scholarly journals The Application of a Novel Statistical Method for Syndromic Surveillance in England

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Roger Morbey ◽  
Helen Hughes ◽  
Alex Elliot ◽  
Neville Verlander ◽  
Nick Andrews ◽  
...  
Keyword(s):  
Public Health ◽  
Statistical Method ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Mixed Effects ◽  
Local Models ◽  
Multi Level ◽  
First Time ◽  
Design And Application

This paper describes the design and application of a new statistical method for real-time syndromic surveillance, used by Public Health England. The Rising Activity, Multi-level Mixed effects, Indicator Emphasis (RAMMIE) statistical method was developed and tested alongside existing methods before being applied to a suite of syndromic surveillance in operation in England. The RAMMIE method has proved to be a reliable, effective method for generating automated alarms for syndromic surveillance. The multi-level models have enabled local models to be created for the first time across all systems and models have proved themselves to be robust across all the signals.

scholarly journals Comparison of statistical algorithms for syndromic surveillance aberration detection

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
R. Morbey ◽  
A. Noufaily ◽  
F. D. Colón-González ◽  
A. Elliot ◽  
S. Harcourt ◽  
...  
Keyword(s):  
Public Health ◽  
Sensitivity And Specificity ◽  
Syndromic Surveillance ◽  
Mixed Effects ◽  
Outbreak Detection ◽  
Aberration Detection ◽  
Day Of The Week ◽  
Multi Level ◽  
Public Holidays

ObjectiveTo investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in England.IntroductionSyndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods.MethodsFor this purpose, we compare RAMMIE to the improved quasi-Poisson regression-based approach (Noufaily et al, 2013), currently implemented at PHE for weekly infectious disease laboratory surveillance, and to the Early Aberration Reporting System (EARS) method (Rossi et al, 1999), which is used for syndromic surveillance aberration detection in many other countries. We model syndromic datasets, capturing real data aspects such as long-term trends, seasonality, public holidays, and day-of-the-week effects, with or without added outbreaks. Then, we compute the sensitivity and specificity to compare how well each of the algorithms detects synthetic outbreaks to provide recommendations for the most suitable statistical methods to use during different public health scenarios.ResultsPreliminary results suggest all methods provide high sensitivity and specificity, with the (Noufaily et al, 2013) approach having the highest sensitivity and specificity. We showed that for syndromes with long-term increasing trends, RAMMIE required modificaiton to prevent excess false alarms. Also, our study suggests further work is needed to fully account for public holidays and day-of-the-week effects.ConclusionsOur study will provide recommendations for which algorithm is most effective for PHE's syndromic surveillance for a range of different syndromes. Furthermore our work to generate standardised synthetic syndromic datasets and a range of outbreaks can be used for future evaluations in England and elsewhere.ReferencesNoufaily, A., Enki, D. G., Farrington, C. P., Garthwaite, P., Andrews, N. and Charlett, A. (2013). An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems. Statistics in Medicine, 32(7), 1206-1222.Morbey, R. A., Elliot, A. J., Charlett, A., Verlander, A. Q, Andrews, N. and Smith, G. (2013). The application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method for syndromic surveillance in England, Bioinformatics, 31(22), 3660-3665.Rossi, G, Lampugnani, L, Marchi, M. (1999), An approximate CUSUM procedure for surveillance of health events. Statistics in Medicine, 18, 2111–2122

scholarly journals Application of Syndromic Surveillance to Describe Gunshot-related Injuries in Houston

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Ryan M. Arnold ◽  
Wesley McNeely ◽  
Kasimu Muhetaer ◽  
Biru Yang ◽  
Raouf R. Arafat
Keyword(s):  
Public Health ◽  
United States ◽  
Health Risk ◽  
Real Time ◽  
Emergency Departments ◽  
Syndromic Surveillance ◽  
The United States ◽  
Public Health Risk ◽  
The Past ◽  
Houston Area

Firearm-related injuries pose a substantial public health risk in the United States, and traditional means of studying this issue rely primarily on retrospective analyses. Syndromic surveillance, collected in over 30 Houston area emergency departments, is well suited to characterize and analyze gunshot injuries in the area in near real-time. Over the past two years, more than 900 gunshot-related injury visits were identified using this method, and ArcGIS effectively identified incident densities in ZIP codes throughout Houston. Most patients were males (86.3%), between the ages of 18 and 34 (64.7%).

scholarly journals How Missing Discharge Diagnosis Data in Syndromic Surveillance Leads to Coverage Gaps

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Kayley Dotson ◽  
Mandy Billman
Keyword(s):  
Public Health ◽  
Real Time ◽  
Random Sample ◽  
Syndromic Surveillance ◽  
Urgent Care ◽  
Discharge Diagnosis ◽  
Disease Clusters ◽  
Department Of Health ◽  
Coverage Gap ◽  
Administrative Code

ObjectiveTo identify surveillance coverage gaps in emergency department (ED) and urgent care facility data due to missing discharge diagnoses.IntroductionIndiana utilizes the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) to collect and analyze data from participating hospital emergency departments. This real-time collection of health related data is used to identify disease clusters and unusual disease occurrences. By Administrative Code, the Indiana State Department of Health (ISDH) requires electronic submission of chief complaints from patient visits to EDs. Submission of discharge diagnosis is not required by Indiana Administrative Code, leaving coverage gaps. Our goal was to identify which areas in the state may see under reporting or incomplete surveillance due to the lack of the discharge diagnosis field.MethodsEmergency department data were collected from Indiana hospitals and urgent care clinics via ESSENCE. Discharge diagnosis was analyzed by submitting facility to determine percent completeness of visits. A descriptive analysis was conducted to identify the distribution of facilities that provide discharge diagnosis. A random sample of 20 days of data were extracted from visits that occurred between January 1, 2017 and September 6, 2017.ResultsA random sample of 179,039 (8%) ED entries from a total of 2,220,021 were analyzed from 121 reporting facilities. Of the sample entries, 102,483 (57.24%) were missing the discharge diagnosis field. Over 40 (36%) facilities were missing more than 90% of discharge diagnosis data. Facilities are more likely to be missing >90% or <19% of discharge diagnoses, rather than between those points.Comparing the percent of syndromic surveillance entries missing discharge diagnosis across facilities reveals large variability. For example, some facilities provide no discharge diagnoses while other facilities provide 100%. The number of facilities missing 100% of discharge diagnoses (n = 19) is 6.3 times that of the facilities that are missing 0% (n = 3).The largest coverage gap was identified in Public Health Preparedness District (PHPD)1 three (93.16%), with districts five (64.97%), seven (61.94%), and four (61.34%) making up the lowest reporting districts. See Table 2 and Figure 12 for percent missing by district and geographic distribution. PHPD three and five contain a large proportion (38%) of the sample population ED visits which results in a coverage gap in the most populated areas of the state.ConclusionsQuerying ESSENCE via chief complaint data is useful for real-time surveillance, but is more informative when discharge diagnoses are available. Indiana does not require facilities to report discharge diagnosis, but regulatory changes are being proposed that would require submission of discharge diagnosis data to ISDH. The addition of discharge diagnose is aimed to improve the completeness of disease clusters and unusual disease occurrence surveillance data.References1. Preparedness Districts [Internet]. Indianapolis (IN): Indiana State Department of Health, Public Health Preparedness; 2017 [Cited 2017 Sept 20]. Available from: https://www.in.gov/isdh/17944.htm. 

scholarly journals Semantic Analysis of Open Source Data for Syndromic Surveillance

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Erica Briscoe ◽  
Scott Appling ◽  
Edward Clarkson ◽  
Nikolay Lipskiy ◽  
James Tyson ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Real Time ◽  
Topic Modeling ◽  
Syndromic Surveillance ◽  
Semantic Analysis ◽  
Human In The Loop ◽  
Media Messages ◽  
Recent Advances ◽  
Post Hoc

ObjectiveThe objective of this analysis is to leverage recent advances innatural language processing (NLP) to develop new methods andsystem capabilities for processing social media (Twitter messages)for situational awareness (SA), syndromic surveillance (SS), andevent-based surveillance (EBS). Specifically, we evaluated the useof human-in-the-loop semantic analysis to assist public health (PH)SA stakeholders in SS and EBS using massive amounts of publiclyavailable social media data.IntroductionSocial media messages are often short, informal, and ungrammatical.They frequently involve text, images, audio, or video, which makesthe identification of useful information difficult. This complexityreduces the efficacy of standard information extraction techniques1.However, recent advances in NLP, especially methods tailoredto social media2, have shown promise in improving real-time PHsurveillance and emergency response3. Surveillance data derived fromsemantic analysis combined with traditional surveillance processeshas potential to improve event detection and characterization. TheCDC Office of Public Health Preparedness and Response (OPHPR),Division of Emergency Operations (DEO) and the Georgia TechResearch Institute have collaborated on the advancement of PH SAthrough development of new approaches in using semantic analysisfor social media.MethodsTo understand how computational methods may benefit SS andEBS, we studied an iterative refinement process, in which the datauser actively cultivated text-based topics (“semantic culling”) in asemi-automated SS process. This ‘human-in-the-loop’ process wascritical for creating accurate and efficient extraction functions in large,dynamic volumes of data. The general process involved identifyinga set of expert-supplied keywords, which were used to collect aninitial set of social media messages. For purposes of this analysisresearchers applied topic modeling to categorize related messages intoclusters. Topic modeling uses statistical techniques to semanticallycluster and automatically determine salient aggregations. A user thensemantically culled messages according to their PH relevance.In June 2016, researchers collected 7,489 worldwide English-language Twitter messages (tweets) and compared three samplingmethods: a baseline random sample (C1, n=2700), a keyword-basedsample (C2, n=2689), and one gathered after semantically cullingC2 topics of irrelevant messages (C3, n=2100). Researchers utilizeda software tool, Luminoso Compass4, to sample and perform topicmodeling using its real-time modeling and Twitter integrationfeatures. For C2 and C3, researchers sampled tweets that theLuminoso service matched to both clinical and layman definitions ofRash, Gastro-Intestinal syndromes5, and Zika-like symptoms. Laymanterms were derived from clinical definitions from plain languagemedical thesauri. ANOVA statistics were calculated using SPSSsoftware, version. Post-hoc pairwise comparisons were completedusing ANOVA Turkey’s honest significant difference (HSD) test.ResultsAn ANOVA was conducted, finding the following mean relevancevalues: 3% (+/- 0.01%), 24% (+/- 6.6%) and 27% (+/- 9.4%)respectively for C1, C2, and C3. Post-hoc pairwise comparison testsshowed the percentages of discovered messages related to the eventtweets using C2 and C3 methods were significantly higher than forthe C1 method (random sampling) (p<0.05). This indicates that thehuman-in-the-loop approach provides benefits in filtering socialmedia data for SS and ESB; notably, this increase is on the basis ofa single iteration of semantic culling; subsequent iterations could beexpected to increase the benefits.ConclusionsThis work demonstrates the benefits of incorporating non-traditional data sources into SS and EBS. It was shown that an NLP-based extraction method in combination with human-in-the-loopsemantic analysis may enhance the potential value of social media(Twitter) for SS and EBS. It also supports the claim that advancedanalytical tools for processing non-traditional SA, SS, and EBSsources, including social media, have the potential to enhance diseasedetection, risk assessment, and decision support, by reducing the timeit takes to identify public health events.

scholarly journals 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

2018 ◽  
Vol 23 (40) ◽  
Author(s):  
Navina Sarma ◽  
Alexander Ullrich ◽  
Hendrik Wilking ◽  
Stéphane Ghozzi ◽  
Andreas K. Lindner ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Detection Algorithm ◽  
Time Data ◽  
Gastrointestinal Infections ◽  
Robert Koch Institute ◽  
Syndromic Surveillance System ◽  
Real Time Data

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.

scholarly journals Syndromic surveillance insights from a symptom assessment app before and during COVID-19 measures in Germany and the United Kingdom: results from repeated cross-sectional analyses

2020 ◽  
Author(s):  
Alicia Mehl ◽  
Francois Bergey ◽  
Caoimhe Cawley ◽  
Andreas Gilsdorf
Keyword(s):  
Public Health ◽  
United Kingdom ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Symptom Assessment ◽  
Assessment Tools ◽  
Added Value ◽  
Cross Sectional ◽  
Syndromic Surveillance System ◽  
The United Kingdom

AbstractBackgroundUnprecedented lockdown measures have been introduced in countries across the world to mitigate the spread and consequences of COVID-19. While attention has focused on the effects of these measures on epidemiological indicators relating directly to the infection, there is increased recognition of their broader health implications. However, assessing these implications in real time is a challenge, due to limitations of existing syndromic surveillance data and tools.ObjectiveTo explore the added value of mobile phone app-based symptom assessment tools as real time health insight providers to inform public health policy makers.MethodsA comparative and descriptive analysis of the proportion of all self-reported symptoms entered by users during an Ada assessment in Germany and the United Kingdom (UK) was conducted between two periods: before and after the implementation of “Phase One” COVID-19 measures. Additional analyses were performed to explore the association between symptom trends and seasonality, and symptom trends and weather. Differences in the proportion of unique symptoms between the periods were analysed using Pearson’s Chi-squared test and reported as Log2 Fold Changes (Log2 FC).ResultsBetween 48,300-54,900 symptomatic users reported 140,500-170,400 symptoms during the Baseline and Measures periods in Germany. Between 34,200-37,400 symptomatic users in the UK reported 112,100-131,900 symptoms during the Baseline and Measures periods. The majority of symptomatic users were female (Germany 68,600/103,200, 66.52%; UK 51,200/71,600, 72.74%). The majority (Germany 68,500/100,000, 68.45%; UK 50,900/68,800, 73.91%) were aged between 10 and 29 years, and about a quarter (Germany 26,200/100,000, 26.15%; UK 14,900/68,800, 21.65%) were between 30-59 years. 103 symptoms were reported either more or less frequently (with statistically significant differences) during the Measures as compared to the Baseline period, and 34 of these were found in both countries. The following mental health symptoms (Log2 FC, P-value) were reported less often during the Measures period: inability to manage constant stress and demands at work (−1.07, P<.001), memory difficulty (−0.56, P<.001), depressed mood (−0.42, P<.001), and impaired concentration (−0.46, P<.001). Diminished sense of taste (2.26, P<.001) and hyposmia (2.20, P<.001) were reported more frequently during the Measures period. None of the 34 symptoms were found to be different between the same dates in 2019. Fourteen of the 34 symptoms had statistically significant associations with weather variables.ConclusionsSymptom assessment apps have an important role to play in facilitating improved understanding of the implications of public health policies such as COVID-19 lockdown measures. Not only do they provide the means to complement and cross-validate hypotheses based on data collected through more traditional channels, they can also generate novel insights through a real-time syndromic surveillance system.

The most under-used statistical method in corpus linguistics: multi-level (and mixed-effects) models

Corpora ◽  
2015 ◽  
Vol 10 (1) ◽  
pp. 95-125 ◽  
Author(s):  
Stefan Th. Gries
Keyword(s):  
Statistical Analysis ◽  
Statistical Method ◽  
Gold Standard ◽  
Corpus Linguistics ◽  
Regression Models ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Regression Modelling ◽  
Corpus Linguistic ◽  
Multi Level

Much statistical analysis of psycholinguistic data is now being done with so-called mixed-effects regression models. This development was spearheaded by a few highly influential introductory articles that (i) showed how these regression models are superior to what was the previous gold standard and, perhaps even more importantly, (ii) showed how these models are used practically. Corpus linguistics can benefit from mixed-effects/multi-level models for the same reason that psycholinguistics can – because, for example, speaker-specific and lexically specific idiosyncrasies can be accounted for elegantly; but, in fact, corpus linguistics needs them even more because (i) corpus-linguistic data are observational and, thus, usually unbalanced and messy/noisy, and (ii) most widely used corpora come with a hierarchical structure that corpus linguists routinely fail to consider. Unlike nearly all overviews of mixed-effects/multi-level modelling, this paper is specifically written for corpus linguists to get more of them to start using these techniques more. After a short methodological history, I provide a non-technical introduction to mixed-effects models and then discuss in detail one example – particle placement in English – to show how mixed-effects/multi-level modelling results can be obtained and how they are far superior to those of traditional regression modelling.

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

2015 ◽  
pp. btv418 ◽  
Author(s):  
Roger A. Morbey ◽  
Alex J. Elliot ◽  
Andre Charlett ◽  
Neville Q. Verlander ◽  
Nick Andrews ◽  
...  
Keyword(s):  
Syndromic Surveillance ◽  
Mixed Effects ◽  
Multi Level
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