Syndromic Surveillance System Research Articles

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.

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.

Evaluation of an ad hoc paper-based syndromic surveillance system in Ibaraki evacuation centres following the 2011 Great East Japan Earthquake and Tsunami.

Outbreaks of infectious diseases can occur after natural disasters as vital services are disrupted and populations move into evacuation centres. National notifiable disease surveillance may be inadequate in these situations because of resource-consuming disease confirmation or system interruptions. Although syndromic surveillance has been used as an alternative in post-disaster situations, no systematic evaluations of it have been published. We evaluated the ad hoc paper-based syndromic surveillance system implemented in evacuation centres in Ibaraki prefecture after the 2011 Great East Japan Earthquake and Tsunami. We assessed the simplicity, acceptability, data quality, timeliness and portability of this system and reviewed its usefulness. We concluded that the system was simple, acceptable, portable and useful. The documentation and monitoring of disease events and trends were useful for developing interventions in evacuation centres and have since been used to improve post-disaster infectious disease and surveillance knowledge in Japan. We believe timeliness was a challenge due to the chain of data transmission and communication passing through an intermediary. Future implementations of this system could consider a more direct chain of data transmission and communication from collectors to analysers. Too few key informant interviewees and the inability to obtain original paper-based data from evacuation centres limited our findings; we conducted this evaluation four years after the response occurred. Future evaluations should be completed closer to when operations cease. The usefulness of the system suggests adopting it in future disasters. A simple, plain-language manual should be developed to improve future employment.

Evaluation of the French reactive mortality surveillance system supporting decision making.

In France, a mortality syndromic surveillance system was set up with objectives of early detection and reactive evaluation of the impact of expected and unexpected events to support decision makers. This study aims to describe the characteristics of the system and its usefulness for decision makers. Anonymized data from the administrative part of death certificates were daily collected from 3062 computerized city halls and were transmitted to Santé publique France in routine. Coverage of the system was measured as the proportion of deaths registered by the system among the complete number of deaths and analyzed by age, month and region. Deaths were described by gender, age and geographical level using proportion. The excess periods of deaths were described based on the comparison of the weekly observed and expected numbers of deaths between 2012 and 2016. The system recorded 77.5% of the national mortality covering the whole territory. About 81% of deaths were aged 65 years old and more. The surveillance system identified mortality variations mainly during winter and summer, for some concomitant with influenza epidemic or heatwave period, and thus provided information for decision makers. The ability of the system to detect and follow mortality outbreaks in routine in the whole territory has been demonstrated. It is a useful tool to provide early evaluation of the impact of threats on mortality and alert decision makers to adapt control measures. However, the absence of information on medical causes of death may limit the ability to target recommendations.

Issues in Building a Nursing Home Syndromic Surveillance System with Textmining: Longitudinal Observational Study.

BackgroundNew nursing homes (NH) data warehouses fed from residents’ medical records allow monitoring the health of elderly population on a daily basis. Elsewhere, syndromic surveillance has already shown that professional data can be used for public health (PH) surveillance but not during a long-term follow-up of the same cohort.ObjectiveThis study aimed to build and assess a national ecological NH PH surveillance system (SS).MethodsUsing a national network of 126 NH, we built a residents’ cohort, extracted medical and personal data from their electronic health records, and transmitted them through the internet to a national server almost in real time. After recording sociodemographic, autonomic and syndromic information, a set of 26 syndromes was defined using pattern matching with the standard query language-LIKE operator and a Delphi-like technique, between November 2010 and June 2016. We used early aberration reporting system (EARS) and Bayes surveillance algorithms of the R surveillance package (Höhle) to assess our influenza and acute gastroenteritis (AGE) syndromic data against the Sentinelles network data, French epidemics gold standard, following Centers for Disease Control and Prevention surveillance system assessment guidelines.ResultsBy extracting all sociodemographic residents’ data, a cohort of 41,061 senior citizens was built. EARS_C3 algorithm on NH influenza and AGE syndromic data gave sensitivities of 0.482 and 0.539 and specificities of 0.844 and 0.952, respectively, over a 6-year period, forecasting the last influenza outbreak by catching early flu signals. In addition, assessment of influenza and AGE syndromic data quality showed precisions of 0.98 and 0.96 during last season epidemic weeks’ peaks (weeks 03-2017 and 01-2017) and precisions of 0.95 and 0.92 during last summer epidemic weeks’ low (week 33-2016).ConclusionsThis study confirmed that using syndromic information gives a good opportunity to develop a genuine French national PH SS dedicated to senior citizens. Access to senior citizens’ free-text validated health data on influenza and AGE responds to a PH issue for the surveillance of this fragile population. This database will also make possible new ecological research on other subjects that will improve prevention, care, and rapid response when facing health threats.

Epidemic surveillance in a low resource setting: lessons from an evaluation of the Solomon Islands syndromic surveillance system, 2017

BackgroundSolomon Islands is one of the least developed countries in the world. Recognising that timely detection of outbreaks is needed to enable early and effective response to disease outbreaks, the Solomon Islands government introduced a simple syndromic surveillance system in 2011. We conducted the first evaluation of the system and the first exploration of a national experience within the broader multi-country Pacific Syndromic Surveillance System to determine if it is meeting its objectives and to identify opportunities for improvement.MethodsWe used a multi-method approach involving retrospective data collection and statistical analysis, modelling, qualitative research and observational methods.ResultsWe found that the system was well accepted, highly relied upon and designed to account for contextual limitations. We found the syndromic algorithm used to identify outbreaks was moderately sensitive, detecting 11.8% (IQR: 6.3–25.0%), 21.3% (IQR: 10.3–36.8%), 27.5% (IQR: 12.8–52.3%) and 40.5% (IQR: 13.5–65.7%) of outbreaks that caused small, moderate, large and very large increases in case presentations to health facilities, respectively. The false alert rate was 10.8% (IQR: 4.8–24.5%). Rural coverage of the system was poor. Limited workforce, surveillance resourcing and other ‘upstream’ health system factors constrained performance.ConclusionsThe system has made a significant contribution to public health security in Solomon Islands, but remains insufficiently sensitive to detect small-moderate sized outbreaks and hence should not be relied upon as a stand-alone surveillance strategy. Rather, the system should sit within a complementary suite of early warning surveillance activities including event-based, in-patient- and laboratory-based surveillance methods. Future investments need to find a balance between actions to address the technical and systems issues that constrain performance while maintaining simplicity and hence sustainability.

430. The First Case Reports of Leptospirosis and Melioidosis in the Aftermath of Hurricanes Irma and Maria—Saint Thomas/Saint John District, US Virgin Islands, September–October 2017

BackgroundFollowing Hurricanes Irma and Maria, the first cases of leptospirosis (n = 1) and melioidosis (n = 2) were identified in the US Virgin Islands (USVI). Leptospirosis and melioidosis are potentially fatal bacterial diseases caused by Leptospira species and Burkholderia pseudomallei, respectively; both are found in contaminated water/soil and outbreaks have been documented following extreme weather events.MethodsQuerying the USVI arbovirus syndromic surveillance system and Emergency Department (ED) records from two hospitals, we identified patients in the 2.5 months post-hurricanes demonstrating symptoms consistent with leptospirosis/melioidosis. Available patient blood samples underwent rapid diagnostic testing (RDT) for anti-Leptospira IgM and were sent to the US Centers for Disease Control and Prevention for confirmatory microscopic agglutination testing (MAT). A subset were tested with a B. pseudomallei antigen RDT, and water collected from sites of potential Leptospira-contamination were tested by PCR.ResultsAn initial query of the syndromic surveillance database yielded 17 patients warranting testing; 15 available patient samples were tested for leptospirosis and were negative (2 tested for melioidosis—negative). Following efforts to enhance this system to proactively detect leptospirosis/melioidosis, 15 additional patient samples were obtained and tested for leptospirosis; one tested positive. We reviewed 5,220 ED charts, identifying six patients warranting testing; five available patient samples were tested for leptospirosis; one tested positive (1 tested for melioidosis—negative). Altogether, as of April 2018, there are threeleptospirosis and two melioidosis confirmed cases in USVI. One of threewater samples collected from sites associated with patients with leptospirosis tested PCR-positive for Leptospira species.ConclusionThis investigation documents the first cases of leptospirosis and melioidosis in USVI and demonstrates how USVI’s surveillance system was adapted to establish ongoing leptospirosis/melioidosis surveillance. Collectively, although not confirmed by detection of B. pseudomallei in the environment, both leptospirosis and melioidosis may be endemic in the USVI.Disclosures All authors: No reported disclosures.

Bioinformatics Tools in Clinical Microbiology and Infectious Disease Prevention Algorithms

Bioinformatics resource is the exploitation of genome sequence data for diagnostic, therapeutic, and preventionpurposes. The role of clinical genomics in infectious disease is aimed at describing the structure of outbreakcommunities, the methodology works equally well to identify pathogens in clinical samples. Furthermore, early detectionof infectious disease in outbreaks is one of the significant issues in syndromic surveillance systems. The key concept inusing clinical genomics methodology is a process of generating large-scale sequence data sets that adequately samplea specimen for microbial content and then of applying computational methods to resolve the sequences into individualspecies, genes, pathways, or other features. It helps to provide a rapid epidemiological response and reduce morbidityand mortality. Therefore, bioinformatics model algorithms for virus transmission and prevention in terms of resistomewould be taken into consideration. The relevant future study of resistome reveals strategies that can be applied in newantibiotic discoveries.

Surveillance on speed: Being aware of infectious diseases in migrants mass accommodations - an easy and flexible toolkit for field application of syndromic surveillance, Germany, 2016 to 2017.

Europe received an increased number of migrants in 2015. Housing in inadequate mass accommodations (MA) made migrants prone to infectious disease outbreaks. In order to enhance awareness for infectious diseases (ID) and to detect clusters early, we developed and evaluated a syndromic surveillance system in three MA with medical centres in Berlin, Germany. Healthcare workers transferred daily data on 14 syndromes to the German public health institute (Robert Koch-Institute). Clusters of ID syndromes and single cases of outbreak-prone diseases produced a signal according to a simple aberration-detection algorithm that computes a statistical threshold above which a case count is considered unusually high. Between May 2016–April 2017, 9,364 syndromes were reported; 2,717 (29%) were ID, of those 2,017 (74%) were respiratory infections, 262 (10%) skin parasites, 181 (7%) gastrointestinal infections. The system produced 204 signals, no major outbreak was detected. The surveillance reinforced awareness for public health aspects of ID. It provided real-time data on migrants' health and stressed the burden of non-communicable diseases. The tool is available online and was evaluated as being feasible and flexible. It complements traditional notification systems. We recommend its usage especially when laboratory testing is not available and real-time data are needed.

Social patterning of telephone health-advice for diarrhoea and vomiting: analysis of 24 million telehealth calls in England

Gastrointestinal (GI) infections are common and most people do not see a physician. There is conflicting evidence of the impact of socioeconomic status (SES) on risk of GI infections. We assessed the relationship between SES and GI calls to two National Health Service (NHS) telephone advice services in England. Over 24 million calls to NHS Direct (2010-13) and NHS 111 (2013-15) were extracted from Public Health England (PHE) syndromic surveillance systems. The relationship between SES and GI calls was assessed using generalised linear models (GLM). Adjusting for rurality and age-sex interactions, in NHS Direct, children in disadvantaged areas were at lower risk of GI calls; in NHS 111 there was a higher risk of GI calls in disadvantaged areas for all ages (0-4 years RR 1.27, 95% CI 1.25-1.29; 5-9 years RR 1.43, 95% CI 1.36-1.51; 10-14 years RR 1.36, 95% CI 1.26-1.41; 15-19 years RR 1.59, 95% CI 1.52-1.67; 20-59 years RR 1.50, 95% CI 1.47-1.53, 60 years and over RR 1.12, 95% CI 1.09-1.14). Disadvantaged areas had higher risk of GI calls in NHS 111. This may relate to differences in exposure or vulnerability to GI infections, or propensity to call about GI infections.

Nipah virus infection: a deadly disease emerging in India

Nipah virus infection is an emerging zoonotic disease caused by a virus called Nipah virus (NiV). First outbreak was occurred among pig farmers in Malaysia and Singapore in the year 1998-99 with 30% case fatality rate. In India, two outbreaks of Nipah virus have been reported in the eastern state of West Bengal in the years 2001 and 2007 where nearly 70% deaths were occurred. Recently, third outbreak of Nipah virus infection has been reported in India from two districts Kozhikode and Mallapuram of Kerala on 19 May 2018 and 17 patients were died from 19 reported cases (18 laboratory confirmed cases) from these two affected districts. Till now, no reasons of the outbreak in Kerala have been identified and situation was controlled immediately because of taking stringent containment and preventive measures like enhancement of acute fever and acute encephalitis syndrome surveillance system and strengthening of Hospital and community surveillance. There is no effective treatment or vaccine available that’s why the primary focus should be on the prevention like avoidance of drinking of raw palm sap (palm toddy) contaminated by bat excrete, don’t consumed partially eaten fruits by bats and water from wells infested by bats and avoid exposure to bats in endemic areas and sick pigs. The WHO suggests that health care professionals should wear gloves and other protective clothing during any pig slaughtering and culling procedures.

Choosing the best algorithm for event detection based on the intended application: A conceptual framework for syndromic surveillance.

There is an extensive list of methods available for the early detection of an epidemic signal in syndromic surveillance data. However, there is no commonly accepted classification system for the statistical methods used for event detection in syndromic surveillance. Comparing and choosing appropriate event detection algorithms is an increasingly challenging task. Although lists of selection criteria, and statistical methods used for signal detection have been reported, selection criteria are rarely linked to a specific set of appropriate statistical methods. The paper presents a practical approach for guiding surveillance practitioners to make an informed choice from among the most popular event detection algorithms based on the intended application of the algorithm. We developed selection criteria by mapping the assumptions and performance characteristics of event detection algorithms directly to important characteristics of the time series used in syndromic surveillance. We also considered types of epidemics that may be expected and other characteristics of the surveillance system. These guidelines will provide decisions makers, data analysts, public health practitioners, and researchers with a comprehensive but practical overview of the domain, which may reduce the technical barriers to the development and implementation of syndromic surveillance systems in animal and human health. The classification scheme was restricted to univariate and temporal methods because they are the most commonly used algorithms in syndromic surveillance.

Early assessment of public health impacts of major floods in France in June 2016 and winter 2018

Introduction The Seine River rises at the northeast of France and flows through Paris before emptying into the English Channel. On June 2016 (week 22) and January 2018 (weeks 4 to 6), major floods occurred in the Basin of Seine River, after important rainy periods. The second period was also marked by the occurrence on the same area of a cold wave including heavy snowfall and ice conditions on week 6. Floods are known to have potential health impacts on population living in those regions, both at short-, medium- and long-term both on physical and mental health. The objective of the study is to present the results of the daily monitoring of health indicators conducted by the French public health agency (SpFrance) during the two major floods, in order to early identify potential impact of those disasters on the population. Methods Since 2004, SpFrance set up a national syndromic surveillance system SurSaUD, enabling to ensure morbidity and mortality surveillance. Morbidity data are daily collected from a network involving emergency departments (ED) and emergency general practitioners’ associations SOS Medecins. Both administrative (age, gender, date and location of consultation) and medical information (medical diagnosis using ICD10 codes in ED and specific thesauri in SOS Medecins associations) are recorded for each patient. The daily and weekly evolution of the number of all-cause ED attendances and SOS Medecins consultations during the flood periods were compared to the evolution on the two previous years. The number of hospitalisations after ED discharge was also monitored. The immediate health impact of floods was assessed by monitoring eight syndromic indicators related to flood exposure: gastroenteritis, carbon monoxide poisoning, burnt, stress, faintness, drowning, injuries and hypothermia. Analyses were performed by age group and at different geographical levels (national, Paris region and districts located in the Basin of Seine River). Results During week 22-2016, a decrease of total number of ED attendances all ages and more specifically for children aged less than 15 years old was observed in region Centre (−15% compared with weeks 21 and 23) and to a lesser extent in Paris area (−4% and −12% respectively compared with week 21 and week 23). Locally, in the most impacted cities, an increase of total ED attendances was also observed for people aged 65 years and more. At the opposite, the number of all-cause SOS Medecins consultations remained stable in all age groups. Analysis by syndromic indicator did not show unusual variations during the first flood period. In 2018, syndromic surveillance did not show any major impact on all-cause ED attendances and SOS Medecins consultations from week 4 to week 6, neither in Paris area nor in other areas along the Seine River. The numbers were comparable to the two precedent years in all age groups. During week 6 in Paris area, an increase of ED attendances was observed for injuries (+4% compared to the past weeks) and to a lesser extent for hypothermia (16 attendances compared to less than 9 for the past weeks). Conclusion For both flood episodes the rising water level was slow with foreseeable evolution, compared to other sudden flood events occurring in south of France in 2010 due to violent thunderstorms. This progressive evolution allows French authority to deploy wide specific organization in order to mitigate impact on concerned populations. That may explain the limited impact observed during the two flood disasters. The evolution of injuries during 2018 episode is attributable to the cold wave that occurred simultaneously. As the French syndromic surveillance system is implemented on the whole territory and collects emergency data routinely since several years, it constitutes a reactive tool to assess the potential public health impact of both sudden and predictable disasters. It can either contribute to adapt management action or reassure decision makers if no major impact is observed.

A reactive mortality surveillance system in France for alert and decision-making

Background In France, after the 2003 heat wave, a mortality syndromic surveillance system was set up by the National Public Health Agency, with objectives of early detection and reactive evaluation of the impact of expected and unexpected events to support decision makers. This study aims to describe the characteristics of the system and the main excess mortality period detected by the system since 2011 that led to alert decision makers for actions. Methods Anonymized data from the administrative part of death certificate were daily collected from 3042 computerized city halls and were transmitted to the agency in routine. Representativeness was measured as the proportion of death registered by the system among the exhaustive number of deaths and analyzed by age, month and region. Deaths were described by gender, age group at national and regional levels using proportion. Mortality surveillance is performed routinely based on the comparison of the weekly observed and expected number of deaths estimated with the model developed by the European consortium EuroMomo between 2012 and 2016. Based on criteria, when an excess of death is observed, decision makers are alerted and an investigation is launched. Results The system recorded 77.5% of the national mortality covering the whole territory and varying from 67% to 96.8% in metropolitan regions and overseas. Three major excess deaths episodes occurred during winters 2011–12, 2012–13 and 2014–15 and were highly correlated with flu epidemics periods. Five punctual excess periods were identified during the summer months, among them 4 were concomitant with a heat wave or elevated temperature period. No particular event was found correlated to the fifth excess. The system was also able to detect events such as an increase in mortality in Guadeloupe in 2014 concomitant with chikungunya epidemics. National and regional epidemiological mortality reports were published weekly (daily if required) and transmitted to the Ministry of Health and regional health authorities with potential recommendations. Conclusion The effectiveness of the reactive mortality surveillance system to alert decision makers has been demonstrated. It is useful to adapt along the event recommendation for control measures as well as evaluation of impact of the event on mortality. However, the absence of medical causes of death may limit the ability to target recommendations. Facing the challenge of emergence of epidemics and climate changes, this reactive mortality system will be complementary for other surveillance systems for the future health crisis. The development of mortality surveillance with causes of death will ensure more information for interpretation of all-cause mortality changes.

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.

Chikungunya outbreak (2017) in Bangladesh: Clinical profile, economic impact and quality of life during the acute phase of the disease.

BackgroundChikungunya virus causes mosquito-transmitted infection that leads to extensive morbidity affecting substantial quality of life. Disease associated morbidity, quality of life, and financial loss are seldom reported in resources limited countries, such as Bangladesh. We reported the acute clinical profile, quality of life and consequent economic burden of the affected individuals in the recent chikungunya outbreak (May to September 2017) in Dhaka city, Bangladesh.MethodsWe conducted a cross-sectional study during the peak of chikungunya outbreak (July 24 to August 5, 2017) to document the clinical profiles of confirmed cases (laboratory test positive) and probable cases diagnosed by medical practitioners. Data related to clinical symptoms, treatment cost, loss of productivity due to missing work days, and quality of life during their first two-weeks of symptom onset were collected via face to face interview using a structured questionnaire. World Health Organization endorsed questionnaire was used to assess the quality of life.ResultsA total of 1,326 chikungunya cases were investigated. Multivariate analysis of major clinical variables showed no statistically significant differences between confirmed and probable cases. All the patients reported joint pain and fever. Other more frequently reported symptoms include headache, loss of appetite, rash, myalgia, and itching. Arthralgia was polyarticular in 56.3% of the patients. Notably, more than 70% patients reported joint pain as the first presenting symptom. About 83% of the patients reported low to very low overall quality of life. Nearly 30% of the patients lost more than 10 days of productivity due to severe arthropathy.ConclusionsThis study represents one of the largest samples studied so far around the world describing the clinical profile of chikungunya infection. Our findings would contribute to establish an effective syndromic surveillance system for early detection and timely public health intervention of future chikungunya outbreaks in resource-limited settings like Bangladesh.

BLOOD PRESSURE CHANGES IN AFRICAN MALES DURING APPLICATION FOR ASYLUM STATUS IN ITALY

Objective: The Central Mediterranean route from Libya to Italy has become the major port of entry to Europe for migrants and refugees. In Italy few studies analysed health of asylum seekers, being mainly focused on infectious diseases. The aim of the present study was to explore cardiovascular risk factors of young asylum seekers during their first phase of residence in Italy. Design and method: Cross-sectional study performed on male African migrants, aged 18–35 years, during the period of application for asylum status in Prato, Italy. Subjects were classified in two groups according to their months of stay in Italy. Variables (Table 1) were measured with validated instruments according to guidelines. The relationship with months of stay in Italy (independent variable) was investigated with stepwise linear regression. Results: Subjects characteristics are reported (Table 1). After 6 months, 11 subjects had impaired fasting glucose (FG > 100 mg/dL) (3 in the < 6mo Group) (RR 3.4; 95%CL 1.0 to 11.5). The significant increase of systolic BP (months as the independent variable) was selected at stepwise multivariable adjusted linear regression (B 0.154;95%Cl 0.003 to 0.305, p = 0.046). https://services.aimgroup.eu/ASPClient/files/3647/Abstract/584_20180112140808.jpgConclusions: The syndromic surveillance system now active in immigration centres in Italy is mainly focused on infectious diseases. When considering the importance of hypertension for African descendent living in Europe, changes of CV risk factors in the new environment probably need more attention.

Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification

Interest in real-time syndromic surveillance based on social media data has greatly increased in recent years. The ability to detect disease outbreaks earlier than traditional methods would be highly useful for public health officials. This paper describes a software system which is built upon recent developments in machine learning and data processing to achieve this goal. The system is built from reusable modules integrated into data processing pipelines that are easily deployable and configurable. It applies deep learning to the problem of classifying health-related tweets and is able to do so with high accuracy. It has the capability to detect illness outbreaks from Twitter data and then to build up and display information about these outbreaks, including relevant news articles, to provide situational awareness. It also provides nowcasting functionality of current disease levels from previous clinical data combined with Twitter data.The preliminary results are promising, with the system being able to detect outbreaks of influenza-like illness symptoms which could then be confirmed by existing official sources. The Nowcasting module shows that using social media data can improve prediction for multiple diseases over simply using traditional data sources.

Infectious diseases and their ICD coding : What could be improved by the introduction of ICD-11?

ZusammenfassungDie Revision der Internationalen statistischen Klassifikation der Krankheiten und verwandter Gesundheitsprobleme (International Classification of Diseases – ICD) geht mit grundlegenden Änderungen der Morbiditäts- und Mortalitätsstatistik einher, die auch den Bereich der Infektionskrankheiten betreffen. Die Zuordnung der einzelnen Infektionskrankheiten zu den Kapiteln in der aktuellen ICD-10 erfolgt aufgrund unterschiedlicher Konzepte, teilweise nach auslösendem Agens, nach betroffenem Organsystem oder nach Lebensperiode. Besondere Herausforderungen der Klassifizierung der Infektionskrankheiten bestehen u. a. darin, dass regelmäßig ein Anpassungsbedarf durch neu auftretende Erreger entstehen kann. Außerdem reichen die Angaben hinsichtlich Umfang und Tiefe in der ICD-10 teilweise nicht aus, um epidemiologische Auswertungen der Daten durchzuführen.Die ICD ermöglicht den weltweiten Vergleich von Statistiken zu Infektionskrankheiten. Zunehmend wird die ICD jedoch auch für die Erhebung von Surveillance- und Forschungsdaten eingesetzt, z. B. im Rahmen des Meldewesens (Identifizierung von Meldetatbeständen), aber auch in der syndromischen Surveillance akuter Atemwegsinfektionen und für den Aufbau neuer Surveillance-Systeme sowie der Evaluation der Datenqualität durch Abgleich mit Sekundärdaten.Die Chancen der ICD-11 liegen vor allem darin, dass Infektionskrankheiten eindeutiger codiert werden können und ihre Codierung mehr relevante Informationen für die epidemiologische Bewertung enthält. Durch die hohe Komplexität können jedoch Verzerrungen in den Daten entstehen, die die Fortschreibung der Morbiditäts- und Mortalitätsstatistiken erschweren.

Using real-time syndromic surveillance to monitor the health effects of air pollution

ObjectiveTo explore the utility of syndromic surveillance systems for detecting and monitoring the impact of air pollution incidents on health-care seeking behaviour in England between 2012 and 2017.IntroductionThe negative effect of air pollution on human health is well documented illustrating increased risk of respiratory, cardiac and other health conditions. [1] Currently, during air pollution episodes Public Health England (PHE) syndromic surveillance systems [2] provide a near real-time analysis of the health impact of poor air quality. In England, syndromic surveillance has previously been used on an ad hoc basis to monitor health impact; this has usually happened during widespread national air pollution episodes where the air pollution index has reached ‘High’ or ‘Very High’ levels on the UK Daily Air Quality Index (DAQI). [3-5]We now aim to undertake a more systematic approach to understanding the utility of syndromic surveillance for monitoring the health impact of air pollution. This would improve our understanding of the sensitivity and specificity of syndromic surveillance systems for contributing to the public health response to acute air pollution incidents; form a baseline for future interventions; assess whether syndromic surveillance systems provide a useful tool for public health alerting; enable us to explore which pollutants drive changes in health-care seeking behaviour; and add to the knowledge base.MethodsThe systematic approach will involve accessing historical data for air pollution incidents and syndromic surveillance data over the period 2012-17 across England. We will use PM10, PM2.5, ozone, NO2 , SO2 and DAQI data to define air pollution periods, and historical syndromic surveillance system data for respiratory syndromes (asthma, difficulty breathing, wheeze, cough, bronchitis, sore throat and allergic rhinitis), cardiac (all cardiovascular and myocardial infarction) and eye irritation/conjunctivitis syndromes. We will use regression modelling and cross-correlation analyses to determine the effects of air pollution, weather and pollen upon these syndromes and thus provide evidence of the sensitivity of these systems. Historical data on additional environmental variables including temperature and precipitation, humidity and thunderstorm activity, pollen and fungal spores will be accounted for in the regression models, as well as data on influenza and respiratory syncytial virus (RSV) laboratory reports. We will include sub-national geographies and age/gender analyses in the study depending on the data availability and suitability.ResultsInitial results presented will include the preliminary descriptive epidemiology with a focus on asthma and the impact of air pollution incidents on health-care seeking behaviour using data from the PHE national syndromic surveillance systems.ConclusionsWe aim to demonstrate an innovative use of syndromic surveillance data to explore the impact of air pollution incidents on health-care seeking behaviour in England, in turn improving our understanding of the sensitivity and specificity of these systems for detecting the impact of air pollution incidents and to contribute to the knowledge base. This understanding will improve the public health response to future incidents.