The value proposition of the Global Health Security Index

Precognition of Known And Unknown Biothreats: A Risk-Based Approach

Data mining and artificial intelligence algorithms can estimate the probability of future occurrences with defined precision. Yet, the prediction of infectious disease outbreaks remains a complex and difficult task. This is demonstrated by the limited accuracy and sensitivity of current models in predicting the emergence of previously unknown pathogens such as Zika, Chikungunya, and SARS-CoV-2, and the resurgence of Mpox, along with their impacts on global health, trade, and security. Comprehensive analysis of infectious disease risk profiles, vulnerabilities, and mitigation capacities, along with their spatiotemporal dynamics at the international level, is essential for preventing their transnational propagation. However, annual indexes about the impact of infectious diseases provide a low level of granularity to allow stakeholders to craft better mitigation strategies. A quantitative risk assessment by analytical platforms requires billions of near real-time data points from heterogeneous sources, integrating and analyzing univariable or multivariable data with different levels of complexity and latency that, in most cases, overwhelm human cognitive capabilities. Autonomous biosurveillance can open the possibility for near real-time, risk- and evidence-based policymaking and operational decision support.

High Global Health Security Index is a determinant of early detection and response to monkeypox: A cross-sectional study

INTRODUCTION

Recent outbreaks of monkeypox (Mpox) have occurred in countries outside of Africa, with large numbers of cases spreading rapidly to almost every continent. We aimed to analyze the correlation between the Global Health Security (GHS) Index (categories and indicators) and the Mpox case rate in different regions and globally.

METHODS

In this cross-sectional study, we used data from Mpox cases from the WHO, and the GHS categories and indicators for detection, prevention, reporting, health system, rapid response, international norms compliance, and risk environment. Outcome measures were the relationship between GHS index (categories and indicators) and Mpox case rate using crude and adjusted non-linear regression models.

RESULTS

After performing adjusted analyses, only risk environment and detection and reporting index were associated with Mpox case rates in the 99 countries and the Region of the Americas, respectively. Antimicrobial resistance (AMR) indicators of the prevention category, risk communication of the rapid response category, the joint external evaluation and performance veterinary services of the norms category, and the infrastructure adequacy of the risk environment category were associated with Mpox case rates in the 99 countries (p<0.05). Laboratory systems strength and quality indicator of the detection and reporting category, and emergency response operation indicator of the response rapid category were associated with Mpox case rates in the countries of the region of the Americas (p<0.05). AMR indicator of the prevention of the emergence category, and the infrastructure adequacy of the risk environment category were associated with Mpox case rates in the countries of the European Region (p<0.05). In the countries of the other regions, only the trade and travel restrictions indicator of the rapid response category was associated with Mpox case rates (p<0.05).

CONCLUSIONS

Countries, particularly in the Americas region, with high levels of infrastructure adequacy and laboratory system strength and quality as measured by the GHS index are better equipped to detect more Mpox cases. Therefore, they have higher Mpox case detection rates and can successfully respond to Mpox outbreaks.

Comparison of the Bulut Index-Beta method and Global Health Security Index: results from the world's countries

Background/aim

The Global Health Security Index (GHSI), which is used to assess the global health security preparedness levels of countries, and the Bulut Index-Beta (BI-β) method, developed as a multicriteria decision-making method, were compared in terms of global health security in the context of the world's countries. It was aimed to demonstrate the feasibility of the BI-β method by testing it on GHSI datasets and contribute to the methodological development of the GHSI.

Materials and methods

The datasets used in this study were the publicly available GHSI datasets, which allow for comparative evaluations of countries. The BI-β method and GHSI were used to compare countries in terms of global health security.

Results

In 2021, the top three best-performing countries in terms of global health security were the United States (BI-β = 85.46), Australia (BI-β = 82.53), and the United Kingdom (BI-β = 82.29). For 2019, the United States (BI-β = 85.44) and Australia (BI-β = 81.69) had the same ranks as in 2021, but the United Kingdom (BI-β = 76.63) dropped to 9th place. There was a statistically significant positive weak monotonic relationship between BI-β and GHSI rankings.

Conclusion

Since the GHSI scoring system is not consistent or questionable, the weighting process needs to be objectively reconsidered and the reasons for the weighting process need to be explained. The GHSI was conceptualized based on a narrow and technical framework. Therefore, it is recommended that the social and political determinants of public health be taken into account in the GHSI. On the other hand, the BI-β method can be easily used in solving other multicriteria decision-making problems, especially in public health areas such as global health security.

The biosafety incident response competence scale for clinical nursing staff: a development and validation study

AIMS

This study was designed to develop a biosafety incident response competence scale and evaluate its validity and reliability among clinical nurses.

DESIGN

This study employed a sequential approach, comprising four phases: (1) the establishment of a multidimensional conceptual model, (2) the preliminary selection of the items, (3) further exploration and psychometric testing of the items, (4) the application of the scale among clinical nurses.

METHODS

The biosafety incident response competence conceptual model was developed through literature review and the Delphi method. A total of 1,712 clinical nurses participated in the preliminary items selection, while 1,027 clinical nurses were involved in the further psychometric testing from July 2023 to August 2023. The item analysis, exploratory factor analysis and confirmatory factor analysis were conducted to evaluate the construct validity. Reliability was measured using Cronbach's alpha, split-half reliability, and test-retest reliability, while validity analysis included content validity, structural validity, convergent validity, and discriminant validity. From September to November 2023, we conducted a survey using the established scale with a total of 4338 valid questionnaires collected. T-test and variance analysis was employed to determine potential variations in biosafety incident response competence based on participants characteristics.

RESULTS

The final scale is composed of 4 factors and 29 items, including monitoring and warning abilities, nursing disposal abilities, biosafety knowledge preparedness, and infection protection abilities. The explanatory variance of the 4 factors was 75.100%. The Cronbach's alpha, split-half reliability and test-retest reliability were 0.974, 0.945 and 0.840 respectively. The Scale-level content validity index was 0.866. The Average Variance Extracted of the 4 factors was larger than 0.5, the Construct Reliability was larger than 0.7, and the Heterotrait-Monotrait ratio were less than 0.9. There were significant differences in the scores of response competence among nurses of different ages, working years, titles, positions, departments, marital status and participation in biosafety training (all P < 0.05).

CONCLUSIONS

The biosafety incident response competence scale for nurses exhibits satisfactory reliability and validity, making it a valuable tool for assessing clinical nurses' abilities in responding to biosafety incidents.

Cracking the code of health security: unveiling the balanced indices through rank-ordered effect analysis

BACKGROUND

Health security is a critical issue which involves multiple dimensions. It has received increasing attention in recent years, especially in China. In order to improve the national health level, China has made many efforts, such as the "Healthy China 2030" plan proposed several years ago. However, due to the complexity of its national conditions and the difficulty of index design, the results of these efforts are not significant. Therefore, it is necessary to construct a new measurement index system.

METHODS

Based on the questionnaire of "Health China 2030", we have collected a total of 3,000 participants from all 31 provinces, autonomous regions, and municipalities in China. We used statistical methods such as multiple correspondence analysis and rank-ordered effect analysis to process the data. The balance index is constructed by a series of actions such as weight division, order calculation and ranking.

RESULTS

Through multiple correspondence analysis, we can find that there was a close relation in the correspondence space between the satisfaction degrees 1, 2, and 3, while a far distance from satisfaction degrees 4 and 5. There were four positive and four negative indices separately based on the average expected level and four clusters after ordinal rank cluster analysis. Generally speaking, there are no prominent discrepancies across gender and residential areas.

CONCLUSIONS

We created and examined balanced indicators for health security in China based on the "Health China 2030" questionnaire. The findings of this study give insight into the overall situation of health security in China and indicate opportunities for improvement.

The application of policy composite indicators to predicting the health risk and recovery: a global comparative investigation

OBJECTIVES

There emerges increasing doubt regarding whether the policy composite indicators are applicable to the COVID-19 pandemic. A few early studies demonstrate that the association between some composite indicators of policy preparedness and the risk of COVID-19 is statistically insignificant, and the relation between any composite indicators and recovery process (e.g., vaccination coverage) remains unexplored. To examine the relation between composite indicators and pandemic risk (as well as the vaccination coverage) with robustness, this study applies different policy preparedness indicators by using data from multi-sources.

STUDY DESIGN

A cross-sectional analysis was performed.

METHOD

Regression analysis is adopted to examine the relation between four policy preparedness indicators (i.e., [1] International Health Regulations core capacity index, [2] Global Health Security Index, [3] epidemic preparedness index, and [4] World Governance Index) and COVID-19-confirmed cases/death/vaccination coverage at different time points. The linear regression is performed, and the spatial distribution of indicators are illustrated.

RESULTS

Countries with higher ranking in policy preparedness indexes can experience less severity of pandemic risk (e.g., confirmed cases and mortality) and faster recovery process (e.g., higher vaccination coverage). However, slight disparity in effectiveness exists across different indicators.

CONCLUSION

Results show that the policy preparedness indicators have predictive value of the confirmed cases, mortality, and vaccination coverage of COVID-19 pandemic, given sufficiently long-time span is observed.

Achieving a Healthy Oklahoma: Cross-Sector Cooperation to Improve Public Health Preparedness and Pandemic Response

In response to the COVID-19 pandemic, the University of Oklahoma Hudson College of Public Health launched the Achieving a Healthy Oklahoma (AHO) initiative in 2021. The goals of AHO were to assess lessons learned in Oklahoma from COVID-19 and set the foundation for enhanced public-private community collaboration by developing recommendations to prepare for future public health crises and promote health across all major economic sectors. Over 700 stakeholders were engaged in surveys, interviews, workgroup meetings, community listening sessions, and steering committee meetings over 8 months to accomplish these goals. Stakeholders produced 60 sector- and stakeholder-specific policy recommendations to address the major issues uncovered during the initiative. The AHO team then distilled them into 5 recommendations: (1) invest in the future of Oklahoma's health workforce to include critically needed public health professions in Oklahoma's healthcare loan repayment programs; (2) establish contracts between higher education institutions in Oklahoma and state and local health departments to monitor health sector workforce needs and provide training; (3) strengthen the delivery of coordinated public health services within local communities during emergencies and daily operations by dedicating health department roles to coordinate public health projects and services; (4) improve preparedness by coordinating annual emergency management exercises across local and state health departments; and (5) emphasize the efficiency and effectiveness of cross-sector collaborative efforts between public, private, and tribal partners. The AHO initiative serves as an action guide for assessing and improving state-level public health emergency responses and strengthening public health infrastructure. Implementing the recommendations in Oklahoma and assessing and addressing similar needs across the nation are necessary to prepare the United States for future public health emergencies.

Evaluation of the Global Health Security Index as a predictor of COVID-19 excess mortality standardised for under-reporting and age structure

BACKGROUND

Previous studies have observed that countries with the strongest levels of pandemic preparedness capacities experience the greatest levels of COVID-19 burden. However, these analyses have been limited by cross-country differentials in surveillance system quality and demographics. Here, we address limitations of previous comparisons by exploring country-level relationships between pandemic preparedness measures and comparative mortality ratios (CMRs), a form of indirect age standardisation, of excess COVID-19 mortality.

METHODS

We indirectly age standardised excess COVID-19 mortality, from the Institute for Health Metrics and Evaluation modelling database, by comparing observed total excess mortality to an expected age-specific COVID-19 mortality rate from a reference country to derive CMRs. We then linked CMRs with data on country-level measures of pandemic preparedness from the Global Health Security (GHS) Index. These data were used as input into multivariable linear regression analyses that included income as a covariate and adjusted for multiple comparisons. We conducted a sensitivity analysis using excess mortality estimates from WHO and The Economist.

RESULTS

The GHS Index was negatively associated with excess COVID-19 CMRs (table 2; β= -0.21, 95% CI= -0.35 to -0.08). Greater capacities related to prevention (β= -0.11, 95% CI= -0.22 to -0.00), detection (β= -0.09, 95% CI= -0.19 to -0.00), response (β = -0.19, 95% CI= -0.36 to -0.01), international commitments (β= -0.17, 95% CI= -0.33 to -0.01) and risk environments (β= -0.30, 95% CI= -0.46 to -0.15) were each associated with lower CMRs. Results were not replicated using excess mortality models that rely more heavily on reported COVID-19 deaths (eg, WHO and The Economist).

CONCLUSION

The first direct comparison of COVID-19 excess mortality rates across countries accounting for under-reporting and age structure confirms that greater levels of preparedness were associated with lower excess COVID-19 mortality. Additional research is needed to confirm these relationships as more robust national-level data on COVID-19 impact become available.

Assessing the impact of regional laboratory networks in East and West Africa on national health security capacities

National laboratories are a fundamental capacity for public health, contributing to disease surveillance and outbreak response. The establishment of regional laboratory networks has been posited as a means of improving health security across multiple countries. Our study objective was to assess whether membership in regional laboratory networks in Africa has an effect on national health security capacities and outbreak response. We conducted a literature review to select regional laboratory networks in the Eastern and Western African regions. We examined data from the World Health Organization Joint External Evaluation (JEE) mission reports, the 2018 WHO States Parties Annual Report (SPAR), and the 2019 Global Health Security Index (GHS). We compared the average scores of countries that are members of a regional laboratory network to those that are not. We also assessed country-level diagnostic and testing indicators during the COVID-19 pandemic. We found no significant differences in any of the selected health security metrics for member versus non-member countries of the either the East Africa Public Health Laboratory Networking Project (EAPHLNP) in the Eastern Africa region, nor for the West African Network of Clinical Laboratories (RESAOLAB) in the Western Africa region. No statistically significant differences were observed in COVID-19 testing rates in either region. Small sample sizes and the inherent heterogeneities in governance, health, and other factors between countries within and between regions limited all analyses. These results suggest potential benefit in setting baseline capacity for network inclusion and developing regional metrics for measuring network impact, but also beyond national health security capacities, other effects that may be required to justify continued support for regional laboratory networks.

Inferring the impact of humanitarian responses on population mortality: methodological problems and proposals

Reducing excess population mortality caused by crises due to armed conflict and natural disasters is an existential aim of humanitarian assistance, but the extent to which these deaths are averted in different humanitarian responses is mostly unknown. This information gap arguably weakens governance and accountability. This paper considers methodological challenges involved in making inferences about humanitarian assistance's effect on excess mortality, and outlines proposed approaches. Three possible measurement questions, each of which contributes some inferential evidence, are presented: (1) whether mortality has remained within an acceptable range during the crisis (for which different direct estimation options are presented); (2) whether the humanitarian response is sufficiently appropriate and performant to avert excess mortality (a type of contribution analysis requiring in-depth audits of the design of humanitarian services and of their actual availability, coverage and quality); and (3) the actual extent to which humanitarian assistance has reduced excess deaths (potentially the most complex question to answer, requiring application of causal thinking and careful specification of the exposure, and for which either quasi-experimental statistical modelling approaches or a combination of verbal and social autopsy methods are proposed). The paper concludes by considering possible 'packages' of the above methods that could be implemented at different stages of a humanitarian response, and calls for investment in improved methods and actual measurement.

The level of countries' preparedness to health risks during Covid-19 and pre-pandemic: the differential response to health systems building blocks and socioeconomic indicators

The global health security (GHS) Index assesses countries' level of preparedness to health risks. However, there is no evidence on how and whether the effects of health systems building blocks and socioeconomic indicators on the level of preparedness differ for low and high prepared countries. The aim of this study was to examine the contributions of health systems building blocks and socioeconomic indicators to show differences in the level of preparedness to health risks. The study also aimed to examine trends in the level of preparedness and the World Health Organization (WHO) regional differences before and during the Covid-19 pandemic. We used the 2021 GHS index report data and employed quantile regression, log-linear, double-logarithmic, and time-fixed effects models. As robustness checks, these functional form specifications corroborated with one another, and interval validity tests confirmed. The results show that increases in effective governance, supply chain capacity in terms of medicines and technologies, and health financing had positive effects on countries' level of preparedness to health risks. These effects were considerably larger for countries with higher levels of preparedness to health risks. The positive gradient trends signaled a sense of capacity on the part of countries with higher global health security. However, the health workforce including doctors, and health services including hospital beds, were not statistically significant in explaining variations in countries' level of preparedness. While economic factors had positive effects on the level of preparedness to health risks, their impacts across the distribution of countries' level of preparedness to health risks were mixed. The effects of Social Development Goals (SDGs) were greater for countries with higher levels of preparedness to health risks. The effect of the Human Development Index (HDI) was greatest for countries whose overall GHS index lies at the midpoint of the distribution of countries' level of preparedness. High-income levels were associated with a negative effect on the level of preparedness, especially if countries were in the lower quantiles across the distributions of preparedness. Relative to poor countries, middle- and high-income groups had lower levels of preparedness to health risks, an indication of a sense of complacency. We find the pandemic period (year 2021) was associated with a decrease in the level of preparedness to health risks in comparison to the pre-pandemic period. There were significant WHO regional differences. Apart from the Eastern Mediterranean, the rest of the regions were more prepared to health risks compared to Africa. There was a negative trend in the level of preparedness to health risks from 2019 to 2021 although regional differences in changes over time were not statistically significant. In conclusion, attempts to strengthen countries' level of preparedness to health shocks should be more focused on enhancing essentials such as supply chain capacity in terms of medicines and technologies; health financing, and communication infrastructure. Countries should also strengthen their already existing health workforce and health services. Together, strengthening these health systems essentials will be beneficial to less prepared countries where their impact we find to be weaker. Similarly, boosting SDGs, particularly health-related sub-scales, will be helpful to less prepared countries. Moreover, there is a need to curb complacency in preparedness to health risks during pandemics by high-income countries. The negative trend in the level of preparedness to health risks would suggest that there is a need for better preparedness during pandemics by conflating national health with global health risks. This will ensure the imperative of having a synergistic response to global health risks, which is understood by and communicated to all countries and regions.

A perspective on quantifying resilience: Combining community and infrastructure capitals

The resilience of communities has emerged as a major goal in policy and practice. Cities, states, and counties within the United States and around the world are passing laws requiring the incorporation of climate-related hazard vulnerability assessments within their master plan updates for resilience planning and design. The resilience of communities under present and future scenarios is thus becoming a cornerstone of decision making and actions. Decisions that would enhance resilience, however, span multiple sectors and involve various stakeholders. Quantifying community resilience is a key step in order to describe the preparedness level of communities, and subsequently locating non-resilient areas to further enhance their capacity to endure disasters. Two main approaches are currently being pursued to evaluate resilience. The first approach is the "community resilience" developed mainly by social scientists and planners, and it captures social resilience using numerous pre-disaster attributes to describe the functioning of a community. This approach subsumes that pre-disaster attributes can predict the community resilience to a disaster. The second approach is adopted for infrastructure resilience, mostly used by engineers, and it focuses on robustness, redundancy, resourcefulness, and rapidity. This approach is appropriate for systems that are operated by highly skilled personnel and where the actions are of engineering type. In this paper, we provide an overview of the two approaches, and we leverage their limitations to propose a hybrid approach that combines community and infrastructure capitals into an Area Resilience metric, called ARez. ARez captures the role/impact of both infrastructure and community and combines five sectors: energy, public health, natural ecosystem, socio-economic, and transportation. We present a proof-of-concept for the ARez metric, showing its practicality and applicability as a direct measure for resilience, over various time scales.

Global law, policy, and governance for effective prevention and control of COVID-19: A comparative analysis of the law and policy of Pakistan, China, and Russia

Global health governance is a developing system in this complex institutional regime. The local and regional health policies sometimes challenge global health governance due to diverse discourse in various countries. In the wake of COVID-19, global health governance was reaffirmed as indifferent modules to control and eliminate the pandemic; however, the global agencies later dissected their own opinion and said that "countries must learn to live with a pandemic." Given the controversial statement, this research focuses on the strong and effective policies of the Russian Federation, Pakistan, and China. The research uses the law and governance results and newly developed policies of the three countries formed under the global health policies. The conclusion is based on the statement that in order to live with the pandemic, strong health measures are required at each level.

COVID-19 in Europe: from outbreak to vaccination

BACKGROUND

COVID-19 is a pandemic of unprecedented proportions in recent human history. To date, the world has paid a high toll in terms of human lives lost, and on economic, financial, and social repercussions. In Europe, countries tried to mobilize all resources available to contain the COVID-19 effects, but the outcomes are diverse across countries. There have also been massive efforts geared towards finding safe and effective vaccines and to distribute them massively to the population. The main objective of this paper is to describe the COVID-19 prevalence in Europe. Secondly, it aims to identify epidemiological typologies allowing to distinguish the countries in terms of their response to the pandemic, and finally assess the effect of vaccination on pandemic control.

METHODS

The study covers 30 European countries: EU 27 in addition to Norway, Switzerland, and United Kingdom. Four epidemiological variables are analyzed at two distinct moments, at the end of 2020 and at the beginning of 2022: total number of cases per million, total number of deaths per million, total number of tests per thousand, and case fatality rate. In a second step, it uses a fuzzy approach, namely archetypal analysis, to identify epidemiological typologies, and positions countries by their response to the pandemic. Finally, it assesses how vaccination, stringency measures, booster doses and population age affect the case fatality rate, using a multiple regression model.

RESULTS

The outcomes unveil four epidemiological typologies for both periods. The clearest sign of change in the two periods concerns the case fatality rate that is found to be low in a single typology in 2020 but occurs in three typologies in 2022, although to different degrees. There is also statistical evidence of the positive impact of the primary vaccination on mortality reduction; however, the same does not hold for the booster dose and stringency measures.

CONCLUSIONS

The study shows that primary vaccination is the most effective measure to reduce mortality by COVID-19 suggesting that vaccination provides hope for an end to the pandemic. However, a worldwide access to vaccination is needed to make this happen.

Analyzing the GHSI puzzle of whether highly developed countries fared worse in COVID-19

Global Health Security Index (GHSI) categories are formulated to assess the capacity of world countries to deal with infectious disease risks. Thus, higher values of these indices were expected to translate to lower COVID-19 severity. However, it turned out to be the opposite, surprisingly suggesting that higher estimated country preparedness to epidemics may lead to higher disease mortality. To address this puzzle, we: (i) use a model-derived measure of COVID-19 severity; (ii) employ a range of statistical learning approaches, including non-parametric machine learning methods; (iii) consider the overall excess mortality, in addition to official COVID-19 fatality counts. Our results suggest that the puzzle is, to a large extent, an artifact of oversimplified data analysis and a consequence of misclassified COVID-19 deaths, combined with the higher median age of the population and earlier epidemics onset in countries with high GHSI scores.

Does climate help modeling COVID-19 risk and to what extent?

A growing number of studies suggest that climate may impact the spread of COVID-19. This hypothesis is supported by data from similar viral contagions, such as SARS and the 1918 Flu Pandemic, and corroborated by US influenza data. However, the extent to which climate may affect COVID-19 transmission rates and help modeling COVID-19 risk is still not well understood. This study demonstrates that such an understanding is attainable through the development of regression models that verify how climate contributes to modeling COVID-19 transmission, and the use of feature importance techniques that assess the relative weight of meteorological variables compared to epidemiological, socioeconomic, environmental, and global health factors. The ensuing results show that meteorological factors play a key role in regression models of COVID-19 risk, with ultraviolet radiation (UV) as the main driver. These results are corroborated by statistical correlation analyses and a panel data fixed-effect model confirming that UV radiation coefficients are significantly negatively correlated with COVID-19 transmission rates.

Governing and Measuring Health Security: The Global Push for Pandemic Preparedness Indicators

Providing collective solutions to global pandemics requires the coordination of information that is accurate and accountable. In recent years there has been a global push for reliable pandemic preparedness indicators. This push has come from U.S. foreign policy, the World Health Organization (WHO), NGOs, and private foundations. These actors want control over how data for preparedness indicators is collected, analysed, and promoted. Governments want to influence how they are assessed, using poor performance to attract attention and good performance to deflect blame. In this article we discuss how the push for pandemic preparedness indicators comes from the dual aims of repelling national risk, the spread of disease, while reducing global harm through stronger transnational governance arrangements. We delve into the development of indicators from the WHO and the privately-run Global Health Security Index, and examine how their claims to authority measure-up against standards of transparency, veracity, and accountability. We stress the importance of understanding how these indicators are composed. This is vital given the current drive to include social and governance metrics in revised efforts at data collection, as well as efforts to include pandemic preparedness indicators in how intergovernmental organizations, NGOs, donors, and funders devise health and development policies.

Global antimicrobial resistance: a system-wide comprehensive investigation using the Global One Health Index

BACKGROUND

Antimicrobial resistance (AMR) is one of the top ten global public health challenges. However, given the lack of a comprehensive assessment of worldwide AMR status, our objective is to develop a One Health-based system-wide evaluation tool on global AMR.

METHODS

We have further developed the three-hierarchical Global One Health Index (GOHI)-AMR indicator scheme, which consists of five key indicators, 17 indicators, and 49 sub-indicators, by incorporating 146 countries' data from diverse authoritative databases, including WHO's Global Antimicrobial Resistance and Use Surveillance System (GLASS) and the European CDC. We investigated the overall- or sub-rankings of GOHI-AMR at the international/regional/national levels for data preprocessing and score calculation utilizing the existing GOHI methodology. Additionally, a correlation analysis was conducted between the GOHI-AMR and other socioeconomic factors.

RESULTS

The average GOHI-AMR score for 146 countries is 38.45. As expected, high-income countries (HICs) outperform the other three income groups on overall rankings and all five key indicators of GOHI-AMR, whereas low-income countries unexpectedly outperform upper-middle-income countries and lower-middle-income countries on the antibiotics-resistant key indicator (ARR) and ARR-subordinate indicators, including carbapenem-, β-lactam-, and quinolone resistance, and even HICs on aminoglycoside resistance. There were no significant differences among the four groups on the environmental-monitoring indicator (P > 0.05). GOHI-AMR was positively correlated with gross domestic product, life expectancy, and AMR-related publications, but negatively with natural growth rate and chronic respiratory disease. In contrast to Cyprus, the remarkably lower prevalence of "ESKAPE pathogens" in high-scoring Sweden and Denmark highlights Europe's huge gaps. China and Russia outperformed the other three BRICS countries on all key indicators, particularly India's ARR and Brazil's AMR laboratory network and coordination capacity. Furthermore, significant internal disparities in carbapenem-resistant Klebsiella pneumoniae (CRKP) and methicillin-resistant Staphylococcus aureus (MRSA) prevalence were observed between China and the USA, with MRSA prevalence both gradually declining, whereas CRKP prevalence has been declining in the USA but increasing in China, consistent with higher carbapenems-related indicator' performance in USA.

CONCLUSIONS

GOHI-AMR is the most comprehensive tool currently available for the assessment of AMR status worldwide. We discovered unique features impacting AMR in each country and offered precise recommendations to improve the capacity to tackle AMR in low-ranking countries.

Pandemic preparedness and COVID-19: an exploratory analysis of infection and fatality rates, and contextual factors associated with preparedness in 177 countries, from Jan 1, 2020, to Sept 30, 2021

BACKGROUND

National rates of COVID-19 infection and fatality have varied dramatically since the onset of the pandemic. Understanding the conditions associated with this cross-country variation is essential to guiding investment in more effective preparedness and response for future pandemics.

METHODS

Daily SARS-CoV-2 infections and COVID-19 deaths for 177 countries and territories and 181 subnational locations were extracted from the Institute for Health Metrics and Evaluation's modelling database. Cumulative infection rate and infection-fatality ratio (IFR) were estimated and standardised for environmental, demographic, biological, and economic factors. For infections, we included factors associated with environmental seasonality (measured as the relative risk of pneumonia), population density, gross domestic product (GDP) per capita, proportion of the population living below 100 m, and a proxy for previous exposure to other betacoronaviruses. For IFR, factors were age distribution of the population, mean body-mass index (BMI), exposure to air pollution, smoking rates, the proxy for previous exposure to other betacoronaviruses, population density, age-standardised prevalence of chronic obstructive pulmonary disease and cancer, and GDP per capita. These were standardised using indirect age standardisation and multivariate linear models. Standardised national cumulative infection rates and IFRs were tested for associations with 12 pandemic preparedness indices, seven health-care capacity indicators, and ten other demographic, social, and political conditions using linear regression. To investigate pathways by which important factors might affect infections with SARS-CoV-2, we also assessed the relationship between interpersonal and governmental trust and corruption and changes in mobility patterns and COVID-19 vaccination rates.

FINDINGS

The factors that explained the most variation in cumulative rates of SARS-CoV-2 infection between Jan 1, 2020, and Sept 30, 2021, included the proportion of the population living below 100 m (5·4% [4·0-7·9] of variation), GDP per capita (4·2% [1·8-6·6] of variation), and the proportion of infections attributable to seasonality (2·1% [95% uncertainty interval 1·7-2·7] of variation). Most cross-country variation in cumulative infection rates could not be explained. The factors that explained the most variation in COVID-19 IFR over the same period were the age profile of the country (46·7% [18·4-67·6] of variation), GDP per capita (3·1% [0·3-8·6] of variation), and national mean BMI (1·1% [0·2-2·6] of variation). 44·4% (29·2-61·7) of cross-national variation in IFR could not be explained. Pandemic-preparedness indices, which aim to measure health security capacity, were not meaningfully associated with standardised infection rates or IFRs. Measures of trust in the government and interpersonal trust, as well as less government corruption, had larger, statistically significant associations with lower standardised infection rates. High levels of government and interpersonal trust, as well as less government corruption, were also associated with higher COVID-19 vaccine coverage among middle-income and high-income countries where vaccine availability was more widespread, and lower corruption was associated with greater reductions in mobility. If these modelled associations were to be causal, an increase in trust of governments such that all countries had societies that attained at least the amount of trust in government or interpersonal trust measured in Denmark, which is in the 75th percentile across these spectrums, might have reduced global infections by 12·9% (5·7-17·8) for government trust and 40·3% (24·3-51·4) for interpersonal trust. Similarly, if all countries had a national BMI equal to or less than that of the 25th percentile, our analysis suggests global standardised IFR would be reduced by 11·1%.

INTERPRETATION

Efforts to improve pandemic preparedness and response for the next pandemic might benefit from greater investment in risk communication and community engagement strategies to boost the confidence that individuals have in public health guidance. Our results suggest that increasing health promotion for key modifiable risks is associated with a reduction of fatalities in such a scenario.

FUNDING

Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.

On the resilience of health systems: A methodological exploration across countries in the WHO African Region

The need for resilient health systems is recognized as important for the attainment of health outcomes, given the current shocks to health services. Resilience has been defined as the capacity to "prepare and effectively respond to crises; maintain core functions; and, informed by lessons learnt, reorganize if conditions require it". There is however a recognized dichotomy between its conceptualization in literature, and its application in practice. We propose two mutually reinforcing categories of resilience, representing resilience targeted at potentially known shocks, and the inherent health system resilience, needed to respond to unpredictable shock events. We determined capacities for each of these categories, and explored this methodological proposition by computing country-specific scores against each capacity, for the 47 Member States of the WHO African Region. We assessed face validity of the computed index, to ensure derived values were representative of the different elements of resilience, and were predictive of health outcomes, and computed bias-corrected non-parametric confidence intervals of the emergency preparedness and response (EPR) and inherent system resilience (ISR) sub-indices, as well as the overall resilience index, using 1000 bootstrap replicates. We also explored the internal consistency and scale reliability of the index, by calculating Cronbach alphas for the various proposed capacities and their corresponding attributes. We computed overall resilience to be 48.4 out of a possible 100 in the 47 assessed countries, with generally lower levels of ISR. For ISR, the capacities were weakest for transformation capacity, followed by mobilization of resources, awareness of own capacities, self-regulation and finally diversity of services respectively. This paper aims to contribute to the growing body of empirical evidence on health systems and service resilience, which is of great importance to the functionality and performance of health systems, particularly in the context of COVID-19. It provides a methodological reflection for monitoring health system resilience, revealing areas of improvement in the provision of essential health services during shock events, and builds a case for the need for mechanisms, at country level, that address both specific and non-specific shocks to the health system, ultimately for the attainment of improved health outcomes.

What matters: non-pharmaceutical interventions for COVID-19 in Europe

OBJECTIVES

The purpose of this study is to describe the situation of COVID-19 in European countries and to identify important factors related to prevention and control.

METHODS

We obtained data from World Health Statistics 2020 and the Institute for Health Metrics and Evaluation (IHME). We calculated the Rt values of 51 countries in Europe under different prevention and control measures. We used lasso regression to screen factors associated with morbidity and mortality. For the selected variables, we used quantile regression to analyse the relevant influencing factors in countries with different levels of morbidity or mortality.

RESULTS

The government has a great influence on the change in Rt value through prevention and control measures. The most important factors for personal and group prevention and control are the mobility index, testing, the closure of educational facilities, restrictions on large-scale gatherings, and commercial restrictions. The number of ICU beds and doctors in medical resources are also key factors. Basic sanitation facilities, such as the proportion of safe drinking water, also have an impact on the COVID-19 epidemic.

CONCLUSIONS

We described the current status of COVID-19 in European countries. Our findings demonstrated key factors in individual and group prevention measures.

Analysing COVID-19 outcomes in the context of the 2019 Global Health Security (GHS) Index

Introduction

The Global Health Security Index benchmarks countries’ capacities to carry out the functions necessary to prevent, detect and respond to biological threats. The COVID-19 pandemic served as an opportunity to evaluate whether the Index contained the correct array of variables that influence countries’ abilities to respond to these threats; assess additional variables that may influence preparedness; and examine how the impact of preparedness components change during public health crises.

Methods

Linear regression models were examined to determine the relationship between excess mortality per capita for the first 500 days of countries’ COVID-19 pandemic and internal Index variables, as well as external variables including social cohesion; island status; perceived corruption; elderly population size; previous epidemic experience; stringency of non-pharmaceutical interventions; and social and political polarisation.

Results

COVID-19 outcomes were significantly associated with sociodemographic, political and governance variables external to the 2019 Index: social cohesion, reduction in social polarisation and reduced perceptions of corruption were consistently correlated with reduced excess mortality throughout the pandemic. The association of other variables assessed by the Index, like epidemiological workforce robustness, changed over time. Fixed country features, including geographic connectedness, larger elderly population and lack of prior coronavirus outbreak experience were detrimental to COVID-19 outcomes. Finally, there was evidence that countries that lacked certain capacities were able to develop these over the course of the pandemic.

Conclusions

Additional sociodemographic, political and governance variables should be included in future indices to improve their ability to characterise preparedness. Fixed characteristics, while not directly addressable, are useful for establishing countries’ inherent risk profile and can motivate those at greater risk to invest in preparedness. Particular components of preparedness vary in their impact on outcomes over the course of the pandemic, which may inform resource direction during ongoing crises. Future research should seek to further characterise time-dependent impacts as additional COVID-19 outcome data become available.

Global cancer research in the era of COVID-19: a bibliometric analysis

Background

Patients with cancer across the world have been impacted by the COVID-19 pandemic due to increased risk of infection and disruption to cancer diagnosis and treatment. Widening of healthcare disparities is expected as the gap between health systems with and without adequate resources to mitigate the pandemic become more apparent. We undertook a bibliometric analysis of research related to cancer and COVID-19 to understand (1) the type of research that has been conducted (e.g. patients, services and systems) and (2) whether the pandemic has impacted the state of global cancer research as measured by research outputs to date.

Methods

An existing filter for cancer research consisting of title words and the names of specialist cancer journals was used to identify cancer and COVID-19 related articles and reviews in the Web of Science (©Clarivate Analytics) between January 2019 and February 2021.

Results

One thousand five hundred and forty-five publications were identified. The majority (57%) were reviews, opinion pieces or concerned with modelling impact of delays to diagnosis and treatment. The main research domains focused on managing or estimating COVID-19 risk to cancer patients accounting for 384 papers (25%). High Income countries contributed the largest volume (n = 1,115; 72%), compared to Upper Middle (n = 302; 20%), Lower Middle (n = 122; 8%) and Low Income countries (n = 2.4; 0.2%). No evidence of a reduction in global cancer research output was observed in 2020.

Conclusions

We observed a shift in research focus rather than a decline in absolute output. However, there is variation based on national income and collaborations are minimal. There has been a focus on pan-cancer studies rather than cancer site-specific studies. Strengthening global multidisciplinary research partnerships with teams from diverse backgrounds with regard to gender, clinical expertise and resource setting is essential to prevent the widening of cancer inequalities.

Organising knowledge to prevent global health crises: a comparative analysis of pandemic preparedness indicators

Preparing for the possibility of a global pandemic presents a transnational organisational challenge: to assemble and coordinate knowledge over institutionally diverse countries with high fidelity. The COVID-19 pandemic has laid these problems bare. This article examines the construction of the three main cross-national indicators of pandemic preparedness: a database with self-reported data by governments, external evaluations organised by the WHO and a global ranking known as the Global Health Security Index. Each of these presents a different model of collecting evidence and organising knowledge: the collation of self-reports by national authorities; the coordination of evaluation by an epistemic community authorised by an intergovernmental organisation and on the basis of a strict template; and the cobbling together of different sources into a common indicator by a transnational multi-stakeholder initiative. We posit that these models represent different ways of creating knowledge to inform policy choices, and each has different forms of potential bias. In turn, this shapes how policymakers understand what is ‘best practice’ and appropriate policy in pandemic preparedness.

Monitoring Quality of Care in the WHO Africa Region-A study design for measurement and tracking, towards UHC attainment

This paper reports on the design of a study to generate a quality of care index for countries in the World Health Organization Africa Region.

Quality of care, for all people at all times, remains pivotal to the advancement of the 2030 agenda and the attainment of Universal Health Coverage. We present a study protocol for deriving a quality of care index, hinged on indicators and data elements currently monitored through routine information systems and institutionalized facility assessments in the World Health Organization Africa Region.

This paper seeks to offer more insight into options in the Region for strengthening monitoring processes of quality of care, as a step towards generating empirical evidence which can galvanize action towards an improved care process.

The methodology proposed in this study design has broad implications for policymaking and priority setting for countries, emphasizing the need for robust empirical measures to understand the functionality of health systems for the delivery of quality essential services. Application of this protocol will guide policymaking, as countries work to increasingly improve quality of care and adopt policies that will best facilitate their advancement towards Universal Health Coverage.

Known and Unknown Transboundary Infectious Diseases as Hybrid Threats

The pathogenicity, transmissibility, environmental stability, and potential for genetic manipulation make microbes hybrid threats that could blur the distinction between peace and war. These agents can fall below the detection, attribution, and response capabilities of a nation and seriously affect their health, trade, and security. A framework that could enhance horizon scanning regarding the potential risk of microbes used as hybrid threats requires not only accurately discriminating known and unknown pathogens but building novel scenarios to deploy mitigation strategies. This demands the transition of analyst-based biosurveillance tracking a narrow set of pathogens toward an autonomous biosurveillance enterprise capable of processing vast data streams beyond human cognitive capabilities. Autonomous surveillance systems must gather, integrate, analyze, and visualize billions of data points from different and unrelated sources. Machine learning and artificial intelligence algorithms can contextualize capability information for different stakeholders at different levels of resolution: strategic and tactical. This document provides a discussion of the use of microorganisms as hybrid threats and considerations to quantitatively estimate their risk to ensure societal awareness, preparedness, mitigation, and resilience.