scholarly journals Syndromic Surveillance of Acute Liver Failure in Emergency Departments (France, 2010-2012)

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Corinne Pioche ◽  
Christine Larsen ◽  
Céline Caserio-Schonemann ◽  
Vanina Héraud-Bousquet
Keyword(s):  
Liver Failure ◽  
Acute Liver Failure ◽  
Emergency Departments ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Surveillance Data ◽  
Syndromic Surveillance System ◽  
Icd10 Code

Our objectives were to explore the relevance of emergency departments' (ED) data, collected daily through the French syndromic surveillance system (414 EDs, 65% attendances), to describe the characteristics of patients with acute liver failure (ALF). Data corresponding to ICD10 codes related to hepatitis diagnosis that include ALF ICD10 code (K720) were extracted and analyzed. During 2010-2012, 246 730 attendances with hepatitis were recorded of which 2 475 (1%) were linked to ALF. Patients with ALF were male (60%), their median age was 55 years. This study shows the relevance of French syndromic surveillance data to assess the burden of ALF.

scholarly journals Impact of Patient Self-Registration in Emergency Departments on Syndromic Surveillance Data

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Melinda C. Thomas ◽  
David Atrubin ◽  
Janet J. Hamilton
Keyword(s):  
Best Practices ◽  
Emergency Departments ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Surveillance Data ◽  
Hospital Emergency ◽  
Syndromic Surveillance System ◽  
Hospital Emergency Departments

This session discusses an assessment of the effect of patient self-registration methods in hospital emergency departments on data in a syndromic surveillance system and provides suggestions for best practices in identifying and analyzing these data.

Use of a syndromic surveillance system to describe the trend in cycling-related presentations to emergency departments in Sydney

2015 ◽  
Vol 27 (4) ◽  
pp. 343-347 ◽  
Author(s):  
Michael M Dinh ◽  
Christopher Kastelein ◽  
Kendall J Bein ◽  
Timothy C Green ◽  
Tanya Bautovich ◽  
...  
Keyword(s):  
Emergency Departments ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Syndromic Surveillance System

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 Determinants of Daily Attendances in Emergency Departments for Asthma in the Paris Area

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Céline Caserio-Schönemann ◽  
Alice Sanna ◽  
Vanina Bousquet ◽  
Sylvia Medina ◽  
Mathilde Pascal ◽  
...  
Keyword(s):  
Air Pollution ◽  
Emergency Departments ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Age Group ◽  
Short Term ◽  
Syndromic Surveillance System ◽  
Intrinsic Factors ◽  
Societal Factors ◽  
Paris Area

Asthma is one of the numerous syndromic indicators daily monitored at the regional and national levels by the French syndromic surveillance system based on the emergency departments Oscour network. This indicator presents important daily fluctuations and can be influenced by several environmental, infectious and societal factors. Particularly the short-term impact on asthma of episodes like the air pollution peak experienced in March 2014 and the thunderstorm occurred in July 2014 has been analysed by age group on the Paris area, as well as the effect intrinsic factors (day-of-week, seasonal period, holidays).

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

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

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

Algorithm for Statistical Detection of Peaks - Syndromic Surveillance System for the Athens 2004 Olympic Games

2004 ◽  
Author(s):  
Urania G. Dafni ◽  
S. Tsiodras ◽  
D. Panagiotakos ◽  
K. Gkolfinopoulou ◽  
G. Kouvatscas ◽  
...  
Keyword(s):  
Surveillance System ◽  
Syndromic Surveillance ◽  
Olympic Games ◽  
Syndromic Surveillance System ◽  
Statistical Detection
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