Retrospective Observational Study of Atypical Winter Respiratory Illness Season Using Real-Time Syndromic Surveillance, England, 2014-15.
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SUBMITTER: Smith S
PROVIDER: S-EPMC5652417 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC7010388 | biostudies-literature
Project description:The 2002 Olympic Winter Games were held in Utah from February 8 to March 16, 2002. Following the terrorist attacks on September 11, 2001, and the anthrax release in October 2001, the need for bioterrorism surveillance during the Games was paramount. A team of informaticists and public health specialists from Utah and Pittsburgh implemented the Real-time Outbreak and Disease Surveillance (RODS) system in Utah for the Games in just seven weeks. The strategies and challenges of implementing such a system in such a short time are discussed. The motivation and cooperation inspired by the 2002 Olympic Winter Games were a powerful driver in overcoming the organizational issues. Over 114,000 acute care encounters were monitored between February 8 and March 31, 2002. No outbreaks of public health significance were detected. The system was implemented successfully and operational for the 2002 Olympic Winter Games and remains operational today.
| S-EPMC264432 | biostudies-literature
Project description:Thunderstorm asthma is often characterised by a sudden surge in patients presenting with exacerbated symptoms of asthma linked to thunderstorm activity. Here, we describe a large spike in asthma and difficulty breathing symptoms observed across parts of England on 17 June 2021. The number of healthcare presentations during the asthma event was compared to expected levels for the overall population and across specific regions. Across affected geographical areas, emergency department attendances for asthma increased by 560% on 17 June compared to the average number of weekday daily attendances during the previous 4 weeks. General practitioner out of hours contacts increased by 349%, National Health Service (NHS) 111 calls 193%, NHS 111 online assessments 581% and ambulance call outs 54%. Increases were particularly noted in patient age groups 5-14 and 15-44 years. In non-affected regions, increases were small (<10%) or decreased, except for NHS 111 online assessments where there was an increase of 39%. A review of the meteorological conditions showed several localised, weak, or moderate thunderstorms specifically across parts of Southeast England on the night of June 16. In this unprecedented episode of asthma, the links to meteorologically defined thunderstorm activity were not as clear as previous episodes, with less evidence of 'severe' thunderstorm activity in those areas affected, prompting further discussion about the causes of these events and implications for public health management of the risk.
| S-EPMC8657080 | biostudies-literature
Project description:INTRODUCTION:Poor air quality (AQ) is a global public health issue and AQ events can span across countries. Using emergency department (ED) syndromic surveillance from England and France, we describe changes in human health indicators during periods of particularly poor AQ in London and Paris during 2014. METHODS:Using daily AQ data for 2014, we identified three periods of poor AQ affecting both London and Paris. Anonymised near real-time ED attendance syndromic surveillance data from EDs across England and France were used to monitor the health impact of poor AQ.Using the routine English syndromic surveillance detection methods, increases in selected ED syndromic indicators (asthma, difficulty breathing and myocardial ischaemia), in total and by age, were identified and compared with periods of poor AQ in each city. Retrospective Wilcoxon-Mann-Whitney tests were used to identify significant increases in ED attendance data on days with (and up to 3 days following) poor AQ. RESULTS:Almost 1.5?million ED attendances were recorded during the study period (27 February 2014 to 1 October 2014). Significant increases in ED attendances for asthma were identified around periods of poor AQ in both cities, especially in children (aged 0-14?years). Some variation was seen in Paris with a rapid increase during the first AQ period in asthma attendances among children (aged 0-14?years), whereas during the second period the increase was greater in adults. DISCUSSION:This work demonstrates the public health value of syndromic surveillance during air pollution incidents. There is potential for further cross-border harmonisation to provide Europe-wide early alerting to health impacts and improve future public health messaging to healthcare services to provide warning of increases in demand.
| S-EPMC5914706 | biostudies-literature
Project description:BackgroundSyndromic 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.MethodsWe 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.ResultsInfluenza 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.ConclusionsThe proposed framework allows the exhaustive evaluation of any syndromic surveillance system and constitutes a useful tool for emergency preparedness and response.
| S-EPMC5921418 | biostudies-literature
Project description:Predicting the magnitude of the annual seasonal peak in influenza-like illness (ILI)-related emergency department (ED) visit volumes can inform the decision to open influenza care clinics (ICCs), which can mitigate pressure at the ED. Using ILI-related ED visit data from the Alberta Real Time Syndromic Surveillance Net for Edmonton, Alberta, Canada, we developed (training data, 1 August 2004-31 July 2008) and tested (testing data, 1 August 2008-19 February 2014) spatio-temporal statistical prediction models of daily ILI-related ED visits to estimate high visit volumes 3 days in advance. Our Main Model, based on a generalised linear mixed model with random intercept, incorporated prediction residuals over 14 days and captured increases in observed volume ahead of peaks. During seasonal influenza periods, our Main Model predicted volumes within ±30% of observed volumes for 67%-82% of high-volume days and within 0.3%-21% of observed seasonal peak volumes. Model predictions were not as successful during the 2009 H1N1 pandemic. Our model can provide early warning of increases in ILI-related ED visit volumes during seasonal influenza periods of differing intensities. These predictions may be used to support public health decisions, such as if and when to open ICCs, during seasonal influenza epidemics.
| S-EPMC7003624 | biostudies-literature
Project description:Influenza viruses typically cause the most severe disease in children and elderly individuals. However, H1N1 viruses disproportionately affected middle-aged adults during the 2013-2014 influenza season. Although H1N1 viruses recently acquired several mutations in the hemagglutinin (HA) glycoprotein, classic serological tests used by surveillance laboratories indicate that these mutations do not change antigenic properties of the virus. Here, we show that one of these mutations is located in a region of HA targeted by antibodies elicited in many middle-aged adults. We find that over 42% of individuals born between 1965 and 1979 possess antibodies that recognize this region of HA. Our findings offer a possible antigenic explanation of why middle-aged adults were highly susceptible to H1N1 viruses during the 2013-2014 influenza season. Our data further suggest that a drifted H1N1 strain should be included in future influenza vaccines to potentially reduce morbidity and mortality in this age group.
| S-EPMC4226110 | biostudies-literature
Project description:We use the Weather Research and Forecast model using the Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) dust module (WRF-CHEM) to simulate the particulate matter (PM) variations in the Sahel during the winter seasons (January-March) of 1960-2014. Two simulations are undertaken where the direct aerosol feedback is turned off, and only transport is considered and where the direct aerosol feedback is turned on. We find that simulated Sahelian PM10 and PM2.5 concentrations were lower in the 1960s and after 2003 and higher during the period between 1988 and 2002. Higher Sahelian PM10 concentrations are due to stronger winds between the surface and 925 hPa over the Sahara, which transport dust into the Sahel. Negative PM10 concentration anomalies are found over the Bodele Depression and associated with weaker 925 wind anomalies after 1997 through 2014. Further west, positive PM10 concentration anomalies are found across the Adrar Plateau in the Sahara and responsible for dust transport to the Western Sahel. The North Atlantic Oscillation (NAO) is positively correlated to Sahelian dust concentrations especially during the periods of 1960-1970 and 1988-2002. The temporal/spatial patterns of PM10 concentrations have significant respiratory health implications for inhabitants of the Sahel.
| S-EPMC7007090 | biostudies-literature
Project description:BACKGROUND:New 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. OBJECTIVE:This study aimed to build and assess a national ecological NH PH surveillance system (SS). METHODS:Using 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. RESULTS:By 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). CONCLUSIONS:This 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.
| S-EPMC6315244 | biostudies-literature
Project description:BackgroundInternet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study.ObjectiveThis study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset.MethodsThis study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users' tweets.ResultsOf 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants' tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P?.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P?.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19).ConclusionsTo our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.
| S-EPMC7210500 | biostudies-literature