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Editorial. Introduces an article on the importance of bioterrorism preparedness for family physicians, published on the May 1, 2003 issue of the journal 'American Family Physician.' Actions that will help physicians prepare for a bioterror event; Other ways in dealing with bioterrorism.

A letter to the editor is presented in response to the article "Distinguishing Health Expectancies and Health-Adjusted Life Expectancies From Quality-Adjusted Life Years," by J. M. Robine et al in the 1999 issue.

A letter to the editor is presented in response to the study "Estimation of Population Denominators for Public Health Studies at the Tract, Gender, and Age-Specific Level," by M. Aickin et al.

ABSTRACT BACKGROUND The 2009 H1N1 vaccination campaign was the largest in US history. State health departments received vaccines from the federal government and sent them to local health departments ( LHDs) who were responsible for getting vaccines to the public. Many LHD's used school-based clinics to ensure children were the first to receive limited vaccine supplies, but the success of school-based distribution strategies varied in different locations. The goal of this project was to identify and learn from high-performing school-based vaccination clinics in order to share successes and improve performance in future school-based vaccination campaigns. METHODS We used a combination of process mapping and comparative analysis to identify and derive lessons from positive outlier cases observed during 2009 H1N1 school-based vaccination clinic implementation. We created process maps to identify the activities of LHDs conducting school-based vaccinations and used them as the basis for in-depth interviews of LHD staff. We asked interviewees to describe their activities during the 2009 H1N1 pandemic ( pH1N1) school-based vaccination campaign with a focus on successful processes. RESULTS We identified positive deviants, that is, those that performed better than expected, and categorized qualitative data from in-depth interviews with 13 successful LHDs according to the process maps. Key mechanisms for school-based vaccination success included having a relationship with local school authorities, communicating effectively with parents, and ensuring clinic logistics allowed for an easy flow of students through the vaccination process. CONCLUSIONS Utilizing rigorous methodology, we defined and learned lessons from successful LHDs when conducting school-based vaccination clinics, which can be applied to future school-based vaccination campaigns. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article the authors discuss the future of the World Health Organization's (WHO's) Pandemic Influenza Preparedness (PIP) Framework, which promotes the fair sharing of public health-related pandemic influenza samples between countries for vaccine development. Topics include the use of genetic sequencing data (GSD) in vaccine development, Indonesia's refusal to share avian influenza A (H5N1) samples, the omission of GSD in the PIP Framework, and biosafety and biosecurity concerns.

Abstract: Background: Although previous studies have found no differences in response to antidepressant pharmacotherapy between selective serotonin reuptake inhibitors (SSRIs), some recent trials suggest benefits associated with more rapid onset of action. Objective: The aim of this work was to compare the likelihood that patients initiating treatment with branded escitalopram, rather than with any of 3 SSRIs (ie, citalopram, fluoxetine, and paroxetine) that are available in generic or branded formulations, would continue therapy with the initial medication after 2 and 6 months. Methods: We used propensity score—weighted logistic regression to assess the effect of antidepressant choice on the likelihood of continuing treatment, based on data from a large administrative claims database with information about US patients. We modeled the propensity to initiate treatment with escitalopram based on demographic, diagnostic, insurance, and service-use characteristics in the 6 months before treatment initiation and used the results to calculate weights for analysis of treatment continuation. The primary outcome measures were receipt of 2 prescriptions of the index drug in the first 2 months and, among those continuing at 2 months, 4 prescriptions in the first 6 months. Antidepressant choice, cost, and service-use characteristics during the treatment period were included as covariates. Patients who initiated therapy between July 2002 and April 2005 were eligible for inclusion. Results: Based on data for 43,921 patients, at 2 months, escitalopram initiators were more likely to have continued initial medication than those receiving the other SSRIs (66.1% vs 61.9%, respectively; P < 0.01) and less likely to have switched or augmented treatment (4.8% vs 7.6%; P < 0.01). At 6 months, escitalopramtreated patients were also more likely to have continued initial medication (47.1% vs 41.0%; P < 0.01) and less likely to have switched or augmented treatment (9.4% vs 14.4%; P < 0.01). Conclusion: Patients initiating treatment with escitalopram were more likely to continue and less likely to switch or augment treatment at 2 and 6 months of therapy compared with those who initiated with alternative SSRIs. [Copyright &y& Elsevier]

Background: The new reality of biologic terrorism and warfare has ignited a debate about whether to reintroduce smallpox vaccination. Methods: We developed scenarios of smallpox attacks and built a stochastic model of outcomes under various control policies. We conducted a systematic literature review and estimated model parameters on the basis of European and North American outbreaks since World War II. We assessed the trade-offs between vaccine-related harms and benefits. Results: Nations or terrorists possessing a smallpox weapon could feasibly mount attacks that vary with respect to tactical complexity and target size, and patterns of spread can be expected to vary according to whether index patients are hospitalized early. For acceptable results, vaccination of contacts must be accompanied by effective isolation. Vaccination of contacts plus isolation is expected to result in 7 deaths (from vaccine or smallpox) in a scenario involving the release of variola virus from a laboratory, 19 deaths in a human-vector scenario, 300 deaths in a building-attack scenario, 2735 deaths in a scenario involving a low-impact airport attack, and 54,729 deaths in a scenario involving a high-impact airport attack. Immediate vaccination of the public in an attacked region would provide little additional benefit. Prior vaccination of health care workers, who would be disproportionately affected, would save lives in large local or national attacks but would cause 25 deaths nationally. Prior vaccination of health care workers and the public would save lives in a national attack but would cause 482 deaths nationally. The expected net benefits of vaccination depend on the assessed probability of an attack. Prior vaccination of health care workers would be expected to save lives if the probability of a building attack exceeded 0.22 or if the probability of a high-impact airport attack exceeded 0.002. The probability would have to be much higher to make vaccination of the public life-saving. Conclusions: The analysis favors prior vaccination of health care workers unless the likelihood of any attack is very low, but it favors vaccination of the public only if the likelihood of a national attack or of multiple attacks is high. N Engl J Med 2003;348:416-25. [ABSTRACT FROM AUTHOR]

Background: Heterogeneity in national SARS-CoV-2 infection surveillance capabilities may compromise global enumeration and tracking of COVID-19 cases and deaths and bias analyses of the pandemic's tolls. Taking account of heterogeneity in data completeness may thus help clarify analyses of the relationship between COVID-19 outcomes and standard preparedness measures., Methods: We examined country-level associations of pandemic preparedness capacities inventories, from the Global Health Security (GHS) Index and Joint External Evaluation (JEE), on SARS-CoV-2 infection and COVID-19 death data completion rates adjusted for income. Analyses were stratified by 100, 100-300, 300-500, and 500-700 days after the first reported case in each country. We subsequently reevaluated the relationship of pandemic preparedness on SARS-CoV-2 infection and age-standardized COVID-19 death rates adjusted for cross-country differentials in data completeness during the pre-vaccine era., Results: Every 10% increase in the GHS Index was associated with a 14.9% (95% confidence interval 8.34-21.8%) increase in SARS-CoV-2 infection completion rate and a 10.6% (5.91-15.4%) increase in the death completion rate during the entire observation period. Disease prevention (infections: β = 1.08 [1.05-1.10], deaths: β = 1.05 [1.04-1.07]), detection (infections: β = 1.04 [1.01-1.06], deaths: β = 1.03 [1.01-1.05]), response (infections: β = 1.06 [1.00-1.13], deaths: β = 1.05 [1.00-1.10]), health system (infections: β = 1.06 [1.03-1.10], deaths: β = 1.05 [1.03-1.07]), and risk environment (infections: β = 1.27 [1.15-1.41], deaths: β = 1.15 [1.08-1.23]) were associated with both data completeness outcomes. Effect sizes of GHS Index on infection completion (Low income: β = 1.18 [1.04-1.34], Lower Middle income: β = 1.41 [1.16-1.71]) and death completion rates (Low income: β = 1.19 [1.09-1.31], Lower Middle income: β = 1.25 [1.10-1.43]) were largest in LMICs. After adjustment for cross-country differences in data completeness, each 10% increase in the GHS Index was associated with a 13.5% (4.80-21.4%) decrease in SARS-CoV-2 infection rate at 100 days and a 9.10 (1.07-16.5%) decrease at 300 days. For age-standardized COVID-19 death rates, each 10% increase in the GHS Index was with a 15.7% (5.19-25.0%) decrease at 100 days and a 10.3% (- 0.00-19.5%) decrease at 300 days., Conclusions: Results support the pre-pandemic hypothesis that countries with greater pandemic preparedness capacities have larger SARS-CoV-2 infection and mortality data completeness rates and lower COVID-19 disease burdens. More high-quality data of COVID-19 impact based on direct measurement are needed., (© 2024. The Author(s).)

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., (© 2023. BioMed Central Ltd., part of Springer Nature.)

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., (© 2023. The Author(s).)

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., Competing Interests: Conflict of interest: None., (© Copyright by the American Board of Family Medicine.)

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., Competing Interests: The authors have declared that no competing interests exist.

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., (© 2022. The Author(s).)

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.

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., Competing Interests: Conflict of interest: None., (© Copyright 2021 by the American Board of Family Medicine.)

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.

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., Competing Interests: The authors have no competing interests to declare.

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., Competing Interests: The authors have no competing interests to declare.

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.

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.

Introduction: Whether the focus of population-health improvement efforts, the measurement of health outcomes, risk factors, and interventions to improve them are central to achieving collective impact in the population health perspective. And because of the importance of a shared measurement system, appropriate measures can help to ensure the accountability of and ultimately integrate the efforts of public health, the health care delivery sector, and other public and private entities in the community to improve population health. Yet despite its importance, population health measurement efforts in the United States are poorly developed and uncoordinated., Collaborative Measurement Development: To achieve the potential of the population health perspective, public health officials, health system leaders, and others must work together to develop sets of population health measures that are suitable for different purposes yet are harmonized so that together they can help to improve a community's health. This begins with clearly defining the purpose of a set of measures, distinguishing between outcomes for which all share responsibility and actions to improve health for which the health care sector, public health agencies, and others should be held accountable., Framework for Population Health Measurement: Depending on the purpose of the analysis, then, measurement systems should clearly specify what to measure-in particular the population served (the denominator), what the critical health dimensions are in a measurement framework, and how the measures can be used to ensure accountability. Building on a clear understanding of the purpose and dimensions of population health that must be measured, developers can then choose specific measures using existing data or developing new data sources if necessary, with established validity, reliability, and other scientific characteristics. Rather than indiscriminately choosing among the proliferating data streams, this systematic approach to measure development can yield measurement systems that are more appropriate and useful for improving population health.

Meta-analysis has increasingly been used to identify adverse effects of drugs and vaccines, but the results have often been controversial. In one respect, meta-analysis is an especially appropriate tool in these settings. Efficacy studies are often too small to reliably assess risks that become important when a medication is in widespread use, so meta-analysis, which is a statistically efficient way to pool evidence from similar studies, seems like a natural approach. But, as the examples in this paper illustrate, different syntheses can come to qualitatively different conclusions, and the results of any one analysis are usually not as precise as they seem to be. There are three reasons for this: the adverse events of interest are rare, standard meta-analysis methods may not be appropriate for the clinical and methodological heterogeneity that is common in these studies, and adverse effects are not always completely or consistently reported. To address these problems, analysts should explore heterogeneity and use random-effects or more complex statistical methods, and use multiple statistical models to see how dependent the results are to the choice of models.

The biosurveillance capabilities needed to rapidly detect and characterize emerging biological threats are an essential part of the Global Health Security Agenda (GHSA). The analyses of the global public health system's functioning during the 2009 H1N1 pandemic suggest that while capacities such as those identified in the GHSA are essential building blocks, the global biosurveillance system must possess 3 critical capabilities: (1) the ability to detect outbreaks and determine whether they are of significant global concern, (2) the ability to describe the epidemiologic characteristics of the pathogen responsible, and (3) the ability to track the pathogen's spread through national populations and around the world and to measure the impact of control strategies. The GHSA capacities-laboratory and diagnostic capacity, reporting networks, and so on-were essential in 2009 and surely will be in future events. But the 2009 H1N1 experience reminds us that it is not just detection but epidemiologic characterization that is necessary. Similarly, real-time biosurveillance systems are important, but as the 2009 H1N1 experience shows, they may contain inaccurate information about epidemiologic risks. Rather, the ability of scientists in Mexico, the United States, and other countries to make sense of the emerging laboratory and epidemiologic information that was critical-an example of global social capital-enabled an effective global response. Thus, to ensure that it is meeting its goals, the GHSA must track capabilities as well as capacities.

Health departments use after-action reports to collect data on their experience in responding to actual public health emergencies. To address deficiencies in the use of such reports revealed in the 2009 H1N1 influenza pandemic and to develop an effective approach to learning from actual public health emergencies, we sought to understand how the concept and operations of a "critical incident registry," commonly used in other industries, could be adapted for public health emergency preparedness. We conducted a workshop with public health researchers and practitioners, reviewed the literature on learning from rare events, and sought to identify the optimal characteristics of a critical incident registry (CIR) for public health emergency preparedness. Several key critical characteristics are needed for a CIR to be feasible and useful. A registry should: (1) include incidents in the response in which public health agencies played a substantial role, are "meaningful," test one or more emergency preparedness capabilities, and are sufficiently limited in scope to isolate specific response issues; (2) be supported by a framework and standard protocols for including reports based on rigorous analysis of individual incidents and methods for cross-case analysis; and (3) include explicit incentives for reporting, to overcome intrinsic disincentives. With proper incentives in place, a critical incident registry can be a useful tool for improving public health emergency preparedness. Standard protocols for reporting critical events and probing analysis are needed to enable identification of patterns of successes and failures.

Antiviral medications can decrease the severity and duration of influenza, but they are most effective if started within 48 hours of the onset of symptoms. In a severe influenza pandemic, normal channels of obtaining prescriptions and medications could become overwhelmed. To assess public perception of the acceptability and feasibility of alternative strategies for prescribing, distributing, and dispensing antivirals and disseminating information about influenza and its treatment, the Institute of Medicine, with technical assistance from the Centers for Disease Control and Prevention (CDC), convened public engagement events in 3 demographically and geographically diverse communities: Fort Benton, MT; Chattanooga, TN; and Los Angeles, CA. Participants were introduced to the issues associated with pandemic influenza and the challenges of ensuring timely public access to information and medications. They then discussed the advantages and disadvantages of 5 alternative strategies currently being considered by the CDC and its partners. Participants at all 3 venues expressed high levels of acceptance for each of the proposed strategies and contributed useful ideas to support their implementation. This article discusses the key findings from these sessions.

Background: Syndromic surveillance has been widely adopted as a real-time monitoring tool for timely response to disease outbreaks. During the second wave of the pH1N1 pandemic in Fall 2009, two major universities in Washington, DC collected data that were potentially indicative of influenza-like illness (ILI) cases in students and staff. In this study, our objectives were three-fold. The primary goal of this study was to characterize the impact of pH1N1 on the campuses as clearly as possible given the data available and their likely biases. In addition, we sought to evaluate the strengths and weaknesses of the data series themselves, in order to inform these two universities and other institutions of higher education (IHEs) about real-time surveillance systems that are likely to provide the most utility in future outbreaks (at least to the extent that it is possible to generalize from this analysis)., Methods: We collected a wide variety of data that covered both student ILI cases reported to medical and non-medical staff, employee absenteeism, and hygiene supply distribution records (from University A only). Communication data were retrieved from university broadcasts, university preparedness websites, and H1N1-related on campus media reports. Regional data based on the Centers for Disease Control and Prevention Outpatient Influenza-like Illness Surveillance Network (CDC ILINet) surveillance network, American College Health Association (ACHA) pandemic influenza surveillance data, and local Google Flu Trends were used as external data sets. We employed a "triangulation" approach for data analysis in which multiple contemporary data sources are compared to identify time patterns that are likely to reflect biases as well as those that are more likely to be indicative of actual infection rates., Results: Medical personnel observed an early peak at both universities immediately after school began in early September and a second peak in early November; only the second peak corresponded to patterns in the community at large. Self-reported illness to university deans' offices was also relatively increased during mid-term exam weeks. The overall volume of pH1N1-related communication messages similarly peaked twice, corresponding to the two peaks of student ILI cases., Conclusions: During the 2009 H1N1 pandemic, both University A and B experienced a peak number of ILI cases at the beginning of the Fall term. This pattern, seen in surveillance systems at these universities and to a lesser extent in data from other IHEs, most likely resulted from students bringing the virus back to campus from their home states coupled with a sudden increase in population density in dormitories and lecture halls. Through comparison of data from different syndromic surveillance data streams, paying attention to the likely biases in each over time, we have determined, at least in the case of the pH1N1 pandemic, that student health center data more accurately depicted disease transmission on campus at both universities during the Fall 2009 pandemic than other available data sources.

Background: The purpose of syndromic surveillance is early detection of a disease outbreak. Such systems rely on the earliest data, usually chief complaint. The growing use of electronic medical records (EMR) raises the possibility that other data, such as emergency department (ED) diagnosis, may provide more specific information without significant delay, and might be more effective in detecting outbreaks if mechanisms are in place to monitor and report these data., Objective: The purpose of this study is to characterize the added value of the primary ICD-9 diagnosis assigned at the time of ED disposition compared to the chief complaint for patients with influenza-like illness (ILI)., Methods: The study was a retrospective analysis of the EMR of a single urban, academic ED with an annual census of over 60, 000 patients per year from June 2005 through May 2006. We evaluate the objective in two ways. First, we characterize the proportion of patients whose ED diagnosis is inconsistent with their chief complaint and the variation by complaint. Second, by comparing time series and applying syndromic detection algorithms, we determine which complaints and diagnoses are the best indicators for the start of the influenza season when compared to the Centers for Disease Control regional data for Influenza-Like Illness for the 2005 to 2006 influenza season using three syndromic surveillance algorithms: univariate cumulative sum (CUSUM), exponentially weighted CUSUM, and multivariate CUSUM., Results: In the first analysis, 29% of patients had a different diagnosis at the time of disposition than suggested by their chief complaint. In the second analysis, complaints and diagnoses consistent with pneumonia, viral illness and upper respiratory infection were together found to be good indicators of the start of the influenza season based on temporal comparison with regional data. In all examples, the diagnosis data outperformed the chief-complaint data., Conclusion: Both analyses suggest the ED diagnosis contains useful information for detection of ILI. Where an EMR is available, the short time lag between complaint and diagnosis may be a price worth paying for additional information despite the brief potential delay in detection, especially considering that detection usually occurs over days rather than hours.