Baseline Evaluation of the DC Emergency Healthcare Coalition

The DC Healthcare Facilities Emergency Care Coalition, funded by a grant by the U.S. Department of Health and Human Services Assistant Secretary for Preparedness and Response (ASPR), was designed to significantly improve the state of emergency preparedness in health care facilities in the District of Columbia (DC), and to create a model for emergency preparedness that can be used by other large cities or regions of care across the nation. Its goal is to provide a comprehensive, uniform, and consistent framework and infrastructure for emergency preparedness across the full continuum of patient care. Devised to address the inconsistencies, shortcomings, fragmentations, and gaps present in current District hospital and healthcare facility emergency preparedness and response capabilities, the Coalition will achieve the following durable results and benefits for the District\u27s medical preparedness and response to a catastrophic event

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

Using Learning Collaboratives to Improve Public Health Emergency Preparedness Systems

The U.S. National Health Security Strategy calls for the development and wide-spread implementation of quality improvement (QI) tools in public health emergency preparedness (PHEP), including the development of “learning collaboratives,” a structured way for organizations with common interests to close the gap between potential and practice by learning from each other. To test this approach, we developed and assessed separate learning collaboratives focused on PHEP emergency communications and on the use of Medical Reserve Corps (MRC) volunteers. Although participants carried out improvement projects that they felt were useful, each collaborative struggled to identify a common theme, participation was limited, and leadership buy-in was not strong. This suggests that the learning collaborative model may not be appropriate in this context. Because some of the factors that limited their success are inherent (the lack of an established evidence base and agreed upon outcome and performance measures and the difficulty of carrying out rapid Plan-Do-Study-Act (PDSA) cycles and measuring the results), this suggests that the learning collaborative model may not be appropriate in this context

Risk-communication capability for public health emergencies varies by community diversity

<p>Abstract</p> <p>Background</p> <p>Public health emergencies heighten several challenges in risk-communication: providing trustworthy sources of information, reaching marginalized populations, and minimizing fear and public confusion. In emergencies, however, information may not diffuse equally among all social groups, and gaps in knowledge may increase. Such knowledge gaps vary by social structure and the size, socioeconomic status, and diversity of the population. This study explores the relationship between risk-communication capabilities, as perceived by public officials participating in emergency tabletop exercises, and community size and diversity.</p> <p>Findings</p> <p>For each of the three communication functions tested, risk-communication capabilities are perceived to be greater in communities with fewer then 10% of the population speaking a language other than English at home, decreasing as the percentage grows to 20% (ANOVA P ≤ 0.02). With respect to community size, however, we found an N-shaped relationship between perceived risk communication capabilities and population size. Capabilities are perceived highest in the largest communities and lowest in the smallest, but lower in communities with 20,000–49,999 inhabitants compared to those with 2,500–19,999.</p> <p>Conclusion</p> <p>The results of this study suggest the need to factor population diversity into risk communication plans and the need for improved state or regional risk-communication capabilities, especially for communities with limited local capacity.</p

Public response to the 2014 chemical spill in West Virginia: knowledge, opinions and behaviours

Background: On January 9th 2014, a faulty storage tank leaked 10,000 gal of an industrial coal processing liquid into the Elk River in West Virginia (WV), contaminating the drinking water of the nine counties collectively known as the Kanawha Valley. The aim of this study was to 1) explore how and when people obtained information about the water contamination and 2) understand how individual and social factors such as socio-demographic characteristics, timing of information, trust in government, and risk perception influenced compliance with recommended behaviours and the public’s views on the need for environmental regulations. Methods: Between February 7–26, 2014, a survey was conducted of adult residents of West Virginia including geographic areas affected and non-affected by the chemical spill. The total population-based sample size was 690 and the survey was administered online. Descriptive statistics and multivariate statistical models were created to determine what factors influenced compliance and public opinions. Results: Findings from this study show that, during the 2014 West Virginia water crisis, information about water contamination spread quickly, as 73 % of survey respondents across the state and 89 % within the affected counties reported they heard about the incident the same day it occurred. Most people received the information promptly, understood what happened, and understood what to do to prevent exposure to the contaminant. The majority of respondents living in affected counties (70 %) followed the recommended behaviours. Among participants who voiced an opinion on the role of government in environmental regulations, the majority of respondents (54 %) reported there is “too little regulation.” Conclusion: Data from this study show that a higher perception of risk and timely receipt of information are associated with compliance with recommended behaviours, underlying the importance of releasing information to the public as quickly as possible during a crisis. This study also highlights the importance of coordinating risk communication activities beyond the area of the incident to assure public understanding of what measures are recommended, which are not and where

Early detection of influenza outbreaks using the DC Department of Health's syndromic surveillance system

<p>Abstract</p> <p>Background</p> <p>Since 2001, the District of Columbia Department of Health has been using an emergency room syndromic surveillance system to identify possible disease outbreaks. Data are received from a number of local hospital emergency rooms and analyzed daily using a variety of statistical detection algorithms. The aims of this paper are to characterize the performance of these statistical detection algorithms in rigorous yet practical terms in order to identify the optimal parameters for each and to compare the ability of two syndrome definition criteria and data from a children's hospital versus vs. other hospitals to determine the onset of seasonal influenza.</p> <p>Methods</p> <p>We first used a fine-tuning approach to improve the sensitivity of each algorithm to detecting simulated outbreaks and to identifying previously known outbreaks. Subsequently, using the fine-tuned algorithms, we examined (i) the ability of unspecified infection and respiratory syndrome categories to detect the start of the flu season and (ii) how well data from Children's National Medical Center (CNMC) did versus all the other hospitals when using unspecified infection, respiratory, and both categories together.</p> <p>Results</p> <p>Simulation studies using the data showed that over a range of situations, the multivariate CUSUM algorithm performed more effectively than the other algorithms tested. In addition, the parameters that yielded optimal performance varied for each algorithm, especially with the number of cases in the data stream. In terms of detecting the onset of seasonal influenza, only "unspecified infection," especially the counts from CNMC, clearly delineated influenza outbreaks out of the eight available syndromic classifications. In three of five years, CNMC consistently flags earlier (from 2 days up to 2 weeks earlier) than a multivariate analysis of all other DC hospitals.</p> <p>Conclusions</p> <p>When practitioners apply statistical detection algorithms to their own data, fine tuning of parameters is necessary to improve overall sensitivity. With fined tuned algorithms, our results suggest that emergency room based syndromic surveillance focusing on unspecified infection cases in children is an effective way to determine the beginning of the influenza outbreak and could serve as a trigger for more intensive surveillance efforts and initiate infection control measures in the community.</p

Emergency Department Chief Complaint and Diagnosis Data to Detect Influenza-Like Illness with an Electronic Medical Record

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. [West J Emerg Med. 2010; 11(1):1-9]

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

Transmission patterns of smallpox: systematic review of natural outbreaks in Europe and North America since World War II

BACKGROUND: Because smallpox (variola major) may be used as a biological weapon, we reviewed outbreaks in post-World War II Europe and North America in order to understand smallpox transmission patterns. METHODS: A systematic review was used to identify papers from the National Library of Medicine, Embase, Biosis, Cochrane Library, Defense Technical Information Center, WorldCat, and reference lists of included publications. Two authors reviewed selected papers for smallpox outbreaks. RESULTS: 51 relevant outbreaks were identified from 1,389 publications. The median for the effective first generation reproduction rate (initial R) was 2 (range 0–38). The majority outbreaks were small (less than 5 cases) and contained within one generation. Outbreaks with few hospitalized patients had low initial R values (median of 1) and were prolonged if not initially recognized (median of 3 generations); outbreaks with mostly hospitalized patients had higher initial R values (median 12) and were shorter (median of 3 generations). Index cases with an atypical presentation of smallpox were less likely to have been diagnosed with smallpox; outbreaks in which the index case was not correctly diagnosed were larger (median of 27.5 cases) and longer (median of 3 generations) compared to outbreaks in which the index case was correctly diagnosed (median of 3 cases and 1 generation). CONCLUSION: Patterns of spread during Smallpox outbreaks varied with circumstances, but early detection and implementation of control measures is a most important influence on the magnitude of outbreaks. The majority of outbreaks studied in Europe and North America were controlled within a few generations if detected early