scholarly journals Monitoring Respiratory Syncytial Virus Regionally In Children Aged < 5 Years Old Using Emergency Department and Urgent Care Center Chief Complaint Data in Florida’s Syndromic Surveillance System, Week 1, 2010 - Week 32, 2014

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Heather Rubino ◽  
David Atrubin ◽  
Janet J. Hamilton
Keyword(s):  
Surveillance System ◽  
Syndromic Surveillance ◽  
Hospital Admissions ◽  
Care Center ◽  
Laboratory Data ◽  
Chief Complaint ◽  
Urgent Care ◽  
Surveillance Systems ◽  
Syndromic Surveillance System ◽  
Syncytial Virus

ED chief complaint and discharge diagnosis data accessed through a syndromic surveillance system can be used for effective, timely monitoring of RSV hospitalizations in children < 5 years old and may be a more efficient and complete means of monitoring seasonality of RSV activity by region and statewide compared to hospital-based laboratory data reporting. Additionally, this surveillance technique can efficiently monitor RSV activity as well as estimate hospital admissions due to RSV and may be a useful approach for other states with syndromic surveillance systems.

Utility of Ambulance Data for Real-Time Syndromic Surveillance: A Pilot in the West Midlands Region, United Kingdom

2017 ◽  
Vol 32 (6) ◽  
pp. 667-672 ◽  
Author(s):  
Dan Todkill ◽  
Paul Loveridge ◽  
Alex J. Elliot ◽  
Roger A. Morbey ◽  
Obaghe Edeghere ◽  
...  
Keyword(s):  
United Kingdom ◽  
Real Time ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Lessons Learned ◽  
Surveillance Systems ◽  
Preliminary Assessment ◽  
The West ◽  
Syndromic Surveillance System ◽  
West Midlands

AbstractIntroductionThe Public Health England (PHE; United Kingdom) Real-Time Syndromic Surveillance Team (ReSST) currently operates four national syndromic surveillance systems, including an emergency department system. A system based on ambulance data might provide an additional measure of the “severe” end of the clinical disease spectrum. This report describes the findings and lessons learned from the development and preliminary assessment of a pilot syndromic surveillance system using ambulance data from the West Midlands (WM) region in England.Hypothesis/ProblemIs an Ambulance Data Syndromic Surveillance System (ADSSS) feasible and of utility in enhancing the existing suite of PHE syndromic surveillance systems?MethodsAn ADSSS was designed, implemented, and a pilot conducted from September 1, 2015 through March 1, 2016. Surveillance cases were defined as calls to the West Midlands Ambulance Service (WMAS) regarding patients who were assigned any of 11 specified chief presenting complaints (CPCs) during the pilot period. The WMAS collected anonymized data on cases and transferred the dataset daily to ReSST, which contained anonymized information on patients’ demographics, partial postcode of patients’ location, and CPC. The 11 CPCs covered a broad range of syndromes. The dataset was analyzed descriptively each week to determine trends and key epidemiological characteristics of patients, and an automated statistical algorithm was employed daily to detect higher than expected number of calls. A preliminary assessment was undertaken to assess the feasibility, utility (including quality of key indicators), and timeliness of the system for syndromic surveillance purposes. Lessons learned and challenges were identified and recorded during the design and implementation of the system.ResultsThe pilot ADSSS collected 207,331 records of individual ambulance calls (daily mean=1,133; range=923-1,350). The ADSSS was found to be timely in detecting seasonal changes in patterns of respiratory infections and increases in case numbers during seasonal events.ConclusionsFurther validation is necessary; however, the findings from the assessment of the pilot ADSSS suggest that selected, but not all, ambulance indicators appear to have some utility for syndromic surveillance purposes in England. There are certain challenges that need to be addressed when designing and implementing similar systems.TodkillD, LoveridgeP, ElliotAJ, MorbeyRA, EdeghereO, Rayment-BishopT, Rayment-BishopC, ThornesJE, SmithG. Utility of ambulance data for real-time syndromic surveillance: a pilot in the West Midlands region, United Kingdom. Prehosp Disaster Med. 2017;32(6):667–672.

scholarly journals Inconsistency of Timeliness in a Chief Complaint-Based Syndromic Surveillance System During Two Influenza Epidemic Seasons

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Tao Tao ◽  
Qi Zhao ◽  
Jun Zong ◽  
Xue Li ◽  
Vinod Diwan ◽  
...  
Keyword(s):  
Surveillance System ◽  
Syndromic Surveillance ◽  
Clinical Manifestations ◽  
Chief Complaint ◽  
Influenza Surveillance ◽  
Influenza Epidemic ◽  
Surveillance Network ◽  
Syndromic Surveillance System ◽  
Positive Rate ◽  
Patient Groups

This study estimated the early warning timeliness of a chief complaint-based syndromic surveillance system towards seasonal influenza epidemics. Findings showed that the timliness of ILI data sources changed across two influenza epidemic seasons. ILI reported from different levels of health facilities and patient groups showed distinct timeliness towards influenza epidemics indicated by virus positive rate (VPR) from National Influenza Surveillance Network. The changes of dominant strains, clinical manifestations, population groups affected in different influenza seasons might account for this inconsistency.

Framework for the Development of Response Protocols for Public Health Syndromic Surveillance Systems: Case Studies of 8 US States

2009 ◽  
Vol 3 (S1) ◽  
pp. S29-S36 ◽  
Author(s):  
Lori Uscher-Pines ◽  
Corey L. Farrell ◽  
Steven M. Babin ◽  
Jacqueline Cattani ◽  
Charlotte A. Gaydos ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
The United States ◽  
Essential Elements ◽  
System Response ◽  
Surveillance Systems ◽  
Health Departments ◽  
Public Health Response ◽  
Syndromic Surveillance System

ABSTRACTObjectives: To describe current syndromic surveillance system response protocols in health departments from 8 diverse states in the United States and to develop a framework for health departments to use as a guide in initial design and/or enhancement of response protocols.Methods: Case study design that incorporated in-depth interviews with health department staff, textual analysis of response plans, and a Delphi survey of syndromic surveillance response experts.Results: All 8 states and 30 of the 33 eligible health departments agreed to participate (91% response rate). Fewer than half (48%) of surveyed health departments had a written response protocol, and health departments reported conducting in-depth investigations on fewer than 15% of syndromic surveillance alerts. A convened panel of experts identified 32 essential elements for inclusion in public health protocols for response to syndromic surveillance system alerts.Conclusions: Because of the lack of guidance, limited resources for development of response protocols, and few examples of syndromic surveillance detecting previously unknown events of public health significance, health departments have not prioritized the development and refinement of response protocols. Systems alone, however, are not effective without an organized public health response. The framework proposed here can guide health departments in creating protocols that will be standardized, tested, and relevant given their goals with such systems. (Disaster Med Public Health Preparedness. 2009;3(Suppl 1):S29–S36)

scholarly journals Using a Syndromic Surveillance System to Evaluate the Impact of a Change in Alcohol Law

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Julia A. Dilley ◽  
Atar Baer ◽  
Jeff Duchin ◽  
Julie E. Maher
Keyword(s):  
Surveillance System ◽  
Syndromic Surveillance ◽  
Washington State ◽  
Chief Complaint ◽  
King County ◽  
Syndromic Surveillance System ◽  
Liquor Stores ◽  
Diagnosis Codes ◽  
Ed Visits ◽  
The Impact

In 2011, Washington State voters passed an initiative which closed state liquor stores and opened private sector liquor sales. We examined trends in alcohol-related emergency department (ED) visits associated with this law change. Data were from the King County syndromic surveillance system. Alcohol-related ED visits were identified using chief complaint search strings and diagnosis codes. We used a linear regression model with a spline at the date of law change and controlled for other factors. Significant increases in alcohol-related ED visits were observed associated with the law change among minors (age <21) and adults ages 40 and older.

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 Active syndromic surveillance of COVID-19 in Israel

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elad Yom-Tov
Keyword(s):  
Medical Care ◽  
Syndromic Surveillance ◽  
Lead Time ◽  
Urgent Care ◽  
Surveillance Systems ◽  
Syndromic Surveillance System ◽  
Disease Indicators ◽  
Chat Bot ◽  
Optimization Mechanism ◽  
Google Search

AbstractSyndromic surveillance systems monitor disease indicators to detect emergence of diseases and track their progression. Here, we report on a rapidly deployed active syndromic surveillance system for tracking COVID-19 in Israel. The system was a novel combination of active and passive components: Ads were shown to people searching for COVID-19 symptoms on the Google search engine. Those who clicked on the ads were referred to a chat bot which helped them decide whether they needed urgent medical care. Through its conversion optimization mechanism, the ad system was guided to focus on those people who required such care. Over 6 months, the ads were shown approximately 214,000 times and clicked on 12,000 times, and 722 people were informed they needed urgent care. Click rates on ads and the fraction of people deemed to require urgent care were correlated with the hospitalization rate ($$R^2=0.54$$ R 2 = 0.54 and $$R^2=0.50$$ R 2 = 0.50 , respectively) with a lead time of 9 days. Males and younger people were more likely to use the system, and younger people were more likely to be determined to require urgent care (slope: $$- \,0.009$$ - 0.009 , $$P=0.01$$ P = 0.01 ). Thus, the system can assist in predicting case numbers and hospital load at a significant lead time and, simultaneously, help people determine if they need medical care.

scholarly journals The National Capitol Region’s Emergency Department Syndromic Surveillance System: 
Do Chief Complaint and Discharge Diagnosis Yield Different Results?

2003 ◽  
Vol 9 (3) ◽  
pp. 393-396 ◽  
Author(s):  
Elizabeth M. Begier ◽  
Denise Sockwell ◽  
Leslie M. Branch ◽  
John O. Davies-Cole ◽  
LaVerne H. Jones ◽  
...  
Keyword(s):  
Emergency Department ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Discharge Diagnosis ◽  
Syndromic Surveillance System

scholarly journals Value of syndromic surveillance in monitoring a focal waterborne outbreak due to an unusual Cryptosporidium genotype in Northamptonshire, United Kingdom, June – July 2008

2010 ◽  
Vol 15 (33) ◽  
Author(s):  
S Smith ◽  
A J Elliot ◽  
C Mallaghan ◽  
D Modha ◽  
J Hippisley-Cox ◽  
...  
Keyword(s):  
United Kingdom ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Small Scale ◽  
Local Health ◽  
Surveillance Systems ◽  
National Surveillance ◽  
Syndromic Surveillance System ◽  
Nhs Direct ◽  
National Surveillance System

The United Kingdom (UK) has several national syndromic surveillance systems. The Health Protection Agency (HPA)/NHS Direct syndromic surveillance system uses pre-diagnostic syndromic data from a national telephone helpline, while the HPA/QSurveillance national surveillance system uses clinical diagnosis data extracted from general practitioner (GP)-based clinical information systems. Data from both of these systems were used to monitor a local outbreak of cryptosporidiosis that occurred following Cryptosporidium oocyst contamination of drinking water supplied from the Pitsford Reservoir in Northamptonshire, United Kingdom, in June 2008. There was a peak in the number of calls to NHS Direct concerning diarrhoea that coincided with the incident. QSurveillance data for the local areas affected by the outbreak showed a significant increase in GP consultations for diarrhoea and gastroenteritis in the week of the incident but there was no increase in consultations for vomiting. A total of 33 clinical cases of cryptosporidiosis were identified in the outbreak investigation, of which 23 were confirmed as infected with the outbreak strain. However, QSurveillance data suggest that there were an estimated 422 excess diarrhoea cases during the outbreak, an increase of about 25% over baseline weekly levels. To our knowledge, this is the first time that data from a syndromic surveillance system, the HPA/QSurveillance national surveillance system, have been able to show the extent of such a small outbreak at a local level. QSurveillance, which covers about 38% of the UK population, is currently the only GP database that is able to provide data at local health district (primary care trust) level. The Cryptosporidium contamination incident described demonstrates the potential usefulness of this information, as it is unusual for syndromic surveillance systems to be able to help monitor such a small-scale outbreak.

scholarly journals Development and assessment of a hospital admissions-based syndromic surveillance system for COVID-19 in Ontario, Canada: ACES Pandemic Tracker

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nicholas Papadomanolakis-Pakis ◽  
Allison Maier ◽  
Adam van Dijk ◽  
Nancy VanStone ◽  
Kieran Michael Moore
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
Hospital Admissions ◽  
Calculated Data ◽  
Global Public Health ◽  
Syndromic Surveillance System ◽  
Spearman's Rho ◽  
Spearman’S Rho

Abstract Background The COVID-19 pandemic has continued to pose a major global public health risk. The importance of public health surveillance systems to monitor the spread and impact of COVID-19 has been well demonstrated. The purpose of this study was to describe the development and effectiveness of a real-time public health syndromic surveillance system (ACES Pandemic Tracker) as an early warning system and to provide situational awareness in response to the COVID-19 pandemic in Ontario, Canada. Methods We used hospital admissions data from the Acute Care Enhanced Surveillance (ACES) system to collect data on pre-defined groupings of symptoms (syndromes of interest; SOI) that may be related to COVID-19 from 131 hospitals across Ontario. To evaluate which SOI for suspected COVID-19 admissions were best correlated with laboratory confirmed admissions, laboratory confirmed COVID-19 hospital admissions data were collected from the Ontario Ministry of Health. Correlations and time-series lag analysis between suspected and confirmed COVID-19 hospital admissions were calculated. Data used for analyses covered the period between March 1, 2020 and September 21, 2020. Results Between March 1, 2020 and September 21, 2020, ACES Pandemic Tracker identified 22,075 suspected COVID-19 hospital admissions (150 per 100,000 population) in Ontario. After correlation analysis, we found laboratory-confirmed hospital admissions for COVID-19 were strongly and significantly correlated with suspected COVID-19 hospital admissions when SOI were included (Spearman’s rho = 0.617) and suspected COVID-19 admissions when SOI were excluded (Spearman’s rho = 0.867). Weak to moderate significant correlations were found among individual SOI. Laboratory confirmed COVID-19 hospital admissions lagged in reporting by 3 days compared with suspected COVID-19 admissions when SOI were excluded. Conclusions Our results demonstrate the utility of a hospital admissions syndromic surveillance system to monitor and identify potential surges in severe COVID-19 infection within the community in a timely manner and provide situational awareness to inform preventive and preparatory health interventions.

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