Syndromic Surveillance

Outbreaks of waterborne gastrointestinal disease occur routinely in North America, resulting in considerable morbidity, mortality, and cost (Hrudey, Payment et al. 2003). Outbreak detection methods generally attempt to identify anomalies in time, but do not identify the type or source of an outbreak. We seek to develop a framework for both detection and classification of outbreaks using information in both space and time. Outbreak detection can be improved by using simulated outbreak data to build, validate, and evaluate models that aim to improve accuracy and timeliness of outbreak detection.

Objective

To develop a methodological framework for detecting and classifying outbreaks of gastrointestinal disease on the island of Montreal, with the goal of improving early outbreak detection using simulated surveillance data.

BioSense 2.0, a redesigned national syndromic surveillance system, provides users with timely regional and national data classified into disease syndromes, with views of health outcomes and trends for use in situational awareness. As of July 2014, there are 60 jurisdictions nationwide feeding data into BioSense 2.0. In New Jersey, the state’s syndromic surveillance system, EpiCenter, receives registration data from 75 of NJ’s 80 acute care and satellite emergency departments. EpiCenter is a system developed by Health Monitoring Systems, Inc. (HMS) that incorporates statistical management and analytical techniques to process health-related data in real time. To participate in BioSense 2.0, New Jersey worked with HMS to connect existing data to BioSense. In May, 2013, HMS established a single data feed of New Jersey’s facility data to BioSense 2.0. This transfer from HMS servers occurs twice daily via SFTP. The average daily visit volume in the transfer is around 10,000 records. This data validation project was initiated by the New Jersey Department of Health (NJDOH) in 2013 to assure that the registration records are delivered successfully to BioSense 2.0.

Objective

To assess and validate New Jersey’s ED registration data feed from EpiCenter to BioSense 2.0.

The Louisiana Office of Public Health conducts ED syndromic surveillance using the Louisiana Early Event Detection System (LEEDS). Using outpatient data for syndromic surveillance is a relatively new concept, brought about due to the increasing use of EHRs and HIEs making such data readily available. Previously, there has been limited means of syndrome classification validation for the LEEDS data and the GNOHIE data has not been studied widely as a population sample, so this analysis and comparison is valuable on both fronts. Due to differences in the types of data (ADT messages from EDs and CCD from outpatient clinics), as well as different patient populations and site visit capability, the percentages of patients classified as ILI between data sets are unequal. The main focus of this analysis is determining whether the ILI classifiers applied to both data sets detect similar syndrome trends.

Each indicator used in the study represents the percentage of total patients seen that week who are classified as ILI cases. The study period covered the 13-14 influenza season, CDC week 1340 through 1420 (9/29/2013-5/17/2014). Two ILI classifiers were applied to both the GNOHIE and LEEDS data:the first classifier consisted of ICD-9 influenza codes and the second classifier consisted of keywords applied to encounter notes(GNOHIE data) and chief complaint, admit reason and diagnoses (LEEDS data). A graph of the data, below, shows the four data sets.

Objective

The goal of this analysis is to compare the results of influenza-like illness (ILI) text and International Classification of Diseases (ICD) code classifiers applied to the Louisiana Office of Public Health’s (OPH) syndromic surveillance data reported by New Orleans area emergency departments and the Greater New Orleans Health Information Exchange’s (GNOHIE) data reported by New Orleans area outpatient clinics.

UIs are among the leading causes of injury in children younger than 5 years in NYC. About 3000 calls are received each year by the NYC Poison Control Center (PCC) for this age group. Common UI exposures include medications, cosmetics, household cleaners, foreign bodies, and pesticides. We examined UIs in NYC from January 2010 to July 2014 for children <5 years to investigate the utility of syndromic surveillance in conjunction with the PCC in capturing real-time pediatric UIs over time.

Objective

To describe unintentional ingestions (UIs) in children <5 years using syndromic data from emergency departments in New York City (NYC) from 2010 to 2014.

Utilization and overcrowding of EDs has been a prominent component of the health care reform debate in the United States for the last several years. In Virginia, the ED utilization rate has increased 27.5% between 2000 and 2012 from 34.5 visits to 44.0 visits per 100 persons. Individuals with high frequency utilization of EDs account for a disproportionate number of visits, which can place burden on already strained health care resources. This study aims to use existing syndromic surveillance data received electronically by the Virginia Department of Health (VDH) to describe demographic and utilization characteristics among chronic high frequency ED users in order to better understand the health complaints affecting this population.

Objective

Leverage existing syndromic surveillance data to characterize the population of chronic high frequency emergency department (ED) users and to understand the health complaints for which this population utilizes emergent health care services.

Over several months in 2012, NYC DOHMH syndromic surveillance staff met with directors of all 49 participating EDs in our syndromic system to collect information on their health information systems coding practices. During these interviews, ED directors expressed interest in receiving summary reports of the data they send to the syndromic unit, such as number of ED visits, most common complaints, and temporal and spatial trends. This effort was done to increase communication and cooperation between the syndromic unit and the EDs that provide data to the syndromic system.

Objective

To share monthly summary reports of syndromic data to participating EDs in NYC.

A retrospective analysis of emergency department data in NC for drug and opioid overdoses has been explained previously [1]. We built on this initial work to develop new poisoning and surveillance reports to facilitate near real time surveillance by health department and hospital users. In North Carolina, the availability for mortality and hospital discharge data are approximately one and two years after the event date, respectively. NC DETECT data are near real time and over 75% of ED visits receive at least one ICD-9-CM final diagnosis code within two weeks of the initial record receipt.

Objective

Twelve new case definitions were added to the NC DETECT Web Application to facilitate timely surveillance for poisoning and overdose. The process for developing these case definitions and the most recent outputs are described.

The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE-FL) receives daily (or bi-hourly) data from 184 emergency departments (ED) from around Florida. Additionally, 30 urgent care centers submit daily data to the system. These 214 facilities are grouped together in an acute care data source category. Five to six days after the start of each school year in Florida, ESSENCE-FL shows increased respiratory illness visits in the school aged population. Previous analyses of these data have shown that this increase is a result of increased transmission of the common cold among school children. In early September 2014, during this sustained yearly increase in respiratory visits, reports of more severe infection caused by Enterovirus D68 (EV-D68) in children in other parts of the country began circulating. Public health officials in Florida, as well as the media, questioned whether children in the state were being infected by this virus capable of causing more severe illness, especially among asthmatics. As is the case with many incipient outbreaks, syndromic surveillance played an integral role in early efforts to detect the presence of this illness. The task of providing situational awareness during this period was complicated by this outbreak coinciding with the start of the school year.

Objective

To provide situational awareness using Florida’s syndromic surveillance system during a 2014 outbreak of EV-D68 in other regions of the country.

Timely access to Emergency Department (ED) Chief Complaint (CC) data, before the definitive diagnosis is established, allows for early outbreak detection and prompt response by public health officials.BioSense 2.0 is a cloud-based application that securely collects, tracks, and shares ED data from participating hospitals around the country. Denver Health (DH) is one of several Colorado hospitals contributing ED Chief Complaint data to BioSense 2.0. In August 2013, ED clinicians reported an increase in patients presenting with excited delirium, possibly related to synthetic marijuana (SM). We used this event to test the use of CC field of ED data for detection of a novel public health event (i.e., serious adverse events related to synthetic marijuana use) not currently categorized in the BioSense syndromic surveillance library.

Objective

The aims of this presentation is to use ED chief complaint data, to test BioSense 2.0 for detection of a novel public health event (i.e., serious adverse events related to synthetic marijuana use) not currently categorized in the BioSense syndromic surveillance library.