1
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Dynamic preparedness metric: a paradigm shift to measure and act on preparedness

      discussion
      a , a , WHO Technical Working Group of the Dynamic Preparedness Metric and Health Security Preparedness Department
      The Lancet. Global Health
      World Health Organization. Published by Elsevier Ltd

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Multiple indices currently exist to measure a country's preparedness for health emergencies.1, 2, 3, 4, 5 However, most of these metrics are based on cross-sectional assessments that do not reflect current and changing risks, particularly as related to major epidemics. Moreover, the COVID-19 pandemic has highlighted that existing measurement approaches are not sufficient to predict how a preparedness system for severe public health emergencies would perform during an emergency.6, 7 WHO aims to address these needs with the creation of a new dynamic, multi-hazards, evidence-based preparedness metric that can gauge preparedness capacity dynamically and inform key action plans for improving capacities for each country or region on the basis of identified capacity gaps. The dynamic preparedness metric (DPM) is a composite measure with three main conceptual dimensions: hazard, vulnerability, and capacity (figure ). Hazard represents the source of potential harm that a country will need to handle, based on both severity and the probability of an epidemic event or the exposure to it. Vulnerability describes the physical, social, economic, and environmental factors that could increase the susceptibility of an individual, community, asset, or system to the impact of hazards. Capacity refers to all the systems of knowledge, institutions, and infrastructure required to effectively anticipate, mitigate, respond to, and recover from the impact of a health emergency. 8 The three dimensions are combined by informative weighting of indicators representing necessary subcomponents and elements within each dimension. Figure Dynamic preparedness metric workflow Two metrics have been developed to assess the country preparedness status: (1) the risk-based DPM index, which supports countries in understanding current and changing capacities in line with hazards and vulnerabilities to strengthen their preparedness capacities for managing health emergencies effectively; and (2) the preparedness capacity gap, which helps countries identify gaps and prioritise actions necessary to address those gaps—such as those from the WHO benchmarks for International Health Regulations (IHR) capacities. The DPM index is dynamic as it is frequently updated with publicly available data and addresses five specific disease syndromes (ie, respiratory, diarrhoeal, neurological, haemorrhagic, and acute febrile syndromes in an initial phase). The potential pathways of spread and the impact on society for each syndrome are heterogeneous; thus, the preparedness capacities and actions needed to contain each syndrome are specific. The ability of the DPM index to estimate preparedness status relative to specific syndromes and risks will better inform effective actions to increase capacities and reduce epidemic spread and its management. Moreover, the aim of the DPM index is not to rank countries but rather to facilitate the identification of countries’ specific limitations in preparedness and possible mitigation strategies. Therefore, the DPM index is designed to support countries and regions to make evidence-based improvements in emergency preparedness considering the unique contributions of multiple sectors and disciplines. This dynamic and multisectoral approach is in line with best practices to limit the impact of future epidemics or pandemics through a health systems for health security approach. 9 As a result, the goal of the DPM index is ultimately to inform national strategic plans, highlighting crucial areas for prioritised improvements. To achieve this goal, an online interactive dashboard has been developed for countries to access quarterly estimates of health emergency preparedness with links to suggested actions. These key actions will be informed by the goals of the WHO benchmarks for IHR (2005) capacities, 10 which are connected to capacity scores in the DPM for each country or region. Furthermore, the DPM index can pinpoint areas where data are scarce and thus guide global health research agendas. The WHO has finalised phase 1 of the DPM through ongoing discussion with experts in the disciplines of health security, data science, animal–human interface, infrastructure, and epidemiology, among others. The DPM index is in use for the Universal Health and Preparedness Review (UHPR) to inform the baseline preparedness status in three dimensions in addition to UHRP indicators. 11 A working phase 1 DPM index is planned to launch in the first quarter of 2022 and implemented as a new, improved metric used in the 13th General Programme of Work (GPW 13). After phase 1, continued validation and improvement of the DPM index will be implemented to ensure that its utility will meet WHO's goals: promote health, keep the world safe, and serve the vulnerable. I declare no competing interests. The members of the WHO Technical Working Group of the Dynamic Preparedness Metric are Woody Ang Woo Teck, Cynthia Bell, Lucy Boulanger, Garrett Brown, Orlando Cenciarelli, Matthew H Cochran, Stephane De La Rocque, Khassoum Diallo, Ande Elisha Ashasim, Kaylee Errecaborde, Sane Jussi, Julie Kae, Nirmal Kandel, Masaya Kato, Benjamin Downs Lane, Lorenzo Lionello, Rafael Lozano, Aragaw Merawi, Robert Nguni, Jennifer Nuzzo, Heather Page, Ihor Perehinets, Amit Prasad, Amelie Rioux, Dalia Samhouri, Reuben Samuel, Nahoko Shindo, Jonathan Suk, Ambrose Otau Talisuna, Schmidt Tanja, Andrew Thow, and Luca Vernaccini. The members of the Health Security Preparedness Department at WHO are Stella Chungong, Marc Ho, Qudsia Huda, Abbas Omaar, Rajesh Sreedharan, Ludy Suryantoro, Liviu Vedrasco, and Jun Xing.

          Related collections

          Most cited references5

          • Record: found
          • Abstract: found
          • Article: not found

          Health security capacities in the context of COVID-19 outbreak: an analysis of International Health Regulations annual report data from 182 countries

          Summary Background Public health measures to prevent, detect, and respond to events are essential to control public health risks, including infectious disease outbreaks, as highlighted in the International Health Regulations (IHR). In light of the outbreak of 2019 novel coronavirus disease (COVID-19), we aimed to review existing health security capacities against public health risks and events. Methods We used 18 indicators from the IHR State Party Annual Reporting (SPAR) tool and associated data from national SPAR reports to develop five indices: (1) prevent, (2) detect, (3) respond, (4) enabling function, and (5) operational readiness. We used SPAR 2018 data for all of the indicators and categorised countries into five levels across the indices, in which level 1 indicated the lowest level of national capacity and level 5 the highest. We also analysed data at the regional level (using the six geographical WHO regions). Findings Of 182 countries, 52 (28%) had prevent capacities at levels 1 or 2, and 60 (33%) had response capacities at levels 1 or 2. 81 (45%) countries had prevent capacities and 78 (43%) had response capacities at levels 4 or 5, indicating that these countries were operationally ready. 138 (76%) countries scored more highly in the detect index than in the other indices. 44 (24%) countries did not have an effective enabling function for public health risks and events, including infectious disease outbreaks (7 [4%] at level 1 and 37 [20%] at level 2). 102 (56%) countries had level 4 or level 5 enabling function capacities in place. 32 (18%) countries had low readiness (2 [1%] at level 1 and 30 [17%] at level 2), and 104 (57%) countries were operationally ready to prevent, detect, and control an outbreak of a novel infectious disease (66 [36%] at level 4 and 38 [21%] at level 5). Interpretation Countries vary widely in terms of their capacity to prevent, detect, and respond to outbreaks. Half of all countries analysed have strong operational readiness capacities in place, which suggests that an effective response to potential health emergencies could be enabled, including to COVID-19. Findings from local risk assessments are needed to fully understand national readiness capacities in relation to COVID-19. Capacity building and collaboration between countries are needed to strengthen global readiness for outbreak control. Funding None.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The Global Health Security index and Joint External Evaluation score for health preparedness are not correlated with countries' COVID-19 detection response time and mortality outcome

            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 R 2 = 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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Rethinking pandemic preparation: Global Health Security Index (GHSI) is predictive of COVID-19 burden, but in the opposite direction

              Dear Editor, A recent article in Journal of Infection by Lv et al. projected that many countries could face similar COVID-19 situations as witnessed in Hubei in China. 1 There are many factors that can influence the course of an infectious disease outbreak. In the wake of the Ebola outbreak in 2014, the Global Health Security Index (GHSI) was developed with the aim of gauging countries’ capacity to deal with infectious disease outbreaks. 2 The GHSI highlights the shortcomings of existing pandemic policies and procedures, with the aim of spurring improvement of future practices. The index ranges from 0 to 100, and assesses six core elements: prevention, detection and reporting, response, health system, compliance with norms and risk of infectious disease outbreaks. 2 A higher GHSI indicates better preparedness. In the present study, we examined the correlation between GHSI and various measures of COVID-19 burden across different countries. We hypothesised that higher GHSI was inversely associated with measures of COVID-19 burden. Country-level data on COVID-19 as at 11 April 2020 were sourced from the ‘worldometer’. 3 Countries without testing data, or those with no assigned GHSI score were excluded. Furthermore, we included only countries with at least 100 confirmed cases of COVD-19. Data on countries’ median age and proportion of females in 2019 were sourced from the United Nations population database. 4 We analysed the association between GHSI and COVID-19 burden, represented by numbers of tests confirmed cases and deaths per million people per day since the first confirmed case in each country. First, we plotted GHSI against natural log transformed values of these outcomes (to provide more symmetrical distributions). Secondly, we used a generalised linear model (GLM) to determine the association between GHSI and confirmed cases and deaths per million people per day, with adjustment for testing rate, population median age and proportion of females. We considered GHSI both as a continuous variable and as a categorical variable comprising four quartiles. In the latter analyses, the first (lowest) quarter of GHSI was considered as the reference category. A total of 100 countries with complete data were included in the analysis (Supplementary Table S1). At the time of the analyses, there were 1,431,533 confirmed COVID-19 cases globally and 82,058 deaths. The median number of tests per million population across the included countries was 2486 (interquartile range [IQR] 623-9515). The countries with the highest and lowest testing rates were Iceland (84,957 per million population) and Nigeria (24 per million population), respectively. The median number of cases and deaths per million population were 207 (IQR: 35-498) and 3 (IQR: 0•8-11), respectively. COVID-19 metrics (log transformed) plotted against GHSI are presented in Figure 1 . These suggest a positive correlation between GHSI and testing rate, as well as cases and deaths per million people per day since the first recorded case. Of note, the US was the highest ranked country in terms of GHSI of all 100 countries analysed yet had the largest number of COVID-19 cases worldwide at the time of analyses. 2 , 3 Second-ranked UK was also bearing a large burden of disease. Figure 1 Scatter plot of showing unadjusted correlation between GHSI and testing rate (A), COVID-19 cases (B) and deaths (C) per million people per day. Figure 1 The results from the GLM model are presented in Table 1 . There was no statistically significant association observed between GHSI and testing rate. After adjusting for testing rate, median age and the proportion of females, a positive association was also observed between GHSI and COVID-19 cases and deaths, with the biggest burden borne by countries at the highest quartile of GHSI. Table 1 Relationship between GHSI and COVID-19 measures. Table 1 COVID-19 metric Incidence rate ratio (95% confidence interval) GHSI (continuous) GHSI quartile Q1 Q2 Q3 Q4 Tests per million people 1.01 (0.97-1.03), p=0.210 1.0 (ref) 2.22 (0.78-6.30), p=0.135 1.08 (0.47-2.47), p=0.858 1.88 (0.89-4.01), p=0.099 Cases per million people per day (unadjusted) 0.99 (0.96-1.04), p=0.858 1.0 (ref) 0.87 (0.22-3.45), p=0.842 0.46 (0.14-1.48), p=0.193 1.16 (0.37-3.67), p=0.798 Cases per million people per day (adjusted)a 1.02 (1.01-1.03), p=0.011 1.0 (ref) 1.69 (0.76-3.71), p=0.195 1.46 (0.80-2.65), p=0.212 2.56 (1.49-4.55), p=0.001 Deaths per million people per day (unadjusted) 0.99 (0.91-1.07), p=0.728 1.0 (ref) 0.15 (0.03-0.74), p=0.020 0.22 (0.38-1.30), p=0.096 0.87 (0.19-3.90), p=0.854 Deaths per million people per day (adjusted)a 1.05 (1.02-1.07), p<0.001 1.0 (ref) 1.09 (0.34-3.47), p=0.879 1.28 (0.43-3.86), p=0.655 3.56 (1.25-10.1), p=0.017 a Adjusted for testing rate, age and sex; IRR=incidence rate ratio The findings of our study were unexpected. First, no association was noted between GHSI and testing rate, despite that GHSI should serve as a surrogate for healthcare capacity, including COVID-19 testing. Effective pandemic response requires significant investment in testing, with adequate training of healthcare workers in testing, as well as sufficient supply of PPE and testing kits. 5 In addition, effective and widespread dissemination of information to the general population regarding testing criteria assists case detection. 5 Secondly, the associations between GHSI and COVID-19 cases and deaths were positive, meaning that the GHSI can reflect a country's capacity to deal with epidemics or pandemics, but in the opposite manner than intended. No doubt there was confounding by increased globalisation among more developed countries (with higher GHSI). Increased exposure to foreigners travelling for the purposes of tourism, business and use of healthcare is likely to increase the risk of new infectious pathogens being introduced. Similarly, mass migration contributes to disruption of local bacterial and viral environments. 2 Furthermore, the rarity of pandemics in conjunction with false reassurance from a high GHSI may have contributed to more lenient adherence to infection control mechanisms in recent years. 6 The intent of the GHSI is noble, and the findings of our study should not discourage future endeavours to gauge capacity to respond to pandemics. However, as the world becomes increasingly interconnected, the value of assessing the capacity of countries to manage infectious outbreaks individually is redundant. This interconnectedness extends beyond social, political, and business interactions to pathogenic environments. Consequently, identifying and controlling spread of newly arising infectious agents is only as effective as the practices within the poorest performing countries. The COVID-19 pandemic has revealed insufficiencies in existing knowledge of pandemic preparedness and response. A more integrated global approach is necessary, as is further research into alternative factors related to infection control that have not yet been considered. Development of international response protocols and effective communication channels will permit coordinated global action. Furthermore, establishment of dynamic models and tools will ensure the world is better prepared for future outbreaks.
                Bookmark

                Author and article information

                Journal
                Lancet Glob Health
                Lancet Glob Health
                The Lancet. Global Health
                World Health Organization. Published by Elsevier Ltd
                2214-109X
                12 April 2022
                May 2022
                12 April 2022
                : 10
                : 5
                : e615-e616
                Affiliations
                [a ]WHO Headquarters, Geneva 27 1211, Switzerland
                Article
                S2214-109X(22)00097-3
                10.1016/S2214-109X(22)00097-3
                9005120
                35427510
                bf69e1e8-5b34-4236-8be7-e30888d50ec2
                © 2022 World Health Organization

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Categories
                Comment

                Comments

                Comment on this article