Does it matter that standard preparedness indices did not predict COVID-19 outcomes?

A number of scientific publications and commentaries have suggested that standard preparedness indices such as the Global Health Security Index (GHSI) and Joint External Evaluation (JEE) scores did not predict COVID-19 outcomes. To some, the failure of these metrics to be predictive demonstrates the need for a fundamental reassessment which better aligns preparedness measurement with operational capacities in real-world stress situations, including the points at which coordination structures and decision-making may fail. There are, however, several reasons why these instruments should not be so easily rejected as preparedness measures.From a methodological point of view, these studies use relatively simple outcome measures, mostly based on cumulative numbers of cases and deaths at a fixed point of time. A country's "success" in dealing with the pandemic is highly multidimensional - both in the health outcomes and type and timing of interventions and policies - is too complex to represent with a single number. In addition, the comparability of mortality data over time and among jurisdictions is questionable due to highly variable completeness and representativeness. Furthermore, the analyses use a cross-sectional design, which is poorly suited for evaluating the impact of interventions, especially for COVID-19.Conceptually, a major reason that current preparedness measures fail to predict pandemic outcomes is that they do not adequately capture variations in the presence of effective political leadership needed to activate and implement existing system, instill confidence in the government's response; or background levels of interpersonal trust and trust in government institutions and country ability needed to mount fast and adaptable responses. These factors are crucial; capacity alone is insufficient if that capacity is not effectively leveraged. However, preparedness metrics are intended to identify gaps that countries must fill. As important as effective political leadership and trust in institutions, countries cannot be held accountable to one another for having good political leadership or trust in institutions. Therefore, JEE scores, the GHSI, and similar metrics can be useful tools for identifying critical gaps in capacities and capabilities that are necessary but not sufficient for an effective pandemic response.

The European experience with testing and surveillance during the first phase of the COVID-19 pandemic.

BACKGROUND: COVID-19 pandemic provides a unique opportunity to learn the challenges encountered by public health emergency preparedness systems, both in terms of problems encountered and adaptations during and after the first wave, as well as successful responses to them. RESULTS: This work draws on published literature, interviews with countries and institutional documents as part of a European Centre for Disease Prevention and Control project that aims to identify the implications for preparedness measurement derived from COVID-19 pandemic experience in order to advance future preparedness efforts in European Union member states. The analysis focused on testing and surveillance themes and five countries were considered, namely Italy, Germany, Finland, Spain and Croatia. Our analysis shown that a country's ability to conduct testing at scale was critical, especially early in the pandemic, and the inability to scale up testing operations created critical issues for public health operations such as contact tracing. Countries were required to develop new strategies, approaches, and policies under pressure and to review and revise them as the pandemic evolved, also considering that public health systems operate at the national, regional, and local level with respect to testing, contact tracing, and surveillance, and involve both government agencies as well as private organizations. Therefore, communication among multiple public and private entities at all levels and coordination of the testing and surveillance activities was critical. CONCLUSION: With regard to testing and surveillance, three capabilities that were essential to the COVID-19 response in the first phase, and presumably in other public health emergencies: the ability to scale-up testing, contact tracing, surveillance efforts; flexibility to develop new strategies, approaches, and policies under pressure and to review and revise them as the pandemic evolved; and the ability to coordinate and communicate in complex public health systems that operate at the national, regional, and local level with respect and involve multiple government agencies as well as private organizations.

An Estimate of Severe Harms Due to Screening Colonoscopy: A Systematic Review.

OBJECTIVE: This study aims to comprehensively assess the direct, severe harms of screening colonoscopy in the United States. Whereas other investigators have completed systematic reviews estimating the harms of all types of colonoscopy, this analysis focuses on screening colonoscopies that had adequate follow up to avoid undercounting delayed harms. DATA SOURCES: PubMed and Embase were queried for relevant studies on screening colonoscopy harms published between January 1, 2002, and April 1, 2022. STUDY SELECTION: English-language studies of screening colonoscopy for average risk patients were included. Studies must have followed patients for adequate time post procedure, defined as 30 days after colonoscopy. MAIN OUTCOMES: The primary outcome was the number of severe bleeding events and gastrointestinal (GI) perforations within 30 days of screening colonoscopy. RESULTS: A total of 1951 studies were reviewed for inclusion; 94 were reviewed in full text. Of those reviewed in full, 6 studies, including a total of 467,139 colonoscopies, met our inclusion criteria and were included in our analysis of harms related to screening colonoscopies. The rate of severe bleeding ranged credibly from 16.4 to 36.18 per 10,000 colonoscopies; the rate of perforation ranged credibly from 7.62 to 8.50 per 10,000 colonoscopies. CONCLUSIONS: This study is the first to estimate direct harms from screening colonoscopy, including harms that occur up to 30 days after the procedure. The risk of harm subsequent to screening colonoscopy is higher than previously reported and should be discussed with patients when engaging in shared decision making.

COVID-19 mortality in the United States: It's been two Americas from the start.

During the summer of 2021, a narrative of "two Americas" emerged: one with high demand for the COVID-19 vaccine and the second with widespread vaccine hesitancy and opposition to masks and vaccines. We analyzed "excess mortality" rates (the difference between total deaths and what would have been expected based on earlier time periods) prepared by the CDC for the United States from January 3, 2020 to September 26, 2021. Between Jan. 3, 2020 and Sept. 26, 2021, there were 895,693 excess deaths associated with COVID-19, 26% more than reported as such. The proportion of deaths estimated by the excess mortality method that was reported as COVID-19 was highest in the Northeast (92%) and lowest in the West (72%) and South (76%). Of the estimated deaths, 43% occurred between Oct. 4, 2020 and Feb. 27, 2021. Before May 31, 2020, approximately 56% of deaths were in the Northeast, where 17% of the population resides. Subsequently, 48% of deaths were in the South, which makes up 38% of the population. Since May 31, 2020, the South experienced COVID-19 mortality 26% higher than the national rate, whereas the Northeast's rate was 42% lower. If each region had the same mortality rate as the Northeast, more than 316,234 COVID-19 deaths between May 31, 2020 and Sept. 26, 2021 were "avoidable." More than half (63%) of the avoidable deaths occurred between May 31, 2020 and February, 2021, and more than half (60%) were in the South. Regional differences in COVID-19 mortality have been strong throughout the pandemic. The South has had higher mortality rates than the rest of the U.S. since May 31, 2020, and experienced 62% of the avoidable deaths. A comprehensive COVID-19 policy, including population-based restrictions as well as vaccines, is needed to control the pandemic.

An Estimate of the US Rate of Overuse of Screening Colonoscopy: a Systematic Review.

BACKGROUND: This study aims to assess the rate at which screening colonoscopy is performed on patients younger or older than the age range specified in national guidelines, or at shorter intervals than recommended. Such non-indicated use of the procedure is considered low-value care, or overuse. This study is the first systematic review of the rate of non-indicated completed screening colonoscopy in the USA. METHODS: PubMed and Embase were queried for relevant studies on overuse of screening colonoscopy published from January 1, 2002, until January 23, 2019. English-language studies that were conducted for screening colonoscopy after 2001 for average-risk patients were included. Studies must have followed national guidelines for detecting rates of overuse. We followed methods outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the reporting recommendations of the Meta-analysis of Observational Studies in Epidemiology group (MOOSE). RESULTS: A total of 772 papers were reviewed for inclusion; 42 were reviewed in full text. Of those reviewed, six studies met eligibility criteria, including a total of 459,503 colonoscopies of which 242,756 were screening colonoscopies. The rate of overuse ranged credibly from 17 to 25.7%. DISCUSSION: This study demonstrates that screening colonoscopy is regularly performed in the USA more often, and in populations older or younger, than recommended by national guidelines. Such overuse wastes resources and places patients at unnecessary risk of harm. Efforts to reduce non-indicated screening colonoscopy are needed.

The impact of health policies and vaccine rollout on the COVID-19 pandemic waves in Italy.

Background: Over the course of the COVID-19 pandemic in Italy, different response measures were taken to contain the spread of the virus. These include a variety of non-pharmaceutical interventions and a mass vaccination campaign. While not definitive, epidemiological measures provide some indication of the impact of such measures on the dynamics of the pandemic and lessons to better prepare for future emergencies. Objective: To describe the impact of vaccine rollout and health policies on the evolution of the COVID-19 pandemic in Italy from March 2020 to October 2021 using a set of epidemiological indicators. Methods: We performed a time-trend analysis of new confirmed COVID-19 cases, patients in hospital, and deaths. Using line charts, we informally assessed the relationship of these indicators with the immunization campaign and other health policies. Daily aggregate data were gathered from GitHub repositories of certified data from Italy's Government and Civil Protection. Results: The immunization coverage increased starting in March 2021, with a parallel decrease in COVID-19 infections, hospitalizations, and deaths. Despite different implementation approaches, the vaccine coverage growth rate had a similar pattern across regions. A comprehensive approach including measures such as requiring face masks and a "Green Pass" to enter indoor places also helped contain the pandemic. Conclusions: The vaccine rollout had a major effect on COVID-19 in Italy, especially on hospitalizations and deaths. Before the vaccine was available, however, other non-pharmaceutical interventions also helped to contain the spread of the virus and mitigate its effect on the population.

COVID-19 data are messy: analytic methods for rigorous impact analyses with imperfect data.

BACKGROUND: The COVID-19 pandemic has led to an avalanche of scientific studies, drawing on many different types of data. However, studies addressing the effectiveness of government actions against COVID-19, especially non-pharmaceutical interventions, often exhibit data problems that threaten the validity of their results. This review is thus intended to help epidemiologists and other researchers identify a set of data issues that, in our view, must be addressed in order for their work to be credible. We further intend to help journal editors and peer reviewers when evaluating studies, to apprise policy-makers, journalists, and other research consumers about the strengths and weaknesses of published studies, and to inform the wider debate about the scientific quality of COVID-19 research. RESULTS: To this end, we describe common challenges in the collection, reporting, and use of epidemiologic, policy, and other data, including completeness and representativeness of outcomes data; their comparability over time and among jurisdictions; the adequacy of policy variables and data on intermediate outcomes such as mobility and mask use; and a mismatch between level of intervention and outcome variables. We urge researchers to think critically about potential problems with the COVID-19 data sources over the specific time periods and particular locations they have chosen to analyze, and to choose not only appropriate study designs but also to conduct appropriate checks and sensitivity analyses to investigate the impact(s) of potential threats on study findings. CONCLUSIONS: In an effort to encourage high quality research, we provide recommendations on how to address the issues we identify. Our first recommendation is for researchers to choose an appropriate design (and the data it requires). This review describes considerations and issues in order to identify the strongest analytical designs and demonstrates how interrupted time-series and comparative longitudinal studies can be particularly useful. Furthermore, we recommend that researchers conduct checks or sensitivity analyses of the results to data source and design choices, which we illustrate. Regardless of the approaches taken, researchers should be explicit about the kind of data problems or other biases that the design choice and sensitivity analyses are addressing.

Adjusting COVID-19 Seroprevalence Survey Results to Account for Test Sensitivity and Specificity.

Population-based seroprevalence surveys can provide useful estimates of the number of individuals previously infected with serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and still susceptible, as well as contribute to better estimates of the case-fatality rate and other measures of coronavirus disease 2019 (COVID-19) severity. No serological test is 100% accurate, however, and the standard correction that epidemiologists use to adjust estimates relies on estimates of the test sensitivity and specificity often based on small validation studies. We have developed a fully Bayesian approach to adjust observed prevalence estimates for sensitivity and specificity. Application to a seroprevalence survey conducted in New York State in 2020 demonstrates that this approach results in more realistic-and narrower-credible intervals than the standard sensitivity analysis using confidence interval endpoints. In addition, the model permits incorporating data on the geographical distribution of reported case counts to create informative priors on the cumulative incidence to produce estimates and credible intervals for smaller geographic areas than often can be precisely estimated with seroprevalence surveys.

A National Framework to Improve Mortality, Morbidity, and Disparities Data for COVID-19 and Other Large-Scale Disasters.

Timely and accurate data on COVID-19 cases and COVID-19‒related deaths are essential for making decisions with significant health, economic, and policy implications. A new report from the National Academies of Sciences, Engineering, and Medicine proposes a uniform national framework for data collection to more accurately quantify disaster-related deaths, injuries, and illnesses. This article describes how following the report's recommendations could help improve the quality and timeliness of public health surveillance data during pandemics, with special attention to addressing gaps in the data necessary to understand pandemic-related health disparities.

Interpreting COVID-19 Test Results in Clinical Settings: It Depends!

Tests for Coronavirus disease 2019 (COVID-19) are intended for a disparate and shifting range of purposes: (1) diagnosing patients who present with symptoms to inform individual treatment decisions; (2) organizational uses such as "cohorting" potentially infected patients and staff to protect others; and (3) contact tracing, surveillance, and other public health purposes. Often lost when testing is encouraged is that testing does not by itself confer health benefits. Rather, testing is useful to the extent it forms a critical link to subsequent medical or public health interventions. Such interventions might be individual level, like better diagnosis, treatment, isolation, or quarantine of contacts. They might aid surveillance to understand levels and trends of disease within a defined population that enables informed decisions to implement or relax social distancing measures. In this article, we describe the range of available COVID-19 tests; their accuracy and timing considerations; and the specific clinical, organizational, and public health considerations that warrant different testing strategies. Three representative clinical scenarios illustrate the importance of appropriate test use and interpretation. The reason a patient seeks testing is often a strong indicator of the pretest probability of infection, and thus how to interpret test results. In addition, the level of population spread of the virus and the timing of testing play critical roles in the positive or negative predictive value of the test. We conclude with practical recommendations regarding the need for testing in various contexts, appropriate tests and testing methods, and the interpretation of test results.

SARS-CoV-2/COVID-19 Testing: The Tower of Babel.

BACKGROUND AND AIM: Testing represents one of the main pillars of public health response to SARS-CoV-2/COVID-19 pandemic. This paper shows how accuracy and utility of testing programs depend not just on the type of tests, but on the context as well. METHODS: We describe the testing methods that have been developed and the possible testing strategies; then, we focus on two possible methods of population-wide testing, i.e., pooled testing and testing with rapid antigen tests. We show the accuracy of split-pooling method and how, in different pre-test probability scenarios, the positive and negative predictive values vary using rapid antigen tests. RESULTS: Split-pooling, followed by retesting of negative results, shows a higher sensitivity than individual testing and requires fewer tests. In case of low pre-test probability, a negative result with antigen test could allow to rule out the infection, while, in case of a positive result, a confirmatory molecular test would be necessary. CONCLUSIONS: Test performance alone is not enough to properly choose which test to use; goals and context of the testing program are essential. We advocate the use of pooled strategies when planning population-wide screening, and the weekly use of rapid tests for close periodic monitoring in low-prevalence populations.

Getting the most from after action reviews to improve global health security.

BACKGROUND: After Action Reviews (AARs) provide a means to observe how well preparedness systems perform in real world conditions and can help to identify - and address - gaps in national and global public health emergency preparedness (PHEP) systems. WHO has recently published guidance for voluntary AARs. This analysis builds on this guidance by reviewing evidence on the effectiveness of AARs as tools for system improvement and by summarizing some key lessons about ensuring that AARs result in meaningful learning from experience. RESULTS: Empirical evidence from a variety of fields suggests that AARs hold considerable promise as tools of system improvement for PHEP. Our review of the literature and practical experience demonstrates that AARs are most likely to result in meaningful learning if they focus on incidents that are selected for their learning value, involve an appropriately broad range of perspectives, are conducted with appropriate time for reflection, employ systems frameworks and rigorous tools such as facilitated lookbacks and root cause analysis, and strike a balance between attention to incident specifics vs. generalizable capacities and capabilities. CONCLUSIONS: Employing these practices requires a PHEP system that facilitates the preparation of insightful AARs, and more generally rewards learning. The barriers to AARs fall into two categories: concerns about the cultural sensitivity and context, liability, the political response, and national security; and constraints on staff time and the lack of experience and the requisite analytical skills. Ensuring that AARs fulfill their promise as tools of system improvement will require ongoing investment and a change in mindset. The first step should be to clarify that the goal of AARs is organizational learning, not placing blame or punishing poor performance. Based on experience in other fields, the buy-in of agency and political leadership is critical in this regard. National public health systems also need support in the form of toolkits, guides, and training, as well as research on AAR methods. An AAR registry could support organizational improvement through careful post-event analysis of systems' own events, facilitate identification and sharing of best practices across jurisdictions, and enable cross-case analyses.

Beyond CHNAS: Performance Measurement for Community Health Improvement.

RESEARCH OBJECTIVE: Non-profit hospitals are required to work with community organizations to prepare Community Health Needs Assessment (CHNA) and implementation strategy (IS). In concert with the health care delivery system's transformation from volume to value and efforts to enhance multi-sector collaboration, such community health improvement (CHI) processes have the potential to bridge efforts of the health care delivery sector, public health agencies, and community organizations to improve population health. Having a shared measurement system is critical to achieving collective impact, yet despite the availability of community-level data from a variety of sources, many CHI processes lack clear, measurable objectives and evaluation plans. Through an in-depth analysis of ten exemplary CHI processes, we sought to identify best practices for population health measurement with a focus on monitoring collaborative implementation strategies. STUDY DESIGN: Based on a review of the scientific literature, professional publications and presentations, and nominations from a national advisory panel, we identified 10 exemplary CHI processes. Criteria of choice were whether (1) the CHIs articulate a clear definition of intended outcomes; (2) clear, focused, measurable objectives and expected outcomes, including health equity; (3) expected outcomes are realistic and addressed with specific action plans; and (4) whether the plans and their associated performance measures become fully integrated into agencies and become a way of being for the agencies. We then conducted an in-depth analysis of CHNA, IS, and related documents created by health departments and leading hospitals in each process. POPULATION STUDIED: U.S. hospitals. PRINCIPAL FINDINGS: Community health improvement processes benefit from a shared measurement system that indicate accountability for specific activities. Despite the importance of measurement and evaluation, existing community health improvement efforts often fall short in these areas. There is more variability in format and content of ISs than CHNAs; the most developed models include population-level goals/objectives and strategies with clear accountability and metrics. Other hospital IS's are less developed.Although all U.S. hospitals are familiar with performance measurement in their management, this familiarity does not seem to carry over to Community Benefit and CHNA efforts. Indeed, 5 of the 10 CHI processes we examined have some Accountable Care Organization (ACO) involvement, where population-health performance measures are commonplace. Yet this involvement is not mentioned in the CHNAs and ISs, nor are ACO data cited. CONCLUSIONS: Strengthening the CHNA regulations to require that hospitals report the evaluation measures they intend to monitor based on an established community health improvement model could help communities demonstrate impact. As in other areas of health care, performance measures should be tailored to implementation strategy, with clear indication of accountability, and move from outputs to process and outcome measures with established validity and reliability. IMPLICATIONS FOR POLICY OR PRACTICE: Although performance measurement is now commonplace throughout the health care system, the individuals who manage CHI processes may not be that familiar with this approach. This suggests that it is important to develop practitioners' knowledge and skills needed to use it population health data effectively.

Making Better Use of Population Health Data for Community Health Needs Assessments.

RESEARCH OBJECTIVE: Non-profit hospitals are required to work with community organizations to prepare a Community Health Needs Assessment (CHNA) and implementation strategy (IS). In concert with the health care delivery system's transformation from volume to value and efforts to enhance multi-sector collaboration, such community health improvement (CHI) processes have the potential to bridge efforts of the health care delivery sector, public health agencies, and community organizations to improve population health. Having a shared measurement system is critical to achieving collective impact, yet despite the availability of community-level data from a variety of sources, many CHI processes lack clear, measurable objectives and evaluation plans. Through an in-depth analysis of ten exemplary CHI processes, we sought to identify best practices for population health measurement with a focus on measures for needs assessments and priority setting. STUDY DESIGN: Based on a review of the scientific literature, professional publications and presentations, and nominations from a national advisory panel, we identified 10 exemplary CHI processes. Criteria of choice were whether (1) the CHIs articulate a clear definition of intended outcomes; (2) clear, focused, measurable objectives and expected outcomes, including health equity; (3) expected outcomes are realistic and addressed with specific action plans; and (4) whether the plans and their associated performance measures become fully integrated into agencies and become a way of being for the agencies. We then conducted an in-depth analysis of CHNA, IS, and related documents created by health departments and leading hospitals in each process. POPULATION STUDIED: U.S. hospitals. PRINCIPAL FINDINGS: Census, American Community Survey, and similar data are available for smaller areas are used to describe the populations covered, and, to a lesser extent, to identify health issues where there are disparities and inequities.Common data sources for population health profiles, including risk factors and population health outcomes, are vital statistics, survey data including BRFSS, infectious disease surveillance data, hospital & ED data, and registries. These data are typically available only at the county level, and only occasionally are broken down by race, ethnicity, age, poverty.There is more variability in format and content of ISs than CHNAs; the most developed models include population-level goals/objectives and strategies with clear accountability and metrics. Other hospital IS's are less developed. CONCLUSIONS: The county is the unit of choice because most population health profile data are not available for sub-county areas, but when a hospital serves a population more broadly or narrowly defined, appropriate data are not available to set priorities or monitor progress.Measure definitions are taken from the original data sources, so comparisons across measures is difficult. Thus, although CHNAs cover many of the same topics, the measures used vary markedly. Using the same community health profile, e.g. County Health Rankings, would simplify benchmarking and trend analysis.Implications for Policy or Practice: It is important to develop population health data that can be disaggregated to the appropriate geographical level and to groups defined by race and ethnicity, socioeconomic status, and other factors associated with health outcomes.

The Complex Interpretation and Management of Zika Virus Test Results.

Zika virus disease provides the latest example of a critical nexus between public health and clinical practice. Interpreting Zika virus test results is complicated by the absence of a single testing approach with superior validity across contexts and populations. Molecular tests are highly specific, variably sensitive, and have a short window period. Serologic tests identify antibodies against Zika virus and are more likely than molecular tests to cross-react with other related viruses, reducing specificity. The type of test performed and timing relative to possible Zika virus exposure depend on public health guidance, testing algorithms, test availability, and capacity. Guidance from the Centers for Disease Control and Prevention and local health departments have changed throughout the course of the US epidemic based on prevalence, geography, and clinical concerns. Women with a low pretest probability of infection should be counseled against testing. Women with a high pretest probability of Zika virus infection should still receive enhanced prenatal monitoring and newborn evaluation, regardless of the test result. An appropriate interpretation of results depends on what tests are used, patient characteristics, and reasons for testing. Clinicians should take these factors into account in shared decision making discussions with pregnant women about Zika virus testing.

Public Health Surveillance for Zika Virus: Data Interpretation and Report Validity.

Zika virus provides an example for which public health surveillance is based primarily on health care provider notifications to health departments of potential cases. This case-based surveillance is commonly used to understand the spread of disease in a population. However, case-based surveillance is often biased-whether testing is done and which tests are used and the accuracy of the results depend on a variety of factors including test availability, patient demand, perceptions of transmission, and patient and provider awareness, leading to surveillance artifacts that can provide misleading information on the spread of a disease in a population and have significant public health practice implications. To better understand this challenge, we first summarize the process that health departments use to generate surveillance reports, then describe factors influencing testing and reporting patterns at the patient, provider, and contextual level. We then describe public health activities, including active surveillance, that influence both patient and provider behavior as well as surveillance reports, and conclude with a discussion about the interpretation of surveillance data and approaches that could improve the validity of surveillance reports.

Facilitated Look-Back Analysis of Public Health Emergencies to Enhance Preparedness: A Brief Report of a Chemical Spill in Charleston, West Virginia.

To demonstrate how public health emergency systems can use health systems tools to analyze and learn from critical incidents, we employed a facilitated look-back approach to review the public response to a chemical spill in Charleston, West Virginia. We reviewed official reports, news articles, and other documents; conducted in-person interviews with key public health and emergency response officials and local community stakeholders; and organized a facilitated look-back meeting to identify root causes of the problems that were encountered. The primary response challenges were (1) public distrust stemming from scientific uncertainty about potential harms of chemicals involved in the spill and how this uncertainty was communicated and (2) communication within the public health system, broadly defined. We found that to address inherent uncertainty, health officials should acknowledge uncertainty and tell the public what is known and unknown, and what they are doing to get more information.

Root-Cause Analysis for Enhancing Public Health Emergency Preparedness: A Brief Report of a Salmonella Outbreak in the Alamosa, Colorado, Water Supply.

To demonstrate how public health systems can use root-cause analysis (RCA) to improve learning from critical incidents, the research team utilized a facilitated look-back meeting to examine the public health systems' response to a Salmonella outbreak in the water supply in Alamosa, Colorado. We worked with public health, emergency management agencies, and other stakeholders to identify response challenges related to public health emergency preparedness capabilities, root causes, and lessons learned. The results demonstrate that RCA can help identify systems issues that, if addressed, can improve future responses. Furthermore, RCA can identify more basic issues that go beyond a specific incident or setting, such as the need for effective communication and coordination throughout the public health system, and the social capital needed to support it.

Development of a public health emergency preparedness competency model for European Union countries.

In 2017, the European Centre for Disease Prevention and Control (ECDC) developed a competency model for individuals who work in public health emergency preparedness (PHEP) in European Union (EU) countries. The model serves as the basis for developing competency-based training programmes to support professionals in PHEP efforts at the country level. The competency model describes the knowledge and skills professionals need when working in national-level PHEP, such as preparedness committee members or their equivalents. In order to develop the model, existing competency statements were reviewed, as well as case studies and reports. Fifty-three professionals from the EU and other countries provided feedback to the model by participating in a three-stage consultation process. The model includes 102 competency, 100 knowledge and 158 skill statements. In addition to specifying the appropriate content for training programmes, the proposed common competency model can help to standardise terminology and approaches to PHEP training.

A Public Health Preparedness Logic Model: Assessing Preparedness for Cross-border Threats in the European Region.

Improving preparedness in the European region requires a clear understanding of what European Union (EU) member states should be able to do, whether acting internally or in cooperation with each other or the EU and other multilateral organizations. We have developed a preparedness logic model that specifies the aims and objectives of public health preparedness, as well as the response capabilities and preparedness capacities needed to achieve them. The capabilities, which describe the ability to effectively use capacities to identify, characterize, and respond to emergencies, are organized into 5 categories. The first 3 categories-(1) assessment; (2) policy development, adaptation, and implementation; and (3) prevention and treatment services in the health sector-represent what the public health system must accomplish to respond effectively. The fourth and fifth categories represent a series of interrelated functions needed to ensure that the system fulfills its assessment, policy development, and prevention and treatment roles: (4) coordination and communication regards information sharing within the public health system, incident management, and leadership, and (5) emergency risk communication focuses on communication with the public. This model provides a framework for identifying what to measure in capacity inventories, exercises, critical incident analyses, and other approaches to assessing public health emergency preparedness, not how to measure them. Focusing on a common set of capacities and capabilities to measure allows for comparisons both over time and between member states, which can enhance learning and sharing results and help identify both strengths and areas for improvement of public health emergency preparedness in the EU.