Coletta Michael

The National Syndromic Surveillance Program's (NSSP) instance of ESSENCE* in the BioSense Platform generates about 35,000 statistical alerts each week. Local ESSENCE instances can generate as many as 5,000 statistical alerts each week. While some states have well-coordinated processes for delegating data and statistical alerts to local public health jurisdictions for review, many do not have adequate resources. By design, statistical alerts should indicate potential clusters that warrant a syndromic surveillance practitioner's time and focus. However, practitioners frequently ignore statistical alerts altogether because of the overwhelming volume of data and alerts. In 2008, staff in the Virginia Department of Health experimented with rules that could be used to rank the statistical output generated in ESSENCE alert lists. Results were shared with Johns Hopkins University Applied Physics Lab (JHU/APL), the developer of ESSENCE, and were early inputs into what is now known as "myAlerts," an ESSENCE function that syndromic surveillance practitioners can use to customize alerting and sort through statistical noise. NSSP ESSENCE produces a shared alert list by syndrome, county, and age-group strata, which generates an unwieldy but rich data set that can be studied to learn more about the importance of these statistical alerts. Ultimately, guidance can be developed to help syndromic surveillance practitioners set up meaningful ESSENCE myAlerts effective in identifying clusters with public health importance.

Objective: Find practical ways to sort through statistical noise in syndromic data and make use of alerts most likely to have public health importance.

In 2016, a half million people were treated in U.S. emergency departments (EDs) as a result of self-harm. 1 Not only is self-harm a major cause of morbidity in the U.S., but it is also one of the best predictors of suicide. Given that approximately 40% of suicide decedents visited an ED in the year prior to their death and that the majority of medically-serious self-harm patients are treated in EDs2, EDs serve as a critical setting in which to monitor rates and trends of suicidal behavior. To date, the majority of ED data for self-harm are generally two to three years old and thereby can only be used to describe historical patterns in suicidal behavior. Thus, in 2018, a syndrome definition for suicide attempts and suicidal ideation (SA/SI) was developed by the International Society for Disease Surveillance (ISDS) Syndrome Definition Committee in conjunction with Centers for Disease Control and Prevention (CDC) staff, allowing researchers to better monitor recent trends in medically treated suicidal behavior using data from the CDC's National Syndromic Surveillance Program (NSSP). These data serve as a valuable resource to help detect deviations from typical patterns of SA/SI and can help drive public health response if atypical activity, such as geospatial or temporal clusters of SA/SI, is observed. Such patterns may be indicative of suicide contagion (i.e., exposure to the suicide or suicidal behavior of a friend or loved one, or through media content, that may put individuals at increased risk of suicidal behavior). Research has demonstrated that suicide contagion is a real phenomenon. 3 13 Reasons Why is a Netflix series focused on social, school, and family-related challenges experienced by a high school sophomore; each episode in the 13-episode series describes a problem faced by the main character, which she indicates contributed to her decision to die by suicide. The series premiered March 31, 2017 and is rated TV-MA by TV Parental Guidelines4 (may be unsuitable for those under age 18 years due to graphic content). Nevertheless, the series has become popular among youth under 18 years of age. Of note, in the final episode, the main character'™s suicide by wrist laceration is graphically depicted. Following the premiere of the series, researchers and psychologists across the U.S. expressed concern that this graphic depiction of suicide could result in a contagion effect, potentially exacerbating suicidal thoughts and behavior among vulnerable youth viewers. To date, the only empirical data demonstrating the potential iatrogenic effects of this graphic portrayal of suicide comes from a study of Google Trends data demonstrating an increase in online suicide queries in the weeks following the show, with most of the queries focusing on suicidal ideation (e.g., how to commit suicide, how to kill yourself).5 However, there has been no study to examine changes in nonfatal self-harm trends following the series debut.

Objective: To describe national-level trends in nonfatal self-harm and suicidal ideation among 10-19 year old youth from January 2016 through December 2017 and examine the impact of popular entertainment on suicidal behavior.

Syndromic surveillance systems, although initially developed in response to bioterrorist threats, are increasingly being used at the local, state, and national level to support early identification of infectious disease and other emerging threats to public health. To facilitate detection, one of the goals of CDC's National Syndromic Surveillance Program (NSSP) is to develop and share new sets of syndrome codes with the syndromic surveillance Community of Practice. Before analysts, epidemiologists, and other practitioners begin customizing queries to meet local needs, especially monitoring ED visits in near-real time during public health emergencies, they need to understand how syndromes are developed. More than 4,000 hospital routinely send data to NSSP's BioSense Platform, representing about 55 percent of ED visits in the United States (2). The platform's surveillance component, ESSENCE,* is a web-based application for analyzing and visualizing prediagnostic hospital ED data. ESSENCE's Chief Complaint Query Validation (CCQV) data source, which is a national-level data source with access to chief complaint (CC) and discharge diagnoses (DD) from reporting sites, was designed for testing new queries.

Objective: Emergency department (ED) visits related to mental health (MH) disorders have increased since 2006 (1), indicating a potential burden on the healthcare delivery system. Surveillance systems has been developed to identify and understand these changing trends in how EDs are used and to characterize populations seeking care. Many state and local health departments are using syndromic surveillance to monitor MH-related ED visits in near real-time. This presentation describes how queries can be created and customized to identify select MH sub-indicators (for adults) by using chief complaint text terms and diagnoses codes. The MH sub-indicators examined are mood and depressive disorders, schizophrenic disorders, and anxiety disorders. Wider adoption of syndromic surveillance for characterizing MH disorders can support long-term planning for healthcare resources and service delivery.

Although the advent of the ONCs "meaningful use" criteria has added significant new incentives for healthcare organizations to provide the necessary data for implementing syndromic surveillance, incentives alone are not sufficient to sustain a robust community of practice that engages public health and healthcare practitioners working together to fully achieve meaningful use objectives. The process for building a successful community of practice around syndromic surveillance is primarily application-agnostic. The business process has many of the same characteristics regardless of application features, and can be incrementally customized for each community based on the unique needs and opportunities and the functional characteristics of the application. This presentation will explore lessons-learned in the north central Texas region with BioSense 1 and ESSENCE over the past six years, and will describe the multi-phase process currently underway for BioSense 2.0. Key program process steps and success criteria for public health and healthcare practitioners will be described. This road map will enable other local health department jurisdictions to replicate proven methodologies in their own communities. The presentation will also highlight what it takes for an existing community of practice with a home grown system to move processes and protocols to the cloud.

Objective

To explore the lessons learned from the Advanced Practice Center methodology regarding the implementation of syndromic surveillance while considering what it takes to create, enhance, and sustain relationships between hospitals, public health practitioners, and the community.

To date, most syndromic surveillance systems rely heavily on complicated statistical algorithms to identify aberrations. The assumption is that when the statistics identify something unusual, follow-up should occur. However, with multiple strata analyzed, small numbers for some strata, and wide variances in daily counts, the statistical algorithms will generate flags too often. Experience has shown that these flags usually have little or no public health significance. As a result, syndromic surveillance systems suffer from the ‘boy who cried wolf’ syndrome. It is clear that the analyst’s ability to use professional judgment to sift through multitudes of flags is very important to the success of the system, which suggests that statistics alone cannot identify issues of public health importance from ED data.

Objective

This study's aim was to refine an automated biosurveillance system in order to better suit the daily monitoring capabilities and resources of a health department.

As system users develop queries within ESSENCE, they step through the user-interface to select data sources and parameters needed for their query. Then they select from the available output options (e.g., time series, table builder, data details). These activities execute a SQL query on the database, the majority of which are saved in a log so that system developers can troubleshoot problems. Secondarily, these data can be used as a form of web analytics to describe user query choices, query volume, query execution time, and develop an understanding of ESSENCE query patterns.

Objective:

The objective of this work is to describe the use and performance of the NSSP ESSENCE system by analyzing the structured query language (SQL) logs generated by users of the National Syndromic Surveillance Program'™s (NSSP) Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE).

In this panel, the presenters will discuss their perspective in responding to Hurricanes Harvey and Irma. Hurricane Harvey made landfall on August 25th and over the course of 4 days dropped approximately 27 trillion gallons of water on Texas and Louisiana. The flooding that ensued was unprecedented and forced over 13,000 people into shelters. These individuals needed to have their basic needs -food, shelter, clothing, sanitation- met as well as their physical and mental health needs. The George R Brown Conference Center (GRB) and NRG Stadium Center were set up as mega-shelters to house shelterees. Hurricane Irma made landfall on September 10th in the Florida Keys as a Category 4 Hurricane. The Hurricane caused 72 deaths and forced thousands of people into shelters. These weather events created novel challenges for local response efforts. Decision makers needed timely and actionable data, including surveillance data.

Objective:

In this panel, attendees will learn about how disaster surveillance was conducted in response to Hurricanes Irma and Harvey, as well as the role of CDC at the federal level in supporting local response efforts. By hearing and discussing the challenges faced and solutions identified, attendees will be better able to respond in the event of a low-frequency/high-consequence disaster occurring within their jurisdiction.

Overdose deaths involving opioids (i.e., opioid pain relievers and illicit opioids such as heroin) accounted for at least 63% (N = 33,091) of overdose deaths in 2015. Overdose deaths related to illicit opioids, heroin and illicitly-manufactured fentanyl, have rapidly increased since 2010. For instance, heroin overdose deaths quadrupled from 3,036 in 2010 to 12,989 in 2015. Unfortunately, timely response to emerging trends is inhibited by time lags for national data on both overdose mortality via vital statistics (8-12 months) and morbidity via hospital discharge data (over 2 years). Emergency department (ED) syndromic data can be leveraged to respond more quickly to emerging drug overdose trends as well as identify drug overdose outbreaks. CDC’s NSSP BioSense Platform collects near real-time ED data on approximately two-thirds of ED visits in the US. NSSP’s data analysis and visualization tool, Electronic Surveillance System for the Notification of Community-based Epidemics (ESSENCE), allows for tailored syndrome queries and can monitor ED visits related to heroin overdose at the local, state, regional, and national levels quicker than hospital discharge data.

Objective:

This paper analyzes emergency department syndromic data in the Centers for Disease Control and Prevention's (CDC) National Syndromic Surveillance Program’s (NSSP) BioSense Platform to understand trends in suspected heroin overdose.