Developing a new syndromic surveillance system for the London 2012 Olympic and Paralympic Games

Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease

The rapid detection of outbreaks is a key step in the effective control and containment of infectious diseases. In particular, the identification of cases which might be epidemiologically linked is crucial in directing outbreak-containment efforts and shaping the intervention of public health authorities. Often this requires the detection of clusters of cases whose numbers exceed those expected by a background of sporadic cases. Quantifying exceedances rapidly is particularly challenging when only few cases are typically reported in a precise location and time. To address such important public health concerns, we present a general method which can detect spatio-temporal deviations from a Poisson point process and estimate the odds of an isolate being part of a cluster. This method can be applied to diseases where detailed geographical information is available. In addition, we propose an approach to explicitly take account of delays in microbial typing. As a case study, we considered invasive group A Streptococcus infection events as recorded and typed by Public Health England from 2015 to 2020.

Observational study of mental health presentations across healthcare setting during the first 9 months of the COVID-19 pandemic in England

Background

The COVID-19 pandemic has resulted in an unprecedented impact on the day-to-day lives of people, with several features potentially adversely affecting mental health. There is growing evidence of the size of the impact of COVID-19 on mental health, but much of this is from ongoing population surveys using validated mental health scores.

Objective

This study investigated the impact of the pandemic and control measures on mental health conditions presenting to a spectrum of national health care services monitored using real-time syndromic surveillance in England.

Methods

We conducted a retrospective observational descriptive study of mental health presentations (those calling the national medical helpline, National Health Service [NHS] 111; consulting general practitioners [GPs] in and out-of-hours; calling ambulance services; and attending emergency departments) from January 1, 2019, to September 30, 2020. Estimates for the impact of lockdown measures were provided using an interrupted time series analysis.

Results

Mental health presentations showed a marked decrease during the early stages of the pandemic. Postlockdown, attendances for mental health conditions reached higher than prepandemic levels across most systems—a rise of 10% compared to that expected for NHS 111 and 21% for GP out-of-hours service—while the number of consultations to GP in-hours service was 13% lower compared to the same time previous year. Increases were observed in calls to NHS 111 for sleep problems.

Conclusions

These analyses showed marked changes in the health care attendances and prescribing for common mental health conditions across a spectrum of health care provision, with some of these changes persisting. The reasons for such changes are likely to be complex and multifactorial. The impact of the pandemic on mental health may not be fully understood for some time, and therefore, these syndromic indicators should continue to be monitored.

The Utility of Ambulance Dispatch Call Syndromic Surveillance for Detecting and Assessing the Health Impact of Extreme Weather Events in England

Extreme weather events present significant global threats to health. The National Ambulance Syndromic Surveillance System collects data on 18 syndromes through chief presenting complaint (CPC) codes. We aimed to determine the utility of ambulance data to monitor extreme temperature events for action. Daily total calls were observed between 01/01/2018−30/04/2019. Median daily ’Heat/Cold’ CPC calls during “known extreme temperature” (identified a priori), “extreme temperature”; (within 5th or 95th temperature percentiles for central England) and meteorological alert periods were compared to all other days using Wilcoxon signed-rank test. During the study period, 12,585,084 calls were recorded. In 2018, median daily “Heat/Cold” calls were higher during periods of known extreme temperature: heatwave (16/day, 736 total) and extreme cold weather events (28/day, 339 total) compared to all other days in 2018 (6/day, 1672 total). Median daily “Heat/Cold” calls during extreme temperature periods (16/day) were significantly higher than non-extreme temperature periods (5/day, p < 0.001). Ambulance data can be used to identify adverse impacts during periods of extreme temperature. Ambulance data are a low resource, rapid and flexible option providing real-time data on a range of indicators. We recommend ambulance data are used for the surveillance of presentations to healthcare related to extreme temperature events.

Environmental factors associated with general practitioner consultations for allergic rhinitis in London, England: a retrospective time series analysis

OBJECTIVES

To identify key predictors of general practitioner (GP) consultations for allergic rhinitis (AR) using meteorological and environmental data.

DESIGN

A retrospective, time series analysis of GP consultations for AR.

SETTING

A large GP surveillance network of GP practices in the London area.

PARTICIPANTS

The study population was all persons who presented to general practices in London that report to the Public Health England GP in-hours syndromic surveillance system during the study period (3 April 2012 to 11 August 2014).

PRIMARY MEASURE

Consultations for AR (numbers of consultations).

RESULTS

During the study period there were 186 401 GP consultations for AR. High grass and nettle pollen counts (combined) were associated with the highest increases in consultations (for the category 216-270 grains/m³, relative risk (RR) 3.33, 95% CI 2.69 to 4.12) followed by high tree (oak, birch and plane combined) pollen counts (for the category 260-325 grains/m³, RR 1.69, 95% CI 1.32 to 2.15) and average daily temperatures between 15°C and 20°C (RR 1.47, 95% CI 1.20 to 1.81). Higher levels of nitrogen dioxide (NO₂) appeared to be associated with increased consultations (for the category 70-85 µg/m³, RR 1.33, 95% CI 1.03 to 1.71), but a significant effect was not found with ozone. Higher daily rainfall was associated with fewer consultations (15-20 mm/day; RR 0.812, 95% CI 0.674 to 0.980).

CONCLUSIONS

Changes in grass, nettle or tree pollen counts, temperatures between 15°C and 20°C, and (to a lesser extent) NO₂ concentrations were found to be associated with increased consultations for AR. Rainfall has a negative effect. In the context of climate change and continued exposures to environmental air pollution, intelligent use of these data will aid targeting public health messages and plan healthcare demand.

Adapting Syndromic Surveillance Baselines After Public Health Interventions

BACKGROUND

Public health surveillance requires historical baselines to identify unusual activity. However, these baselines require adjustment after public health interventions. We describe an example of such an adjustment after the introduction of rotavirus vaccine in England in July 2013.

METHODS

We retrospectively measured the magnitude of differences between baselines and observed counts (residuals) before and after the introduction of a public health intervention, the introduction of a rotavirus vaccine in July 2013. We considered gastroenteritis, diarrhea, and vomiting to be indicators for national syndromic surveillance, including telephone calls to a telehealth system, emergency department visits, and unscheduled consultations with general practitioners. The start of the preintervention period varied depending on the availability of surveillance data: June 2005 for telehealth, November 2009 for emergency departments, and July 2010 for general practitioner data. The postintervention period was July 2013 to the second quarter of 2016. We then determined whether baselines incorporating a step-change reduction or a change in seasonality resulted in more accurate models of activity.

RESULTS

Residuals in the unadjusted baseline models increased by 42%-198% from preintervention to postintervention. Increases in residuals for vomiting indicators were 19%-44% higher than for diarrhea. Both step-change and seasonality adjustments improved the surveillance models; we found the greatest reduction in residuals in seasonally adjusted models (4%-75%).

CONCLUSION

Our results demonstrated the importance of adjusting surveillance baselines after public health interventions, particularly accounting for changes in seasonality. Adjusted baselines produced more representative expected values than did unadjusted baselines, resulting in fewer false alarms and a greater likelihood of detecting public health threats.

Comparison of statistical algorithms for daily syndromic surveillance aberration detection

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.

Developing influenza and respiratory syncytial virus activity thresholds for syndromic surveillance in England

Influenza and respiratory syncytial virus (RSV) are common causes of respiratory tract infections and place a burden on health services each winter. Systems to describe the timing and intensity of such activity will improve the public health response and deployment of interventions to these pressures. Here we develop early warning and activity intensity thresholds for monitoring influenza and RSV using two novel data sources: general practitioner out-of-hours consultations (GP OOH) and telehealth calls (NHS 111). Moving Epidemic Method (MEM) thresholds were developed for winter 2017-2018. The NHS 111 cold/flu threshold was breached several weeks in advance of other systems. The NHS 111 RSV epidemic threshold was breached in week 41, in advance of RSV laboratory reporting. Combining the use of MEM thresholds with daily monitoring of NHS 111 and GP OOH syndromic surveillance systems provides the potential to alert to threshold breaches in real-time. An advantage of using thresholds across different health systems is the ability to capture a range of healthcare-seeking behaviour, which may reflect differences in disease severity. This study also provides a quantifiable measure of seasonal RSV activity, which contributes to our understanding of RSV activity in advance of the potential introduction of new RSV vaccines.

Can syndromic surveillance help forecast winter hospital bed pressures in England?

Background

Health care planners need to predict demand for hospital beds to avoid deterioration in health care. Seasonal demand can be affected by respiratory illnesses which in England are monitored using syndromic surveillance systems. Therefore, we investigated the relationship between syndromic data and daily emergency hospital admissions.

Methods

We compared the timing of peaks in syndromic respiratory indicators and emergency hospital admissions, between 2013 and 2018. Furthermore, we created forecasts for daily admissions and investigated their accuracy when real-time syndromic data were included.

Results

We found that syndromic indicators were sensitive to changes in the timing of peaks in seasonal disease, especially influenza. However, each year, peak demand for hospital beds occurred on either 29^th or 30^th December, irrespective of the timing of syndromic peaks. Most forecast models using syndromic indicators explained over 70% of the seasonal variation in admissions (adjusted R square value). Forecast errors were reduced when syndromic data were included. For example, peak admissions for December 2014 and 2017 were underestimated when syndromic data were not used in models.

Conclusion

Due to the lack of variability in the timing of the highest seasonal peak in hospital admissions, syndromic surveillance data do not provide additional early warning of timing. However, during atypical seasons syndromic data did improve the accuracy of forecast intensity.

Machine learning to refine decision making within a syndromic surveillance service

Background

Worldwide, syndromic surveillance is increasingly used for improved and timely situational awareness and early identification of public health threats. Syndromic data streams are fed into detection algorithms, which produce statistical alarms highlighting potential activity of public health importance. All alarms must be assessed to confirm whether they are of public health importance. In England, approximately 100 alarms are generated daily and, although their analysis is formalised through a risk assessment process, the process requires notable time, training, and maintenance of an expertise base to determine which alarms are of public health importance. The process is made more complicated by the observation that only 0.1% of statistical alarms are deemed to be of public health importance. Therefore, the aims of this study were to evaluate machine learning as a tool for computer-assisted human decision-making when assessing statistical alarms.

Methods

A record of the risk assessment process was obtained from Public Health England for all 67,505 statistical alarms between August 2013 and October 2015. This record contained information on the characteristics of the alarm (e.g. size, location). We used three Bayesian classifiers- naïve Bayes, tree-augmented naïve Bayes and Multinets - to examine the risk assessment record in England with respect to the final ‘Decision’ outcome made by an epidemiologist of ‘Alert’, ‘Monitor’ or ‘No-action’. Two further classifications based upon tree-augmented naïve Bayes and Multinets were implemented to account for the predominance of ‘No-action’ outcomes.

Results

The attributes of each individual risk assessment were linked to the final decision made by an epidemiologist, providing confidence in the current process. The naïve Bayesian classifier performed best, correctly classifying 51.5% of ‘Alert’ outcomes. If the ‘Alert’ and ‘Monitor’ actions are combined then performance increases to 82.6% correctly classified. We demonstrate how a decision support system based upon a naïve Bayes classifier could be operationalised within an operational syndromic surveillance system.

Conclusions

Within syndromic surveillance systems, machine learning techniques have the potential to make risk assessment following statistical alarms more automated, robust, and rigorous. However, our results also highlight the importance of specialist human input to the process.

A Syndrome-Based Surveillance System for Infectious Diseases Among Asylum Seekers in Austrian Reception Centers, 2015-2018: Analysis of Reported Data

Background

Austria has been among the main European countries hosting incoming asylum seekers since 2015. Consequently, there was an urgent need to predict any public health threats associated with the arriving asylum seekers. The Department of Surveillance and Infectious Disease Epidemiology at the Austrian Agency for Health and Food Safety (AGES) was mandated to implement a national syndrome-based surveillance system in the 7 reception centers by the Austrian Ministry of Interior and Ministry of Health.

Objective

We aimed to analyze the occurrence and spread of infectious diseases among asylum seekers using data reported by reception centers through the syndrome-based surveillance system from September 2015 through February 2018.

Methods

We deployed a daily data collection system for 13 syndromes: rash with fever; rash without fever; acute upper respiratory tract infection; acute lower respiratory tract infection; meningitis or encephalitis; fever and bleeding; nonbloody gastroenteritis or watery diarrhea; bloody diarrhea; acute jaundice; skin, soft tissue, or bone abnormalities; acute flaccid paralysis; high fever with no other signs; and unexplained death. General practitioners, the first professionals to consult for health problems at reception centers in Austria, sent the tally sheets on identified syndromes daily to the AGES.

Results

We identified a total of 2914 cases, presenting 8 of the 13 syndromes. A total of 405 signals were triggered, and 6.4% (26/405) of them generated alerts. Suspected acute upper respiratory tract infection (1470/2914, 50.45% of cases), rash without fever (1174/2914, 40.29% of cases), suspected acute lower respiratory tract infection (159/2914, 5.46% of cases), watery diarrhea (73/2914, 2.51% of cases), and skin, soft tissue, or bone abnormalities (32/2914, 1.10% of cases) were the top 5 syndromes.

Conclusions

The cooperation of the AGES with reception center health care staff, supported by the 2 involved ministries, was shown to be useful for syndromic surveillance of infectious diseases among asylum seekers. None of the identified alerts escalated to an outbreak.

Estimating the burden on general practitioner services in England from increases in respiratory disease associated with seasonal respiratory pathogen activity

Understanding the burden of respiratory pathogens on health care is key to improving public health emergency response and interventions. In temperate regions, there is a large seasonal rise in influenza and other respiratory pathogens. We have examined the associations between individual pathogens and reported respiratory tract infections to estimate attributable burden. We used multiple linear regression to model the relationship between doctor consultation data and laboratory samples from week 3 2011 until week 37 2015. We fitted separate models for consultation data with in-hours and out-of-hours doctor services, stratified by different age bands. The best fitting all ages models (R2 > 80%) for consultation data resulted in the greatest burden being associated with influenza followed by respiratory syncytial virus (RSV). For models of adult age bands, there were significant associations between consultation data and invasive Streptococcus pneumoniae. There were also smaller numbers of consultations significantly associated with rhinovirus, parainfluenza, and human metapneumovirus. We estimate that a general practice with 10 000 patients would have seen an additional 18 respiratory tract infection consultations per winter week of which six had influenza and four had RSV. Our results are important for the planning of health care services to minimise the impact of winter pressures. •Respiratory pathogen incidence explains over 80% of seasonal variation in respiratory consultation data.•Influenza and RSV are associated with the biggest seasonal rises in respiratory consultation counts.•A third of consultation counts associated with respiratory pathogens were due to influenza.

Novel public health risk assessment process developed to support syndromic surveillance for the 2012 Olympic and Paralympic Games

Background

Syndromic surveillance aims to provide early warning and real time estimates of the extent of incidents; and reassurance about lack of impact of mass gatherings. We describe a novel public health risk assessment process to ensure those leading the response to the 2012 Olympic Games were alerted to unusual activity that was of potential public health importance, and not inundated with multiple statistical 'alarms'.

Methods

Statistical alarms were assessed to identify those which needed to result in 'alerts' as reliably as possible. There was no previously developed method for this. We identified factors that increased our concern about an alarm suggesting that an 'alert' should be made.

Results

Between 2 July and 12 September 2012, 350 674 signals were analysed resulting in 4118 statistical alarms. Using the risk assessment process, 122 'alerts' were communicated to Olympic incident directors.

Conclusions

Use of a novel risk assessment process enabled the interpretation of large number of statistical alarms in a manageable way for the period of a sustained mass gathering. This risk assessment process guided the prioritization and could be readily adapted to other surveillance systems. The process, which is novel to our knowledge, continues as a legacy of the Games.

A methodological framework for the evaluation of syndromic surveillance systems: a case study of England

BACKGROUND

Syndromic surveillance complements traditional public health surveillance by collecting and analysing health indicators in near real time. The rationale of syndromic surveillance is that it may detect health threats faster than traditional surveillance systems permitting more timely, and hence potentially more effective public health action. The effectiveness of syndromic surveillance largely relies on the methods used to detect aberrations. Very few studies have evaluated the performance of syndromic surveillance systems and consequently little is known about the types of events that such systems can and cannot detect.

METHODS

We introduce a framework for the evaluation of syndromic surveillance systems that can be used in any setting based upon the use of simulated scenarios. For a range of scenarios this allows the time and probability of detection to be determined and uncertainty is fully incorporated. In addition, we demonstrate how such a framework can model the benefits of increases in the number of centres reporting syndromic data and also determine the minimum size of outbreaks that can or cannot be detected. Here, we demonstrate its utility using simulations of national influenza outbreaks and localised outbreaks of cryptosporidiosis.

RESULTS

Influenza outbreaks are consistently detected with larger outbreaks being detected in a more timely manner. Small cryptosporidiosis outbreaks (<1000 symptomatic individuals) are unlikely to be detected. We also demonstrate the advantages of having multiple syndromic data streams (e.g. emergency attendance data, telephone helpline data, general practice consultation data) as different streams are able to detect different outbreak types with different efficacy (e.g. emergency attendance data are useful for the detection of pandemic influenza but not for outbreaks of cryptosporidiosis). We also highlight that for any one disease, the utility of data streams may vary geographically, and that the detection ability of syndromic surveillance varies seasonally (e.g. an influenza outbreak starting in July is detected sooner than one starting later in the year). We argue that our framework constitutes a useful tool for public health emergency preparedness in multiple settings.

CONCLUSIONS

The proposed framework allows the exhaustive evaluation of any syndromic surveillance system and constitutes a useful tool for emergency preparedness and response.

Socioeconomic and geographical variation in general practitioner consultations for allergic rhinitis in England, 2003-2014: an observational study

OBJECTIVE

Allergic rhinitis (AR) is a global health problem, potentially impacting individuals' sleep, work and social life. We aimed to use a surveillance network of general practitioners (GPs) to describe the epidemiology of AR consultations in England.

SETTING

A large GP surveillance network covering approximately 53% of the English population.

METHODS

GP consultations for AR across England between 30 December 2002 and 31 December 2014 were analysed. Using more granular data available between 2 April 2012 and 31 December 2014 rates and rate ratios (RR) of AR were further analysed in different age groups, gender, rural-urban classification and index of multiple deprivation score quintile of location of GP.

RESULTS

The mean weekly rate for AR consultations was 19.8 consultations per 100 000 GP registered patients (range 1.13-207), with a regular peak occurring during June (weeks 24-26), and a smaller peak during April. Between 1 April 2012 and 31 December 2014, the highest mean daily rates of consultations per 1 00 000 were: in age group 5-14 years (rate=8.02, RR 6.65, 95% CI 6.38 to 6.93); females (rate=4.57, RR 1.12 95% CI 1.12 to 1.13); persons registered at a GP in the most socioeconomically deprived quintile local authority (rate=5.69, RR 1.48, 95% CI 1.47 to 1.49) or in an urban area with major conurbation (rate=5.91, RR 1.78, 95% CI 1.69 to 1.87).

CONCLUSIONS

AR rates were higher in those aged 5-14 years, females and in urban and socioeconomically deprived areas. This needs to be viewed in the context of this study's limitations but should be considered in health promotion and service planning.

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

Introduction

The 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/Problem

Is an Ambulance Data Syndromic Surveillance System (ADSSS) feasible and of utility in enhancing the existing suite of PHE syndromic surveillance systems?

Methods

An 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.

Results

The 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.

Conclusions

Further 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.

Correcting for day of the week and public holiday effects: improving a national daily syndromic surveillance service for detecting public health threats

BACKGROUND

As service provision and patient behaviour varies by day, healthcare data used for public health surveillance can exhibit large day of the week effects. These regular effects are further complicated by the impact of public holidays. Real-time syndromic surveillance requires the daily analysis of a range of healthcare data sources, including family doctor consultations (called general practitioners, or GPs, in the UK). Failure to adjust for such reporting biases during analysis of syndromic GP surveillance data could lead to misinterpretations including false alarms or delays in the detection of outbreaks. The simplest smoothing method to remove a day of the week effect from daily time series data is a 7-day moving average. Public Health England developed the working day moving average in an attempt also to remove public holiday effects from daily GP data. However, neither of these methods adequately account for the combination of day of the week and public holiday effects.

METHODS

The extended working day moving average was developed. This is a further data-driven method for adding a smooth trend curve to a time series graph of daily healthcare data, that aims to take both public holiday and day of the week effects into account. It is based on the assumption that the number of people seeking healthcare services is a combination of illness levels/severity and the ability or desire of patients to seek healthcare each day. The extended working day moving average was compared to the seven-day and working day moving averages through application to data from two syndromic indicators from the GP in-hours syndromic surveillance system managed by Public Health England.

RESULTS

The extended working day moving average successfully smoothed the syndromic healthcare data by taking into account the combined day of the week and public holiday effects. In comparison, the seven-day and working day moving averages were unable to account for all these effects, which led to misleading smoothing curves.

CONCLUSIONS

The results from this study make it possible to identify trends and unusual activity in syndromic surveillance data from GP services in real-time independently of the effects caused by day of the week and public holidays, thereby improving the public health action resulting from the analysis of these data.

An Observational Study Using English Syndromic Surveillance Data Collected During the 2012 London Olympics - What did Syndromic Surveillance Show and What Can We Learn for Future Mass-gathering Events?

Introduction In preparation for the London 2012 Olympic Games, existing syndromic surveillance systems operating in England were expanded to include daily general practitioner (GP) out-of-hours (OOH) contacts and emergency department (ED) attendances at sentinel sites (the GP OOH and ED syndromic surveillance systems: GPOOHS and EDSSS). Hypothesis/Problem The further development of syndromic surveillance systems in time for the London 2012 Olympic Games provided a unique opportunity to investigate the impact of a large mass-gathering event on public health and health services as monitored in near real-time by syndromic surveillance of GP OOH contacts and ED attendances. This can, in turn, aid the planning of future events.

METHODS

The EDSSS and GPOOHS data for London and England from July 13 to August 26, 2012, and a similar period in 2013, were divided into three distinct time periods: pre-Olympic period (July 13-26, 2012); Olympic period (July 27 to August 12); and post-Olympic period (August 13-26, 2012). Time series of selected syndromic indicators in 2012 and 2013 were plotted, compared, and risk assessed by members of the Real-time Syndromic Surveillance Team (ReSST) in Public Health England (PHE). Student's t test was used to test any identified changes in pattern of attendance.

RESULTS

Very few differences were found between years or between the weeks which preceded and followed the Olympics. One significant exception was noted: a statistically significant increase (P value = .0003) in attendances for "chemicals, poisons, and overdoses, including alcohol" and "acute alcohol intoxication" were observed in London EDs coinciding with the timing of the Olympic opening ceremony (9:00 pm July 27, 2012 to 01:00 am July 28, 2012).

CONCLUSIONS

Syndromic surveillance was able to provide near to real-time monitoring and could identify hourly changes in patterns of presentation during the London 2012 Olympic Games. Reassurance can be provided to planners of future mass-gathering events that there was no discernible impact in overall attendances to sentinel EDs or GP OOH services in the host country. The increase in attendances for alcohol-related causes during the opening ceremony, however, may provide an opportunity for future public health interventions. Todkill D , Hughes HE , Elliot AJ , Morbey RA , Edeghere O , Harcourt S , Hughes T , Endericks T , McCloskey B , Catchpole M , Ibbotson S , Smith G . An observational study using English syndromic surveillance data collected during the 2012 London Olympics - what did syndromic surveillance show and what can we learn for future mass-gathering events? Prehosp Disaster Med. 2016;31(6):628-634.

SCM: a practical tool to implement hospital-based syndromic surveillance

BACKGROUND

Syndromic surveillance has been widely used for the early warning of infectious disease outbreaks, especially in mass gatherings, but the collection of electronic data on symptoms in hospitals is one of the fundamental challenges that must be overcome during operating a syndromic surveillance system. The objective of our study is to describe and evaluate the implementation of a symptom-clicking-module (SCM) as a part of the enhanced hospital-based syndromic surveillance during the 41st World Exposition in Shanghai, China, 2010.

METHODS

The SCM, including 25 targeted symptoms, was embedded in the sentinels' Hospital Information Systems (HIS). The clinicians used SCM to record these information of all the visiting patients, and data were collated and transmitted automatically in daily batches. The symptoms were categorized into seven targeted syndromes using pre-defined criteria, and statistical algorithms were applied to detect temporal aberrations in the data series.

RESULTS

SCM was deployed successfully in each sentinel hospital and was operated during the 184-day surveillance period. A total of 1,730,797 patient encounters were recorded by SCM, and 6.1 % (105,352 visits) met the criteria of the seven targeted syndromes. Acute respiratory and gastrointestinal syndromes were reported most frequently, accounted for 92.1 % of reports in all syndromes, and the aggregated time-series presented an obvious day-of-week variation over the study period. In total, 191 aberration signals were triggered, and none of them were identified as outbreaks after verification and field investigation.

CONCLUSIONS

SCM has acted as a practical tool for recording symptoms in the hospital-based enhanced syndromic surveillance system during the 41st World Exposition in Shanghai, in the context of without a preexisting electronic tool to collect syndromic data in the HIS of the sentinel hospitals.

The application of a novel 'rising activity, multi-level mixed effects, indicator emphasis' (RAMMIE) method for syndromic surveillance in England

MOTIVATION

Syndromic surveillance is the real-time collection and interpretation of data to allow the early identification of public health threats and their impact, enabling public health action. The 'rising activity, multi-level mixed effects, indicator emphasis' method was developed to provide a single robust method enabling detection of unusual activity across a wide range of syndromes, nationally and locally.

RESULTS

The method is shown here to have a high sensitivity (92%) and specificity (99%) compared to previous methods, whilst halving the time taken to detect increased activity to 1.3 days.

AVAILABILITY AND IMPLEMENTATION

The method has been applied successfully to syndromic surveillance systems in England providing realistic models for baseline activity and utilizing prioritization rules to ensure a manageable number of 'alarms' each day.

CONTACT

roger.morbey@phe.gov.uk.

Internet-based remote health self-checker symptom data as an adjuvant to a national syndromic surveillance system

Syndromic 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.

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.

London 2012 Olympic and Paralympic Games: public health surveillance and epidemiology

Mass gatherings are regarded as potential risks for transmission of infectious diseases, and might compromise the health system of countries in which they are hosted. The evidence for increased transmission of infectious diseases at international sporting mass gatherings that attract many visitors from all over the world is not clear, and the evidence base for public health surveillance, epidemiology, and response at events such as the Olympics is small. However, infectious diseases are a recognised risk, and public health planning is, and should remain, a crucial part of the overall planning of sporting events. In this Series paper, we set out the planning and the surveillance systems that were used to monitor public health risks during the London 2012 Olympic and Paralympic Games in the summer of 2012, and draw attention to the public health issues-infectious diseases and chemical, radiation, and environmental hazards-that arose. Although the absolute risk of health-protection problems, including infectious diseases, at sporting mass gatherings is small, the need for reassurance of the absence of problems is higher than has previously been considered; this could challenge conventional public health surveillance systems. Recognition of the limitations of health-surveillance systems needs to be part of the planning for future sporting events.

Report and session summary from the 18th World Congress on Disaster and Emergency Medicine

This section of Prehospital and Disaster Medicine (PDM) presents a report and summary of a session at the 18th World Congress on Disaster and Emergency Medicine (WCDEM) held in Manchester, UK in May of 2013. Additional reports and summaries were published in PDM (Volume 28, No. 6). Abstracts of Congress oral and poster presentations were published in May, 2013 as a supplement to PDM (Volume 28, Supplement 1).

Report and session summary from the 18th World Congress on Disaster and Emergency Medicine. Prehosp Disaster Med. 2014:29(1):1-3.

Impact of the London 2012 Olympic and Paralympic Games on demand for microbiology gastrointestinal diagnostic services at the Public Health Laboratory London

Planning for the London 2012 Olympic and Paralympic Games at the Public Health Laboratory London was based on the requirement to meet potential increased demand with scalable capacity. The aim of this study was to determine the impact on demand for microbiology gastrointestinal diagnostic services during the Games period. Retrospective cross-sectional time-series data analysis was used to assess the number of gastrointestinal specimens received in the laboratory and the number of positive results. There was no increase in the number of gastrointestinal specimens received during the Games period, thus the Games had no impact on demand for microbiology gastrointestinal diagnostic services at the laboratory. There was a decrease in the number of public health specimens received for culture [incidence rate ratio = 0.34, 95% confidence interval (CI) = 0.13-0.86, P = 0.02] and a decrease in the number of culture positive community specimens (odds ratio = 0.59, 95 % CI = 0.40-0.85, P = 0.005), suggesting a decrease in gastrointestinal illness during the Games period. As previous planning assumptions were not based on actual specimen activity, the results of this study may modify the extent of additional planning for microbiological services required for mass gatherings.

Using public health scenarios to predict the utility of a national syndromic surveillance programme during the 2012 London Olympic and Paralympic Games

During 2012 real-time syndromic surveillance formed a key part of the daily public health surveillance for the London Olympic and Paralympic Games. It was vital that these systems were evaluated prior to the Games; in particular what types and scales of incidents could and could not be detected. Different public health scenarios were created covering a range of potential incidents that the Health Protection Agency would require syndromic surveillance to rapidly detect and monitor. For the scenarios considered it is now possible to determine what is likely to be detectable and how incidents are likely to present using the different syndromic systems. Small localized incidents involving food poisoning are most likely to be detected the next day via emergency department surveillance, while a new strain of influenza is more likely to be detected via GP or telephone helpline surveillance, several weeks after the first seed case is introduced.

Development and refinement of new statistical methods for enhanced syndromic surveillance during the 2012 Olympic and Paralympic Games

Prior to the 2012 London Olympic and Paralympic Games, new statistical methods had to be developed for the enhanced syndromic surveillance during the Games. Different methods were developed depending on whether or not historical data were available. Practical solutions were needed to cope with the required daily reporting and data quality issues. During the Games, nearly 4800 signals were tested on average each day, generating statistical alarms that were assessed to provide information on areas of potential public health concern and reassurance that no major adverse incident had occurred.

GRAPHICAL ABSTRACT

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Microbiological aspects of public health planning and preparedness for the 2012 Olympic Games

Although communicable diseases have hitherto played a small part in illness associated with Olympic Games, an outbreak of infection in a national team, Games venue or visiting spectators has the potential to disrupt a global sporting event and distract from the international celebration of athletic excellence. Preparation for hosting the Olympic Games includes implementation of early warning systems for detecting emerging infection problems. Ensuring capability for rapid microbiological diagnoses to inform situational risk assessments underpins the ability to dispel rumours. These are a prelude to control measures to minimize impact of any outbreak of infectious disease at a time of intense public scrutiny. Complex multidisciplinary teamwork combined with laboratory technical innovation and efficient information flows underlie the Health Protection Agency's preparation for the London 2012 Olympic and Paralympic Games. These will deliver durable legacies for clinical and public health microbiology, outbreak investigation and control in the coming years.