Interpreting specific and general respiratory indicators in syndromic surveillance

ObjectiveTo improve understanding of the relative burden of differentcausative respiratory pathogens on respiratory syndromic indicatorsmonitored using syndromic surveillance systems in England.IntroductionPublic Health England (PHE) uses syndromic surveillance systemsto monitor for seasonal increases in respiratory illness. Respiratoryillnesses create a considerable burden on health care services andtherefore identifying the timing and intensity of peaks of activity isimportant for public health decision-making. Furthermore, identifyingthe incidence of specific respiratory pathogens circulating in thecommunity is essential for targeting public health interventionse.g. vaccination. Syndromic surveillance can provide early warningof increases, but cannot explicitly identify the pathogens responsiblefor such increases.PHE uses a range of general and specific respiratory syndromicindicators in their syndromic surveillance systems, e.g. “allrespiratory disease”, “influenza-like illness”, “bronchitis” and“cough”. Previous research has shown that “influenza-like illness”is associated with influenza circulating in the community1whilst“cough” and “bronchitis” syndromic indicators in children under 5are associated with respiratory syncytial virus (RSV)2, 3. However, therelative burden of other pathogens, e.g. rhinovirus and parainfluenzais less well understood. We have sought to further understand therelationship between specific pathogens and syndromic indicators andto improve estimates of disease burden. Therefore, we modelled theassociation between pathogen incidence, using laboratory reports andhealth care presentations, using syndromic data.MethodsWe used positive laboratory reports for the following pathogens as aproxy for community incidence in England: human metapneumovirus(HMPV), RSV, coronavirus, influenza strains, invasivehaemophilusinfluenzae, invasivestreptococcus pneumoniae, mycoplasmapneumoniae, parainfluenza and rhinovirus. Organisms were chosenthat were found to be important in previous work2and were availablefrom routine laboratory testing. Syndromic data included consultationswith family doctors (called General Practitioners or GPs), calls to anational telephone helpline “NHS 111” and attendances at emergencydepartments (EDs). Associations between laboratory reports andsyndromic data were examined over four winter seasons (weeks40 to 20), between 2011 and 2015. Multiple linear regression was usedto model correlations and to estimate the proportion of syndromicconsultations associated with specific pathogens. Finally, burdenestimates were used to infer the proportion of patients affected byspecific pathogens that would be diagnosed with different symptoms.ResultsInfluenza and RSV exhibited the greatest seasonal variation andwere responsible for the strongest associated burden on generalrespiratory infections. However, associations were found with theother pathogens and the burden ofstreptococcus pneumoniaewasimportant in adult age groups (25 years and over).The model estimates suggested that only a small proportion ofpatients with influenza receive a specific diagnosis that is coded toan “influenza-like illness” syndromic indicator, (6% for both GPin-hours consultations and for emergency department attendances),compared to a more general respiratory diagnosis. Also, patients withinfluenza calling NHS 111 were more likely to receive a diagnosisof fever or cough than cold/flu. Despite these findings, the specificsyndromic indicators remained more sensitive to changes in influenzaincidence than the general indicators.ConclusionsThe majority of patients affected by a seasonal respiratory pathogenare likely to receive a non-specific respiratory diagnosis. Therefore,estimates of community burden using more specific syndromicindicators such as “influenza-like illness” are likely to be a severeunderestimate. However, these specific indicators remain importantfor detecting changes in incidence and providing added intelligenceon likely causative pathogens.Specific syndromic indicators were associated with multiplepathogens and we were unable to identify indicators that were goodmarkers for pathogens other than influenza or RSV. However, futurework focusing on differences between ages and the relative levels ofa range of pathogens may be able to provide estimates for the mix ofpathogens present in the community in real-time

Developing a multidisciplinary syndromic surveillance academic research programme in the United Kingdom: benefits for public health surveillance

Syndromic surveillance is growing in stature internationally as a recognised and innovative approach to public health surveillance. Syndromic surveillance research uses data captured by syndromic surveillance systems to investigate specific hypotheses or questions. However, this research is often undertaken either within established public health organisations or the academic setting, but often not together. Public health organisations can provide access to health-related data and expertise in infectious and non-infectious disease epidemiology and clinical interpretation of data. Academic institutions can optimise methodological rigour, intellectual clarity and establish routes for applying to external research funding bodies to attract money to fund projects. Together, these competencies can complement each other to enhance the public health benefits of syndromic surveillance research. This paper describes the development of a multidisciplinary syndromic surveillance academic research programme in England, United Kingdom, its aims, goals and benefits to public health

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 67505 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

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 29th or 30th 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

Using emergency department syndromic surveillance to investigate the impact of a national vaccination program: A retrospective observational study

BackgroundRotavirus infection is a common cause of gastroenteritis in children worldwide, with a high mortality burden in developing countries, particularly during the first two years of life. Rotavirus vaccination was introduced into the United Kingdom childhood vaccination schedule in July 2013, with high coverage (>90%) achieved by June 2016. We used an emergency department (ED) syndromic surveillance system to assess the impact of the rotavirus vaccination programme, specifically through the demonstration of any immediate and continuing impact on ED gastroenteritis visits in England.MethodsThis retrospective, observational study used syndromic surveillance data collected from 3 EDs in the two years before (July 2011-June 2013) and 3 years post (July 2013-June 2016) introduction of rotavirus vaccination. The weekly levels of ED visits for gastroenteritis (by age group and in total) during the period before rotavirus vaccination was first described alongside the findings of laboratory surveillance of rotavirus during the same period. An interrupted time-series analysis was then performed to demonstrate the impact of rotavirus vaccination introduction on gastroenteritis ED visit levels.ResultsDuring the two years before vaccine introduction ED visits for gastroenteritis in total and for the 0-4 years age group were seen to rise and fall in line with the seasonal rotavirus increases reported by laboratory surveillance. ED gastroenteritis visits by young children were lower in the three years following introduction of rotavirus vaccination (reduced from 8% of visits to 6% of visits). These attendance levels in young children (0-4years) remained higher than in older age groups, however the previously large seasonal increases in children were greatly reduced, from peaks of 16% to 3-10% of ED visits per week.ConclusionsED syndromic surveillance demonstrated a reduction in gastroenteritis visits following rotavirus vaccine introduction. This work establishes ED syndromic surveillance as a platform for rapid impact assessment of future vaccine programmes

Retrospective Observational Study of Atypical Winter Respiratory Illness Season Using Real-Time Syndromic Surveillance, England, 2014-15

During winter 2014–15, England experienced severe strains on acute health services. We investigated whether syndromic surveillance could contribute to understanding of the unusually high level of healthcare needs. We compared trends for several respiratory syndromic indicators from that winter to historical baselines. Cumulative and mean incidence rates were compared by winter and age group. All-age influenza-like illness was at expected levels; however, severe asthma and pneumonia levels were above those expected. Across several respiratory indicators, cumulative incidence rates during 2014–15 were similar to those of previous years, but higher for older persons; we saw increased rates of acute respiratory disease, including influenza like illness, severe asthma, and pneumonia, in the 65–74- and >75-year age groups. Age group–specific statistical algorithms may provide insights into the burden on health services and improve early warning in future winters

The influence of a major sporting event upon emergency department attendances; A retrospective cross-national European study

Major sporting events may influence attendance levels at hospital emergency departments (ED). Previous research has focussed on the impact of single games, or wins/losses for specific teams/countries, limiting wider generalisations. Here we explore the impact of the Euro 2016 football championships on ED attendances across four participating nations (England, France, Northern Ireland, Wales), using a single methodology. Match days were found to have no significant impact upon daily ED attendances levels. Focussing upon hourly attendances, ED attendances across all countries in the four hour pre-match period were statistically significantly lower than would be expected (OR 0.97, 95% CI 0.94–0.99) and further reduced during matches (OR 0.94, 95% CI 0.91–0.97). In the 4 hour post-match period there was no significant increase in attendances (OR 1.01, 95% CI 0.99–1.04). However, these impacts were highly variable between individual matches: for example in the 4 hour period following the final, involving France, the number of ED attendances in France increased significantly (OR 1.27, 95% CI 1.13–1.42). Overall our results indicate relatively small impacts of major sporting events upon ED attendances. The heterogeneity observed makes it difficult for health providers to predict how major sporting events may affect ED attendances but supports the future development of compatible systems in different countries to support cross-border public health surveillance