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 Comparing and Contrasting Two ESSENCE Syndrome Definition Query Methods

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Zachary M. Stein ◽  
Sophia Crossen
Keyword(s):  
Best Practices ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Discharge Diagnosis ◽  
True Positive ◽  
Patient Visit ◽  
Production Facility ◽  
Time Period ◽  
Ed Visits

ObjectiveTo compare and contrast two ESSENCE syndrome definition query methods and establish best practices for syndrome definition creation.IntroductionThe Kansas Syndromic Surveillance Program (KSSP) utilizes the ESSENCE v.1.20 program provided by the National Syndromic Surveillance Program to view and analyze Kansas Emergency Department (ED) data.Methods that allow an ESSENCE user to query both the Discharge Diagnosis (DD) and Chief Complaint (CC) fields simultaneously allow for more specific and accurate syndromic surveillance definitions. As ESSENCE use increases, two common methodologies have been developed for querying the data in this way.The first is a query of the field named “CC and DD.” The CC and DD field contains a concatenation of the parsed patient chief complaint and the discharge diagnosis. The discharge diagnosis consists of the last non-null value for that patient visit ID and the chief complaint parsed is the first non-null chief complaint value for that patient visit ID that is parsed by the ESSENCE platform. For this comparison, this method shall be called the CCDD method.The second method involves a query of the fields named, “Chief Complaint History” and “Discharge Diagnosis History.” While the first requires only one field be queried, this method queries the CC History and DD History fields, combines the resulting data and de-duplicates this final data set by the C_BioSense_ID. Chief Complaint History is a list of all chief complaint values related to a singular ED visit, and Discharge Diagnosis History is the same concept, except involving all Discharge Diagnosis values. For this comparison, this method shall be called the CCDDHX method.While both methods are based on the same query concept, each method can yield different results.MethodsA program was created in R Studio to analyze a user-provided query.Simple queries were randomly generated. Twenty randomly generated queries were run through the R Studio program and disparities between data sets were recorded. All KSSP production facility ED visits during the month of August 2017 were analyzed.Secondly, three queries actively utilized in KSSP practice were run through the program. These queries were Firework-Related Injuries, Frostbite and Cold Exposure, and Rabies Exposure. The queries were run on all KSSP production facility ED visits, and coincided with the timeline of relevant exposures.ResultsIn the random query trials, an average of 5.4% of the cases captured using the CCDD field method were unique and not captured by the same query in the CCDDHX method. Using the CCDDHX method, an average of 6.1% of the cases captured were unique and not captured by the CCDD method.When using the program to compare syndromes from actively utilized KSSP practice, the disparity between the two methods was much lower.Firework-Related InjuriesDuring the time period queried, the CCDD method returned 171 cases and the CCDDHX method returned 169 cases. All CCDDHX method cases were captured by the CCDD method. The CCDD method returned 2 cases not captured by the CCDDHX method. These two cases were confirmed as true positive firework-related injury cases.Frostbite and Cold ExposureDuring the time period queried, CCDD method returned 328 cases and the CCDDHX method returned 344 cases. The CCDDHX method captured 16 cases that the CCDD method did not. The CCDD method did not capture any additional cases when compared to the CCDDHX method. After review, 10 (62.5%) of these 16 cases not captured by the CCDD method were true positive cases.Rabies ExposureDuring the time period queried, the CCDD method returned 474 cases and the CCDDHX method returned 473 cases. The CCDDHX method captured 7 cases that the CCDD method did not. The CCDD method returned 8 cases not captured by the CCDDHX method. After review, the 7 unique cases captured in the CCDDHX method contained 3 (42.9%) true positive cases and 3 (37.5%) of the 8 cases not captured by the CCDDHX method were true positives.ConclusionsThe twenty random queries showed a disparity between methods. When utilizing the same program to analyze three actively utilized KSSP definitions, both methods yielded similar results with a much smaller disparity. The CCDDHX method inherently requires more steps and requires more queries to be run through ESSENCE, making the method less timely and more difficult to share. Despite these downsides, CCDDHX will capture cases that appear throughout the history of field updates.Further variance between methods is likely due to the CCDD field utilizing the ESSENCE-processed CC while the CCDDHX field utilizes the CC verbatim as produced by the ED facility. This allows the CCDD method to tap into the powerful spelling correction and abbreviation-parsing steps that ESSENCE employs, but incorrect machine corrections and replacements, while rare, can negatively affect syndrome definition performance.The greater disparity in methods for the random queries may be due to the short (3 letter) text portion of the queries. Short segments are more likely to be found in multiple words than text of actual queries. Utilizing larger randomly generated text segments may resolve this and is a planned next step for this research.Our next step is to share the R Studio program to allow further replication. The Kansas Syndromic Surveillance Program is also continuing similar research to ensure that best practices are being met. 

scholarly journals Tracking drug-related overdoses at the local level: Using Syndromic Surveillance in the CO-NCR

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Emery Shekiro ◽  
Lily Sussman ◽  
Talia Brown
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Real Time ◽  
Drug Overdose ◽  
Syndromic Surveillance ◽  
Case Definition ◽  
Chief Complaint ◽  
Discharge Diagnosis ◽  
Diagnosis Codes ◽  
Drug Overdoses

Objective: In order to better describe local drug-related overdoses, we developed a novel syndromic case definition using discharge diagnosis codes from emergency department data in the Colorado North Central Region (CO-NCR). Secondarily, we used free text fields to understand the use of unspecified diagnosis fields.Introduction: The United States is in the midst of a drug crisis; drug-related overdoses are the leading cause of unintentional death in the country. In Colorado the rate of fatal drug overdose increased 68% from 2002-2014 (9.7 deaths per 100,000 to 16.3 per 100,000, respectively)1, and non-fatal overdose also increased during this time period (23% increase in emergency department visits since 2011)2. The CDC’s National Syndromic Surveillance Program (NSSP) provides near-real time monitoring of emergency department (ED) events across the country, with information uploaded daily on patient demographics, chief complaint for visit, diagnosis codes, triage notes, and more. Colorado North Central Region (CO-NCR) receives data for 4 local public health agencies from 25 hospitals across Adams, Arapahoe, Boulder, Denver, Douglas, and Jefferson Counties.Access to local syndromic data in near-real time provides valuable information for local public health program planning, policy, and evaluation efforts. However, use of these data also comes with many challenges. For example, we learned from key informant interviews with ED staff in Boulder and Denver counties, about concern with the accuracy and specificity of drug-related diagnosis codes, specifically for opioid-related overdoses.Methods: Boulder County Public Health (BCPH) and Denver Public Health (DPH) developed a query in Early Notification of Community Based Epidemics (ESSENCE) using ICD-10-CM codes to identify cases of drug-related overdose [T36-T51] from October 2016 to September 2017. The Case definition included unintentional, self-harm, assault and undetermined poisonings, but did not include cases coded as adverse effects or underdosing of medication. Cases identified in the query were stratified by demographic factors (i.e., gender and age) and substance used in poisoning. The first diagnosis code in the file was considered the primary diagnosis. Chief complaint and triage note fields were examined to further describe unspecified cases and to describe how patients present to emergency departments in the CO-NCR. We also explored whether detection of drug overdose visits captured by discharge diagnosis data varied by patient sex, age, or county.Results: The query identified 2,366 drug-related overdoses in the CO-NCR. The prevalence of drug overdoses differed across age groups. The detection of drug overdoses was highest among our youth and young adult populations; 16 to 20 year olds (16.0%), 21-25 year olds (11.4%), 26-30 year olds (11.4%). Females comprised 56.1% of probable general drug overdoses. The majority of primary diagnoses (31.0%) included poisonings related to diuretics and other unspecified drugs (T50), narcotics (T40) (12.6%), or non-opioid analgesics (T39) (7.8%). For some cases with unspecified drug overdose codes there was additional information about drugs used and narcan administration found in the triage notes and chief complaint fields.Conclusions: Syndromic surveillance offers the opportunity to capture drug-related overdose data in near-real time. We found variation in drug-related overdose by demographic groups. Unspecified drug overdose codes are extremely common, which likely negatively impacts the quality of drug-specific surveillance. Leveraging chief complaint and triage notes could improve our understanding of factors involved in drug-related overdose with limitations in discharge diagnosis. Chart reviews and access to more fields from the ED electronic health record could improve local drug surveillance.

scholarly journals Use of Emergency Department Data to Monitor and Respond to an Increase in Opioid Overdoses in New Hampshire, 2011-2015

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 73S-79S ◽  
Author(s):  
Elizabeth R. Daly ◽  
Kenneth Dufault ◽  
David J. Swenson ◽  
Paul Lakevicius ◽  
Erin Metcalf ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
New Hampshire ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
The United States ◽  
Chief Complaint ◽  
Public Health Response ◽  
Syndromic Surveillance System ◽  
Icd Codes

Objectives: Opioid-related overdoses and deaths in New Hampshire have increased substantially in recent years, similar to increases observed across the United States. We queried emergency department (ED) data in New Hampshire to monitor opioid-related ED encounters as part of the public health response to this health problem. Methods: We obtained data on opioid-related ED encounters for the period January 1, 2011, through December 31, 2015, from New Hampshire’s syndromic surveillance ED data system by querying for (1) chief complaint text related to the words “fentanyl,” “heroin,” “opiate,” and “opioid” and (2) opioid-related International Classification of Diseases ( ICD) codes. We then analyzed the data to calculate frequencies of opioid-related ED encounters by age, sex, residence, chief complaint text values, and ICD codes. Results: Opioid-related ED encounters increased by 70% during the study period, from 3300 in 2011 to 5603 in 2015; the largest increases occurred in adults aged 18-29 and in males. Of 20 994 total opioid-related ED visits, we identified 18 554 (88%) using ICD code alone, 690 (3%) using chief complaint text alone, and 1750 (8%) using both chief complaint text and ICD code. For those encounters identified by ICD code only, the corresponding chief complaint text included varied and nonspecific words, with the most common being “pain” (n = 3335, 18%), “overdose” (n = 1555, 8%), “suicidal” (n = 816, 4%), “drug” (n = 803, 4%), and “detox” (n = 750, 4%). Heroin-specific encounters increased by 827%, from 4% of opioid-related encounters in 2011 to 24% of encounters in 2015. Conclusions: Opioid-related ED encounters in New Hampshire increased substantially from 2011 to 2015. Data from New Hampshire’s ED syndromic surveillance system provided timely situational awareness to public health partners to support the overall response to the opioid epidemic.

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

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Kayley Dotson ◽  
Mandy Billman
Keyword(s):  
Public Health ◽  
Real Time ◽  
Random Sample ◽  
Syndromic Surveillance ◽  
Urgent Care ◽  
Discharge Diagnosis ◽  
Disease Clusters ◽  
Department Of Health ◽  
Coverage Gap ◽  
Administrative Code

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 >90% or <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.References1. Preparedness Districts [Internet]. Indianapolis (IN): Indiana State Department of Health, Public Health Preparedness; 2017 [Cited 2017 Sept 20]. Available from: https://www.in.gov/isdh/17944.htm. 

scholarly journals The Validity of Chief Complaint and Discharge Diagnosis in Emergency Department-based Syndromic Surveillance

2004 ◽  
Vol 11 (12) ◽  
pp. 1262-1267 ◽  
Author(s):  
Aaron T. Fleischauer ◽  
Benjamin J. Silk ◽  
Mare Schumacher ◽  
Ken Komatsu ◽  
Sarah Santana ◽  
...  
Keyword(s):  
Emergency Department ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Discharge Diagnosis

scholarly journals Using Syndromic Surveillance Alert Protocols for Epidemiologic Response in Georgia

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Rene Borroto ◽  
Bill Williamson ◽  
Patrick Pitcher ◽  
Lance Ballester ◽  
Wendy Smith ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Health Care Providers ◽  
Syndromic Surveillance ◽  
Emergency Department Patient ◽  
Chief Complaint ◽  
Care Providers ◽  
Public Health Response ◽  
Presumptive Diagnosis ◽  
Notifiable Diseases

ObjectiveDescribe how the Georgia Department of Public Health (DPH) usessyndromic surveillance to initiate review by District Epidemiologists(DEs) to events that may warrant a public health response (1).IntroductionDPH uses its State Electronic Notifiable Disease SurveillanceSystem (SendSS) Syndromic Surveillance (SS) Module to collect,analyze and display results of emergency department patient chiefcomplaint data from hospitals throughout Georgia.MethodsDPH prepares a daily SS report, based upon the analysis ofdaily visits to 112 Emergency Department (EDs). The visits areclassified in 33 syndromes. Queries of chief complaint and dischargediagnosis are done using the internal query capability of SendSS-SSand programming in SAS/BASE. Charting of the absolute countsor percentage of ED visits by syndromes is done using the internalcharting capability of SendSS-SS. A daily SS report includes thefollowing sections:Statewide Emergency Department Visitsby Priority Syndromes(Bioterrorism, BloodyRespiratory,FeverRespiratory, FeverChest, FeverFluAdmit, FeverFluDeaths,VeryIll, andPoxRashFever, Botulism, Poison, BloodyDiarrhea,BloodyVomit, FeverGI, ILI, FeverFlu, RashFever, Diarrhea,Vomit).Statewide Flag Analysis: Is intended to detect statewideflags, by using theChartscapability in SendSS SS.Possible caseswith presumptive diagnosis of potentially notifiable diseases: Isintended to provide early-warning to the DEs of possible cases thatare reportable to public health immediately or within 7 days usingqueries in the Chief Complaint and Preliminary Diagnosis fields ofSendSS-SS.Possible clusters of illness: Since any cluster of illnessmust be reported immediately to DPH, this analysis is aimed atquerying and identifying possible clusters of patients with similarsymptoms (2).Possible travel-related illness: Is intended to identifypatients with symptoms and recent travel history.Other events ofinterest: Exposures to ill patients in institutional settings (e.g. chiefcomplaint indicates that other children in the daycare have similarsymptoms).Trend Analysis: Weekly analysis of seasonality andtrends of 14 syndromes. Finally, specific events are notified to andreviewed by the 18 DEs, who follow up by contacting the InfectionPreventionists of the hospitals to identify the patients using medicalrecords or other hospital-specific identification numbers and followup on the laboratory test results.ResultsSince 05/15/2016, 12 travel-related illnesses, 29 vaccine-preventable diseases, 14 clusters, and 3 chemical exposures havebeen notified to DEs. For instance, a cluster of chickenpox in childrenwas identified after the DE contacted the Infection Preventionist ofa hospital, who provided the DE with the laboratory results and thephysician notes about the symptoms of the patients. These actionshave resulted in earlier awareness of single cases or cluster of illness,prompt reporting of notifiable diseases, and successful interactionbetween DEs and health care providers. In addition, SS continues totrack the onset, peak, and decline of seasonal illnesses.ConclusionsThe implementation of SS in the State of Georgia is helping withthe timely detection and early responses to disease events and couldprove useful in reducing the disease burden caused by a bioterroristattack.

scholarly journals NIST Syndromic Surveillance Test Suite 2015 Edition

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Sheryl L. Taylor ◽  
Robert Snelick
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Test Suite ◽  
Conformance Test ◽  
Test Tool ◽  
Certification Testing ◽  
Key Features ◽  
Relationship Of ◽  
The Relationship

ObjectiveThe NIST Syndromic Surveillance Test Suite for 2015 EditionONC certification testing was published in February 2016. Keyinformation related to the purpose, development, and use of thisconformance test tool is provided via snapshots on a poster.IntroductionDetails about the ONC 2015 Edition certification criteria forSyndromic Surveillance and the related NIST Test Suite wereexplained previously. We now provide an overview and keyinformation regarding updates to the Test Suite and how it is designedto be used.MethodsSnapshots are provided on a poster and are used by the presenterto explain the steps involved in developing the NIST SyndromicSurveillance Test Suite 2015 Edition, to show key features of andupdates to the Test Suite, and to illustrate the relationship of the TestSuite to various releases of the PHIN Messaging Guide for SyndromicSurveillance.ResultsThe NIST Syndromic Surveillance Test Suite for 2015 EditionONC certification testing was published in February 2016. As thetarget stakeholders began using it and providing feedback, this tooland associated documentation were updated. The Test Suite is beingused by test labs for ONC certification testing of health informationtechnologies, by developers in preparation for certification testing,and ultimately by public health jurisdictions for on-boarding ofprovider organizations that need to submit surveillance data.ConclusionsAutomated conformance test tools enable validation of healthinformation technologies’ ability to support the requirementspublished in the PHIN Messaging Guide for Syndromic Surveillance.Having this standard and the means to validate conformance helpsdrive the industry toward the level of interoperability needed topromote efficient reporting and utilization of syndrome-based publichealth surveillance information.

scholarly journals Using ESSENCE to Detect Bomb-Making Activity: What’s Appropriate?

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Andrew Torgerson
Keyword(s):  
Public Health ◽  
Law Enforcement ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Chemical Mechanism ◽  
Mass Gathering ◽  
Surveillance Period ◽  
Saint Louis ◽  
Ed Visits

ObjectiveTo describe a novel application of ESSENCE by the Saint Louis County Department of Public Health (DPH) in preparation for a mass gathering and to encourage discussion about the appropriateness of sharing syndromic surveillance data with law enforcement partners.IntroductionIn preparation for mass gathering events, DPH conducts enhanced syndromic surveillance activities to detect potential cases of anthrax, tularemia, plague, and other potentially bioterrorism-related communicable diseases. While preparing for Saint Louis to host a Presidential Debate on October 9, 2016, DPH was asked by a partner organization whether we could also detect emergency department (ED) visits for injuries (e.g., burns to the hands or forearms) that could possibly indicate bomb-making activities.MethodsUsing the Electronic Surveillance System for the Notification of Community-Based Epidemics (ESSENCE), version 1.9, DPH developed a simple query to detect visits to EDs in Saint Louis City or Saint Louis County with chief complaints including the word “burn” and either “hand” or “arm.” A DPH epidemiologist reviewed the results of the query daily for two weeks before and after the debate (i.e., from September 25, 2016 to October 23, 2016). If any single ED visit was thought to be “suspicious” – if, for example, the chief complaint mentioned an explosive or chemical mechanism of injury – then DPH would contact the ED for details and relay the resulting information to the county’s Emergency Operations Center.ResultsDuring the 29 day surveillance period, ESSENCE detected 27 ED visits related to arm or hand burns. The ESSENCE query returned a median of 1 ED visit per day (IQR 0 to 2 visits). Of these, one was deemed to merit further investigation – two days before the debate, a patient presented to an ED in Saint Louis County complaining of a burned hand. The patient’s chief complaint data also mentioned “explosion of unspecified explosive materials.” Upon investigation, DPH learned that the patient had been injured by a homemade sparkler bomb. Subsequently, law enforcement determined that the sparkler bomb had been made without any malicious intent.ConclusionsDPH succeeded in using ESSENCE to detect injuries related to bomb-making. However, this application of ESSENCE differs in at least two ways from more traditional uses of syndromic surveillance. First, conventional syndromic surveillance is designed to detect trends in ED visits resulting from an outbreak already in progress or a bioterrorist attack already carried out. In this case, syndromic surveillance was used to detect a single event that could be a prelude to an attack. The potential to prevent widespread injury or illness is a strength of this approach. Second, conventional syndromic surveillance identifies potential outbreak cases or, in the case of a bioterrorist attack, potential victims. In this case, syndromic surveillance was used to identify a potential perpetrator of an attack. While public health and law enforcement agencies would ideally coordinate their investigative efforts in the wake of an attack, this practice has led to conversations within DPH about the appropriateness of routinely sharing public health surveillance data with law enforcement. 

scholarly journals Data Quality Improvements in National Syndromic Surveillance Program (NSSP) Data

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Girum S. Ejigu ◽  
Kakshmi Radhakrishnan ◽  
Paul McMurray ◽  
Roseanne English
Keyword(s):  
Data Quality ◽  
Syndromic Surveillance ◽  
Data Flow ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Discharge Diagnosis ◽  
Standard Data ◽  
Quality Checks ◽  
State And Local ◽  
Data Elements

ObjectiveReview the impact of applying regular data quality checks to assess completeness of core data elements that support syndromic surveillance.IntroductionThe National Syndromic Surveillance Program (NSSP) is a community focused collaboration among federal, state, and local public health agencies and partners for timely exchange of syndromic data. These data, captured in nearly real time, are intended to improve the nation's situational awareness and responsiveness to hazardous events and disease outbreaks. During CDC’s previous implementation of a syndromic surveillance system (BioSense 2), there was a reported lack of transparency and sharing of information on the data processing applied to data feeds, encumbering the identification and resolution of data quality issues. The BioSense Governance Group Data Quality Workgroup paved the way to rethink surveillance data flow and quality. Their work and collaboration with state and local partners led to NSSP redesigning the program’s data flow. The new data flow provided a ripe opportunity for NSSP analysts to study the data landscape (e.g., capturing of HL7 messages and core data elements), assess end-to-end data flow, and make adjustments to ensure all data being reported were processed, stored, and made accessible to the user community. In addition, NSSP extensively documented the new data flow, providing the transparency the community needed to better understand the disposition of facility data. Even with a new and improved data flow, data quality issues that were issues in the past, but went unreported, remained issues in the new data. However, these issues were now identified. The newly designed data flow provided opportunities to report and act on issues found in the data unlike previous versions. Therefore, an important component of the NSSP data flow was the implementation of regularly scheduled standard data quality checks, and release of standard data quality reports summarizing data quality findings.MethodsNSSP data was assessed for the national-level completeness of chief complaint and discharge diagnosis data. Completeness is the rate of non- null values (Batini et al., 2009). It was defined as the percent of visits (e.g., emergency department, urgent care center) with a non-null value found among the one or more records associated with the visit. National completeness rates for visits in 2016 were compared with completeness rates of visits in 2017 (a partial year including visits through August 2017). In addition, facility-level progress was quantified after scoring each facility based on the percent completeness change between 2016 and 2017. Legacy data processed prior to introducing the new NSSP data flow were not included in this assessment.ResultsNationally, the percent completeness of chief complaint for visits in 2016 was 82.06% (N=58,192,721), and the percent completeness of chief complaint for visits in 2017 was 87.15% (N=80,603,991). Of the 2,646 facilities that sent visits data in 2016 and 2017, 114 (4.31%) facilities showed an increase of at least 10% in chief complaint completeness in 2017 compared with 2016. As for discharge diagnosis, national results showed the percent completeness of discharge diagnosis for 2016 visits was 50.83% (N=36,048,334), and the percent completeness of discharge diagnosis for 2017 was 59.23% (N=54,776,310). Of the 2,646 facilities that sent data for visits in 2016 and 2017, 306 (11.56%) facilities showed more than a 10% increase in percent completeness of discharge diagnosis in 2017 compared with 2016.ConclusionsNationally, the percent completeness of chief complaint for visits in 2016 was 82.06% (N=58,192,721), and the percent completeness of chief complaint for visits in 2017 was 87.15% (N=80,603,991). Of the 2,646 facilities that sent visits data in 2016 and 2017, 114 (4.31%) facilities showed an increase of at least 10% in chief complaint completeness in 2017 compared with 2016. As for discharge diagnosis, national results showed the percent completeness of discharge diagnosis for 2016 visits was 50.83% (N=36,048,334), and the percent completeness of discharge diagnosis for 2017 was 59.23% (N=54,776,310). Of the 2,646 facilities that sent data for visits in 2016 and 2017, 306 (11.56%) facilities showed more than a 10% increase in percent completeness of discharge diagnosis in 2017 compared with 2016.ReferencesBatini, C., Cappiello. C., Francalanci, C. and Maurino, A. (2009) Methodologies for data quality assessment and improvement. ACM Comput. Surv., 41(3). 1-52.

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