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Todkill, Dan, Paul Loveridge, Alex J. Elliot, Roger A. Morbey, Obaghe Edeghere, Tracy Rayment-Bishop, Chris Rayment-Bishop, John E. Thornes, and Gillian Smith. "Utility of Ambulance Data for Real-Time Syndromic Surveillance: A Pilot in the West Midlands Region, United Kingdom." Prehospital and Disaster Medicine 32, no. 6 (August 1, 2017): 667–72. http://dx.doi.org/10.1017/s1049023x17006690.
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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.
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HARCOURT, S. E., J. FLETCHER, P. LOVERIDGE, A. BAINS, R. MORBEY, A. YEATES, B. McCLOSKEY, et al. "Developing a new syndromic surveillance system for the London 2012 Olympic and Paralympic Games." Epidemiology and Infection 140, no. 12 (August 15, 2012): 2152–56. http://dx.doi.org/10.1017/s0950268812001781.
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SUMMARYSyndromic surveillance is vital for monitoring public health during mass gatherings. The London 2012 Olympic and Paralympic Games represents a major challenge to health protection services and community surveillance. In response to this challenge the Health Protection Agency has developed a new syndromic surveillance system that monitors daily general practitioner out-of-hours and unscheduled care attendances. This new national system will fill a gap identified in the existing general practice-based syndromic surveillance systems by providing surveillance capability of general practice activity during evenings/nights, over weekends and public holidays. The system will complement and supplement the existing tele-health phone line, general practitioner and emergency department syndromic surveillance systems. This new national system will contribute to improving public health reassurance, especially to meet the challenges of the London 2012 Olympic and Paralympic Games.
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Garrett-Cherry, Tiana A., Andrew K. Hennenfent, Sasha McGee, and John Davies-Cole. "Enhanced One Health Surveillance during the 58th Presidential Inauguration—District of Columbia, January 2017." Disaster Medicine and Public Health Preparedness 14, no. 2 (July 23, 2019): 201–7. http://dx.doi.org/10.1017/dmp.2019.38.
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ABSTRACTObjective:In January 2017, Washington, DC, hosted the 58th United States presidential inauguration. The DC Department of Health leveraged multiple health surveillance approaches, including syndromic surveillance (human and animal) and medical aid station–based patient tracking, to detect disease and injury associated with this mass gathering.Methods:Patient data were collected from a regional syndromic surveillance system, medical aid stations, and an internet-based emergency department reporting system. Animal health data were collected from DC veterinary facilities.Results:Of 174 703 chief complaints from human syndromic data, there were 6 inauguration-related alerts. Inauguration attendees who visited aid stations (n = 162) and emergency departments (n = 180) most commonly reported feeling faint/dizzy (n = 29; 17.9%) and pain/cramps (n = 34;18.9%). In animals, of 533 clinical signs reported, most were gastrointestinal (n = 237; 44.5%) and occurred in canines (n = 374; 70.2%). Ten animals that presented dead on arrival were investigated; no significant threats were identified.Conclusion:Use of multiple surveillance systems allowed for near-real-time detection and monitoring of disease and injury syndromes in humans and domestic animals potentially associated with inaugural events and in local health care systems.
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Miller, Stephanie, Kim Fallon, and Ludmila Anderson. "New Hampshire emergency department syndromic surveillance system." Journal of Urban Health 80, S1 (March 2003): i118. http://dx.doi.org/10.1007/bf02416900.
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Moore, Kieran M., Bronwen L. Edgar, and Donald McGuinness. "Implementation of an automated, real-time public health surveillance system linking emergency departments and health units: rationale and methodology." CJEM 10, no. 02 (March 2008): 114–19. http://dx.doi.org/10.1017/s1481803500009817.
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ABSTRACTIn September 2004, Kingston, Frontenac, Lennox and Addington (KFL&A) Public Health, in collaboration with the Public Health Division of the Ontario Ministry of Health and Long-Term Care, Queen's University, the Public Health Agency of Canada, Kingston General Hospital and Hotel Dieu Hospital, began a 2-year pilot project to implement and evaluate an emergency department (ED) chief complaint syndromic surveillance system. Our objective was to evaluate a comprehensive and readily deployable real-time regional syndromic surveillance program and to determine its ability to detect gastrointestinal or respiratory outbreaks well in advance of traditional reporting systems. In order to implement the system, modifications were made to the University of Pittsburgh's Real-time Outbreak and Disease Surveillance (RODS) system, which has been successfully integrated into public health systems, and has enhanced communication and collaboration between them and EDs. This paper provides an overview of a RODS-based syndromic surveillance system as adapted for use at a public health unit in Kingston, Ontario. We summarize the technical specifications, privacy and security considerations, data capture, classification and management of the data streams, alerting and public health response. We hope that the modifications described here, including the addition of unique data streams, will provide a benchmark for future Canadian syndromic surveillance systems.
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Kajita, Emily, Monica Z. Luarca, Han Wu, Bessie Hwang, and Laurene Mascola. "Harnessing Syndromic Surveillance Emergency Department Data to Monitor Health Impacts During the 2015 Special Olympics World Games." Public Health Reports 132, no. 1_suppl (July 2017): 99S—105S. http://dx.doi.org/10.1177/0033354917706956.
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Introduction: Mass gatherings that attract a large international presence may cause or amplify point-source outbreaks of emerging infectious disease. The Los Angeles County Department of Public Health customized its syndromic surveillance system to detect increased syndrome-specific utilization of emergency departments (EDs) and other medical encounters coincident to the 2015 Special Olympics World Games. Materials and Methods: We queried live databases containing data on ED visits, California Poison Control System calls, and Los Angeles County coroner-investigated deaths for increases in daily counts from July 19 to August 6, 2015. We chose syndrome categories based on the potential for disease outbreaks common to international travel and dormitory settings, morbidity amplified by high temperatures, and bioterrorism threats inherent to mass gatherings. We performed line-list reviews and trend analyses of total, syndrome-specific, and region-specific daily counts, using cumulative sum-based signals. We also piloted a novel strategy of requesting that ED registrars proactively tag Special Olympics attendees in chief complaint data fields. Results: The syndromic surveillance system showed that the 2015 Special Olympics did not generate large-scale acute morbidities leading to detectable stress on local EDs. We recruited 10 hospitals for proactive patient tagging, from which 16 Special Olympics attendees were detected; these patients reported various symptoms, such as injury, vomiting, and syncope. Practice Implications: As an enhancement to traditional syndromic surveillance, proactive patient tagging can illuminate potential epidemiologic links among patients in challenging syndromic surveillance applications, such as mass gatherings. Syndromic surveillance has the potential to enhance ED patient polling and reporting of exposure, symptom, and other epidemiologic case definition criteria to public health agencies in near-real time.
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Lall, Ramona, Jasmine Abdelnabi, Stephanie Ngai, Hilary B. Parton, Kelly Saunders, Jessica Sell, Amanda Wahnich, Don Weiss, and Robert W. Mathes. "Advancing the Use of Emergency Department Syndromic Surveillance Data, New York City, 2012-2016." Public Health Reports 132, no. 1_suppl (July 2017): 23S—30S. http://dx.doi.org/10.1177/0033354917711183.
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Introduction: The use of syndromic surveillance has expanded from its initial purpose of bioterrorism detection. We present 6 use cases from New York City that demonstrate the value of syndromic surveillance for public health response and decision making across a broad range of health outcomes: synthetic cannabinoid drug use, heat-related illness, suspected meningococcal disease, medical needs after severe weather, asthma exacerbation after a building collapse, and Ebola-like illness in travelers returning from West Africa. Materials and Methods: The New York City syndromic surveillance system receives data on patient visits from all emergency departments (EDs) in the city. The data are used to assign syndrome categories based on the chief complaint and discharge diagnosis, and analytic methods are used to monitor geographic and temporal trends and detect clusters. Results: For all 6 use cases, syndromic surveillance using ED data provided actionable information. Syndromic surveillance helped detect a rise in synthetic cannabinoid-related ED visits, prompting a public health investigation and action. Surveillance of heat-related illness indicated increasing health effects of severe weather and led to more urgent public health messaging. Surveillance of meningitis-related ED visits helped identify unreported cases of culture-negative meningococcal disease. Syndromic surveillance also proved useful for assessing a surge of methadone-related ED visits after Superstorm Sandy, provided reassurance of no localized increases in asthma after a building collapse, and augmented traditional disease reporting during the West African Ebola outbreak. Practice Implications: Sharing syndromic surveillance use cases can foster new ideas and build capacity for public health preparedness and response.
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ELLIOT, A. J., E. O. KARA, P. LOVERIDGE, Z. BAWA, R. A. MORBEY, M. MOTH, S. LARGE, and G. E. SMITH. "Internet-based remote health self-checker symptom data as an adjuvant to a national syndromic surveillance system." Epidemiology and Infection 143, no. 16 (April 10, 2015): 3416–22. http://dx.doi.org/10.1017/s0950268815000503.
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SUMMARYSyndromic surveillance is an innovative surveillance tool used to support national surveillance programmes. Recent advances in the use of internet-based health data have demonstrated the potential usefulness of these health data; however, there have been limited studies comparing these innovative health data to existing established syndromic surveillance systems. We conducted a retrospective observational study to assess the usefulness of a national internet-based ‘symptom checker’ service for use as a syndromic surveillance system. NHS Direct online data were extracted for 1 August 2012 to 1 July 2013; a time-series analysis on the symptom categories self-reported by online users was undertaken and compared to existing telehealth syndromic data. There were 3·37 million online users of the internet-based self-checker compared to 1·43 million callers to the telephone triage health service. There was a good correlation between the online and telephone triage data for a number of syndromic indicators including cold/flu, difficulty breathing and eye problems; however, online data appeared to provide additional early warning over telephone triage health data. This assessment has illustrated some potential benefit of using internet-based symptom-checker data and provides the basis for further investigating how these data can be incorporated into national syndromic surveillance programmes.
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Kam, H. J., S. Choi, J. P. Cho, Y. G. Min, and R. W. Park. "Acute Diarrheal Syndromic Surveillance." Applied Clinical Informatics 01, no. 02 (2010): 79–95. http://dx.doi.org/10.4338/aci-2009-12-ra-0024.
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Summary Objective: In an effort to identify and characterize the environmental factors that affect the number of patients with acute diarrheal (AD) syndrome, we developed and tested two regional surveillance models including holiday and weather information in addition to visitor records, at emergency medical facilities in the Seoul metropolitan area of Korea. Methods: With 1,328,686 emergency department visitor records from the National Emergency Department Information system (NEDIS) and the holiday and weather information, two seasonal ARIMA models were constructed: (1) The simple model (only with total patient number), (2) the environmental factor-added model. The stationary R-squared was utilized as an in-sample model goodness-of-fit statistic for the constructed models, and the cumulative mean of the Mean Absolute Percentage Error (MAPE) was used to measure post-sample forecast accuracy over the next 1 month. Results: The (1,0,1)(0,1,1)7 ARIMA model resulted in an adequate model fit for the daily number of AD patient visits over 12 months for both cases. Among various features, the total number of patient visits was selected as a commonly influential independent variable. Additionally, for the environmental factor-added model, holidays and daily precipitation were selected as features that statistically significantly affected model fitting. Stationary R-squared values were changed in a range of 0.651-0.828 (simple), and 0.805-0.844 (environmental factor-added) with p<0.05. In terms of prediction, the MAPE values changed within 0.090-0.120 and 0.089-0.114, respectively. Conclusion: The environmental factor-added model yielded better MAPE values. Holiday and weather information appear to be crucial for the construction of an accurate syndromic surveillance model for AD, in addition to the visitor and assessment records. Citation: Kam HJ, Choi S, Cho JP, Min YG, Park RW. Acute diarrheal syndromic surveillance – effects of weather and holidays. Appl Clin Inf 2010; 1: 79–95 http://dx.doi.org/10.4338/ACI-2009-12-RA-0024
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Jia, Peng, and Shujuan Yang. "China needs a national intelligent syndromic surveillance system." Nature Medicine 26, no. 7 (May 20, 2020): 990. http://dx.doi.org/10.1038/s41591-020-0921-5.
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KARA, E. O., A. J. ELLIOT, H. BAGNALL, D. G. F. FOORD, R. PNAISER, H. OSMAN, G. E. SMITH, and B. OLOWOKURE. "Absenteeism in schools during the 2009 influenza A(H1N1) pandemic: a useful tool for early detection of influenza activity in the community?" Epidemiology and Infection 140, no. 7 (October 21, 2011): 1328–36. http://dx.doi.org/10.1017/s0950268811002093.
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SUMMARYCertain influenza outbreaks, including the 2009 influenza A(H1N1) pandemic, can predominantly affect school-age children. Therefore the use of school absenteeism data has been considered as a potential tool for providing early warning of increasing influenza activity in the community. This study retrospectively evaluates the usefulness of these data by comparing them with existing syndromic surveillance systems and laboratory data. Weekly mean percentages of absenteeism in 373 state schools (children aged 4–18 years) in Birmingham, UK, from September 2006 to September 2009, were compared with established syndromic surveillance systems including a telephone health helpline, a general practitioner sentinel network and laboratory data for influenza. Correlation coefficients were used to examine the relationship between each syndromic system. In June 2009, school absenteeism generally peaked concomitantly with the existing influenza surveillance systems in England. Weekly school absenteeism surveillance would not have detected pandemic influenza A(H1N1) earlier but daily absenteeism data and the development of baselines could improve the timeliness of the system.
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Uscher-Pines, Lori, Corey L. Farrell, Steven M. Babin, Jacqueline Cattani, Charlotte A. Gaydos, Yu-Hsiang Hsieh, Michael D. Moskal, and Richard E. Rothman. "Framework for the Development of Response Protocols for Public Health Syndromic Surveillance Systems: Case Studies of 8 US States." Disaster Medicine and Public Health Preparedness 3, S1 (June 2009): S29—S36. http://dx.doi.org/10.1097/dmp.0b013e31819f4483.
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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)
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Muchaal, PK, S. Parker, K. Meganath, L. Landry, and J. Aramini. "Évaluation d'un système national de surveillance syndromique en milieu pharmaceutique." Relevé des maladies transmissibles au Canada 41, no. 9 (September 3, 2015): 234–40. http://dx.doi.org/10.14745/ccdr.v41i09a01f.
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Kuramoto-Crawford, S. Janet, Erica L. Spies, and John Davies-Cole. "Detecting Suicide-Related Emergency Department Visits Among Adults Using the District of Columbia Syndromic Surveillance System." Public Health Reports 132, no. 1_suppl (July 2017): 88S—94S. http://dx.doi.org/10.1177/0033354917706933.
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Objectives: Limited studies have examined the usefulness of syndromic surveillance to monitor emergency department (ED) visits involving suicidal ideation or attempt. The objectives of this study were to (1) examine whether syndromic surveillance of chief complaint data can detect suicide-related ED visits among adults and (2) assess the added value of using hospital ED data on discharge diagnoses to detect suicide-related visits. Methods: The study data came from the District of Columbia electronic syndromic surveillance system, which provides daily information on ED visits at 8 hospitals in Washington, DC. We detected suicide-related visits by searching for terms in the chief complaints and discharge diagnoses of 248 939 ED visits for which data were available for October 1, 2015, to September 30, 2016. We examined whether detection of suicide-related visits according to chief complaint data, discharge diagnosis data, or both varied by patient sex, age, or hospital. Results: The syndromic surveillance system detected 1540 suicide-related ED visits, 950 (62%) of which were detected through chief complaint data and 590 (38%) from discharge diagnosis data. The source of detection for suicide-related ED visits did not vary by patient sex or age. However, whether the suicide-related terms were mentioned in the chief complaint or discharge diagnosis differed across hospitals. Conclusions: ED syndromic surveillance systems based on chief complaint data alone would underestimate the number of suicide-related ED visits. Incorporating the discharge diagnosis into the case definition could help improve detection.
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Perry, Alexander G., Michael J. Korenberg, Geoffrey G. Hall, and Kieran M. Moore. "Modeling and Syndromic Surveillance for Estimating Weather-Induced Heat-Related Illness." Journal of Environmental and Public Health 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/750236.
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This paper compares syndromic surveillance and predictive weather-based models for estimating emergency department (ED) visits for Heat-Related Illness (HRI). A retrospective time-series analysis of weather station observations and ICD-coded HRI ED visits to ten hospitals in south eastern Ontario, Canada, was performed from April 2003 to December 2008 using hospital data from the National Ambulatory Care Reporting System (NACRS) database, ED patient chief complaint data collected by a syndromic surveillance system, and weather data from Environment Canada. Poisson regression and Fast Orthogonal Search (FOS), a nonlinear time series modeling technique, were used to construct models for the expected number of HRI ED visits using weather predictor variables (temperature, humidity, and wind speed). Estimates of HRI visits from regression models using both weather variables and visit counts captured by syndromic surveillance as predictors were slightly more highly correlated with NACRS HRI ED visits than either regression models using only weather predictors or syndromic surveillance counts.
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Rosewell, Alexander, Berry Ropa, Heather Randall, Rosheila Dagina, Samuel Hurim, Sibauk Bieb, Siddhartha Datta, et al. "Mobile Phone–based Syndromic Surveillance System, Papua New Guinea." Emerging Infectious Diseases 19, no. 11 (November 2013): 1811–18. http://dx.doi.org/10.3201/eid1911.121843.
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Muchaal, PK, S. Parker, K. Meganath, L. Landry, and J. Aramini. "Evaluation of a national pharmacy‐based syndromic surveillance system." Canada Communicable Disease Report 41, no. 9 (September 3, 2015): 203–8. http://dx.doi.org/10.14745/ccdr.v41i09a01.
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Chan, Ta-Chien, Jia-Hong Tang, Cheng-Yu Hsieh, Kevin J. Chen, Tsan-Hua Yu, and Yu-Ting Tsai. "Approaching precision public health by automated syndromic surveillance in communities." PLOS ONE 16, no. 8 (August 6, 2021): e0254479. http://dx.doi.org/10.1371/journal.pone.0254479.
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Background Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named “Sentinel plus” which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians. Methods Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019. Results The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC. Conclusions This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis.
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Gould, Deborah W., David Walker, and Paula W. Yoon. "The Evolution of BioSense: Lessons Learned and Future Directions." Public Health Reports 132, no. 1_suppl (July 2017): 7S—11S. http://dx.doi.org/10.1177/0033354917706954.
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The BioSense program was launched in 2003 with the aim of establishing a nationwide integrated public health surveillance system for early detection and assessment of potential bioterrorism-related illness. The program has matured over the years from an initial Centers for Disease Control and Prevention–centric program to one focused on building syndromic surveillance capacity at the state and local level. The uses of syndromic surveillance have also evolved from an early focus on alerts for bioterrorism-related illness to situational awareness and response, to various hazardous events and disease outbreaks. Future development of BioSense (now the National Syndromic Surveillance Program) includes, in the short term, a focus on data quality with an emphasis on stability, consistency, and reliability and, in the long term, increased capacity and innovation, new data sources and system functionality, and exploration of emerging technologies and analytics.
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Shih, Fuh-Yuan, Muh-Yong Yen, Jiunn-Shyan Wu, Fang-Kuei Chang, Lih-Wen Lin, Mei-Shang Ho, Chao A. Hsiung, et al. "Challenges Faced by Hospital Healthcare Workers in Using a Syndrome-Based Surveillance System During the 2003 Outbreak of Severe Acute Respiratory Syndrome in Taiwan." Infection Control & Hospital Epidemiology 28, no. 3 (March 2007): 354–57. http://dx.doi.org/10.1086/508835.
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Because the severe acute respiratory syndrome (SARS) outbreak in Taiwan in 2003 was worsened by hospital infections, we analyzed 229 questionnaires (84.8% of 270 sent) completed by surveyed healthcare workers who cared for patients with SARS in 3 types of hospitals, to identify surveillance problems. Atypical clinical presentation was the most often reported problem, regardless of hospital type, which strongly indicates that more timely syndromic surveillance was needed.
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Stoto, M. A., A. Jain, J. O. Davies-Cole, and C. Glymph. "Evaluation of the Dc Department of Health's Syndromic Surveillance System." American Journal of Epidemiology 163, suppl_11 (June 1, 2006): S187. http://dx.doi.org/10.1093/aje/163.suppl_11.s187-b.
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Jia, Peng, and Shujuan Yang. "Publisher Correction: China needs a national intelligent syndromic surveillance system." Nature Medicine 26, no. 7 (June 18, 2020): 1149. http://dx.doi.org/10.1038/s41591-020-0977-2.
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Ong, M. S., F. Magrabi, and E. Coiera. "Syndromic surveillance for health information system failures: a feasibility study." Journal of the American Medical Informatics Association 20, no. 3 (May 1, 2013): 506–12. http://dx.doi.org/10.1136/amiajnl-2012-001144.
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Chang, H. G., J. H. Chen, D. Cochrane, J. Allegra, and P. Smith. "The Use of Sub-syndromes to Investigate Peaks in a Syndromic Surveillance System." Academic Emergency Medicine 14, no. 5 Supplement 1 (May 1, 2007): S179—S180. http://dx.doi.org/10.1197/j.aem.2007.03.1217.
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van Dijk, Adam, Emily Dawson, Kieran Michael Moore, and Paul Belanger. "Risk Assessment During the Pan American and Parapan American Games, Toronto, 2015." Public Health Reports 132, no. 1_suppl (July 2017): 106S—110S. http://dx.doi.org/10.1177/0033354917708356.
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During the summer of 2015, the Pan American and Parapan American Games took place in the Greater Toronto area of Ontario, Canada, bringing together thousands of athletes and spectators from around the world. The Acute Care Enhanced Surveillance (ACES) system—a syndromic surveillance system that captures comprehensive hospital visit triage information from acute care hospitals across Ontario—monitored distinct syndromes throughout the games. We describe the creation and use of a risk assessment tool to evaluate alerts produced by ACES during this period. During the games, ACES generated 1420 alerts, 4 of which were considered a moderate risk and were communicated to surveillance partners for further action. The risk assessment tool was useful for public health professionals responsible for surveillance activities during the games. Next steps include integrating the tool within the ACES system.
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Smith, G. E., D. L. Cooper, P. Loveridge, F. Chinemana, E. Gerard, and N. Verlander. "A national syndromic surveillance system for England and Wales using calls to a telephone helpline." Eurosurveillance 11, no. 12 (December 1, 2006): 9–10. http://dx.doi.org/10.2807/esm.11.12.00667-en.
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Routine primary care data provide the means to monitor a variety of syndromes which could give early warning of health protection issues. In the United Kingdom, a national syndromic surveillance system, operated jointly by the UK Health Protection Agency (HPA) and NHS Direct (a national telephone health helpline), examines symptoms reported to NHS Direct. The aim of the system is to identify an increase in syndromes indicative of common infections and diseases, or the early stages of illness caused by the deliberate release of a biological or chemical agent. Data relating to 11 key symptoms/syndromes are received electronically from all 22 NHS Direct call centres covering England and Wales and analysed by the HPA on a daily basis. Statistically significant excesses in calls are automatically highlighted and assessed by a multi-disciplinary team. Although the surveillance system has characterised many sudden rises in syndromes reported to NHS Direct, no evidence of a biological or chemical attack has been detected. Benefits of this work, however, are early warning and tracking of rises in community morbidity (e.g. influenza-like illness, heatstroke); providing reassurance during times of perceived high risk (e.g. after the 7 July 2005 London bombs and December 2005 Buncefield oil depot fire); and timely surveillance data for influenza pandemic planning and epidemic modeling.
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COOPER, D. L., G. E. SMITH, F. CHINEMANA, C. JOSEPH, P. LOVERIDGE, P. SEBASTIONPILLAI, E. GERARD, and M. ZAMBON. "Linking syndromic surveillance with virological self-sampling." Epidemiology and Infection 136, no. 2 (March 30, 2007): 222–24. http://dx.doi.org/10.1017/s0950268807008412.
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SUMMARYCalls to a UK national telephone health helpline (NHS Direct) have been used for syndromic surveillance, aiming to provide early warning of rises in community morbidity. We investigated whether self-sampling by NHS Direct callers could provide viable samples for influenza culture. We recruited 294 NHS Direct callers and sent them self-sampling kits. Callers were asked to take a swab from each nostril and post them to the laboratory. Forty-two per cent of the samples were returned, 16·2% were positive on PCR for influenza (16 influenza A(H3N2), three influenza A (H1N1), four influenza B) and eight for RSV (5·6%). The mean time between the NHS Direct call and laboratory analysis was 7·4 days. These samples provided amongst the earliest influenza reports of the season, detected multiple influenza strains, and augmented a national syndromic surveillance system. Self-sampling is a feasible method of enhancing community-based surveillance programmes for detection of influenza.
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Meurice, Laure, Thierry Chapon, Frédéric Chemin, Laurence Gourinchas, Stéphane Sauvagnac, Sébastien Uijttewaal, and Stéphanie Vandentorren. "General Practitioner House Call Network (SOS Médecins): An Essential Tool for Syndromic Surveillance – Bordeaux, France." Prehospital and Disaster Medicine 35, no. 3 (March 5, 2020): 326–30. http://dx.doi.org/10.1017/s1049023x20000308.
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AbstractIntroduction:In the French mainland administrative region Nouvelle-Aquitaine, syndromic surveillance is based on hospital emergency data, mortality data, and data from associations belonging to the SOS Médecins network. The aim of the present article is to describe the functioning of this network and to illustrate its use for syndromic surveillance in Nouvelle-Aquitaine.Method:The SOS Médecins network participates in the syndromic surveillance system SurSaUD, developed by Santé publique France (SpF; the French National Public Health Agency; Saint-Maurice, Paris, France). Near real-time data are automatically transmitted daily to a data server and analyzed by SpF’s Nouvelle Aquitaine’s regional unit to identify, monitor, and evaluate the impact of expected and unexpected health events in the region.Results:The SOS Médecins network has five local associations spread across the region with 146 participating physicians. Data have been recorded for more than 10 years and represented nearly 481,000 visits in 2017. The resulting database has helped to identify and monitor seasonal epidemics and unexpected events, as well as measure the health impact of these events.Conclusion:The data from the SOS Médecins network are an essential source in syndromic surveillance. They complement surveillance data from other sources. More specifically, mortality and emergency unit traffic reflect the most severe cases, while SOS Médecins data help early detection of epidemics and health events in the general population. The network has shown its responsiveness and its reliability, not only for the surveillance of seasonal epidemics, but also for the detection of unusual signals. It therefore constitutes an essential link in syndromic surveillance in France, and specifically in the Nouvelle-Aquitaine region.
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Noufaily, Angela, Roger A. Morbey, Felipe J. Colón-González, Alex J. Elliot, Gillian E. Smith, Iain R. Lake, and Noel McCarthy. "Comparison of statistical algorithms for daily syndromic surveillance aberration detection." Bioinformatics 35, no. 17 (January 25, 2019): 3110–18. http://dx.doi.org/10.1093/bioinformatics/bty997.
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Abstract Motivation Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the ‘rising activity, multilevel mixed effects, indicator emphasis’ (RAMMIE) method and the improved quasi-Poisson regression-based method known as ‘Farrington Flexible’ both currently used at Public Health England, and the ‘Early Aberration Reporting System’ (EARS) method used at the US Centre for Disease Control and Prevention. We model the wide range of data structures encountered within the daily syndromic surveillance systems used by PHE. We undertake extensive simulations to identify which algorithms work best across different types of syndromes and different outbreak sizes. We evaluate RAMMIE for the first time since its introduction. Performance metrics were computed and compared in the presence of a range of simulated outbreak types that were added to baseline data. Results We conclude that amongst the algorithm variants that have a high specificity (i.e. >90%), Farrington Flexible has the highest sensitivity and specificity, whereas RAMMIE has the highest probability of outbreak detection and is the most timely, typically detecting outbreaks 2–3 days earlier. Availability and implementation R codes developed for this project are available through https://github.com/FelipeJColon/AlgorithmComparison Supplementary information Supplementary data are available at Bioinformatics online.
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MA, T., H. ENGLUND, P. BJELKMAR, A. WALLENSTEN, and A. HULTH. "Syndromic surveillance of influenza activity in Sweden: an evaluation of three tools." Epidemiology and Infection 143, no. 11 (December 4, 2014): 2390–98. http://dx.doi.org/10.1017/s0950268814003240.
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SUMMARYAn evaluation was conducted to determine which syndromic surveillance tools complement traditional surveillance by serving as earlier indicators of influenza activity in Sweden. Web queries, medical hotline statistics, and school absenteeism data were evaluated against two traditional surveillance tools. Cross-correlation calculations utilized aggregated weekly data for all-age, nationwide activity for four influenza seasons, from 2009/2010 to 2012/2013. The surveillance tool indicative of earlier influenza activity, by way of statistical and visual evidence, was identified. The web query algorithm and medical hotline statistics performed equally well as each other and to the traditional surveillance tools. School absenteeism data were not reliable resources for influenza surveillance. Overall, the syndromic surveillance tools did not perform with enough consistency in season lead nor in earlier timing of the peak week to be considered as early indicators. They do, however, capture incident cases before they have formally entered the primary healthcare system.
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Roberts, Stephen L., and Stefan Elbe. "Catching the flu: Syndromic surveillance, algorithmic governmentality and global health security." Security Dialogue 48, no. 1 (September 21, 2016): 46–62. http://dx.doi.org/10.1177/0967010616666443.
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How do algorithms shape the imaginary and practice of security? Does their proliferation point to a shift in the political rationality of security? If so, what is the nature and extent of that shift? This article argues that efforts to strengthen global health security are major drivers in the development and proliferation of new algorithmic security technologies. In response to a seeming epidemic of potentially lethal infectious disease outbreaks – including HIV/AIDS, Severe Acute Respiratory Syndrome (SARS), pandemic flu, Middle East Respiratory Syndrome (MERS), Ebola and Zika – governments and international organizations are now using several next-generation syndromic surveillance systems to rapidly detect new outbreaks globally. This article analyses the origins, design and function of three such internet-based surveillance systems: (1) the Program for Monitoring Emerging Diseases, (2) the Global Public Health Intelligence Network and (3) HealthMap. The article shows how each newly introduced system became progressively more reliant upon algorithms to mine an ever-growing volume of indirect data sources for the earliest signs of a possible new outbreak – gradually propelling algorithms into the heart of global outbreak detection. That turn to the algorithm marks a significant shift in the underlying problem, nature and role of knowledge in contemporary security policy.
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Kim, Min-Jung, Harriet Black Nembhard, Bruno Lambert, Clément Turbelin, Antoine Flahault, and Elisabeta Vergu. "A syndromic surveillance system for clinical and non-clinical health data." IIE Transactions on Healthcare Systems Engineering 1, no. 1 (March 16, 2011): 37–48. http://dx.doi.org/10.1080/19488300.2011.555877.
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Dey, Achintya N., Matthew Miller, Michael Coletta, and Umed Ajani. "Use of Syndromic Data for Enhanced Surveillance: MERS Like-Syndrome." Online Journal of Public Health Informatics 7, no. 1 (February 26, 2015). http://dx.doi.org/10.5210/ojphi.v7i1.5735.
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The goal is to identify and monitor MERS like syndrome cases in the syndromic surveillance system. In consultation with the state and local jurisdictions, five case definitions were developed to monitor MERS like syndromes. From May through July, 2014 fifteen reporting jurisdictions participated in MERS enhanced surveillance. . During this enhanced surveillance time period 171 probable MERS cases were identified and all of them were ruled out. The MERS collaborative efforts between BioSense programs, CDC subject matter experts and jurisdictions will help develop more comprehensive definitions to conduct enhanced surveillance at the national level using multiple syndromic surveillance systems.
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Rhee, ChulWoo, Howard Burkom, Changgyo Yoon, Sangwoo Tak, Aaron Katz, and Miles Stewart. "Military Real-time Syndromic Surveillance System for Biosurveillance Portal in Korea." Online Journal of Public Health Informatics 7, no. 1 (February 26, 2015). http://dx.doi.org/10.5210/ojphi.v7i1.5713.
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As a part of the Korea-US joint Biosurveillance Portal project, we have developed the military syndromic surveillance system with electronic medical records from military hospitals for early identification of bio-terror related events among Armed Forces in Korea. Respiratory, Gastrointestinal, Botulism, Dermatologic, Neurologic, Hemorrhagic and Fever syndromes were defined by different ICD-10 codes and their alert thresholds were developed based on the characteristics of time series derived using daily counts of ICD-10 codes for each syndrome.
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Bouchouar, Etran, Benjamin M. Hetman, and Brendan Hanley. "Development and validation of an automated emergency department-based syndromic surveillance system to enhance public health surveillance in Yukon: a lower-resourced and remote setting." BMC Public Health 21, no. 1 (June 29, 2021). http://dx.doi.org/10.1186/s12889-021-11132-w.
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Abstract Background Automated Emergency Department syndromic surveillance systems (ED-SyS) are useful tools in routine surveillance activities and during mass gathering events to rapidly detect public health threats. To improve the existing surveillance infrastructure in a lower-resourced rural/remote setting and enhance monitoring during an upcoming mass gathering event, an automated low-cost and low-resources ED-SyS was developed and validated in Yukon, Canada. Methods Syndromes of interest were identified in consultation with the local public health authorities. For each syndrome, case definitions were developed using published resources and expert elicitation. Natural language processing algorithms were then written using Stata LP 15.1 (Texas, USA) to detect syndromic cases from three different fields (e.g., triage notes; chief complaint; discharge diagnosis), comprising of free-text and standardized codes. Validation was conducted using data from 19,082 visits between October 1, 2018 to April 30, 2019. The National Ambulatory Care Reporting System (NACRS) records were used as a reference for the inclusion of International Classification of Disease, 10th edition (ICD-10) diagnosis codes. The automatic identification of cases was then manually validated by two raters and results were used to calculate positive predicted values for each syndrome and identify improvements to the detection algorithms. Results A daily secure file transfer of Yukon’s Meditech ED-Tracker system data and an aberration detection plan was set up. A total of six syndromes were originally identified for the syndromic surveillance system (e.g., Gastrointestinal, Influenza-like-Illness, Mumps, Neurological Infections, Rash, Respiratory), with an additional syndrome added to assist in detecting potential cases of COVID-19. The positive predictive value for the automated detection of each syndrome ranged from 48.8–89.5% to 62.5–94.1% after implementing improvements identified during validation. As expected, no records were flagged for COVID-19 from our validation dataset. Conclusions The development and validation of automated ED-SyS in lower-resourced settings can be achieved without sophisticated platforms, intensive resources, time or costs. Validation is an important step for measuring the accuracy of syndromic surveillance, and ensuring it performs adequately in a local context. The use of three different fields and integration of both free-text and structured fields improved case detection.
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Wiedeman, Caleb, Tonya McKennley, Glenn Yates, and Erin Holt. "So Long and Thanks for All the EARS: Lessons Learned from Tennessee’s Ongoing Syndromic Surveillance Transition." Online Journal of Public Health Informatics 7, no. 1 (February 26, 2015). http://dx.doi.org/10.5210/ojphi.v7i1.5841.
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Tennessee has been slowly transitioning from a jurisidcitional syndromic surveillance system to a statewide, centralized system. During this process, old jurisdictional systems are being maintained, while infrastructure is being put in place to support a statewide syndromic surveillance solution. Successess, obstacles, and lessons learned throughout this process will be discussed.
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Arkin, Kristin. "Syndromic Surveillance for Situation Awareness: Understanding Syndrome Performance." Online Journal of Public Health Informatics 10, no. 1 (May 22, 2018). http://dx.doi.org/10.5210/ojphi.v10i1.8971.
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ObjectiveIn August 2017, a large influx of visitors was expected to view the total solar eclipse in Idaho. The Idaho Syndromic Surveillance program planned to enhance situation awareness during the event. In preparation, we sought to examine syndrome performance of several newly developed chief complaint and combination chief complaint and diagnosis code syndrome definitions to aid in interpretation of syndromic surveillance data during the event.IntroductionThe August 21, 2017 total solar eclipse in Idaho was anticipated to lead to a large influx of visitors in many communities, prompting a widespread effort to assure Idaho was prepared. To support these efforts, the Idaho Syndromic Surveillance program (ISSp) developed a plan to enhance situation awareness during the event by conducting syndromic surveillance using emergency department (ED) visit data contributed to the National Syndromic Surveillance Program’s BioSense platform by Idaho hospitals. ISSp sought input on anticipated threats from state and local emergency management and public health partners, and selected 8 syndromes for surveillance.Ideally, the first electronic message containing information on an emergency department visit is sent to ISSp within 24 hours of the visit and includes the chief complaint for the visit. Data on other variables, such as diagnosis codes, are updated by subsequent messages for several days after the visit. Chief complaint (CC) text and discharge diagnosis (DD) codes are the primary variables used for syndrome match; delay in reporting these variables adversely affects timely syndrome match of visits. Because our plan included development of new syndrome definitions and querying data within 24 hours of visits, earlier than ISSp had done previously for trend analysis, we sought to better understand syndrome performance.MethodsWe defined messages with completed CC and DD as the last message regarding a visit where term count increased from previous messages regarding that visit, indicating new information was added to the field. We retrospectively assessed the total number of ED visits and calculated the daily frequency of completed CC and DD by days since visit date for visits during June 1–July 31, 2017. Additionally, we calculated facility mean word count in CC fields by averaging the word count of parsed, complete CC fields for visits occurring June 1–July 31, 2017 for each facility.During July 10–24, 2017, we calculated the daily frequency of visits occurring in the previous 90 days for total ED visits and syndrome-matched visits for 8 selected syndromes (heat-related illness; cold exposure; influenza-like-illness; nausea, vomiting, and diarrhea; animal/bug bites and stings; drowning/submersion; alcohol/drug intoxication; and medication replacement). Syndrome-matched visits were defined as visits with CC or DD that match the syndrome definition. We calculated the percent of syndrome-matched visits by syndromes defined with CC or CC and DD combined (CCDD) over time. Syndromes with fewer than 5 matched visits were excluded from analysis.ResultsComplete CCs were received for 99.1% of visits and complete DDs were received for 89.8% of visits. Complete CCs were submitted for 58.2% of visits within 1 day of the visit, 88.9% of visits within 3 days, and 98.9% of visits within 7 days. In contrast, complete DDs were submitted for 24.3% of visits within 1 day, 38.7% of visits within 3 days, and 53.7% of visits within 7 days (Table 1).During the observation period, data submission from facilities representing approximately 33% of visits was interrupted for 5 (36%) of 14 days. Heat-related illness, cold exposure, and drowning/submersion, were excluded from syndrome-match analysis. During the 9 days of uninterrupted data submission, 100% syndrome-matched visits for syndromes defined by CC alone and 69.1% syndrome-matched visits for syndromes defined by CCDD were identified within 6–7 days of initial visit. Facilities with interrupted data submission contributed 75% of CC syndrome-matched visits and 33% of CCDD syndrome-matched visits. The facility mean word count in CC fields from these facilities was >15 compared with 2–4 from other facilities.ConclusionsExamination of syndrome performance prior to a known event quantitated differences in timeliness of CC and DD completeness and syndrome match. CCs and DDs in visit messages were not complete within 24 hours of initial visit. CC completion was nearly 34 percentage points greater than DD completeness 1 day after initial visit and did not converge until ≥15 days after initial visit. Higher percentages of syndrome match within 6–7 days of initial visit were seen by CC alone than CCDD defined syndromes. Facilities using longer CCs contributed disproportionately to syndrome matching using CC, but not CCDD syndrome definitions. Syndromic surveillance system characteristics, including timeliness of CCs and DDs, length of CCs, and characteristics of facilities from which data transmission is interrupted should be considered when building syndrome definitions that will be used for surveillance within 7 days of emergency department visits and when interpreting syndromic surveillance findings.
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Harcourt, Sally, Lydia Izon-Cooper, Felipe D. Colón-González, Roger Morbey, Gillian Smith, Naima Bradley, Karen Exley, Alec Dobney, Iain Lake, and Alex Elliot. "Using real-time syndromic surveillance to monitor the health effects of air pollution." Online Journal of Public Health Informatics 10, no. 1 (May 22, 2018). http://dx.doi.org/10.5210/ojphi.v10i1.8651.
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ObjectiveTo explore the utility of syndromic surveillance systems for detecting and monitoring the impact of air pollution incidents on health-care seeking behaviour in England between 2012 and 2017.IntroductionThe negative effect of air pollution on human health is well documented illustrating increased risk of respiratory, cardiac and other health conditions. [1] Currently, during air pollution episodes Public Health England (PHE) syndromic surveillance systems [2] provide a near real-time analysis of the health impact of poor air quality. In England, syndromic surveillance has previously been used on an ad hoc basis to monitor health impact; this has usually happened during widespread national air pollution episodes where the air pollution index has reached ‘High’ or ‘Very High’ levels on the UK Daily Air Quality Index (DAQI). [3-5]We now aim to undertake a more systematic approach to understanding the utility of syndromic surveillance for monitoring the health impact of air pollution. This would improve our understanding of the sensitivity and specificity of syndromic surveillance systems for contributing to the public health response to acute air pollution incidents; form a baseline for future interventions; assess whether syndromic surveillance systems provide a useful tool for public health alerting; enable us to explore which pollutants drive changes in health-care seeking behaviour; and add to the knowledge base.MethodsThe systematic approach will involve accessing historical data for air pollution incidents and syndromic surveillance data over the period 2012-17 across England. We will use PM10, PM2.5, ozone, NO2 , SO2 and DAQI data to define air pollution periods, and historical syndromic surveillance system data for respiratory syndromes (asthma, difficulty breathing, wheeze, cough, bronchitis, sore throat and allergic rhinitis), cardiac (all cardiovascular and myocardial infarction) and eye irritation/conjunctivitis syndromes. We will use regression modelling and cross-correlation analyses to determine the effects of air pollution, weather and pollen upon these syndromes and thus provide evidence of the sensitivity of these systems. Historical data on additional environmental variables including temperature and precipitation, humidity and thunderstorm activity, pollen and fungal spores will be accounted for in the regression models, as well as data on influenza and respiratory syncytial virus (RSV) laboratory reports. We will include sub-national geographies and age/gender analyses in the study depending on the data availability and suitability.ResultsInitial results presented will include the preliminary descriptive epidemiology with a focus on asthma and the impact of air pollution incidents on health-care seeking behaviour using data from the PHE national syndromic surveillance systems.ConclusionsWe aim to demonstrate an innovative use of syndromic surveillance data to explore the impact of air pollution incidents on health-care seeking behaviour in England, in turn improving our understanding of the sensitivity and specificity of these systems for detecting the impact of air pollution incidents and to contribute to the knowledge base. This understanding will improve the public health response to future incidents.References1. World Health Organization (WHO). Preventing disease through healthy environments. Exposure to air pollution: A major public health concern. (http://www.who.int/ipcs/features/air_pollution.pdf). Accessed 28/09/20172. Public Health England. Syndromic surveillance: systems and analyses. (https://www.gov.uk/government/collections/syndromic-surveillance-systems-and-analyses). Accessed 20/09/20173. Department for Environment Food and Rural Affairs (Defra). Daily Air Quality Index (DAQI). (https://uk-air.defra.gov.uk/air-pollution/daqi). Accessed 28/06/20174. Smith GE, et al. Using real-time syndromic surveillance systems to help explore the acute impact of the air pollution incident of March/April 2014 in England. Environ Res 2015; 136: 500-504.5. Elliot AJ, et al. Monitoring the effect of air pollution episodes on health care consultations and ambulance call-outs in England during March/April 2014: A retrospective observational analysis. Environ Pollut 2016; 214: 903-911.
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Davis, Cassandra N. "Enhancing the BioSense Platform: Findings from an ESSENCE and SAS Pilot Project." Online Journal of Public Health Informatics 8, no. 1 (March 24, 2016). http://dx.doi.org/10.5210/ojphi.v8i1.6520.
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BioSense was created in 2003 with the intent of establishing an integrated system of nationwide public health surveillance for the early detection of potential bioterrorism-related syndromes or other public health emergencies. BioSense has evolved into the National Syndromic Surveillance Program that includes the BioSense Platform - an improved suite of analytical tools based in a cloud environment. To address the user community's priorities of the platform's current system requirements and the preference for including other software on the platform to improve syndromic surveillance data processing, CDC conducted a pilot project to evaluate the tools, SAS and ESSENCE.
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Olson, Don, Willem Van der Mei, Sungwoo Lim, Carol Yoon, Melissa Kull, and Marivel Davila. "Monitoring child mental health related emergency department visits in New York City." Online Journal of Public Health Informatics 9, no. 1 (May 2, 2017). http://dx.doi.org/10.5210/ojphi.v9i1.7720.
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ObjectiveTo assess the use of syndromic surveillance to assess trends inmental health-related emergency department (ED) visits amongschool-aged children and adolescents in New York City (NYC).IntroductionFrom 2001-2011, mental health-related hospitalizations and EDvisits increased among United States children nationwide [1]. Duringthis period, mental health-related hospitalizations among NYCchildren increased nearly 23% [2]. To estimate mental health-relatedED visits in NYC and assess the use of syndromic surveillance chiefcomplaint data to monitor these visits, we compared trends from anear real-time syndromic system with those from a less timely, codedED visit database.MethodsThe NYC ED syndromic surveillance system receives anonymizedpatient chief complaint and basic demographic data for nearly everyED visit citywide to provide timely surveillance information tohealth authorities. Using NYC ED syndromic surveillance datafrom 2003-2015, we applied previously developed definitions forgeneral psychiatric syndromes. We aggregated ED visits by agegroup (5-12 years, 13-17 years, and 18-20 years), geography, andtemporality. Syndromic data were compared with Statewide Planningand Research Collaborative System (SPARCS) data from 2006-2014which reported mental health diagnosis (ICD-9), treatment, service,and basic demographics for patients visiting facilities in NYC. Usingthese two data sources, we compared daily visit patterns and annualtrends overall as well as stratified by age group, area-based poverty(ZIP code), and time of visit.ResultsBoth syndromic surveillance and SPARCS data for NYC showedan increasing trend during the period. While both showed relativeincreases with similar slopes, mental health-related chief complaintdata captured fewer overall visits than the ICD-9 coded SPARCSdata. Trends in syndromic data during 2003-2015 differed by age-group and area-based poverty, e.g., among children ages 5-12 yearsthe annual proportion of mental health-related ED visits increasedroughly 3-fold from 1.2% to 3.8% in the poorest areas, which wasgreater than the increase in the richest areas (1.7% to 2.6%). Seasonal,day-of-week, and school holiday patterns found far fewer visits duringthe periods of NYC public school breaks (Figure).ConclusionsWe conclude that syndromic surveillance data can provide areliable indicator of mental health-related ED visit trends. Thesefindings suggest potential benefit of syndromic surveillance data asthey may help capture temporal and spatial clustering of events in amuch more timely manner than the >1 year delay in availability ofED discharge data. Next steps include a qualitative study exploringthe causes of these patterns and the role of various factors drivingthem, as well as use of patient disposition and matched data to bettercharacterize ED visit patient outcomes.
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Smith, S., A. J. Elliot, C. Mallaghan, D. Modha, J. Hippisley-Cox, S. Large, M. Regan, and G. E. Smith. "Value of syndromic surveillance in monitoring a focal waterborne outbreak due to an unusual Cryptosporidium genotype in Northamptonshire, United Kingdom, June – July 2008." Eurosurveillance 15, no. 33 (August 19, 2010). http://dx.doi.org/10.2807/ese.15.33.19643-en.
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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.
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Chan, Ta-Chien, Yung-Chu Teng, Yen-Hua Chu, and Tzu-Yu Lin. "Precision public health through clinic-based syndromic surveillance in communities." Online Journal of Public Health Informatics 11, no. 1 (May 30, 2019). http://dx.doi.org/10.5210/ojphi.v11i1.9887.
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ObjectiveSentinel physician surveillance in the communities has played an important role in detecting early aberrations in epidemics. The traditional approach is to ask primary care physicians to actively report some diseases such as influenza-like illness (ILI), and hand, foot, and mouth disease (HFMD) to health authorities on a weekly basis. However, this is labor-intensive and time-consuming work. In this study, we try to set up an automatic sentinel surveillance system to detect 23 syndromic groups in the communites.IntroductionIn December 2009, Taiwan’s CDC stopped its sentinel physician surveillance system. Currently, infectious disease surveillance systems in Taiwan rely on not only the national notifiable disease surveillance system but also real-time outbreak and disease surveillance (RODS) from emergency rooms, and the outpatient and hospitalization surveillance system from National Health Insurance data. However, the timeliness of data exchange and the number of monitored syndromic groups are limited. The spatial resolution of monitoring units is also too coarse, at the city level. Those systems can capture the epidemic situation at the nationwide level, but have difficulty reflecting the real epidemic situation in communities in a timely manner. Based on past epidemic experience, daily and small area surveillance can detect early aberrations. In addition, emerging infectious diseases do not have typical symptoms at the early stage of an epidemic. Traditional disease-based reporting systems cannot capture this kind of signal. Therefore, we have set up a clinic-based surveillance system to monitor 23 kinds of syndromic groups. Through longitudinal surveillance and sensitive statistical models, the system can automatically remind medical practitioners of the epidemic situation of different syndromic groups, and will help them remain vigilant to susceptible patients. Local health departments can take action based on aberrations to prevent an epidemic from getting worse and to reduce the severity of the infected cases.MethodsWe collected data on 23 syndromic groups from participating clinics in Taipei City (in northern Taiwan) and Kaohsiung City (in southern Taiwan). The definitions of 21 of those syndromic groups with ICD-10 diagnoses were adopted from the International Society for Disease Surveillance (https://www.surveillancerepository.org/icd-10-cm-master-mapping-reference-table). The definitions of the other two syndromic groups, including dengue-like illness and enterovirus-like illness, were suggested by infectious disease and emergency medicine specialists.An enhanced sentinel surveillance system named “Sentinel plus” was designed for sentinel clinics and community hospitals. The system was designed with an interactive interface and statistical models for aberration detection. The data will be computed for different combinations of syndromic groups, age groups and gender groups. Every day, each participating clinic will automatically upload the data to the provider of the health information system (HIS) and then the data will be transferred to the research team.This study was approved by the committee of the Institutional Review Board (IRB) at Academia Sinica (AS-IRB02-106262, and AS-IRB02-107139). The databases we used were all stripped of identifying information and thus informed consent of participants was not required.ResultsThis system started to recruit the clinics in May 2018. As of August 2018, there are 89 clinics in Kaohsiung City and 33 clinics and seven community hospitals in Taipei City participating in Sentinel plus. The recruiting process is still ongoing. On average, the monitored volumes of outpatient visits in Kaohsiung City and Taipei City are 5,000 and 14,000 per day.Each clinic is provided one list informing them of the relative importance of syndromic groups, the age distribution of each syndromic group and a time-series chart of outpatient rates at their own clinic. In addition, they can also view the village-level risk map, with different alert colors. In this way, medical practitioners can know what’s going on, not only in their own clinics and communities but also in the surrounding communities.The Department of Health (Figure 1) can know the current increasing and decreasing trends of 23 syndromic groups by red and blue color, respectively. The spatial resolution has four levels including city, township, village and clinic. The map and bar chart represent the difference in outpatient rate between yesterday and the average for the past week. The line chart represents the daily outpatient rates for one selected syndromic group in the past seven days. The age distribution of each syndromic group and age-specific outpatient rates in different syndromic groups can be examined.ConclusionsSentinel plus is still at the early stage of development. The timeliness and the accuracy of the system will be evaluated by comparing with some syndromic groups in emergency rooms and the national notifiable disease surveillance system. The system is designed to assist with surveillance of not only infectious diseases but also some chronic diseases such as asthma. Integrating with external environmental data, Sentinel plus can alert public health workers to implement better intervention for the right population.References1. James W. Buehler AS, Marc Paladini, Paula Soper, Farzad Mostashari: Syndromic Surveillance Practice in the United States: Findings from a Survey of State, Territorial, and Selected Local Health Departments. Advances in Disease Surveillance 2008, 6(3).2. Ding Y, Fei Y, Xu B, Yang J, Yan W, Diwan VK, Sauerborn R, Dong H: Measuring costs of data collection at village clinics by village doctors for a syndromic surveillance system — a cross sectional survey from China. BMC Health Services Research 2015, 15:287.3. Kao JH, Chen CD, Tiger Li ZR, Chan TC, Tung TH, Chu YH, Cheng HY, Liu JW, Shih FY, Shu PY et al.: The Critical Role of Early Dengue Surveillance and Limitations of Clinical Reporting -- Implications for Non-Endemic Countries. PloS one 2016, 11(8):e0160230.4. Chan TC, Hu TH, Hwang JS: Daily forecast of dengue fever incidents for urban villages in a city. International Journal of Health Geographics 2015, 14:9.5. Chan TC, Teng YC, Hwang JS: Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models. BMC Public Health 2015, 15:168.6. Ma HT: Syndromic surveillance system for detecting enterovirus outbreaks evaluation and applications in public health. Taipei, Taiwan: National Taiwan University; 2007.
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Snelick, Snelick, and Sheryl L. Taylor. "HIT Conformance Testing: Advancing Syndromic Surveillance System Interoperability." Online Journal of Public Health Informatics 8, no. 1 (March 24, 2016). http://dx.doi.org/10.5210/ojphi.v8i1.6452.
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Glatman-Freedman, Aharona, Lea Gur-Arie, Hanna Sefty, Zalman Kaufman, Michal Bromberg, Rita Dichtiar, Alina Rosenberg, et al. "The impact of SARS-CoV-2 on respiratory syndromic and sentinel surveillance in Israel, 2020: a new perspective on established systems." Eurosurveillance 27, no. 16 (April 21, 2022). http://dx.doi.org/10.2807/1560-7917.es.2022.27.16.2100457.
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Background The COVID-19 pandemic presented new challenges for the existing respiratory surveillance systems, and adaptations were implemented. Systematic assessment of the syndromic and sentinel surveillance platforms during the pandemic is essential for understanding the value of each platform in the context of an emerging pathogen with rapid global spread. Aim We aimed to evaluate systematically the performance of various respiratory syndromic surveillance platforms and the sentinel surveillance system in Israel from 1 January to 31 December 2020. Methods We compared the 2020 syndromic surveillance trends to those of the previous 3 years, using Poisson regression adjusted for overdispersion. To assess the performance of the sentinel clinic system as compared with the national SARS-CoV-2 repository, a cubic spline with 7 knots and 95% confidence intervals were applied to the sentinel network's weekly percentage of positive SARS-CoV-2 cases. Results Syndromic surveillance trends changed substantially during 2020, with a statistically significant reduction in the rates of visits to physicians and emergency departments to below previous years' levels. Morbidity patterns of the syndromic surveillance platforms were inconsistent with the progress of the pandemic, while the sentinel surveillance platform was found to reflect the national circulation of SARS-CoV-2 in the population. Conclusion Our findings reveal the robustness of the sentinel clinics platform for the surveillance of the main respiratory viruses during the pandemic and possibly beyond. The robustness of the sentinel clinics platform during 2020 supports its use in locations with insufficient resources for widespread testing of respiratory viruses.
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Rubino, Heather, David Atrubin, and Janet J. Hamilton. "Identifying and Communicating the Importance of the Variable Nature of SyS Data." Online Journal of Public Health Informatics 9, no. 1 (May 2, 2017). http://dx.doi.org/10.5210/ojphi.v9i1.7655.
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ObjectiveThis roundtable will provide a forum for national, state, and localmanagers of syndromic surveillance systems to discuss how theyidentify, monitor, and respond to changes in the nature of their data.Additionally, this session will focus on the strengths and weaknessof the syndromic surveillance systems for supporting programevaluation and trend analysis. This session will also provide a forumwhere subject matter experts can discuss the ways in which this deepunderstanding of their data can be leveraged to forge and improvepartnerships with academic partners.IntroductionAs syndromic surveillance systems continue to grow, newopportunities have arisen to utilize the data in new or alternativeways for which the system was not initially designed. For example,in many jurisdictions syndromic surveillance has recently becomepopulation-based, with 100% coverage of targeted emergencydepartment encounters. This makes the data more valuable for real-time evaluation of public health and prevention programs. There hasalso been increasing pressure to make more data publicly available –to the media, academic partners, and the general public.
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Graef, J., M. Omar, and A. Abbara. "Syndromic infectious disease surveillance of refugees in Greece: a mixed methods analysis." European Journal of Public Health 29, Supplement_4 (November 1, 2019). http://dx.doi.org/10.1093/eurpub/ckz186.061.
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Abstract Background An estimated 1,174,140 refugees have migrated into Greece, a main entry point for refugees into Europe, since 2014. Their infectious disease profile is monitored by a national-level ad-hoc syndromic surveillance system in refugee-migrant reception centres. The utility of this system is explored to contribute evidence to and improve syndromic surveillance in European refugee responses. Methods Proportional morbidities, numbers of cases and signals, cases above expected numbers, of 14 syndromes are collated from weekly reports between 2016-2019, graphed and analysed in the context of the humanitarian response. Semi-structured key informant interviews are conducted and thematically analysed. Results Between 20.06.2016 and 17.02.2019, 36358 cases and 116 signals occurred. Public health responses resulted and there were no significant outbreaks. On average 5% of all consultations in centres were on infectious syndromes. Respiratory infections with fever (57%), gastroenteritis (22%), suspected scabies (13%) and rashes with fever (5%) were most commonly reported. Every week, between 68-100% of 25-58 participating centres completed reporting adequately. 6 informants reported on their syndromic system user experience. The system’s benefits, providing information and safeguarding refugees, outweighed harms. Data was timely and complete, but likely under-reported for common conditions. Poor living conditions and inter-agency coordination complicated reporting and public health responses. Conclusions Infectious burdens and trends were provided by the system and allowed for timely responses. Data quality was adequate. The system was valuable and feasible to informants. The set-up of the humanitarian response, inadequate ownership and poor coordination of authorities reduced the system’s utility. Key messages Syndromic surveillance is useful for monitoring refugee infectious health. Structural barriers need to be resolved to improve systems’ data and user experience.
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Parton, Hilary B., Robert Mathes, Jasmine Abdelnabi, Lisa Alleyne, Andrea Econome, Robert Fitzhenry, Kristen Forney, Megan Halbrook, Stephanie Ngai, and Don Weiss. "Investigating a Syndromic Surveillance Signal with Complimentary Data Systems." Online Journal of Public Health Informatics 8, no. 1 (March 24, 2016). http://dx.doi.org/10.5210/ojphi.v8i1.6569.
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In early June, the New York City syndromic surveillance system detected five signals in sales of over-the-counter antidiarrheal medications. To determine if this increase reflected a concerning cluster of diarrheal illness, we examined multiple communicable disease surveillance data systems. After further investigation of syndromic and other systems, we determined that findings possibly reflected sales promotions but did not suggest increased diarrheal illness in NYC.
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Elliot, Alex J., Sally E. Harcourt, Helen E. Hughes, Paul Loveridge, Roger A. Morbey, Sue Smith, Ana Soriano, et al. "The COVID-19 pandemic: a new challenge for syndromic surveillance." Epidemiology and Infection 148 (2020). http://dx.doi.org/10.1017/s0950268820001314.
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Abstract The COVID-19 pandemic is exerting major pressures on society, health and social care services and science. Understanding the progression and current impact of the pandemic is fundamental to planning, management and mitigation of future impact on the population. Surveillance is the core function of any public health system, and a multi-component surveillance system for COVID-19 is essential to understand the burden across the different strata of any health system and the population. Many countries and public health bodies utilise ‘syndromic surveillance’ (using real-time, often non-specific symptom/preliminary diagnosis information collected during routine healthcare provision) to supplement public health surveillance programmes. The current COVID-19 pandemic has revealed a series of unprecedented challenges to syndromic surveillance including: the impact of media reporting during early stages of the pandemic; changes in healthcare-seeking behaviour resulting from government guidance on social distancing and accessing healthcare services; and changes in clinical coding and patient management systems. These have impacted on the presentation of syndromic outputs, with changes in denominators creating challenges for the interpretation of surveillance data. Monitoring changes in healthcare utilisation is key to interpreting COVID-19 surveillance data, which can then be used to better understand the impact of the pandemic on the population. Syndromic surveillance systems have had to adapt to encompass these changes, whilst also innovating by taking opportunities to work with data providers to establish new data feeds and develop new COVID-19 indicators. These developments are supporting the current public health response to COVID-19, and will also be instrumental in the continued and future fight against the disease.
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Morbey, Roger. "Communicating the detection capabilities of syndromic surveillance systems." Online Journal of Public Health Informatics 11, no. 1 (May 30, 2019). http://dx.doi.org/10.5210/ojphi.v11i1.9666.
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ObjectiveTo communicate the detection capabilities of syndromic surveillance systems to public health decision makers.IntroductionIncreasingly public health decision-makers are using syndromic surveillance for real-time reassurance and situational awareness in addition to early warning1. Decision-makers using intelligence, including syndromic data, need to understand what the systems are capable of detecting, what they cannot detect and specifically how much reassurance should be inferred when syndromic systems report ‘nothing detected’. In this study we quantify the detection capabilities of syndromic surveillance systems used by Public Health England (PHE).The key measures for detection capabilities are specificity and sensitivity (although timeliness is also very important for surveillance systems)2. However, measuring the specificity and sensitivity of syndromic surveillance systems is not straight forward. Firstly, syndromic systems are usually multi-purpose and may be better at identifying certain types of public health threat than others. Secondly, whilst it is easy to quantify statistical aberration detection algorithms, surveillance systems involve other stages, including data collection and human decision-making, which also affect detection capabilities. Here, we have taken a ‘systems thinking’ approach to understand potential barriers to detection, and summarize what we know about detection capabilities of syndromic surveillance systems in England.MethodsWithin the systems thinking approach all stages in surveillance (data collection, automated statistical analysis, expert risk assessment and reporting of any aberrations) were considered. Sensitivity and specificity were then calculated for the system as a whole, and the separate impact of each process stage.To communicate these findings to decision-makers, we created an evidence synthesis. Evidence was synthesised from research involving PHE syndromic surveillance systems and retrospective incidents detected and/or investigated by PHE. We then summarized the evidence for different types of incident.ResultsWe identified the following stages which influence detection:The proportion of people who become symptomatic;The proportion of symptomatic people who present to different types of health care;The coding of symptomatic patients;Coverage of different health care systems by syndromic surveillance;Statistical algorithms used to identify unusual clusters within syndromic data;Risk assessment process used to determine action resulting following automated statistical alarms3.Stages 1 to 3 depend on the type of incident that is affecting peoples’ health or healthcare seeking behaviour: stages 3 to 6 depend on the capabilities of the syndromic surveillance system. In general, each stage increases the time until detection, and reduces sensitivity but should improve specificity.Our evidence synthesis identified a wide range of threats to public health including: seasonal outbreaks of respiratory infections; allergic rhinitis; insect bites; gastrointestinal outbreaks; air pollution; and heat waves. We ranked the available evidence, giving more weight to actual events detected and validated against independent evidence, and less to purely descriptive epidemiology or modelled simulations. We created different measures for sensitivity, specificity and timeliness depending on the type of evidence available. Sensitivity ranged from 100% for seasonal influenza to 0% for seasonal adenovirus. Specificity also varied, with high specificity where we had a specific syndromic indicators, e.g. sunstroke, and lower for those associated only with more generic multi-purpose indicators e.g. acute respiratory infections. Timeliness varied from being able to provide early warning of up to seven days prior to traditional surveillance methods for some respiratory illnesses, to being able to detect and report on the health impact of air pollution within four days of a period of poor air quality.ConclusionsThis study has shown that a syndromic surveillance systems’ utility depends on more than just an algorithm’s specificity and sensitivity measure. We’ve identified the impact of the different surveillance stages and separately considered different types of incident. Thus, we can identify the impact of issues such as local population coverage and an individual investigator’s risk assessment practices. Furthermore, the evidence synthesis will provide a summary for decision makers, and help identify gaps in our knowledge where more research is required.References1. Colon-Gonzalez FJ, Lake IR, Morbey RA, Elliot AJ, Pebody R, Smith GE. A methodological framework for the evaluation of syndromic surveillance systems: a case study of England. BMC Public Health. 2018;18(1):544. http://dx.doi.org/10.1186/s12889-018-5422-92. Kleinman KP, Abrams AM. Assessing surveillance using sensitivity, specificity and timeliness. Stat Methods Med Res. 2006;15(5):445-64.3. Smith GE, Elliot AJ, Ibbotson S, Morbey R, Edeghere O, Hawker J, et al. Novel public health risk assessment process developed to support syndromic surveillance for the 2012 Olympic and Paralympic Games. J Public Health. 2016. http://dx.doi.org/10.1093/pubmed/fdw054
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Morbey, R., A. Noufaily, F. D. Colón-González, A. Elliot, S. Harcourt, and G. Smith. "Comparison of statistical algorithms for syndromic surveillance aberration detection." Online Journal of Public Health Informatics 10, no. 1 (May 22, 2018). http://dx.doi.org/10.5210/ojphi.v10i1.8302.
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ObjectiveTo investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in England.IntroductionSyndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods.MethodsFor this purpose, we compare RAMMIE to the improved quasi-Poisson regression-based approach (Noufaily et al, 2013), currently implemented at PHE for weekly infectious disease laboratory surveillance, and to the Early Aberration Reporting System (EARS) method (Rossi et al, 1999), which is used for syndromic surveillance aberration detection in many other countries. We model syndromic datasets, capturing real data aspects such as long-term trends, seasonality, public holidays, and day-of-the-week effects, with or without added outbreaks. Then, we compute the sensitivity and specificity to compare how well each of the algorithms detects synthetic outbreaks to provide recommendations for the most suitable statistical methods to use during different public health scenarios.ResultsPreliminary results suggest all methods provide high sensitivity and specificity, with the (Noufaily et al, 2013) approach having the highest sensitivity and specificity. We showed that for syndromes with long-term increasing trends, RAMMIE required modificaiton to prevent excess false alarms. Also, our study suggests further work is needed to fully account for public holidays and day-of-the-week effects.ConclusionsOur study will provide recommendations for which algorithm is most effective for PHE's syndromic surveillance for a range of different syndromes. Furthermore our work to generate standardised synthetic syndromic datasets and a range of outbreaks can be used for future evaluations in England and elsewhere.ReferencesNoufaily, A., Enki, D. G., Farrington, C. P., Garthwaite, P., Andrews, N. and Charlett, A. (2013). An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems. Statistics in Medicine, 32(7), 1206-1222.Morbey, R. A., Elliot, A. J., Charlett, A., Verlander, A. Q, Andrews, N. and Smith, G. (2013). The application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method for syndromic surveillance in England, Bioinformatics, 31(22), 3660-3665.Rossi, G, Lampugnani, L, Marchi, M. (1999), An approximate CUSUM procedure for surveillance of health events. Statistics in Medicine, 18, 2111–2122