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 Using State and National Surveillance Systems during World Equestrian Games in NC

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Lana Deyneka ◽  
Zachary Faigen ◽  
Anne Hakenwerth ◽  
Nicole Lee ◽  
Amy Ising ◽  
...  
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Urgent Care ◽  
Poison Center ◽  
Surveillance Systems ◽  
Mass Gathering ◽  
Mass Gatherings ◽  
Epidemic Intelligence ◽  
Polk County ◽  
Ed Visits

ObjectiveTo describe surveillance activities and use of existing state (NC DETECT) and national (NSSP) syndromic surveillance systems during the International Federation for Equestrian Sports (FEI) World Equestrian Games (WEG), in Mill Spring, NC from September 11 to September 23, 2018MethodsNC DETECT collects statewide data from hospital emergency department (ED) visits and Carolinas Poison Center (CPC) calls. NC DETECT also collects data from select Urgent Care Centers (UCC) in the Charlotte area. CPC data are updated hourly, while ED data are updated twice a day. NC DETECT data were monitored daily for census (total ED visits), communicable disease syndromes, injury syndromes, and other occurrences of public health significance related to the event. The geographic areas monitored were Polk County (the location of the main event), the counties where the guests were lodging in the Western NC Region (Henderson, Transylvania, Buncombe, Rutherford, McDowell, and Cleveland), the Charlotte Metropolitan area, and statewide. Because of the large number of people from other states and countries who attended, ED surveillance was mainly conducted by hospitals so that visits were captured for all patients and not just NC residents. WEG dashboards containing ED data were created prior to the event using NC DETECT and NSSP ESSENCE systems, and were accessible to epidemiologists at the state level. NSSP syndrome queries were shared with the neighboring state (SC) public health agency. Surveillance began two weeks prior to the event to establish baseline levels for all ED visits for hospitals in Polk County and the Western NC Region. Surveillance occurred daily before the event, during the event, and for two weeks following the event to account for incubation periods of potential diseases.ResultsThe 2018 Equestrian games in Western NC were affected by heavy rain and heat. The weather led to low attendance and cancellation of a few competitions. During the observation period, ED admissions and most of the mass gathering related syndromes in both NC DETECT and NSSP systems were at baseline. ED admissions for motor vehicle collisions and dehydration syndromes were above baseline for 09/19 and 09/21/18 (Figures 3-4). CPC calls and UC admissions for selected UC centers in the Charlotte area were also monitored, and were at baseline.ConclusionsNC DETECT and NSSP Dashboards provided effective and timely surveillance for the WEG event to assist local public health in the rural NC area with epidemiologic investigations and appropriate response. NC DETECT’s CPC and UC data provided additional valuable information, and complemented ED surveillance during the mass gathering event. Syndromic surveillance became essential during WEG, as NC DPH deployment plans and resource availability changed when Hurricane Florence bore down on the region.References1. Joseph S. Lombardo, Carol A. Sniegoski, Wayne A. Loschen, Matthew Westercamp, Michael Wade, Shandy Dearth, and Guoyan Zhang Public Health Surveillance for Mass Gatherings Johns Hopkins APL Technical Digest , Volume 27, Number 4 (2008)2. Kaiser R, Coulombier D. Epidemic intelligence during mass gatherings. Euro Surveill. 2006;113. Ising A, Li M, Deyneka L, Vaughan-Batten H, Waller A. Improving syndromic surveillance for nonpower users: NC DETECT dashboards. Emerging Health Threats Journal 2011, 4: 11702 - DOI: 10.3402/ehtj.v4i0.11702 

scholarly journals Evaluation of ESSENCE Syndromic Definitions for ED Visits Related to Falls in Icy Weather

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jessica Hensley ◽  
Sandra Gonzalez ◽  
Derry Stover ◽  
Thomas Safranek ◽  
Ming Qu
Keyword(s):  
Public Health ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Syndromic Surveillance ◽  
Hospital Admissions ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Search Terms ◽  
Ed Visits ◽  
Sensitivity Specificity

ObjectiveThis project evaluated and compared two ESSENCE syndromic surveillance definitions for emergency department (ED) visits related to injuries associated with falls in icy weather using 2016-2017 data from two hospitals in Douglas County, Nebraska. The project determined the validity of the syndromic surveillance definition as applied to chief complaint and triage notes and compared the chief complaint data alone to chief complaint plus triage notes definitions to find the most reliable definition for ED visits resulting from fall-related injuries.IntroductionIcy weather events increase the risk for injury from falls on untreated or inadequately treated surfaces. These events often result in ED visits, which represents a significant public health and economic impact1.The goal of this project was to start the process toward an evaluation of the public health impact and the economic impact of falls associated to icy weather in Douglas County, NE for the ultimate purpose of designing and implementing injury prevention related public health protection measures. Additionally, the validated definition will be used by NE DHHS Occupational Health Surveillance Program to identify work related ice-related fall injuries that were covered by workers compensation. To achieve the goal, the first step was to identify a valid and reliable syndromic surveillance. Specifically, this project looked at the applicability of the ESSENCE syndromic surveillance definitions related to injuries associated with falls. Two syndromic surveillance definitions were compared, one that includes triage note and chief complaint search terms, and another that only includes chief complaint. The hypothesis was that the ESSENCE syndromic surveillance definition that includes triage note and chief complaint search terms, rather than the syndromic surveillance definition that only includes chief complaint, would be more effective at identifying ED visits resulting from fall-related injuries.MethodsThis project included 751 EDs visits from two hospitals located in Douglas County Nebraska, during ice events on December 16-18, 2016, January 10-12, 2017, and January 15-18, 2017.Two ESSENCE syndromic surveillance definitions, “Chief Complaint or Triage Note” and “Chief Complaint Only,” were used to identify fall-related ED visits from two participating EDs in Douglas County, NE. In the chief complaint and the triage note fields, the keywords selected were: fall, fell, or slip. In that the ESSENCE time series analysis indicated the increase in the number of falls were associated with ice events from baseline, an assumption was made that the increase was a result of the weather. Then, the Syndromic Surveillance Event Detection of Nebraska database was used to find the patient and visit identification numbers. These two identification numbers were used to identify the EHRs needed for a gold standard review. Chart data was used to evaluate the reliability and validity of the two syndromic surveillance definitions for the detection of falls on the study dates. This analysis was used to find the sensitivity, specificity and predictive value.ResultsThe sensitivity, specificity and positive predictive value for the “Chief Complaint Only” definition yielded 71.7%, 100%, and 100% respectively. The “Chief Complaint or Triage Note” definition results were 90.9%, 98.8%, and 95.5% for these analyses. Negative predictive value for both definitions was 97.5%.ConclusionsThe sensitivity indicates both definitions are unlikely to give false positives, and the positive predictive value indicates both definitions successfully identify most of the true positives found in the visits. However, the “Chief Complaint Only” definition resulted in a minimally higher specificity and positive predictive value. Therefore, the results indicate that although both definitions have similar specificity and positive predictive value, the “Chief Complaint or Triage Note” definition is more likely than the “Chief Complaint Only” definition to correctly identify ED visits related to falls in icy weather.References1. Beynon C, Wyke S, Jarman I, Robinson M, Mason J, Murphy K, Bellis MA, Perkins C. The cost of emergency hospital admissions for falls on snow and ice in England during winter 2009/10: a cross sectional analysis. Environmental Health 2011;10(60).

An Observational Study Using English Syndromic Surveillance Data Collected During the 2012 London Olympics – What did Syndromic Surveillance Show and What Can We Learn for Future Mass-gathering Events?

2016 ◽  
Vol 31 (6) ◽  
pp. 628-634 ◽  
Author(s):  
Dan Todkill ◽  
Helen E. Hughes ◽  
Alex J. Elliot ◽  
Roger A. Morbey ◽  
Obaghe Edeghere ◽  
...  
Keyword(s):  
Public Health ◽  
Observational Study ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Olympic Games ◽  
Surveillance Data ◽  
Surveillance Systems ◽  
Mass Gathering ◽  
Opening Ceremony ◽  
London 2012

AbstractIntroductionIn preparation for the London 2012 Olympic Games, existing syndromic surveillance systems operating in England were expanded to include daily general practitioner (GP) out-of-hours (OOH) contacts and emergency department (ED) attendances at sentinel sites (the GP OOH and ED syndromic surveillance systems: GPOOHS and EDSSS).Hypothesis/ProblemThe further development of syndromic surveillance systems in time for the London 2012 Olympic Games provided a unique opportunity to investigate the impact of a large mass-gathering event on public health and health services as monitored in near real-time by syndromic surveillance of GP OOH contacts and ED attendances. This can, in turn, aid the planning of future events.MethodsThe EDSSS and GPOOHS data for London and England from July 13 to August 26, 2012, and a similar period in 2013, were divided into three distinct time periods: pre-Olympic period (July 13-26, 2012); Olympic period (July 27 to August 12); and post-Olympic period (August 13-26, 2012). Time series of selected syndromic indicators in 2012 and 2013 were plotted, compared, and risk assessed by members of the Real-time Syndromic Surveillance Team (ReSST) in Public Health England (PHE). Student’s t test was used to test any identified changes in pattern of attendance.ResultsVery few differences were found between years or between the weeks which preceded and followed the Olympics. One significant exception was noted: a statistically significant increase (P value = .0003) in attendances for “chemicals, poisons, and overdoses, including alcohol” and “acute alcohol intoxication” were observed in London EDs coinciding with the timing of the Olympic opening ceremony (9:00 pm July 27, 2012 to 01:00 am July 28, 2012).ConclusionsSyndromic surveillance was able to provide near to real-time monitoring and could identify hourly changes in patterns of presentation during the London 2012 Olympic Games. Reassurance can be provided to planners of future mass-gathering events that there was no discernible impact in overall attendances to sentinel EDs or GP OOH services in the host country. The increase in attendances for alcohol-related causes during the opening ceremony, however, may provide an opportunity for future public health interventions.TodkillD, HughesHE, ElliotAJ, MorbeyRA, EdeghereO, HarcourtS, HughesT, EndericksT, McCloskeyB, CatchpoleM, IbbotsonS, SmithG. An observational study using English syndromic surveillance data collected during the 2012 London Olympics – what did syndromic surveillance show and what can we learn for future mass-gathering events?Prehosp Disaster Med. 2016;31(6):628–634.

scholarly journals Validation of Syndromic ILI Data for Use in CDC’s ILINet Surveillance, Pennsylvania

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Sameh W Boktor ◽  
Kristen Waller ◽  
Lenee Blanton ◽  
Krista Kniss
Keyword(s):  
Sore Throat ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Correlation Coefficients ◽  
Chief Complaint ◽  
The State ◽  
Surveillance Data ◽  
Syndromic Surveillance System ◽  
Ed Visits ◽  
Number Of Visits

Objective: Discuss use of syndromic surveillance as a source for the state’s ILI/Influenza surveillanceDiscuss reliability of syndromic data and methods to address problems caused by data outliers and inconsistencies.Introduction: ILINet is a CDC program that has been used for years for influenza-like illness (ILI) surveillance, using a network of outpatient providers who volunteer to track and report weekly the number of visits due to ILI and the total number of visits to their practice. Pennsylvania has a network of 95 providers and urgent care clinics that submit data to ILINet. However, ongoing challenges in recruiting and retaining providers, and inconsistent weekly reporting are barriers to receiving accurate, representative, and timely ILI surveillance data year-round. Syndromic surveillance data have been used to enhance outpatient ILI surveillance in a number of jurisdictions, including Pennsylvania. At present, 156 hospitals, or 90% of all Pennsylvania hospitals with emergency departments (EDs), send chief complaint and other information on their ED visits to the Department of Health’s (PADOH) syndromic surveillance system. PADOH evaluated the consistency and reliability of ILI syndromic data as compared to ILINet data, to confirm that syndromic data were suitable for use in ILINet.Methods: Pennsylvania ILINet data from the past 6 influenza seasons (2011-2012 to 2016-2017, or 314 weeks of data) were downloaded from the CDC’s ILINet website. The statewide weekly percent of visits due to ILI in ILINet was used as the standard for comparisons. For syndromic surveillance, PADOH uses the Epicenter platform hosted by Health Monitoring Systems (HMS); visit-level data are also stored in SAS datasets at PADOH, and HMS forwards a subset of data to the National Syndromic Surveillance System Program. Using syndromic data from the same time period, the proportion of weeks with no syndromic data available was calculated for each facility. A state-developed ILI algorithm (very similar to the 2016 algorithm developed by the ISDS Syndrome Definitions Workgroup) was applied to ED visit chief complaint data to identify visits likely to be due to ILI. The algorithm flags the ER visit as ILI if chief complaint has any combinations of words for flu or fever plus either cough and sore throat or fever and both cough or sore throat . The percent of ED visits due to ILI per the syndromic algorithm (ILIsyn) was calculated for each week by hospital and state-wide. Facility ILIsyn trends were compared to the State level percent ILI data from ILINet by visually examining plots and by calculating Pearson correlation coefficients. Facilities that had >=15 weeks where ILIsyn differed from percent ILI in ILINet by more than 5% were considered to be poorly correlated.Results: A total of 156 hospitals were evaluated in the study. Twenty of the hospitals were excluded because they did not have syndromic data for at least 50% of the weeks in the study period, and an additional 20 were excluded because they had not agreed to have data forwarded to CDC. Of the remaining 116 facilities, individual facility correlation coefficients between ILIsyn and ILINet trends ranged from 0.03 to 0.82 (examples are in Figure 1). Twenty-four hospitals (20.7%) were determined to be poorly correlated. When data from the remaining 92 hospitals were combined, the state ILINet and state-wide ILIsyn trends were strongly correlated statistically and graphically (r=0.82, p <0.0001, Figure 2). Syndromic data from these 92 facilities were deemed acceptable for inclusion in ILINet. Conclusions: Syndromic surveillance data are a valuable source for ILI surveillance. However, evaluation at the hospital-specific level revealed that useful information is not obtained from all facilities. This project demonstrated that validation of data at the facility level is crucial to obtaining reliable and meaningful information. More work is needed to understand which factors distinguish well-correlated from poorly-correlated facilities, and how to improve the quality of information obtained from poorly-correlated facilities.

scholarly journals Enhanced syndromic surveillance during the 9 th Indian Ocean Island Games, 2015

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Pascal Vilain ◽  
Frédéric Pages ◽  
Guy Henrion ◽  
Xavier Combes ◽  
Marc Weber ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Reunion Island ◽  
Mass Gathering ◽  
Secondary Diagnosis ◽  
Syndromic Surveillance System ◽  
Ocean Island ◽  
Réunion Island ◽  
Ed Visits

ObjectiveTo describe how syndromic surveillance was enhanced to detecthealth events during the 9thIndian Ocean Island Games (IOIG) inReunion Island.IntroductionThe 9thIOIG took place in Reunion Island from July 31 to August9, 2015. This sport event gathered approximatively 1 640 athletes,2 000 volunteers and several thousand spectators from seven islands:Comoros, Madagascar, Maldives, Mauritius, Mayotte, Seychelles andReunion.In response to the import risk of infectious diseases from thesecountries where some of them are endemics, the syndromicsurveillance system, which captures 100% of all EmergencyDepartment visits, was enhanced in order to detect any health event.MethodsIn Reunion Island, syndromic surveillance system is based onOSCOUR® network (Organisation de la surveillance coordonnéedes urgences) that collects data from all emergency departments ofthe island. Data are daily transmitted to the French national publichealth agency then are available to the regional office. At the regionallevel, data are integrated into an application that allows the built ofpredefined syndromic groups according to the health risks related tomass gatherings (Table 1, parts 1 to 3) and complemented by specificsyndromic groups (table 1, part 4). Daily analyses with temporal[1] and spatial-temporal [2] algorithms were performed during thesurveillance period of July 27 to August 13, 2015. In addition to thismonitoring, ED physicians were requested to proactively tag Y33(ICD-10) as secondary diagnosis, each ED visits related to IOIG. Linelists were reviewed daily. Each day, an epidemiological report wassend to public health authorities.ResultsFrom July 31 to August 9, 2015, the activity of EDs was inaccordance with that expected. No health events were detected bythe syndromic surveillance system except for the syndrome “alcoholintoxication” for which consecutive signals were observed fromAugust 6 to 9, 2015. This increase occurs commonly at the beginningof each month (due to the social benefits payday) [3] nevertheless thisevent has probably been increased by IOIG (finals for team sportsand games closing ceremony). In total, 8 ED visits were tagged Y33as secondary diagnosis. In over half the cases, visits were related totrauma.ConclusionsThe syndromic surveillance system proved to be useful for thesurveillance of mass gathering events due to its capacity to detecthealth events but also to provide reassurance public health authorities[4]. As described in literature [5], few ED visits were tagged in relationto IOIG. Indeed, the tag of ED visits was implemented two weeksbefore the games, and given the shifts of ED physicians, some of themmay have not been informed. In the future, preparation meetings withphysicians will have to be planned several months before in order toimprove the response rate for mass gathering events.

scholarly journals Travel and Triage: Pilot project to detect infections after medical tourism procedures

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Pinar Erdogdu ◽  
Barbara Carothers ◽  
Rebecca Greeley ◽  
Stella Tsai
Keyword(s):  
Public Health ◽  
Data Mining ◽  
New Jersey ◽  
Syndromic Surveillance ◽  
Surgical Site Infections ◽  
Chief Complaint ◽  
Syndromic Surveillance System ◽  
Cosmetic Procedure ◽  
The Us ◽  
Ed Visits

Objective: Medical notes provide a rich source of information that can be used as additional supporting information for healthcare-associated infection (HAI) investigations. The medical notes from 10 New Jersey (NJ) emergency departments (ED) were searched to identify cases of surgical-site infections (SSI).Introduction: EpiCenter, NJ’s statewide syndromic surveillance system, collects ED registration data. The system uses chief complaint data to classify ED visits into syndrome categories and provides alerts to state and local health departments for surveillance anomalies.After the 2014 Ebola outbreak in West Africa, the New Jersey Department of Health (NJDOH) started collecting medical notes including triage notes, which contain more specific ED visit information than chief complaint, from 10 EDs to strengthen HAI syndromic surveillance efforts.In 2017, the NJDOH was aware of one NJ resident whose surgical site was infected following a cosmetic procedure outside of the US. This event triggered an intensive data mining using medical notes collected in EpiCenter. The NJDOH staff searched one week of medical notes data in EpiCenter with a specific keyword to identify additional potential cases of surgical-site infections (SSI) that could be associated with medical tourism.Methods: The NJ resident whose surgical site was infected following a cosmetic procedure outside of the US was interviewed by NJDOH staff for details about their procedure. First, the patient’s interview results were reviewed to prepare a set of SSI and travel related keywords to be used in performing data mining in medical notes collected in EpiCenter. The interviewed patient had tummy tuck and liposuction surgeries; therefore, it was decided to search for “tummy tuck” as a keyword in EpiCenter. The medical notes from August 31, 2017 through September 8, 2017 were reviewed to identify patients who developed SSI following a cosmetic procedure outside of the US.Results: The search yielded 8 ED visits, one of which was identified as possible surgical site infection. The medical notes details indicated that the ED patient, a 21-year old female who had abdominoplasty (tummy tuck) and liposuction surgeries about a month prior, presented with post-surgical complaints such as pain, surgical dehiscence, and purulent drainage at the surgery site. Chief complaint text for the same ED patient indicated the patient had headache and dizziness which were less specific than medical notes.The NJDOH staff contacted the ED to obtain additional information regarding the infection. The lab results from the ED showed that the patient was identified as having a post-surgery infection, which prompted public health to follow-up whether it was an HAI.Conclusions: The limitation for this project was that the keyword search was conducted only on one week of data. The timeframe was kept short to pilot testing the keyword identified. The Centers for Disease Control and Prevention suggests clinicians should consider nontuberculous mycobacteria (NTM) infections in the differential diagnosis for all people who have wound infections after surgery abroad, including surgery that has occurred weeks to months previously (1). Future studies will explore larger data sets with additional keywords (e.g. country and organism) to see if potential cases can be identified as possible HAI and/or outbreak that will lead to public health investigations.

scholarly journals Customizing ESSENCE Queries for Select Mental Health Sub-indicators

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Achintya N. Dey ◽  
Michael Coletta ◽  
Hong Zhou ◽  
Nelson Adekoya ◽  
Deborah Gould
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Anxiety Disorders ◽  
Syndromic Surveillance ◽  
Depressive Disorders ◽  
National Level ◽  
Chief Complaint ◽  
Co Morbidity ◽  
Icd 10 ◽  
Ed Visits

ObjectiveEmergency department (ED) visits related to mental health (MH) disorders have increased since 2006 (1), indicating a potential burden on the healthcare delivery system. Surveillance systems has been developed to identify and understand these changing trends in how EDs are used and to characterize populations seeking care. Many state and local health departments are using syndromic surveillance to monitor MH-related ED visits in near real-time. This presentation describes how queries can be created and customized to identify select MH sub-indicators (for adults) by using chief complaint text terms and diagnoses codes. The MH sub-indicators examined are mood and depressive disorders, schizophrenic disorders, and anxiety disorders. Wider adoption of syndromic surveillance for characterizing MH disorders can support long-term planning for healthcare resources and service delivery.IntroductionSyndromic surveillance systems, although initially developed in response to bioterrorist threats, are increasingly being used at the local, state, and national level to support early identification of infectious disease and other emerging threats to public health. To facilitate detection, one of the goals of CDC’s National Syndromic Surveillance Program (NSSP) is to develop and share new sets of syndrome codes with the syndromic surveillance Community of Practice. Before analysts, epidemiologists, and other practitioners begin customizing queries to meet local needs, especially monitoring ED visits in near-real time during public health emergencies, they need to understand how syndromes are developed.More than 4,000 hospital routinely send data to NSSP’s BioSense Platform, representing about 55 percent of ED visits in the United States (2). The platform’s surveillance component, ESSENCE,* is a web-based application for analyzing and visualizing prediagnostic hospital ED data. ESSENCE’s Chief Complaint Query Validation (CCQV) data source, which is a national-level data source with access to chief complaint (CC) and discharge diagnoses (DD) from reporting sites, was designed for testing new queries.MethodsWe used ESSENCE CCQV to query weekly data for the nine week period from the first quarter of 2018 and looked at three common MH sub-indicators: mood and depressive disorders, schizophrenic disorders, and anxiety disorders. We developed four query types for each MH sub-indicator. Query-1 focused on DD codes; query-2 focused on CC text terms; query-3 focused on a combination of CC, DD, and no exclusion for mental health co-morbidity; and query-4 focused on a combination of CC and DD and excluded mental health co-morbidity. We also examined the summary distribution of CC texts to identify keywords related to MH sub-indicators.For mood and depressive disorders, we queried ICD-9 codes 296, 311; ICD-10 codes F30–F39; CC text terms for words “depressive disorder,” bipolar disorder,” “mood disorder,” “depression,” “manic episodes,” and “psychotic.” For schizophrenic disorders, we queried ICD-9 codes 295; ICD-10 codes F20–F29; CC text terms for words “psychosis,” “psychotic,” “schizo,” “delusional,” “paranoid,” “auditory,” “hallucinations,” and “hearing voices.” For anxiety disorders, we queried ICD-9 codes 300, 306, 307, 308, 309; ICD-10 codes F40–F48; CC text terms for words “anxiety,” “anexiy,” “aniety,” “aniexty,” “ansiety,” “anxety,” “anxity,” “anxiety,” “phobia,” and “panic attack.”ResultsWe identified 2.3 million average weekly ED visits for the 9-week period queried. Table 1 shows average weekly ED visits of select MH sub-indicators from the four query types. Because query 4 focused on specific MH outcomes and excluded MH co-morbidities, the average weekly ED visit for all three sub-indicators was almost half that of query 3, which focused on broader concepts by including MH co-morbidities. Among mood and depressive disorders, query 4 identified on average 23,352 ED visits per week versus 45,504 visits per week for query 3. Similarly, for schizophrenic disorders and anxiety disorders, query 4 identified on average 4,988 and 32,790 visits per week compared with 9,816 and 53,868 visits, respectively, for query 3. Further, more MH-related visits were identified using the DD-coded query (query 1) than CC-based text terms (query 2).ConclusionsAnalysts can benefit from having queries on select sub-indicators readily available and can use these to facilitate routine MH-related monitoring of ED visits, or customize the queries by including local text terms. Consistent with our previous work (3), this analysis demonstrated that MH-related ED visits are more likely to be found in DD codes than in CC alone.* Electronic Surveillance for the Early Notification of Community-based EpidemicsReferences[1] Weiss AJ, Barrett ML, Heslin KC , Stocks C. Trends in Emergency Department Visits Involving Mental and Substance Use Disorders, 2006–2013. HCUP Statistical Brief #216 [Internet]. Rockville (MD): Agency for Healthcare Research and Quality; 2016 Dec [cited 2018 Aug 14]. Available from: http://www.hcup-us.ahrq.gov/reports/statbriefs/sb216-Mental-Substance-Use-Disorder-ED-Visit-Trends.pdf.[2] Gould DW, Walker D, Yoon PW. The Evolution of BioSense: Lessons Learned and Future Directions. Public Health Reports. 2017 Jul/Aug;132(Suppl 1):S7–S11.[3] Dey AN, Gould D, Adekoya N, Hicks P, Ejigu GS, English R, Couse J, Zhou H. Use of Diagnosis Code in Mental Health Syndrome Definition. Online Journal of Public Health Informatics [Internet]. 2018 [cited 2018 Aug 14];10(1). Available from: https://doi.org/10.5210/ojphi.v10i1.8983

scholarly journals Real-time monitoring of a mass K2-related overdose outbreak – Connecticut, 2018

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Kristen Soto ◽  
Erin Grogan ◽  
Alexander Senetcky ◽  
Susan Logan
Keyword(s):  
Emergency Department ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Emergency Department Utilization ◽  
Syndromic Surveillance System ◽  
Ed Visits ◽  
State And Local

ObjectiveTo describe the use of syndromic surveillance data for real-time situational awareness of emergency department utilization during a localized mass overdose event related to the substance K2.IntroductionOn August 15, 2018, the Connecticut Department of Public Health (DPH) became aware of a cluster of suspected overdoses in an urban park related to the synthetic cannabinoid K2. Abuse of K2 has been associated with serious adverse effects and overdose clusters have been reported in multiple states. This investigation aimed to characterize the use of syndromic surveillance data to monitor a cluster of suspected overdoses in real time.MethodsThe EpiCenter syndromic surveillance system collects data on all emergency department (ED) visits at Connecticut hospitals. ED visits associated with the event were identified using ad hoc keyword analyses. The number of visits by facility location for the state, county, and city were communicated to state and local partners in real time. Gender, age, and repeated ED visits were assessed. After the event, surveillance findings were summarized for partnersResultsDuring the period of August 15–16, 2018 the number of ED visits with a mention of K2 in the chief complaint increased from three to 30 in the impacted county, compared to a peak of 5 visits during the period of March–July, 2018. An additional 25 ED visits were identified using other related keywords (e.g., weed). After the event, 72 ED visits were identified with K2 and location keywords in the chief complaint or triage notes. These 72 visits comprised 53 unique patients, with 12 patients returning to the ED 2–5 times over the two day period. Of 53 patients, 77% were male and the median age was 40 years (interquartile range 35–51 years). Surveillance findings were shared with partners in real time for situational awareness, and in a summary report on August 21.ConclusionsData from the EpiCenter system were consistent with reports from other data sources regarding this cluster of suspected drug overdoses. Next steps related to this event involve: monitoring data for reference to areas of concentrated substance use, enabling automated alerts to detect clusters of interest, and developing a plan to improve coordinate real-time communication with stakeholderswithin DPH and with external partners during events.

scholarly journals Tracking suspected heroin overdoses in CDC's National Syndromic Surveillance Program

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Alana M. Vivolo-Kantor ◽  
R. Matthew Gladden ◽  
Aaron Kite-Powell ◽  
Michael Coletta ◽  
Grant Baldwin
Keyword(s):  
Public Health ◽  
United States ◽  
Emergency Department ◽  
Drug Overdose ◽  
Syndromic Surveillance ◽  
Case Definition ◽  
Chief Complaint ◽  
Heroin Overdose ◽  
Ed Visits ◽  
Overdose Deaths

ObjectiveThis paper analyzes emergency department syndromic data in the Centers for Disease Control and Prevention’s (CDC) National Syndromic Surveillance Program’s (NSSP) BioSense Platform to understand trends in suspected heroin overdose.IntroductionOverdose deaths involving opioids (i.e., opioid pain relievers and illicit opioids such as heroin) accounted for at least 63% (N = 33,091) of overdose deaths in 2015. Overdose deaths related to illicit opioids, heroin and illicitly-manufactured fentanyl, have rapidly increased since 2010. For instance, heroin overdose deaths quadrupled from 3,036 in 2010 to 12,989 in 2015. Unfortunately, timely response to emerging trends is inhibited by time lags for national data on both overdose mortality via vital statistics (8-12 months) and morbidity via hospital discharge data (over 2 years). Emergency department (ED) syndromic data can be leveraged to respond more quickly to emerging drug overdose trends as well as identify drug overdose outbreaks. CDC’s NSSP BioSense Platform collects near real-time ED data on approximately two-thirds of ED visits in the US. NSSP’s data analysis and visualization tool, Electronic Surveillance System for the Notification of Community-based Epidemics (ESSENCE), allows for tailored syndrome queries and can monitor ED visits related to heroin overdose at the local, state, regional, and national levels quicker than hospital discharge data.MethodsWe analyzed ED syndromic data using ESSENCE to detect monthly and annual trends in suspected unintentional or undetermined heroin overdose by sex and region for those 11 years and older. An ED visit was categorized as a suspected heroin overdose if it met several criteria, including heroin overdose ICD-9-CM and ICD-10-CM codes (i.e., 965.01 and E850.0; T40.1X1A, T40.1X4A) and chief complaint text associated with a heroin overdose (e.g., “heroin overdose”). Using computer code developed specifically for ESSENCE based on our case definition, we queried data from 9 of the 10 HHS regions from July 2016-July 2017. One region was excluded due to large changes in data submitted during the time period. We conducted trend analyses using the proportion of suspected heroin overdoses by total ED visits for a given month with all sexes and regions combined and then stratified by sex and region. To determine significant linear changes in monthly and annual trends, we used the National Cancer Institute’s Joinpoint Regression Program.ResultsFrom July 2016-July 2017, over 72 million total ED visits were captured from all sites and jurisdictions submitting data to NSSP. After applying our case definition to these records, 53,786 visits were from a suspected heroin overdose, which accounted for approximately 7.5 heroin overdose visits per 10,000 total ED visits during that timeframe. The rate of suspected heroin overdose visits to total ED visits was highest in June 2017 (8.7 per 10,000) and lowest in August 2016 (6.6 per 10,000 visits). Males accounted for a larger rates of visits over all months (range = 10.7 to 14.2 per 10,000 visits) than females (range = 3.8 to 4.7 per 10,000 visits). Overall, compared to July 2016, suspected heroin overdose ED visits from July 2017 were significantly higher for all sexes and US regions combined (β = .010, p = .036). Significant increases were also demonstrated over time for males (β = .009, p = .044) and the Northeast (β = .012, p = .025). No other significant increases or decreases were detected by demographics or on a monthly basis.ConclusionsEmergency department visits related to heroin overdose increased significantly from July 2016 to July 2017, with significant increases in the Northeast and among males. Urgent public health action is needed reduce heroin overdoses including increasing the availability of naloxone (an antidote for opioid overdose), linking people at high risk for heroin overdose to medication-assisted treatment, and reducing misuse of opioids by implementing safer opioid prescribing practices. Despite these findings, there are several limitations of these data: not all states sharing data have full participation thus limiting the representativeness of the data; not all ED visits are shared with NSSP; and our case definition may under-identify (e.g., visits missing discharge diagnosis codes and lacking specificity in chief complaint text) or over-identify (e.g., reliance on hospital staff impression and not drug test results) heroin overdose visits. Nonetheless, ED syndromic surveillance data can provide timely insight into emerging regional and national heroin overdose trends.ReferencesWarner M, Chen LH, Makuc DM, Anderson RN, Minino AM. Drug poisoning deaths in the United States, 1980-2008. NCHS Data Brief 2011(81):1-8.Rudd RA, Seth P, David F, Scholl L. Increases in Drug and Opioid-Involved Overdose Deaths - United States, 2010-2015. MMWR Morb Mortal Wkly Rep 2016;65(5051):1445-1452.Spencer MRA, F. Timeliness of Death Certificate Data for Mortality Surveillance and Provisional Estimates. National Center for Health Statistics 2017.Richards CL, Iademarco MF, Atkinson D, Pinner RW, Yoon P, Mac Kenzie WR, et al. Advances in Public Health Surveillance and Information Dissemination at the Centers for Disease Control and Prevention. Public Health Rep 2017;132(4):403-410.

scholarly journals Monitoring suicide-related events using National Syndromic Surveillance Program data

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Marissa L. Zwald ◽  
Kristin M. Holland ◽  
Francis Annor ◽  
Aaron Kite-Powell ◽  
Steven A. Sumner ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Real Time ◽  
Syndromic Surveillance ◽  
The United States ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Joinpoint Regression ◽  
Self Harm ◽  
Ed Visits

ObjectiveTo describe epidemiological characteristics of emergency department (ED) visits related to suicidal ideation (SI) or suicidal attempt (SA) using syndromic surveillance data.IntroductionSuicide is a growing public health problem in the United States.1 From 2001 to 2016, ED visit rates for nonfatal self-harm, a common risk factor for suicide, increased 42%.2–4 To improve public health surveillance of suicide-related problems, including SI and SA, the Data and Surveillance Task Force within the National Action Alliance for Suicide Prevention recommended the use of real-time data from hospital ED visits.5 The collection and use of real-time ED visit data on SI and SA could support a more targeted and timely public health response to prevent suicide.5 Therefore, this investigation aimed to monitor ED visits for SI or SA and to identify temporal, demographic, and geographic patterns using data from CDC’s National Syndromic Surveillance Program (NSSP).MethodsCDC’s NSSP data were used to monitor ED visits related to SI or SA among individuals aged 10 years and older from January 1, 2016 through July 31, 2018. A syndrome definition for SI or SA, developed by the International Society for Disease Surveillance’s syndrome definition committee in collaboration with CDC, was used to assess SI or SA-related ED visits. The syndrome definition was based on querying the chief complaint history, discharge diagnosis, and admission reason code and description fields for a combination of symptoms and Boolean operators (for example, hang, laceration, or overdose), as well as ICD-9-CM, ICD-10-CM, and SNOMED diagnostic codes associated with SI or SA. The definition was also developed to include common misspellings of self-harm-related terms and to exclude ED visits in which a patient “denied SI or SA.”The percentage of ED visits involving SI or SA were analyzed by month and stratified by sex, age group, and U.S. region. This was calculated by dividing the number of SI or SA-related ED visits by the total number of ED visits in each month. The average monthly percentage change of SI or SA overall and for each U.S. region was also calculated using the Joinpoint regression software (Surveillance Research Program, National Cancer Institute).6ResultsAmong approximately 259 million ED visits assessed in NSSP from January 2016 to July 2018, a total of 2,301,215 SI or SA-related visits were identified. Over this period, males accounted for 51.2% of ED visits related to SI or SA, and approximately 42.1% of SI or SA-related visits were comprised of patients who were 20-39 years, followed by 40-59 years (29.7%), 10-19 years (20.5%), and ≥60 years (7.7%).During this period, the average monthly percentage of ED visits involving SI or SA significantly increased 1.1%. As shown in Figure 1, all U.S. regions, except for the Southwest region, experienced significant increases in SI or SA ED visits from January 2016 to July 2018. The average monthly increase of SI or SA-related ED visits was 1.9% for the Midwest, 1.5% for the West (1.5%), 1.1% for the Northeast, 0.9% for the Southeast, and 0.5% for the Southwest.ConclusionsED visits for SI or SA increased from January 2016 to June 2018 and varied by U.S. region. In contrast to previous findings reporting data from the National Electronic Injury Surveillance Program – All-Injury Program, we observed different trends in SI or SA by sex, where more ED visits were comprised of patients who were male in our investigation.2 Syndromic surveillance data can fill an existing gap in the national surveillance of suicide-related problems by providing close to real-time information on SI or SA-related ED visits.5 However, our investigation is subject to some limitations. NSSP data is not nationally representative and therefore, these findings are not generalizable to areas not participating in NSSP. The syndrome definition may under-or over-estimate SI or SA based on coding differences and differences in chief complaint or discharge diagnosis data between jurisdictions. Finally, hospital participation in NSSP can vary across months, which could potentially contribute to trends observed in NSSP data. Despite these limitations, states and communities could use this type of surveillance data to detect abnormal patterns at more detailed geographic levels and facilitate rapid response efforts. States and communities can also use resources such as CDC’s Preventing Suicide: A Technical Package of Policy, Programs, and Practices to guide prevention decision-making and implement comprehensive suicide prevention approaches based on the best available evidence.7References1. Stone DM, Simon TR, Fowler KA, et al. Vital Signs: Trends in State Suicide Rates — United States, 1999–2016 and Circumstances Contributing to Suicide — 27 States, 2015. Morb Mortal Wkly Rep. 2018;67(22):617-624.2. CDCs National Center for Injury Prevention and Control. Web-based Injury Statistics Query and Reporting System (WISQARS). https://www.cdc.gov/injury/wisqars/index.html. Published 2018. Accessed September 1, 2018.3. Mercado M, Holland K, Leemis R, Stone D, Wang J. Trends in emergency department visits for nonfatal self-inflicted injuries among youth aged 10 to 24 years in the United States, 2005-2015. J Am Med Assoc. 2017;318(19):1931-1933. doi:10.1001/jama.2017.133174. Olfson M, Blanco C, Wall M, et al. National Trends in Suicide Attempts Among Adults in the United States. JAMA Psychiatry. 2017;10032(11):1095-1103. doi:10.1001/jamapsychiatry.2017.25825. Ikeda R, Hedegaard H, Bossarte R, et al. Improving national data systems for surveillance of suicide-related events. Am J Prev Med. 2014;47(3 SUPPL. 2):S122-S129. doi:10.1016/j.amepre.2014.05.0266. National Cancer Institute. Joinpoint Regression Software. https://surveillance.cancer.gov/joinpoint/. Published 2018. Accessed September 1, 2018.7. Centers for Disease Control and Prevention. Preventing Suicide: A Technical Package of Policy, Programs, and Practices. 

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