Electronic Surveillance System For The Early Notification Of Community-based Epidemics Research Articles

Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE): Overview, Components, and Public Health Applications.

BackgroundThe Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) is a secure web-based tool that enables health care practitioners to monitor health indicators of public health importance for the detection and tracking of disease outbreaks, consequences of severe weather, and other events of concern. The ESSENCE concept began in an internally funded project at the Johns Hopkins University Applied Physics Laboratory, advanced with funding from the State of Maryland, and broadened in 1999 as a collaboration with the Walter Reed Army Institute for Research. Versions of the system have been further developed by Johns Hopkins University Applied Physics Laboratory in multiple military and civilian programs for the timely detection and tracking of health threats.ObjectiveThis study aims to describe the components and development of a biosurveillance system increasingly coordinating all-hazards health surveillance and infectious disease monitoring among large and small health departments, to list the key features and lessons learned in the growth of this system, and to describe the range of initiatives and accomplishments of local epidemiologists using it.MethodsThe features of ESSENCE include spatial and temporal statistical alerting, custom querying, user-defined alert notifications, geographical mapping, remote data capture, and event communications. To expedite visualization, configurable and interactive modes of data stratification and filtering, graphical and tabular customization, user preference management, and sharing features allow users to query data and view geographic representations, time series and data details pages, and reports. These features allow ESSENCE users to gather and organize the resulting wealth of information into a coherent view of population health status and communicate findings among users.ResultsThe resulting broad utility, applicability, and adaptability of this system led to the adoption of ESSENCE by the Centers for Disease Control and Prevention, numerous state and local health departments, and the Department of Defense, both nationally and globally. The open-source version of Suite for Automated Global Electronic bioSurveillance is available for global, resource-limited settings. Resourceful users of the US National Syndromic Surveillance Program ESSENCE have applied it to the surveillance of infectious diseases, severe weather and natural disaster events, mass gatherings, chronic diseases and mental health, and injury and substance abuse.ConclusionsWith emerging high-consequence communicable diseases and other health conditions, the continued user requirement–driven enhancements of ESSENCE demonstrate an adaptable disease surveillance capability focused on the everyday needs of public health. The challenge of a live system for widely distributed users with multiple different data sources and high throughput requirements has driven a novel, evolving architecture design.

Recent advances in the surveillance for measles: when will these be feasible in Africa?

Measles is a highly contagious viral infection with worldwide prevalence. It is transmitted by airborne route and has an incubation period is 8–12 days. Symptoms include fever, coryza, cough, rash, while signs include exudative conjunctivitis, maculopapular rashes. The population at risk for measles are those less than five years of age especially infants, those above 20 years of age, the immuno-suppressed and immune-compromised patients such as those with HIV/AIDS, those on steroids and chemotherapy, those with severe malnutrition. We aim to describe the different surveillance methods for measles including the traditional and modern methods, their evaluation and analysis. The traditional surveillance for measles includes over the counter sales of antipyretics and cough syrups, data from school absenteeism and school nurses, daily physician office visit, hospital ED visits, hospital admission data, nurse hotline data, emergency medical services and ambulance 911 calls and laboratory test requests. Measles surveillance can also be done using modern methods which can be used at the local, state and regional levels for the detection of measles outbreak. These modern methods include the Electronic Surveillance System for the early notification of community-based epidemics (ESSENCE), the real-time outbreak detection system (RODS), early aberration reporting system (EARS), Redbat and the syndrome reporting information system (SYRIS). Telehealth is another modern method for the surveillance of measles which detects outbreak of measles earlier regardless of how far the patient is from the healthcare centers. The various disease surveillance methods above are effective and beneficial in terms of early detection of outbreaks and situation analysis for measles and are hence recommended for use.

Use of ESSENCE APIs to Support Flexible Analysis and Reporting

ObjectiveTo describe and provide examples of the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) application programming interface (API) as a part of disease surveillance workflows.IntroductionThe ESSENCE application supports users’ interactive analysis of data by clicking through menus in a user interface (UI), and provides multiple types of user defined data visualization options, including various charts and graphs, tables of statistical alerts, table builder functionality, spatial mapping, and report generation. However, no UI supports all potential analysis and visualization requirements. Rapidly accessing data processed through ESSENCE using existing access control mechanisms, but de-coupled from the UI, supports innovative analyses, visualizations and reporting of these data using other tools.MethodsThe ESSENCE API gives users the ability to query ESSENCE data and functionality via a Representational State Transfer (REST) web API designed to use HTTPs protocol. As with logging into the ESSENCE application normally, use of the API also requires users to authenticate with their username and password by including it in the code. This makes programmatic interfaces with the application possible whereby a tool or program makes a request to the API endpoint and the API returns the result of the query in a structured form. The ESSENCE API is a collection of endpoints that return different sets of data, including ESSENCE time series graphs, time series data, data details data, aggregated data created using the table builder functionality, number of unique facilities or regions (i.e. counties) reporting for a query, and results from the detector algorithms and alert list. All of the query parameter information is stored in the API URL, which the user can create programmatically or by first creating their query from within ESSENCE, and then clicking the “API URL” to generate the necessary URL. API results are generally available in both json and csv formats.ResultsEpidemiologists in the CDC NSSP have developed R code that uses these APIs to create customized Rmarkdown reports and visualizations not possible within the ESSENCE application, as well as to automate extraction of data from ESSENCE to support routine reporting for other CDC program areas (e.g., influenza-like illness, and suspected opioid encounters). Anecdotally, some Sites utilize the API to populate publically facing dashboards with aggregated data from ESSENCE. Programmatic access to processed ESSENCE data via the APIs also supports easily sharable exploratory analysis and visualization that can serve as a sandbox for testing new methods for future inclusion within ESSENCE.ConclusionsThe development and use of the ESSENCE APIs in public health surveillance will support more efficient and timely access to machine-readable data de-coupled from point and click user interfaces, and has the potential to spur new and innovative ways of using data that has traditionally been less programmatically accessible to users. New tools and programs can leverage the data in web or mobile applications, traditional reports, and more easily integrate disparate data sources for comprehensive surveillance.

Monitoring and Improving Syndromic Surveillance Data Quality

Monitoring and Improving Syndromic Surveillance Data Quality

Analysis of Emergency Department Visits for Motor Vehicle Injuries in Utah, 2016

ObjectiveTo describe the characteristics of emergency department (ED) visits for motor vehicle injuries in Utah using 2016 syndromic surveillance data.IntroductionMotor vehicle injury is the leading cause of death in injury category in the United States. In 2016, motor vehicle crashes were one of the main causes of death resulting from injury (8.8 per 100,000 population) in Utah. Motor vehicle crashes can lead to physical and economic consequences that impact the lives of individuals and their families. In addition, the treatment of injuries places an enormous burden on hospital Emergency Departments (EDs). Currently; there are no data sources other than syndromic data in the Utah Department of Health to monitor ED visits due to motor vehicle injuries in real time.MethodsUtah participates in the National Syndromic Surveillance Program (NSSP) to which all hospitals in the state submit ED visit data via the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). ESSENCE was used to analyze 2016 ED visit data. Total population data were obtained from Utah population estimates. Data from 2017 was not included due to major system changes at a major healthcare system that interrupted data feeds resulting in lower than expected data volume.Motor vehicle injury is defined by existing subsyndrome definition in the Centers for Disease Control and Prevention ESSENCE system. All ED visit data were analyzed by querying key terms in the chief complaint field including any mention of: vehicle, wheeler, motorcycle, motor scooter, motor cycle, motor cross, truck, motorbike etc. Exclusion terms included any mention of: car dealership, hit head and car door. Ages were divided into seven groups for data distribution and comparison: 0–17, 18–24, 25–34, 35–44, 45–54, 55–64 and ≥ 65 years.ResultsIn 2016, a total of 28,472 ED visits (2% of total visits) were identified using the motor vehicle injury query. The ED visit rate for motor vehicle injuries was highest among persons aged 18–24 years (1,682 per 100,000 population). Rates continued to decline with increasing age after 18–24 years. The rate of females visiting the ED was higher than males (1,040 versus 826 per 100,000 population respectively; p < 0.01) (Figure 1). The majority of injuries (11722(52%)) were reported between 10:00 a.m. and 5:59 p.m. Injuries were highest August-September (5913(22%)).ConclusionsSyndromic data is a robust source of data for analyzing ED visits due to motor vehicle injuries in real time, and providing information to injury prevention programs for targeting interventions. Our data suggest an increased risk of visiting an ED due to motor vehicle injuries by age group (18-24 year olds), sex (females), month (August-September), and time (10:00 a.m. to 5:59 p.m.). These results do not include visits with incomplete or incorrectly coded chief complaints or discharge codes, patients of motor vehicle injuries who do not present to the ED, or not classified as ‘emergency’ patient class.

21st Century Approach: Using data and novel technologies to address the opioid crisis

ObjectiveTo use novel technologies to develop a rapid response framework to reach opioid overdose patients in an area which is challenging from both a geography and population distribution standpoint.IntroductionClark County, Indiana is geographically located in between the urban area of Louisville, Kentucky and Scott County, Indiana. Scott County is the site for the largest HIV outbreak in the history of the United States, directly related to high rates of IV drug abuse. The unique geographic location of Clark County in combination with the recent HIV and Hepatitis C outbreaks in Clark and neighboring counties has greatly informed the development of an effective response to overdoses and the opioid epidemic in general. Furthermore, Clark County has a unique population distribution, with a population of over 125,000 and a land area of over 300 square miles. Despite this large area, over 80% of the population lives within 9 miles of the southern border of the county. This leads to a mix of both urban and rural challenges. There are several areas of the county that have greater than 15 minute emergency response times, which is often the difference between life and death in an overdose situation. These factors led to the development of the Clark County Rapid Response Project. The rapid response project is a community-based, multidisciplinary framework to address the opioid addicted patient, from initial use to successful recovery. The project uses data driven technology to initiate the care of opiate overdose patients and administer lifesaving interventions.MethodsClark County has partnered with the Indiana State Department of Health utilizing the early notification system that monitors statewide overdose activity. Once an alert is sent out, the response involves the use of two early notification systems. Everbridge is a one touch notification system that allows rapid dissemination of information to various community partners to allow them to initiate the appropriate response. Pulse Point is a smart phone application that allows CPR and trained community laypeople to respond to a cardiac arrest or overdose patient in a public place. It provides directions to the patient as well as to the nearest AED. Clark County has also simultaneously instituted a county-wide CPR training initiative and offered Narcan training as well. This is a major paradigm shift, as prior methods of deployment of trained laypeople essentially relied on the chance that an overdose will be reached by a first responder.ResultsEverbridge has allowed for the rapid notification of county entities and deployment of resources to overdose ‘hot spot’ areas. The Pulse Point initiative has dramatically increased the number of CPR and Narcan trained responders and provided means of delivering them to the appropriate patient population in a timely manner. Both these technologies have dramatically increased the delivery of resources to the overdose patient and decreased response times to the delivery of care.ConclusionsUsing data driven technology to inform how Clark County Health Department and first responders collectively address the opioid crisis is a novel approach. Since January 2018, Clark County Health Department has used ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) to determine where and when an increase of drug overdose activity is occurring throughout the county. This affords county health officials the ability to inform in “near real-time” first responders, the emergency department and other community stakeholders, relevant information thus allowing for the rapid deployment of county resources to the areas most affected. Our collective efforts to save lives is further enhanced by the county using of novel technologies like Pulse Point which is used to deploy both CPR and Narcan trained laypersons directly to sites in the community where overdoses are occurring. In a community, which is in large part considered rural and, in many places, has a greater than 15 minute emergency response time, using Pulse Point and Everbridge technologies has uniquely positioned Clark County to be on the cutting edge of saving lives as we leverage data and technology to address the opioid epidemic in our communities. This has markedly improved access to treatment and response times to overdose patients in Clark County, Indiana.

Surveilling Non-Opioid Substance Use: Utilizing Multiple Data Sources in Marion County, Indiana

ObjectiveTo assess the prevalence of non-opioid substance use—including cocaine, methamphetamine and “spice”—within Marion County, Indiana and propose response recommendations utilizing a current opioid response plan.IntroductionCocaine, methamphetamine, and “spice” are addictive, non-opioid substances that negatively impact a person’s health through direct and indirect means. Direct health concerns of non-opioid substance use include anxiety, paranoia, seizure, heart attack, stroke, and potentially death while indirect health concerns include the acquisition of disease and infections, particularly sexually transmitted infections (STIs). Substance users experience an increased risk of acquiring STIs since they may exchange sex for substances, use substances within a social setting that may lead to sexual activity, or engage in risky sexual behavior as a result of impaired judgement associated with substance use. The current study evaluated the use of multiple data sources to monitor changes in the rate of cocaine, methamphetamine, and “spice” related emergency department visits as well as cocaine- and methamphetamine-related death rates, within Marion County, Indiana between 2013 and 2017.MethodsTwo data sources were used in this study. First, prevalence rates of non-opioid substance related emergency department (ED) visits were calculated using Marion County (IN) ED data from Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) between 2013 and 2017. Second, cocaine and methamphetamine death rates were calculated using coroner toxicology data related to Marion County deaths between 2013 and 2017. Cocaine and methamphetamine deaths were defined as any death in which cocaine and methamphetamine was found in the toxicology results, respectively. All rates were calculated per 100,000 and age-adjusted to the 2000 U.S. Census using SAS Enterprise Guide v7.1.ResultsNon-opioid substance related ED visits have persistently risen between 2013 and 2017 (Figure 1). Methamphetamine and “spice” related ED visits exhibited similar prevalence patterns, increasing from 0.99 (0.72, 1.58) to 5.32 (4.67, 6.21) and 0.46 (0.28, 1.00) to 4.13 (3.57, 4.94) per 100,000, respectively, between 2013 and 2016. Cocaine-related ED visits consistently exhibited the highest prevalence rates, ranging from 3.72 (3.17, 4.44) to 23.56 (22.16, 25.11) per 100,000 in 2013 and 2016, respectively. In 2017, all non-opioid substance related ED visits drastically increased to 47.78 (45.79, 49.91), 48.48 (46.48, 50.67), and 42.08 (40.23, 44.13) per 100,000 for cocaine, methamphetamine, and “spice,” respectively. Further, we looked at cocaine- and methamphetamine-related death rates using coroner toxicology results. We found that between 2013 and 2017, the cocaine-related death rate nearly tripled, from 4.82 (4.20, 5.64) per 100,000 in 2013 to 13.01 (11.97, 14.23) per 100,000 in 2017 (Figure 2). Similarly, methamphetamine-related death rates increased from 1.31 (0.99, 1.92) per 100,000 in 2013 to 10.15 (9.25, 11.28) per 100,000 in 2017 (Figure 2). We did not calculate death rates of those who were found to have “spice” in their system at the time of death due to low prevalence.ConclusionsThe increase of non-opioid substance related ED visits in Marion County may indicate that non-opioid substance use—particularly cocaine, methamphetamine, and “spice”—may be an emerging public health issue in Marion County. This growing concern is further supported by the consistent increase in cocaine- and methamphetamine-related death rates. A limitation to our study is the inconsistent reporting of the substance in ED chief complaints and missing fields for discharge diagnoses and triage notes. As such, this inconsistency may have led to an underestimation of the prevalence rates of non-opioid substance related ED visits. The addition of triage notes and more reliable discharge diagnoses in 2017 ultimately culminated in a sharp increase in non-opioid substance related ED visits in 2017.Certain aspects of Marion County Public Health Department’s established opioid response plan may be used to address the growing concern of non-opioid substance use. These aspects include, but are not limited to, engaging community partners, creating a task force, establishing focus groups, and providing resources. While these aspects are critical to effectively respond to non-opioid substance use epidemics, establishing the various components prior to an outbreak enable communities to reduce the impact of such epidemics, if not prevent them from occurring. Additionally, it is important to incorporate participatory aspects into a non-opioid substance response plan such that community members are the driving force to provide context for the impact that non-opioid substance use is having on the community while also offering insight into which interventions would be most effective.

Building Electronic Disease Surveillance Capacity in the Peruvian Navy with SAGES

ObjectiveTo 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.IntroductionIn 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.MethodsJHU/APL is engaged with the Peruvian Navy Health System to upgrade the existing SAGES-based Alerta DISAMAR surveillance system which relies on remote data collection using IVR (interactive voice recording) technology, with a SAGES-based system that uses SMS (short message service) text messages for remote data collection. Based on Peruvian Navy requirements, JHU/APL created mobile data entry forms for Android smartphones using the SAGES mCollect application. SAGES mCollect is built using Open Data Kit open source tools along with added features such as 128-bit encryption and quality checks.The JHU/APL team engages closely with end users and other stakeholders to determine system requirements and to deploy the system, as well as to train end users and the system administrators who will need to maintain the system once it is deployed. The JHU/APL team, consisting of both information technology and public health expertise, conduct a country-level capabilities and needs assessment to address design considerations and operational end user requirements. This assessment takes into account the requirements and objectives of the Peruvian Navy, while keeping in mind infrastructure, cost, and personnel constraints. A pilot test of SMS-based data collection is currently underway with 10 health clinics within the Navy.ResultsMany challenges exist when implementing electronic disease surveillance tools in resource-limited settings, but using a tailored approach to implementation in which specific needs, constraints, and expectations are identified with stakeholders helps increase the overall adoption and sustainment of the system. JHU/APL believes SMS-based data collection will be more sustainable than IVR-based data collection for the Peruvian Navy.ConclusionsJHU/APL is deploying a SAGES-based electronic disease surveillance system for the Peruvian Navy that has great potential to increase reporting rates from its health facilities as well as improve data quality and timeliness, thus resulting in greater awareness and enhanced public health decision making.

Syndromic Surveillance of Emergency Department Visits for Acute Adverse Effects of Marijuana, Tri-County Health Department, Colorado, 2016-2017.

In Colorado, legalization of recreational marijuana in 2014 increased public access to marijuana and might also have led to an increase in emergency department (ED) visits. We examined the validity of using syndromic surveillance data to detect marijuana-associated ED visits by comparing the performance of surveillance queries with physician-reviewed medical records. We developed queries of combinations of marijuana-specific International Classification of Diseases, Tenth Revision (ICD-10) diagnostic codes or keywords. We applied these queries to ED visit data submitted through the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) syndromic surveillance system at 3 hospitals during 2016-2017. One physician reviewed the medical records of ED visits identified by ≥1 query and calculated the positive predictive value (PPV) of each query. We defined cases of acute adverse effects of marijuana (AAEM) as determined by the ED provider's clinical impression during the visit. Of 44 942 total ED visits, ESSENCE queries detected 453 (1%) as potential AAEM cases; a review of 422 (93%) medical records identified 188 (45%) true AAEM cases. Queries using ICD-10 diagnostic codes or keywords in the triage note identified all true AAEM cases; PPV varied by hospital from 36% to 64%. Of the 188 true AAEM cases, 109 (58%) were among men and 178 (95%) reported intentional use of marijuana. Compared with noncases of AAEM, cases were significantly more likely to be among non-Colorado residents than among Colorado residents and were significantly more likely to report edible marijuana use rather than smoked marijuana use ( P < .001). ICD-10 diagnostic codes and triage note keyword queries in ESSENCE, validated by medical record review, can be used to track ED visits for AAEM.

The RHINO Community of Practice: Building a Space for Data Users and Enthusiasts in Washington State

ObjectiveTo grow and facilitate a community of syndromic surveillance data users in Washington State, improving and expanding local syndromic practice.IntroductionPrior to June 2016, there were 45 registered users of syndromic surveillance data in Washington State, with 29 (64.4%) representing 5 of Washington’s 35 local health jurisdictions and 16 (35.6%) at the state level. Of those registered users, 9 (8.8%) had logged into ESSENCE at least once in the 6 months before October 2016.In June 2016, the Washington State syndromic surveillance program began accepting Meaningful Use data and sought to increase its user base. To accomplish this, the Washington State Department of Health (WA DOH) designated a staff member to oversee outreach efforts to increase the visibility of syndromic data in the state, including the establishment of a Community of Practice.MethodsThe Washington State syndromic surveillance program—the Rapid Health Information NetwOrk (RHINO)—began the process of stakeholder engagement by delivering a needs assessment to 15 current and potential users of the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) platform. The survey assessed interest in participation in a Community of Practice for Washington State syndromic data users, the timing and format for meetings, needs for technical assistance, and topic areas of interest. RHINO then used the survey results to create a bank of topics for Community of Practice calls and develop a strategy for long-term outreach and engagement.In April of 2017, the RHINO team developed a new strategic plan and outlined metrics for evaluating growth and challenges in the program’s outreach efforts, including plans for outreach to novel disciplines like emergency preparedness. These metrics included counts of invitations for speaking engagements, ESSENCE users, onsite ESSENCE trainings and attendees at those trainings, organizations and disciplines represented in the Community of Practice, Community members, and webinars facilitated for the Community. RHINO staff compiled monthly tabulations of these metrics to track progress over the course of the year and aid in adjustments to outreach efforts as necessary.ResultsRHINO received 10 responses to the survey, with 9 respondents from local health jurisdictions and 1 from WA DOH. Respondents indicated particularly strong interest in regular webinars, a database of resources, and live trainings to support syndromic practice in their work. They also expressed concerns about the distance which would be required for in-person meetings.RHINO established that meetings would occur via webinar every other month and held 6 webinars between October 2016 and October 2017 on a broad range of topics including developing syndrome definitions, basic ESSENCE functions, using ESSENCE’s Report Manager tool, monitoring influenza-like-illness in ESSENCE, and using syndromic data for situational surveillance. In addition to the Community of Practice webinars, RHINO staff developed technical guides for both the Washington and National Surveillance Program’s (NSSP) ESSENCE platforms, a handbook for using syndromic surveillance data in Washington State, and a curriculum for onsite ESSENCE training.Between October 2016 and October 2017, RHINO offered 8 onsite ESSENCE trainings for groups of users at the Washington State Department of Health and local health jurisdictions, serving a total of 36 attendees. Over the course of the year, ESSENCE users in Washington State increased to 75, with 40 (53.3%) of them logging into the system at least once over the previous 6 months and 20 (26.7%) listed as “new users” who have not yet activated their accounts. The Community of Practice itself has 86 members representing 16 agencies and 19 disciplines.As RHINO’s profile increased and more potential users became aware of the availability of syndromic data, RHINO began receiving invitations to present for external partners. Between March 2016 and October 2017, RHINO received 8 invitations to present to audiences of potential syndromic data users. These audiences included leadership at the Washington State Department of Health and emergency preparedness and response organizations.In the next year, the program will continue offering data trainings and partner meetings to better serve the needs of both current and potential data users in Washington State. As more jurisdictions begin to have production-quality data, RHINO will continue offering onsite training. RHINO has also built a relationship with the Northwest Tribal Epidemiology Center in Portland, Oregon to begin the process of exploring data sharing with the Tribal Nations and Organizations located within Washington.ConclusionsThrough the development and implementation of a detailed outreach plan, RHINO increased the user base and profile of syndromic surveillance data in Washington State. This work was made possible through the careful construction of strong relationships with new and potential partners and the decision to diversify RHINO’s staff to include members with backgrounds beyond epidemiology.

User Generated SQL Queries Inform Evaluation of NSSP ESSENCE

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).Introduction: 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.Methods: ESSENCE SQL query logs were extracted from April 1, 2016 to August 23th, 2017. Overall query volume was assessed by summarizing volume of queries over time (e.g., by hour, day, and week), and by Site. To better understand system performance the mean, median, and maximum query execution times were summarized over time and by Site. SQL query text was parsed so that we could isolate, 1) Syndromes queried, 2) Sub-syndromes queried, 3) Keyword categories queried, and 4) Free text query terms used. Syndromes, sub-syndromes, and keyword categories were tabulated in total and by Site. Frequencies of free text query terms were analyzed using n-grams, wordclouds, and term co-occurrence relationships. Term co-occurrence network graphs were used to visualize the structure and relationships among terms.Results: There were a total of 354,101 SQL queries generated by users of ESSENCE between April 1, 2016 and August 23rd, 2017. Over this entire time period there was a weekly mean of 4,785 SQL queries performed by users. When looking at 2017 data through August 23rd this figure increases to a mean of 7,618 SQL queries per week for 2017, and since May 2017 the mean number of SQL queries has increased to 10,485 per week. The maximum number of user generated SQL queries in a week was 29,173. The mean, median, and maximum query execution times for all data was 0.61 minutes, 0 minutes, and 365 minutes, respectively. When looking at only queries with a free text component the mean query execution time increases slightly to 0.94 minutes, though the median is still 0 minutes. The peak usage period based on number of SQL queries performed is between 12:00pm and 3:00pm EST.Conclusions: The use of NSSP ESSENCE has grown since implementation. This is the first time the ESSENCE system has been used at a National level with this volume of data, and number of users. Our focus to date has been on successfully on-boarding new Sites so that they can benefit from use of the available tools, providing trainings to new users, and optimizing ESSENCE performance. Routine analysis of the ESSENCE SQL logs can assist us in understanding how the system is being used, how well it is performing, and in evaluating our system optimization efforts.

Monitoring Out-of-State Patients during a 2017 Hurricane Response using ESSENCE

ObjectiveTo demonstrate the use of ESSENCE in the BioSense Platform to monitor out-of-State patients seeking emergency healthcare in Tennessee during Hurricanes Harvey and Irma.IntroductionSyndromic surveillance is the monitoring of symptom combinations (i.e., syndromes) or other indicators within a population to inform public health actions. The Tennessee Department of Health (TDH) collects emergency department (ED) data from more than 70 hospitals across Tennessee to support statewide syndromic surveillance activities. Hospitals in Tennessee typically provide data within 48 hours of a patient encounter. While syndromic surveillance often supplements disease- or condition-specific surveillance, it can also provide general situational awareness about emergency department patients during an event or response.During Hurricanes Harvey (continental US landfall on August 25, 2017) and Irma (continental US landfall on September 10, 2017), TDH supported all hazards situational awareness using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) in the BioSense Platform supported by the National Syndromic Surveillance Program (NSSP). The volume of out-of-state patients in Tennessee was monitored to assess the impact on the healthcare system and any geographic- or hospital-specific clustering of out-of-state patients within Tennessee. Results were included in daily State Health Operations Center (SHOC) situation reports and shared with agency response partners such as the Tennessee Emergency Management Agency (TEMA).MethodsData were monitored from August 18, 2017 through September 24, 2017. A simple query was established in ESSENCE using the Patient Location (Full Details) dataset. Data were limited to hospital ED visits reported by Tennessee (Site = “Tennessee”). To monitor ED visits among residents of Texas before, during, and after Major Hurricane Harvey, data were queried for a patient zip code within Texas (State = “Texas”). ED visits among Florida residents were monitored similarly (State = “Florida”) before, during, and after Major Hurricane Irma. Additionally, a free text chief complaint search was implemented for the terms “Harvey”, “Irma, “hurricane”, “evacuee”, “evacuate”, “Florida”, and “Texas”. Chief complaint search results were then filtered to remove encounters with patient zip codes within Tennessee.ResultsFrom August 18, 2017 through September 24, 2017, Tennessee hospital EDs reported 277 patient encounters among Texas residents and 1,041 patient encounters among Florida residents. The number of encounters among patients from Texas remained stable throughout the monitoring period. In contrast, the number of encounters among patients from Florida exceeded the expected value on September 7, peaked September 10 at 116 patient encounters, and returned to expected levels on September 16 (Figure 1). The increase in patients from Florida was evenly distributed across most of Tennessee, with some clustering around a popular tourism area in East Tennessee. No concerning trends in reported syndromes or chief complaints were identified among Texas or Florida patients.The free text chief complaint query first exceeded the expected value on September 9, peaked on September 11 with 5 patient encounters, and returned to expected levels on September 14. From August 18 through September 24, 21 of 30 visits captured by the query were among Florida residents. One Tennessee hospital appeared to be intentionally using the term “Irma” in their chief complaint field to indicate patients from Florida impacted by the hurricane.ConclusionsThe ESSENCE instance in the BioSense platform provided TDH the opportunity to easily locate and monitor out-of-state patients seen in Tennessee hospital EDs. While TDH was unable to validate whether all patients identified as residents of Florida were displaced because of Major Hurricane Irma, the timing of the rise and fall of patient encounters was highly suggestive. Likewise, seeing no substantial increase ED patients with residence in Texas reassured TDH that the effects of Hurricane Harvey were not impacting hospital emergency departments in Tennessee.TDH used information and charts from ESSENCE to support situational awareness in our SHOC and at TEMA. Use of patient zip code to identify out-of-state residents was more sensitive than chief complaint searches by keyword during this event. ESSENCE allowed TDH to see where out-of-state patients appeared to be concentrating in Tennessee and monitor the need for targeting messaging and resources to heavily affected areas. Additionally, close surveillance of chief complaints among out-of-state patients provided assurance that no unusual patterns in illness or injury were occurring.ESSENCE is the only TDH information source capable of rapidly collecting health information on out-of-state patients. ESSENCE allowed TDH to quickly identify a change within the patient population seen at Tennessee emergency departments and monitor the situation until the patient population returned to baseline levels.

Free-Text Mining to Improve Syndrome Definition Matching Across Emergency Departments

ObjectiveWe sought to use free text mining tools to improve emergency department (ED) chief complaint and discharge diagnosis data syndrome definition matching across facilities with differing robustness of data in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) application in Idaho’s syndromic surveillance system.IntroductionStandard syndrome definitions for ED visits in ESSENCE rely on chief complaints. Visits with more words in the chief complaint field are more likely to match syndrome definitions. While using ESSENCE, we observed geographic differences in chief complaint length, apparently related to differences in electronic health record (EHR) systems, which resulted in disparate syndrome matching across Idaho regions. We hypothesized that chief complaint and diagnosis code co-occurrence among ED visits to facilities with long chief complaints could help identify terms that would improve syndrome match among facilities with short chief complaints.MethodsThe ESSENCE-defined influenza-like illness (ILI) chief complaint syndrome was used as the base syndrome for this analysis. Syndrome-matched visits were defined as visits that match the syndrome definition.We assessed chief complaints and diagnosis code co-occurrence of syndrome-matched visits using the RCRAN TidyText package and developed a bigram network from normalized, concatenated chief complaint and diagnosis code (CCDD) fields and normalized diagnosis code (DD) fields per previously described methodologies.1 Common connections were defined by a natural break in frequency of pair occurrence for CCDD pairs (30 occurrences) and DD pairs (5 occurrences).The ESSENCE syndrome was revised by adding relevant bigram network clusters and logic operators. We compared time series of the percent of ED visits matched to the ESSENCE syndrome with those matched to the revised syndrome. We stratified the time series by facilities grouped by short (average &lt; 4 words, “Group A”) and long (average ≥ 4 words, “Group B”) chief complaint fields (Figure 1). Influenza season start was defined as two consecutive weeks above baseline, or the 95% upper confidence limit of percent syndrome-matched visits outside of the CDC ILI surveillance season. Season trends and influenza-related deaths in Idaho residents were compared.ResultsDuring August 1, 2016 through July 31, 2017, 1,587 (1.17%) of 135,789 ED visits matched the ESSENCE syndrome. Bigram networks of CCDD fields produced clusters already included by the ESSENCE syndrome. The bigram network of DD fields (Figure 2) produced six clusters. The revised syndrome definition included the ESSENCE syndrome, 3 single DD terms, and 3 two DD terms combined. The start of influenza season was identified as the same week for both ILI syndrome definitions (ESSENCE baseline 0.70%; revised baseline 2.21%). The ESSENCE syndrome indicated the season peaked during Morbidity and Mortality Weekly Report (MMWR) week 2017-05 with the season ending MMWR week 2017-14. The revised syndrome indicated 2017-20 as the season end. Multiple peaks seen with the revised syndrome during MMWR weeks 2017-02, 2017-05, and 2017-10 mirrored peaks in influenza-related deaths during MMWR weeks 2017-03, 2017-06, and 2017-11.ILI season onset was five weeks earlier with the revised syndrome compared with the ESSENCE syndrome in Group A facilities, but remained the same in Group B. The annual percentage of ED visits related to ILI was more uniform between facility groups under the revised syndrome than the ESSENCE syndrome. Unlike the trend seen with the ESSENCE syndrome, the revised syndrome shows low-level ILI activity in both groups year-round.ConclusionsIn Idaho, dramatic differences in ED visit chief complaint word counts were seen between facilities; bigram networks were found to be an important tool to identify diagnosis codes and logical operators that built more inclusive syndrome definitions when added to an existing chief complaint syndrome. Bigram networks may aid understanding the relationship between chief complaints and diagnosis codes in syndrome-matched visits.Use of trade names and commercial sources is for identification only and does not imply endorsement by the Centers for Disease Control and Prevention, the Public Health Service, or the U.S. Department of Health and Human Services.

How Missing Discharge Diagnosis Data in Syndromic Surveillance Leads to Coverage Gaps

ObjectiveTo identify surveillance coverage gaps in emergency department (ED) and urgent care facility data due to missing discharge diagnoses.IntroductionIndiana utilizes the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) to collect and analyze data from participating hospital emergency departments. This real-time collection of health related data is used to identify disease clusters and unusual disease occurrences. By Administrative Code, the Indiana State Department of Health (ISDH) requires electronic submission of chief complaints from patient visits to EDs. Submission of discharge diagnosis is not required by Indiana Administrative Code, leaving coverage gaps. Our goal was to identify which areas in the state may see under reporting or incomplete surveillance due to the lack of the discharge diagnosis field.MethodsEmergency department data were collected from Indiana hospitals and urgent care clinics via ESSENCE. Discharge diagnosis was analyzed by submitting facility to determine percent completeness of visits. A descriptive analysis was conducted to identify the distribution of facilities that provide discharge diagnosis. A random sample of 20 days of data were extracted from visits that occurred between January 1, 2017 and September 6, 2017.ResultsA random sample of 179,039 (8%) ED entries from a total of 2,220,021 were analyzed from 121 reporting facilities. Of the sample entries, 102,483 (57.24%) were missing the discharge diagnosis field. Over 40 (36%) facilities were missing more than 90% of discharge diagnosis data. Facilities are more likely to be missing &gt;90% or &lt;19% of discharge diagnoses, rather than between those points.Comparing the percent of syndromic surveillance entries missing discharge diagnosis across facilities reveals large variability. For example, some facilities provide no discharge diagnoses while other facilities provide 100%. The number of facilities missing 100% of discharge diagnoses (n = 19) is 6.3 times that of the facilities that are missing 0% (n = 3).The largest coverage gap was identified in Public Health Preparedness District (PHPD)1 three (93.16%), with districts five (64.97%), seven (61.94%), and four (61.34%) making up the lowest reporting districts. See Table 2 and Figure 12 for percent missing by district and geographic distribution. PHPD three and five contain a large proportion (38%) of the sample population ED visits which results in a coverage gap in the most populated areas of the state.ConclusionsQuerying ESSENCE via chief complaint data is useful for real-time surveillance, but is more informative when discharge diagnoses are available. Indiana does not require facilities to report discharge diagnosis, but regulatory changes are being proposed that would require submission of discharge diagnosis data to ISDH. The addition of discharge diagnose is aimed to improve the completeness of disease clusters and unusual disease occurrence surveillance data.

Using Syndromic Data for Opioid Overdose Surveillance in Utah

Objective: To monitor opioid-related overdose in real-time using emergency department visit data and to develop an opioid overdose surveillance report for Utah Department of Health (UDOH) and its public health partners.Introduction: The current surveillance system for opioid-related overdoses at UDOH has been limited to mortality data provided by the Office of the Medical Examiner (OME). Timeliness is a major concern with OME data due to the considerable lag in its availability, often up to six months or more. To enhance opioid overdose surveillance, UDOH has implemented additional surveillance using timely syndromic data to monitor fatal and nonfatal opioid-related overdoses in Utah.Methods: As one of the agencies participating in the National Syndromic Surveillance Program (NSSP), UDOH submits de-identified data on emergency department visit from Utah’s hospitals and urgent care facilities in close to real-time to the NSSP platform. Emergency department visit data are available for analysis using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) system provided by NSSP. ESSENCE provides UDOH with patient-level syndromic data for analysis and early detection of abnormal patterns in emergency visits. A total of 38 out of 48 acute care hospitals and multiple urgent care facilities are enrolled in the system in Utah. More than 90% of these hospitals report chief complaint data, and discharge data are available from about 15% of the facilities. Data were analyzed by querying key terms in the chief complaint field including: any entry of: ‘overdose’, drug and brand names for opioids, street names, ‘naloxone’, and miss-spellings. Exclusion terms included any mention of: ‘denies’, ‘quit’, ‘refill’, ‘withdraw’, ‘dependence’, etc. Data containing any ICD entry of: T40.0-T40.4, T40.60, and T40.69 were included in the analysis.Results: Between September 1, 2016 and August 31, 2017, Utah Department of Health identified 4,063 opioid-related overdose emergency department (ED) visits through the ESSENCE system using both chief complaint and discharge diagnosis queries. Of these visits, 3,865 (95%) were identified using chief complaints alone and 198 (5%) visits were added by searching the discharge diagnosis field. Opioid-related visits comprised approximately 0.3% of the total ED visits (1,267,244) reported during this time (Graph 1). More than half of the opioid-related emergency visits were reported from just five facilities. Rate of opioid-related visits ranging from 0 to 292 visits per 100,000 population per year (median: 108 visits per 100,000 population per year), with an overall rate for the state of 129 visits per100, 000 population per year. The highest rate of opioid-related visits occurred among patients aged 18 to 24 (219 visits per 100,000 population per year), and 59% of all opioid-related patients in Utah were female.Conclusions: The results presented are estimates of opioid-related overdoses reported using close to real-time data. These results would not include visits with incomplete or incorrectly coded chief complaints or discharge codes, or cases of opioid overdose who do not present to an emergency department or urgent care facility. The results from using syndromic data are consistent with existing surveillance findings using mortality data in Utah. This suggests that syndromic surveillance data are useful for rapidly capturing opioid events, which may allow for a timelier public health response. UDOH is currently evaluating syndromic surveillance data versus hospital discharge data for opioid-related emergency department visits, which may further optimize queries in ESSENCE, in order to provide improved opioid surveillance data to local public health partners. This analysis demonstrates that using syndromic surveillance data provides a more time-efficient alternative, enabling more rapid public health interventions, which improved opportunities to reduce opioid-related morbidity and mortality in Utah.

Comparison of National and Local Syndromic Surveillance Data - Cook County, IL, 2017

ObjectiveThis analysis was undertaken to determine how the data completeness, consistency, and other attributes of our local syndromic surveillance program compared to the National Syndromic Surveillance Platform.IntroductionIn 2005, the Cook County Department of Public Health (CCDPH) began using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) as an emergency department (ED)-based local syndromic surveillance program (LSSP); 23 (100%) of 23 hospitals in suburban Cook County report to the LSSP. Data are transmitted in delimited ASCII text files (i.e., flat files) and contain a unique patient identifier, visit date and time, zip code, age, sex, and chief complaint. Discharge diagnosis and disposition are optional data elements. Prior to 2017, the Illinois Department of Public Health placed facilities participating in the Cook LSSP in a holding queue to transform their flat file submissions into a HL7 compliant message; however as of 2017, eligible hospitals must submit HL7 formatted production data to IDPH to fulfill Meaningful Use. The primary syndromic surveillance system for Illinois is the National Syndromic Surveillance Program (NSSP), which transitioned to an ESSENCE interface in 2016. As of December 2016, 20 (87%) of 23 hospitals reporting to the LSSP also reported to IDPH and the NSSP. As both syndromic surveillance systems aim to collect the same data, and now can be analyzed with the same interface, CCDPH sought to compare the LSSP and NSSP for data completeness, consistency, and other attributes.MethodsOur comparison of NSSP to the LSSP focused on data completeness for key demographic and medical variables and consistency in total visit counts. Analysis of completeness utilized data from December 2016 for 20 hospitals contributing HL7 production data to IDPH at that time. Total visit counts in both systems were compared for the same 20 hospitals from February 5th-11th 2017, a randomly chosen time period. A target threshold of less than 3% difference in total visit counts was set by the CCDPH system users. Analysis was completed in Microsoft Excel 2010. Other attributes of the surveillance systems were qualitatively assessed by the primary system users at CCDPH.ResultsAll variables required by the LSSP had 98-100% completeness in both the LSSP and NSSP (unique patient identifier, age, sex, zip code, visit time and date, and chief complaint). However, the LSSP optional data elements, discharge diagnosis and discharge disposition, were less complete in the LSSP, compared to the NSSP (Diagnosis: 56% versus 83%, Disposition: 66% versus 80%). Among variables required for NSSP reporting but not reported to the LSSP, completeness ranged from 100% (race, ethnicity) to 82% (county). Optional data elements within NSSP ranged in completeness from 73% (initial pulse oximetry) to 0% (initial blood pressure, insurance coverage). Of the 20 hospitals evaluated for visit counts, only one hospital had &lt;3% difference in visit counts in the LSSP and NSSP for all 7 days assessed. Ten hospitals had &gt;3% difference in visit counts on all seven days. Average seven day differences for hospitals ranged from 0% to 54%. Eighteen (90%) of 20 hospitals were reporting larger numbers of visits to NSSP than to the LSSP.ConclusionsOverall completeness of data was similar between the national and our local ESSENCE systems with most required variables having over 98% completeness. NSSP had higher completeness over the LSSP for discharge diagnosis and disposition. Additional data elements required by NSSP, but unavailable in the LSSP, had similarly high completeness but optional NSSP variables of interest showed greater variability in reporting. Differences in visit counts were higher than expected. An ongoing exploration of these differences has shown they are multifaceted and require hospital-specific interventions. There are strengths and limitations to both the NSSP and LSSP. CCDPH has direct control over data sharing between jurisdictions in the LSSP and there has historically been less system “down time” in the LSSP compared to the NSSP; however, the use of flat files instead of HL7, as well as having fewer incentives for hospital participation (e.g. Meaningful Use) after 2016, results in limited data collection and stagnant growth compared to the NSSP. Jurisdictions using their own LSSPs should consider analyzing their data completeness, consistency, and quality compared to the NSSP.

Developing and Validating a Fireworks-Related Syndrome Definition in Kansas

ObjectiveTo develop a syndrome definition and analyze syndromic surveillance data usefulness in surveillance of firework-related emergency department visits in Kansas. Introduction Across the U.S.A., multiple people seek treatment for fireworks-related injuries around the July 4th holiday. Syndromic surveillance in Kansas allows for near real-time analysis of the injuries occurring during the firework selling season. During the 2017 July 4thholiday, the Kansas Syndromic Surveillance Program (KSSP) production data feed received data from 88 EDs at excellent quality and timeliness. Previous and current firework safety messaging in Kansas is dependent on voluntary reporting from hospitals across the state. With widespread coverage of EDs by KSSP, data can be more complete and timely to better drive analysis and public information Methods:KSSP data was queried through the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) v.1.20 provided by the National Syndromic Surveillance Program. Data between June 12, 2017 and August 13, 2017 were queried. The first query (Query A, Table 1.) searched the Discharge Diagnosis History field for the “W39” ICD-10 Diagnosis code, “Discharge of firework.” These records were searched for common firework terms contained in the Chief Complaint History field. These firework-related free text terms (Query B, Table 1.) were then combined with other potential firework-related terms to create a preliminary free text query (Query C, Table 1.). This preliminary query was run on the Chief Complaint History field. Data were then searched for false positive cases and appropriate negation terms were included to accommodate this. The new query with negation terms (Query D, Table 1.) was run on the Chief Complaint History field, combined with the results from the Discharge Diagnosis History field, and then combined records were de-duplicated based on a unique visit identifier. The final data set was then classified by the anatomical location of the injury and the gender and age group of the patient. Results:The initial query (Query A, Table 1.) for the diagnosis code “W39” returned 101 unique ED visits. Of these 101 unique ED visits, the following terms were identified in the Chief Complaint History field: shell, artillery, bomb, sparkler, grenade, fire cracker, firework, and firework show. These key terms were translated into Query B, Table 1. Other key terms deemed likely to capture specific firework-related exposures were then included into Query C, Table 1. , including roman, candle, lighter, M80, and punk. Query C was then used to query the Chief Complaint History field, returning 144 unique ED visits. Cases captured by Query C were then reviewed by hand for false positives and the negation terms, lighter fluid, fish, nut, and pistachio, were incorporated the Query D, Table 1. The previous process for Query C was then repeated on Query D, leaving a remaining 136 unique cases. Query A’s 101 unique ED visits was then combined with the 136 unique ED visits captured by Query D and de-duplicated. The de-duplicated data set contained 170 unique ED visits which were then reviewed by hand for false positives. The final removal of false positives from the combined and de-duplicated data set left a remaining 154 unique ED visits for firework-related injuries during this time period.For these data, the most common victims of firework injuries were males, accounting for 65.5% of all firework related ED visits and children ages 0 to 19 accounting for 44.2% of these visits. At every age breakout, male injuries exceeded female injuries. The most common anatomical location of the injury was one or both hands with 38.3% of all injuries mentioned hands as their primary injury. Injuries to the eyes, face, and head accounted for the second most injuries (28.6% of all patients). Conclusions: The selling of fireworks will be a yearly occurrence of a specific exposure that can potentially lead to injuries. Utilizing syndromic surveillance to review the holiday firework injuries is a very rapid method to assess the impact of these injuries and may allow for future direction of public information during the holiday. Having a syndrome definition that builds on knowledge from previous years will allow for quicker case identification as well.State public information regarding firework safety can be significantly bolstered by accurate and rapid data assessment. Developing a firework injury syndrome definition that is accurate and returns information rapidly has allowed for increased buy-in to the Kansas Syndromic Surveillance Program from public information offices, fire marshal’s offices, and other program fields.

Correlation of Tweets Mentioning Influenza Illness and Traditional Surveillance Data

ObjectiveTo determine if social media data can be used as a surveillance tool for influenza at the local level.IntroductionThe use of social media as a syndromic sentinel for diseases is an emerging field of growing relevance as the public begins to share more online, particularly in the area of influenza. Several applications have been developed to predict or monitor influenza activity using publicly posted or self-reported online data; however, few have prioritized accuracy at the local level. In 2016, the Cook County Department of Public Health (CCDPH) collected localized Twitter information to evaluate its utility as a potential influenza sentinel data source. Tweets from MMWR week 40 through MMWR week 20 indicating influenza-like illness (ILI) in our jurisdiction were collected and analyzed for correlation with traditional surveillance indicators. Social media has the potential to join other sentinels, such as emergency room and outpatient provider data, to create a more accurate and complete picture of influenza in Cook County.MethodsWe developed a JAVA program which included a customized geofence around suburban Cook County to collect tweets from Twitter’s STREAM application programming interface (API) (available at https://github.com/FoodSafeCookCo/TwitterStream-Program). The JAVA program looked for tweets within the geofence or for tweets with a profile location naming a suburban Cook County municipality and copied them to a text file if the tweet contained “flu” or “influenza”. Captured data included the user’s Twitter handle, Tweet text, Tweet time and date, x and y coordinates (if available), and profile location. Tweets were then manually reviewed to determine if they met the following criteria: 1) language indicated the user was recently ill with influenza; 2) user appeared to reside in CCDPH jurisdiction. Tweets meeting these criteria were included in the analysis. Tweets were aggregated by MMWR week and analyzed for correlation, using Pearson methods (data were normal), with two traditional surveillance sources: 1) the percent of visits to all suburban Cook County emergency departments for ILI as reported to the Cook County Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE), and 2) the percent of laboratory specimens testing positive for influenza at seven local sentinel laboratories. Analysis was performed in Excel 2013 and SAS 9.4.ResultsFrom MMWR week 40-20, 113 tweets indicating influenza-like illness were collected within Cook County’s jurisdiction. Due to technical issues with the program, data were not collected for weeks 52, 2, and 17-19. Correlations were compared for the percent of laboratory specimens testing positive for influenza (LSL) and percent of visits to emergency departments for ILI (EDILI) to the total number of tweets per MMWR week. A strong correlation exists between LSL and EDILI r=0.92 (p-value&lt;0.0001) indicating the traditional sentinels have a strong positive relationship. The correlation between number of tweets and LSL was 0.46 (p-value =0.0138), indicating a moderate positive relationship. Correlation between number of tweets and EDILI was similarly moderate, r=0.52 (p-value=0.0049). Correlations to EDILI stratified by age (0-4, 5-17, 18-64, 65+) also showed a moderate positive relationship (range 0.50 to 0.55, all p-values &lt; 0.01). Twitter use peaked one week before the recorded peak of other surveillance indicators. When Twitter counts were shifted one week to align the peak in tweets with the peak of the influenza season, the correlations were 0.54 for LSL and 0.61 for EDILI (p-value=0.0034 and 0.0007, respectively).ConclusionsOverall, the tweets collected had a moderately positive relationship with the severity of influenza activity in Cook County. When the data were transitioned to match peaks, there was an increase in the correlations’ strength for both LSL and EDILI. This data indicates that publicly shared social media data may be an underutilized source of syndromic data at the local level, potentially capable of predicting seasonal influenza peaks before traditional data sources. Other jurisdictions may consider using the open source program created by CCDPH to determine the relationship of influenza related social media to their own local influenza surveillance data. For the 2017-2018 influenza season, we established a redundant system for tweet collection in case one of the systems goes down. Exploring machine learning (in place of manual review) to detect tweets indicating illness is also a promising avenue to simplify data collection and cleaning. Data will be collected using the same system for the 2017-2018 influenza season and correlations re-evaluated with more complete data.

Enhancing Biosurveillance Specificity Using PraedicoTM, A Next Generation Application

We evaluated the specificity of Praedico Biosurveillance, a next generation biosurveillance application leveraging multiple detection algorithms, big data and machine learning, for VA outpatient syndromic surveillance alerting during the period of June 2014 thru May 2015, and compared it to the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). Praedicoâ„¢ Biosurveillance generated alerts were significantly lower compared to ESSENCE generated alerts across all major syndromic syndromes and demonstrated higher sensitivity to seasons (i.e., ILI activity in winter). Reducing alerting fatigue would enhance specificity of computer-generated alerts, promoting more usage and gradual improvement in the algorithm's output.

Use of Severity Indicators in a Public Health Surveillance System

While other surveillance systems may only use death and admissions as severity indicators, these serious events may overshadow the more subtle severity signals based on appointment type, disposition from an outpatient setting, and whether that patient had to return for care if they their condition has not improved. This abstract discusses how these additional data fields were utilized in a fusion model to improve the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE).