scholarly journals Detection of a Salmonellosis Outbreak using Syndromic Surveillance in Georgia

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Rene Borroto ◽  
Jessica Pavlick ◽  
Karl Soetebier ◽  
Bill Williamson ◽  
Patrick Pitcher ◽  
...  
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Food Poisoning ◽  
Cluster Detection ◽  
Free Text ◽  
Family Reunion ◽  
Text Query ◽  
Diarrhea Syndrome ◽  
Zip Code

ObjectiveDescribe how the Georgia Department of Public Health (DPH) used data from its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) module for early detection of an outbreak of salmonellosis in Camden County, Georgia.IntroductionEvidence about the value of syndromic surveillance data for outbreak detection is limited (1). In July 2018, a salmonellosis outbreak occurred following a family reunion of 300 persons held in Camden County, Georgia, where one meal was served on 7/27/2018 and on 7/28/2018.MethodsSendSS-SS and SAS were used for cluster detection of Emergency Department (ED) patients with similar Chief Complaint (CC), Triage Notes (TN), or Discharge Diagnoses (DDx) by facility, time of ED visit, and zip code / county of residence. A SAS-based free-text query related to food poisoning in the CC and DDx fields was also performed on a daily basis. County- and hospital-specific charting of the Diarrhea syndrome was also conducted in SendSS-SS, whereas county- and zip code-specific charting of the same syndrome were done in both SendSS-SS and SAS (2).ResultsOn Sunday July 29th, 2018, three children and three adults were seen within 18 hours at the ED of Hospital A in Camden County, Georgia. All patients complained of diarrhea, vomiting, and food poisoning, after a large family reunion that had been held the day before. This early cluster was detected by the SAS-based free-text query of ‘food poisoning’ and the SAS-based cluster detection tool for patients with Diarrhea syndrome. The District Epidemiologists (DE) in the Coastal Health District were notified on Monday, July 30th, 2018. One-year high daily spikes of the Diarrhea syndrome occurred from July 29th to July 31st, 2018 in a local hospital ED (Fig 1), Camden County, and zip code 31548. Two HIPAA-compliant line lists with a total of 27 patients seen at EDs were emailed to the DEs to support active case finding. No further spikes of the Diarrhea syndrome were detected in Camden County during the 2-week period after the 3-day spike.ConclusionsSyndromic surveillance was a useful surveillance tool for early detection of a salmonellosis outbreak, helping with the active search for outbreak cases, tracking the peak of the outbreak, and assuring that no further spikes were occurring.References1.R Hopkins, C Tong, H Burkom, et al. A Practitioner-Driven Research Agenda for Syndromic Surveillance. Public Health Reports 2017; 132(Supplement1): 116S-126S.2. G Zhang, A Llau, J Suarez, E O'Connell, E Rico, R Borroto, F Leguen. Using ESSENCE to Track a Gastrointestinal Outbreak in a Homeless Shelter in Miami-Dade County, 2008. Advances in Disease Surveillance. 2008; 5:139. 

Window of Opportunity for New Disease Surveillance: Developing Keyword Lists for Monitoring Mental Health and Injury Through Syndromic Surveillance

2016 ◽  
Vol 11 (2) ◽  
pp. 173-178 ◽  
Author(s):  
Ursula Lauper ◽  
Jian-Hua Chen ◽  
Shao Lin
Keyword(s):  
Public Health ◽  
Mental Health ◽  
New York ◽  
Syndromic Surveillance ◽  
New York State ◽  
Hurricane Sandy ◽  
York State Department ◽  
Free Text ◽  
York State ◽  
The Impact

AbstractStudies have documented the impact that hurricanes have on mental health and injury rates before, during, and after the event. Since timely tracking of these disease patterns is crucial to disaster planning, response, and recovery, syndromic surveillance keyword filters were developed by the New York State Department of Health to study the short- and long-term impacts of Hurricane Sandy. Emergency department syndromic surveillance is recognized as a valuable tool for informing public health activities during and immediately following a disaster. Data typically consist of daily visit reports from hospital emergency departments (EDs) of basic patient data and free-text chief complaints. To develop keyword lists, comparisons were made with existing CDC categories and then integrated with lists from the New York City and New Jersey health departments in a collaborative effort. Two comprehensive lists were developed, each containing multiple subcategories and over 100 keywords for both mental health and injury. The data classifiers using these keywords were used to assess impacts of Sandy on mental health and injuries in New York State. The lists will be validated by comparing the ED chief complaint keyword with the final ICD diagnosis code. (Disaster Med Public Health Preparedness. 2017;11:173–178)

scholarly journals Precision public health through clinic-based syndromic surveillance in communities

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Ta-Chien Chan ◽  
Yung-Chu Teng ◽  
Yen-Hua Chu ◽  
Tzu-Yu Lin
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Early Stage ◽  
Age Distribution ◽  
Local Health ◽  
Syndromic Surveillance System ◽  
Taipei City ◽  
Kaohsiung City

ObjectiveSentinel physician surveillance in the communities has played an important role in detecting early aberrations in epidemics. The traditional approach is to ask primary care physicians to actively report some diseases such as influenza-like illness (ILI), and hand, foot, and mouth disease (HFMD) to health authorities on a weekly basis. However, this is labor-intensive and time-consuming work. In this study, we try to set up an automatic sentinel surveillance system to detect 23 syndromic groups in the communites.IntroductionIn December 2009, Taiwan’s CDC stopped its sentinel physician surveillance system. Currently, infectious disease surveillance systems in Taiwan rely on not only the national notifiable disease surveillance system but also real-time outbreak and disease surveillance (RODS) from emergency rooms, and the outpatient and hospitalization surveillance system from National Health Insurance data. However, the timeliness of data exchange and the number of monitored syndromic groups are limited. The spatial resolution of monitoring units is also too coarse, at the city level. Those systems can capture the epidemic situation at the nationwide level, but have difficulty reflecting the real epidemic situation in communities in a timely manner. Based on past epidemic experience, daily and small area surveillance can detect early aberrations. In addition, emerging infectious diseases do not have typical symptoms at the early stage of an epidemic. Traditional disease-based reporting systems cannot capture this kind of signal. Therefore, we have set up a clinic-based surveillance system to monitor 23 kinds of syndromic groups. Through longitudinal surveillance and sensitive statistical models, the system can automatically remind medical practitioners of the epidemic situation of different syndromic groups, and will help them remain vigilant to susceptible patients. Local health departments can take action based on aberrations to prevent an epidemic from getting worse and to reduce the severity of the infected cases.MethodsWe collected data on 23 syndromic groups from participating clinics in Taipei City (in northern Taiwan) and Kaohsiung City (in southern Taiwan). The definitions of 21 of those syndromic groups with ICD-10 diagnoses were adopted from the International Society for Disease Surveillance (https://www.surveillancerepository.org/icd-10-cm-master-mapping-reference-table). The definitions of the other two syndromic groups, including dengue-like illness and enterovirus-like illness, were suggested by infectious disease and emergency medicine specialists.An enhanced sentinel surveillance system named “Sentinel plus” was designed for sentinel clinics and community hospitals. The system was designed with an interactive interface and statistical models for aberration detection. The data will be computed for different combinations of syndromic groups, age groups and gender groups. Every day, each participating clinic will automatically upload the data to the provider of the health information system (HIS) and then the data will be transferred to the research team.This study was approved by the committee of the Institutional Review Board (IRB) at Academia Sinica (AS-IRB02-106262, and AS-IRB02-107139). The databases we used were all stripped of identifying information and thus informed consent of participants was not required.ResultsThis system started to recruit the clinics in May 2018. As of August 2018, there are 89 clinics in Kaohsiung City and 33 clinics and seven community hospitals in Taipei City participating in Sentinel plus. The recruiting process is still ongoing. On average, the monitored volumes of outpatient visits in Kaohsiung City and Taipei City are 5,000 and 14,000 per day.Each clinic is provided one list informing them of the relative importance of syndromic groups, the age distribution of each syndromic group and a time-series chart of outpatient rates at their own clinic. In addition, they can also view the village-level risk map, with different alert colors. In this way, medical practitioners can know what’s going on, not only in their own clinics and communities but also in the surrounding communities.The Department of Health (Figure 1) can know the current increasing and decreasing trends of 23 syndromic groups by red and blue color, respectively. The spatial resolution has four levels including city, township, village and clinic. The map and bar chart represent the difference in outpatient rate between yesterday and the average for the past week. The line chart represents the daily outpatient rates for one selected syndromic group in the past seven days. The age distribution of each syndromic group and age-specific outpatient rates in different syndromic groups can be examined.ConclusionsSentinel plus is still at the early stage of development. The timeliness and the accuracy of the system will be evaluated by comparing with some syndromic groups in emergency rooms and the national notifiable disease surveillance system. The system is designed to assist with surveillance of not only infectious diseases but also some chronic diseases such as asthma. Integrating with external environmental data, Sentinel plus can alert public health workers to implement better intervention for the right population.References1. James W. Buehler AS, Marc Paladini, Paula Soper, Farzad Mostashari: Syndromic Surveillance Practice in the United States: Findings from a Survey of State, Territorial, and Selected Local Health Departments. Advances in Disease Surveillance 2008, 6(3).2. Ding Y, Fei Y, Xu B, Yang J, Yan W, Diwan VK, Sauerborn R, Dong H: Measuring costs of data collection at village clinics by village doctors for a syndromic surveillance system — a cross sectional survey from China. BMC Health Services Research 2015, 15:287.3. Kao JH, Chen CD, Tiger Li ZR, Chan TC, Tung TH, Chu YH, Cheng HY, Liu JW, Shih FY, Shu PY et al.: The Critical Role of Early Dengue Surveillance and Limitations of Clinical Reporting -- Implications for Non-Endemic Countries. PloS one 2016, 11(8):e0160230.4. Chan TC, Hu TH, Hwang JS: Daily forecast of dengue fever incidents for urban villages in a city. International Journal of Health Geographics 2015, 14:9.5. Chan TC, Teng YC, Hwang JS: Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models. BMC Public Health 2015, 15:168.6. Ma HT: Syndromic surveillance system for detecting enterovirus outbreaks evaluation and applications in public health. Taipei, Taiwan: National Taiwan University; 2007. 

scholarly journals Development and validation of an automated emergency department-based syndromic surveillance system to enhance public health surveillance in Yukon: a lower-resourced and remote setting

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Etran Bouchouar ◽  
Benjamin M. Hetman ◽  
Brendan Hanley
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Validation Dataset ◽  
Free Text ◽  
Mass Gathering ◽  
Syndromic Surveillance System ◽  
Development And Validation ◽  
Remote Setting

Abstract Background Automated Emergency Department syndromic surveillance systems (ED-SyS) are useful tools in routine surveillance activities and during mass gathering events to rapidly detect public health threats. To improve the existing surveillance infrastructure in a lower-resourced rural/remote setting and enhance monitoring during an upcoming mass gathering event, an automated low-cost and low-resources ED-SyS was developed and validated in Yukon, Canada. Methods Syndromes of interest were identified in consultation with the local public health authorities. For each syndrome, case definitions were developed using published resources and expert elicitation. Natural language processing algorithms were then written using Stata LP 15.1 (Texas, USA) to detect syndromic cases from three different fields (e.g., triage notes; chief complaint; discharge diagnosis), comprising of free-text and standardized codes. Validation was conducted using data from 19,082 visits between October 1, 2018 to April 30, 2019. The National Ambulatory Care Reporting System (NACRS) records were used as a reference for the inclusion of International Classification of Disease, 10th edition (ICD-10) diagnosis codes. The automatic identification of cases was then manually validated by two raters and results were used to calculate positive predicted values for each syndrome and identify improvements to the detection algorithms. Results A daily secure file transfer of Yukon’s Meditech ED-Tracker system data and an aberration detection plan was set up. A total of six syndromes were originally identified for the syndromic surveillance system (e.g., Gastrointestinal, Influenza-like-Illness, Mumps, Neurological Infections, Rash, Respiratory), with an additional syndrome added to assist in detecting potential cases of COVID-19. The positive predictive value for the automated detection of each syndrome ranged from 48.8–89.5% to 62.5–94.1% after implementing improvements identified during validation. As expected, no records were flagged for COVID-19 from our validation dataset. Conclusions The development and validation of automated ED-SyS in lower-resourced settings can be achieved without sophisticated platforms, intensive resources, time or costs. Validation is an important step for measuring the accuracy of syndromic surveillance, and ensuring it performs adequately in a local context. The use of three different fields and integration of both free-text and structured fields improved case detection.

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 Improving Timeliness of Georgia Emergency Room Data

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Lance Ballester ◽  
Karl Soetebier ◽  
Bill Williamson ◽  
Rene Borroto ◽  
Jessica Grippo ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Room ◽  
Disease Control ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Kernel Density ◽  
Meaningful Use ◽  
Patient Records ◽  
Data Analyses ◽  
Triage Time

ObjectiveTo explore the timeliness of emergency room surveillance data after the advent of federal Meaningful Use initiatives and determine potential areas for improvement.IntroductionTimeliness of emergency room (ER) data is arguably its strongest attribute in terms of its contribution to disease surveillance. Timely data analyses may improve the efficacy of prevention and control measures.There are a number of studies that have looked at timeliness prior to the advent of Meaningful Use, and these studies note that ER data were not fast enough for them to be useful in real time2,3. However, the change in messaging practices in the Meaningful Use era potentially changes this.Other studies have shown that changes in processes and protocol can dramatically improve timeliness1,4 and this motivates the current study of timeliness to identify processes that can be changed to improve timeliness.MethodsER data were collected from March 2017 through September 2017 from both the Georgia Department of Public Health’s (GDPH) State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance Module and the Centers for Disease Control and Prevention (CDC) National Syndromic Surveillance Program’s (NSSP) ESSENCE systems. Patients from hospitals missing 10 or more days of data, as well as patients with missing or invalid triage times, and all visits after August 1st were excluded in order to ensure data were representative of a “typical” time period and that a sufficient amount of time was given for visits to arrive from hospitals.The timeliness of individual records was determined in a number of different ways. All timeliness measurements were determined by subtracting the earlier time event from the later time of the event. The overall measure of timeliness is the time between the patient’s triage time and the data being present in the ESSENCE data system. In between, Georgia’s SendSS system receives and processes the data. This is illustrated in Figure 1. Due to the skewed nature of these measures, they were analyzed using medians and Gaussian kernel density plots.ResultsThe study in total included records from 118 Georgia hospitals, 14,203 data files and 1,897,501 patient records. Overall median timeliness of data from Triage Time to being available in SendSS for analyses was 33.62 hours (IQR=28.5), and in ESSENCE was 45.08 hours (IQR=37.05).The distributions of Triage Time of Day, Time Available in SendSS Staging, and Time Available in ESSENCE Analysis can be seen in Figure 2. Additionally, lines were added for when SendSS makes data available for its own analyses and when it sends data to ESSENCE. These latter lines represent places where the SendSS system itself could improve, and potential improved times were noted based on the kernel densities.Peak triage times for Georgia hospitals were between 10 am to 11 pm, shown in black. This represents the ideal timeliness if Hospitals sent their data immediately. However, data was all batched by Georgia hospitals and sent at different times of the day. The distribution of the time patient records arrived at SendSS staging was indicated in blue.During the period of this study, Georgia processed data into its SendSS system at 6:30am and 11:30am every day and sent data to the ESSENCE system at 1pm each day. These times are highlighted on the plot in green, and red respectively. New potential improved times, based on the kernel density of data being available in SendSS staging, are shown in the lighter shades of these colors at 8:30am and 12pm every day, while being sent to ESSENCE at 9am and 12:30pm to ensure time for data to be properly processed. These were determined to be optimal times for reducing lag in the data, however, may not be optimal for daily analysis.The purple line on the plot represents the times that data were available in ESSENCE’s system for analysis. This was notably delayed by a median 4.15 hours after the data was sent to ESSENCE on a typical day.ConclusionsA data driven approach to choosing processing times could improve timeliness of data analyses in the SendSS and ESSENCE systems. By conducting this type of analysis in an ongoing periodic basis, processing lag times can be kept at a minimum.1. Centers for Disease Control. Progress in improving state and local disease surveillance--United States, 2000-2005. MMWR Morbidity and mortality weekly report. 2005;54(33):822-825.2. Jajosky R, Groseclose S. Evaluation of reporting timeliness of public health surveillance systems for infectious diseases. BMC Public Health. 2004;4(1).3. Travers D, Barnett C, Ising A, Waller A. Timeliness of emergency department diagnoses for syndromic surveillance. AMIA Annual Symposium Proceedings. 2006;Vol. 2006:769.4. Ward M, Brandsema P, van Straten E, Bosman A. Electronic reporting improves timeliness and completeness of infectious disease notification, The Netherlands, 2003. Eurosurveillance. 2005;10(1):7-8.

scholarly journals “That was then, this is now” improving public health syndromic surveillance baselines

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Roger Morbey ◽  
Alex J. Elliot ◽  
Paul Loveridge ◽  
Helen Hughes ◽  
Sally Harcourt ◽  
...  
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Regression Models ◽  
Major Change ◽  
Surveillance Systems ◽  
Care Activity ◽  
Working Practices ◽  
Available Information ◽  
Traditional Surveillance

ObjectiveTo improve the ability of syndromic surveillance systems to detectunusual events.IntroductionSyndromic surveillance systems are used by Public Health England(PHE) to detect changes in health care activity that are indicative ofpotential threats to public health. By providing early warning andsituational awareness, these systems play a key role in supportinginfectious disease surveillance programmes, decision making andsupporting public health interventions.In order to improve the identification ofunusualactivity, wecreated new baselines to modelseasonally expectedactivity inthe absence of outbreaks or other incidents. Although historicaldata could be used to model seasonality, changes due to publichealth interventions or working practices affected comparability.Specific examples of these changes included a major change in theway telehealth services were provided in England and the rotavirusvaccination programme introduced in July 2013 that changed theseasonality of gastrointestinal consultations. Therefore, we needed toincorporate these temporal changes in our baselines.MethodsWe used negative binominal regression to model daily syndromicsurveillance, allowing for day of week and public holiday effects.To account for step changes in data caused by changes in healthcaresystem working practices or public health interventions we introducedspecific independent variables into the models. Finally, we smoothedthe regression models to provide short term forecasts of expectedtrends.The new baselines were applied to PHE’s four syndromicsurveillance systems for daily surveillance and public-facing weeklybulletins.ResultsWe replaced traditional surveillance baselines (based on simpleaverages of historical data) with the regression models for dailysurveillance of 53 syndromes across four syndromic surveillancesystems. The improved models captured current seasonal trends andmore closely reflected actual data outside of outbreaks.ConclusionsSyndromic surveillance baselines provide context forepidemiologists to make decisions about seasonal disease activity andemerging public health threats. The improved baselines developedhere showed whether current activity was consistent with expectedactivity, given all available information, and improved interpretationwhen trends diverged from expectations.

scholarly journals Syndrome Development to Assess IDU, HIV, and Homelessness in MA Emergency Departments

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Stefanie P. Albert ◽  
Rosa Ergas ◽  
Sita Smith ◽  
Gillian Haney ◽  
Monina Klevens
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Drug Use ◽  
Hiv Infection ◽  
Injection Drug Use ◽  
Syndromic Surveillance ◽  
Injection Drug ◽  
Free Text ◽  
Increased Risk ◽  
Ed Visits

ObjectiveWe sought to measure the burden of emergency department (ED) visits associated with injection drug use (IDU), HIV infection, and homelessness; and the intersection of homelessness with IDU and HIV infection in Massachusetts via syndromic surveillance data.IntroductionIn Massachusetts, syndromic surveillance (SyS) data have been used to monitor injection drug use and acute opioid overdoses within EDs. Currently, Massachusetts Department of Public Health (MDPH) SyS captures over 90% of ED visits statewide. These real-time data contain rich free-text and coded clinical and demographic information used to categorize visits for population level public health surveillance.Other surveillance data have shown elevated rates of opioid overdose related ED visits, Emergency Medical Service incidents, and fatalities in Massachusetts from 2014-20171,2,3. Injection of illicitly consumed opioids is associated with an increased risk of infectious diseases, including HIV infection. An investigation of an HIV outbreak among persons reporting IDU identified homelessness as a social determinant for increased risk for HIV infection.MethodsTo accomplish our objectives staff used an existing MDPH SyS IDU syndrome definition4, developed a novel syndrome definition for HIV-related visits, and adapted Maricopa County's homelessness syndrome definition. Syndromes were applied to Massachusetts ED data through the CDC’s BioSense Platform. Visits meeting the HIV and homelessness syndromes were randomly selected and reviewed to assess accuracy; inclusion and exclusion criteria were then revised to increase specificity. The final versions of all three syndrome definitions incorporate free-text elements from the chief complaint and triage notes, as well as International Statistical Classification of Diseases and Related Health Problems, 9th (ICD-9) and 10th Revision (ICD-10) diagnostic codes. Syndrome categories were not mutually exclusive, and all reported visits occurring at Massachusetts EDs were included in the analysis.Syndromes CreatedFor the HIV infection syndrome definition, we incorporated the free-text term “HIV” in both the chief complaint and triage notes. Visit level review demonstrated that the following exclusions were needed to reduce misspellings, inclusion of partial words, and documentation of HIV testing results: “negative for HIV”, “HIV neg”, “negative test for HIV”, “hive”, “hivies”, and “vehivcle”. Additionally, the following diagnostic codes were incorporated: V65.44 (Human immunodeficiency virus [HIV] counseling), V08 (asymptomatic HIV infection status), V01.79 (contact with or exposure to other viral diseases), 795.71 (nonspecific serologic evidence of HIV), V73.89 (special screening examination for other specified viral diseases), 079.53 (HIV, type 2 [HIV-2]), Z20.6 (contact with and (suspected) exposure to HIV), Z71.7 (HIV counseling), B20 (HIV disease), Z21 (asymptomatic HIV infection status), R75 (inconclusive laboratory evidence of HIV), Z11.4 (encounter for screening for HIV), and B97.35 (HIV-2 as the cause of diseases classified elsewhere).Building on the Maricopa County homeless syndrome definition, we incorporated a variety of free-text inclusion and exclusion terms. To meet this definition visits had to mention: “homeless”, or “no housing”, or, “lack of housing”, or “without housing”, or “shelter” but not animal and domestic violence shelters. We also selected the following ICD-10 codes for homelessness and inadequate housing respectively, Z59.0 and Z59.1.We analyzed MDPH SyS data for visits occurring from January 1, 2016 through June 30, 2018. Rates per 10,000 ED visits categorized as IDU, HIV, or homeless were calculated. Subsequently, visits categorized as IDU, HIV, and meeting both IDU and HIV syndrome definitions (IDU+HIV) were stratified by homelessness.ResultsSyndrome Burden on EDThe MDPH SyS dataset contains 6,767,137 ED visits occurring during the study period. Of these, 82,819 (1.2%) were IDU-related, 13,017 (0.2%) were HIV-related, 580 (<0.01%) were related to IDU + HIV, and 42,255 visits (0.6%) were associated with homelessness.The annual rate of IDU-related visits increased 15% from 2016 through June of 2018 (from 113.63 to 130.57 per 10,000 visits); while rates of HIV-related and IDU + HIV-related visits remained relatively stable. The overall rate of visits associated with homelessness increased 47% (from 49.99 to 73.26 per 10,000 visits).Rates of IDU, HIV, and IDU + HIV were significantly higher among visits associated with homelessness. Among visits that met the homeless syndrome definition compared to those that did not: the rate of IDU-related visits was 816.0 versus 118.03 per 10,000 ED visits (X2= 547.12, p<0. 0001); the rate of visits matching the HIV syndrome definition was 145.54 versus 18.44 per 10,000 ED visits (X2= 99.33, p<0.0001); and the rate of visits meeting the IDU+HIV syndrome definition was 15.86 versus 0.76 per 10,000 visits (X2= 13.72, p= 0.0002).ConclusionsMassachusetts is experiencing an increasing burden of ED visits associated with both IDU and homelessness that parallels increases in opioid overdoses. Higher rates of both IDU and HIV-related visits were associated with homelessness. An understanding of the intersection between opioid overdoses, IDU, HIV, and homelessness can inform expanded prevention efforts, introduction of alternatives to ED care, and increase consideration of housing status during ED care.Continued surveillance for these syndromes, including collection and analysis of demographic and clinical characteristics, and geographic variations, is warranted. These data can be useful to providers and public health authorities for planning healthcare services.References1. Vivolo-Kantor AM, Seth P, Gladden RM, et al. Vital Signs: Trends in Emergency Department Visits for Suspected Opioid Overdoses — United States, July 2016–September 2017. MMWR Morbidity and Mortality Weekly Report 2018; 67(9);279–285 DOI: http://dx.doi.org/10.15585/mmwr.mm6709e12. Massachusetts Department of Public Health. Chapter 55 Data Brief: An assessment of opioid-related deaths in Massachusetts, 2011-15. 2017 August. Available from: https://www.mass.gov/files/documents/2017/08/31/data-brief-chapter-55-aug-2017.pdf3. Massachusetts Department of Public Health. MA Opioid-Related EMS Incidents 2013-September 2017. 2018 Feb. Available from: https://www.mass.gov/files/documents/2018/02/14/emergency-medical-services-data-february-2018.pdf4. Bova, M. Using emergency department (ED) syndromic surveillance to measure injection-drug use as an indicator for hepatitis C risk. Powerpoint presented at: 2017 Northeast Epidemiology Conference. 2017 Oct 18 – 20; Northampton, Massachusetts, USA.

scholarly journals Comparing Emergency Department Gunshot Wound Data with Mass Casualty Shooting Reports

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrew Walsh
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Syndromic Surveillance ◽  
Gunshot Wound ◽  
Gunshot Wounds ◽  
Mass Casualty ◽  
Gun Violence ◽  
Mass Shootings ◽  
Mass Shooting ◽  
Zip Code

ObjectiveTo determine whether mass casualty shooting events are capturedvia syndromic surveillance data.IntroductionShootings with multiple victims are a concern for public safetyand public health. The precise impact of such events and the trendsassociated with them is dependent on which events are counted. Somereports only consider events with multiple deaths, typically four ormore, while other reports also include events with multiple victimsand at least one death.1Underreporting is also a concern. Somecommonly cited databases for these events are based on media reportsof shootings which may or may not capture the complete set of eventsthat meet whatever criteria are being considered.Many gunshot wounds are treated in the emergency departmentsetting. Emergency department registrations routinely collected forsyndromic surveillance will capture all of those visits. Analysis ofthat data may be useful as a supplement to mass shooting databases byidentifying unreported events. In addition, clusters of gunshot woundincidents which are not the result of a single shooting event but stillrepresent significant public safety and public health concerns mayalso be identified.MethodsEmergency department registration data was collected fromhospitals via the EpiCenter syndromic surveillance system. Gunshot-related visits were identified based on chief complaint contentsusing EpiCenter’s regular expression-based classification system.The gunshot wound classifier attempts to exclude patients with pre-existing wounds and shooting incidents involving weapon classes thatare lesser concerns for public safety, such as nail guns and toy guns.Gunshot-related visits were clustered by day of registration andseparately by facility, by patient home zip code, and by patienthome county. The largest clusters of each type were compared viamanual search against media reports of shootings and against the GunViolence Archive mass shooting database.ResultsA total of 23,132 gunshot-related visits were identified from 635healthcare facilities from 2013 to 2015. From these, the five largestclusters by facility, by zip code, and by county were identified. Theclusters included 112 gunshot wounds in total, ranging in size from4 to 12 with a median of 7.Of the 5 facility clusters, 5 had a corresponding media story and 2were located in the shooting database. Of the 5 zip code clusters, 1 hada corresponding media story and none were located in the shootingdatabase. Of the 5 county clusters, 4 had a corresponding media storyand 1 was located in the shooting database.ConclusionsMultiple gunshot wound patients being treated on the same daywere not necessarily all shot during the same incident or by the sameshooter. The information available in a syndromic surveillance feeddoes not allow for direct identification of the shooter or shooters.Given that limitation, a complete correspondence between clustersidentified in syndromic surveillance data and mass shootings was notexpected. The strong correlation between clusters and media coverageindicates that the news is a reasonable source for shooting data. Thesmaller overlap with the mass shooting database is likely due to themore stringent criteria required for an incident to qualify as a massshooting.It is still notable that the majority of gunshot clusters were notassociated with any particular mass shooting incident. This serves asa reminder that mass shootings represent only a small portion of thetotal gun violence in the United States. Healthcare data representsa significant additional data source for understanding the completeimpact of gun violence on public health and safety.Weekly time series of gunshot-related emergency department visits

scholarly journals A Practitioner-Driven Research Agenda for Syndromic Surveillance

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 116S-126S ◽  
Author(s):  
Richard S. Hopkins ◽  
Catherine C. Tong ◽  
Howard S. Burkom ◽  
Judy E. Akkina ◽  
John Berezowski ◽  
...  
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Research Agenda ◽  
Syndromic Surveillance ◽  
Surveillance Network ◽  
Systems Research ◽  
Health Practitioners ◽  
Research Questions ◽  
Surveillance Practice ◽  
Public Health Practitioners

Syndromic surveillance has expanded since 2001 in both scope and geographic reach and has benefited from research studies adapted from numerous disciplines. The practice of syndromic surveillance continues to evolve rapidly. The International Society for Disease Surveillance solicited input from its global surveillance network on key research questions, with the goal of improving syndromic surveillance practice. A workgroup of syndromic surveillance subject matter experts was convened from February to June 2016 to review and categorize the proposed topics. The workgroup identified 12 topic areas in 4 syndromic surveillance categories: informatics, analytics, systems research, and communications. This article details the context of each topic and its implications for public health. This research agenda can help catalyze the research that public health practitioners identified as most important.

scholarly journals Syndromic infectious disease surveillance of refugees in Greece: a mixed methods analysis

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
J Graef ◽  
M Omar ◽  
A Abbara
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
User Experience ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Respiratory Infections ◽  
National Level ◽  
Structural Barriers ◽  
Syndromic Surveillance System ◽  
Humanitarian Response

Abstract Background An estimated 1,174,140 refugees have migrated into Greece, a main entry point for refugees into Europe, since 2014. Their infectious disease profile is monitored by a national-level ad-hoc syndromic surveillance system in refugee-migrant reception centres. The utility of this system is explored to contribute evidence to and improve syndromic surveillance in European refugee responses. Methods Proportional morbidities, numbers of cases and signals, cases above expected numbers, of 14 syndromes are collated from weekly reports between 2016-2019, graphed and analysed in the context of the humanitarian response. Semi-structured key informant interviews are conducted and thematically analysed. Results Between 20.06.2016 and 17.02.2019, 36358 cases and 116 signals occurred. Public health responses resulted and there were no significant outbreaks. On average 5% of all consultations in centres were on infectious syndromes. Respiratory infections with fever (57%), gastroenteritis (22%), suspected scabies (13%) and rashes with fever (5%) were most commonly reported. Every week, between 68-100% of 25-58 participating centres completed reporting adequately. 6 informants reported on their syndromic system user experience. The system’s benefits, providing information and safeguarding refugees, outweighed harms. Data was timely and complete, but likely under-reported for common conditions. Poor living conditions and inter-agency coordination complicated reporting and public health responses. Conclusions Infectious burdens and trends were provided by the system and allowed for timely responses. Data quality was adequate. The system was valuable and feasible to informants. The set-up of the humanitarian response, inadequate ownership and poor coordination of authorities reduced the system’s utility. Key messages Syndromic surveillance is useful for monitoring refugee infectious health. Structural barriers need to be resolved to improve systems’ data and user experience.

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