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

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

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

scholarly journals Real-time monitoring of a mass K2-related overdose outbreak – Connecticut, 2018

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Kristen Soto ◽  
Erin Grogan ◽  
Alexander Senetcky ◽  
Susan Logan
Keyword(s):  
Emergency Department ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Emergency Department Utilization ◽  
Syndromic Surveillance System ◽  
Ed Visits ◽  
State And Local

ObjectiveTo describe the use of syndromic surveillance data for real-time situational awareness of emergency department utilization during a localized mass overdose event related to the substance K2.IntroductionOn August 15, 2018, the Connecticut Department of Public Health (DPH) became aware of a cluster of suspected overdoses in an urban park related to the synthetic cannabinoid K2. Abuse of K2 has been associated with serious adverse effects and overdose clusters have been reported in multiple states. This investigation aimed to characterize the use of syndromic surveillance data to monitor a cluster of suspected overdoses in real time.MethodsThe EpiCenter syndromic surveillance system collects data on all emergency department (ED) visits at Connecticut hospitals. ED visits associated with the event were identified using ad hoc keyword analyses. The number of visits by facility location for the state, county, and city were communicated to state and local partners in real time. Gender, age, and repeated ED visits were assessed. After the event, surveillance findings were summarized for partnersResultsDuring the period of August 15–16, 2018 the number of ED visits with a mention of K2 in the chief complaint increased from three to 30 in the impacted county, compared to a peak of 5 visits during the period of March–July, 2018. An additional 25 ED visits were identified using other related keywords (e.g., weed). After the event, 72 ED visits were identified with K2 and location keywords in the chief complaint or triage notes. These 72 visits comprised 53 unique patients, with 12 patients returning to the ED 2–5 times over the two day period. Of 53 patients, 77% were male and the median age was 40 years (interquartile range 35–51 years). Surveillance findings were shared with partners in real time for situational awareness, and in a summary report on August 21.ConclusionsData from the EpiCenter system were consistent with reports from other data sources regarding this cluster of suspected drug overdoses. Next steps related to this event involve: monitoring data for reference to areas of concentrated substance use, enabling automated alerts to detect clusters of interest, and developing a plan to improve coordinate real-time communication with stakeholderswithin DPH and with external partners during events.

scholarly journals Use of Emergency Department Data to Monitor and Respond to an Increase in Opioid Overdoses in New Hampshire, 2011-2015

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 73S-79S ◽  
Author(s):  
Elizabeth R. Daly ◽  
Kenneth Dufault ◽  
David J. Swenson ◽  
Paul Lakevicius ◽  
Erin Metcalf ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
New Hampshire ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
The United States ◽  
Chief Complaint ◽  
Public Health Response ◽  
Syndromic Surveillance System ◽  
Icd Codes

Objectives: Opioid-related overdoses and deaths in New Hampshire have increased substantially in recent years, similar to increases observed across the United States. We queried emergency department (ED) data in New Hampshire to monitor opioid-related ED encounters as part of the public health response to this health problem. Methods: We obtained data on opioid-related ED encounters for the period January 1, 2011, through December 31, 2015, from New Hampshire’s syndromic surveillance ED data system by querying for (1) chief complaint text related to the words “fentanyl,” “heroin,” “opiate,” and “opioid” and (2) opioid-related International Classification of Diseases ( ICD) codes. We then analyzed the data to calculate frequencies of opioid-related ED encounters by age, sex, residence, chief complaint text values, and ICD codes. Results: Opioid-related ED encounters increased by 70% during the study period, from 3300 in 2011 to 5603 in 2015; the largest increases occurred in adults aged 18-29 and in males. Of 20 994 total opioid-related ED visits, we identified 18 554 (88%) using ICD code alone, 690 (3%) using chief complaint text alone, and 1750 (8%) using both chief complaint text and ICD code. For those encounters identified by ICD code only, the corresponding chief complaint text included varied and nonspecific words, with the most common being “pain” (n = 3335, 18%), “overdose” (n = 1555, 8%), “suicidal” (n = 816, 4%), “drug” (n = 803, 4%), and “detox” (n = 750, 4%). Heroin-specific encounters increased by 827%, from 4% of opioid-related encounters in 2011 to 24% of encounters in 2015. Conclusions: Opioid-related ED encounters in New Hampshire increased substantially from 2011 to 2015. Data from New Hampshire’s ED syndromic surveillance system provided timely situational awareness to public health partners to support the overall response to the opioid epidemic.

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 Using Syndromic Surveillance Alert Protocols for Epidemiologic Response in Georgia

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Rene Borroto ◽  
Bill Williamson ◽  
Patrick Pitcher ◽  
Lance Ballester ◽  
Wendy Smith ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Health Care Providers ◽  
Syndromic Surveillance ◽  
Emergency Department Patient ◽  
Chief Complaint ◽  
Care Providers ◽  
Public Health Response ◽  
Presumptive Diagnosis ◽  
Notifiable Diseases

ObjectiveDescribe how the Georgia Department of Public Health (DPH) usessyndromic surveillance to initiate review by District Epidemiologists(DEs) to events that may warrant a public health response (1).IntroductionDPH uses its State Electronic Notifiable Disease SurveillanceSystem (SendSS) Syndromic Surveillance (SS) Module to collect,analyze and display results of emergency department patient chiefcomplaint data from hospitals throughout Georgia.MethodsDPH prepares a daily SS report, based upon the analysis ofdaily visits to 112 Emergency Department (EDs). The visits areclassified in 33 syndromes. Queries of chief complaint and dischargediagnosis are done using the internal query capability of SendSS-SSand programming in SAS/BASE. Charting of the absolute countsor percentage of ED visits by syndromes is done using the internalcharting capability of SendSS-SS. A daily SS report includes thefollowing sections:Statewide Emergency Department Visitsby Priority Syndromes(Bioterrorism, BloodyRespiratory,FeverRespiratory, FeverChest, FeverFluAdmit, FeverFluDeaths,VeryIll, andPoxRashFever, Botulism, Poison, BloodyDiarrhea,BloodyVomit, FeverGI, ILI, FeverFlu, RashFever, Diarrhea,Vomit).Statewide Flag Analysis: Is intended to detect statewideflags, by using theChartscapability in SendSS SS.Possible caseswith presumptive diagnosis of potentially notifiable diseases: Isintended to provide early-warning to the DEs of possible cases thatare reportable to public health immediately or within 7 days usingqueries in the Chief Complaint and Preliminary Diagnosis fields ofSendSS-SS.Possible clusters of illness: Since any cluster of illnessmust be reported immediately to DPH, this analysis is aimed atquerying and identifying possible clusters of patients with similarsymptoms (2).Possible travel-related illness: Is intended to identifypatients with symptoms and recent travel history.Other events ofinterest: Exposures to ill patients in institutional settings (e.g. chiefcomplaint indicates that other children in the daycare have similarsymptoms).Trend Analysis: Weekly analysis of seasonality andtrends of 14 syndromes. Finally, specific events are notified to andreviewed by the 18 DEs, who follow up by contacting the InfectionPreventionists of the hospitals to identify the patients using medicalrecords or other hospital-specific identification numbers and followup on the laboratory test results.ResultsSince 05/15/2016, 12 travel-related illnesses, 29 vaccine-preventable diseases, 14 clusters, and 3 chemical exposures havebeen notified to DEs. For instance, a cluster of chickenpox in childrenwas identified after the DE contacted the Infection Preventionist ofa hospital, who provided the DE with the laboratory results and thephysician notes about the symptoms of the patients. These actionshave resulted in earlier awareness of single cases or cluster of illness,prompt reporting of notifiable diseases, and successful interactionbetween DEs and health care providers. In addition, SS continues totrack the onset, peak, and decline of seasonal illnesses.ConclusionsThe implementation of SS in the State of Georgia is helping withthe timely detection and early responses to disease events and couldprove useful in reducing the disease burden caused by a bioterroristattack.

scholarly journals Development of Syndrome Definitions for Acute Unintentional Drug and Heroin Overdose

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Em Stephens
Keyword(s):  
Public Health ◽  
Chronic Pain ◽  
Real Time ◽  
Correlation Coefficient ◽  
Drug Overdose ◽  
Chief Complaint ◽  
Opioid Overdose ◽  
Free Text ◽  
Discharge Data ◽  
Heroin Overdose

ObjectiveTo develop and evaluate syndrome definitions for the identificationof acute unintentional drug overdose events including opioid, heroin,and unspecified substances among emergency department (ED) visitsin Virginia.IntroductionNationally, deaths due to opioid overdose have continuallyincreased for the past 15 years1. Deaths specifically related to heroinincreased more than four-fold between 2002 and 20142. Hospitalinpatient discharge data provide information on non-fatal overdoses,but include a significant lag in reporting time3. Syndromic ED visitdata provide near real-time identification of public health issues andcan be leveraged to inform public health actions on the emergingthreat of drug overdose.MethodsVirginia Department of Health (VDH) developed two syndromedefinitions in 2014 to capture acute unintentional drug overdoseevents among syndromic ED visit data. Syndrome 1 captured visitsfor overdose, whether or not a specific substance was mentioned.Syndrome 2 captured only visits for heroin overdose. Definitionswere based on free-text terms found within the chief complaintand standardized text or International Classification of Diseases(ICD) codes within the diagnosis field. In 2016, both definitionswere revised to identify additional inclusion and exclusion criteriaaccording to CDC guidance documentation and syndrome definitionsused by other state jurisdictions.Microsoft SQL was used to modify both definitions based on thenewly identified chief complaint and diagnosis criteria. Record leveldata were analyzed for their adherence to established criteria using aniterative evaluation process.The scope of Syndrome 1 (2016) was narrowed from the 2014version by excluding visits for non-opioid substances, heroin, andnon-acute indicators. It included chief complaint and diagnosisterms related to opioids, unspecified substance overdose, narcotics,and Narcan or naloxone, and excluded terms related to suicide,alcohol overdose alone, withdrawal, detoxification, rehab, addiction,constipation, chronic pain, and any specified non-opioid drug ormedication. Syndrome 2 (2016) included chief complaint or diagnosisterms mentioning heroin overdose and excluded suicide, withdrawal,detoxification, rehab, and addiction. Visits with mention of suicide,rehab, or addiction were identified during the evaluation process,resulting in the exclusion of these terms in the revised query.From January 1, 2015 to July 31, 2016, the number of visitscaptured by the revised syndrome definitions was compared to thenumber captured by the 2014 definitions. Correlation coefficientswere calculated using SAS 9.3.ResultsThe revised Syndrome 1 found 4296 fewer ED visits(29% decrease) for acute unintentional drug overdose betweenJanuary 1, 2015 and July 31, 2016 compared to the 2014 definition.Despite the drop in volume, the monthly trends were similar forthe 2014 and 2016 definitions (correlation coefficient = 0.95,p < 0.001). For the same time period, the revised Syndrome 2 definitionreturned 108 fewer visits (6% decrease) for acute unintentional heroinoverdose. The monthly trends were also similar for the 2014 and 2016definitions (correlation coefficient = 0.98, p < 0.001).ConclusionsBoth revised syndrome definitions improved specificity incapturing overdose visits as Syndrome 1 (2016) identified 29% fewervisits and Syndrome 2 (2016) identified 6% fewer visits found to beunrelated to the desired overdose criteria.When developing the revised syndrome definitions, VDH decidedto exclude non-acute drug-related visits. Terms such as addiction,detoxification, rehab, withdrawal, chronic pain, and constipation wereindicative of habitual drug use or abuse instead of acute overdose andwere thus excluded. In narrowing the scope of Syndrome 1, VDHalso identified and excluded visits for specified drug and medicationoverdose. Together, these expanded exclusion criteria resulted ingreater specificity with both updated syndromes.These revised syndrome definitions enable VDH to better trackopioid and heroin overdose trends in near real-time and overextended time periods which can be used to inform public healthactions. Limitations include the inconsistency of diagnosis codingamong syndromic data submitters, which may lead to geographicunderrepresentation of unintentional drug overdose visits based onthe location of health care systems. VDH will continue to evaluate andrefine these overdose syndrome definitions as this emerging healthissue evolves.

scholarly journals Using ESSENCE to Detect Bomb-Making Activity: What’s Appropriate?

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Andrew Torgerson
Keyword(s):  
Public Health ◽  
Law Enforcement ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Chemical Mechanism ◽  
Mass Gathering ◽  
Surveillance Period ◽  
Saint Louis ◽  
Ed Visits

ObjectiveTo describe a novel application of ESSENCE by the Saint Louis County Department of Public Health (DPH) in preparation for a mass gathering and to encourage discussion about the appropriateness of sharing syndromic surveillance data with law enforcement partners.IntroductionIn preparation for mass gathering events, DPH conducts enhanced syndromic surveillance activities to detect potential cases of anthrax, tularemia, plague, and other potentially bioterrorism-related communicable diseases. While preparing for Saint Louis to host a Presidential Debate on October 9, 2016, DPH was asked by a partner organization whether we could also detect emergency department (ED) visits for injuries (e.g., burns to the hands or forearms) that could possibly indicate bomb-making activities.MethodsUsing the Electronic Surveillance System for the Notification of Community-Based Epidemics (ESSENCE), version 1.9, DPH developed a simple query to detect visits to EDs in Saint Louis City or Saint Louis County with chief complaints including the word “burn” and either “hand” or “arm.” A DPH epidemiologist reviewed the results of the query daily for two weeks before and after the debate (i.e., from September 25, 2016 to October 23, 2016). If any single ED visit was thought to be “suspicious” – if, for example, the chief complaint mentioned an explosive or chemical mechanism of injury – then DPH would contact the ED for details and relay the resulting information to the county’s Emergency Operations Center.ResultsDuring the 29 day surveillance period, ESSENCE detected 27 ED visits related to arm or hand burns. The ESSENCE query returned a median of 1 ED visit per day (IQR 0 to 2 visits). Of these, one was deemed to merit further investigation – two days before the debate, a patient presented to an ED in Saint Louis County complaining of a burned hand. The patient’s chief complaint data also mentioned “explosion of unspecified explosive materials.” Upon investigation, DPH learned that the patient had been injured by a homemade sparkler bomb. Subsequently, law enforcement determined that the sparkler bomb had been made without any malicious intent.ConclusionsDPH succeeded in using ESSENCE to detect injuries related to bomb-making. However, this application of ESSENCE differs in at least two ways from more traditional uses of syndromic surveillance. First, conventional syndromic surveillance is designed to detect trends in ED visits resulting from an outbreak already in progress or a bioterrorist attack already carried out. In this case, syndromic surveillance was used to detect a single event that could be a prelude to an attack. The potential to prevent widespread injury or illness is a strength of this approach. Second, conventional syndromic surveillance identifies potential outbreak cases or, in the case of a bioterrorist attack, potential victims. In this case, syndromic surveillance was used to identify a potential perpetrator of an attack. While public health and law enforcement agencies would ideally coordinate their investigative efforts in the wake of an attack, this practice has led to conversations within DPH about the appropriateness of routinely sharing public health surveillance data with law enforcement. 

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.

Emergency Medical Services and Syndromic Surveillance: A Comparison With Traditional Surveillance and Effects on Timeliness

2021 ◽  
Vol 136 (1_suppl) ◽  
pp. 72S-79S
Author(s):  
Peter J. Rock ◽  
Dana Quesinberry ◽  
Michael D. Singleton ◽  
Svetla Slavova
Keyword(s):  
Public Health ◽  
Time Series ◽  
Emergency Medical Services ◽  
Syndromic Surveillance ◽  
Surveillance Data ◽  
Medical Services ◽  
Opioid Overdose ◽  
Billing Data ◽  
Public Health Response ◽  
Emergency Medical

Objective Traditional public health surveillance of nonfatal opioid overdose relies on emergency department (ED) billing data, which can be delayed substantially. We compared the timeliness of 2 new data sources for rapid drug overdose surveillance—emergency medical services (EMS) and syndromic surveillance—with ED billing data. Methods We used data on nonfatal opioid overdoses in Kentucky captured in EMS, syndromic surveillance, and ED billing systems during 2018-2019. We evaluated the time-series relationships between EMS and ED billing data and syndromic surveillance and ED billing data by calculating cross-correlation functions, controlling for influences of autocorrelations. A case example demonstrates the usefulness of EMS and syndromic surveillance data to monitor rapid changes in opioid overdose encounters in Kentucky during the COVID-19 epidemic. Results EMS and syndromic surveillance data showed moderate-to-strong correlation with ED billing data on a lag of 0 ( r = 0.694; 95% CI, 0.579-0.782; t = 9.73; df = 101; P < .001; and r = 0.656; 95% CI, 0.530-0.754; t = 8.73; df = 101; P < .001; respectively) at the week-aggregated level. After the COVID-19 emergency declaration, EMS and syndromic surveillance time series had steep increases in April and May 2020, followed by declines from June through September 2020. The ED billing data were available for analysis 3 months after the end of a calendar quarter but closely followed the trends identified by the EMS and syndromic surveillance data. Conclusion Data from EMS and syndromic surveillance systems can be reliably used to monitor nonfatal opioid overdose trends in Kentucky in near–real time to inform timely public health response.

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