Ising Amy

North Carolina hosted the 2018 FEI WEG in Polk County at the Tryon Equestrian Center in September 2018. Polk County, located in the Mountain region of Western North Carolina, is home to 20,357 people, and the population is widely distributed. Event organizers expected approximately 300,000 to 500,000 people to visit the area, with 800 horses from 71 countries taking part in the games. Providing adequate public health epidemiologic investigations and response for the large scale event in the predominantly rural area presented a challenge. The NC Surveillance & Response Team was created to facilitate enhanced surveillance for significant public health events during the WEG, assist local public health agencies with epidemiologic investigations and response, develop public health risk assessments, and implement control measures. Surveillance data were collected from the North Carolina Electronic Disease Surveillance System (NC EDSS), North Carolina’s and CDC’s National syndromic surveillance systems (NC DETECT and NSSP ESSENCE), Public Health Epidemiologists from Atrium Health and Mission Hospital, and reports from the on-site medical facility (MED-1) at the Tryon Equestrian Center. The data were reviewed and summarized in internal and external situation reports.

Objective: To describe surveillance activities and use of existing state (NC DETECT) and national (NSSP) syndromic surveillance systems during the International Federation for Equestrian Sports (FEI) World Equestrian Games (WEG), in Mill Spring, NC from September 11 to September 23, 2018

The opioid overdose crisis has rapidly expanded in North Carolina (NC), paralleling the epidemic across the United States. The number of opioid overdose deaths in NC has increased by nearly 40% each year since 2015.1 Critical to preventing overdose deaths is increasing access to the life-saving drug naloxone, which can reverse overdose symptoms and progression. Over 700 EMS agencies across NC respond to over 1,000,000 calls each year; naloxone administration was documented in over 15,000 calls in 2017.2 Linking EMS encounters with naloxone administration to the corresponding ED visit assists in understanding the health outcomes of these patients. However, less than 66% of NC EMS records with naloxone administration in 2017 were successfully linked to an ED visit record. This study explored methods to improve EMS and ED data linkage, using a multistage process to maximize the number of correctly linked records while avoiding false linkages.

Objective: To improve linkage between North Carolina's Emergency Medical Services (EMS) and Emergency Department (ED) data using an iterative, deterministic approach.

Suicide is a leading cause of mortality in the United States, causing about 45,000 deaths annually. Research suggests that universal screening in health care settings may be beneficial for prevention, but few studies have combined detailed suicide circumstances with ED encounter data to better understand care-seeking behavior prior to death.

Objective: To identify potential emergency department (ED) visits prior to suicide deaths in North Carolina (NC) and describe pre-suicide care-seeking in EDs.

Over the last few decades, the United States has made considerable progress in decreasing the incidence of motor vehicle occupants injured and killed in traffic collisions.1 However, there is still a need for continued motor vehicle crash (MVC) injury surveillance, particularly for vulnerable road users, such as pedestrians and bicyclists. In NC, the average annual number of pedestrian-motor vehicle crashes increased by 13.5 percent during the period 2011-2015, as compared to 2006-2010.2 Therefore, the Carolina Center for Health Informatics (CCHI), as part of a NC Governor's Highway Safety Program-funded project to improve statewide MVC injury surveillance, developed and evaluated four ICD-10-CM based case definitions for use with NC DETECT, NC's statewide syndromic surveillance system.

Objective: To evaluate four ICD-10-CM based case definitions designed to capture pedestrian and bicycle crash-related emergency department (ED) visits in North Carolina's statewide syndromic surveillance system, NC DETECT.

Despite considerable effort since the turn of the century to develop Natural Language Processing (NLP) methods and tools for detecting negated terms in chief complaints, few standardised methods have emerged. Those methods that have emerged (e.g. the NegEx algorithm) are confined to local implementations with customised solutions. Important reasons for this lack of progress include (a) limited shareable datasets for developing and testing methods (b) jurisdictional data silos, and (c) the gap between resource-constrained public health practitioners and technical solution developers, typically university researchers and industry developers. To address these three problems ISDS, funded by a grant from the Defense Threat Reduction Agency, organized a consultancy meeting at the University of Utah designed to bring together (a) representatives from public health departments, (b) university researchers focused on the development of computational methods for public health surveillance, (c) members of public health oriented non-governmental organisations, and (d) industry representatives, with the goal of developing a roadmap for the development of validated, standardised and portable resources (methods and data sets) for negation detection in clinical text used for public health surveillance.

Objective: This abstract describes an ISDS initiative to bring together public health practitioners and analytics solution developers from both academia and industry to define a roadmap for the development of algorithms, tools, and datasets to improve the capabilities of current text processing algorithms to identify negated terms (i.e. negation detection).

The field of syndromic surveillance has received increased attention over the past decade as an expansion of traditional disease detection methods. There is, however, little or no consensus, regarding a standard definition encompassing the full scope of the term 'syndromic surveillance'. Several researchers have proposed at least 36 alternative names to differentiate various forms of syndromic surveillance but none has taken hold (including early warning, health indicator surveillance, enhanced surveillance, among others). Katz et al presented a redefining of syndromic surveillance as two overarching categories of 'syndrome based'“ versus 'syndrome non-specific'“ surveillance1. In addition, the Meaningful Use Stage 2 standard for syndromic surveillance includes both pre-diagnostic and diagnostic data elements, further broadening the scope of this surveillance method.

Objective

To provide a forum for stakeholders from various sectors of syndromic surveillance research and practice to discuss and establish a more accurate and comprehensive yet succinct definition of syndromic surveillance, based on lessons learned and innovations in public health surveillance practice.

Time-of-arrival (TOA) surveillance methodology consists of identifying clusters of patients arriving to a hospital emergency department (ED) with similar complaints within a short temporal interval. TOA monitoring of ED visit data is currently conducted by the Florida Department of Health at the county level for multiple subsyndromes [1]. In 2011, North Carolina's NC DETECT system and CDC's Biosense Program collaborated to enhance and adapt this capability for 10 hospital-based Public Health Epidemiologists (PHEs), an ED-based monitoring group established in 2003, for North Carolina's largest hospital systems. At the present time, PHE hospital systems include coverage for approximately 44% of the statewide general/acute care hospital beds and 32% of all emergency department visits statewide. We present findings from TOA monitoring in one hospital system.

Objective

To describe collaborations between North Carolina Division of Public Health and the Centers for Disease Control and Prevention (CDC) implementing time-of-arrival (TOA) surveillance to monitor for exposure-related visits to emergency departments (ED) in small groups of North Carolina hospitals.

The North Carolina Division of Public Health (NC DPH) has been collecting emergency department data in collaboration with the Carolina Center for Health Informatics in the UNC Department of Emergency Medicine since 1999. As of August 2011, there are 113 of 115 emergency departments sending data electronically at least once daily to NC DETECT. Data elements include disposition, initial vital signs, up to 11 ICD-9-CM final diagnosis codes, up to five external cause of injury codes (E-codes),as well as the arrival date and time, patient sex and age, patient zip and county, and chief complaint. As of January 2008, NC DETECT emergency department data covered 99% of the NC population and captures approximately 4.5 million ED visits each year. As a result, requests for data from researchers continue to increase. Use of the data for public health purposes is covered by the mandate requiring hospitals to submit their emergency department data to NC DPH.

Objective

To describe the process by which researchers request access to data sets of emergency department data from NC DETECT,the history of this process,and the resulting best practices and lessons learned.

NC DETECT provides near-real-time statewide surveillance capacity to local, regional and state level users across NC with twice daily data feeds from 117 (99%) emergency departments (EDs), hourly updates from the statewide poison center, and daily feeds from statewide EMS runs and select urgent care centers. The NC DETECT Web Application provides access to aggregate and line listing analyses customized to users' respective jurisdictions. The most active users are state-level epidemiologists (DPH) and hospital-based public health epidemiologists (PHEs). The use of NC DETECT is included in PHE job descriptions and NC DETECT functionality has been developed specifically to meet the surveillance needs of this group, including data entry of aggregated lab results for flu and respiratory panels. Interviews of local health department (LHD) users completed as part of an evaluation project have suggested that functionality specifically tailored to LHDs may increase their use of the NC DETECT Web application [1]. As of June 2011, there were 139 LHD users with active accounts to use the Web application (out of 384 total users with active accounts).

Objective

To describe the development, implementation and preliminary evaluation of new dashboard interfaces in NC DETECT, designed primarily for infrequent users of NC DETECT at local health departments.