Syndromic Surveillance

Current local, state, and national initiatives related to meaningful use and the modernization of electronic health records, and the growing availability of electronic information exchanges, have become important drivers to establishing syndromic surveillance systems. Effective implementation of electronic syndromic surveillance interfaces requires approaches that ensure the receipt of quality, timely, and reliable information.

While there are published specifications for the HL7 ADT message and National Institute of Standards and Technology (NIST) validation tools, there has been little documentation about the necessary steps for a local public health department to validate and confirm that an interface that is producing consistent and quality information. The lack of effective validation efforts has led to incomplete or inconsistent data utilized by syndromic systems and their intended audiences.

The County of San Diego has developed and utilized a framework for validating new syndromic interfaces. This presentation will highlight several pragmatic methods to validate the HL7 message content, provide specific examples of validation, and describe the pitfalls that could result from a poorly validated syndromic interface.

A local foundation commissioned a project to determine the leading causes of childhood injury in Wake County, NC. Multiple sources of secondary data, including syndromic surveillance data, were used to describe leading causes of childhood injury in the county.

Objective

To utilize secondary data sources to describe childhood injury and prioritize prevention efforts in Wake County, NC.

 

The New York City (NYC) syndromic surveillance system has monitored syndromes from NYC emergency department (ED) visits since 2001, using the temporal and spatial scan statistic in SaTScan for aberration detection. Since our syndromic system was initiated, alternative methods have been proposed for outbreak identification. Our goal was to evaluate methods for outbreak detection and apply the best performing method(s) to our daily analysis of syndromic data.

Objective

To evaluate temporal and spatial aberration detection methods for implementation in a local syndromic surveillance system.

The Environmental Public Health Tracking Network (Tracking Network) is a national surveillance system that integrates environmental hazard, exposure, and health outcome data into one system. The Tracking Network launched in July 2009, and has since been receiving data from 23 funded state and local health departments, and several national partners, e.g., Environmental Protection Agency. Despite this success, some challenges exist in obtaining more timely and complete data to link risk factors, and assign exposure for health outcomes with long latency periods before their detection.

The Health Information Technology for Economic and Clinical Health (HITECH) Act (2009) facilitates the adoption of electronic health records (EHR) through incentivizing the meaningful use of certified EHR Technology. Meaningful use is a set of specific objectives and data exchange standards that eligible healthcare professionals and hospitals must achieve to qualify for the Centers for Medicare and Medicaid Services (CMS) Electronic Health Records Incentive Programs. Public health agencies in turn need to have the capacity to accept these data in the mandated standard and determine the potential and use of this increased data.

This presentation will discuss how the Tracking Network is exploring the use of EHR to meet Tracking Network surveillance challenges and provide other opportunities to enhance environmental public health surveillance. We will also present some results of these efforts.

Objective

This presentation will discuss how the Tracking Network is exploring the use of EHR to meet Tracking Network surveillance challenges and provide other opportunities to enhance environmental public health surveillance

Real-time emergency department (ED) data are currently received from 78 of 80 New Jersey acute care and satellite EDs by Health Monitoring Systems Inc.’s (HMS) EpiCenter system. EpiCenter collects, manages and analyzes ED registration data for syndromic surveillance, and provides alerts to state and local health departments for surveillance anomalies. After the 2012 Superstorm Sandy devastated parts of New Jersey, NJDOH initiated a plan to develop severe weather surveillance using EpiCenter to provide the Department with the ability to track both health and mental health concerns during adverse weather conditions to alert the public about emerging health hazards.

Objective

To describe the development and validation of a mental health classification to track emergency department visits for potential needed public health response during severe weather events.

Since its inception in 2008, PHO has grown through new funding to establish the agency, as well as a series of program transfers from the Government of Ontario, including ID surveillance. PHO’s current role in ID surveillance in Ontario is to support the public health and health care systems with surveillance information, tools, and resources for the prevention and control of IDs. PHO also provides scientific and technical expertise for IDs, including different aspects of surveillance (e.g., data entry requirements, statistical algorithms, provincial surveillance reports).

The overarching aim of the framework is to establish PHO’s key priorities, strategies, and actions to guide ID surveillance over the next five years and will help advance ID surveillance across Ontario. This is PHO’s first step towards a strategic and coordinated approach to ID surveillance.

Objective

This presentation will outline the development process for Public Health Ontario’s (PHO’s) first Infectious Disease Surveillance Framework (the framework), highlight key elements of the framework, and identify examples of infectious disease (ID) surveillance activities and projects that align with the framework.

The Louisiana Office of Public Health (OPH) Infectious Disease Epidemiology Section (IDEpi) conducts emergency department (ED) syndromic surveillance using the Louisiana Early Event Detection System (LEEDS). IDEpi has the capability to define and change syndrome definitions in LEEDS based on surveillance needs and quality assurance activities. IDEpi submits all of the ED data to BioSense, which uses different syndrome definitions than LEEDS. Both BioSense and LEEDS use text and ICD code searches in any available chief complaint, admit reason and diagnosis data. The results of LEEDS and BioSense syndrome classifications for influenza-likeillness (ILI), gastrointestinal (GI), and upper respiratory infections (URI) applied to Louisiana’s ED data were compared to examine if the different syndrome definitions yield similar results when applied to the same data.

Objective

To compare the results of BioSense and Louisiana syndrome classifications for influenza-like-illness, gastrointestinal, and upper respiratory infections applied to Louisiana emergency department data.

Data from the Emergency Departments (EDs) of 49 hospitals in New York City (NYC) is sent to the Department of Health and Mental Hygiene (DOHMH) daily as part of the syndromic surveillance system. Currently, thirty-four of the EDs transmit data as flat files. As part of the Center for Medicare and Medicaid Services Electronic Health Record Incentive Program, otherwise known as Meaningful Use, many EDs in our system have switched or are in the process of switching to HL7 Messaging Standard Version 2.5.1. Given there may be differences in data completeness, quality, and content between the new HL7 data and legacy data, we evaluated data sent in both formats in parallel by several EDs.

Objective

To evaluate potential changes in emergency department (ED) syndromic surveillance data quality, as hospitals shift from sending data as flat file format (Legacy Data) to real-time/batch HL7 Messaging Standard Version 2.5.1, in compliance with Meaningful Use requirements.