Ising Amy

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.

 

The International Society for Disease Surveillance held its eleventh annual conference in San Diego on December 4th and 5th, 2012, under the theme Expanding Collaborations to Chart a New Course in Public Health Surveillance. During these two days, practitioners and researchers across many disciplines gathered to share best practices, lessons learned and cutting edge approaches to timely disease surveillance. A record number of abstracts were received, reviewed and presented – the schedule included 99 orals, 4 panels, 94 posters, 5 roundtables and 12 system demonstrations. Presenters represented 24 different countries from Africa, North and South America, Europe, and Asia . Topics covered included, but were not limited to, statistical methods for outbreak detection, border health, data quality, evaluation of novel data streams, influenza surveillance, best practices and policies for information sharing, social network analysis, data mining techniques, surveillance during weather events and mass gatherings, syndrome development, and novel uses of syndromic surveillance data. There were also discussions on the impact of regulations and standards development on disease surveillance, including Meaningful Use and the International Health Regulations.

The 2012 Conference was also host to several exciting keynote and plenary talks, including those given by James Fowler, Professor of Medical Genetics and Political Science at the University of California, San Diego and Bill Davenhall, Global manager of Esri's Health and Human Service Solutions Group. Plenary speakers Steve Waterman, Centers for Disease Control and Prevention (CDC); Simon Hay, University of Oxford; and Brian McCloskey, Health Protection Agency in London, reflected on the importance of effective collaborations in their respective topics of migrant and border health, malaria disease epidemiology and mass gathering health. National and international representatives from the CDC, the World Health Organization and the Department of Homeland Security also discussed their respective strategic plans for disease surveillance.

In addition, the 2012 Data Visualization Event enabled conference attendees to collaborate and gain knowledge of visualization tools and techniques applied to a rich, de-identified set of ambulatory electronic health record (EHR) data. Participants developed visualizations of chronic disease events using this common data set and presented their work during the evening poster session. The goals for this event were to demonstrate and share visualization tools and techniques that attendees could learn to apply to their own data and also to provide exposure to data elements available in ambulatory EHR systems and highlight their potential for surveillance and research.

My hope is that attendees of the 2012 ISDS Conference strengthened existing collaborations and fostered new ones, and returned to their places of work or study energized with new ideas and approaches to disease surveillance. The challenge for all of us is to sustain this new energy throughout the coming year and to leverage the tools available to us to share best practices and reach out for assistance when needed. We all want to improve the health of our populations, and collaborations will enable us to achieve that goal.

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.

 

LHDs are operating in a changing data environment. As household telephone use declines, national surveys are not sampling large enough populations to report representative local health statistics. As a result, reliable indicators from surveys such as the Behavioral Risk Factors Surveillance Survey (BRFSS) are becoming scarce. Soon, these indicators may not be sufficient for county assessments. NC DETECT primarily uses data from emergency departments, the Carolinas Poison Center, and the Pre-hospital Medical Information System (PreMIS) to identify outbreaks and facilitate emergency response. However, while built to aggregate “real-time” data, NC DETECT also provides a source for rich, long-term indicators. The challenge for LHDs is that they may not have the knowledge, training, or technical assistance needed to fully utilize NC DETECT services. This project capitalizes on available human, organizational, and technical resources to increase LHD situational awareness and to demonstrate the usefulness of both “real-time” surveillance data as aggregate indicators of county health, and of low-cost prototyping using Excel’s more advanced Business Intelligence (BI) features.

Objective

This project aims to fill a growing county-level health data gap through the development of a low-cost, Excel-based surveillance tool. This prototype utilizes emergency department data (ED) collected by NC DETECT, a state-wide syndromic surveillance system, in order to visualize, monitor, and compare annual local health indicators for use in local decision making. In this way, the project aims to increase noncommunicable disease surveillance capacity and improve situational awareness within North Carolina local health departments (LHDs).