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Hamby Teresa

Description

The opioid drug overdose crisis presents serious challenges to state-based public health surveillance programs, not the least of which is uncertainty in the detection of cases in existing data systems. New Jersey historically had slightly higher unintentional drug overdose death rates than the national average, but by 2001 dramatic increases in drug overdose deaths in states like West Virginia began to drive up the national rate (Figure 1). Although the rise in New Jersey's fatal overdose rates has mirrored the national rate since 1999, the rate has dramatically increased since 2011- from 9.7 per 100,000 (868 deaths) to 21.9 per 100,000 in 2016 (1,931 deaths), an increase of 125% in five years.1 The New Jersey Department of Health has been funded by the Centers for Disease Control and Prevention (CDC) to conduct surveillance of opioid-involved overdoses through the Enhanced Surveillance of Opioid-Involved Overdose in States (ESOOS) program, and to conduct syndromic surveillance through the National Syndromic Surveillance Program (NSSP); this has presented a collaboration opportunity for the Department's surveillance grantee programs to use existing resources to evaluate and refine New Jersey’s drug overdose case definitions and develop new indicators to measure the burden of overdose throughout the state and to facilitate effective responses.

Objective: Link syndromic surveillance data for potential opioid-involved overdoses with hospital discharge data to assess positive predictive value of CDC Opioid Classifiers for conducting surveillance on acute drug overdoses.

Submitted by elamb on
Description

In early May of 2013, two chemical spills occurred within high schools in Atlantic county. These incidents, occurring within a week of each other, highlighted the need to strengthen statewide syndromic surveillance of illnesses caused by such exposures. In response to these spills, a new 'chemical exposure' classifier was created in EpiCenter, New JerseyÕs syndromic surveillance system, to track future events by monitoring registration chief complaint data taken from emergency department visits. The primary objective behind creation of the new classifier is to provide local epidemiologists with prompt notification once EpiCenter detects an abnormal numbers of chemical exposure cases.

Objective

To describe the development of a new chemical exposure classifier in New Jersey's syndromic surveillance system (EpiCenter).

Submitted by elamb on
Description

In the summer of 2001, New Jersey (NJ) was in the process of developing surveillance activities for bioterrorism. On September 11, 2001, the U.S. suffered a major terrorist attack. Approximately a month later, Anthrax-laced letters were processed through a New Jersey Postal Distribution Center (PDC). As a result of these events, the state instituted simplistic surveillance activities in emergency departments (ED's). Over time, this initial system has developed into a broader, more streamlined approach to surveillance that now includes syndromic data e.g., Influenza-like illness (ILI) as well as the use of technology (automated surveys, real-time data connections, and alert analysis) to achieve surveillance goals and provide daily information to public health partners in local health departments and DHSS response colleagues.

Objective

To describe the improvements in New Jersey's Emergency Department surveillance system over time.

Submitted by elamb on
Description

A goal of biosurveillance is to identify incidents that require a public health response. The challenge is creating specific definitions of such incidents so they can be detected. In syndromic surveillance, this is accomplished by classifying emergency department chief complaints, nurse triage calls, and other prediagnostic data into categories, and then looking for increases in visits related to those categories. This approach can only find incidents that match those predefined categories. It is well-suited to handle common diseases; data from prior years provides information not only on which symptoms correlate with the disease, but also on how patients report them and how they appear in prediagnostic data streams. For unique or rare events, it is hard to know in advance how they will be described or recorded. Another approach is to look for similarities in the time of the healthcare encounters alone. This method can detect events which are missed by syndrome-oriented surveillance, but healthcare encounters that only have time of occurrence aren't necessarily related. To address this limitation, we propose a set of similarity criteria which incorporates both timing and reason.

Objective

Develop a method for detecting groups of related healthcare encounters without having to specify details of the reasons for those encounters in advance.

Submitted by knowledge_repo… on
Description

BioSense is a national Centers for Disease Control and Prevention (CDC) initiative to improve the nation's capabilities for early event detection and situational awareness. BioSense data includes Department of Defense and Veterans Affairs ambulatory care diagnoses and procedures, as well as Laboratory Corporation of America lab test orders.  The data are collected, assigned to syndromes based upon definitions developed by a multi-agency working group, analyzed using several detection algorithms, and displayed in various visualizations [2,3].  BioIntelligence Center (BIC) staff at CDC monitors BioSense national data on a daily basis and are available to support state and local public health officials’ monitoring and investigations [3]. As part of its ongoing bioterrorism surveillance, the New Jersey Department of Health and Senior Services (NJDHSS) reviews the BioSense application for syndrome activity and disease alerts of potential public health importance.  In November, 2004, staff noted a Sentinel Infection Alert for Smallpox two days before the Thanksgiving holiday.  The investigation of this Sentinel Alert by NJDHSS was the first Sentinel Alert follow-up investigation by a state health department and helped state and CDC colleagues identify ways to enhance BioSense.

Objective:

This paper describes a situation in November, 2004, regarding a Sentinel Infection Alert for Smallpox that appeared in the BioSense application.

Submitted by elamb on
Description

On July 11, 2012, New Jersey Department of Health (DOH) Communicable Disease Service (CDS) surveillance staff received email notification of a statewide anomaly in EpiCenter for Paralysis. Two additional anomalies followed within three hours. Since Paralysis Anomalies are uncommon, staff initiated an investigation to determine if there was an outbreak or other event of concern taking place. Also at question was whether receipt of multiple anomalies in such a short time span was statistically or epidemiologically significant.

Objective

To describe the investigation of a statewide anomaly detected by a newly established state syndromic surveillance system and usage of that system.

Submitted by dbedford on
Description

The NJ syndromic surveillance system, EpiCenter, developed an algorithm to quantify HRI visits using chief complaint data. While heat advisories are released by the National Weather Service, an effective HRI algorithm could provide real-time health impact information that could be used to provide supplemental warnings to the public during a prolonged heat wave.

Objective:

The purpose of this evaluation is to characterize the relationship between a patient’s initial hospital emergency room chief complaint potentially related to a heat-related illness (HRI) with final primary and secondary ICD-9 diagnoses.

 

Submitted by Magou on
Description

Syndromic surveillance uses near-real-time emergency department and other health care data for enhancing public health situational awareness and informing public health activities. In recent years, continued progress has been made in developing and strengthening syndromic surveillance activities. At the national level, syndromic surveillance activities are facilitated by the National Syndromic Surveillance Program (NSSP), a collaboration among state and local health departments, the CDC, other federal organizations, and other organizations that enabled collection of syndromic surveillance data in a timely manner, application of advanced data monitoring and analysis techniques, and sharing of best practices. This panel will highlight the importance of success stories. Examples of successes from state and local health departments will be presented and the audience will be encouraged to provide feedback.

Objective:

This panel will: 

  • Discuss the importance of identifying and developing success stories
  • Highlight successes from state and local health departments to show how syndromic surveillance activities enhance situational awareness and address public health concerns
  • Encourage discussion on how to further efforts for developing and disseminating success stories.
Submitted by elamb on
Description

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.

Submitted by teresa.hamby@d… on