Dataset Information

The Global Health Security Index is not predictive of coronavirus pandemic responses among Organization for Economic Cooperation and Development countries.

ABSTRACT: The ongoing COVID-19 pandemic has devastated many countries with ripple effects felt in various sectors of the global economy. In November 2019, the Global Health Security (GHS) Index was released as the first detailed assessment and benchmarking of 195 countries to prevent, detect, and respond to infectious disease threats. This paper presents the first comparison of Organization for Economic Cooperation and Development OECD countries' performance during the pandemic, with the pre-COVID-19 pandemic preparedness as determined by the GHS Index. Using a rank-based analysis, four indices were compared between select countries, including total cases, total deaths, recovery rate, and total tests performed, all standardized for comparison. Our findings suggest a discrepancy between the GHS index rating and the actual performance of countries during this pandemic, with an overestimation of the preparedness of some countries scoring highly on the GHS index and underestimation of the preparedness of other countries with relatively lower scores on the GHS index.

SUBMITTER: Abbey EJ

PROVIDER: S-EPMC7540886 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Global Health Security Index is not predictive of coronavirus pandemic responses among Organization for Economic Cooperation and Development countries.

Abbey Enoch J EJ Khalifa Banda A A BAA Oduwole Modupe O MO Ayeh Samuel K SK Nudotor Richard D RD Salia Emmanuella L EL Lasisi Oluwatobi O Bennett Seth S Yusuf Hasiya E HE Agwu Allison L AL Karakousis Petros C PC

PloS one 20201007 10

The ongoing COVID-19 pandemic has devastated many countries with ripple effects felt in various sectors of the global economy. In November 2019, the Global Health Security (GHS) Index was released as the first detailed assessment and benchmarking of 195 countries to prevent, detect, and respond to infectious disease threats. This paper presents the first comparison of Organization for Economic Cooperation and Development OECD countries' performance during the pandemic, with the pre-COVID-19 pand ...[more]

PMID: 33027257

Similar Datasets

Project description:The term neonatal sepsis is used to describe a generalized bloodstream infection of bacterial, viral, or fungal origin which is associated with hemodynamic changes and other clinical symptoms and signs, however, there is no unified definition. There are no basic criteria regarding differentiation of early-onset sepsis (EOS) versus late-onset sepsis (LOS). Stratification used in studies on neonatal sepsis also rarely includes the general condition of the newborn according to unambiguous assessment at birth, which hampers the establishment of a clear, uniform epidemiological description of neonatal sepsis. We aim to review the published data about the epidemiology and microbiology of sepsis in Organization for Economic Cooperation and Development (OECD) countries. Data was also collected on sepsis prevention programs that can be implemented in neonatal units. The outcomes of interest were incidence or incidence density of EOS and LOS, microbiology of EOS and LOS, and data on the methodology of the research, in particular the criteria for inclusion and exclusion of newborns from the study. Pubmed, EMBASE, LILACS Embase, Scopus, and Google Scholar were used. For the preselection step, inclusion criteria included: "bloodstream infection" or "neonatal sepsis" (MesH), "very low birth weight", and "country" full-text studies, human, and English language. Exclusion criteria included: studies published in languages other than English and studies available only as an abstracts. For proper selection, inclusion criteria included: information about epidemiology or microbiology bloodstream infection (BSI), study population and case definitions, exclusion criteria, narrative reviews, commentaries, case studies, pilot studies, study protocols, pediatric studies, and only clinical data (without microbiology or epidemiology) or studies with only one etiological factor analysis. The data review indicated the lack of an unequivocal, unified definition and no unambiguous basic criteria with regard to differentiation of EOS versus LOS. Among infants <1500 g, studies reported an EOS rate from 7% to 2%. For studies using other definitions (mostly all inborn babies), the rate of EOS ranged from 1% to 3%. The LOS incidences were much more varied among countries; the highest rates were in the multicenter studies focused on very low birth weight (VLBW) infants. The main pathogens in EOS are GBS and Gram-negative bacteria in LOS. Our review data shows that LOS microbiology is very diverse and that Gram-positive cocci, especially staphylococci, predominate versus Gram-negative rods. Unfortunately, the lack of uniform, international prevention programs results in high newborn morbidity and insufficient postnatal prevention of late-onset infections.

Project description:BackgroundAlthough there is rapid progress in vaccine research regarding influenza pandemic vaccines it is expected that pandemic influenza vaccine production can only start once the pandemic virus has been recognized. Therefore, pandemic vaccine capacity will be limited at least during the first phase of an influenza pandemic, requiring vaccine prioritization strategies. WHO recommends developing preliminary priorities for pandemic vaccine use. The goal of this review is to provide a thorough overview of pandemic vaccine prioritization concepts in the 27 European Union (EU) member states and the four non-EU countries of the Global Health Security Action Group.MethodsBetween September and December 2006 data was collected for each country through two data sources: (i) the national influenza pandemic plan; (ii) contacting key persons involved in pandemic planning by email and/or phone and/or fax.ResultsTwenty-six (84%) countries had established at least one vaccine priority group. Most common reported vaccine priority groups were health care workers (HCW) (100%), essential service providers (ESP) (92%) and high risk individuals (HRI) (92%). Ranking of at least one vaccine priority group was done by 17 (65%) of 26 countries. Fifteen (88%) of these 17 countries including a ranking strategy, decided that HCW with close contact to influenza patients should be vaccinated first; in most countries followed and/or ranked equally by ESP and subsequently HRI. Rationales for prioritization were provided by 22 (85%) of 26 countries that established vaccine priority groups. There was large variation in the phrasing and level of detailed specification of rationales. Seven (32%) of 22 countries providing rationales clearly associated each vaccine priority group with the specific rationale. Ten (32% of the 31 countries studied) countries have consulted and involved ethical experts to guide decisions related to vaccine prioritization.ConclusionIn the majority of the countries the establishment of vaccine priority groups, ranking and underlying rationales are in line with WHO recommendations. In most public plans the criteria by which prioritized groups are identified are not easily recognizable. Clarity however, may be necessary to assure public acceptability of the prioritization. Ethical experts, results of modelling exercises could play an increasing role in the future decision making process.

Project description:Global Health Security Index (GHSI) and Joint External Evaluation (JEE) are two well-known health security and related capability indices. We hypothesised that countries with higher GHSI or JEE scores would have detected their first COVID-19 case earlier, and would experience lower mortality outcome compared to countries with lower scores. We evaluated the effectiveness of GHSI and JEE in predicting countries' COVID-19 detection response times and mortality outcome (deaths/million). We used two different outcomes for the evaluation: (i) detection response time, the duration of time to the first confirmed case detection (from 31st December 2019 to 20th February 2020 when every country's first case was linked to travel from China) and (ii) mortality outcome (deaths/million) until 11th March and 1st July 2020, respectively. We interpreted the detection response time alongside previously published relative risk of the importation of COVID-19 cases from China. We performed multiple linear regression and negative binomial regression analysis to evaluate how these indices predicted the actual outcome. The two indices, GHSI and JEE were strongly correlated (r = 0.82), indicating a good agreement between them. However, both GHSI (r = 0.31) and JEE (r = 0.37) had a poor correlation with countries' COVID-19-related mortality outcome. Higher risk of importation of COVID-19 from China for a given country was negatively correlated with the time taken to detect the first case in that country (adjusted R2 = 0.63-0.66), while the GHSI and JEE had minimal predictive value. In the negative binomial regression model, countries' mortality outcome was strongly predicted by the percentage of the population aged 65 and above (incidence rate ratio (IRR): 1.10 (95% confidence interval (CI): 1.01-1.21) while overall GHSI score (IRR: 1.01 (95% CI: 0.98-1.01)) and JEE (IRR: 0.99 (95% CI: 0.96-1.02)) were not significant predictors. GHSI and JEE had lower predictive value for detection response time and mortality outcome due to COVID-19. We suggest introduction of a population healthiness parameter, to address demographic and comorbidity vulnerabilities, and reappraisal of the ranking system and methods used to obtain the index based on experience gained from this pandemic.

Project description:The stability of food supply chains is crucial to the food security of people around the world. Since the beginning of 2020, this stability has been undergoing one of the most vigorous pressure tests ever due to the COVID-19 outbreak. From a mere health issue, the pandemic has turned into an economic threat to food security globally in the forms of lockdowns, economic decline, food trade restrictions, and rising food inflation. It is safe to assume that the novel health crisis has badly struck the least developed and developing economies, where people are particularly vulnerable to hunger and malnutrition. However, due to the recency of the COVID-19 problem, the impacts of macroeconomic fluctuations on food insecurity have remained scantily explored. In this study, the authors attempted to bridge this gap by revealing interactions between the food security status of people and the dynamics of COVID-19 cases, food trade, food inflation, and currency volatilities. The study was performed in the cases of 45 developing economies distributed to three groups by the level of income. The consecutive application of the autoregressive distributed lag method, Yamamoto's causality test, and variance decomposition analysis allowed the authors to find the food insecurity effects of COVID-19 to be more perceptible in upper-middle-income economies than in the least developed countries. In the latter, food security risks attributed to the emergence of the health crisis were mainly related to economic access to adequate food supply (food inflation), whereas in higher-income developing economies, availability-sided food security risks (food trade restrictions and currency depreciation) were more prevalent. The approach presented in this paper contributes to the establishment of a methodology framework that may equip decision-makers with up-to-date estimations of health crisis effects on economic parameters of food availability and access to staples in food-insecure communities.

Dataset Information

The Global Health Security Index is not predictive of coronavirus pandemic responses among Organization for Economic Cooperation and Development countries.

Publications

The Global Health Security Index is not predictive of coronavirus pandemic responses among Organization for Economic Cooperation and Development countries.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets