Falls Dennis

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.

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) provides early event detection and public health situational awareness to hospital-based and public health users statewide. Authorized users are currently able to view data from emergency departments (n=110), the statewide poison control center, the statewide EMS data system, a regional wildlife center and pilot data from a college veterinary laboratory as well as select urgent care centers. While NC DETECT has over 200 registered users, there are public health officials, hospital clinicians and administrators who do not access the system on a regular basis, but still like to be kept abreast of syndromic trends in their jurisdictions. In order to accommodate this interest and reduce redundant data entry among active users, we developed a summary report that can be easily exported and distributed outside of NC DETECT.

Objective

This paper describes a user driven weekly syndromic report designed and developed to improve the efficiency of sharing syndromic information statewide.

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) serves public health users across NC at the local, regional and state levels, providing early event detection and situational awareness capabilities. At the state level, our primary users are in the General Communicable Disease Control Branch of the NC Division of Public Health. NC DETECT receives 10 different data feeds daily including emergency department visits, emergency medical service runs, poison center calls, veterinary laboratory test results, and wildlife treatment.

In order to fulfill our users’ needs with NC DETECT’s limited staff, business intelligence tools are utilized for the acquisition and processing of our multiple, disparate data sources as well as reporting our findings to our numerous end users. Business intelligence can be described as a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.

Objective

We report here on how NC DETECT uses business intelligence tools to automate both data capture and reporting in order to run a comprehensive surveillance system with limited resources.

NC BEIPS is a system designed and developed by the NC Division of Public Health (DPH) for early detection of disease and bioterrorism outbreaks or events. It analyzes emergency department (ED) data on a daily basis from 33 (29%) EDs in North Carolina. With a new mandate requiring the submission of ED data to DPH, NC BEIPS will soon have data from all 114 EDs. NC BEIPS also receives data on a daily basis from the Carolinas Poison Center, the Prehospital Medical Information System and the Piedmont Wildlife Center, although syndromic surveillance output from these data sources is still in testing.

Objective

 This paper describes the North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS). NC BEIPS is the syndromic surveillance arm of NC PHIN.

The North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS) serves public health users across North Carolina at the local, regional and state levels, providing syndromic surveillance capabilities.  At the state level, our primary users are in the General Communicable Disease Control Branch of the NC Division of Public Health.  NC BEIPS currently receives daily data from the North Carolina Emergency Department Database (NCEDD), Carolina Poison Control Center (CPC), Prehospital Medical Information System (PreMIS) and the Piedmont Wildlife Center (PWC). Future data sources will include the North Carolina State University College of Veterinary Medicine Laboratories.  The PWC is a non-profit organization dedicated to wildlife rehabilitation, education, and scientific study of health and disease in wildlife populations.  PWC admits approximately 3,000 animals annually, including mammals, birds, and reptiles, the majority of which are from 21 counties in central North Carolina.  

Objective

This poster will illustrate how a novel data source, wildlife health center data, is being incorporated and used in a syndromic surveillance system.

Text-based syndrome case definitions published by the Center for Disease Control (CDC)1 form the basis for the syndrome queries used by the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT). Keywords within these case definitions were identified by public health epidemiologists for use as search terms with the goal of capturing symptom complexes from free-text chief complaint and triage note data for the purpose of early event detection and situational awareness. Initial attempts at developing SQL queries incorporating these search terms resulted in the return of many unwanted records due to the inability to control for certain terms imbedded within unrelated free text strings. For example, a query containing the search term “h/a”, a common abbreviation for headache, also returns false positives such as “cough/asthma”, “skin rash/allergic reaction” or “psych/anxiety”.  Simple abbreviations without punctuation, such as “ha”, were even more problematic.  Global wildcards ('%') indicate that zero or more characters of any type may substitute for the wildcard.2 The term “ha” as a synonym for "headache" appears frequently in the data, but searching this term bracketed by global wildcards returns any instance where the two letters appear together (e.g. pharyngitis, hand, hallucinations, toothache). Using global wild cards to search for common symptoms such as headache using simple abbreviations, with or without specialized punctuation, results in the return of many unwanted false positive records. We describe here the advanced application of SQL character set wildcards to address this problem.

Objective

This paper describes a novel approach to the construction of syndrome queries written in Structured Query Language (SQL). Through the advanced application of character set wildcards, we are able to increase the number of valid records identified by our queries while simultaneously decreasing the number of false positives.