scholarly journals Impact of the NSSP’s transition to ESSENCE on chief complaint field-based syndromes

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Rasneet S Kumar ◽  
Jessica R White
Keyword(s):  
Time Series ◽  
Syndromic Surveillance ◽  
Data Access ◽  
Case Finding ◽  
Chief Complaint ◽  
Analytical Tool ◽  
Free Text ◽  
Guidance Document ◽  
Maricopa County ◽  
Analytical Tools

Objective: To evaluate the effect and implications of changing the chief complaint field during the National Syndromic Surveillance Program (NSSP) transition from BioSense 2.0 analytical tools to BioSense Platform – ESSENCEIntroduction: In January 2017, the NSSP transitioned their BioSense analytical tools to Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE). The chief complaint field in BioSense 2.0 was a concatenation of the record’s chief complaint, admission reason, triage notes, and diagnostic impression. Following the transition to ESSENCE, the chief complaint field was comprised of the first chief complaint entered or the first admission reason, if the chief complaint was blank. Furthermore, the ESSENCE chief complaint field was electronically parsed (i.e., the original chief complaint text was altered to translate abbreviations and remove punctuation). This abstract highlights key findings from Maricopa County Department of Public Health’s evaluation of the new chief complaint field, its impact on heat-related illness syndromic surveillance, and implications for ongoing surveillance efforts.Methods: For this evaluation, we used the heat-related illness query recommended in Council of State and Territorial Epidemiologists’ (CSTE)2016 Guidance Document for Implementing Heat-Related Illness Syndromic Surveillance. Before the transition, we used BioSense 2.0’s, phpMyAdmin analytical tool to generate a list of patients who visited Maricopa County emergency departments or inpatient hospitals between 5/1/2016 – 9/30/2016 due to heat-related illness. After the transition, we used the CC and DD Category “Heat-related Illness, v1” in ESSENCE, which was based on the CSTE heat-related illness query, to generate a list of patients for the same time period. We compared the line-lists and time-series trends from phpMyAdmin and ESSENCE.Results: The phpMyAdmin analytical tool identified 785 heat-related illness records with the query (Figure). 642 (82%) of these heat-related illness records were also captured by ESSENCE. Reasons for 143 (18%) records not being identified by ESSENCE included: the patient’s admission reason field contained keywords that were not available in the ESSENCE chief complaint field (n=94, 66%); data access changed, which disabled access to patients who resided in zip codes that crossed a county border (30, 21%); discrepancies between ESSENCE parsing and text in the original chief complaint (11, 8%); heat-related illness discharge diagnoses were removed by the facility after the phpMyAdmin line-list for heat-related illness was extracted (7, 5%); and one record was undetermined. Conversely, ESSENCE captured 36 additional heat-related illness records, not previously captured by phpMyAdmin. Reasons included: a query exclusion term was located in the patient’s admission reason but not the ESSENCE chief complaint field (16, 44%); a heat-related illness discharge diagnosis code was added by the facility after the data were extracted by phpMyAdmin (4, 11%); and 16 (44%) were undetermined. Time-series trend evaluation revealed a significant correlation between the two surveillance tools (Pearson coefficient = 0.97, p < 0.01).Conclusions: Though the data trends over time were not significantly affected by changes in the chief complaint field, differences in the field’s composition have important implications for syndromic surveillance practitioners. Free-text queries designed to search the chief complaint field in ESSENCE may not retrieve records previously identified with BioSense 2.0 analytical tools, which may limit individual case-finding capacity. The elimination of admission reason from the chief complaint field in ESSENCE has the greatest effect on case-finding capacity. Furthermore, surveillance reports produced by ESSENCE cannot be directly compared to reports that were previously published with data from BioSense 2.0. These limitations may be addressed if ESSENCE creates a feature that allows users to easily query fields (e.g., admission reason) in addition to the chief compliant field.

scholarly journals The Evaluation of Triage Notes Using ESSENCE-FL for Active Case Finding of Zika

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Allison B. Culpepper ◽  
David Atrubin ◽  
Janet J. Hamilton
Keyword(s):  
Syndromic Surveillance ◽  
Case Finding ◽  
Chief Complaint ◽  
Urgent Care ◽  
Outbreak Detection ◽  
Free Text ◽  
Active Case Finding ◽  
Discharge Diagnosis ◽  
Active Case ◽  
Foreign Travel

ObjectiveThis study assesses the utilization of triage notes from emergencydepartments (EDs) and urgent care centers (UCCs) for active casefinding in ESSENCE-FL during the Zika response.IntroductionThe Florida Department of Health (DOH) utilizes the ElectronicSurveillance System for the Early Notification of Community BasedEpidemics (ESSENCE-FL) as its statewide syndromic surveillancesystem. ESSENCE-FL comprises of chief complaint data from231 of 240 EDs, representing 96 percent of the total number of EDsin Florida. Historically, syndromic surveillance has categorizedpatient chief complaint data into syndromes for the purpose of diseasesurveillance or outbreak detection. Triage notes are much longer free-text, pre-diagnostic data that capture the presenting symptoms andcomplaints of a patient.MethodsTriage notes are being collected from 24 EDs, representing tenpercent of total reporting EDs, and seven UCCs, representing 17%of total reporting UCCs. Triage notes were made a searchable fieldin ESSENCE-FL during Zika enhanced surveillance efforts, whichfacilitated additional case finding of Zika.During the period of February 3, 2016 – July 25, 2016, a free-textquery was created to run against the concatenated chief complaint-discharge diagnosis (CCDD) and triage note fields:^zika^,or,^ziki^,or,^zica^,or,^zeeka^,or,^zeeca^,or,^microcep^,or,^zyka^Additional queries were created to detect foreign travel visits ofinterest within the CCDD and triage note fields. Results of thesequeries were analyzed and communicated to county and regionalepidemiologists daily for investigation.ResultsThe triage note specific queries identified 18 Zika triage note and11 foreign travel triage note visits of interest. All of these visits werereviewed and investigated by county epidemiologists. These triagenote queries identified one case of Zika that had not been previouslyreported to public health. Of note, seven additional cases of Zikainfection were identified using the CCDD field in ESSENCE-FL (fiveof the seven flagged in both the CCDD and triage note field).ConclusionsResults from this analysis provide evidence that triage notes withinsyndromic surveillance systems play a role in active case finding whenemerging diseases arise. However, only 31 out of 272 total reportingfacilities are submitting triage note to ESSENCE-FL, representingonly 11% of reporting facilities.Relying on chief complaint and discharge diagnosis data onlywould have resulted in an undetected case of Zika that would havenot been captured by our free-text Zika query.The increased detection of Zika cases allows for public healthintervention, including mosquito control response, which in turnreduces the chance of Zika spreading locally in Florida. Triagenotes often provide pertinent information for determining when aflagged CCDD needs to be investigated further. Making triage notesa required data element for Meaningful Use compliance would benefitcase finding conducted through syndromic surveillance.

scholarly journals Developing and Validating a Fireworks-Related Syndrome Definition in Kansas

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Zachary M Stein
Keyword(s):  
Syndromic Surveillance ◽  
Public Information ◽  
False Positives ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Anatomical Location ◽  
Free Text ◽  
Data Set ◽  
Ed Visits ◽  
Key Terms

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.

scholarly journals Evaluation of a Novel Syndromic Surveillance Query for Heat-Related Illness Using Hospital Data From Maricopa County, Arizona, 2015

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 31S-39S ◽  
Author(s):  
Jessica R. White ◽  
Vjollca Berisha ◽  
Kathryn Lane ◽  
Henri Ménager ◽  
Aaron Gettel ◽  
...  
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Hospital Data ◽  
Maricopa County ◽  
Predictive Values ◽  
Diagnosis Codes ◽  
Illness Prevention ◽  
Council Of State

Objectives: We evaluated a novel syndromic surveillance query, developed by the Council of State and Territorial Epidemiologists (CSTE) Heat Syndrome Workgroup, for identifying heat-related illness cases in near real time, using emergency department and inpatient hospital data from Maricopa County, Arizona, in 2015. Methods: The Maricopa County Department of Public Health applied 2 queries for heat-related illness to area hospital data transmitted to the National Syndromic Surveillance Program BioSense Platform: the BioSense “heat, excessive” query and the novel CSTE query. We reviewed the line lists generated by each query and used the diagnosis code and chief complaint text fields to find probable cases of heat-related illness. For each query, we calculated positive predictive values (PPVs) for heat-related illness. Results: The CSTE query identified 674 records, of which 591 were categorized as probable heat-related illness, demonstrating a PPV of 88% for heat-related illness. The BioSense query identified 791 patient records, of which 589 were probable heat-related illness, demonstrating a PPV of 74% for heat-related illness. The PPV was substantially higher for the CSTE novel and BioSense queries during the heat season (May 1 to September 30; 92% and 85%, respectively) than during the cooler seasons (55% and 29%, respectively). Conclusion: A novel query for heat-related illness that combined diagnosis codes, chief complaint text terms, and exclusion criteria had a high PPV for heat-related illness, particularly during the heat season. Public health departments can use this query to meet local needs; however, use of this novel query to substantially improve public health heat-related illness prevention remains to be seen.

scholarly journals Triage Notes in Syndromic Surveillance – A Double Edged Sword

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Zachary Faigen ◽  
Amy Ising ◽  
Lana Deyneka ◽  
Anna E. Waller
Keyword(s):  
Syndromic Surveillance ◽  
Meaningful Use ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Free Text ◽  
Detailed Data ◽  
Hospital Level ◽  
Data Field ◽  
Emergency Department Triage ◽  
The Many

The advent of Meaningful Use has allowed for the expansion of data collected at the hospital level and received by public health for syndromic surveillance. The triage note, a free text expansion on the chief complaint, is one of the many variables that are becoming commonplace in syndromic surveillance data feeds. This roundtable will provide a forum for the ISDS community to discuss the use of emergency department triage notes in syndromic surveillance. It will be an opportunity to discuss both the benefits of having this variable included in syndromic surveillance data feeds, as well as the drawbacks and challenges associated with working with such a detailed data field.

scholarly journals Use of N-grams and Term Relationship Graphs in the Syndrome Definition Development Process

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Nimi Idaikkadar ◽  
Nelson Adekoya ◽  
Aaron Kite-Powell ◽  
Achintya N Dey
Keyword(s):  
Public Health ◽  
Time Series ◽  
Syndromic Surveillance ◽  
Development Process ◽  
Chief Complaint ◽  
Discharge Diagnosis ◽  
Diagnosis Code ◽  
Diagnostic Codes ◽  
Time Period ◽  
The Relationship

ObjectiveTo describe the use of uni-grams, bi-grams, and tri-grams relationships in the development of syndromic categories.IntroductionThe use of syndromic surveillance systems has evolved over the last decade, and increasingly includes both infectious and non-infectious topic areas. Public health agencies at the national, state, and local levels often need to rapidly develop new syndromic categories, or improve upon existing categories, to enhance their public health surveillance efforts. Documenting this development process can help support increased understanding and user acceptance of syndromic surveillance. This presentation will highlight the visualization process being used by CDC’s National Syndromic Surveillance Program (NSSP) program to develop and refine definitions for syndromes of interest to public health programs.MethodsDevelopment of a syndromic definition is an iterative process that starts with an analyst testing how different terms, which are assumed to be associated with the topic of interest, and diagnostic codes are noted in the chief complaint and discharge diagnosis code fields. The analyst then manually scans through the resulting line list of patient chief complaint text and diagnostic codes to determine whether the query terms match the intended syndromic concept. Typically, more terms and diagnostic codes are then added to the query using Boolean operators, and other terms are negated and removed. To facilitate summarization of the resulting terms and diagnostic codes CDC’s NSSP program developed programs with R that extracted data using the ESSENCE application programming interface (API), and the chief complaint query validation data source (CCQV). We use N-gram analysis, which is extensively used in text mining, to show co-occurrences of words in a consecutive order. The co-occurrences of words can be a uni-gram which represents a single word, bi-gram for two words, and tri-grams for three words. The process tokenizes the chief complaint text and diagnosis code fields, with some pre-processing of the text and removal of stopwords. Uni-grams, bi-grams, and tri-grams are then calculated for the top 200 combinations along with term and diagnostic code co-occurrence. Other visualizations that can be used are network graphs, which show the connections between different chief complaints terms and also between discharge diagnosis codes and chief complaint terms. The use of these graphs provides an insight into the frequency and relationship between terms and codes.ResultsTo support the development of new syndrome definitions we used the R program to produce two time series graphs. The first time series graph is used to show the volume of visits over the user’s indicated time period and the second shows the median chief complaint compared over the user’s indicated time period. A series of histograms showing frequency of the uni-gram, bi-grams, and tri-grams are also used during the development process. Lastly, two network diagrams are used to show the co-occurrence between term and diagnostic codes. The use of this range of graphs during the syndrome definition development process provides multiple ways to view the characteristics of the chief complaint and discharge diagnosis fields.The sample graphs below can be used by the analyst to illustrate key information.ConclusionsThrough this development process and the use of graphs the relationship between the syndrome definition and search terms can be visualized. In addition when using this process, the analyst could be specific as to the syndrome of interest or be broad, allowing a generic trend series monitoring of the syndrome. The search words can also be based on specific local or regional terms and the relationship terms set to include or exclude certain terms. Use of this process for the development of syndrome definitions can support the use of syndromic surveillance and offer the opportunity to further refine the process. After the syndrome has been developed, the analyst can consider spatial or temporal analysis. 

scholarly journals Using Evaluation to Inform the BioSense Platform: Results from a 2018 Survey

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Cassandra N. Davis
Keyword(s):  
User Satisfaction ◽  
Present Status ◽  
Syndromic Surveillance ◽  
Data Flow ◽  
Data Access ◽  
Comfort Level ◽  
Acceptance Testing ◽  
Free Text ◽  
Data Access Control ◽  
System Usability Scale

ObjectiveTo assess the present status of utility, functionality, usability and user satisfaction of the BioSense Platform.IntroductionSince 2015, CDC’s Division of Health Informatics and Surveillance staff have conducted evaluations to provide information on the utility, functionality, usability and user satisfaction associated with the National Syndromic Surveillance Program’s BioSense Platform tools. The BioSense Platform tools include: 1) Access and Management Center (AMC), a tool that enables site administrators to manage users and data permissions; 2) Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE), a software application that enables syndromic surveillance related data visualization and analysis; 3) Adminer, a tool that allows users to access site data on the datamart; and 4) Rstudio, an application that can be used for data analysis and visualization. The evaluation findings have informed activities that led to improvements in functionality, development or procurement of platform associated tools, and development of resource materials. In May 2018, NSSP conducted an evaluation with eight jurisdictions that participated in the first user acceptance testing (UAT) evaluations in 2015. The purpose of the evaluation was to assess the present status of utility, functionality, usability and user satisfaction of the tools on the BioSense Platform, and delineate progress since 2015.MethodsCDC’s evaluation framework and utilization-focused evaluation were used to inform and engage stakeholders, develop the evaluation questions, metrics, and methodology. Eight selected jurisdictions participated in an online, Epi-Info survey that captured quantitative and qualitative information. Prior to the survey, participants received a presentation about the evolution of the BioSense Platform since 2015, and were provided an overview of components to evaluate. The participants were asked to assess the following key areas based on use of the BioSense Platform within the past 30 days: 1) the utility, functionality and usability of the AMC, ESSENCE, Adminer and Rstudio; 2) how well the enhanced data flow has enabled them to conduct syndromic surveillance activities; 3) usefulness of the quick start guides. Additionally, participants were asked to provide suggestions for other improvements to the BioSense Platform, and to indicate their overall satisfaction. Descriptive statistics were generated and thematic analysis was conducted to identify themes from qualitative responses.ResultsOverall, participant’s responses remained positive about the utility, functionality, usability and overall satisfaction of the BioSense Platform. Participants indicated using the BioSense Platform regularly (e.g. daily, weekly and/or monthly) within those 30 days. Certain functions have been used more than others across the various tools to conduct syndromic surveillance, with at least 50% of participants reporting use. These included creating data access rules, viewing and verifying raw and processed data, running time series, conducting free-text queries, and assessing data details and total ER visit counts by hospital, county/region, or state. The challenges ranged from tool performance to user interpretation of the function. Participants reported that the enhanced data flow improved their data quality and helped identify issues. Although participants scored ESSENCE to have average usability per the system usability scale (SUS score=63.5 in 2018), the BioSense Platform and its tools were reported as useful by 88% of participants. Further, participants continue to be comfortable using the AMC, however creating data access rules that are outside of simple use cases continue to be a challenge. Participants comfort level with Adminer improved from 2016 to 2018 with all participants reporting comfortable in using the tool. The use of each tool’s quick start guides varied. Of those who used the guides, all of the participants agreed that the Adminer and Data Dictionary guides were useful. There was a smaller number of participants agreeing that the other guides were useful. Lastly, participants provided recommendations to improving the BioSense Platform. The most frequent recommendations were improving the data access control architecture, and sharing aggregate data with hospitals in their state.ConclusionsThe development and operationalization of the BioSense Platform and associated tools has been in an environment of continuing advancements in technology and changing public health needs and priorities. Up-to-date evaluation activities have helped to ensure that BioSense is best suited to address these challenges and meet the syndromic surveillance needs of users. Overall, the findings outlined above indicate that the functionality and utility of BioSense are well suited to meet user needs.ReferencesBangor A, Kortm P, Miller J. Determining What Individual SUS Scores Mean: Adding an Adjective Rating Scale. Journal of Usability Studies. 2009; 4:114-123. 

scholarly journals Using Syndromic Surveillance to Classify and Capture Non-Fatal Occupational Injuries and Illnesses

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Marija Borjan ◽  
Margaret Lumia
Keyword(s):  
Real Time ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Occupational Injuries ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Free Text ◽  
Syndromic Surveillance System ◽  
Work Related ◽  
Data Source

ObjectivesTo evaluate the use of a real-time surveillance tool to track a variety of occupationally-related emergency room visits through the state based syndromic surveillance system, EpiCenter.IntroductionThis study uses data from the New Jersey syndromic surveillance system (EpiCenter) as a data source to enhance surveillance of current non-fatal occupational injuries, illnesses, and poisonings. EpiCenter was originally developed for early detection and monitoring of the health of communities using chief complaints from people seeking acute care in hospital emergency rooms to identify health trends. Currently, syndromic surveillance has not been widely applied to identify occupational injuries and illnesses. Incorporating syndromic surveillance data from EpiCenter, along with hospital discharge data, will enhance the classification and capture of work-related non-fatal injuries with possible improved efforts at prevention.MethodsEpiCenter Emergency Department data from January to December 2014 was evaluated, using work-related keywords and ICD-9 codes, to determine its ability to capture non-fatal work-related injuries. A collection of keywords and phrases specific to work-related injuries was developed by manually assessing the free text chief complaint data field’s. Sensitivity, specificity, and positive predictive value (PPV), along with descriptive statistics was used to evaluate and summarize the occupational injuries identified in EpiCenter.ResultsOverall, 11,919 (0.3%) possible work-related injuries were identified via EpiCenter. Of these visits 956 (8%) indicated Workman’s Compensation as payer. Events that resulted in the greatest number of ED visits were falls, slips, trips (1,679, 14%). Nature of injury included cuts, lacerations (1,041, 9%), burns (255, 2%), and sprains, strains, tears (185, 2). The part of the body most affected were the back (1,414, 12%). This work-related classifier achieved a sensitivity of 5.4%, a specificity of 99.8%, and a PPV of 2.8%.ConclusionsEvaluating the ability and performance of a new and existing surveillance data source to capture work-related injuries can lead to enhancements in current data collection methods. This evaluation successfully demonstrated that the chief complaint reporting system can yield real-time knowledge of incidents and local conditions for use in identifying opportunities for prevention of work-related injuries. 

scholarly journals Keyword Surveillance and the Development of Evolving Reporting in Austin, Texas

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Ashley N. Hawes
Keyword(s):  
Public Health ◽  
Social Media ◽  
Emergency Preparedness ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Public Health Emergency ◽  
Free Text ◽  
Local Health ◽  
Open Communication ◽  
Public Health Emergency Preparedness

ObjectiveAustin Public Health's Public Health Emergency Preparedness program utilizes a variety of tools and resources to create informative, event-specific, and engaging syndromic surveillance reports to share 1) internally within Austin Public Health; 2) with City of Austin and Travis County partners; 3) local health care coalition members; and 4) the public during events that affect the Austin, Texas metropolitan area.IntroductionAustin Public Health creates a variety of syndromic surveillance reports for events throughout the Austin, Texas metropolitan area. These events range from responses to major disasters such as the 2017 Hurricane Harvey sheltering to ongoing special event monitoring such as University of Texas football games and the Austin City Limits music festival. Partnerships within the Austin metropolitan region are crucial to ensuring the information-sharing necessary to create robust reports, as well as during the follow-up process of requesting feedback from partners on the usefulness of the reports. Austin Public Health's Public Health Emergency Preparedness program utilizes a variety of tools and resources to create informative, event-specific, and engaging reports, fulfilling multiple reporting needs for all partners.MethodsThe process of generating syndromic surveillance reports begins by keyword surveillance of hospital emergency room chief complaint data. Keywords are keyed into the Austin metropolitan area's hospital free-text chief complaints via the Capital Area Public Health and Medical Coalition. The searchable keywords are queried to create a baseline picture of an evolving event. Data are also requested and gathered from multiple partners including local news stations, the National Weather Service, the City of Austin’s Office of Vital Records (birth and death certificates), social media platforms, Austin 3-1-1, and Austin/Travis County Emergency Medical Services. All data are then analyzed, visualized and displayed in reports that are distributed via multiple platforms including email, social media, governmental websites, Geographic Information System (GIS) storymaps, and WebEOC. Reports are then combined into event end summaries. Accompanying the final summary report are feedback surveys.ResultsThe ability to request keywords in an open communication pathway between hospitals, the Capital Area Public Health and Medical Coalition, and the local health department has bolstered area partnerships. Previous surveillance reports have been reported to be both useful and beneficial to departmental, community and health coalition partners. For example, the 2017 report following Hurricane Harvey was used by local hospitals for planning staffing and surge needs, and the 2018 heat report is being used to determine the placement of future cooling stations at special events. A 2019 surveillance report on dockless scooter injuries will be used to inform risk factors and trauma injury severity. Requested changes from partners have included: the addition of graphs, keyword-specific changes, inclusion of social media and broadcast media data, and the use of information from other partners to create a final event or year-end summary report.ConclusionsKeyword surveillance of hospital chief complaint data and of other local real-time data are innovative tools to creating meaningful syndromic surveillance reports that provide situational awareness and are adaptable to the needs of events and situations in the area. The development and evolution of these syndromic surveillance reports has helped to build a rapidly deployable syndromic surveillance system that can provide key data for preparing for and responding to future disaster events. By engaging local and regional partners in an iterative process for developing these reports, APH ensures ongoing improvement, thereby providing more powerful and useful reports to all partners involved. 

scholarly journals Using Emergency Department Data to Conduct Dog and Animal Bite Surveillance in New York City, 2003–2006

2012 ◽  
Vol 127 (2) ◽  
pp. 195-201 ◽  
Author(s):  
Brooke Bregman ◽  
Sally Slavinski
Keyword(s):  
Emergency Department ◽  
New York ◽  
New York City ◽  
York City ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
The United States ◽  
Chief Complaint ◽  
Free Text ◽  
Animal Bites

Objectives. Most animal bites in the United States are due to dogs, with approximately 4.7 million reports per year. Surveillance for dog and other animal bites requires a substantial investment of time and resources, and underreporting is common. We described the use and findings of electronic hospital emergency department (ED) chief complaint data to characterize patients and summarize trends in people treated for dog and other animal bites in New York City (NYC) EDs between 2003 and 2006. Methods. Retrospective data were obtained from the syndromic surveillance system at the NYC Department of Health and Mental Hygiene. We used a statistical program to identify chief complaint free-text fields as one of four categories of animal bites. We evaluated descriptive statistics and univariate associations on the available demographic data. The findings were also compared with data collected through the existing passive reporting animal bite surveillance system. Results. During the study period, more than 6,000 animal bite patient visits were recorded per year. The proportion of visits for animal bites did not appear to change over time. Dog bites accounted for more than 70% and cat bites accounted for 13% of animal bite patient visits. Demographic characteristics of patients were similar to those identified in NYC's passive surveillance system. Conclusions. Our findings suggest that the use of ED data offers a simple, less resource-intensive, and sustainable way of conducting animal bite surveillance and a novel use of syndromic surveillance data. However, it cannot replace traditional surveillance used to manage individual patients for potential rabies exposures.

scholarly journals Evaluation of an arboviral syndrome query used in Maricopa County, Arizona

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Kaitlyn Sykes ◽  
Rasneet S. Kumar ◽  
Melissa Kretschmer ◽  
Jessica R. White
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Maricopa County ◽  
Diagnosis Codes ◽  
Arboviral Diseases ◽  
Surveillance Protocol ◽  
The Difference ◽  
Arboviral Disease

ObjectiveTo evaluate Arizona’s arboviral syndromic surveillance protocol in Maricopa County.IntroductionTimely identification of arboviral disease is key to prevent transmission in the community, but traditional surveillance may take up to 14 days between specimen collection and health department notification. Arizona state and county health agencies began monitoring National Syndromic Surveillance Program BioSense 2.0 data for patients infected with West Nile virus (WNV), St. Louis encephalitis virus (SLEV), chikungunya, or dengue virus in August 2015. Zika virus was added in April 2016. Our novel methods were presented at the International Society for Disease Surveillance 2015 Annual Conference. [1] Twice per week, we queried patient records from 15 Maricopa County BioSense-enrolled emergency department and inpatient hospitals for chief complaint keywords and discharge diagnosis codes. Our “Case Investigation Decision Tree” helped us determine whether records had a high or low degree of evidence for arboviral disease and necessitated further investigation. This study evaluated how Arizona’s protocol for conducting syndromic surveillance compared to traditional arboviral surveillance in terms of accuracy and timeliness in Maricopa County from August 2015 through December 2016.MethodsWe followed guidelines from the Centers for Disease Control and Prevention (CDC) to evaluate two major attributes of the protocol: accuracy [measured as positive predictive value (PPV) and sensitivity] and timeliness. [2] Arizona’s Medical Electronic Disease Surveillance Intelligence System (MEDSIS) was considered the “gold standard” system and BioSense was the test system. PPV was calculated as the proportion of records identified by BioSense that were reported to MEDSIS, regardless of final case classification. Sensitivity was the proportion of confirmed or probable cases in MEDSIS identified by BioSense. Though not all MEDSIS cases were seen at BioSense-reporting facilities, the sensitivity demonstrates how each query contributed to arboviral surveillance overall. We assessed timeliness in two ways: (1) the difference between the date when keywords or diagnosis codes were first identified by BioSense and the date the same patient was first reported to MEDSIS; and (2) the difference between the date the BioSense record was first reviewed by the Maricopa County Department of Public Health (MCDPH) syndromic surveillance team and the date the same patient was first investigated through MEDSIS by the MCDPH disease investigators. We assessed whether timeliness was affected by the method in which a record was identified in BioSense (i.e., chief complaint keyword or discharge diagnosis code).ResultsThe arboviral syndromic surveillance queries identified 62 records during the evaluation period (Table). For each arboviral query, the proportion of BioSense records that were also reported through MEDSIS ranged from 25.0% to 32.4%, except chikungunya, which had a PPV of 0%. BioSense records that had a high degree of evidence for arboviral disease tended to have a higher PPV compared to those with low evidence. BioSense records that were not already reported to MEDSIS met neither clinical nor exposure criteria for the arboviral diseases and were not deemed a public health risk. The sensitivities of the WNV and Zika queries to detect confirmed or probable cases in MEDSIS were 8.2% and 5.6%, respectively, while SLEV, chikungunya, and dengue queries were 0%. On average, patients were reported to MEDSIS 7 days prior to BioSense identifying keywords or diagnosis codes. In addition, MEDSIS cases were investigated by MCDPH disease investigators 10 days prior to MCDPH syndromic surveillance team review of BioSense records, on average. The average time between MEDSIS report date and BioSense identification date was shorter for BioSense records identified by chief complaint keywords than by diagnosis codes (4 and 8 days after MEDSIS, respectively).ConclusionsArizona’s arboviral syndromic surveillance protocol provided MCDPH with situational awareness, but BioSense data were not available more quickly than traditional mandated reporting. Through this process, we reviewed patient records that mentioned arboviral diseases and confirmed that these reportable conditions were captured in our traditional surveillance system. The decision tree was effective at prioritizing records for further investigation. Timeliness may be improved by updating the queries to include more chief complaint keywords and reviewing BioSense more than twice per week. MCDPH plans to evaluate Arizona’s updated arboviral syndromic surveillance protocol, which was adapted for BioSense Platform’s Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE).References1. White, J. R., Imholte, S., & Collier, K. (2016). Using Syndromic Surveillance to Enhance Arboviral Surveillance in Arizona. Online J Public Health Inform, 8(1), e81.2. German, R. R., et al. (2001). Updated guidelines for evaluating public health surveillance systems: recommendations from the Guidelines Working Group. MMWR Recomm Rep, 50(RR-13), 1-35.

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