Use Of Syndromic Surveillance Research Articles

Syndromic surveillance system during mass gathering of Panchkroshi Yatra festival, Ujjain, Madhya Pradesh, India

BackgroundThe health implications surrounding a mass gathering pose significant challenges to public health officials. The use of syndromic surveillance provides an ideal method for achieving the public health goals and objectives at such events. In the absence of published reports of systematic documentation of public health preparedness in mass gatherings in the local context, we describe the public health preparedness and demonstrate the operational feasibility of a tablet-based participatory syndromic surveillance among pilgrims during the annual ritual circumambulation- Panchkroshi Yatra. MethodsA real-time surveillance system was established from 2017-2019 to capture all the health consultations done at the designated points (medical camps) in the Panchkroshi yatra area of the city Ujjain in Madhya Pradesh. We also surveyed a subset of pilgrims in 2017 to gauge satisfaction with the public health measures such as sanitation, water, safety, food, and cleanliness. ResultsIn 2019, injuries were reported in the highest proportion (16.7%; 794/4744); most numbers of fever cases (10.6%; 598/5600) were reported in 2018, while 2017 saw the highest number of patient presentations of abdominal pain (7.73%; 498/6435). ConclusionPublic health and safety measures were satisfactory except for the need for setting up urinals along the fixed route of the circumambulation. A systematic data collection of selected symptoms among yatris and their surveillance through tablet could be established during the panchkroshi yatra, which can complement the existing surveillance for detecting early warning signals. We recommend the implementation of such tablet-based surveillance during such mass gathering events.

Veterinary syndromic surveillance using swine production data for farm health management and early disease detection

The use of syndromic surveillance (SyS) has grown in animal health since the 2010s, but the use of production data has been underexplored due to methodological and practical challenges. This paper aimed to tackle some of those challenges by developing a SyS system using production data routinely collected in pig breeding farms. Health-related indicators were created from the recorded data, and two different time-series types emerged: the weekly counts of events traditionally used in SyS; and continuous time-series, where every new event is a new observation, and grouping by time-unit is not applied. Exponentially Weighted Moving Average (EWMA) and Shewhart control charts were used for temporal aberration detection, using three detection limits to create a “severity” score. The system performance was evaluated using simulated outbreaks of porcine respiratory and reproduction syndrome (PRRS) as a disease introduction scenario. The system proved capable of providing early detection of unexpected trends, serving as a useful health and management decision support tool for farmers. Further research is needed to combine results of monitoring multiple parallel time-series into an overall assessment of the risk of reproduction failure.

Syndromic Surveillance Systems for Mass Gatherings: A Scoping Review.

Syndromic surveillance involves the near-real-time collection of data from a potential multitude of sources to detect outbreaks of disease or adverse health events earlier than traditional forms of public health surveillance. The purpose of the present study is to elucidate the role of syndromic surveillance during mass gathering scenarios. In the present review, the use of syndromic surveillance for mass gathering scenarios is described, including characteristics such as methodologies of data collection and analysis, degree of preparation and collaboration, and the degree to which prior surveillance infrastructure is utilized. Nineteen publications were included for data extraction. The most common data source for the included syndromic surveillance systems was emergency departments, with first aid stations and event-based clinics also present. Data were often collected using custom reporting forms. While syndromic surveillance can potentially serve as a method of informing public health policy regarding specific mass gatherings based on the profile of syndromes ascertained, the present review does not indicate that this form of surveillance is a reliable method of detecting potentially critical public health events during mass gathering scenarios.

Utilizing the electronic health records to create a syndromic staff surveillance system during the COVID-19 outbreak

ObjectivesSince December 2019, COVID-19 has caused a worldwide pandemic and Singapore has seen escalating cases with community spread. Aggressive contact tracing and identification of suspects has helped to identify local community clusters, surveillance being the key to early intervention. Healthcare workers (HCWs) have contracted COVID-19 infection both at the workplace and community. We aimed to create a prototype staff surveillance system for the detection of acute respiratory infection (ARI) clusters amongst our HCWs and describe its effectiveness.MethodsA prototypical surveillance system was built on existing electronic health record infrastructure.ResultsOver a 10-week period, we investigated 10 ARI clusters amongst 7 departments. One of the ARI clusters was later determined to be related to COVID-19 infection. We demonstrate the feasibility of syndromic surveillance to detect ARI clusters during the COVID-19 outbreak.ConclusionThe use of syndromic surveillance to detect ARI clusters amongst HCWs in the COVID-19 pandemic may enable early case detection and prevent onward transmission. It could be an important tool in infection prevention within healthcare institutions.

Novel use of syndromic surveillance to monitor the impact of synthetic cannabinoid control measures on morbidity

BackgroundUsing data from syndromic surveillance, the New York City Department of Health and Mental Hygiene (DOHMH) identified an increase in the number of emergency department (ED) visits related to synthetic cannabinoids. Syndromic surveillance data were used to target community-level interventions and assess the real-time impact of control measures in reducing synthetic cannabinoid (“K2”)-related morbidity.MethodsFrom April 2015 through September 2015, DOHMH implemented 3 separate interventions to reduce K2-related morbidity by limiting the availability of K2 products. Difference-in-difference analyses compared pre- and post-intervention differences in cannabinoid-related ED visit rates between neighborhoods and controls for Interventions A and B. City-wide count data were used to compare K2-related ED visits before and after Intervention C.ResultsSyndromic data showed a reduction in K2-related ED visits following the 3 interventions. Respective decreases in rates of synthetic cannabinoid-related ED visits of 33 and 38% were detected at the neighborhood-level due to Interventions A and B, respectively. A decrease of 29% was calculated at the city level following Intervention C.ConclusionsIn addition to identifying emerging public health concerns, syndromic data can provide valuable real-time evidence on the effectiveness of public health interventions.

Use of N-grams and Term Relationship Graphs in the Syndrome Definition Development Process

ObjectiveTo describe the use of uni-grams, bi-grams, and tri-grams relationships in the development of syndromic categories.IntroductionThe use of syndromic surveillance systems has evolved over the last decade, and increasingly includes both infectious and non-infectious topic areas. Public health agencies at the national, state, and local levels often need to rapidly develop new syndromic categories, or improve upon existing categories, to enhance their public health surveillance efforts. Documenting this development process can help support increased understanding and user acceptance of syndromic surveillance. This presentation will highlight the visualization process being used by CDC’s National Syndromic Surveillance Program (NSSP) program to develop and refine definitions for syndromes of interest to public health programs.MethodsDevelopment of a syndromic definition is an iterative process that starts with an analyst testing how different terms, which are assumed to be associated with the topic of interest, and diagnostic codes are noted in the chief complaint and discharge diagnosis code fields. The analyst then manually scans through the resulting line list of patient chief complaint text and diagnostic codes to determine whether the query terms match the intended syndromic concept. Typically, more terms and diagnostic codes are then added to the query using Boolean operators, and other terms are negated and removed. To facilitate summarization of the resulting terms and diagnostic codes CDC’s NSSP program developed programs with R that extracted data using the ESSENCE application programming interface (API), and the chief complaint query validation data source (CCQV). We use N-gram analysis, which is extensively used in text mining, to show co-occurrences of words in a consecutive order. The co-occurrences of words can be a uni-gram which represents a single word, bi-gram for two words, and tri-grams for three words. The process tokenizes the chief complaint text and diagnosis code fields, with some pre-processing of the text and removal of stopwords. Uni-grams, bi-grams, and tri-grams are then calculated for the top 200 combinations along with term and diagnostic code co-occurrence. Other visualizations that can be used are network graphs, which show the connections between different chief complaints terms and also between discharge diagnosis codes and chief complaint terms. The use of these graphs provides an insight into the frequency and relationship between terms and codes.ResultsTo support the development of new syndrome definitions we used the R program to produce two time series graphs. The first time series graph is used to show the volume of visits over the user’s indicated time period and the second shows the median chief complaint compared over the user’s indicated time period. A series of histograms showing frequency of the uni-gram, bi-grams, and tri-grams are also used during the development process. Lastly, two network diagrams are used to show the co-occurrence between term and diagnostic codes. The use of this range of graphs during the syndrome definition development process provides multiple ways to view the characteristics of the chief complaint and discharge diagnosis fields.The sample graphs below can be used by the analyst to illustrate key information.ConclusionsThrough this development process and the use of graphs the relationship between the syndrome definition and search terms can be visualized. In addition when using this process, the analyst could be specific as to the syndrome of interest or be broad, allowing a generic trend series monitoring of the syndrome. The search words can also be based on specific local or regional terms and the relationship terms set to include or exclude certain terms. Use of this process for the development of syndrome definitions can support the use of syndromic surveillance and offer the opportunity to further refine the process. After the syndrome has been developed, the analyst can consider spatial or temporal analysis.

Lessons Learned from an Extreme Heat Event using ACES for Situational Awareness, Ontario, Canada

ObjectiveTo describe the lessons learned for public health decision-makers from an analysis of Acute Care Enhanced Surveillance (ACES) data for the heatwaves experienced in Ontario, Canada in the summer of 2018.IntroductionThe Acute Care Enhanced Surveillance (ACES) system provides syndromic surveillance for Ontario’s acute care hospitals. ACES receives over 99% of acute care records for emergency department (ED) visits; mean daily volume is 17,500 visits. ACES uses a maximum entropy classifier and generates more than 80 standard syndromes, fifteen of which are actively monitored for aberrational activity and are considered of higher public health relevance, including RESP (respiratory infection, non-croup), ILI (influenza-like illness), TOX (toxicological, chemical/drug exposure), AST (asthma), OPI (opioid exposure), CELL (cellulitis), GASTRO (gastroenteritis), ENVIRO (environmental, heat/cold exposure), MH (mental health), EOH (alcohol intoxication), DERM (rash), and SEP (bacteremia, sepsis).Syndromic surveillance provides a salient source of public health surveillance during extreme heat events; monitoring real-time ED visits can inform local public health authorities of health impacts, provide situation awareness to initiate and/or inform public health response, and help decision-makers allocate resources according to geographic (or demographic) vulnerability. While the use of syndromic surveillance has been well-characterized to monitor infectious disease outbreaks, its use to monitor the heat-health impacts is relatively novel for ACES users, specifically local public health authorities. This report describes the the data collected during an extended extreme heat event in Ontario, Canada, to highlight the value of syndromic surveillance during extreme heat events and make recommendations regarding incorporating ACES data into routine workflows.MethodsTemperature data were retrieved from Environment Canada historical databases for mid-June to mid-July 2018. Aggregate counts per day for total ED visits and and for individual syndromes were retrieved from ACES databases. Descriptive statistics were used to analyze all datasets.ResultsAn extreme heat event occurred in the southern region of Ontario in early summer, 2018. Environment Canada issues heat warnings for regions throughout Canada according to region-specific criteria; for southern Ontario, heat warnings are issued when 2 or more consecutive days of daytime maximum temperatures are expected to reach 31°C or when 2 or more consecutive days of humidex values are expected to reach 40. Extended heat warnings are issued when the event lasts beyond 2 days. An extended heat event occurred June 29 to July 5, 2018. Although the region is large, temperature data from Environment Canada’s climate monitoring station at Toronto’s Pearson Airport are shown (Figure 1) as an example of the temperatures observed for this time period in the region.ConclusionsLessons learned from an analysis of ACES data during an extreme heat event:1. The ENVIRO syndrome provides real-time monitoring of the health impacts during a heat event and may provide proxy for estimating the indirect effects of heat (e.g., impacts on chronic conditions). Public health authorities can monitor local health impacts during an extreme heat event.2. Patients seeking help at the ED do not appear to be skewed in acuity, sex nor age. This does not necessarily reflect the population that experiences the greatest impact from extreme heat, but rather those that are seeking help at the ED for the direct effects of heat. That said, an increase in ENVIRO counts does not indicate whether the increase is due to greater exposure to the heat (or sun), engaging in vigorous outdoor activity during the event (recreational or occupational), or lack of access to air conditioning.3. ED visits for ENVIRO can be geolocated to determine areas experiencing greater health impacts. This may allow allocation of resources to specifically address vulnerabilities. ACES has built-in mapping capabilities that allows a geovisualization of the home addresses for patients. Furthermore, aggregate counts for relevant syndromes are available for registered users on the Public Health Information Management System (PHIMS), a web-accessible GIS tool for situational awareness that gives public health decision-makers access to real time health impacts in concert with demographics, weather, and other emergency management information.

Using longitudinal syndromic surveillance to describe small ruminant health in village production systems in Myanmar

A novel syndromic surveillance approach was used to describe small ruminant health in Myanmar, to help overcome limitations in disease diagnosis common in many parts of the world, especially in low and middle income countries (LMICs). Observations were made from July 2015 to June 2016 of ten clinical syndromes in 12 goat herds and sheep flocks owned by smallholders in the Central Dry Zone. Strengths and weaknesses to using syndromic surveillance in a village setting were identified using a formal surveillance evaluation framework, ‘SERVAL’. Larger reporting teams made disproportionately more reports than smaller ones (86% compared to 14% of all reports, with a reporting rate ratio of 4.3 95% CI 3.5–5.4), which may have affected surveillance sensitivity. The benefits of the syndromic surveillance included its relatively low cost and ability to produce quantitative disease estimates that could be used to prioritise further disease investigation and extension activities. In particular, significant mortality was observed, with monthly mortality of 3.0% (95% CI 2.5–3.7%) and 0.28% (0.15–0.53%) in young and adult animals, respectively, and a population attributable fraction of mortality for young animals of 82% (68–91%). Mortality was associated with ill-thrift in young animals but had not previously been considered an important production-limiting condition in Myanmar. This information contributes to an understanding of the prevalence of excessive mortality in smallholder goat and sheep production systems. It is a practical example of the use of syndromic surveillance in a LMIC livestock production system, the results of which can direct future disease research, treatment and prevention to improve the health and productivity of small ruminants in Myanmar.

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

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

Detecting Suicide-Related Emergency Department Visits Among Adults Using the District of Columbia Syndromic Surveillance System.

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. 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. 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. 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.

Advancing the Use of Emergency Department Syndromic Surveillance Data, New York City, 2012-2016.

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. 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. 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. Sharing syndromic surveillance use cases can foster new ideas and build capacity for public health preparedness and response.

Monitoring child mental health related emergency department visits in New York City

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.

Workforce Competencies in Syndromic Surveillance Practice at Local Health Departments.

As the science and practice of syndromic surveillance (SyS) evolve, it has increasing utility for public health surveillance at the local level. Local health departments (LHDs) require specific organizational and workforce capabilities to use SyS data. In 2013, more than half of the LHDs reported using SyS, although little has been reported about LHD workforce capabilities in SyS. To conduct an assessment of self-reported knowledge and skills in SyS tasks to effectively target technical assistance to different levels of LHD need. A stratified sampling design based on LHD jurisdiction population and SyS status was employed. Data were drawn from the 2015 Biosurveillance Needs Assessment Survey, which captured variables related to LHD use of SyS, management of systems, and self-reported proficiencies in a typology of SyS functionalities developed by a workgroup of subject matter experts in SyS. Respondents were US-based LHD public health practitioners. Estimation weights were applied during analysis to determine the national representation of the responses. Respondents self-reported proficiency in 26 SyS tasks within 5 categories, analyzed by LHD jurisdiction size and respondents' years of SyS experience. SyS expertise varied widely across LHDs. Less than 50% of workers who have access to SyS demonstrated overall proficiency within any of the task areas: communication, data use, data analysis, quality monitoring and assurance, and system design and development. SyS users were strongest in data use tasks. Proficiency in SyS practice corresponded directly with respondents' years of SyS experience and the LHD jurisdiction size. SyS practitioners display a wide range of proficiencies both within and across SyS tasks. Considerable gaps in proficiencies of all areas of SyS practice indicate a need for technical assistance and knowledge dissemination to improve SyS practice as an important component of an LHD surveillance strategy.

The use of syndromic surveillance to monitor the incidence of arthropod bites requiring healthcare in England, 2000-2013: a retrospective ecological study.

Climate change experts predict the number of nuisance-biting arthropods in England will increase but there is currently no known surveillance system in place to monitor or assess the public health impact of arthropod bites. This retrospective ecological study utilized arthropod bites requiring healthcare from five national real-time syndromic surveillance systems monitoring general practitioner (GP) consultations (in-hours and out-of-hours), emergency department (ED) attendances and telephone calls to remote advice services to determine baseline incidence in England between 2000 and 2013 and to assess the association between arthropod bites and temperature. During summer months (weeks 20-40) we estimated that arthropod bites contribute a weekly median of ~4000 GP consultations, 750 calls to remote advice services, 700 ED and 1300 GP out-of-hours attendances. In all systems, incidence was highest during summer months compared to the rest of the year. Arthropod bites were positively associated with temperature with incidence rate ratios (IRRs) that ranged between systems from 1·03 [95% confidence interval (CI) 1·01-1·06] to 1·14 (95% CI 1·11-1·16). Using syndromic surveillance systems we have established and described baseline incidence of arthropod bites and this can now be monitored routinely in real time to assess the impact of extreme weather events and climate change.

What is the utility of using syndromic surveillance systems during large subnational infectious gastrointestinal disease outbreaks? An observational study using case studies from the past 5 years in England.

Syndromic surveillance systems in England have demonstrated utility in the early identification of seasonal gastrointestinal illness (GI) tracking its spatio-temporal distribution and enabling early public health action. There would be additional public health utility if syndromic surveillance systems could detect or track subnational infectious disease outbreaks. To investigate using syndromic surveillance for this purpose we retrospectively identified eight large GI outbreaks between 2009 and 2014 (four randomly and four purposively sampled). We then examined syndromic surveillance information prospectively collected by the Real-time Syndromic Surveillance team within Public Health England for evidence of possible outbreak-related changes. None of the outbreaks were identified contemporaneously and no alerts were made to relevant public health teams. Retrospectively, two of the outbreaks - which happened at similar times and in proximal geographical locations - demonstrated changes in the local trends of relevant syndromic indicators and exhibited a clustering of statistical alarms, but did not warrant alerting local health protection teams. Our suite of syndromic surveillance systems may be more suited to their original purposes than as means of detecting or monitoring localized, subnational GI outbreaks. This should, however, be considered in the context of this study's limitations; further prospective work is needed to fully explore the use of syndromic surveillance for this purpose. Provided geographical coverage is sufficient, syndromic surveillance systems could be able to provide reassurance of no or minor excess healthcare systems usage during localized GI incidents.

Meeting the International Health Regulations (2005) surveillance core capacity requirements at the subnational level in Europe: the added value of syndromic surveillance.

BackgroundThe revised World Health Organization’s International Health Regulations (2005) request a timely and all-hazard approach towards surveillance, especially at the subnational level. We discuss three questions of syndromic surveillance application in the European context for assessing public health emergencies of international concern: (i) can syndromic surveillance support countries, especially the subnational level, to meet the International Health Regulations (2005) core surveillance capacity requirements, (ii) are European syndromic surveillance systems comparable to enable cross-border surveillance, and (iii) at which administrative level should syndromic surveillance best be applied?DiscussionDespite the ongoing criticism on the usefulness of syndromic surveillance which is related to its clinically nonspecific output, we demonstrate that it was a suitable supplement for timely assessment of the impact of three different public health emergencies affecting Europe. Subnational syndromic surveillance analysis in some cases proved to be of advantage for detecting an event earlier compared to national level analysis. However, in many cases, syndromic surveillance did not detect local events with only a small number of cases.The European Commission envisions comparability of surveillance output to enable cross-border surveillance. Evaluated against European infectious disease case definitions, syndromic surveillance can contribute to identify cases that might fulfil the clinical case definition but the approach is too unspecific to comply to complete clinical definitions. Syndromic surveillance results still seem feasible for comparable cross-border surveillance as similarly defined syndromes are analysed.We suggest a new model of implementing syndromic surveillance at the subnational level. In this model, syndromic surveillance systems are fine-tuned to their local context and integrated into the existing subnational surveillance and reporting structure. By enhancing population coverage, events covering several jurisdictions can be identified at higher levels. However, the setup of decentralised and locally adjusted syndromic surveillance systems is more complex compared to the setup of one national or local system.SummaryWe conclude that syndromic surveillance if implemented with large population coverage at the subnational level can help detect and assess the local and regional effect of different types of public health emergencies in a timely manner as required by the International Health Regulations (2005).

An early warning system based on syndromic surveillance to detect potential health emergencies among migrants: results of a two-year experience in Italy.

Profound geopolitical changes have impacted the southern and eastern Mediterranean since 2010 and defined a context of instability that is still affecting several countries today. Insecurity combined with the reduction of border controls has led to major population movements in the region and to migration surges from affected countries to southern Europe, especially to Italy. To respond to the humanitarian emergency triggered by this migration surge, Italy implemented a syndromic surveillance system in order to rapidly detect potential public health emergencies in immigrant reception centres. This system was discontinued after two years. This paper presents the results of this experience detailing its strengths and weaknesses in order to document the applicability and usefulness of syndromic surveillance in this specific context.

Use of Syndromic Surveillance at Local Health Departments

Syndromic surveillance systems enhance public health practice in both large and small population settings. However, data from these systems are typically monitored by state and federal agencies and less frequently used by small public health agencies, such as local health departments (LHDs). Syndromic surveillance system modifications may facilitate use by LHDs. To describe syndromic surveillance system modifications and survey LHD staff to assess subsequent changes in system use. Pre- and postintervention cross-sectional analysis. North Carolina (NC) LHDs, 2009 and 2012. LHD nursing and preparedness staff. Use of syndromic surveillance data by LHDs for outbreak response, seasonal event response, program management, and stakeholder reports. In NC, syndromic surveillance system modifications made between 2009 and 2012 included implementation of LHD-specific data "dashboards" and increased distribution of LHD-specific surveillance information by the state public health agency. Users of LHD syndromic surveillance system increased from 99 in 2009 to 175 in 2012. Twenty-seven of 28 (96%) and 62 of 72 (86%) respondents completed the 2009 and 2012 surveys, respectively. Among respondents, 23% used syndromic surveillance data for outbreak response in 2009, compared with 25% in 2012. In 2009, 46% of respondents used these data for seasonal event response, compared with 57% in 2012. Syndromic surveillance data were used for program management by 25% of respondents in 2009 (compared with 30% in 2012) and for stakeholder reports by 23% of respondents in 2009 (compared with 33% in 2012). Syndromic surveillance system changes supported modest increases in LHD use of syndromic surveillance information. Because use of syndromic surveillance information at smaller LHD is rare, these modest increases indicate effective modification of the NC syndromic surveillance system.

Usefulness of Syndromic Surveillance during Ultra-endurance Running Races: Example with the “Grand Raid de la Réunion” Ultra Trail

The "Grand Raid de la Réunion" is one of the hardest ultra trails in the world. This endurance running race has consequences on health's runners. We used syndromic surveillance to estimate the health impact of this sporting event on the Emergency Departments (ED) of Reunion Island. During the race's period, a global increase of the ED visits all causes was observed. The syndromic surveillance system detected a significant ED visits' increase for hydro-electrolytic disorders. These results highlight the usefulness of syndromic surveillance to estimate the impact on health of a mass gathering on a sporting event of great magnitude.

Usefulness of Syndromic Surveillance for Early Outbreak Detection in Small Islands: The Case of Mayotte

Syndromic surveillance can be a useful tool for the early detection outbreak. Mayotte Island is a French overseas department located in the North of Mozambique Channel in the Indian Ocean. Due to its geographical and socio-demographical situation, the population of this island is widely exposed to infectious diseases. The implementation of a syndromic surveillance system allowed to detect several unusual events as an outbreak of conjunctivitis in March 2012.