scholarly journals Enhancing TX SyS by integrating EMS and Poison Data for Opioid Overdose Surveillance

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jyllisa Mabion
Keyword(s):  
Public Health ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Data Sources ◽  
Opioid Overdose ◽  
Poison Center ◽  
State Health ◽  
Health Statistics ◽  
Time Data ◽  
Department Of State

ObjectiveTo improve Texas Syndromic Surveillance by integrating data from the Texas Poison Center and Emergency Medical Services for opioid overdose surveillance.IntroductionIn recent years, the number of deaths from illicit and prescription opioids has increased significantly resulting in a national and local public health crisis. According to the Texas Center for Health Statistics, there were 1340 opioid related deaths in 2015.1 In 2005, by comparison, there were 913 opioid related deaths. Syndromic surveillance can be used to monitor overdose trends in near real-time and provide much needed information to public health officials. Texas Syndromic Surveillance (TxS2) is the statewide syndromic surveillance system hosted by the Texas Department of State Health Services (DSHS). To enhance the capabilities of TxS2 and to better understand the opioid epidemic, DSHS is integrating both Texas Poison Center (TPC) data and Emergency Medical Services (EMS) data into the system.Much of the data collected at public health organizations can be several years old by the time it is released for public use. As a result, there have been major efforts to integrate more real-time data sources for a variety of surveillance needs and during emergency response activities.MethodsGuided by the Oregon Public Health Division’s successful integration of poison data into Oregon ESSENCE, DSHS has followed a similar path.2 DSHS already receives TPC data from the Commission on State Emergency Communication (CSEC), hence copying and routing that data into TxS2 requires a Memorandum of Understanding (MOU) with CSEC, which is charged with administering the implementation of the Texas Poison Control Network.EMS records are currently received by the DSHS Office of Injury Prevention (OIP) via file upload and extracted from web services as an XML file. Regional and Local Health Operations, the division where the syndromic surveillance program is located, and OIP, are both sections within DSHS. Therefore, it is not necessary to have a formal MOU in place. Both parties would operate under the rules and regulations that are established for data under the Community Health Improvement Division.CSEC and EMS will push data extracts to a DSHS SFTP folder location for polling by Rhapsody in Amazon Web Services. The message data will be extracted and transformed into the ESSENCE database format. Data are received at least once every 24 hours.ResultsTxS2 will now include TPC and EMS data, giving system users the ability to analyze and overlay real-time data for opioid overdose surveillance in one application. The integration of these data sources in TxS2 can be used for both routine surveillance and for unexpected public health events. This effort has led to discussions on how different sections within DSHS can collaborate by using syndromic surveillance data, and has generated interest in incorporating additional data streams into TxS2 in the future.ConclusionsWhile this venture is still a work in progress, it is anticipated that adding TPC and EMS data to TxS2 will be beneficial in surveilling not just opioid overdoses but other conditions and illnesses, as well as capturing disaster related injuries.References1. Texas Health Data, Center for Health Statistics [Internet]. Austin (TX): Department of State Health Services. Available from: http://healthdata.dshs.texas.gov/Opioids/Deaths2. Laing R, Powell M. Integrating Poison Center Data into Oregon ESSENCE using a Low-Cost Solution. OJPHI. 2017 May 1; 9(1).

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 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 Analytic Fusion for Essential Indicators of the Opioid Epidemic

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Howard Burkom ◽  
Joseph Downs ◽  
Raghav Ramachandran ◽  
Wayne Loschen ◽  
Laurel Boyd ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Law Enforcement ◽  
Bayesian Networks ◽  
Historical Data ◽  
Opioid Overdose ◽  
Poison Center ◽  
Machine Learning Methods ◽  
Fusion Methods ◽  
Data Source

ObjectiveIn a partnership between the Public Health Division of the Oregon Health Authority (OHA) and the Johns Hopkins Applied Physics Laboratory (APL), our objective was develop an analytic fusion tool using streaming data and report-based evidence to improve the targeting and timing of evidence-based interventions in the ongoing opioid overdose epidemic. The tool is intended to enable practical situational awareness in the ESSENCE biosurveillance system to target response programs at the county and state levels. Threats to be monitored include emerging events and gradual trends of overdoses in three categories: all prescription and illicit opioids, heroin, and especially high-mortality synthetic drugs such as fentanyl and its analogues. Traditional sources included emergency department (ED) visits and emergency management services (EMS) call records. Novel sources included poison center calls, death records, and report-based information such as bad batch warnings on social media. Using available data and requirements analyses thus far, we applied and compared Bayesian networks, decision trees, and other machine learning approaches to derive robust tools to reveal emerging overdose threats and identify at-risk subpopulations.IntroductionUnlike other health threats of recent concern for which widespread mortality was hypothetical, the high fatality burden of opioid overdose crisis is present, steadily growing, and affecting young and old, rural and urban, military and civilian subpopulations. While the background of many public health monitors is mainly infectious disease surveillance, these epidemiologists seek to collaborate with behavioral health and injury prevention programs and with law enforcement and emergency medical services to combat the opioid crisis. Recent efforts have produced key terms and phrases in available data sources and numerous user-friendly dashboards allowing inspection of hundreds of plots. The current effort seeks to distill and present combined fusion alerts of greatest concern from numerous stratified data outputs. Near-term plans are to implement best-performing fusion methods as an ESSENCE module for the benefit of OHA staff and other user groups.MethodsBy analyzing historical OHA data, we formed features to monitor in each data source to adapt diagnosis codes and text strings suggested by CDC’s injury prevention division, published EMS criteria [Reference 1], and generic product codes from CDC toxicologists, with guidance from OHA Emergency Services Director David Lehrfeld and from Oregon Poison Center Director Sandy Giffen. These features included general and specific opioid abuse indicators such as daily counts of records labelled with the “poisoning” subcategory and containing “fentanyl” or other keywords in the free-text. Matrices of corresponding time series were formed for each of 36 counties and the entire state as inputs to region-specific fusion algorithms.To obtain truth data for detection, the OHA staff provided guidance and design help to generate plausible overdose threat scenarios that were quantified as realistic data distributions of monitored features accounting for time delays and historical distributions of counts in each data source. We sampled these distributions to create 1000 target sets for detection based on the event duration and affected counties for each event scenario.We used these target datasets to compare the detection performance of fusion detection algorithms. Tested algorithms included Bayesian Networks formed with the R package gRain, and also random forest, logistic regression, and support vector machine models implemented with the Python scikit-learn package using default settings. The first 800 days of the data were used for model training, and the last 400 days for testing. Model results were evaluated with the metrics:Sensitivity = (number of target event days signaled) / (all event days) andPositive predictive value (PPV) = (number of target event days signaled) / (all days signaled).These metrics were combined with specificity regarded as the expected fusion alert rate calculated from the historical dataset with no simulated cases injected.ResultsThe left half of Figure 1 illustrates a threat scenario along Oregon’s I5 corridor in which string of fentanyl overdoses with a few fatalities affects the monitored data streams in three counties over a seven-day period. The right half of the figure charts the performance metrics for random forest and Bayesian network machine learning methods applied to both training and test datasets assuming total case counts of 50, 20, and 10 overdoses. Sensitivity values were encouraging, especially for the Bayesian networks and even for the 10-case scenario. Computed PPV levels suggested a manageable public health investigation burden.ConclusionsThe detection results were promising for a threat scenario of particular concern to OHA based on a data scenario deemed plausible and realistic based on historical data. Trust and acceptance from public health surveillance of outputs from supervised machine learning methods beyond traditional statistical methods will require user experience and similar evaluation with additional threat scenarios and authentic event data.Credible truth data can be generated for testing and evaluation of analytic fusion methods with the advantages of several years of historical data from multiple sources and the expertise of experienced monitors. The collaborative generation process may be standardized and extended to other threat types and data environments.Next steps include the addition to the analytic fusion capability of report-based data that can influence data interpretation, including mainstream and social media reports, events in neighboring regions, and law enforcement data.References1. Rhode Island Enhanced State Opioid Overdose Surveillance (ESOOS) Case Definition for Emergency Medical Services (EMS), http://www.health.ri.gov/publications/guidelines/ESOOSCaseDefinitionForEMS.pdf, last accessed: Sept. 9, 2018.

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 Influenza forecasting for the French regions by using EHR, web and climatic data sources with an ensemble approach ARGONet

2019 ◽  
Author(s):  
Canelle Poirier ◽  
Yulin Hswen ◽  
Guillaume Bouzillé ◽  
Marc Cuggia ◽  
Audrey Lavenu ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Disease Activity ◽  
Real Time ◽  
Data Sources ◽  
Monitoring Systems ◽  
Surveillance Systems ◽  
Influenza Activity ◽  
Public Health Officials ◽  
The Right

AbstractEffective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by 1 to 3 weeks. This temporal data gap introduces uncertainty that prevents public health officials from having a timely perspective on the population-level disease activity. Here, we present a machine-learning modeling approach that produces real-time estimates and short-term forecasts of influenza activity for the 12 continental regions of France by leveraging multiple disparate data sources that include, Google search activity, real-time and local weather information, flu-related Twitter micro-blogs, electronic health records data, and historical disease activity synchronicities across regions. Our results show that all data sources contribute to improving influenza surveillance and that machine-learning ensembles that combine all data sources lead to accurate and timely predictions.Author summaryThe role of public health is to protect the health of populations by providing the right intervention to the right population at the right time. In France and all around the world, Influenza is a major public health problem. Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with one-to three-week delay. Accurate real-time monitoring systems of influenza outbreaks could be useful for public health decisions. By combining different data sources and different statistical models, we propose an accurate and timely forecasting platform to track the flu in France at a spatial resolution that, to our knowledge, has not been explored before.

Using Emergency Medical Services Data to Monitor Nonfatal Opioid Overdoses in Real Time

2021 ◽  
Vol 136 (1_suppl) ◽  
pp. 40S-46S
Author(s):  
Benjamin D. Hallowell ◽  
Laura C. Chambers ◽  
Jason Rhodes ◽  
Melissa Basta ◽  
Samara Viner-Brown ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Medical Services ◽  
Real Time ◽  
Rhode Island ◽  
Keyword Search ◽  
Case Definition ◽  
Outbreak Detection ◽  
Medical Services ◽  
Opioid Overdose ◽  
Emergency Medical

Objective No case definition exists that allows public health authorities to accurately identify opioid overdoses using emergency medical services (EMS) data. We developed and evaluated a case definition for suspected nonfatal opioid overdoses in EMS data. Methods To identify suspected opioid overdose–related EMS runs, in 2019 the Rhode Island Department of Health (RIDOH) developed a case definition using the primary impression, secondary impression, selection of naloxone in the dropdown field for medication given, indication of medication response in a dropdown field, and keyword search of the report narrative. We developed the case definition with input from EMS personnel and validated it using an iterative process of random medical record review. We used naloxone administration in consideration with other factors to avoid misclassification of opioid overdoses. Results In 2018, naloxone was administered during 2513 EMS runs in Rhode Island, of which 1501 met our case definition of a nonfatal opioid overdose. Based on a review of 400 randomly selected EMS runs in which naloxone was administered, the RIDOH case definition accurately identified 90.0% of opioid overdoses and accurately excluded 83.3% of non–opioid overdose–related EMS runs. Use of the case definition enabled analyses that identified key patterns in overdose locations, people who experienced repeat overdoses, and the creation of hotspot maps to inform outbreak detection and response. Practice Implications EMS data can be an effective tool for monitoring overdoses in real time and informing public health practice. To accurately identify opioid overdose–related EMS runs, the use of a comprehensive case definition is essential.

A distributed real-time data prediction framework for large-scale time-series data using stream processing

2017 ◽  
Vol 10 (2) ◽  
pp. 145-165 ◽  
Author(s):  
Kehe Wu ◽  
Yayun Zhu ◽  
Quan Li ◽  
Ziwei Wu
Keyword(s):  
Time Series ◽  
Real Time ◽  
Large Scale ◽  
Time Series Data ◽  
Data Sources ◽  
Series Data ◽  
Time Data ◽  
Content Type ◽  
Data Prediction ◽  
Real Time Data

Purpose The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources, e.g., sensor networks, securities exchange, electric power secondary system, etc. Concretely, the proposed framework should handle several difficult requirements including the management of gigantic data sources, the need for a fast self-adaptive algorithm, the relatively accurate prediction of multiple time series, and the real-time demand. Design/methodology/approach First, the autoregressive integrated moving average-based prediction algorithm is introduced. Second, the processing framework is designed, which includes a time-series data storage model based on the HBase, and a real-time distributed prediction platform based on Storm. Then, the work principle of this platform is described. Finally, a proof-of-concept testbed is illustrated to verify the proposed framework. Findings Several tests based on Power Grid monitoring data are provided for the proposed framework. The experimental results indicate that prediction data are basically consistent with actual data, processing efficiency is relatively high, and resources consumption is reasonable. Originality/value This paper provides a distributed real-time data prediction framework for large-scale time-series data, which can exactly achieve the requirement of the effective management, prediction efficiency, accuracy, and high concurrency for massive data sources.

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