Using ESSENCE-FL for Situational Awareness after National Reports of Increased Enterovirus D68 (EV-D68) Infections with Severe Outcomes, September 2014

In early September 2014, during the yearly increase in respiratory visits associated with the start of the school year, reports of more severe infection caused by Enterovirus D68 (EV-D68) in children in other parts of the country began circulating. Public health officials in Florida, as well as the media, questioned whether children in the state were being infected by this virus capable of causing more severe illness, especially among asthmatics. As is the case with many incipient outbreaks, syndromic surveillance played an integral role in early efforts to detect the presence of this illness

Jurisdictional Usage of the New ESSENCE Word Alert Feature

ObjectiveThe objective of this presentation is to describe the new word alertcapability in ESSENCE and how it has been used by the FloridaDepartment of Health (FDOH). Specifically, this presentation willdescribe how the word alert feature works to find individual chiefcomplaint terms that are occurring at an abnormal rate. It willthen provide usage statistics and first-person accounts of how thealerts have impacted public health practice for the users. Finally,the presentation will offer future enhancement possibilities and asummary of the benefits and shortcomings of this new feature.IntroductionSyndromic surveillance systems have historically focused onaggregating data into syndromes for analysis and visualization. Thesesyndromes provide users a way to quickly filter large amounts ofdata into a manageable number of streams to analyze. Additionally,ESSENCE users have the ability to build their own case definitionsto look for records matching particular sets of criteria. Those user-defined queries can be stored and analyzed automatically, along withthe pre-defined syndromes. Aside from these predefined and user-defined syndromic categories, ESSENCE did not previously providealerts based on individual words in the chief complaint text that hadnot been specified a priori. Thus, an interesting cluster of recordslinked only by non-syndromic keywords would likely not be broughtto a user’s attention.MethodsIn the FDOH ESSENCE system a new detection feature wasdeveloped to trigger alerts based on anomalous occurrence of termsin chief complaints.1This feature used Fisher’s Exact Test to testfrequencies of individual chief complaint terms relative to all termsin a 1-month baseline. The feature used a 7-day guard-band, andautomatically switched to an efficient chi-square test for sufficientlylarge term counts. A term triggered an alert if its p-value≤10E-4.This algorithm was then run on chief complaint sets both by hospitaland by region, with region assignment according to patient zip code.Results were then displayed in new visualizations showing alerts inword cloud and line listing form. Additionally, users were given theoption to ignore stop words, syndromic terms, and a user-created listof ignorable words in order to focus on words of greater interest.ResultsThe result of using the tool since June 2016 has seen three majorbenefits. First, the original intent for the system to notify users ofabnormal word clusters has proven useful. Users have been able to seeterms such asDisaster, ShelterandFireworkswhich were not part ofany prior syndromes and use these notifications to investigate possibleissues. The second benefit found by users was the ability to find newmisspellings or abbreviations commonly used by hospitals. The termsZykaandGLF(Ground Level Fall) are examples of these. Finally,the system has helped discover new trends in hospital processes. Forexample, the tool has helped discover first person and non-Englishphrases in the chief complaint. This observation led to the discoverythat some hospitals are using kiosks or mobile phone apps to allowpatients to enter their own chief complaints.ConclusionsThe word alert feature has provided value to the users of FDOHESSENCE. While accomplishing its initial goal of triggeringabnormal non-syndromic term usage, the additional ability to findnew misspellings and abbreviations may have even larger impact bykeeping syndrome and subsyndrome definitions up-to-date over timefor traditional syndromic alerting. Beyond these current benefits,additional visualization enhancements are under consideration.Additionally, the resources required to perform the detection aresubstantial, and implementation improvements are under developmentto improve the performance and enable more advanced free-textanomaly detection

How Should We Be Conducting Routine Analysis of Traditional Emergency Department Syndromic Surveillance Data?

This roundtable will focus on how traditional emergency department syndromic surveillance systems should be used to conduct daily or periodic disease surveillance.  As outbreak detection using these systems has demonstrated an equivocal track record, epidemiologists have sought out other interesting uses for these systems.  Over the numerous years of the International Society for Disease Surveillance (ISDS) Conference, many of these studies have been presented; however, there has been a dearth of discussion related to how these systems should be used. This roundtable offers a forum to discuss best practices for the routine use of emergency department syndromic surveillance data

Increased Seizure Activity in Florida Associated with Hurricane Irma, September 2017

ObjectiveUsing Florida’s syndromic surveillance data, to describe the increase in seizure activity in the days after Hurricane Irma made landfall in 2017IntroductionOn September 10, 2017, Irma made landfall in the Florida Keys as a Category 4 hurricane and subsequently tracked up the west side of the state. Due to the size of the storm, it impacted nearly all of Florida. The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL), the state’s syndromic surveillance system, captures 98% of the emergency department (ED) visits statewide and has historically served a vital function in providing near real-time ED data that are used to track post-disaster morbidity and mortality. After previous hurricanes and tropical storms, increases in carbon monoxide poisonings, animal bites, and injuries have been documented. During post-Irma surveillance, an additional increase in seizure-related ED visits was observed.MethodsTwice-daily Hurricane Irma surveillance reports were produced from Sept 10-22, 2017. In addition to specialized queries specific to storm surveillance, analysis was conducted using ESSENCE-FL’s syndrome and subsyndrome categories. The subsyndrome category of Seizure captures ED visits which list the words seizure or convulsion in the patient chief complaint. Daily number of seizure visits were compared against a 28-day baseline using an exponentially weighted moving average algorithm. Additionally, daily seizure visits as a percentage of total ED visits were calculated and plotted.ResultsOn September 11, 12, and 13, ED visits for seizures were increased above the expected levels. On these dates respectively, 336 visits (270 expected, p < 0.01), 349 visits (278 expected, p < 0.01), and 306 visits (267 expected, p < 0.01) seizure visits occurred statewide. September 10 showed the largest increase in seizure visits as a percent of all visits.ConclusionsRoutine post-storm surveillance was able to identify an increase in seizure visits at EDs in Florida. This hurricane-related increase, while not detected using our syndromic surveillance system during previous storms, supports findings of increased emergency medical service calls for convulsions and seizures after Hurricanes Katrina and Rita (both in 2005) found by other researchers (Davis et al., 2013). Due to the size, strength, and projected path of Hurricane Irma, stress (a known seizure trigger) is a possible biological explanation for the increase that was observed. A greater understanding of storm-related public health threats allows the Florida Department of Health to better plan for these events and communicate this information to the public and our partners. Post-storm analysis was complicated by large changes in overall ED volumes during and immediately following the hurricane, and further exploration of the association found in this study is encouraged.ReferencesDavis JS, Allan BJ, Pearlman AM, Carvajal DP, Schulman CI., Optimal emergency personnel allocation after a natural disaster, Am J Disaster Med. 2012 Winter;7(1):31-6

Effectiveness of Using a Chief Complaint and Discharge Diagnosis Query in ESSENCE-FL to Identify Possible Tuberculosis Patients and Contacts in Hillsborough County, Florida

The Electronic Surveillance System for the Early Notification of Community-based Epidemics in Florida (ESSENCE-FL) is a web-based application for use by epidemiologists within the Florida Department of Health and other public health professionals. In Hillsborough County, Florida a specific query has been developed to search for and identify possible tuberculosis patients and exposed contacts among the emergency department data in ESSENCE-FL. This study is designed to determine the usefulness of specific term-chief complaint and discharge diagnosis queries in identifying tuberculosis patients and exposed contact

Assessing Best Practices for Grouping and Analyzing Urgent Care Center (UCC) and Emergency Department (ED) Data Sources within Syndromic Surveillance Systems

The Florida Department of Health (FDOH) electronically receives both urgent care center (UCC) data and hospital emergency department (ED) data from health care facilities in 43 of its 67 counties through its Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL). This project will investigate and describe the differences between the data received from these two sources and provide best practices for grouping and analyzing these data sources

Jurisdictional Usage of the New ESSENCE Word Alert Feature

ObjectiveThe objective of this presentation is to describe the new word alertcapability in ESSENCE and how it has been used by the FloridaDepartment of Health (FDOH). Specifically, this presentation willdescribe how the word alert feature works to find individual chiefcomplaint terms that are occurring at an abnormal rate. It willthen provide usage statistics and first-person accounts of how thealerts have impacted public health practice for the users. Finally,the presentation will offer future enhancement possibilities and asummary of the benefits and shortcomings of this new feature.IntroductionSyndromic surveillance systems have historically focused onaggregating data into syndromes for analysis and visualization. Thesesyndromes provide users a way to quickly filter large amounts ofdata into a manageable number of streams to analyze. Additionally,ESSENCE users have the ability to build their own case definitionsto look for records matching particular sets of criteria. Those user-defined queries can be stored and analyzed automatically, along withthe pre-defined syndromes. Aside from these predefined and user-defined syndromic categories, ESSENCE did not previously providealerts based on individual words in the chief complaint text that hadnot been specified a priori. Thus, an interesting cluster of recordslinked only by non-syndromic keywords would likely not be broughtto a user’s attention.MethodsIn the FDOH ESSENCE system a new detection feature wasdeveloped to trigger alerts based on anomalous occurrence of termsin chief complaints.1This feature used Fisher’s Exact Test to testfrequencies of individual chief complaint terms relative to all termsin a 1-month baseline. The feature used a 7-day guard-band, andautomatically switched to an efficient chi-square test for sufficientlylarge term counts. A term triggered an alert if its p-value≤10E-4.This algorithm was then run on chief complaint sets both by hospitaland by region, with region assignment according to patient zip code.Results were then displayed in new visualizations showing alerts inword cloud and line listing form. Additionally, users were given theoption to ignore stop words, syndromic terms, and a user-created listof ignorable words in order to focus on words of greater interest.ResultsThe result of using the tool since June 2016 has seen three majorbenefits. First, the original intent for the system to notify users ofabnormal word clusters has proven useful. Users have been able to seeterms such asDisaster, ShelterandFireworkswhich were not part ofany prior syndromes and use these notifications to investigate possibleissues. The second benefit found by users was the ability to find newmisspellings or abbreviations commonly used by hospitals. The termsZykaandGLF(Ground Level Fall) are examples of these. Finally,the system has helped discover new trends in hospital processes. Forexample, the tool has helped discover first person and non-Englishphrases in the chief complaint. This observation led to the discoverythat some hospitals are using kiosks or mobile phone apps to allowpatients to enter their own chief complaints.ConclusionsThe word alert feature has provided value to the users of FDOHESSENCE. While accomplishing its initial goal of triggeringabnormal non-syndromic term usage, the additional ability to findnew misspellings and abbreviations may have even larger impact bykeeping syndrome and subsyndrome definitions up-to-date over timefor traditional syndromic alerting. Beyond these current benefits,additional visualization enhancements are under consideration.Additionally, the resources required to perform the detection aresubstantial, and implementation improvements are under developmentto improve the performance and enable more advanced free-textanomaly detection

Assessing Best Practices for Grouping and Analyzing Urgent Care Center (UCC) and Emergency Department (ED) Data Sources within Syndromic Surveillance Systems

The Florida Department of Health (FDOH) electronically receives both urgent care center (UCC) data and hospital emergency department (ED) data from health care facilities in 43 of its 67 counties through its Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL). This project will investigate and describe the differences between the data received from these two sources and provide best practices for grouping and analyzing these data sources

Monitoring Respiratory Syncytial Virus Regionally In Children Aged < 5 Years Old Using Emergency Department and Urgent Care Center Chief Complaint Data in Florida’s Syndromic Surveillance System, Week 1, 2010 - Week 32, 2014

ED chief complaint and discharge diagnosis data accessed through a syndromic surveillance system can be used for effective, timely monitoring of RSV hospitalizations in children &lt; 5 years old and may be a more efficient and complete means of monitoring seasonality of RSV activity by region and statewide compared to hospital-based laboratory data reporting. Additionally, this surveillance technique can efficiently monitor RSV activity as well as estimate hospital admissions due to RSV and may be a useful approach for other states with syndromic surveillance systems

How do we present messy syndromic surveillance data to public health’s partners?

Objective: To discuss data disclaimers and caveats that are fundamental to sharing syndromic surveillance (SyS) dataIntroduction: With increasing awareness of SyS systems, there has been a concurrent increase in demand for data from these systems – both from researchers and from the media. The opioid epidemic occurring in the United States has forced the SyS community to determine the best way to present these data in a way that makes sense while acknowledging the incompleteness and variability in how the data are collected at the hospital level and queried at the user level. While significant time and effort are spent discussing optimal queries, responsible presentation of the data - including data disclaimers - is rarely discussed within the SyS community.Description: This roundtable will provide a forum for national, state, and local users of syndromic surveillance systems to discuss these SyS data disclaimers. Over the last few months, an informal working group has crafted data disclaimer language. Members of this working group will facilitate the discussion and present their template for comment and discussion. Other members of the SyS will be encouraged to share their jurisdiction-specific data disclaimer language. The focus of this roundtable will be on effective communication of emergency department SyS data.How the Moderator Intends to Engage the Audience in Discussions on the Topic: This roundtable is well suited to audience participation as all jurisdictions are faced with how to communicate SyS data. Jurisdictions will likely have varying degrees of experience with disclaimers of this sort, so opportunities for sharing of work will be useful to the broader SyS community.Sample Questions:Does your jurisdiction have standardized language that accompanies your SyS data?How does the SyS community best share data that is often incomplete and subject to inter-hospital variability?What kinds of reporting would the SyS community like to come from the NSSP