 1 54059.011 0.365 -63424.093 171542.12 1110.895 0.111 -256.72 2478.509
GHSI score category 2 32520.818 0.534 -70507.909 135549.55 86.631 0.887 -1112.721 1285.983
GHSI score category 3 32423.189 0.612 -93417.898 158264.27 745.511 0.317 -719.398 2210.421
GHSI score category 4 47772.482 0.446 -75682.406 171227.37 478.303 0.512 -958.829 1915.435
GHSI score category 5 -29229.913 0.587 -135211.93 76752.103 -929.527 0.139 -2163.258 304.204
GHSI score category 6 -21474.877 0.752 -155237 112287.24 -499.759 0.527 -2056.877 1057.359
Africa Ref Ref Ref Ref Ref Ref Ref Ref
Central Asia -575980.71 0.861 -7048712.6 5896751.2 -6372.041 0.868 -81720.778 68976.695
Eastern Asia -500457.03 0.877 -6887826.2 5886912.1 -86462.898 0.023 -160817.93 -12107.865
Europe 485011.6 0.825 -3836650.5 4806673.7 10174.111 0.690 -40134.134 60482.355
Latin America/Caribbean 373668.96 0.838 -3229024.4 3976362.3 28234.857 0.186 -13703.91 70173.623
Northern America 32871378 0.000 22323893 43418864 373178.83 0.000 250396.1 495961.56
Oceania 1304242.1 0.558 -3087341.7 5695826 10308.669 0.691 -40813.53 61430.869
South-eastern Asia -1068991 0.655 -5789278 3651296 -17101.567 0.540 -72050.183 37847.05
Southern Asia 2542775.2 0.318 -2471370.1 7556920.5 2896.672 0.922 -55472.734 61266.078
Western Asia -458300.16 0.833 -4730676.2 3814075.9 -9505.975 0.706 -59240.483 40228.533
< million Ref Ref Ref Ref Ref Ref Ref Ref
1–10million 882391.75 0.599 -2427222.5 4192006 8174.867 0.676 -30352.18 46701.915
10–50million 35357.086 0.984 -3546064.7 3616778.9 2590.866 0.903 -39100.28 44282.011
100million+ 10990833 0.000 5682309.5 16299356 210747.06 0.000 148950.81 272543.3
Low income Ref Ref Ref Ref Ref Ref Ref Ref
Lower middle income -719377.99 0.679 -4139984.6 2701228.6 -10160.651 0.615 -49979.755 29658.454
Upper middle income -269480.33 0.895 -4287172.6 3748211.9 3018.908 0.899 -43750.839 49788.655
High income 154745.72 0.956 -5381128.6 5690620 -18099.687 0.580 -82542.513 46343.14
Constant -1502940.4 0.675 -8575465.2 5569584.3 8424.549 0.840 -73906.345 90755.443
Model 1 Model 2
Mean dependent var SD dependent var Mean dependent var SD dependent var 
2810112.864 8349878.210 33005.508 105171.496
R-squared Number of obs R-squared Number of obs 
0.480 191.000 0.556 191.000
F-test Prob  >  F F-test Prob  >  F 
7.419 0.000 10.060 0.000
Akaike crit. (AIC) Bayesian crit. (BIC) Akaike crit. (AIC) Bayesian crit. (BIC) 
6548.471 6620.021 4847.339 4918.889
* ** p  <  .01, * * p  <  .05, * p  <  .1 * ** p  <  .01, * * p  <  .05, * p  <  .1
Source: GHSI Database (2021); Legend: GHSI score category 1 (Prevention of emergence or release of pathogens); GHSI score category 2 (Early detection and reporting for 
epidemics of potential international concern); GHSI score category 3 (Rapid response to and mitigation of the spread of an epidemic); GHSI score category 4 (Sufficient & robust 
health sector to treat the sick & protect health workers); GHSI score category 5 (Commitments to improving national capacity financing and adherence to norms); GHSI score 
category 6 (Overall risk environment and country vulnerability to biological threats)
completeness and synchrony across the 195 participating countries management [41–44]. Unfortunately, same cannot be fully said of 
could potentially impact negatively on the GHSI scores inad- many western countries which appear to have consolidated on UHC 
vertently. Thus, more robust and parsimonious assessment tools and and GHSI gains but seemingly demonstrated weaknesses at the PHC 
strategies could help enhance the otherwise promising global health level [37–40]. These assertions are corroborated by the evidence 
security database. espoused in this paper from the GHSI data sets.
Finally, based on the available evidence, it would be more ben-
eficial for countries to invest in integrated health systems that Limitations
prioritise all building blocks of the health system. For instance, 
successes in health security without commensurate gains in PHC the first, the paper is based mainly on secondary data set without 
and UHC renders a country vulnerable to shocks and ravages from the compliment of primary data. An incorporated primary data 
future pandemics. Empirical literature argues the United States of would have allowed the researchers to explore further on potential 
America (USA) perhaps did not fully maximise its primary health reasons for the empirical findings. Also, any existing methodological 
care (PHC) in response to COVID-19 hence its challenged response to lapses already acknowledged by the GHSI team automatically apply 
COVID-19 [33]. PHC concept is typically designed to respond prag- to this study since there was no methodological review nor com-
matically to infectious disease outbreaks at the community level pliment of data with primary data.
through effective contact tracing and mapping [34,35]. Even though Additionally, critics of the GHSI maintain that the index is not 
USA spends the highest per capita expenditure of US$ 10,921.01 on hinged on a theory as basis to predict countries response to pan-
health in the world [36], the primary healthcare system is highly demics like COVID-19 and thus questions whether the index is in-
fragmented and incapacitated to deal with novel pandemics like deed adequate to measure responsiveness to national health 
COVID-19 [37–40]. systems. Finally, the index is criticized as not adequately accounting 
On other hand, some experts have argued that, holding exo- for health systems that do not have comprehensive UHC especially 
genous factors constant, some African [25–32] and Asian countries in resource-poor settings which as the potential to independently 
[21–24] responded relatively better to COVID-19 pandemic due to a impact on countries response to pandemics.
more robust PHC system in addition to leveraged experiences from Notwithstanding the above limitations, the GHSI promises to be 
earlier pandemics like Ebola (in the case of Africa). In Ghana for important tool that is capable of gauging the global health security 
example, community-based health planning and services (CHPS) situation and preparedness for pandemics. Moreover, the robust-
system was leveraged for contact tracing, isolation and case ness of the GHSI methodology coupled with the rigorous/validated 
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R.K. Alhassan, E. Nketiah-Amponsah, A. Afaya et al. Journal of Infection and Public Health 16 (2023) 196–205
Table 6 
Linear regression on categories of global health security and COVID-19 vaccination cov0.044 0.5erage. 
Independent variables Model 1: Fully vaccinated Model 2: full vaccination per 100
Coef. p-value [95% Conf Interval] Coef. p-value [95% Conf Interval]
GHSI score category 1 196709.88 0.818 -1485822.4 1879242.1 .307 0.022 .044 .569
GHSI score category 2 39479.303 0.958 -1442799.6 1521758.2 .048 0.680 -.183 .28
GHSI score category 3 -2110977.1 0.022 -3919988.3 -301965.87 -.213 0.139 -.495 .07
GHSI score category 4 1550671.2 0.084 -211035.01 3312377.5 .246 0.079 -.029 .52
GHSI score category 5 -1063687 0.166 -2574068.2 446694.1 -.365 0.003 -.6 -.129
GHSI score category 6 1499968.1 0.122 -405088.93 3405025.1 .835 0.000 .538 1.132
Africa Ref Ref Ref Ref Ref Ref Ref Ref
Central Asia -25535837 0.586 -1.180e+ 08 66889072 4.621 0.528 -9.797 19.039
Eastern Asia 2.371e+ 08 0.000 1.327e+ 08 3.414e+ 08 18 0.030 1.722 34.278
Europe -24450830 0.438 -86506039 37604380 -6.887 0.162 -16.568 2.793
Latin America/Caribbean -10205877 0.697 -61815402 41403648 12.479 0.003 4.428 20.53
Northern America 565286.09 0.994 -1.504e+ 08 1.515e+ 08 -9.241 0.440 -32.793 14.311
Oceania 3198243.9 0.919 -58872668 65269156 19.655 0.000 9.972 29.338
South-eastern Asia 7551165.8 0.827 -60497239 75599570 31.408 0.000 20.793 42.024
Southern Asia 39711570 0.276 -31953497 1.114e+ 08 23.169 0.000 11.99 34.349
Western Asia -10927969 0.724 -72033517 50177579 3.115 0.520 -6.418 12.647
< million Ref Ref Ref Ref Ref Ref Ref Ref
1–10million 4913019.7 0.838 -42473632 52299671 -5.885 0.118 -13.278 1.507
10–50million 13290851 0.609 -37977507 64559208 -2.8 0.490 -10.798 5.198
100million+ 2.077e+ 08 0.000 1.319e+ 08 2.835e+ 08 -2.81 0.640 -14.633 9.013
Low income Ref Ref Ref Ref Ref Ref Ref Ref
Lower middle income -6919197.9 0.783 -56333768 42495372 3.004 0.443 -4.705 10.713
Upper middle income 4843529.2 0.869 -53009544 62696603 8.448 0.066 -.577 17.473
High income -28165972 0.484 -1.074e+ 08 51098243 18.351 0.004 5.985 30.716
Constant 4851185.9 0.925 -96814036 1.065e+ 08 1.011 0.900 -14.848 16.871
Model 1 Model 2
Mean dependent var SD dependent var Mean dependent var SD dependent var 
24898578.563 115218529.283 51.439 26.932
R-squared Number of obs. R-squared Number of obs 
0.444 190.000 0.752 190.000
F-test Prob  >  F F-test Prob  >  F 
6.389 0.000 24.307 s 0.000
Akaike crit. (AIC) Bayesian crit. (BIC) 7595.778 Akaike crit. (AIC) Bayesian crit. (BIC) 
7524.344 1568.448 1639.883
* ** p  <  .01, * * p  <  .05, * p  <  .1 * ** p  <  .01, * * p  <  .05, * p  <  .1
Source: GHSI Database (2021); Legend: GHSI score category 1 (Prevention of emergence or release of pathogens); GHSI score category 2 (Early detection and reporting for 
epidemics of potential international concern); GHSI score category 3 (Rapid response to and mitigation of the spread of an epidemic); GHSI score category 4 (Sufficient & robust 
health sector to treat the sick & protect health workers); GHSI score category 5 (Commitments to improving national capacity financing and adherence to norms); GHSI score 
category 6 (Overall risk environment and country vulnerability to biological threats)
tools makes the data fidelity unquestionable and evidence from it validated and proven reliable the assessment criteria and tools might 
compelling. have to be revisited at the next review opportunity to reflect the 
contextual and environmental nuances in pertinent countries. Fi-
Conclusion nally, even though the current GHSI methodology attempts to adjust 
for country-specific conditions there is the need to adapt the tools to 
The main question this paper attempted to addressed is why the account for differences in health systems in low-resource and re-
GHSI appeared not be an indicator with the ability to predict re- source rich settings. This adaption will help avert perpetual com-
sponse to the COVID-19 pandemic and whether or not this in- parison of apples with oranges.
validates the GHSI itself. The paper also raises a vital policy dialogue 
issue on how GHSI can for instance be modified to account for Declarations
country-specific conditions that impinged on their ability to respond 
effectively to the COVID-19 pandemic independent of their GHSI NA.
scores. For instance, the fact that countries with established health 
care capabilities like the United States might have responded poorly Ethical Approval and Consent to Participate
to the COVID-19 pandemic perhaps due to political and internal 
coordination problems in their health care system, as discussed in Paper was written mainly based on analysis of publicly available 
the paper. secondary data; likewise, no human subjects were involved hence 
Overall, evidence from the analysed GHSI data strongly suggests no Ethical Clearance required as per local ethics committees’ 
the world might still not be adequately prepared for the next major guidelines.
pandemic, if no drastic steps are taken to change the narrative. A 
global aggregate score of 38.9 out of the expected optimum score of CRediT authorship contribution statement
100is a palpable manifestation of countries unpreparedness for the 
next major pandemic [5]. Countries that scored higher on the GHSI RKA: provided conceptualization direction, literature search, 
assessment did not translate into optimal response to the COVID-19 synthesis and analysis and manuscript write-up; JJN AAA AA SSM 
pandemic. Nonetheless, the glimpse of hope is that high GHSI scores EN: read and peer reviewed the manuscript based on their re-
correlated positively with vaccination coverage per 100 population. spective technical knowledge and expertise. All authors read and 
Moving forward, even though the GHSI methodology has been approved manuscript for publication.
204
R.K. Alhassan, E. Nketiah-Amponsah, A. Afaya et al. Journal of Infection and Public Health 16 (2023) 196–205
Funding [18] Lal A, Ashworth HC, Dada S, Hoemeke L, Tambo E. Optimizing pandemic pre-
paredness and response systems: lessons learned from Ebola to COVID-19. 
Disaster Med Public Health Prep 2020.
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of COVID-19. Int J Environ Res Public Health 2020;17(9):3161.
Consent for publication [20] Aitken T, Chin KL, Liew D, Ofori-Asenso R. Rethinking pandemic preparation: 
Global Health Security Index (GHSI) is predictive of COVID-19 burden, but in the 
opposite direction. J Infect 2020;81(2):318–56.
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response to recovery for the COVID-19 pandemic. Prog Disaster Sci 
Competing interests 2020;7:100102.
[22] Legido-Quigley H, Asgari N, Teo YY, Leung GM, Oshitani H, Fukuda K, Heymann 
D. Are high-performing health systems resilient against the COVID-19 epidemic? 
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[23] Liu W, Yue XG, Tchounwou PB. Response to the COVID-19 epidemic: the Chinese 
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