Deyneka Lana

North Carolina hosted the 2012 Democratic National Convention, September 3-6, 2012. The NC Epidemiology and Surveillance Team was created to facilitate enhanced surveillance for injuries and illnesses, early detection of outbreaks during the DNC, assist local public health with epidemiologic investigations and response, and produce daily surveillance reports for internal and external stakeholders. Surveillane data were collected from several data sources, including North Carolina Electronic Disease Surveillance System (NC EDSS), triage stations, and the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT). NC DETECT was created by the North Carolina Division of Public Health (NC DPH) in 2004 in collaboration with the Carolina Center for Health Informatics (CCHI) in the UNC Department of Emergency Medicine to address the need for early event detection and timely public health surveillance in North Carolina using a variety of secondary data sources. The data from emergency departments, the Carolinas Poison Center, the Pre-hospital Medical Information System (PreMIS) and selected Urgent Care Centers were available for monitoring by authorized users during the DNC.

Objective:

To describe how the existing state syndromic surveillance system (NC DETECT) was enhanced to facilitate surveillance conducted at the Democratic National Convention in Charlotte, North Carolina from August 31, 2012 to September 10, 2012.

 

Syndromic surveillance systems offer richer understanding of population health. However, because of their complexity, they are less used at small public health agencies, such as many local health departments (LHDs). The evolution of these systems has included modifying user interfaces for more efficient and effective use at the local level. The North Carolina Preparedness and Emergency Response Research Center previously evaluated use of syndromic surveillance information at LHDs in North Carolina. Since this time, both the NC DETECT system and distribution of syndromic surveillance information by the state public health agency have changed. This work describes use following these changes.

Objective

Our objective was to describe changes in use following syndromic surveillance system modifications and assess the effectiveness of these modifications.

 

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.

Typical approaches to monitoring ED data classify cases into pre-defined syndromes and then monitor syndrome counts for anomalies. However, syndromes cannot be created to identify every possible cluster of cases of relevance to public health. To address this limitation, NC DETECT’s approach clusters cases by arrival times and monitors the textual chief complaint data associated with each identified cluster for relevant similarities [1]. This approach is time consuming and limited in its ability to detect emerging outbreaks that are dispersed across time. A new method is needed to automatically identify clusters of interest that would not be detected by existing syndromes. Clusters may be based on symptoms, events, place names, arrival time, or hospital location. The NC DPH dataset describes 198,511 de-identified ED visits over one year at 3 North Carolina hospitals. The data include chief complaint, altered date and time of arrival, hospital A/B/C, and age group. About 40 simulated outbreaks were injected into the data set by the NC DETECT team. For example, an inject cluster might consist of 4 patients who report getting sick after eating at a particular restaurant.

Objective

We apply a novel semantic scan statistic approach to solve a problem posed by the NC DETECT team, North Carolina Division of Public Health (NC DPH) and UNC Department of Emergency Medicine Carolina Center for Health Informatics, and facilitated by the ISDS Technical Conventions Committee. This use case identifies a need for methodology that detects emerging, potentially novel outbreaks in free-text emergency department (ED) chief complaint data.

 

Per a frequently asked questions document on the ISDS website, approximately two thirds of HL7 records received in BioSense do not provide a Visit ID. As a result, BioSense data processing rules use the patient ID, facility ID and earliest date in the record to identify a unique visit. If the earliest dates in records with the same patient ID and facility ID occur within the same 24-hour time frame, those two visits are combined into one visit and the earliest date will be stored. The ED data sent by hospitals to NC DETECT include unique visit IDs and these are used to identify unique visits in NC DETECT. These data are also sent twice daily to BioSense. In order to assess the potential differences between the NC DETECT ED data in NC DETECT and the NC DETECT ED data in BioSense, an initial analysis of the 24-hour rule was performed.

Objective

NC DETECT emergency department (ED) data were analyzed to assess the impact of applying the BioSense “24-hour rule” that combines ED visits into a single visit if the patient ID and facility ID are the same and the earliest recorded dates occur within the same 24-hour time frame.

The advent of Meaningful Use (MU) has allowed for the expansion of data collected at the hospital level and received by public health for syndromic surveillance. The triage note, a free text expansion on the chief complaint, is one of the many variables that are becoming commonplace in syndromic surveillance data feeds. Triage notes are readily available in many ED information systems, including, but not limited to, Allscripts, Cerner, EPIC, HMS, MedHost, Meditech, and T-System. North Carolina’s syndromic surveillance system, NC DETECT, currently collects triage notes from 33 out of 122 hospitals in the State (27%), and this number is likely to increase.

Objective

This roundtable will provide a forum for the ISDS community to discuss the use of emergency department (ED) triage notes in syndromic surveillance. It will be an opportunity to discuss both the benefits of having this variable included in syndromic surveillance data feeds, as well as the drawbacks and challenges associated with working with such a detailed data field.

Recreational drug use is a major problem in the United States and around the world. Specifically, drug abuse results in heavy use of emergency department (ED) services, and is a high financial burden to society and to the hospitals due to chronic ill health and multiple injection drug use complications. Intravenous drug users are at high risk of developing sepsis and endocarditis due to the use of a dirty or infected needle that is either shared with someone else or re-used. It can also occur when a drug user repeatedly injects into an inflamed and infected site or due to the poor overall health of an injection drug user. The average cost of hospitalization for aortic valve replacement in USA is about $165,000, and in order for the valve replacement to be successful, patients must abstain from using drugs.

Objective

To describe how the state syndromic surveillance system (NC DETECT) was used to initiate near real time surveillance for endocarditis, sepsis and skin infection among drug users.

CPC provides the 24/7/365 poison hotline for the entire state of North Carolina and currently handles approximately 80,000 calls per year. CPC consultation services that assist callers with poison exposure, diagnosis, optimal patient management, therapy, and patient disposition guidance remain indispensable to the public and health care providers. Poison control center data have been used for years in syndromic surveillance practice as a reliable data source for early event detection. This information has been useful for a variety of public health issues, including environmental exposures, foodborne diseases, overdoses, medication errors, drug identification, drug abuse trends and other information needs. The North Carolina Department of Health and Human Services started formal integration of CPC information into surveillance activities in 2004. CPC call data are uploaded in real time (hourly), 24/7/365, to the NC DETECT state database.

Objective

To describe Carolinas Poison Control Center (CPC) calls data collected in the NC DETECT syndromic surveillance system.