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Automated analysis of electronic health record (EHR) data is a complementary tool for public health surveillance. Analyzing and presenting these data, however, demands new methods of data communication optimized to the detail, flexibility, and timeliness of EHR data.RiskScape is an open-source, interactive, Web-based, user-friendly data aggregation and visualization platform for public health surveillance using EHR data. RiskScape displays near-real-time surveillance data and enables clinical practices and health departments to review, analyze, map, and trend aggregate data on chronic conditions and infectious diseases. Data presentations include heat maps of prevalence by zip code, time series with statistics for trends, and care cascades for conditions such as HIV and HCV. The platform's flexibility enables it to be modified to incorporate new conditions quickly-such as COVID-19.The Massachusetts Department of Public Health (MDPH) uses RiskScape to monitor conditions of interest using data that are updated monthly from clinical practice groups that cover approximately 20% of the state population. RiskScape serves an essential role in demonstrating need and burden for MDPH's applications for funding, particularly through the identification of inequitably burdened populations.

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The 2014/15 influenza epidemic caused a work overload for healthcare facilities in France. The French national public health agency announced the start of the epidemic - based on indicators aggregated at the national level - too late for many hospitals to prepare. It was therefore decided to improve the influenza alert procedure through (i) the introduction of a pre-epidemic alert level to better anticipate future outbreaks, (ii) the regionalisation of surveillance so that healthcare structures can be informed of the arrival of epidemics in their region, (iii) the standardised use of data sources and statistical methods across regions. A web application was developed to deliver statistical results of three outbreak detection methods applied to three surveillance data sources: emergency departments, emergency general practitioners and sentinel general practitioners. This application was used throughout the 2015/16 influenza season by the epidemiologists of the headquarters and regional units of the French national public health agency. It allowed them to signal the first influenza epidemic alert in week 2016-W03, in Brittany, with 11 other regions in pre-epidemic alert. This application received positive feedback from users and was pivotal for coordinating surveillance across the agency's regional units.

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The BioSense program was launched in 2003 with the aim of establishing a nationwide integrated public health surveillance system for early detection and assessment of potential bioterrorism-related illness. The program has matured over the years from an initial Centers for Disease Control and Prevention-centric program to one focused on building syndromic surveillance capacity at the state and local level. The uses of syndromic surveillance have also evolved from an early focus on alerts for bioterrorism-related illness to situational awareness and response, to various hazardous events and disease outbreaks. Future development of BioSense (now the National Syndromic Surveillance Program) includes, in the short term, a focus on data quality with an emphasis on stability, consistency, and reliability and, in the long term, increased capacity and innovation, new data sources and system functionality, and exploration of emerging technologies and analytics.

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Public health informatics is an evolving domain in which practices constantly change to meet the demands of a highly complex public health and healthcare delivery system. Given the emergence of various concepts, such as learning health systems, smart health systems, and adaptive complex health systems, health informatics professionals would benefit from a common set of measures and capabilities to inform our modeling, measuring, and managing of health system "smartness." Here, we introduce the concepts of organizational complexity, problem/issue complexity, and situational awareness as three codependent drivers of smart public health systems characteristics. We also propose seven smart public health systems measures and capabilities that are important in a public health informatics professional's toolkit.

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OBJECTIVE: The aims of this literature review are to (1) summarise how computer and mobile technology-based health behaviour change applications have been evaluated in unintentional injury prevention, (2) describe how these successes can be applied to injury-prevention programmes in the future and (3) identify research gaps. METHODS: Studies included in this systematic review were education and behaviour change intervention trials and programme evaluations in which the intervention was delivered by either a computer or mobile technology and addressed an unintentional injury prevention topic. Articles were limited to those published in English and after 1990. RESULTS: Among the 44 technology-based injury-prevention studies included in this review, 16 studies evaluated locally hosted software programmes, 4 studies offered kiosk-based programmes, 11 evaluated remotely hosted internet programmes, 2 studies used mobile technology or portable devices and 11 studies evaluated virtual-reality interventions. Locally hosted software programmes and remotely hosted internet programmes consistently increased knowledge and behaviours. Kiosk programmes showed evidence of modest knowledge and behaviour gains. Both programmes using mobile technology improved behaviours. Virtual-reality programmes consistently improved behaviours, but there were little gains in knowledge. No studies evaluated text-messaging programmes dedicated to injury prevention. CONCLUSIONS: There is much potential for computer-based programmes to be used for injury-prevention behaviour change. The reviewed studies provide evidence that computer-based communication is effective in conveying information and influencing how participants think about an injury topic and adopt safety behaviours.

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We systematically reviewed the statistical disclosure control techniques employed for releasing aggregate data in Web-based data query systems listed in the National Association for Public Health Statistics and Information Systems (NAPHSIS). Each Web-based data query system was examined to see whether (1) it employed any type of cell suppression, (2) it used secondary cell suppression, and (3) suppressed cell counts could be calculated. No more than 30 minutes was spent on each system. Of the 35 systems reviewed, no suppression was observed in more than half (n = 18); observed counts below the threshold were observed in 2 sites; and suppressed values were recoverable in 9 sites. Six sites effectively suppressed small counts. This inquiry has revealed substantial weaknesses in the protective measures used in data query systems containing sensitive public health data. Many systems utilized no disclosure control whatsoever, and the vast majority of those that did deployed it inconsistently or inadequately.

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We describe the design and implementation of a novel automated outbreak detection system in Germany that monitors the routinely collected surveillance data for communicable diseases. Detecting unusually high case counts as early as possible is crucial as an accumulation may indicate an ongoing outbreak. The detection in our system is based on state-of-the-art statistical procedures conducting the necessary data mining task. In addition, we have developed effective methods to improve the presentation of the results of such algorithms to epidemiologists and other system users. The objective was to effectively integrate automatic outbreak detection into the epidemiological workflow of a public health institution. Since 2013, the system has been in routine use at the German Robert Koch Institute.

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BACKGROUND: Combining existing data on background characteristics with data from immunization registers might give insight into determinants of vaccine uptake, which can help to improve communication strategies and invitation policy of National Immunisation Programmes. METHODS: The study population consisted of children born in 2005 as registered in the Dutch national immunization register Præventis. A hierarchical logistic regression model was used to quantify associations between individual vaccination status and proxy variables for ethnic background (individual level), socio-economic status (postcode level) and religious objection to vaccination (municipal level). RESULTS: Most children whose both parents were not born in The Netherlands had a somewhat lower full vaccine uptake, for example, children whose both parents were born in Turkey [odds ratio = 0.7 (0.6-0.8)] or in Morocco [odds ratio = 0.8 (0.7-0.9)]. The partial uptake was also relatively high (3.7-8.0%) compared with children whose both parents were born in The Netherlands (3.1%). Municipalities with higher religious objection to vaccination and postcode areas with lower socio-economic status were also associated with a lower full uptake. CONCLUSIONS: Despite the high vaccination coverage in The Netherlands, we were able to identify determinants of vaccine uptake by combining existing data sets. This might be an example for other countries. The impact of ethnic background and socio-economic status is not as well known in The Netherlands as the effect of religious objection to vaccination, and deserves more attention. Groups that have a relatively high partial uptake deserve special attention because they do not reject vaccination in general.

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Vaccination coverage is an important performance indicator of any national immunisation programme (NIP). To monitor the vaccination coverage in the Netherlands, an electronic national immunisation register called 'Præventis' was implemented in 2005. Præventis has a link with the population register and can produce letters of invitation for the NIP, register and validate administered vaccinations. The database is used to monitor the vaccination process, produce reminder letters, control the stock of vaccines and provides information used for paying the fees to the different executive organisations involved. Præventis provides a crucial tool for the evaluation of the NIP by producing (sub)national vaccination coverage estimates with high accuracy and allowing additional research: identifying populations at high risk for low coverage based on existing data, conducting specific studies where individuals included in the immunisation register are approached for further research, using vaccination coverage data for the interpretation of (sero)surveillance data, and linking the immunisation register with disease registers to address vaccine safety or vaccine effectiveness. The ability to combine Præventis data with data from other databases or disease registers and the ability to approach individuals with additional research questions offers opportunities to identify areas of priority for improving the Dutch NIP.

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In Italy, the 21 regional health authorities are in charge of organising and implementing their own vaccination strategy, based on the national vaccine plan. Immunisation coverage varies greatly among the regions for certain vaccines. Efforts to increase childhood immunisation coverage have included initiatives to develop and implement computerised immunisation registers in as many regions as possible. We undertook a cross-sectional online survey in July 2011 to provide an updated picture of the use, heterogeneity and main functions of different computerised immunisation registers used in the Italian regions and to understand the flow of information from local health units to the regional authorities and to the Ministry of Health. Comparing current data with those obtained in 2007, a substantial improvement is evident. A total of 15 regions are fully computerised (previously nine), with 83% of local health units equipped with a computerised register (previously 70%). Eight of the 15 fully computerised regions use the same software, simplifying data sharing. Only four regions are able to obtain data in real time from local health units. Despite the progress made, the capacity to monitor vaccination coverage and to exchange data appears still limited.

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OBJECTIVE: To discuss the current status of public health messaging and how pharmacists can become more active participants. SUMMARY: Public health needs can be addressed by using Internet videos as a model to disseminate medical information. Introducing student pharmacists to new ways of developing and delivering targeted public health messages can increase their awareness that public health intervention is part of the emerging scope of practice. CONCLUSION: For pharmacy to affect change in public health at the local and national level, pharmacists should consider providing public health advocacy messages through virtual platforms.

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OBJECTIVE: We describe a centralized automated multi-function detection and reporting system for public health surveillance--the Alberta Real Time Syndromic Surveillance Net (ARTSSN). This improves upon traditional paper-based systems which are often fragmented, limited by incomplete data collection and inadequate analytical capacity, and incapable of providing timely information for public health action. METHODS: ARTSSN concurrently analyzes multiple electronic data sources in real time to describe results in tables, charts and maps. Detected anomalies are immediately disseminated via alerts to decision-makers for action. RESULTS: ARTSSN provides richly integrated information on a variety of health conditions for early detection of and prompt action on abnormal events such as clusters, outbreaks and trends. Examples of such health conditions include chronic and communicable disease, injury and environment-mediated adverse incidents. DISCUSSION: Key advantages of ARTSSN over traditional paper-based methods are its timeliness, comprehensiveness and automation. Public health surveillance of communicable disease, injury, environmental hazard exposure and chronic disease now occurs in a single system in real time year round. Examples are given to demonstrate the public health value of this system, particularly during Pandemic (H1N1) 2009.

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OBJECTIVE: To develop an early-warning automated surveillance-data-analysis system for early outbreak detection and reporting and to assess its performance on an abortion outbreak in mares in Kentucky. SAMPLE POPULATION: 426 data sets of abortions in mares in Kentucky during December 2000 to July 2001. PROCEDURES: A custom software system was developed to automatically extract and analyze data from a Laboratory Information Management System database. The software system was tested on data on abortions in mares in Kentucky reported between December 1, 2000, and July 31, 2001. The prospective space-time permutations scan statistic, proposed by Kulldorff, was used to detect and identify abortion outbreak signals. RESULTS: Results indicated that use of the system would have detected the abortion outbreak approximately 1 week earlier than traditional surveillance systems. However, the geographic scale of analysis was critical for highest sensitivity in outbreak detection. Use of the lower geographic scale of analysis (ie, postal [zip code]) enhanced earlier detection of significant clusters, compared with use of the higher geographic scale (ie, county). CONCLUSIONS AND CLINICAL RELEVANCE: The automated surveillance-data-analysis system would be useful in early detection of endemic, emerging, and foreign animal disease outbreaks and might help in detection of a bioterrorist attack. Manual analyses of such a large number of data sets (ie, 426) with a computationally intensive algorithm would be impractical toward the goal of achieving near real-time surveillance. Use of this early-warning system would facilitate early interventions that should result in more positive health outcomes.

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After the epidemic of SARS in 2003, China has paid more attention to alert of infectious diseases and has established the credible disease information systems and enhanced the control systems, but the ability of making alert decisions of infectious disease outbreaks by using disease early detection methods needs to be improved. In this paper, the indexes related to alert of infectious diseases, and data analysis, and the timeliness of alert and the evaluation of the alert were reviewed.

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BACKGROUND: SaTScan is a software program written to implement the scan statistic; it can be used to find clusters in space and/or time. It must often be run multiple times per day when doing disease surveillance. Running SaTScan frequently via its graphical user interface can be cumbersome, and the output can be difficult to visualize. RESULTS: The SaTScan Macro Accessory for Cartography (SMAC) package consists of four SAS macros and was designed as an easier way to run SaTScan multiple times and add graphical output. The package contains individual macros which allow the user to make the necessary input files for SaTScan, run SaTScan, and create graphical output all from within SAS software. The macros can also be combined to do this all in one step. CONCLUSION: The SMAC package can make SaTScan easier to use and can make the output more informative.

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BioSense is a CDC initiative to support enhanced early detection, quantification, and localization of possible biologic terrorism attacks and other events of public health concern on a national level. The goals of the BioSense initiative are to advance early detection by providing the standards, infrastructure, and data acquisition for near real-time reporting, analytic evaluation and implementation, and early event detection support for state and local public health officials. BioSense collects and analyzes Department of Defense and Department of Veterans Affairs ambulatory clinical diagnoses and procedures and Laboratory Corporation of America laboratory-test orders. The application summarizes and presents analytical results and data visualizations by source, day, and syndrome for each ZIP code, state, and metropolitan area through maps, graphs, and tables. An initial proof of a concept evaluation project was conducted before the system was made available to state and local users in April 2004. User recruitment involved identifying and training BioSense administrators and users from state and local health departments. User support has been an essential component of the implementation and enhancement process. CDC initiated the BioIntelligence Center (BIC) in June 2004 to conduct internal monitoring of BioSense national data daily. BIC staff have supported state and local system monitoring, conducted data anomaly inquiries, and communicated with state and local public health officials. Substantial investments will be made in providing regional, state, and local data for early event detection and situational awareness, test beds for data and algorithm evaluation, detection algorithm development, and data management technologies, while maintaining the focus on state and local public health needs.

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INTRODUCTION: Laboratory test orders constitute an early outbreak data source. CDC receives laboratory order data in HL7 format from the Laboratory Corporation of America (LabCorp) and plans to use the data in the BioSense Early Event Detection and Situation Awareness System. METHODS: These LabCorp data contain information on tests ordered and include the type of test ordered and the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-coded reasons for the order. A consensus panel was formed to group test orders on the basis of expert opinion into eight standard syndrome categories to provide an additional data source for early outbreak detection. A laboratory order taxonomy was developed and used in the mapping consolidation phase. The five main classes of this taxonomy are miscellaneous functional tests, fluid screening tests, system-specific tests, tests for specific infections (by primary manifestation), and tests for specific noninfectious diseases. RESULTS: Summary of numbers of laboratory order codes in each syndrome category are fever (53), respiratory (53), gastrointestinal (27), neurological (35), rash (37), lymphadenitis (20), localized cutaneous lesion (11), and specific infection (63). CONCLUSION: With the daily use of laboratory order data in BioSense, the actual distribution of laboratory order codes in syndrome groups can be evaluated, allowing modification of the mapping.

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INTRODUCTION: In concert with increased concerns regarding both biologic terrorism and new natural infectious disease threats (e.g., severe acute respiratory syndrome [SARS] and West Nile virus), as a result of advances in medical informatics, various data sources are available to epidemiologists for routine, prospective monitoring of public health. The synthesis of this evidence requires tools to find anomalies within various data stream combinations while maintaining manageable false alarm rates. OBJECTIVES: The objectives of this report are to establish statistical hypotheses to define the compound multivariate problem of surveillance systems, present statistical methods for testing these hypotheses, and examine results of applying these methods to simulated and actual data. METHODS: Canonical problems of parallel monitoring and consensus monitoring are considered in this report. Modified Bonferroni methods are examined for parallel monitoring. Both multiple univariate and multivariate methods are applied for consensus monitoring. A multivariate adaptation of Monte Carlo trials, using the injection of epidemic-curve-like signals in the multiple data streams of interest, is presented for evaluation of the various tests. RESULTS: The Monte Carlo test results demonstrate that the multiple univariate combination methods of Fisher and Edgington provide the most robust detection performance across the scenarios tested. As the number of data streams increases, methods based on Hotelling's T2 offer added sensitivity for certain signal scenarios. This potential advantage is clearer when strong correlation exists among the data streams. CONCLUSION: Parallel and consensus monitoring tools must be blended to enable a surveillance system with distributed sensitivity and controlled alert rates. Whether a multiple univariate or multivariate approach should be used for consensus monitoring depends on the number and distribution of useful data sources and also on their covariance structure and stationarity. Strong, consistent correlation among numerous sources warrants the examination of multivariate control charts.

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