ESSENCE

In recent years, the number of deaths from illicit and prescription opioids has increased significantly resulting in a national and local public health crisis. According to the Texas Center for Health Statistics, there were 1340 opioid related deaths in 2015.1 In 2005, by comparison, there were 913 opioid related deaths. Syndromic surveillance can be used to monitor overdose trends in near real-time and provide much needed information to public health officials. Texas Syndromic Surveillance (TxS2) is the statewide syndromic surveillance system hosted by the Texas Department of State Health Services (DSHS). To enhance the capabilities of TxS2 and to better understand the opioid epidemic, DSHS is integrating both Texas Poison Center (TPC) data and Emergency Medical Services (EMS) data into the system. Much of the data collected at public health organizations can be several years old by the time it is released for public use. As a result, there have been major efforts to integrate more real-time data sources for a variety of surveillance needs and during emergency response activities.

Objective: To improve Texas Syndromic Surveillance by integrating data from the Texas Poison Center and Emergency Medical Services for opioid overdose surveillance.

Since 2008, drug overdose deaths exceeded the number of motor vehicle traffic-related deaths in Indiana, and the gap continues to widen1. While federal funding opportunities are available for states, it often takes years for best practices to be developed, shared, and published. Similarly, local health departments (LHDs) may experience lengthy delays to receive finalized county health statistics. Indiana collects and stores syndromic emergency department data in the Public Health Emergency Surveillance System (PHESS) and uses the Electronic Surveillance System for the Early Notification of Community-based Epidemics version 1.21 (ESSENCE) to monitor public health events and trends. In July 2017, the Indiana Overdose Surveillance Team (IOST) developed a standard process for monitoring and alerting local health partners of increases in drug overdoses captured in ESSENCE at the county level. ISDH is enhancing these alerts by mapping the data in GIS and providing spatiotemporal data to LHDs to inform more targeted intervention and prevention efforts.

Objective: This poster presentation shares Indiana's approach of alerting local health departments (LHDs) with near real-time drug overdose data and how this process has been enhanced through mapping and analysis with a geographic information system (GIS).

In the past 15 years, public health surveillance has undergone a revolution driven by advances in information technology (IT) with vast improvements in the collection, analysis, visualization, and reporting of health data. Mobile technologies and open source software have played a key role in advancing surveillance techniques, particularly in resource-limited settings. Johns Hopkins University Applied Physics Laboratory (JHU/APL) is an internationally recognized leader in the area of electronic disease surveillance. In addition to the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) used by several state and local jurisdictions and the CDC in the U.S., JHU/APL has also developed the Suite for Automated Global Electronic bioSurveillance (SAGES). SAGES is a collection of modular, open-source software tools designed to meet the challenges of electronic disease surveillance in resource-limited settings. JHU/APL is working with the Peruvian Navy health system to improve their electronic disease surveillance capabilities. The Peruvian Navy currently uses a SAGES-based system called Alerta DISAMAR that was implemented several years ago in an effort supported by the Armed Forces Health Surveillance Branch, and in collaboration with the Naval Medical Research Unit No. 6 (NAMRU-6). The system uses both web-based and IVR-based (interactive voice response) data collection from several Navy health facilities in Peru. For the present effort, JHU/APL is implementing a new SMS-based data collection capability for the Peruvian Navy.

Objective: To introduce SMS-based data collection into the Peruvian Navy’s public health surveillance system for increased reporting rates and timeliness, particularly from remote areas, as well as improve capabilities for analysis of surveillance data by decision makers.

Syndromic surveillance has become an integral component of public health surveillance efforts within the state of Florida. The near real-time nature of these data are critical during events such as the Zika virus outbreak in Florida in 2016 and in the aftermath of Hurricane Irma in 2017. Additionally, syndromic surveillance data are utilized to support daily reportable disease detection and other surveillance efforts. Although syndromic systems typically utilize emergency department (ED) visit data, ESSENCE-FL also includes data from non-traditional sources: urgent care center visit data, mortality data, reportable disease data, and Florida Poison Information Center Network (FPICN) data. Inclusion of these data sources within the same system enables the broad accessibility of the data to more than 400 users statewide, and allows for rapid visualization of multiple data sources in order to address public health needs. Currently, the ESSENCE-FL team is actively working to incorporate EMS data into ESSENCE-FL to further increase public health surveillance capacity and data visualization.

Objective: To describe the strategy and process used by the Florida Department of Health (FDOH) Bureau of Epidemiology to onboard emergency medical services (EMS) data into FDOH’s syndromic surveillance system, the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL).

Unlike other health threats of recent concern for which widespread mortality was hypothetical, the high fatality burden of opioid overdose crisis is present, steadily growing, and affecting young and old, rural and urban, military and civilian subpopulations. While the background of many public health monitors is mainly infectious disease surveillance, these epidemiologists seek to collaborate with behavioral health and injury prevention programs and with law enforcement and emergency medical services to combat the opioid crisis. Recent efforts have produced key terms and phrases in available data sources and numerous user-friendly dashboards allowing inspection of hundreds of plots. The current effort seeks to distill and present combined fusion alerts of greatest concern from numerous stratified data outputs. Near-term plans are to implement best-performing fusion methods as an ESSENCE module for the benefit of OHA staff and other user groups.

Objective: In a partnership between the Public Health Division of the Oregon Health Authority (OHA) and the Johns Hopkins Applied Physics Laboratory (APL), our objective was develop an analytic fusion tool using streaming data and report-based evidence to improve the targeting and timing of evidence-based interventions in the ongoing opioid overdose epidemic. The tool is intended to enable practical situational awareness in the ESSENCE biosurveillance system to target response programs at the county and state levels. Threats to be monitored include emerging events and gradual trends of overdoses in three categories: all prescription and illicit opioids, heroin, and especially high-mortality synthetic drugs such as fentanyl and its analogues. Traditional sources included emergency department (ED) visits and emergency management services (EMS) call records. Novel sources included poison center calls, death records, and report-based information such as bad batch warnings on social media. Using available data and requirements analyses thus far, we applied and compared Bayesian networks, decision trees, and other machine learning approaches to derive robust tools to reveal emerging overdose threats and identify at-risk subpopulations.