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 Tracking drug-related overdoses at the local level: Using Syndromic Surveillance in the CO-NCR

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Emery Shekiro ◽  
Lily Sussman ◽  
Talia Brown
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Real Time ◽  
Drug Overdose ◽  
Syndromic Surveillance ◽  
Case Definition ◽  
Chief Complaint ◽  
Discharge Diagnosis ◽  
Diagnosis Codes ◽  
Drug Overdoses

Objective: In order to better describe local drug-related overdoses, we developed a novel syndromic case definition using discharge diagnosis codes from emergency department data in the Colorado North Central Region (CO-NCR). Secondarily, we used free text fields to understand the use of unspecified diagnosis fields.Introduction: The United States is in the midst of a drug crisis; drug-related overdoses are the leading cause of unintentional death in the country. In Colorado the rate of fatal drug overdose increased 68% from 2002-2014 (9.7 deaths per 100,000 to 16.3 per 100,000, respectively)1, and non-fatal overdose also increased during this time period (23% increase in emergency department visits since 2011)2. The CDC’s National Syndromic Surveillance Program (NSSP) provides near-real time monitoring of emergency department (ED) events across the country, with information uploaded daily on patient demographics, chief complaint for visit, diagnosis codes, triage notes, and more. Colorado North Central Region (CO-NCR) receives data for 4 local public health agencies from 25 hospitals across Adams, Arapahoe, Boulder, Denver, Douglas, and Jefferson Counties.Access to local syndromic data in near-real time provides valuable information for local public health program planning, policy, and evaluation efforts. However, use of these data also comes with many challenges. For example, we learned from key informant interviews with ED staff in Boulder and Denver counties, about concern with the accuracy and specificity of drug-related diagnosis codes, specifically for opioid-related overdoses.Methods: Boulder County Public Health (BCPH) and Denver Public Health (DPH) developed a query in Early Notification of Community Based Epidemics (ESSENCE) using ICD-10-CM codes to identify cases of drug-related overdose [T36-T51] from October 2016 to September 2017. The Case definition included unintentional, self-harm, assault and undetermined poisonings, but did not include cases coded as adverse effects or underdosing of medication. Cases identified in the query were stratified by demographic factors (i.e., gender and age) and substance used in poisoning. The first diagnosis code in the file was considered the primary diagnosis. Chief complaint and triage note fields were examined to further describe unspecified cases and to describe how patients present to emergency departments in the CO-NCR. We also explored whether detection of drug overdose visits captured by discharge diagnosis data varied by patient sex, age, or county.Results: The query identified 2,366 drug-related overdoses in the CO-NCR. The prevalence of drug overdoses differed across age groups. The detection of drug overdoses was highest among our youth and young adult populations; 16 to 20 year olds (16.0%), 21-25 year olds (11.4%), 26-30 year olds (11.4%). Females comprised 56.1% of probable general drug overdoses. The majority of primary diagnoses (31.0%) included poisonings related to diuretics and other unspecified drugs (T50), narcotics (T40) (12.6%), or non-opioid analgesics (T39) (7.8%). For some cases with unspecified drug overdose codes there was additional information about drugs used and narcan administration found in the triage notes and chief complaint fields.Conclusions: Syndromic surveillance offers the opportunity to capture drug-related overdose data in near-real time. We found variation in drug-related overdose by demographic groups. Unspecified drug overdose codes are extremely common, which likely negatively impacts the quality of drug-specific surveillance. Leveraging chief complaint and triage notes could improve our understanding of factors involved in drug-related overdose with limitations in discharge diagnosis. Chart reviews and access to more fields from the ED electronic health record could improve local drug surveillance.

scholarly journals Using Syndromic Data for Opioid Overdose Surveillance in Utah

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Wei Hou ◽  
Elizabeth Brutsch ◽  
Angela C Dunn ◽  
Cindy L Burnett ◽  
Melissa P Dimond ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Real Time ◽  
Emergency Department Visit ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Urgent Care ◽  
Mortality Data ◽  
Opioid Overdose

Objective: To monitor opioid-related overdose in real-time using emergency department visit data and to develop an opioid overdose surveillance report for Utah Department of Health (UDOH) and its public health partners.Introduction: The current surveillance system for opioid-related overdoses at UDOH has been limited to mortality data provided by the Office of the Medical Examiner (OME). Timeliness is a major concern with OME data due to the considerable lag in its availability, often up to six months or more. To enhance opioid overdose surveillance, UDOH has implemented additional surveillance using timely syndromic data to monitor fatal and nonfatal opioid-related overdoses in Utah.Methods: As one of the agencies participating in the National Syndromic Surveillance Program (NSSP), UDOH submits de-identified data on emergency department visit from Utah’s hospitals and urgent care facilities in close to real-time to the NSSP platform. Emergency department visit data are available for analysis using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) system provided by NSSP. ESSENCE provides UDOH with patient-level syndromic data for analysis and early detection of abnormal patterns in emergency visits. A total of 38 out of 48 acute care hospitals and multiple urgent care facilities are enrolled in the system in Utah. More than 90% of these hospitals report chief complaint data, and discharge data are available from about 15% of the facilities. Data were analyzed by querying key terms in the chief complaint field including: any entry of: ‘overdose’, drug and brand names for opioids, street names, ‘naloxone’, and miss-spellings. Exclusion terms included any mention of: ‘denies’, ‘quit’, ‘refill’, ‘withdraw’, ‘dependence’, etc. Data containing any ICD entry of: T40.0-T40.4, T40.60, and T40.69 were included in the analysis.Results: Between September 1, 2016 and August 31, 2017, Utah Department of Health identified 4,063 opioid-related overdose emergency department (ED) visits through the ESSENCE system using both chief complaint and discharge diagnosis queries. Of these visits, 3,865 (95%) were identified using chief complaints alone and 198 (5%) visits were added by searching the discharge diagnosis field. Opioid-related visits comprised approximately 0.3% of the total ED visits (1,267,244) reported during this time (Graph 1). More than half of the opioid-related emergency visits were reported from just five facilities. Rate of opioid-related visits ranging from 0 to 292 visits per 100,000 population per year (median: 108 visits per 100,000 population per year), with an overall rate for the state of 129 visits per100, 000 population per year. The highest rate of opioid-related visits occurred among patients aged 18 to 24 (219 visits per 100,000 population per year), and 59% of all opioid-related patients in Utah were female.Conclusions: The results presented are estimates of opioid-related overdoses reported using close to real-time data. These results would not include visits with incomplete or incorrectly coded chief complaints or discharge codes, or cases of opioid overdose who do not present to an emergency department or urgent care facility. The results from using syndromic data are consistent with existing surveillance findings using mortality data in Utah. This suggests that syndromic surveillance data are useful for rapidly capturing opioid events, which may allow for a timelier public health response. UDOH is currently evaluating syndromic surveillance data versus hospital discharge data for opioid-related emergency department visits, which may further optimize queries in ESSENCE, in order to provide improved opioid surveillance data to local public health partners. This analysis demonstrates that using syndromic surveillance data provides a more time-efficient alternative, enabling more rapid public health interventions, which improved opportunities to reduce opioid-related morbidity and mortality in Utah.

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 Monitoring populations at increased risk for SARS-CoV-2 infection in the community

2021 ◽  
Author(s):  
Emma Pritchard ◽  
Joel Jones ◽  
Karina Vihta ◽  
Nicole Stoesser ◽  
Philippa C Matthews ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Multiple Testing ◽  
Reverse Causality ◽  
Screening Process ◽  
Department Of Health ◽  
Increased Risk ◽  
Behavioural Surveillance ◽  
Geographical Regions ◽  
Health And Social Care

Background: The COVID-19 pandemic is rapidly evolving, with emerging variants and fluctuating control policies. Real-time population screening and identification of groups in whom positivity is highest could help monitor spread and inform public health messaging and strategy. Methods: To develop a real-time screening process, we included results from nose and throat swabs and questionnaires taken 19 July 2020-17 July 2021 in the UK's national COVID-19 Infection Survey. Fortnightly, associations between SARS-CoV-2 positivity and 60 demographic and behavioural characteristics were estimated using logistic regression models adjusted for potential confounders, considering multiple testing, collinearity, and reverse causality. Findings: Of 4,091,537 RT-PCR results from 482,677 individuals, 29,903 (0.73%) were positive. As positivity rose September-November 2020, rates were independently higher in younger ages, and those living in Northern England, major urban conurbations, more deprived areas, and larger households. Rates were also higher in those returning from abroad, and working in healthcare or outside of home. When positivity peaked December 2020-January 2021 (Alpha), high positivity shifted to southern geographical regions. With national vaccine roll-out from December 2020, positivity reduced in vaccinated individuals. Associations attenuated as rates decreased between February-May 2021. Rising positivity rates in June-July 2021 (Delta) were independently higher in younger, male, and unvaccinated groups. Few factors were consistently associated with positivity. 25/45 (56%) confirmed associations would have been detected later using 28-day rather than 14-day periods. Interpretation: Population-level demographic and behavioural surveillance can be a valuable tool in identifying the varying characteristics driving current SARS-CoV-2 positivity, allowing monitoring to inform public health policy. Funding: Department of Health and Social Care (UK), Welsh Government, Department of Health (on behalf of the Northern Ireland Government), Scottish Government, National Institutes of Health Research.

scholarly journals Tracking Health Effects of Wildfires: The Oregon ESSENCE Wildfire Pilot Project

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Melissa Powell ◽  
Laurel Boyd
Keyword(s):  
Real Time ◽  
Health Effects ◽  
Migrant Workers ◽  
Environmental Protection Agency ◽  
Syndromic Surveillance ◽  
Local Level ◽  
Pilot Project ◽  
Health Surveillance ◽  
Urgent Care ◽  
Wildfire Season

ObjectiveTo build capacity to conduct syndromic surveillance at the locallevel by leveraging a health surveillance need.IntroductionWildfires occur annually in Oregon, and the health risks of wildfiresmoke are well documented1. Before implementing syndromicsurveillance through Oregon ESSENCE, assessing the health effectsof wildfires in real time was very challenging. Summer 2015 markedthe first wildfire season with 60 of 60 eligible Oregon emergencydepartments (EDs) reporting to ESSENCE. The Oregon ESSENCEteam developed a wildfire surveillance pilot project with two localpublic health authorities (LPHAs) to determine their surveillanceneeds and practices and developed a training program to increasecapacity to conduct surveillance at the local level. Following thetraining, one of the LPHAs integrated syndromic surveillance intoits routine surveillance practices. Oregon ESSENCE also integratedthe evaluation findings into the summer 2016 statewide wildfiresurveillance plan.MethodsOregon ESSENCE staff recruited two LPHA preparednesscoordinators whose jurisdictions are regularly affected by wildfiresmoke to participate in the pilot project. A state public healthemergency preparedness liaison served as facilitator in order toincrease syndromic surveillance capacity among state preparednessstaff.A pre-season interview assessed data and surveillance needs,risk communication practices, and typical response activities duringwildfires. Initial project calls focused on determining specific queriesthat would meet local needs. Participants wanted total ED visitnumbers and health outcomes including asthma, chest pain or heartproblems. Both LPHAs were interested in using the data to assesshealth effects on vulnerable populations, including elderly, children,and migrant workers. Oregon ESSENCE staff also recommendedqueries that would be used if large numbers of people were displaced(e.g., medication refills, dialysis).Before the onset of wildfire season, Oregon ESSENCEepidemiologists created queries and a MyESSENCE page for eachparticipant. LPHA staff practiced running the queries, modifyingthem, and discussed interpretation and data-sharing best practices.During wildfire season, brief weekly webinars enabled participantsto ask questions and learn additional techniques including displayingtime series as proportions and adjusting geographic parameters tofocus on areas with poor air quality.Results2015 was a severe wildfire season in Oregon, with over 685,000acres burned2. For the first time, local and state public health were ableto monitor and share near real-time health information on interagencysmoke calls. In the post project evaluation, participants reportedincreased knowledge of syndromic surveillance, interpretation,and risk communications. There were no marked increases in totalemergency department visits, or visits for asthma, heart palpitations,or other heart complaints. The public may have adhered to warningsand effectively protected themselves against exposure to wildfiresmoke, or health effects may have been less severe and not reflectedin emergency department data. Over the next several years, OregonESSENCE will integrate select urgent care data, which may bettercapture morbidity due to wildfire smoke.ConclusionsFraming syndromic surveillance training around a healthsurveillance need was effective because participants were engagedaround a high-priority health hazard. In summer 2016, OregonESSENCE integrated wildfire health surveillance into a biweeklyESSENCE seasonal hazard surveillance report and invited wildfireresponse partners to subscribe. Local ESSENCE users can use ormodify the queries. In 2017, Oregon ESSENCE will incorporate airquality data from the Environmental Protection Agency so partnerscan monitor air quality and health effects simultaneously.

scholarly journals Recognizing Recreational Water Exposure and Habituating HAB Surveillance in ESSENCE

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Kathryn Kuspis ◽  
Meredith Jagger ◽  
Melissa Powell ◽  
Rebecca Hillwig
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Algal Bloom ◽  
Urgent Care ◽  
Contaminated Water ◽  
Free Text ◽  
Recreational Water ◽  
Environmental Public Health ◽  
Gi Symptoms ◽  
Chief Complaints

ObjectiveUse ESSENCE to create a sustainable process for identifying ED and urgent care visits that may be related to harmful algal bloom exposure in Oregon.IntroductionHarmful algal blooms (HABs) consist of colonies of prokaryotic photosynthetic bacteria algae that can produce harmful toxins. The toxins produced by HABs are considered a One Health issue. HABs can occur in all types of water (fresh, brackish, and salt water) and are composed of cyanobacteria or microalgae. As the climate changes, so do many of the factors that contribute to the growth of HABs, which in turn, can increase the incidence of HAB-related illness in humans.There are three main pathways that HAB toxins can affect human health: dermal, gastrointestinal (GI), and neurological. Swimming in or consuming contaminated water and eating contaminated shellfish are ways to develop HAB-related illnesses. Contact with cells from a bloom while recreating can cause a rash on the body. Most commonly, HAB-related illnesses present with GI symptoms that resemble food poisoning and can affect the liver. Rarely, HABs that produce cyanotoxins can present with neurological symptoms.Issuing and lifting freshwater HAB advisories is within the preview of the Environmental Public Health section at the Oregon Public Health Division. However, most water bodies in the state are not monitored. Because of this, syndromic surveillance was considered as a potentially useful source of HAB exposure information, and the Oregon ESSENCE team was asked to develop a query to help monitor HAB-related complaints.MethodsPreliminary research was done on HABs and the associated health issues, and past advisories were examined to identify locations of interest. Next, keywords and symptoms were evaluated.Initially, the objective was to create a single query for HAB syndromic surveillance, but it became evident that multiple queries would have to be developed to fully encompass the various types of HAB-related illnesses: GI, neurological, and rash.Most commonly Oregon ESSENCE uses chief complaint and discharge diagnosis (CCDD) queries. However, the ICD-10 codes relating to HABs are not widely used, with only two occurrences since June 2015. It was determined that using the already established ESSENCE syndromes of Neuro, GI, and Rash would be most useful. To make the queries HAB-specific, an additional exposure element needed to be added. Exposures to HABs that are of interest occur in recreational freshwater sources. After running this query in the CCDD field, it was determined that the triage note field would yield better results. This is because this field often includes the patient’s verbatim complaints. This produced higher quality results, and a seasonal curve of cases could be seen in the historic data.Since the microcystin threshold for illness is significantly lower for pets; and a permanent HAB alert in southern Oregon was established after several dogs died from drinking contaminated water, tracking neurological cases that followed canine illness was investigated. A free-text triage note query was developed for patients mentioning dogs, and it was combined with the ESSENCE Neuro syndrome. After several attempts, it was clear that this would not be helpful for surveillance of HAB-related illnesses.Ultimately, four query configurations were developed to monitor HAB-related illness. Most importantly, a free-text recreational water query was developed to stand alone and then be paired with three distinct ESSENCE syndromes.Recreational water query text: (, (, ^ lake^ ,andnot, (, ^road^ ,or, ^rd^ ,or, ^sky^ ,or, ^oswego^ ,or, ^view^ ,) ,) ,or, ^swim^ ,or, (, ^ river ^ ,andnot, (, ^driver^, or, ^hood^ ,or, ^rd^ ,or, ^road^ ,or, ^three^ ,) ,) ,or, ^ boat^ ,) ,andnot, ^feels like^All queries were compiled into a myESSENCE page that could be shared for easy monitoring by all members of the team (Figure 1).ResultsThe ESSENCE team monitored the HAB myESSENCE page. The monitoring period for this project stretched from May to early August (MMWR weeks 19-31). Motoring was often informed by HAB alerts and required looking closely at individual visits. Over this time, the number of recreational water related visits varied, but the average was approximately 110 visits a week. This techniques also helped identify cases possibly related to unreported blooms. The months of June and July saw 15 specific cases that were potentially due to HAB exposure. These cases were highlighted and forwarded to Environmental Public Health for investigation.ConclusionsThis process helped refine the use of the triage note field when constructing keyword queries. While not all Oregon facilities provide triage notes, using specific terms allows ESSENCE users to search for words that may not be included in chief complaints. This is most be useful when searching for specific places or events. With further analysis, users can see what chief complaints are most likely to occur in conjunction with specific exposures. Moving forward, the development of a recreational water query has proven to be useful beyond the scope of this HAB project. Alternative versions of this query have been used in other contexts.ReferencesHarmful Algal Bloom (HAB)-Associated Illness. (2017, June 01). Retrieved August 01, 2017, from https://www.cdc.gov/habs/index.html

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 Administrative and syndromic surveillance data can enhance public health surveillance

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Tara C. Anderson ◽  
Hussain Yusuf ◽  
Amanda McCarthy ◽  
Katrina Trivers ◽  
Peter Hicks ◽  
...  
Keyword(s):  
Public Health ◽  
Administrative Data ◽  
Syndromic Surveillance ◽  
Health Surveillance ◽  
Local State ◽  
Surveillance Data ◽  
Data Sources ◽  
Urgent Care ◽  
American Hospital ◽  
State Regional

ObjectiveThis roundtable will address how multiple data sources, includingadministrative and syndromic surveillance data, can enhance publichealth surveillance activities at the local, state, regional, and nationallevels. Provisional findings from three studies will be presented topromote discussion about the complementary uses, strengths andlimitations, and value of these data sources to address public healthpriorities and surveillance strategies.IntroductionHealthcare data, including emergency department (ED) andoutpatient health visit data, are potentially useful to the publichealth community for multiple purposes, including programmaticand surveillance activities. These data are collected through severalmechanisms, including administrative data sources [e.g., MarketScanclaims data1; American Hospital Association (AHA) data2] andpublic health surveillance programs [e.g., the National SyndromicSurveillance Program (NSSP)3]. Administrative data typically becomeavailable months to years after healthcare encounters; however, datacollected through NSSP provide near real time information nototherwise available to public health. To date, 46 state and 16 localhealth departments participate in NSSP, and the estimated nationalpercentage of ED visits covered by the NSSP BioSense platform is54%. NSSP’s new data visualization tool, ESSENCE, also includesadditional types of healthcare visit (e.g., urgent care) data. AlthoughNSSP is designed to support situational awareness and emergencyresponse, potential expanded use of data collected through NSSP(i.e., by additional public health programs) would promote the utility,value, and long-term sustainability of NSSP and enhance surveillanceat the local, state, regional, and national levels. On the other hand,studies using administrative data may help public health programsbetter understand how NSSP data could enhance their surveillanceactivities. Such studies could also inform the collection and utilizationof data reported to NSSP.

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.

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