Real-time Syndromic Surveillance System Research Articles

DiD IT?: a differences-in-differences investigation tool to quantify the impact of local incidents on public health using real-time syndromic surveillance health data.

Syndromic surveillance was originally developed to provide early warning compared to laboratory surveillance, but it is increasing used for real-time situational awareness. When a potential threat to public health is identified, a rapid assessment of its impact is required for public health management. When threats are localised, analysis is more complex as local trends need to be separated from national trends and differences compared to unaffected areas may be due to confounding factors such as deprivation or age distributions. Accounting for confounding factors usually requires an in-depth study, which takes time. Therefore, a tool is required which can provide a rapid estimate of local incidents using syndromic surveillance data.Here, we present 'DiD IT?', a new investigation tool designed to measure the impact of local threats to public health. 'DiD IT?' uses a difference-in-differences statistical approach to account for temporal and spatial confounding and provide a direct estimate of impact due to incidents. Temporal confounding differences are estimated by comparing unaffected locations during and outside of exposure periods. Whilst spatial confounding differences are estimated by comparing unaffected and exposed locations outside of the exposure period. Any remaining differences can be considered to be the direct effect of the local incident.We illustrate the potential utility of the tool through four examples of localised health protection incidents in England. The examples cover a range of data sources including general practitioner (GP) consultations, emergency department (ED) attendances and a telehealth call and online health symptom checker; and different types of incidents including, infectious disease outbreak, mass-gathering, extreme weather and an industrial fire. The examples use the UK Health Security Agency's ongoing real-time syndromic surveillance systems to show how results can be obtained in near real-time.The tool identified 700 additional online difficulty breathing assessments associated with a severe thunderstorm, 53 additional GP consultations during a mumps outbreak, 2-3 telehealth line calls following an industrial fire and that there was no significant increase in ED attendances during the G7 summit in 2021.DiD IT? can provide estimates for the direct impact of localised events in real-time as part of a syndromic surveillance system. Thus, it has the potential for enhancing surveillance and can be used to evaluate the effectiveness of extending national surveillance to a more granular local surveillance.

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

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

Syndromic Surveillance Insights from a Symptom Assessment App Before and During COVID-19 Measures in Germany and the United Kingdom: Results From Repeated Cross-Sectional Analyses

BackgroundUnprecedented lockdown measures have been introduced in countries worldwide to mitigate the spread and consequences of COVID-19. Although attention has been focused on the effects of these measures on epidemiological indicators relating directly to the infection, there is increased recognition of their broader health implications. However, assessing these implications in real time is a challenge, due to the limitations of existing syndromic surveillance data and tools.ObjectiveThe aim of this study is to explore the added value of mobile phone app–based symptom assessment tools as real-time health insight providers to inform public health policy makers.MethodsA comparative and descriptive analysis of the proportion of all self-reported symptoms entered by users during an assessment within the Ada app in Germany and the United Kingdom was conducted between two periods, namely before and after the implementation of “Phase One” COVID-19 measures. Additional analyses were performed to explore the association between symptom trends and seasonality, and symptom trends and weather. Differences in the proportion of unique symptoms between the periods were analyzed using a Pearson chi-square test and reported as log2 fold changes.ResultsOverall, 48,300-54,900 symptomatic users reported 140,500-170,400 symptoms during the Baseline and Measures periods in Germany. Overall, 34,200-37,400 symptomatic users in the United Kingdom reported 112,100-131,900 symptoms during the Baseline and Measures periods. The majority of symptomatic users were female (Germany: 68,600/103,200, 66.52%; United Kingdom: 51,200/71,600, 72.74%). The majority were aged 10-29 years (Germany: 68,500/100,000, 68.45%; United Kingdom: 50,900/68,800, 73.91%), and about one-quarter were aged 30-59 years (Germany: 26,200/100,000, 26.15%; United Kingdom: 14,900/68,800, 21.65%). Overall, 103 symptoms were reported either more or less frequently (with statistically significant differences) during the Measures period as compared to the Baseline period, and 34 of these were reported in both countries. The following mental health symptoms (log2 fold change, P value) were reported less often during the Measures period: inability to manage constant stress and demands at work (–1.07, P<.001), memory difficulty (–0.56, P<.001), depressed mood (–0.42, P<.001), and impaired concentration (–0.46, P<.001). Diminished sense of taste (2.26, P<.001) and hyposmia (2.20, P<.001) were reported more frequently during the Measures period. None of the 34 symptoms were found to be different between the same dates in 2019. In total, 14 of the 34 symptoms had statistically significant associations with weather variables.ConclusionsSymptom assessment apps have an important role to play in facilitating improved understanding of the implications of public health policies such as COVID-19 lockdown measures. Not only do they provide the means to complement and cross-validate hypotheses based on data collected through more traditional channels, they can also generate novel insights through a real-time syndromic surveillance system.

A real-time spatio-temporal syndromic surveillance system with application to small companion animals

Lack of disease surveillance in small companion animals worldwide has contributed to a deficit in our ability to detect and respond to outbreaks. In this paper we describe the first real-time syndromic surveillance system that conducts integrated spatio-temporal analysis of data from a national network of veterinary premises for the early detection of disease outbreaks in small animals. We illustrate the system’s performance using data relating to gastrointestinal disease in dogs and cats. The data consist of approximately one million electronic health records for dogs and cats, collected from 458 UK veterinary premises between March 2014 and 2016. For this illustration, the system predicts the relative reporting rate of gastrointestinal disease amongst all presentations, and updates its predictions as new data accrue. The system was able to detect simulated outbreaks of varying spatial geometry, extent and severity. The system is flexible: it generates outcomes that are easily interpretable; the user can set their own outbreak detection thresholds. The system provides the foundation for prompt detection and control of health threats in companion animals.

Syndromic surveillance: two decades experience of sustainable systems - its people not just data!

Syndromic surveillance is a form of surveillance that generates information for public health action by collecting, analysing and interpreting routine health-related data on symptoms and clinical signs reported by patients and clinicians rather than being based on microbiologically or clinically confirmed cases. In England, a suite of national real-time syndromic surveillance systems (SSS) have been developed over the last 20 years, utilising data from a variety of health care settings (a telehealth triage system, general practice and emergency departments). The real-time systems in England have been used for early detection (e.g. seasonal influenza), for situational awareness (e.g. describing the size and demographics of the impact of a heatwave) and for reassurance of lack of impact on population health of mass gatherings (e.g. the London 2012 Olympic and Paralympic Games).We highlight the lessons learnt from running SSS, for nearly two decades, and propose questions and issues still to be addressed. We feel that syndromic surveillance is an example of the use of 'big data', but contend that the focus for sustainable and useful systems should be on the added value of such systems and the importance of people working together to maximise the value for the public health of syndromic surveillance services.

Google search patterns monitoring the daily health impact of heatwaves in England: How do the findings compare to established syndromic surveillance systems from 2013 to 2017?

One of the implications of climate change is a predicted increase in frequent and severe heatwaves. The impact of heatwaves on the health of the population is captured through real-time syndromic healthcare surveillance systems monitored daily in England during the summer months. Internet search data could potentially provide improved timeliness and help to assess the wider population health impact of heat by capturing a population sub-group who are symptomatic but do not seek healthcare. A retrospective observational study was carried out from June 2013 to September 2017 in England to compare daily trends in validated syndromic surveillance heat-related morbidity indicators against symptom-based heatwave related Google search terms. The degree of correlation was determined with Spearman correlation coefficients and lag assessment was carried out to determine timeliness. Daily increases in frequency in Google search terms during heatwave events correlated well with validated syndromic indicators. Correlation coefficients between search term frequency and syndromic indicators from 2013 to 2017 were highest with the telehealth service NHS 111 (range of 0.684–0.900 by search term). Lag analysis revealed a similar timeliness between the data sources, suggesting Google data did not provide a delayed or earlier signal in the context of England's syndromic surveillance systems. This work highlights the potential benefits for countries which lack established public health surveillance systems to monitor heat-related morbidity and the use of internet search data to assess the wider population health impact of exposure to heat.

Public Health Impact of Syndromic Surveillance Data—A Literature Survey

Public Health Impact of Syndromic Surveillance Data—A Literature Survey

Syndromic Surveillance System for Korea-US Joint Biosurveillance Portal: Design and Lessons Learned.

Driven by the growing importance of situational awareness of bioterrorism threats, the Republic of Korea (ROK) and the United States have constructed a joint military capability, called the Biosurveillance Portal (BSP), to enhance biosecurity. As one component of the BSP, we developed the Military Active Real-time Syndromic Surveillance (MARSS) system to detect and track natural and deliberate disease outbreaks. This article describes the ROK military health data infrastructure and explains how syndromic data are derived and made available to epidemiologists. Queries corresponding to 8 syndromes, based on published clinical effects of weaponized pathogens, were used to classify military hospital patient records to form aggregated daily syndromic counts. A set of ICD-10 codes for each syndrome was defined through literature review and expert panel discussion. A study set of time series of national daily counts for each syndrome was extracted from the Defense Medical Statistical Information System between January 1, 2011, and May 31, 2014. A stratified, adjusted cumulative summation algorithm was implemented for each syndrome group to signal alerts prompting investigation. The algorithm was developed by calculating sensitivity to sets of 1,000 artificial outbreak signals randomly injected in the dataset, with each signal injected in a separate trial. Queries and visualizations were adapted from the Suite for Automated Global bioSurveillance. Findings indicated that early warning of outbreaks affecting fewer than 50 patients will require analysis at subnational levels, especially for common syndrome groups. Developing MARSS to improve sensitivity will require modification of underlying syndromic diagnosis codes, engineering to coordinate alerts among subdivisions, and enhanced algorithms. The bioterrorist threat in the Korean peninsula mandates these efforts.

Post-disaster health impact of natural hazards in the Philippines in 2013

BackgroundIn 2011, the Health Emergency Management Bureau (HEMB) created the Surveillance for Post Extreme Emergencies and Disasters (SPEED), a real-time syndromic surveillance system that allows the early detection and monitoring of post-disaster disease trends. SPEED can assist health leaders in making informed decisions on health systems affected by disasters. There is a need for further validation of current concepts in post-disaster disease patterns in respect to actual field data. This study aims to evaluate the temporal post-disaster patterns of selected diseases after a flood, an earthquake, and a typhoon in the Philippines in 2013.MethodologyWe analyzed the 21 syndromes provided by SPEED both separately and grouped into injuries, communicable diseases, and non-communicable diseases (NCDs) by calculating daily post-disaster consultation rates for up to 150 days post-disaster. These were compared over time and juxtaposed according to the type of disaster.ResultsCommunicable diseases were found to be the predominant syndrome group in all three disaster types. The top six syndromes found were: acute respiratory infections, open wounds, bruises and burns, high blood pressure, skin disease, fever, and acute watery diarrhea.DiscussionOverall, the results aligned with the country's morbidity profile. Within 2 months, the clear gradation of increasing syndrome rates reflected the severity (flood<earthquake<typhoon) and magnitude of the disruption of the health system caused by the disasters. After 2 months, rates dropped, suggesting the beginning of the recovery phase. The most common syndromes can be addressed by measures such as providing for shelter, water, sanitation, hygiene, nutrition, and common health services.ConclusionsMost post-disaster syndromes may be addressed by prevention, early diagnosis, and early treatment. Health needs differ in response and recovery phases.

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

There is an increasing body of evidence illustrating the negative health effects of air pollution including increased risk of respiratory, cardiac and other morbid conditions. During March and April 2014 there were two air pollution episodes in England that occurred in close succession. We used national real-time syndromic surveillance systems, including general practitioner (GP) consultations, emergency department attendances, telehealth calls and ambulance dispatch calls to further understand the impact of these short term acute air pollution periods on the health seeking behaviour of the general public. Each air pollution period was comparable with respect to particulate matter concentrations (PM10 and PM2.5), however, the second period was longer in duration (6 days vs 3 days) and meteorologically driven ‘Sahara dust’ contributed to the pollution. Health surveillance data revealed a greater impact during the second period, with GP consultations, emergency department attendances and telehealth (NHS 111) calls increasing for asthma, wheeze and difficulty breathing indicators, particularly in patients aged 15–64 years. Across regions of England there was good agreement between air quality levels and health care seeking behaviour. The results further demonstrate the acute impact of short term air pollution episodes on public health and also illustrate the potential role of mass media reporting in escalating health care seeking behaviour.

Syndromic surveillance in companion animals utilizing electronic medical records data: development and proof of concept.

In an effort to recognize and address communicable and point-source epidemics in dog and cat populations, this project created a near real-time syndromic surveillance system devoted to companion animal health in the United States. With over 150 million owned pets in the US, the development of such a system is timely in light of previous epidemics due to various causes that were only recognized in retrospect. The goal of this study was to develop epidemiologic and statistical methods for veterinary hospital-based surveillance, and to demonstrate its efficacy by detection of simulated foodborne outbreaks using a database of over 700 hospitals. Data transfer protocols were established via a secure file transfer protocol site, and a data repository was constructed predominantly utilizing open-source software. The daily proportion of patients with a given clinical or laboratory finding was contrasted with an equivalent average proportion from a historical comparison period, allowing construction of the proportionate diagnostic outcome ratio and its confidence interval for recognizing aberrant heath events. A five-tiered alert system was used to facilitate daily assessment of almost 2,000 statistical analyses. Two simulated outbreak scenarios were created by independent experts, blinded to study investigators, and embedded in the 2010 medical records. Both outbreaks were detected almost immediately by the alert system, accurately detecting species affected using relevant clinical and laboratory findings, and ages involved. Besides demonstrating proof-in-concept of using veterinary hospital databases to detect aberrant events in space and time, this research can be extended to conducting post-detection etiologic investigations utilizing exposure information in the medical record.

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.

Developing and validating a new national remote health advice syndromic surveillance system in England.

Public Health England (PHE) coordinates a suite of real-time national syndromic surveillance systems monitoring general practice, emergency department and remote health advice data. We describe the development and informal evaluation of a new syndromic surveillance system using NHS 111 remote health advice data. NHS 111 syndromic indicators were monitored daily at national and local level. Statistical models were applied to daily data to identify significant exceedances; statistical baselines were developed for each syndrome and area using a multi-level hierarchical mixed effects model. Between November 2013 and October 2014, there were on average 19 095 NHS 111 calls each weekday and 43 084 each weekend day in the PHE dataset. There was a predominance of females using the service (57%); highest percentage of calls received was in the age group 1-4 years (14%). This system was used to monitor respiratory and gastrointestinal infections over the winter of 2013-14, the potential public health impact of severe flooding across parts of southern England and poor air quality episodes across England in April 2014. This new system complements and supplements the existing PHE syndromic surveillance systems and is now integrated into the routine daily processes that form this national syndromic surveillance service.

Utility of a near real-time emergency department syndromic surveillance system to track injuries in New York City.

BackgroundThe New York City emergency department (ED) syndromic surveillance (SS) system provides near real-time data on the majority of ED visits. The utility of ED SS for injury surveillance has not been thoroughly evaluated. We created injury syndromes based on ED chief complaint information and evaluated their utility compared to administrative billing data.MethodsSix injury syndromes were developed: traffic-related injuries to pedal cyclists, pedestrians, and motor vehicle occupants; fall-related injuries; firearm-related injuries; and assault-related stabbings. Daily injury counts were compared for ED SS and the administrative billing data for years 2008–2010. We examined characteristics of injury trends and patterns between the two systems, calculating descriptive statistics for temporal patterns and Pearson correlation coefficients (r) for temporal trends. We also calculated proportions of demographic and geospatial patterns for both systems.ResultsAlthough daily volume of the injuries varied between the two systems, the temporal patterns were similar (all r values for daily volume exceeded 0.65). Comparisons of injuries by time of day, day of week, and quarter of year demonstrated high agreement between the two systems—the majority had an absolute percentage point difference of 2.0 or less. Distributions of injury by sex and age group also aligned well. Distribution of injury by neighborhood of residence showed mixed results—some neighborhood comparisons showed a high level of agreement between systems, while others were less successful.ConclusionsAs evidenced by the strong positive correlation coefficients and the small absolute percentage point differences in our comparisons, we conclude that ED SS captures temporal trends and patterns of injury-related ED visits effectively. The system could be used to identify changes in injury patterns, allowing for situational awareness during emergencies, timely response, and public messaging.Electronic supplementary materialThe online version of this article (doi:10.1186/s40621-015-0044-5) contains supplementary material, which is available to authorized users.

Military Real-time Syndromic Surveillance System for Biosurveillance Portal in Korea

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.

Using real-time syndromic surveillance systems to help explore the acute impact of the air pollution incident of March/April 2014 in England

During March and early April 2014 there was widespread poor air quality across the United Kingdom. Public Health England used existing syndromic surveillance systems to monitor community health during the period. Short lived statistically significant rises in a variety of respiratory conditions, including asthma and wheeze, were detected. This incident has demonstrated the value of real-time syndromic surveillance systems, during an air pollution episode, for helping to explore the impact of poor air quality on community health in real-time.

Detecting Public Health Impacts Associated with Air Pollution Events in the UK Using Syndromic Surveillance

Air pollution is well documented to cause adverse health effects in the population. Studies have demonstrated that it is associated with various adverse health outcomes. Classical epidemiological studies of the health effects of air pollution are typically retrospective. In order to assess the effectiveness of any public health messages or interventions in a timely manner there is a need to be able to systematically detect any health effects occurring in 'real-time'. This study examined whether the current UK real-time syndromic surveillance systems can detect public health impacts associated with air pollution events such as fires and ambient air pollution episodes.

Determination of French influenza outbreaks periods between 1985 and 2011 through a web-based Delphi method

BackgroundAssessing the accuracy of influenza epidemic periods determined by statistical models is important to improve the performance of algorithms used in real-time syndromic surveillance systems. This is a difficult problem to address in the absence of a reliable gold standard. The objective of this study is to establish an expert-based determination of the start and the end of influenza epidemics in France.MethodsA three-round international web-based Delphi survey was proposed to 288 eligible influenza experts. Fifty-seven (20%) experts completed the three-rounds of the study. The experts were invited to indicate the starting and the ending week of influenza epidemics, on 32 time-series graphs of influenza seasons drawn using data from the French Sentinelles Network (Influenza-like illness incidence rates) and virological data from the WHO-FluNet. Twenty-six of 32 time-series graphs proposed corresponded to each of the French influenza seasons observed between 1985 and 2011. Six influenza seasons were proposed twice at each round to measure variation among expert responses.ResultsWe obtained consensual results for 88% (23/26) of the epidemic periods. In two or three rounds (depending on the season) answers gathered around modes, and the internal control demonstrated a good reproducibility of the answers. Virological data did not appear to have a significant impact on the answers or the level of consensus, except for a season with a major mismatch between virological and incidence data timings.ConclusionsThanks to this international web-based Delphi survey, we obtained reproducible, stable and consensual results for the majority of the French influenza epidemic curves analysed. The detailed curves together with the estimates from the Delphi study could be a helpful tool for assessing the performance of statistical outbreak detection methods, in order to optimize them.

Use of a Real-Time Syndromic Surveillance System to Improve Influenza Like Illness Screening and Documentation in Emergency Departments during the H1N1 Pandemic

Interventions introduced to increase ILI screening documentation exhibited a correlation with an improved documentation rate. Aggregated data across sites demonstrate that the greatest impact is associated with email reminders for recording ILI screening results, meetings on how to improve adherence and media broadcasts associated with the circulating pandemic influenza. When one site reliably reported a period of one-to-one nurse reminders to record the ILI screening result was analyzed, one of the strongest correlations to increased adherence was demonstrated. While the results suggest more direct interventions have a significant impact, further research to isolate which interventions had the greatest impact is warranted.

Using a real-time syndromic surveillance system to track heat-related illnesses during a heat wave

Using a real-time syndromic surveillance system to track heat-related illnesses during a heat wave