Chief Complaint Data Research Articles

Using Emergency Department Data to Conduct Dog and Animal Bite Surveillance in New York City, 2003–2006

Most animal bites in the United States are due to dogs, with approximately 4.7 million reports per year. Surveillance for dog and other animal bites requires a substantial investment of time and resources, and underreporting is common. We described the use and findings of electronic hospital emergency department (ED) chief complaint data to characterize patients and summarize trends in people treated for dog and other animal bites in New York City (NYC) EDs between 2003 and 2006. Retrospective data were obtained from the syndromic surveillance system at the NYC Department of Health and Mental Hygiene. We used a statistical program to identify chief complaint free-text fields as one of four categories of animal bites. We evaluated descriptive statistics and univariate associations on the available demographic data. The findings were also compared with data collected through the existing passive reporting animal bite surveillance system. During the study period, more than 6,000 animal bite patient visits were recorded per year. The proportion of visits for animal bites did not appear to change over time. Dog bites accounted for more than 70% and cat bites accounted for 13% of animal bite patient visits. Demographic characteristics of patients were similar to those identified in NYC's passive surveillance system. Our findings suggest that the use of ED data offers a simple, less resource-intensive, and sustainable way of conducting animal bite surveillance and a novel use of syndromic surveillance data. However, it cannot replace traditional surveillance used to manage individual patients for potential rabies exposures.

Method selection and adaptation for distributed monitoring of infectious diseases for syndromic surveillance

Background Automated surveillance systems require statistical methods to recognize increases in visit counts that might indicate an outbreak. In prior work we presented methods to enhance the sensitivity of C2, a commonly used time series method. In this study, we compared the enhanced C2 method with five regression models. Methods We used emergency department chief complaint data from US CDC BioSense surveillance system, aggregated by city (total of 206 hospitals, 16 cities) during 5/2008–4/2009. Data for six syndromes (asthma, gastrointestinal, nausea and vomiting, rash, respiratory, and influenza-like illness) was used and was stratified by mean count (1–19, 20–49, ⩾50 per day) into 14 syndrome-count categories. We compared the sensitivity for detecting single-day artificially-added increases in syndrome counts. Four modifications of the C2 time series method, and five regression models (two linear and three Poisson), were tested. A constant alert rate of 1% was used for all methods. Results Among the regression models tested, we found that a Poisson model controlling for the logarithm of total visits (i.e., visits both meeting and not meeting a syndrome definition), day of week, and 14-day time period was best. Among 14 syndrome-count categories, time series and regression methods produced approximately the same sensitivity (<5% difference) in 6; in six categories, the regression method had higher sensitivity (range 6–14% improvement), and in two categories the time series method had higher sensitivity. Discussion When automated data are aggregated to the city level, a Poisson regression model that controls for total visits produces the best overall sensitivity for detecting artificially added visit counts. This improvement was achieved without increasing the alert rate, which was held constant at 1% for all methods. These findings will improve our ability to detect outbreaks in automated surveillance system data.

Assessing the Early Aberration Reporting System's Ability to Locally Detect the 2009 Influenza Pandemic

The Early Aberration Reporting System (EARS) is used by some local health departments (LHDs) to monitor emergency room and clinic data for disease outbreaks. Using actual chief complaint data from local public health clinics, we evaluate how EARS—both the baseline system distributed by the CDC and two variants implemented by one LHD—perform at locally detecting the 2009 influenza A H1N1 pandemic. We also compare the EARS methods to a CUSUM-based method. We find that the baseline EARS system performed poorly in comparison to one of the LHD variants and the CUSUM-based method. These results suggest that changes in how syndromes are defined can substantially improve EARS performance. The results also show that incorporating algorithms that use more historical data will improve EARS performance for routine surveillance by local health departments.

Modeling and Syndromic Surveillance for Estimating Weather-Induced Heat-Related Illness

This paper compares syndromic surveillance and predictive weather-based models for estimating emergency department (ED) visits for Heat-Related Illness (HRI). A retrospective time-series analysis of weather station observations and ICD-coded HRI ED visits to ten hospitals in south eastern Ontario, Canada, was performed from April 2003 to December 2008 using hospital data from the National Ambulatory Care Reporting System (NACRS) database, ED patient chief complaint data collected by a syndromic surveillance system, and weather data from Environment Canada. Poisson regression and Fast Orthogonal Search (FOS), a nonlinear time series modeling technique, were used to construct models for the expected number of HRI ED visits using weather predictor variables (temperature, humidity, and wind speed). Estimates of HRI visits from regression models using both weather variables and visit counts captured by syndromic surveillance as predictors were slightly more highly correlated with NACRS HRI ED visits than either regression models using only weather predictors or syndromic surveillance counts.

Management of Subluxating Ulnar Nerve at the Elbow

Musculotendinous variations around the medial epicondyle can contribute to subluxation of the ulnar nerve at the elbow. This article reviews the presenting symptoms, operative findings, and results of surgery for subluxation of the ulnar nerve at the elbow. A retrospective evaluation was performed of 200 elbows managed operatively for medial elbow pathology over a 17-year period between 1990 and 2007. The patient charts were reviewed for chief complaint, radiographic studies, operative reports, and postoperative examination data. Seventeen patients (18 elbows) were treated for a subluxating ulnar nerve. Three patients were women and 14 were men, with a mean age of 27.6 years. Medial elbow pain was the chief complaint in all 17 patients. Seventeen elbows also demonstrated paresthesias in an ulnar nerve distribution. All patients were treated with anterior transposition of the ulnar nerve, and 11 patients (61%) were found to have a muscular anomaly. At a mean follow-up of 17 months, the mean visual analog scale for pain improved from 6.0 to 2.0. There was no functional impairment reported for any patient at final follow-up. Of the 200 elbows surgically treated for medial elbow pathology, 17 patients (8.5%) demonstrated a subluxating ulnar nerve. These patients tend to be young and present with a primary complaint of medial elbow pain. In addition, a subluxating ulnar nerve is often associated with muscular anomalies, which must be addressed concurrently.

A real-time temporal Bayesian architecture for event surveillance and its application to patient-specific multiple disease outbreak detection

Reliable and accurate detection of disease outbreaks remains an important research topic in disease outbreak surveillance. A temporal surveillance system bases its analysis on data not only from the most recent time period, but also on data from previous time periods. A non-temporal system only looks at data from the most recent time period. There are two difficulties with a non-temporal system when it is used to monitor real data which often contain noise. First, it is prone to produce false positive signals during non-outbreak time periods. Second, during an outbreak, it tends to release false negative signals early in the outbreak, which can adversely affect the decision making process of the user of the system. We conjecture that by converting a non-temporal system to a temporal one, we may attenuate these difficulties inherent in a non-temporal system. In this paper, we propose a Bayesian network architecture for a class of temporal event surveillance models called BayesNet-T. Using this Bayesian network architecture, we can convert certain non-temporal surveillance systems to temporal ones. We apply this architecture to a previously developed non-temporal multiple-disease outbreak detection system called PC and create a temporal system called PCT. PCT takes Emergency Department (ED) patient chief complaint data as its input. The PCT system was constructed using both data (non-outbreak diseases) and expert assessments (outbreak diseases). We compare PCT to PC using a real influenza outbreak. Furthermore, we compare PCT to both PC and the classic statistical methods CUSUM and EWMA using a total of 240 influenza and Cryptosporidium disease outbreaks created by injecting stochastically simulated outbreak cases into real ED admission data. Our results indicate that PCT has a smaller mean time to detection than PC at low false alarm rates, and that PCT is more stable than PC in that once an outbreak is detected, PCT is better at maintaining the detection signal on future days.

Pathology's Contributions to Disease Surveillance: Sending Our Data to Public Health Officials and Encouraging Our Clinical Colleagues to Do So

Pathology and clinical laboratories produce many types of data and information that are of relevance in protecting the health of the public. Electronic linkages between laboratory/pathology information systems and public health surveillance databases help ensure that these data reach the appropriate responder rapidly, completely, and accurately. Many efforts are underway to facilitate the flow of data from laboratory/pathology data sources to public health systems. Likewise, pathologists can encourage the contribution of clinical data from other parts of the health care enterprise. Chief complaint data from the emergency department and usage of over-the-counter medications from the retail pharmacy are useful for real-time population health surveillance. Having directly advocated transmission of data from our laboratories and facilitated other departments' participation in the public health surveillance network, we can also serve the public's health in other ways. Public health jurisdictions have a great need for staff with the skill sets of an experienced laboratorian. So, plan to contribute: data and talent.

347: Performance of a Chief Complaint Classifier for Syndromic Surveillance for Three Gastrointestinal Sub-Syndromes

Syndromic surveillance for disease outbreaks and situational awareness is generally performed in the emergency department (ED) setting by computerized scanning of patient chief complaint (CC) data looking for peaks in patient arrivals. The patients are grouped into broad syndromes such as the respiratory syndrome or the gastrointestinal (GI) syndrome. It may be useful for surveillance to break these broad groupings into smaller groupings called sub-syndromes but little is known about this approach. The Centers for Disease Control and Prevention (CDC) BioSense project has developed CC and ICD9 sub-syndrome classifiers for the major syndromes for early event detection and situational awareness. Previously we reported on the sensitivity and specificity of three GI sub-syndrome (CC) classifiers for abdominal pain, nausea/vomiting and diarrhea using chart review as the criterion standard, and found increased specificity at the expense of decreased sensitivity in the sub-syndromes compared to the broader GI classifier. Our objective in this study was to examine reasons for false-negative (FN) and false-positive (FP) classifications and determine whether they were due to correctable deficiencies in the computerized CC classifiers or due to a lack of information in the CC.

Pathology's Contributions to Disease Surveillance: Sending Our Data to Public Health Officials and Encouraging Our Clinical Colleagues to Do So

Pathology and clinical laboratories produce many types of data and information that are of relevance in protecting the health of the public. Electronic linkages between laboratory/pathology information systems and public health surveillance databases help ensure that these data reach the appropriate responder rapidly, completely, and accurately. Many efforts are underway to facilitate the flow of data from laboratory/pathology data sources to public health systems. Likewise, pathologists can encourage the contribution of clinical data from other parts of the health care enterprise. Chief complaint data from the emergency department and usage of over-the-counter medications from the retail pharmacy are useful for real-time population health surveillance. Having directly advocated transmission of data from our laboratories and facilitated other departments' participation in the public health surveillance network, we can also serve the public's health in other ways. Public health jurisdictions have a great need for staff with the skill sets of an experienced laboratorian. So, plan to contribute: data and talent.

Two-Phase Chief Complaint Mapping to the UMLS Metathesaurus in Korean Electronic Medical Records

The task of automatically determining the concepts referred to in chief complaint (CC) data from electronic medical records (EMRs) is an essential component of many EMR applications aimed at biosurveillance for disease outbreaks. Previous approaches that have been used for this concept mapping have mainly relied on term-level matching, whereby the medical terms in the raw text and their synonyms are matched with concepts in a terminology database. These previous approaches, however, have shortcomings that limit their efficacy in CC concept mapping, where the concepts for CC data are often represented by associative terms rather than by synonyms. Therefore, herein we propose a concept mapping scheme based on a two-phase matching approach, especially for application to Korean CCs, which uses term-level complete matching in the first phase and concept-level matching based on concept learning in the second phase. The proposed concept-level matching suggests the method to learn all the terms (associative terms as well as synonyms) that represent the concept and predict the most probable concept for a CC based on the learned terms. Experiments on 1204 CCs extracted from 15,618 discharge summaries of Korean EMRs showed that the proposed method gave significantly improved F-measure values compared to the baseline system, with improvements of up to 73.57%.

Monitoring the Impact of Influenza by Age: Emergency Department Fever and Respiratory Complaint Surveillance in New York City

BackgroundThe importance of understanding age when estimating the impact of influenza on hospitalizations and deaths has been well described, yet existing surveillance systems have not made adequate use of age-specific data. Monitoring influenza-related morbidity using electronic health data may provide timely and detailed insight into the age-specific course, impact and epidemiology of seasonal drift and reassortment epidemic viruses. The purpose of this study was to evaluate the use of emergency department (ED) chief complaint data for measuring influenza-attributable morbidity by age and by predominant circulating virus.Methods and FindingsWe analyzed electronically reported ED fever and respiratory chief complaint and viral surveillance data in New York City (NYC) during the 2001–2002 through 2005–2006 influenza seasons, and inferred dominant circulating viruses from national surveillance reports. We estimated influenza-attributable impact as observed visits in excess of a model-predicted baseline during influenza periods, and epidemic timing by threshold and cross correlation. We found excess fever and respiratory ED visits occurred predominantly among school-aged children (8.5 excess ED visits per 1,000 children aged 5–17 y) with little or no impact on adults during the early-2002 B/Victoria-lineage epidemic; increased fever and respiratory ED visits among children younger than 5 y during respiratory syncytial virus-predominant periods preceding epidemic influenza; and excess ED visits across all ages during the 2003–2004 (9.2 excess visits per 1,000 population) and 2004–2005 (5.2 excess visits per 1,000 population) A/H3N2 Fujian-lineage epidemics, with the relative impact shifted within and between seasons from younger to older ages. During each influenza epidemic period in the study, ED visits were increased among school-aged children, and each epidemic peaked among school-aged children before other impacted age groups.ConclusionsInfluenza-related morbidity in NYC was highly age- and strain-specific. The impact of reemerging B/Victoria-lineage influenza was focused primarily on school-aged children born since the virus was last widespread in the US, while epidemic A/Fujian-lineage influenza affected all age groups, consistent with a novel antigenic variant. The correspondence between predominant circulating viruses and excess ED visits, hospitalizations, and deaths shows that excess fever and respiratory ED visits provide a reliable surrogate measure of incident influenza-attributable morbidity. The highly age-specific impact of influenza by subtype and strain suggests that greater age detail be incorporated into ongoing surveillance. Influenza morbidity surveillance using electronic data currently available in many jurisdictions can provide timely and representative information about the age-specific epidemiology of circulating influenza viruses.

Direct Cost Associated With the Development and Implementation of a Local Syndromic Surveillance System

Enhancing public health surveillance to include electronic syndromic surveillance systems has received increased attention in recent years. Although cost continually serves as a critical factor in public health decision making, few studies have evaluated direct costs associated with syndromic surveillance systems. In this study, we calculated the direct costs associated with developing and implementing a syndromic surveillance system in Boston, Massachusetts, from the perspective of local, state, and federal governments. Between December 2003 and July 2005, the Boston Public Health Commission (BPHC), in collaboration with the Centers for Disease Control and Prevention (CDC), and the Massachusetts Department of Public Health developed a syndromic surveillance system in which limited demographic and chief complaint data are collected from all Boston acute care emergency departments every 24 hours. Costs were divided into three categories: development, operation, and upgrade. Within these categories, all fixed and variable costs incurred by both BPHC and CDC were assessed, including those associated with development of syndromic surveillance-related city regulations and system enhancements. The total estimated direct cost of system development and implementation during the study period was $422,899 ($396,716 invested by BPHC and $26,183 invested by CDC). Syndromic system enhancements to improve situational awareness accounted for $74,389. Development, implementation, and operation of a syndromic surveillance system accounted for a relatively small proportion of surveillance costs in a large urban health department. Funding made available for a future cost-benefit analysis, and an assessment of local epidemiologic capacity will help to guide decisions for local health departments. Although not a replacement for traditional surveillance, syndromic surveillance in Boston is an important and relatively inexpensive component of a comprehensive local public health surveillance system.

Unified Medical Language System Coverage of Emergency-medicine Chief Complaints

Emergency department (ED) chief-complaint (CC) data increasingly are important for clinical-care and secondary uses such as syndromic surveillance. There is no widely used ED CC vocabulary, but experts have suggested evaluation of existing health-care vocabularies for ED CC. To evaluate the ED CC coverage in existing biomedical vocabularies from the Unified Medical Language System (UMLS). The study sample included all CC entries for all visits to three EDs over one year. The authors used a special-purpose text processor to clean CC entries, which then were mapped to UMLS concepts. The UMLS match rates then were calculated and analyzed for matching concepts and nonmatching entries. A total of 203,509 ED visits was included. After cleaning with the text processor, 82% of the CCs matched a UMLS concept. The authors identified 5,617 unique UMLS concepts in the ED CC data, but many were used for only one or two visits. One thousand one hundred thirty-six CC concepts were used more than ten times and covered 99% of all the ED visits. The largest biomedical vocabulary in the UMLS is the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), which included concepts for 79% of all ED CC entries. However, some common CCs were not found in SNOMED CT. The authors found that ED CC concepts are well covered by the UMLS and that the best source of vocabulary coverage is from SNOMED CT. There are some gaps in UMLS and SNOMED CT coverage of ED CCs. Future work on vocabulary control for ED CCs should build upon existing vocabularies.

An Evaluation of Controlled Terminologies for ED Chief Complaint Documentation in the Department

Chief complaint data from patients' visits to the emergency department (ED) are used to support clinical care, surveillance, and research efforts. Current chief complaint data have limitations due to poor quality and lack of standardization. To document chief complaints, many emergency departments use electronic systems that include a controlled list of predefined terms to choose from, as well as an option to enter free text. However, preliminary evidence suggests that nurses who prefer to document the chief complaint in free-text form bypass the controlled lists often.

Evaluation of Emergency Medical Text Processor, a System for Cleaning Chief Complaint Text Data

Objectives: Emergency Medical Text Processor (EMT-P) version 1, a natural language processing system that cleans emergency department text (e.g., chst pn, chest pai), was developed to maximize extraction of standard terms (e.g., chest pain). The authors compared the number of standard terms extracted from raw chief complaint (CC) data with that for CC data cleaned with EMT-P and evaluated the accuracy of EMT-P. Methods: This cross-sectional observation study included CC text entries for all emergency department visits to three tertiary care centers in 2001. Terms were extracted from CC entries before and after cleaning with EMT-P. Descriptive statistics included number and percentage of all entries (tokens) and all unique entries (types) that matched a standard term from the Unified Medical Language System (UMLS). An expert panel rated the accuracy of the CC–UMLS term matches; inter-rater reliability was measured with κ. Results: The authors collected 203,509 CC entry tokens, of which 63,946 were unique entry types. For the raw data, 89,337 tokens (44%) and 5,081 types (8%) matched a standard term. After EMT-P cleaning, 168,050 tokens (83%) and 44,430 types (69%) matched a standard term. The expert panel reached consensus on 201 of the 222 CC–UMLS term matches reviewed (κ= 0.69–0.72). Ninety-six percent of the 201 matches were rated equivalent or related. Thirty-eight percent of the nonmatches were found to match UMLS concepts.Conclusions: EMT-P version 1 is relatively accurate, and cleaning with EMT-P improved the CC–UMLS term match rate over raw data. The authors identified areas for improvement in future EMT-P versions and issues to be resolved in developing a standard CC terminology.

Evaluation of Emergency Medical Text Processor, a System for Cleaning Chief Complaint Text Data

Emergency Medical Text Processor (EMT-P) version 1, a natural language processing system that cleans emergency department text (e.g., chst pn, chest pai), was developed to maximize extraction of standard terms (e.g., chest pain). The authors compared the number of standard terms extracted from raw chief complaint (CC) data with that for CC data cleaned with EMT-P and evaluated the accuracy of EMT-P. This cross-sectional observation study included CC text entries for all emergency department visits to three tertiary care centers in 2001. Terms were extracted from CC entries before and after cleaning with EMT-P. Descriptive statistics included number and percentage of all entries (tokens) and all unique entries (types) that matched a standard term from the Unified Medical Language System (UMLS). An expert panel rated the accuracy of the CC-UMLS term matches; inter-rater reliability was measured with kappa. The authors collected 203,509 CC entry tokens, of which 63,946 were unique entry types. For the raw data, 89,337 tokens (44%) and 5,081 types (8%) matched a standard term. After EMT-P cleaning, 168,050 tokens (83%) and 44,430 types (69%) matched a standard term. The expert panel reached consensus on 201 of the 222 CC-UMLS term matches reviewed (kappa=0.69-0.72). Ninety-six percent of the 201 matches were rated equivalent or related. Thirty-eight percent of the nonmatches were found to match UMLS concepts. EMT-P version 1 is relatively accurate, and cleaning with EMT-P improved the CC-UMLS term match rate over raw data. The authors identified areas for improvement in future EMT-P versions and issues to be resolved in developing a standard CC terminology.

Syndromic surveillance for infectious disease outbreaks using chief complaint data

Study objectives: Although the ubiquitous threat of biochemical terrorism has spurred newfound interest in disease surveillance, the current set of systems has limited ability to rapidly detect and report an outbreak. By focusing on real-time analysis of patient chief complaint data, this investigation seeks to eliminate the inherent delay in current surveillance systems. The objective is to compare a real-time computerized tracking system to rapid laboratory testing for disease surveillance using influenza as a model. Methods: All patients presenting to the adult and pediatric emergency departments (EDs) of a university teaching hospital with chief complaints of and flu-like illness from October 2004 through December 2004 were included. Using a computerized patient tracking system, patient age, sex, chief complaint, and ZIP code were collected and correlated with the documented outbreak of the 2003 to 2004 influenza season as indicated by the total daily number of positive rapid influenza tests. Determinations of an increase in the number of chief complaints of concern were based on the CUSUM statistic. In addition, these surveillance data were also compared with chief complaint data collected from many EDs within the Nashville area by the Metropolitan Nashville Health Department. Results: It was found that, although chief complaints of flu or fever effectively mirrored flu diagnosis, our surveillance system did not appreciably improve use of rapid influenza testing. The real-time nature of this investigation, however, yielded a novel use for this system in its predictive ability concerning the course of the outbreak and subsequent allowance for improved hospital response time through informed staffing decisions. Conclusion: Although our findings indicate that syndromic surveillance from a single institution may not result in a more effective influenza outbreak detection in a moderate-sized city, they also suggest that real-time analysis of this evolving outbreak would prove valuable in mounting an effective medical response.

The frontlines of medicine project progress report: standardized communication of emergency department triage data for syndromic surveillance

This article reports progress since the original publication of the Frontlines of Medicine Project. This project is a collaborative effort of emergency medicine (including emergency medical services and clinical toxicology), public health, other government agencies involved in health care and preparedness, law enforcement, and informatics to develop nonproprietary, standardized methods for reporting emergency department patient data. These data may be used for a variety of public health or clinical care initiatives, including syndromic surveillance for chemical and biological terrorism. This article reviews the outcome of the Project meeting in April 2002. Also, the article describes a Delphi Survey process to define the data elements in a triage surveillance report and to define a set of codified values for the chief complaint data element. An initial retrospective validation of the codified chief complaint values is provided, and prospective study of the proposed Frontlines' standards is encouraged.

Syndromic analysis of computerized emergency department patients' chief complaints: An opportunity for bioterrorism and influenza surveillance

Study objective: Emergency department computerized triage logs might be useful for automated ED surveillance and potentially for early identification of bioterrorism events. We describe a Web-based surveillance program and its feasibility for surveillance. Methods: A Web-based surveillance program that receives computerized chief complaint data daily from a large academic urban teaching hospital and performs syndromic analysis on these data was developed. On the basis of preset limits, the Web-based surveillance program sends an alert e-mail message when the syndromic analysis reveals an increase in the number of patients in predefined symptom groups. The feasibility of this system was tested by using historical data during an influenza outbreak (December 1999 to January 2000) and applying the anthrax symptom group. Results: The Web-based surveillance program identified the influenza outbreak in the first week. Conclusion: Computerized triage logs might be a feasible method for bioterrorism and influenza surveillance. The Web-based nature of the surveillance program creates the opportunity for other hospitals to contribute data, potentially resulting in an automated network of ED computerized triage log surveillance. [Ann Emerg Med. 2003;41:447-452.]

The use of hospital emergency department chief complaint data as a "Near" real-time marker for assessing public health risk of infectious disease outbreak

S i121 Jersey EDs, with participants consecutive patients seen by ED physicians January 1998 through July 2002. Based on ICD-9 codes, syndromic groups were developed for the following categories: any gastrointestinal, diarrhea, respiratory, asthma, chest pain, fever, skin, headache, and weakness. We then generated daily counts of patients by category and generated time series graphs to display the incidence of disease for these syndromic groups over the 4.5-year period. We also generated similar counts and graphs for the same syndromic groups based on the physician’s choice of charting template rather than ICD-9 code and compared the results. There were 3.2 million patient visits in the database. Visual inspection of the time series graphs showed definite seasonal peaks in the diarrhea, respiratory, asthma, fever, and skin syndromic groups. There was good agreement between ICD-9 codes and templates. The existing ED database identified seasonal peaks in the incidence of several disease syndromes. Tracking physician charting template usage could potentially identify these patterns in real time. This ED database may be able to provide early warning of disease outbreaks and some types of bioterrorist attacks. The Use of Hospital Emergency Department Chief Complaint Data as a “Near” Real-Time Marker for Assessing Public Health Risk of Infectious Disease Outbreak Ronald J. Shannon, Michael Davisson, Trang Nguyen, Carolyn Stetson, and Kevin Jones New York State Department of Health, Division of Epidemiology Since September 11, 2001, and the corresponding anthrax attacks, there has been considerable interest in developing pre-event surveillance methods that would be used for early detection of a bioterrorist event and prevention of widespread morbidity and mortality. Traditionally, active surveillance mechanisms to detect disease outbreaks consist of confirmatory laboratory testing after preliminary diagnosis from a physician. In many cases, the confirmation of infectious disease takes days of testing, many hours of epidemiological analysis, and significant public health resources at the local level before an outbreak is finally diagnosed. Our communities are at significant risk unless public health authorities can develop a preevent early warning system with a high degree of specificity and sensitivity for outbreak detection when patients present themselves to the emergency department or their primary health care provider. A pre-event early warning system depends on quality, “near” real-time data from the medical community. Potential sources of this information are (1) hospital emergency department encounters, (2) outpatient clinic visits, (3) pharmacy data (over the counter and prescription). All data sources have varying degrees of quality, but the hospital emergency department registration information (chief complaint at initial visit) is determined to provide the nearest real-time means for use in a pre-event surveillance system. But, is chief complaint information as reliable as International Classification of Diseases, 9th Revision (ICD-9)–coded discharge diagnosis in predicting an early event? ICD-9 diagnosis is considered to be the best indicator of patient diagnosis, but is not readily available for epidemiological analysis until 3 to 5 days after initial visit. The New York State Department of Health and Emergency Medical Associates of New Jersey Research Foundation (EMARF) have completed a study comparing emergency department chief complaint data with ICD-9 discharge codes from 2.7 million patient encounters presenting to 15 emergency departments in New Jersey to determine the feasibility of using chief complaint for pre-event surveillance. Preliminary findings show a high specificity and sensitivity comparing chief complaint data to ICD9-coded discharge diagnosis. Connecticut Hospital Admissions Syndromic Surveillance Zygmunt Dembek, Myrth Myers, Kenneth Carley, and James Hadler Connecticut Department of Public Health