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 Developing Syndromic Surveillance to Monitor and Respond to Adverse Health Events Related to Psychoactive Substance Use: Methods and Applications

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 65S-72S ◽  
Author(s):  
Michelle L. Nolan ◽  
Hillary V. Kunins ◽  
Ramona Lall ◽  
Denise Paone
Keyword(s):  
Public Health ◽  
New York ◽  
New York City ◽  
York City ◽  
Syndromic Surveillance ◽  
Psychoactive Drug ◽  
Surveillance Systems ◽  
Chief Complaints ◽  
Level Information ◽  
Ed Visits

Introduction: Recent increases in drug overdose deaths, both in New York City and nationally, highlight the need for timely data on psychoactive drug-related morbidity. We developed drug syndrome definitions for syndromic surveillance to monitor drug-related emergency department (ED) visits in real time. Materials and Methods: We used 2012 archived syndromic surveillance data from New York City hospitals to develop definitions for psychoactive drug-related syndromes. The dataset contained ED visit-level information that included patients’ chief complaints, dates of visits, ZIP codes of residence, discharge diagnoses, and dispositions. After manually reviewing chief complaints, we developed a classification scheme comprising 3 categories (overdose, drug mention, and drug abuse/misuse), which we used to define 25 psychoactive drug syndromes. From July 2013 through December 2015, the New York City Department of Health and Mental Hygiene performed daily syndromic surveillance of psychoactive drug-related ED visits using the 25 syndrome definitions. Results: Syndromic surveillance triggered 4 public health investigations, supported 8 other public health investigations that had been triggered by other mechanisms, and resulted in the identification of 5 psychoactive drug-related outbreaks. Syndromic surveillance also identified a substantial increase in synthetic cannabinoid-related visits (from an average of 3 per week in January 2014 to >300 per week in July 2015) and an increase in heroin overdose visits (from 80 to 171 in the first 3 quarters of 2012 and 2014, respectively) in a single neighborhood. Practice Implications: Syndromic surveillance using these novel definitions enabled monitoring of trends in psychoactive drug-related morbidity, initiation and support of public health investigations, and targeting of interventions. Health departments can refine these definitions for their jurisdictions using the described methods and integrate them into existing syndromic surveillance systems.

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 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 Syndromic surveillance from free-text triage chief complaints

2003 ◽  
Vol 80 (S1) ◽  
pp. i120-i120 ◽  
Author(s):  
Wendy W. Chapman ◽  
Michael M. Wagner ◽  
Oleg Ivanov ◽  
Robert Olszewski ◽  
John N. Dowling
Keyword(s):  
Syndromic Surveillance ◽  
Free Text ◽  
Chief Complaints

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 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 Monitoring Heat-Related Illness through Syndromic Surveillance in Los Angeles County

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jimmy Duong ◽  
Michael Lim ◽  
Emily Kajita ◽  
Bessie Hwang
Keyword(s):  
Public Health ◽  
Los Angeles ◽  
Syndromic Surveillance ◽  
Los Angeles County ◽  
Correlation Coefficients ◽  
Temperature Data ◽  
Extreme Heat ◽  
Chief Complaints ◽  
Ed Visits ◽  
Council Of State

ObjectiveTo analyze Los Angeles County’s (LAC) extreme heat season in 2018 and evaluate the Council of State and Territorial Epidemiologists’ (CSTE) syndrome query for heat-related-illness (HRI) in Los Angeles County (LAC)IntroductionLAC experienced several days of record-breaking temperatures during the summer of 2018. Downtown Los Angeles temperatures soared to 108°F in July with an average daily maximum of 92°F. Extreme heat events such as these can pose major risks to human health. Syndromic surveillance can be a useful tool in providing near real-time surveillance of HRI. In 2014, a working group was formed within the CSTE Climate Change Subcommittee to define and analyze HRI. The workgroup’s goal was to provide guidance to public health professionals in adapting and implementing an HRI syndrome surveillance query. The Acute Communicable Disease Control Program’s (ACDC) Syndromic Surveillance Unit utilized CSTE’s HRI query to provide surveillance during the extreme heat season in 2018 in LAC. Additional modifications to the CSTE query were evaluated for potential improvements towards characterizing HRI trends.MethodsFrom May 1 to September 30, 2018, Emergency Department (ED) data were queried for cases using the CSTEs definition for HRI. The queries consisted of key word searches within the chief complaint (CC) data field, and, if available, the diagnosis data fields. The query was derived from the CSTE HRI query published in 20161. In addition, ACDC explored the utility of expanding the CSTE syndrome definition to include additional chief complaints commonly associated with HRI such as dehydration and syncope. Both queries were applied on all participating syndromic EDs in LAC alongside daily high temperature data trends. Local temperature data for downtown Los Angeles weather station KCQT were taken from the Weather Underground website. Spearman correlation coefficients were calculated for each query during the heat season. Similarly, both queries were also applied during colder months from October 1, 2017 to April 30, 2018 for comparison. Lastly, results for dehydration and syncope were independently assessed apart from other HRI query terms during both heat seasons and colder months.ResultsThe CSTE HRI query and the query with the added terms yielded 1,258 and 63,332 ED visits, respectively, during the heat season. On July 6, the maximum daily temperature peaked at 108 °F; the HRI and the query with the added terms yielded 136 and 618 ED visits, respectively. The HRI query and the HRI query with the added terms had a correlation coefficient of 0.714 (p <0.0001) and 0.427 (p <0.0001), respectively. During colder months, the CSTE HRI query and the query with the added terms yielded 377 and 86,008, respectively, with correlation coefficients of 0.342 (p < 0.0001) and 0.133 (p < 0.052). The syncope-only query saw no variation in HRI classified encounters throughout the heat season (mean: 328; min: 228; max: 404) or colder months (mean: 328; min: 261; max: 404) with correlation coefficients of 0.238 (p = 0.003) and 0.155 (p = 0.024), respectively. Similarly, the dehydration-only query saw no variation in HRI classified encounters throughout the heat season (mean: 96; min: 58; max: 258) or colder months (mean: 94; min: 60; max: 160) with correlation coefficients of 0.596 (p < 0.0001) and -0.016 (p = 0.822).ConclusionsThe CSTE HRI query proved to be a strong indicator for HRI, and the addition of terms associated with dehydration and syncope to the CSTE HRI query weakened the correlation with temperature. Compared to the original CSTE HRI query, the added terms yielded a 4934% increase in HRI classified encounters during the heat season; however, these were likely due to causes other than HRI -- adding the extra terms resulted in a weaker correlation with temperature. Additionally, the comparative analysis showed that, with the added terms, the volume of HRI encounters was larger during colder months than hotter months suggesting misclassification of non-HRI illnesses. Surveillance of HRI has proven to be difficult because many of the HRI symptoms are too commonly associated with non-HRI conditions which would explain the weaker correlations when adding additional chief complaints associated with HRI. In conclusion, the CSTE syndrome definition for HRI proved to be the most robust query for HRI during the heat season. Case counts of HRI are difficult due to symptom overlap with many other medical conditions. However, syndromic surveillance using the CSTE HRI query is useful for trend analysis in near real-time during heat events.References1. Council of State and Territorial Epidemiologists. Heat-Related Illness Syndrome Query: A Guidance Document for Implementing Heat-Related Illness Syndromic Surveillance in Public Health Practice. Version 1.0. 2016 Sep. 12 p. 

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 Using State and National Surveillance Systems during World Equestrian Games in NC

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Lana Deyneka ◽  
Zachary Faigen ◽  
Anne Hakenwerth ◽  
Nicole Lee ◽  
Amy Ising ◽  
...  
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Urgent Care ◽  
Poison Center ◽  
Surveillance Systems ◽  
Mass Gathering ◽  
Mass Gatherings ◽  
Epidemic Intelligence ◽  
Polk County ◽  
Ed Visits

ObjectiveTo describe surveillance activities and use of existing state (NC DETECT) and national (NSSP) syndromic surveillance systems during the International Federation for Equestrian Sports (FEI) World Equestrian Games (WEG), in Mill Spring, NC from September 11 to September 23, 2018MethodsNC DETECT collects statewide data from hospital emergency department (ED) visits and Carolinas Poison Center (CPC) calls. NC DETECT also collects data from select Urgent Care Centers (UCC) in the Charlotte area. CPC data are updated hourly, while ED data are updated twice a day. NC DETECT data were monitored daily for census (total ED visits), communicable disease syndromes, injury syndromes, and other occurrences of public health significance related to the event. The geographic areas monitored were Polk County (the location of the main event), the counties where the guests were lodging in the Western NC Region (Henderson, Transylvania, Buncombe, Rutherford, McDowell, and Cleveland), the Charlotte Metropolitan area, and statewide. Because of the large number of people from other states and countries who attended, ED surveillance was mainly conducted by hospitals so that visits were captured for all patients and not just NC residents. WEG dashboards containing ED data were created prior to the event using NC DETECT and NSSP ESSENCE systems, and were accessible to epidemiologists at the state level. NSSP syndrome queries were shared with the neighboring state (SC) public health agency. Surveillance began two weeks prior to the event to establish baseline levels for all ED visits for hospitals in Polk County and the Western NC Region. Surveillance occurred daily before the event, during the event, and for two weeks following the event to account for incubation periods of potential diseases.ResultsThe 2018 Equestrian games in Western NC were affected by heavy rain and heat. The weather led to low attendance and cancellation of a few competitions. During the observation period, ED admissions and most of the mass gathering related syndromes in both NC DETECT and NSSP systems were at baseline. ED admissions for motor vehicle collisions and dehydration syndromes were above baseline for 09/19 and 09/21/18 (Figures 3-4). CPC calls and UC admissions for selected UC centers in the Charlotte area were also monitored, and were at baseline.ConclusionsNC DETECT and NSSP Dashboards provided effective and timely surveillance for the WEG event to assist local public health in the rural NC area with epidemiologic investigations and appropriate response. NC DETECT’s CPC and UC data provided additional valuable information, and complemented ED surveillance during the mass gathering event. Syndromic surveillance became essential during WEG, as NC DPH deployment plans and resource availability changed when Hurricane Florence bore down on the region.References1. Joseph S. Lombardo, Carol A. Sniegoski, Wayne A. Loschen, Matthew Westercamp, Michael Wade, Shandy Dearth, and Guoyan Zhang Public Health Surveillance for Mass Gatherings Johns Hopkins APL Technical Digest , Volume 27, Number 4 (2008)2. Kaiser R, Coulombier D. Epidemic intelligence during mass gatherings. Euro Surveill. 2006;113. Ising A, Li M, Deyneka L, Vaughan-Batten H, Waller A. Improving syndromic surveillance for nonpower users: NC DETECT dashboards. Emerging Health Threats Journal 2011, 4: 11702 - DOI: 10.3402/ehtj.v4i0.11702 

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