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PubMed2004

Daily Emergency Department Surveillance System --- Bergen County, New Jersey.

The purpose of the Daily Emergency Department Surveillance System (DEDSS) is to provide consistent, timely, and robust data that can be used to guide public health activities in Bergen County, New Jersey. DEDSS collects data on all emergency department visits in four hospitals in Bergen County and analyzes them for aberrant patterns of disease or single instances of certain diseases or syndromes. The system monitors for clusters of patients with syndromes consistent with the prodrome of a terrorism-related illness (e.g., anthrax or smallpox) or naturally occurring disease (e.g., pandemic influenza or food and waterborne outbreaks). The health department can use these data to track and characterize the temporal and geographic spread of a known outbreak or demonstrate the absence of cases during the same period (e.g., severe acute respiratory syndrome [SARS] or anthrax). DEDSS was designed to be flexible and readily adaptable as local, state, or federal surveillance needs evolve.

Bioterrorism↗
PubMed2004

BioSense--a national initiative for early detection and quantification of public health emergencies.

BioSense is a national initiative to enhance the nation's capability to rapidly detect, quantify, and localize public health emergencies, particularly biologic terrorism, by accessing and analyzing diagnostic and prediagnostic health data. BioSense will establish near real-time electronic transmission of data to local, state, and federal public health agencies from national, regional, and local health data sources (e.g., clinical laboratories, hospital systems, ambulatory care sites, health plans, U.S. Department of Defense and Veterans Administration medical treatment facilities, and pharmacy chains).

Algorithms↗
PubMed2004

Evaluation challenges for syndromic surveillance--making incremental progress.

INTRODUCTION: The 2003 National Syndromic Surveillance Conference provided an opportunity to examine challenges and progress in evaluating syndromic surveillance systems. OBJECTIVES: Using the conference abstracts as a focus, this paper describes the status of performance measurement of syndromic surveillance systems and ongoing challenges in system evaluation. METHODS: Ninety-nine original abstracts were reviewed and classified descriptively and according to their presentation of evaluation attributes. RESULTS: System evaluation was the primary focus of 35% of the abstracts submitted. Of those abstracts, 63% referenced prospective evaluation methods and 57% reported on outbreak detection. However, no data were provided in 34% of the evaluation abstracts, and only 37% referred to system signals, 20% to investigation of system signals, and 20% to timeliness. CONCLUSIONS: Although this abstract review is not representative of all current syndromic surveillance efforts, it highlights recent attention to evaluation and the need for a basic set of system performance measures. It also proposes questions to be answered of all public health systems used for outbreak detection.

Disease Outbreaks↗
PubMed2004

Measuring outbreak-detection performance by using controlled feature set simulations.

INTRODUCTION: The outbreak-detection performance of a syndromic surveillance system can be measured in terms of its ability to detect signal (i.e., disease outbreak) against background noise (i.e., normally varying baseline disease in the region). Such benchmarking requires training and the use of validation data sets. Because only a limited number of persons have been infected with agents of biologic terrorism, data are generally unavailable, and simulation is necessary. An approach for evaluation of outbreak-detection algorithms was developed that uses semisynthetic data sets to provide real background (which effectively becomes the noise in the signal-to-noise problem) with artificially injected signal. The injected signal is defined by a controlled feature set of variable parameters, including size, shape, and duration. OBJECTIVES: This report defines a flexible approach to evaluating public health surveillance systems for early detection of outbreaks and provides examples of its use. METHODS: The stages of outbreak detection are described, followed by the procedure for creating data sets for benchmarking performance. Approaches to setting parameters for simulated outbreaks by using controlled feature sets are detailed, and metrics for detection performance are proposed. Finally, a series of experiments using semisynthetic data sets with artificially introduced outbreaks defined with controlled feature sets is reviewed. RESULTS: These experiments indicate the flexibility of controlled feature set simulation for evaluating outbreak-detection sensitivity and specificity, optimizing attributes of detection algorithms (e.g., temporal windows), choosing approaches to syndrome groupings, and determining best strategies for integrating data from multiple sources. CONCLUSIONS: The use of semisynthetic data sets containing authentic baseline and simulated outbreaks defined by a controlled feature set provides a valuable means for benchmarking the detection performance of syndromic surveillance systems.

Disease Outbreaks↗
PubMed2004

Evaluation of syndromic surveillance systems--design of an epidemic simulation model.

INTRODUCTION: The paucity of outbreak data from biologic terrorism and emerging infectious diseases limits the evaluation of syndromic surveillance systems. Evaluation using naturally occurring outbreaks of proxy disease (e.g., influenza) is one alternative but does not allow for rigorous evaluation. Another approach is to inject simulated outbreaks into real background data, but existing simulation models generally do not account for such factors as spatial mobility and do not explicitly incorporate knowledge of the disease agent. OBJECTIVE: The objective of this analysis was to design a simulated anthrax epidemic injection model that accounts for the complexity of the background data and enables sensitivity analyses based on uncertain disease-agent characteristics. MODEL REQUIREMENTS AND ASSUMPTIONS: Model requirements are described and used to limit the scope of model development. Major assumptions used to limit model complexity are also described. Available literature on inhalational anthrax is reviewed to ensure that the level of model detail reflects available disease knowledge. MODEL DESIGN: The model is divided into four components: 1) agent dispersion, 2) infection, 3) disease and behavior, and 4) data source. The agent-dispersion component uses a Gaussian plume model to compute spore counts on a fine grid. The infection component uses a cohort approach to identify infected persons by residential zip code, accounting for demographic covariates and spatial mobility. The disease and behavior component uses a discrete-event approach to simulate progression through disease stages and health-services utilization. The data-source component generates records to insert into background data sources. CONCLUSIONS: An epidemic simulation model was designed to enable evaluation of syndromic surveillance systems. The model addresses limitations of existing simulation approaches by accounting for such factors as spatial mobility and by explicitly modeling disease knowledge. Subsequent work entails software implementation and model validation.

Anthrax↗
PubMed2004

Benchmark data and power calculations for evaluating disease outbreak detection methods.

INTRODUCTION: Early detection of disease outbreaks enables public health officials to implement immediate disease control and prevention measures. Computer-based syndromic surveillance systems are being implemented to complement reporting by physicians and other health-care professionals to improve the timeliness of disease-outbreak detection. Space-time disease-surveillance methods have been proposed as a supplement to purely temporal statistical methods for outbreak detection to detect localized outbreaks before they spread to larger regions. OBJECTIVE: The aims of this study were twofold: 1) to design and make available benchmark data sets for evaluating the statistical power of space-time early detection methods and 2) to evaluate the power of the prospective purely temporal and space-time scan statistics by applying them to the benchmark data sets at different parameter settings. METHODS: Simulated data sets based on the geography and population of New York City were created, including effects of outbreaks of varying size and location. Data sets with no outbreak effects were also created. Scan statistics were then run on these data sets, and the resulting power performances were analyzed and compared. RESULTS: The prospective space-time scan statistic performs well for a spectrum of outbreak models. By comparison, the prospective purely temporal scan statistic has higher power for detecting citywide outbreaks but lower power for detecting geographically localized outbreaks. CONCLUSIONS: The benchmark data sets created for this study can be used successfully for formal statistical power evaluations and comparisons. If an anomaly caused by an outbreak is local, purely temporal surveillance methods might be unable to detect it, in which case space-time methods would be necessary for early detection.

Benchmarking↗
PubMed2004

Bio-ALIRT biosurveillance detection algorithm evaluation.

INTRODUCTION: Early detection of disease outbreaks by a medical biosurveillance system relies on two major components: 1) the contribution of early and reliable data sources and 2) the sensitivity, specificity, and timeliness of biosurveillance detection algorithms. This paper describes an effort to assess leading detection algorithms by arranging a common challenge problem and providing a common data set. OBJECTIVES: The objectives of this study were to determine whether automated detection algorithms can reliably and quickly identify the onset of natural disease outbreaks that are surrogates for possible terrorist pathogen releases, and do so at acceptable false-alert rates (e.g., once every 2-6 weeks). METHODS: Historic de-identified data were obtained from five metropolitan areas over 23 months; these data included International Classification of Diseases, Ninth Revision (ICD-9) codes related to respiratory and gastrointestinal illness syndromes. An outbreak detection group identified and labeled two natural disease outbreaks in these data and provided them to analysts for training of detection algorithms. All outbreaks in the remaining test data were identified but not revealed to the detection groups until after their analyses. The algorithms established a probability of outbreak for each day's counts. The probability of outbreak was assessed as an "actual" alert for different false-alert rates. RESULTS: The best algorithms were able to detect all of the outbreaks at false-alert rates of one every 2-6 weeks. They were often able to detect for the same day human investigators had identified as the true start of the outbreak. CONCLUSIONS: Because minimal data exists for an actual biologic attack, determining how quickly an algorithm might detect such an attack is difficult. However, application of these algorithms in combination with other data-analysis methods to historic outbreak data indicates that biosurveillance techniques for analyzing syndrome counts can rapidly detect seasonal respiratory and gastrointestinal illness outbreaks. Further research is needed to assess the value of electronic data sources for predictive detection. In addition, simulations need to be developed and implemented to better characterize the size and type of biologic attack that can be detected by current methods by challenging them under different projected operational conditions.

Algorithms↗
PubMed2004

ESSENCE II and the framework for evaluating syndromic surveillance systems.

INTRODUCTION: The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II) is a prototype syndromic surveillance system for capturing and analyzing public health indicators for early detection of disease outbreaks. OBJECTIVES: This paper presents a preliminary evaluation of ESSENCE II according to a CDC framework for evaluating syndromic surveillance systems. METHODS: Each major topic of the framework is addressed in this assessment of ESSENCE II performance. RESULTS: ESSENCE captures data in multiple formats, parses text strings into syndrome groupings, and applies multiple temporal and spatio-temporal outbreak-detection algorithms. During a recent DARPA evaluation exercise, ESSENCE algorithms detected a set of health events with a median delay of 1 day after the earliest possible detection opportunity. CONCLUSIONS: ESSENCE II has provided excellent performance with respect to the framework and has proven to be a useful and cost-effective approach for providing early detection of health events.

Algorithms↗
PubMed2004

Conducting population behavioral health surveillance by using automated diagnostic and pharmacy data systems.

INTRODUCTION: The Walter Reed Army Institute of Research used the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) to conduct population-based behavioral health surveillance among military-health-system beneficiaries. The study analyzed the effectiveness of using prescribing patterns of psychotropic medications to monitor changes in a community's behavioral health status. OBJECTIVES: The objectives of this study were to 1) determine the feasibility of tracking psychiatric illnesses by monitoring prescriptions for psychiatric medications; 2) assess how often psychiatric medications are prescribed for patients with no record of psychiatric illness; 3) determine at what types of clinics these medications are prescribed most often and what other diagnoses are attributed to these patients; and 4) analyze data for potential changes in the population's mental health after high-stress events. METHODS: Correlation analysis and calculations of sensitivity and specificity were used to determine how well prescription medications correlate with outpatient diagnoses and how well they serve as proxies for outpatient diagnoses. A descriptive analysis was conducted of the types of clinics (e.g., primary care, behavioral health, or other specialty clinics) treating patients and the associated percentage of concurrence between prescriptions and diagnostic codes. RESULTS: In military treatment facilities, a diagnosis of depression or anxiety correlated significantly (r = 0.82) with antidepressant or anxiolytic prescriptions. Sensitivity of prescriptions when compared with outpatient visits was 0.76, and specificity was 0.94. Among those patients who visited a primary care clinic either the day before or the same day as an antidepressant or anxiolytic prescription was filled, 60.1% did not receive a diagnosis of any mental health disorder. Behavioral health clinics had the highest correlation between diagnoses and prescriptions; specialty clinics had the lowest. CONCLUSIONS: Behavioral health trends in a population can be monitored by automated analysis of prescribing patterns alone. This method might be a rapid indicator of needed mental health interventions after acute stress-inducing events and be more sensitive than tracking diagnoses alone.

Disease Outbreaks↗
PubMed2004

Evaluation of an electronic general-practitioner-based syndromic surveillance system--Auckland, New Zealand, 2000-2001.

INTRODUCTION: During 2000 and 2001, Auckland Regional Public Health Service piloted a general-practitioner-based syndromic surveillance system (GPSURV). OBJECTIVES: The pilot evaluated data capture, the method used to distinguish initial from follow-up visits, the definition of denominators, and the external validity of measured influenza-like illness trends. METHODS: GPSURV monitored three acute infectious-disease syndromes: gastroenteritis, influenza-like illness, and skin and subcutaneous tissue infection. Standardized terms were used to describe the syndromes. Data were uploaded daily from clinics and transferred to a database via a secure network after one-way encryption of patient identifiers. Records were matched to allow the distinction of follow-ups from first visits, based on between-visit intervals of </=8 weeks. Denominator populations were based on counts of unique patients treated at participating clinics during the previous 2 years. Record completion was examined by using before-and-after surveys of self-assessed standardized-term recording. Between-visit intervals were counted for matching records and alternative denominators were calculated on the basis of different observation periods. Weekly influenza-like illness rates were compared with rates generated by an alternative system. RESULTS: Physicians' self-reported recording compliance was highest for skin and subcutaneous tissue infection (71%) and lowest for influenza-like illness (48%). Initial visits had 18%-19% greater compliance than follow-up visits. The number of physicians reporting increasing compliance during the pilot was greater than the number reporting decreases for all conditions. Comparison of data with an independent influenza-like illness surveillance system indicated a close agreement between the two data series. CONCLUSIONS: These results indicate that incidence of acute syndromes can be monitored, at least as successfully as a manual system, by using standardized clinical-term data from selected general-practice clinics. The provision of feedback reports appears to have a limited but positive effect on data quality.

Disease Outbreaks↗
PubMed2004

Field investigations of emergency department syndromic surveillance signals--New York City.

INTRODUCTION: The New York City (NYC) Department of Health and Mental Hygiene (DOHMH) has operated a syndromic surveillance system based on emergency department (ED) chief-complaint data since November 2001. This system was created for early detection of infectious-disease outbreaks, either natural or intentional. However, limited documentation exists regarding epidemiologic field investigations conducted in response to syndromic surveillance signals. OBJECTIVE: DOHMH conducted field investigations to characterize syndromic surveillance signals by person, place, and time and to determine whether signals represented true infectious-disease outbreaks. METHODS: A DOHMH physician reviews ED-based syndromic surveillance results daily to look for signals. When necessary, field investigations are conducted and consist of a review of the patient line list, telephone interviews with hospital staff, chart reviews, interviews with patients, and collection and testing of specimens. RESULTS: In November 2002, a series of citywide signals for diarrhea and vomiting syndromes, which coincided with institutional outbreaks consistent with viral gastroenteritis, prompted DOHMH to send mass e-mail notification to NYC ED directors and institute collection of stool specimens. Three of four specimens collected were positive for norovirus. In December 2002, DOHMH investigated why an ED syndromic signal was not generated after 15 ill patients were transferred to a participating ED during a gastrointestinal outbreak at a nursing home. Field investigation revealed varying chief complaints, multiple dates of ED visits, and a coding error in a complementary DOHMH syndromic system, and confirmed a seasonal norovirus outbreak. During March 2003, the system generated a 4-day citywide respiratory signal and a simultaneous 1-day hospital-level fever signal in a predominantly Asian community. In those instances, epidemiologic investigation provided reassurance that severe acute respiratory syndrome was not present. CONCLUSION: Detailed field investigations of syndromic signals can identify the etiology of signals and determine why a given syndromic surveillance system failed to detect an outbreak captured through traditional surveillance. Validation of the utility of syndromic surveillance to detect infectious-disease outbreaks is necessary to justify allocating resources for this new public health tool.

Communicable Diseases, Emerging↗
PubMed2004

Should we be worried? Investigation of signals generated by an electronic syndromic surveillance system--Westchester County, New York.

INTRODUCTION: In January 2003, the Westchester County Department of Health (WCDH) began conducting electronic syndromic surveillance of hospital emergency department (ED) chief complaints. Although methods for data collection and analysis used in syndromic surveillance have been described previously, minimal information exists regarding the responses to and investigations of signals detected by such systems. This paper describes WCDH's experience in responding to syndromic surveillance signals during the first 9 months after the system was implemented. OBJECTIVES: The objectives of this analysis were to examine WCDH's responses to signals detected by the county's syndromic surveillance system. Specific goals were to 1) review the actual complaints reported by hospital EDs to determine whether complaint data were accurately identified and classified into syndrome categories, and provide feedback from this review to data collection and analysis staff to refine text terms or filters used to identify and classify chief complaints; 2) develop procedures and response algorithms for investigating signals; 3) determine whether signals correlated with reportable communicable diseases or other incidents of public health significance requiring investigation and intervention; and 4) quantify the staffing resources and time required to investigate signals. METHODS: During January 27-October 31, 2003, electronic files containing chief-complaint data from seven of the county's 13 EDs were collected daily. Complaints were classified into syndrome categories and analyzed for statistically significant increases. A line listing of each complaint comprising each signal detected was reviewed for exact complaint, number, location, patient demographics, and requirement for hospital admission. RESULTS: A total of 59 signals were detected in eight syndrome categories: fever/influenza (11), respiratory (6), vomiting (11), gastrointestinal illness/diarrhea (8), sepsis (7), rash (7), hemorrhagic events (3), and neurologic (6). Line-listing review indicated that complaints routinely were incorrectly identified and included in syndrome categories and that as few as three complaints could produce a signal. On the basis of hospital, geographic, age, or sex clustering of complaints, whether the complaint indicated a reportable condition (e.g., meningitis) or potentially represented an unusual medical event, and whether rates of hospital admission were consistent with medical conditions, 34 of 59 signals were determined to require further investigation (i.e., obtaining additional information from ED staff or medical providers). Investigation did not identify any reportable communicable disease or other incidents of public health significance that would have been missed by existing traditional surveillance systems. Nine staff members spent 3 hours/week collectively investigating signals detected by syndromic surveillance. CONCLUSIONS: Standardized sets of text terms used to identify and classify hospital ED chief complaints into syndrome categories might require modification on the basis of hospital idiosyncrasies in recording chief complaints. Signal investigations could be reasonably conducted by using local health department resources. Although no communicable disease events were identified, the system provided baseline and timely objective data for hospital visits and improved communication among county health department and hospital ED staff.

Disease Outbreaks↗
PubMed2004

Public Health Information Network--improving early detection by using a standards-based approach to connecting public health and clinical medicine.

Public health departments and their clinical partners are moving ahead rapidly to implement systems for early detection of disease outbreaks. In the urgency to develop useful early detection systems, information systems must adhere to certain standards to facilitate sustainable, real-time delivery of important data and to make data available to the public health partners who verify, investigate, and respond to outbreaks. To ensure this crucial interoperability, all information systems supported by federal funding for state and local preparedness capacity are required to adhere to the Public Health Information Network standards.

Bioterrorism↗
PubMed2004

Perspective of an emergency physician group as a data provider for syndromic surveillance.

The need for enhanced biologic surveillance has led to the search for new sources of data. Beginning in September 2001, Emergency Medical Associates (EMA) of New Jersey, an emergency physician group practice, undertook a series of surveillance projects in collaboration with state and federal agencies. This paper examines EMA's motivations and concerns and discusses the collaborative opportunities available to data suppliers for syndromic surveillance. Motivations for supplying data included altruism and public service, previous involvement in terrorism and disaster preparedness, academic research interests, and the opportunity to find added value in the group's existing information systems. Concerns and barriers included cost, maintaining patient confidentiality, and challenges in interacting with the public health community. The extensive and carefully maintained electronic medical record enabled EMA to conduct multiple studies in collaboration with state and federal agencies. The electronic medical record provides useful data that might be more sensitive and specific in detecting outbreaks than the patient-chief-complaint data more commonly used for surveillance. EMA's experience also indicates that opportunities exist for the public health community to work with emergency physicians and emergency physician groups as suppliers of data. Such collaborations not only are useful for syndromic surveillance systems but also can help build relations that might facilitate a response to an actual biologic attack.

Bioterrorism↗
PubMed2004

SARS Surveillance Project--Internet-enabled multiregion surveillance for rapidly emerging disease.

INTRODUCTION: On March 15, 2003, CDC requested health-care and public health agencies to conduct surveillance for severe acute respiratory syndrome (SARS). The SARS Surveillance Project (SARS-SP) was established to rapidly implement multiregional SARS surveillance in emergency departments (EDs) by using existing Internet-based tools. OBJECTIVES: The objectives of SARS-SP were to 1) disseminate and update SARS screening forms for ED triage, 2) establish surveillance for SARS syndrome elements by using Regional Emergency Medicine Internet (REMI), 3) expand surveillance to multiple regions, and 4) evaluate the usefulness of Internet tools for agile surveillance during a rapidly emerging global epidemic. METHODS: SARS-SP developed, distributed, and updated an Internet-based triage form to identify patients for infection control and public health reporting. EDs then were invited to report visit frequencies with various SARS syndrome elements to local public health authorities by using the REMI Internet application (first in one metropolitan area, and later in four). After pilot-testing in one metropolitan area, the surveillance system was implemented in three others. RESULTS: Active syndromic surveillance was established by health departments in Milwaukee, Wisconsin; Denver, Colorado; Akron, Ohio; and Fort Worth, Texas. A total of 27 EDs reported syndrome frequencies from >146,000 patient encounters. CONCLUSIONS: ED and public health partners reported being satisfied with the system, confirming the usefulness of Internet tools in the rapid establishment of multiregion syndromic surveillance during an emerging global epidemic.

Communicable Diseases, Emerging↗
PubMed2004

From SARS to systems: developing advanced knowledge management for public health.

Historically, public health has been at the forefront of data processing applications but it is lagging behind other areas of health care in the application of advanced interconnected and mobile information technologies. Ready to use technologies are lacking not only for the management of emerging infections or bio-terrorism but also for the coordination of prevention and chronic care initiatives. Advanced information and knowledge management for community health should expedite the transfer of research evidence to practice and provide essential logistical support for action. We need to find ways to integrate new scientific knowledge into our environment in order to expedite the translation of research to practice.

Decision Support Techniques↗