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 Flexibility of ED surveillance system to monitor dengue outbreak in Reunion Island

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Pascal Vilain ◽  
Muriel Vincent ◽  
Anne Fouillet ◽  
Katia Mougin-Damour ◽  
Xavier Combes ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Reunion Island ◽  
Regional Office ◽  
Syndromic Surveillance System ◽  
Dengue Outbreak ◽  
Icd 10 ◽  
Réunion Island ◽  
Ed Visits

ObjectiveTo describe the characteristics of ED vitis related to dengue fever and to show how the syndromic surveillance system can be flexible for the monitoring of this outbreak.IntroductionIn Reunion Island, a French overseas territory located in the southwestern of Indian Ocean, the dengue virus circulation is sporadic. Since 2004, between 10 and 221 probable and confirmed autochthonous dengue fever cases have been reported annually. Since January 2018, the island has experienced a large epidemic of DENV serotype 2. As of 4 September 2018, 6,538 confirmed and probable autochthonous cases have been notified1. From the beginning of the epidemic, the regional office of National Public Health Agency (ANSP) in Indian Ocean enhanced the syndromic surveillance system in order to monitor the outbreak and to provide hospital morbidity data to public health authorities.MethodsIn Reunion Island, the syndromic surveillance system called OSCOUR® network (Organisation de la Surveillance Coordonnée des Urgences) is based on all emergency departments (ED)2. Anonymous data are collected daily directly from the patients’ computerized medical files completed during medical consultations. Every day, data files are sent to the ANSP via a regional server over the internet using a file transfer protocol. Each file transmitted to ANSP includes all patient visits to the ED logged during the previous 24 hours (midnight to midnight). Finally, data are integrated in a national database (including control of data quality regarding authorized thesauri) and are made available to the regional office through an online application3.Following the start of dengue outbreak in week 4 of 2018, the regional office organized meetings with physicians in each ED to present the dengue epidemiological update and to recommend the coding of ED visit related to dengue for any suspect case (acute fever disease and two or more of the following signs or symptoms: nausea, vomiting, rash, headache, retro-orbital pain, myalgia). During these meetings, it was found that the version of ICD-10 (International Classification of Diseases) was different from one ED to another. Indeed, some ED used A90, A91 (ICD-10 version: 2015) for visit related to dengue and others used A97 and subdivisions (ICD-10 version: 2016). As the ICD-10 version: 2015 was implemented at the national server, some passages could be excluded. In this context, the thesaurus of medical diagnosis implemented in the national database has been updated so that all codes can be accepted. ED visits related to dengue fever has been then described according to age group, gender and hospitalization.ResultsFrom week 9 of 2018, the syndromic surveillance system was operational to monitor dengue outbreak. The regional office has provided each week, an epidemic curve of ED visits for dengue and a dashboard on descriptive characteristic of these visits. In total, 441 ED visits for dengue were identified from week 9 to week 34 of 2018 (Figure 1). On this period, the weekly number of ED visits for dengue was correlated with the weekly number of probable and confirmed autochthonous cases (rho=0.86, p<0.001). Among these visits, the male/female ratio was 0.92 and median (min-max) age was 44 (2-98) years. The distribution by age group showed that 15-64 year-old (72.1%, n=127) were most affected. Age groups 65 years and more and 0-14 year-old represented respectively 21.8% (n=96) and 6.1% (n=27) of dengue visits. About 30% of dengue visits were hospitalized.ConclusionsAccording Buehler et al., “the flexibility of a surveillance system refers to the system's ability to change as needs change. The adaptation to changing detection needs or operating conditions should occur with minimal additional time, personnel, or other resources. Flexibility generally improves the more data processing is handled centrally rather than distributed to individual data-providing facilities because fewer system and operator behavior changes are needed...” 4.During this dengue outbreak, the syndromic surveillance system seems to have met this purpose. In four weeks (from week 5 to week 9 of 2018), the system was able to adapt to the epidemiological situation with minimal additional resources and personnel. Indeed, updates were not made in the IT systems of each EDs’ but at the level of the national ANSP server (by one person). This surveillance system was also flexible thank to the reactivity of ED physicians who timely implemented coding of visits related to dengue fever.In conclusion, ED surveillance system constitutes an added-value for the dengue outbreak monitoring in Reunion Island. The automated collection and analysis data allowed to provide hospital morbidity (severe dengue) data to public health authorities. Although the epidemic has decreased, this system also allows to continue a routine active surveillance in order to quickly identify a new increase.References1Santé publique France. Surveillance de la dengue à la Réunion. Point épidémiologique au 4 septembre 2018. http://invs.santepubliquefrance.fr/fr/Publications-et-outils/Points-epidemiologiques/Tous-les-numeros/Ocean-Indien/2018/Surveillance-de-la-dengue-a-la-Reunion.-Point-epidemiologique-au-4-septembre-2018. [Accessed September 8, 2018].2Vilain P, Filleul F. La surveillance syndromique à la Réunion : un système de surveillance intégré. [Syndromic surveillance in Reunion Island: integrated surveillance system]. Bulletin de Veille Sanitaire. 2013;(21):9-12. http://invs.santepubliquefrance.fr/fr/Publications-et-outils/Bulletin-de-veille-sanitaire/Tous-les-numeros/Ocean-indien-Reunion-Mayotte/Bulletin-de-veille-sanitaire-ocean-Indien.-N-21-Septembre-2013. [Accessed September 4, 2018].3Fouillet A, Fournet N, Caillère N et al. SurSaUD® Software: A Tool to Support the Data Management, the Analysis and the Dissemination of Results from the French Syndromic Surveillance System. OJPHI. 2013; 5(1): e118.4Buehler JW, Hopkins RS, Overhage JM, Sosin DM, Tong V; CDC Working Group. Framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the CDC Working Group. MMWR Recomm Rep. 2004;53(RR-5):1-11.

scholarly journals Using Real-Time Syndromic Surveillance to Analyze the Impact of a Cold Weather Event in New Mexico

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Victoria F. Dirmyer
Keyword(s):  
Real Time ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Cold Weather ◽  
Weather Event ◽  
Diagnosis Codes ◽  
Chief Complaints ◽  
Icd 10 ◽  
Ed Visits ◽  
The Impact

Objective. This report describes the development of a novel syndromic cold weather syndrome for use in monitoring the impact of cold weather events on emergency department attendance. Methods. Syndromic messages from seven hospitals were analyzed for ED visits that occurred over a 12-day period. A cold weather syndrome was defined using terms in the self-reported chief complaint field as well as specific ICD-10-CM codes related to cold weather. A κ statistic was calculated to assess the overall agreement between the chief complaint field and diagnosis fields to further refine the cold weather syndrome definition. Results. Of the 3,873 ED visits that were reported, 487 were related to the cold weather event. Sixty-three percent were identified by a combination of diagnosis codes and chief complaints. Overall agreement between chief complaint and diagnosis codes was moderate (κ=0.50; 95% confidence interval = 0.48–0.52). Conclusion. Due to the near real-time reporting of syndromic surveillance data, analysis results can be acted upon. Results from this analysis will be used in the state’s emergency operations plan (EOP) for cold weather and winter storms. The EOP will provide guidance for mobilization of supplies/personnel, preparation of roadways and pedestrian walkways, and the coordination efforts of multiple state agencies.

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 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).

scholarly journals Syndromic Surveillance on the Mental Health Impact of Political Rallies in Charlottesville, Virginia

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Em Stephens
Keyword(s):  
Public Health ◽  
Mental Health ◽  
African Americans ◽  
Syndromic Surveillance ◽  
Primary Diagnosis ◽  
Chief Complaint ◽  
City Council ◽  
Civil Unrest ◽  
The Right ◽  
The Impact

ObjectiveTo describe the impact of civil unrest on the mental health of a community in near real-time using syndromic surveillance.IntroductionAs part of a wide-spread community discussion on the presence of monuments to Confederate Civil War figures, the Charlottesville city council voted to remove a statue of General Robert E. Lee.1 Multiple rallies were then held to protest the statue’s removal. A Ku Klux Klan (KKK) rally on July 8, 2017 (MMWR Week 27) and a Unite the Right rally on August 12, 2017 (MMWR Week 32) held in Charlottesville both resulted in violence and media attention.2,3 The violence associated with the Unite the Right rally included fatalities connected to motor vehicle and helicopter crashes.Syndromic surveillance has been used to study the impact of terrorism on a community’s mental health4 while more traditional data sources have looked at the impact of racially-charged civil unrest.5 Syndromic surveillance, however, has not previously been used to document the effect of racially-charged violence on the health of a community.MethodsThe Virginia Department of Health (VDH) analyzed syndromic surveillance data from three emergency departments (EDs) in the Charlottesville area (defined to include Charlottesville city and Albemarle county), regardless of patient residence following the Unite the Right rally. Visits to these EDs between January 1 and September 2, 2017 were analyzed using the Enhanced Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) and Microsoft SQL 2012. Encounters were identified as acute anxiety-related visits based on an International Classification of Diseases, Tenth Revision (ICD-10) discharge diagnosis beginning with ’F41’. Analyses were conducted using the ESSENCE algorithm EWMA 1.2 and SAS 9.3.ResultsThe greatest number of visits with a primary diagnosis of anxiety in 2017 (N=20) was observed in MMWR week 34 (August 20-26). This represented a statistically significant increase over baseline with a p-value of 0.01.By race, a significant increase over baseline in visits with a primary diagnosis of anxiety was observed among blacks or African Americans. The largest volume of visits was observed in MMWR week 33 with a total of 8 identified visits or 1.8% of total ED visit volume. The increase in visits for anxiety observed in weeks 33-35 was 2.2 times greater among blacks or African Americans than it was among whites, p = 0.016, 95% CI [1.14, 4.16].ConclusionsPrevious work done in Virginia to identify ED visits related to anxiety included only chief complaint criteria in the syndrome definition. Due to a change in how one ED in the Charlottesville area reported data during the study period, this syndrome definition could not be applied. In order to remove any potential data artifacts, only those visits with an initial diagnosis of anxiety were included in the analysis. The resulting syndrome definition likely underestimated the occurrence of anxiety in the Charlottesville area, both because it lacked chief complaint information and because syndromic surveillance does not include data on visits to mental health providers outside of EDs. This analysis presents a trend over time rather than a true measure of the prevalence of anxiety.This analysis, while conservative in its inclusion criteria, still identified an increase in visits for anxiety, particularly among blacks or African Americans. In today’s political environment of race-related civil unrest, a way to measure the burden of mental illness occurring in the community can be invaluable for public health response. In Charlottesville, the identification of a community-wide need for mental health support prompted many local providers to offer their services to those in need pro-bono.6References1 Suarez, C. (2017, February 6). Charlottesville City Council votes to remove statue from Lee Park. The Daily Progress. Retrieved from http://bit.ly/2wYOHhv2 Spencer, H., & Stevens, M. (2017, July 8). 23 Arrested and Tear Gas Deployed After a K.K.K. Rally in Virginia. The New York Times. Retrieved from http://nyti.ms/2tCiBGU3 Hanna, J., Hartung, K., Sayers, D., & Almasy, S. (2017, August 13). Virginia governor to white nationalists: ‘Go home … shame on you’. CNN. Retrieved from http://cnn.it/2vvAGHt4 Vandentorren, S., Paty, A. C., Baffert, E., Chansard, P., Caserio-Schönemann, C. (2016, February). Syndromic surveillance during the Paris terrorist attacks. The Lancet (387(10021), 846-847. doi:10.1016/S0140-6736(16)00507-95 Yimgang, D. P., Wang, Y., Paik, G., Hager, E. R., & Black, M. M. Civil Unrest in the Context of Chronic Community Violence: Impact on Maternal Depressive Symptoms. American Journal of Public Health 107(9), 1455-1462. doi:10.2105/AJPH.2017.3038766 DeLuca, P. (2017, August 19). Downtown Charlottesville Library Offers Free Counseling. NBC29.com. Retrieved from http://bit.ly/2yIzHbl

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 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 Use of Diagnosis Code in Mental Health Syndrome Definition

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Achintya N Dey ◽  
Deborah Gould ◽  
Nelson Adekoya ◽  
Peter Hicks ◽  
Girum S Ejigu ◽  
...  
Keyword(s):  
Mental Health ◽  
Substance Use ◽  
Mental Disorders ◽  
Substance Use Disorders ◽  
Syndromic Surveillance ◽  
Case Definition ◽  
Added Value ◽  
Icd 10 ◽  
Health Related ◽  
Ed Visits

Objective: The objectives of this study are to (1) create a mental health syndrome definition for syndromic surveillance to monitor mental health-related ED visits in near real time; (2) examine whether CC data alone can accurately detect mental health related ED visits; and (3) assess the added value of using Dx data to detect mental health-related ED visits.Introduction: Between 2006 and 2013, the rate of emergency department (ED) visits related to mental and substance use disorders increased substantially. This increase was higher for mental disorders visits (55 percent for depression, anxiety or stress reactions and 52 percent for psychoses or bipolar disorders) than for substance use disorders (37 percent) visits [1]. This increasing number of ED visits by patients with mental disorders indicates a growing burden on the health-care delivery system. New methods of surveillance are needed to identify and understand these changing trends in ED utilization and affected underlying populations.Syndromic surveillance can be leveraged to monitor mental health-related ED visits in near real-time. ED syndromic surveillance systems primarily rely on patient chief complaints (CC) to monitor and detect health events. Some studies suggest that the use of ED discharge diagnoses data (Dx), in addition to or instead of CC, may improve sensitivity and specificity of case identification [2].Methods: We extracted a de-identified random sample of 50,000 ED visits with CC from the National Syndromic Surveillance Program (NSSP) for the period January 1—June 30, 2017. NSSP’s BioSense Platform receives ED data from >4000 hospitals, representing about 55 percent of all ED visits in the country [3]. From this sample we extracted 22868 ED visits that included both CC and Dx data. We then applied our mental health syndrome case definition which comprised mental health-related keywords and ICD-9-CM and ICD-10-CM codes. We queried CC text for the words “stress,” “PTSD,” “anxiety,” “depression,” “clinical depression,” “manic depression,” “unipolar depression,” “agitated,” “nervousness,” “mental health,” “mental disorder,” “affective disorder,” “schizoaffective disorder,” “psycoaffective disorder,” “obsessive-compulsive disorder,” “mood disorder,” “bipolar disorder,” “schizotypal personality disorder,” “panic disorder,” “psychosis,” “paranoia,” “psych,” “manic,” “mania,” “hallucinating,” “hallucination,” “mental episode,” and “mental illness.” We queried Dx fields either for ICD-9- CM codes 295-296; 300, 311 or for ICD-10-CM codes F20-F48. The ICD-9- CM and ICD-10-CM codes used to identify mental health-related ED visits are based on the mental health disorders most frequently seen in EDs. Alcohol and substance use, suicide ideation, and suicide attempt were excluded from this study because they are included in alternate syndromes [2]. We manually reviewed the CC text to validate the search terms. Sensitivity, specificity, and positive predictive value will be calculated based on agreement of coding mental health against the human review of mental health visits.Based on our case definition, the sample of 22868 ED visits with CC and Dx data was further stratified into two groups: (1) mental health identified in either CC or Dx, and (2) no mental health identified in CC and Dx. Group 1 was further stratified into three groups: (a) mental health identified only in CC, (b) mental health identified in both CC and Dx, and (c) mental health identified only in Dx. The sample of 27132 ED visits with CC and no Dx data was further stratified into two groups: (1) mental health identified in CC, and (2) no mental health identified in CC (Figure).Results: Of the 50,000 sample of ED visits with CC data, 22868 visits had both CC and Dx data. Of the 22868 visits, we identified 1560 mental health-related ED visits using the mental health syndrome case definition. Of those visits, 241 were identified by a CC only, 226 were identified by both CC and Dx, and 1093 by a mental health-related Dx. Of the 27132 ED visits without Dx data, 421 had mental health identified in CC.Conclusions: Based on our preliminary analysis these findings suggest potential benefits of including Dx data in syndrome binning for mental health. Mental health terms are more likely to be found in Dx data than in the CC (1093 vs. 662). Using CC alone may underestimate the number of mental health-related ED visits. This study had several limitations. Not all facilities reporting to NSSP provide chief complaint data in the same manner, some provide CC as a drop down menu with predefined terms while others include the full text of CC. Not all records contained a Dx code which limited our ability to examine the added value of Dx code for that subset.

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. 

Is the firearm epidemic in the US getting worse?

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
D Tedesco ◽  
K Y C Adja ◽  
F Rallo ◽  
C Reno ◽  
M P Fantini ◽  
...  
Keyword(s):  
Suicide Attempts ◽  
National Level ◽  
Gun Violence ◽  
Public Health Issue ◽  
Western World ◽  
Diagnostic Code ◽  
The Us ◽  
Icd 10 ◽  
Ed Visits ◽  
Annual Decline

Abstract Background The US is the least regulated firearm market in the Western world and firearm violence is a major public health issue. Firearms account for 40,000 deaths in the US annually, which is higher than other high-income countries. Although most of the gun-related deaths in the US are the result of suicide attempts and self-inflicted injuries, nearly 40% of them come from accidents, assaults, or police intervention. Methods We measured the number of non-self-inflicted firearm-related ED visits, by including patients discharged with diagnostic ICD-9-CM (ICD-10 for 2016) codes of accidents, assaults or legal intervention resulting in firearm injuries between 2006-2016. We used data from the Healthcare Cost and Utilization Project (HCUPnet). From the CDC Wide-ranging Online Data for Epidemiologic Research we obtained data on non-suicidal firearm-related deaths over the period 2006-2017. To identify the cause of death we used the ICD-10 codes. Temporal changes of rates of ED visits and deaths were evaluated using Joinpoint Software. Results In 2006 there were a total of 79,998 ED visits with a diagnostic code of firearm-related injury, and this number showed a non-significant 2.7% annual decline between 2006-2013 (p = 0.06) followed by a significant 19.4% annual increase between 2013-2016 (p &lt; 0.05), resulting in 111.305 visits in 2016. The number of non-suicidal firearm-related deaths showed a significant 2.2% annual decline between 2006-2014 (p &lt; 0.05), followed by a significant 10.3% APC (p &lt; 0.05) between 2014-2017. Conclusions Data showed steady rates until 2013 and a striking increasing trend starting from 2013. Firearm-related deaths followed the same trends. Our data show that in the last four detectable years there has been a new concerning wave of gun violence and consequently a higher number of fatalities. Analysis limitations: we used national-level aggregate data and coding accuracy may be not consistent nationwide. Key messages In the last four detectable years there has been a new concerning wave of gun violence and consequently a higher number of fatalities nationwide. The US firearm related deaths epidemic urges for new policies and preventive measures, such as stricter background checks and restrictions on guns ownership.

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