Emergency Department Syndromic Surveillance Research Articles

Emergency Department Syndromic Surveillance to Monitor Tick-Borne Diseases: A 6-Year Small-Area Analysis in Northeastern Italy.

Tick-borne diseases (TBD) are endemic in Europe. However, surveillance is currently incomplete. Alternative strategies need to be considered. The aim of this study was to test an Emergency Department Syndromic Surveillance (EDSyS) system as a complementary data source to describe the impact of tick bites and TBD using a small-area analysis approach and to monitor the risk of TBD to target prevention. ED databases in the Local Health Authority 8 District (Veneto, Italy) were queried for tick-bite and TBD-related visits between January 2017 and December 2022. Hospitalisations were also collected. Events involving the resident population were used to calculate incidence rates. A total of 4187 ED visits for tick-bite and 143 for TBD were recorded; in addition, 62 TBD-related hospitalisations (of which 72.6% in over 50 s and 22.6% in over 65 s). ED visits peaked in spring and in autumn, followed by a 4-week lag in the increase in hospital admissions. The small-area analysis identified two areas at higher risk of bites and TBD. The use of a EDSyS system allowed two natural foci to be identified. This approach proved useful in predicting temporal and geographic risk of TBD and in identifying local endemic areas, thus enabling an effective multidisciplinary prevention strategy.

Suspected Nonfatal Cocaine-Involved Overdoses Overall and With Co-involvement of Opioids.

The Centers for Disease Control and Prevention (CDC) developed a syndrome definition for detection of suspected nonfatal cocaine-involved overdoses. The definition can be used to monitor trends and detect anomalies in emergency department (ED) syndromic surveillance data at the national, state, and local levels. This study describes the development of the nonfatal, unintentional/undetermined intent cocaine-involved overdose (UUCOD) definition and analysis of trends over time. CDC developed the UUCOD definition to query ED data in CDC's National Syndromic Surveillance Program (NSSP). Data between 2018 and 2021 were analyzed from 29 states sharing data access in the Drug Overdose Surveillance and Epidemiology (DOSE) System via NSSP. Using Joinpoint regression, trends were analyzed for UUCOD overall, by sex and age group, and for UUCOD co-involving opioids. Time trends between 2018 and 2021 were analyzed by examining average monthly percentage change. Individual trend segments and trend inflection points were analyzed by examining monthly percentage change. During 2018-2021, a total of 27 240 UUCOD visits were identified by the syndrome definition. Analyses identified different patterns in trends for males and females, with largely similar trends for persons aged 15 to 44 years and 45 years or older. Analyses also identified seasonal patterns with increases in spring/summer months in UUCOD overall and UUCOD co-involving opioids and declines for both in fall/winter months. This UUCOD syndrome definition will be useful for ongoing monitoring of suspected nonfatal overdoses involving cocaine and co-involving cocaine and opioids. Ongoing assessment of cocaine-involved overdose trends might identify anomalies requiring further investigation and inform deployment of resources.

Lessons from COVID-19 syndromic surveillance through emergency department activity: a prospective time series study from western Switzerland

ObjectiveWe aimed to assess if emergency department (ED) syndromic surveillance during the first and second waves of the COVID-19 outbreak could have improved our surveillance system.Design and settingsWe did an...

Evaluation of Data Sources for Carbon Monoxide Poisoning Surveillance in New York City.

Carbon monoxide (CO) exposure can be life-threatening. Suspected and confirmed cases of CO poisoning warranting health care in New York City (NYC) are reportable to the NYC Poison Control Center (PCC). We evaluated 4 hospital-based sources of CO surveillance data to identify ways to improve data capture and reporting. Suspected and confirmed CO poisoning records from October 2015 through December 2016 were collected from the NYC emergency department (ED) syndromic surveillance system, New York State Statewide Planning and Research Cooperative System (SPARCS) ED billing data, NYC PCC calls made from hospitals, and the Electronic Clinical Laboratory Reporting System (ECLRS). Syndromic and SPARCS records were person- and visit-matched. SPARCS and ECLRS records were also matched to PCC records on combinations of name, demographic characteristics, and visit information. Hospitals in NYC. Individuals who visited NYC hospitals for CO-related health effects. We assessed the validity of syndromic data, with SPARCS records as the gold standard. We matched SPARCS and ECLRS records to PCC records to analyze reporting rates by case characteristics. The sensitivity of syndromic surveillance was 60% (225 true-positives detected among 372 visit-matched SPARCS cases), and positive predictive value was 46%. Syndromic records often missed CO flags because of a nonspecific or absent International Classification of Diseases code in the diagnosis field. Only 15% of 428 SPARCS records (total includes 56 records not visit-matched to syndromic) and 16% of 199 ECLRS records were reported to PCC, with male sex and younger age associated with higher reporting. Mandatory reporting makes PCC useful for tracking CO poisoning in NYC, but incomplete reporting and challenges in distinguishing between confirmed and suspected cases limit its utility. Simultaneous tracking of the systems we evaluated can best reveal surveillance patterns.

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

BackgroundAutomated 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.MethodsSyndromes 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.ResultsA 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.ConclusionsThe 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.

Differences and similarities between emergency department syndromic surveillance and hospital discharge data for nonfatal drug overdose

Emergency department syndromic surveillance and hospital discharge data have been used to detect and monitor nonfatal drug overdose, yet few studies have assessed the differences and similarities between these two data sources. The Centers for Disease Control and Prevention Drug Overdose Surveillance and Epidemiology system data from 14 states were used to compare these two sources at estimating monthly overdose burden and trends from January 2018 through December 2019 for nonfatal all drug, opioid-, heroin-, and stimulant-involved overdoses. Compared to discharge data, syndromic data captured 13.3% more overall emergency department visits, 67.8% more all drug overdose visits, 15.6% more opioid-involved overdose visits, and 78.8% more stimulant-involved overdose visits. Discharge data captured 18.9% more heroin-involved overdoses. Significant trends were identified for all drug (Average Monthly Percentage Change [AMPC]=1.1, 95% CI=0.4,1.8) and stimulant-involved overdoses (AMPC=2.4, 95% CI=1.2,3.7) in syndromic data; opioid-involved overdoses increased in both discharge and syndromic data (AMPCDischarge=0.9, 95% CI=0.2,1.7; AMPCSyndromic=1.9, CI=1.1,2.8). Results demonstrate that discharge data may be better for reporting counts, yet syndromic data are preferable to detect changes quickly and to alert practitioners and public health officials to local overdose clusters. These data sources do serve complementary purposes when examining overdose trends.

Lessons from COVID-19 Syndromic Surveillance Through Emergency Department Activity:  A Prospective Time-Series Study from Western Switzerland

Background: Surveillance reports for COVID-19 use confirmed cases, deaths in the population and intensive care unit (ICU) occupancy, but emergency department (ED) syndromic surveillance is not routinely used. We aimed to assess if ED syndromic surveillance during the first and second waves of the COVID-19 outbreak could have improved our surveillance system. Methods: We did an observational study using aggregated data from the ED of a university hospital and public health authorities in western Switzerland. We used time series methods for ED syndromic surveillance and usual indicators from public health authorities. The relationship between ICU occupancy and surveillance indicators was explored by cross-correlation. We calculated the flu-like syndrome moving average and established Shewhart charts for aberration detection. Findings: Based on 37,319 ED visits during the COVID-19 outbreak, 1421 ED visits (3·8%) were positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Patients with flu-like syndrome or droplet isolation in the ED showed a similar correlation to ICU occupancy as confirmed cases in the general population with a time lag of approximately 13 days (0·73, 95% CI 0·64-0·80; 0·79, 95% CI 0·71-0·86; and 0·76, 95% CI 0·67-0·83, respectively). The proportion of positive tests in the population showed the best correlation with ICU occupancy (0·95, 95% CI 0·85-0·96). Interpretation: ED syndromic surveillance is an effective tool to detect and monitor a COVID-19 outbreak and to predict hospital resource needs, and would have allowed to anticipate ICU occupancy by 13 days, including detection of significant aberration at the beginning of the second wave. Funding Statement: The study had no funding. Declaration of Interests: We declare no competing interest. Ethics Approval Statement: This study was approved by the institutional ethics review board of the Canton of Vaud (CER-VD 2020-00731).

Suspected Nonfatal Drug-Related Overdoses Among Youth in the US: 2016-2019.

During the current drug overdose crisis, the United States is experiencing a significant number of overdose deaths, hospitalizations, and emergency department visits. Given the vulnerability of young persons to substance use, it is important to assess how this crisis affects the nation's youth. In this study, we investigate trends in suspected nonfatal drug-related overdoses (all-drugs, opioids, heroin, and stimulants) among youth using syndromic surveillance data from 2016 to 2019. A retrospective analysis of emergency department syndromic surveillance data were used to detect quarterly trends in suspected drug overdoses from April 2016 through September 2019 among youth aged 0 to 10, 11 to 14, and 15 to 24 years. Syndrome definitions were developed using chief complaint free-text and discharge diagnosis codes to identify overdoses involving all-drugs, opioids, heroin, and stimulants. Pearson χ2 tests detected quarter-to-quarter changes, and joinpoint regression analysis assessed trends over time. On average, there was a 2.0% increase for youth aged 0 to 10 years and a 2.3% increase for youth aged 11 to 14 years for suspected all-drug overdoses. Suspected heroin overdoses decreased by an average of 3.3% per quarter for youth aged 15 to 24 years. Among all age groups, suspected stimulant overdoses increased across the study period, 3.3% for 0 to 10-year-olds, 4.0% for 11- to 14-year-olds, and 2.3% for 15- to 24-year-olds. Suspected stimulant-involved drug overdoses appear to be rising among youth. These findings could inform targeted interventions, such as stimulant-focused prevention, and comprehensive approaches, including school-based prevention and other strategies to lower morbidity and mortality.

Emergency department syndromic surveillance systems: a systematic review

BackgroundSyndromic surveillance provides public health intelligence to aid in early warning and monitoring of public health impacts (e.g. seasonal influenza), or reassurance when an impact has not occurred. Using information collected during routine patient care, syndromic surveillance can be based on signs/symptoms/preliminary diagnoses. This approach makes syndromic surveillance much timelier than surveillance requiring laboratory confirmed diagnoses.The provision of healthcare services and patient access to them varies globally. However, emergency departments (EDs) exist worldwide, providing unscheduled urgent care to people in acute need. This provision of care makes ED syndromic surveillance (EDSyS) a potentially valuable tool for public health surveillance internationally.The objective of this study was to identify and describe the key characteristics of EDSyS systems that have been established and used globally.MethodsWe systematically reviewed studies published in peer review journals and presented at International Society of Infectious Disease Surveillance conferences (up to and including 2017) to identify EDSyS systems which have been created and used for public health purposes. Search criteria developed to identify “emergency department” and “syndromic surveillance” were applied to NICE healthcare, Global Health and Scopus databases.ResultsIn total, 559 studies were identified as eligible for inclusion in the review, comprising 136 journal articles and 423 conference abstracts/papers. From these studies we identified 115 EDSyS systems in 15 different countries/territories across North America, Europe, Asia and Australasia. Systems ranged from local surveillance based on a single ED, to comprehensive national systems. National EDSyS systems were identified in 8 countries/territories: 2 reported inclusion of ≥85% of ED visits nationally (France and Taiwan).ConclusionsEDSyS provides a valuable tool for the identification and monitoring of trends in severe illness. Technological advances, particularly in the emergency care patient record, have enabled the evolution of EDSyS over time. EDSyS reporting has become closer to ‘real-time’, with automated, secure electronic extraction and analysis possible on a daily, or more frequent basis.The dissemination of methods employed and evidence of successful application to public health practice should be encouraged to support learning from best practice, enabling future improvement, harmonisation and collaboration between systems in future.Prospero numberCRD42017069150.

Using emergency department syndromic surveillance to investigate the impact of a national vaccination program: A retrospective observational study.

BackgroundRotavirus infection is a common cause of gastroenteritis in children worldwide, with a high mortality burden in developing countries, particularly during the first two years of life. Rotavirus vaccination was introduced into the United Kingdom childhood vaccination schedule in July 2013, with high coverage (>90%) achieved by June 2016. We used an emergency department (ED) syndromic surveillance system to assess the impact of the rotavirus vaccination programme, specifically through the demonstration of any immediate and continuing impact on ED gastroenteritis visits in England.MethodsThis retrospective, observational study used syndromic surveillance data collected from 3 EDs in the two years before (July 2011—June 2013) and 3 years post (July 2013—June 2016) introduction of rotavirus vaccination. The weekly levels of ED visits for gastroenteritis (by age group and in total) during the period before rotavirus vaccination was first described alongside the findings of laboratory surveillance of rotavirus during the same period. An interrupted time-series analysis was then performed to demonstrate the impact of rotavirus vaccination introduction on gastroenteritis ED visit levels.ResultsDuring the two years before vaccine introduction ED visits for gastroenteritis in total and for the 0–4 years age group were seen to rise and fall in line with the seasonal rotavirus increases reported by laboratory surveillance. ED gastroenteritis visits by young children were lower in the three years following introduction of rotavirus vaccination (reduced from 8% of visits to 6% of visits). These attendance levels in young children (0-4years) remained higher than in older age groups, however the previously large seasonal increases in children were greatly reduced, from peaks of 16% to 3–10% of ED visits per week.ConclusionsED syndromic surveillance demonstrated a reduction in gastroenteritis visits following rotavirus vaccine introduction. This work establishes ED syndromic surveillance as a platform for rapid impact assessment of future vaccine programmes.

Emergency department use during COVID-19 as described by syndromic surveillance

On 12 March 2020 the UK entered the ‘delay phase’ of the COVID-19 pandemic response. The Public Health England Emergency Department Syndromic Surveillance System (EDSSS) carries out daily (near real-time)...

Syndrome definitions for drug overdose: How far down the rabbit hole do we go?

ObjectiveTo discuss the process for developing and revising suspected drug overdose queries in syndromic surveillance (SyS) systems.IntroductionState and local jurisdictions have been exploring the use of SyS data to monitor suspected drug overdose outbreaks in their communities. With the increasing awareness and use of SyS systems, staff from the Centers for Disease Control and Prevention (CDC) worked to develop several queries that jurisdictions could use to better capture suspected drug overdose visits. In 2017, CDC released their first two queries on heroin overdose and opioid overdose, followed in 2018 by stimulant and all drug overdose queries. Over time, and with the assistance from the SyS community and the CDC-funded Enhanced State Opioid Overdose Surveillance (ESOOS) state health departments, CDC has revised the queries to address suggestions from jurisdictions. However, it’s not clear how often and in what way the syndrome definitions are updated over time. This is particularly true as new drugs emerge and the names of those drugs are integrated into syndrome definitions (e.g., recent “Spice” and “K2” synthetic cannabinoid outbreaks).DescriptionThis roundtable will provide a forum for national, state, and local users of SyS and drug overdose syndrome queries to discuss the process of query development, with an eye towards determining when a definition is “good enough.” CDC staff will facilitate the discussion and present the current portfolio of drug-related overdose queries. Participants will be encouraged to provide feedback on the queries, share what has been/has not been working in their jurisdiction with regard to syndrome query development, and discuss the process for revising queries as the epidemic evolves. The focus of this roundtable will be on suspected drug overdose query development and revision with emergency department SyS data.How the Moderator Intends to Engage the Audience in Discussions on the TopicWith most jurisdictions grappling with the impact of the opioid epidemic, this roundtable is well suited for widespread audience participation. Though some jurisdictions have been using SyS to monitor suspected drug overdose outbreaks for some time, others are not using SyS in this way. Thus, opportunities for sharing of work, experiences, barriers, and facilitators will be useful for all SyS users

EMS Heroin Overdoses with Refusal to Transport & Impacts on ED Overdose Surveillance

ObjectiveThe aim of this project was to explore changing patterns in patient refusal to transport by emergency medical services for classified heroin overdoses and possible implications on heroin overdose surveillance in Kentucky.IntroductionAs a Centers for Disease Control and Prevention Enhanced State Opioid Overdose Surveillance (ESOOS) funded state, Kentucky started utilizing Emergency Medical Services (EMS) data to increase timeliness of state data on drug overdose events in late 2016. Using developed definitions of heroin overdose for EMS emergency runs, Kentucky analyzed the patterns of refused/transported EMS runs for both statewide and local jurisdictions. Changes in EMS transportation patterns of heroin overdoses can have a dramatic impact on other surveillance systems, such as emergency department (ED) claims data or syndromic surveillance (SyS) data.MethodsAs part of the ESOOS grant, Kentucky receives all emergency-only EMS runs monthly from Kentucky Board for Emergency Medical Services, Kentucky State Ambulance Reporting System data. Heroin cases were classified based on text and medications (Narcan) administered, with comparisons to historic data discussed elsewhere (Rock & Singleton, 2018). Transportation classifications are based on EMS standard elements defining treatment with transportation vs refusal to transport to hospital and canceled runs were excluded. Initial analysis included trend analysis at state and local levels, as well as demographic comparisons of refusal vs transported heroin overdose encounters.ResultsStatewide trends in EMS heroin overdoses with refusal transport significantly increased from 5% (n=42) in 2016 quarter three to 22% (n=290) in 2018 quarter two (Fig 1). Initial demographic analysis does not show any significant difference between refusals/transported for age, gender, or race. However, there are significant differences among geographic regions in Kentucky with heroin encounter refusal proportion ranging from 3%-48% in 2018 quarter two. Specifically, one urban area (Fig 2) shows the change in proportion of refusal increasing from 15% (n=23) in 2016 quarter three to 47% (n=110) in 2018 quarter two. In this geographic area, combined refused/transported EMS heroin overdoses compared to traditional ED data demonstrates opposing heroin overdose patterns for the same local with EMS showing and increasing trend overtime and ED showing a decreasing trend (Fig 3).ConclusionsTraditional public health surveillance for heroin overdose has historically relied on ED billing data, though agencies are starting to use syndromic surveillance, too (Vivolo-Kantor et al., 2016). These systems share similar underlying ED data, albeit with different components, quality, and limitations. However, in terms of the overdose epidemic, both are limited to only heroin overdoses that result in ED hospital encounters. The recent drastic increase in refused transport can have significant impacts on heroin surveillance. Jurisdictions relying on SyS or ED data for monitoring overdose patterns and/or evaluating interventions may be significantly underestimating acute overdose occurrence in the population. This analysis highlights the importance of this preclinical data source in surveillance of the heroin epidemic.ReferencesRock, P. J., & Singleton, M. D. (2018). Assessing Definitions of Heroin Overdose in ED & EMS Data Using Hospital Billing Data, 10(1), 2579.Vivolo-Kantor, A. M., Seth, P., Gladden, ; R Matthew, Mattson, C. L., Baldwin, G. T., Kite-Powell, A., & Coletta, M. A. (2016). Morbidity and Mortality Weekly Report Vital Signs: Trends in Emergency Department Visits for Suspected Opioid Overdoses — United States, 67(9), 279–285. Retrieved from https://www.cdc.gov/mmwr/volumes/67/wr/pdfs/mm6709e1-H.pdf

Impact of a new diagnoses thesaurus on the French ED syndromic surveillance system

Impact of a new diagnoses thesaurus on the French ED syndromic surveillance system

Comparing Syndromic Data to Discharge Data to Measure Opioid Overdose Emergency Department Visits

ObjectiveEpidemiologists will understand the differences between syndromic and discharge emergency department data sources, the strengths and limitations of each data source, and how each of these different emergency department data sources can be best applied to inform a public health response to the opioid overdose epidemic.IntroductionTimely and accurate measurement of overdose morbidity using emergency department (ED) data is necessary to inform an effective public health response given the dynamic nature of opioid overdose epidemic in the United States. However, from jurisdiction to jurisdiction, differing sources and types of ED data vary in their quality and comprehensiveness. Many jurisdictions collect timely emergency department data through syndromic surveillance (SyS) systems, while others may have access to more complete, but slower emergency department discharge datasets. State and local epidemiologists must make decisions regarding which datasets to use and how to best operationalize, interpret, and present overdose morbidity using ED data. These choices may affect the number, timeliness, and accuracy of the cases identified.MethodsCDC partnered with 45 states and the District of Columbia to combat the worsening opioid overdose epidemic through three cooperative agreements: Prevention for States (PFS), Data Driven Prevention Initiative (DDPI), and Enhanced State Opioid Overdose Surveillance (ESOOS). To support funded jurisdictions in monitoring non-fatal opioid overdoses, CDC developed two different sets of indicator guidance for measuring non-fatal opioid overdoses using ED data, with each focusing on different ED data sources (SyS and discharge). We report on the following attributes for each type of ED data source1,2: 1) timeliness; 2) data quality (e.g., percent completeness by field); 3) validity; and 4) representativeness (e.g., percent of facilities included).ResultsWhen comparing timeliness across data sources, SyS data has clear advantages, with many jurisdictions receiving data within 24 hours of an event. For discharge data, timeliness is more variable with some jurisdictions receiving data within weeks while others wait over 1.5 years before receiving a complete discharge dataset. Data quality and completeness tends to be stronger in discharge datasets as facilities are required to submit complete discharge records with valid ICD-10-CM codes in order to be reimbursed by payers. By contrast, for SyS data systems, participating facilities may not consistently submit data for all possible fields, including diagnosis. Validity is dependent on the data source as well as the case definition or syndrome definition used; with this in mind, SyS data overdose indicators are designed to have high sensitivity, with less attention to specificity. Discharge data overdose indicators are designed to have a high positive predictive value, while sensitivity and specificity are both important considerations. Discharge datasets often include records for 100% of ED visits from all nonfederal, acute care-affiliated facilities in a state included. By contrast, representativeness of facilities in SyS data systems varies widely across states with some states having less than 50% of facilities reporting.ConclusionsCDC funded partners share overdose morbidity data with CDC using either ED SyS data, ED discharge data, or both. CDC indicator guidance for ED discharge data is designed for states to track changes in health outcomes over time for descriptive, performance monitoring, and evaluation purposes and to create rates that are more comparable across injury category, time, and place. Considering these objectives, CDC placed a higher priority on data quality, validity (i.e., positive predictive value), and representativeness, all of which are stronger attributes of discharge data. CDC’s indicator guidance for ED SyS data is designed for states to rapidly identify changes in nonfatal overdoses and to identify areas within a particular state that are experiencing rapid change in the frequency or types of overdose events. When considering these needs, CDC prioritized timeliness and validity in terms of sensitivity, both of which are stronger attributes of SyS data. SyS and discharge ED data each lend themselves to different informational applications and interpretations based on the strengths and limitations of each dataset. An effective, informed public health response to the opioid overdose epidemic requires continued investment in public health surveillance infrastructure, careful consideration of the needs of the data user, and transparency regarding the unique strengths and limitations of each dataset.

Surveillance of a Synthetic Cannabinoid Overdose Outbreak using Syndromic & EMS Data

Objective: The aim of this project was to investigate anecdotal reports of an increase in synthetic cannabinoid (SynCan) overdoses in Lexington-Fayette County area of Kentucky, using rapid surveillance systems including emergency department (ED) syndromic surveillance (SyS) and emergency medical services (EMS) data.Introduction: In mid-2017, the Kentucky Injury Prevention and Research Center (a bonafide agent of Kentucky Department for Public Health-KDPH) was alerted by members of KDPH to anecdotal evidence of a possible increase of SynCan (primarily “Serenity”) overdoses. The situation presented an opportunity to demonstrate the capabilities of syndromic surveillance and emergency medical services (EMS) data systems to provide rapid situational awareness about SynCan overdoses.Methods: SynCan cases were identified based on EMS emergency runs with narratives including mentions of ‘serenity|K2|spice’ and occurred in the Lexington-Fayette County. In ED SyS, SynCan cases were identified for Lexington-Fayette County area ED visits with a chief complaints of ‘serenity|K2|spice’ or diagnosis code of T40.7X[1-4]A. The ICD code was included after analysis of diagnosis codes in positive cases from a chief complaint only query revealed T40.7X[1-4]A as a primary code assigned in these cases. Trends for Lexington-Fayette County area were compared to state-wide total to determine if the trend is unique or related to system-wide pattern changes. EMS and ED SyS trend results were compared for internal validity. EMS incident addresses were geocoded to point-level to enable more granular analysis of geospatial patterns over time for identification of hotspots/clustering.Results: ED SyS and EMS results demonstrated a clear temporal increase in SynCan overdoses beginning around March of 2017[Fig 1]. Further analysis indicated that this increase was most dramatically centered in the Lexington-Fayette County area [Fig 2]. The vast majority of those overdosing were males (SyS: 88.1%) with average age 37 compared to 11.9% and 36.0 for females, respectively. These demographics are similar to those reported by the New York City Department of Health and Mental Hygiene for a K2 outbreak in New York City in 20141. Kernel density mapping demonstrated a strong clustering in a specific area of downtown Lexington. Additionally, analysis of EMS data revealed that a large portion of these overdoses were being admitted for observational care and thus not being captured in SyS data (based on the primary hospital’s submission types).From a practical standpoint, the rapid surveillance results only took 1-2 days to complete and highlight the utility of these data systems in preparing rapid data products. The results of the analysis were shared with local and state health department authorities, including the local Emergency Medical Advisory Board. The geospatial analysis provided local authorities with information to enable precise targeting of public health and public safety messaging.Conclusions: By analyzing data from these systems, we were able to quickly identify the geographic areas and demographic groups that were most affected, and to describe trends in SynCan overdoses over time. As a result, we were able to provide highly-detailed data to local public health and public safety authorities to inform their response.

Public Health Impact of Syndromic Surveillance Data—A Literature Survey

Public Health Impact of Syndromic Surveillance Data—A Literature Survey

Assessing Definitions of Heroin Overdose in ED & EMS Data Using Hospital Billing Data

Objective: The aim of this project was to assess the face validity of surveillance case definitions for heroin overdose in emergency medical services (EMS) and emergency department syndromic surveillance (SyS) data systems by comparing case counts to those found in a statewide emergency department (ED) hospital administrative billing data system.Introduction: In 2016, the Centers for Disease Control and Prevention funded 12 states, under the Enhanced State Opioid Overdose Surveillance (ESOOS) program, to utilize state Emergency Medical Services (EMS) and emergency department syndromic surveillance (SyS) data systems to increase timeliness of state data on drug overdose events. An important component of the ESOOS program is the development and validation of case definitions for drug overdoses for EMS and ED SyS data systems with a focus on small area anomaly detection. In fiscal year one of the grant Kentucky collaborated with CDC to develop case definitions for heroin and opioid overdoses for both SyS and EMS data. These drug overdose case definitions are compared between these two rapid surveillance systems, and further compared to emergency department (ED) hospital administrative claims billing data, to assess their face validity.Methods: The most recent available data were pulled from multiple hospitals in a large healthcare system serving an urban region of Kentucky. Definitions for acute heroin overdose were applied to all three sources. For SyS and ED data, definitions were queried against the same hospitals within this geographic region and aggregated to week-level totals. SyS and ED data are similar with the exception of additional textual information available in SyS (such as chief complaint). Our EMS definition of heroin overdose was loosely based on a draft definition that was produced by the Massachusetts Department of Public Health, and relies more on textual analysis versus ICD10 codes used in SyS and ED data systems. While SyS and ED used the same hospitals as the frame of selection, EMS used incidents that occurred in the approximate catchment area served by those hospitals. Weekly totals from all three data sources were plotted in R studio with LOESS-smoothed trend lines. Unsmoothed times series plots also demonstrate highly correlated trends, but the smoothed trend lines are less cluttered and easier to interpret.Results: Visual interpretation of the LOESS-smoothed trend lines shows very similar trajectories among all three sources [Fig 1]. The resultant graph demonstrates that individually, the time courses described by SyS and EMS data track closely with the one observed in ED data. The absolute counts between the three sources showed some differences, as expected. The EMS system captures a slightly different cohort that may include people that do not go to the ED (observation patients, refused transport, etc.) and SyS/ED have slightly different definitions (as ED does not include a free-text chief complaint. These types of limitations are better explored through data linkage that may or may not include medical record review to establish ground truth.Conclusions: Public health surveillance of drug overdoses has traditionally relied on ED billing data. In most states, however, there is a lag of at least several months before this data becomes available for analysis. In some jurisdictions the delay may be considerably longer. Rapid surveillance data sources may allow for more timely identification of changes in overdose patterns at the local level. In addition, SyS/EMS can be used together to confirm that a spike seen in one rapid system is confirmed within the other, with relative ease.Though the comparison is a rather simple or crude visual analysis of three data systems at a common geographic level, there is still appears to be a common pattern among the three systems. While this does not carry the validity of cross-data matched analysis, it does provide some of the utility of looking at these system collective without match; and therefore may be of use to surveillance users that may be limited by de-identified data.

Assessment of a Surveillance Case Definition for Heroin Overdose in Emergency Medical Services Data

Assessment of a Surveillance Case Definition for Heroin Overdose in Emergency Medical Services Data

Leveraging the NSSP R Studio Server to Automate QA Monitoring and Reporting

Objective: The aim of this project was to develop a nimble system to both monitor and report on the quality of Kentucky emergency department syndromic surveillance (SyS) data at system-wide and facility levels.Introduction: In 2016, the CDC funded 12 states, under the Enhanced State Opioid Overdose Surveillance (ESOOS) program, to utilize SyS to increase timeliness of state data on drug overdose events. In order to operationalize the objectives of the grant, there was a need to assess and monitor the quality of Kentucky’s SyS data, with limited resources. We leveraged the NSSP’s R Studio Server to automate quality assurance (QA) monitoring and reporting to meet these objectives.Methods: Using the R Server, we pulled data from the process messages table, aggregating messages to single patient encounters. In addition to compiling the code on a powerful remote server, the server can access the process table messages relatively quickly. We developed an R Markdown report to produce a report that includes a variety of system- and facility-level metrics that highlight key indicators of system performance and data flows. By using R, we were able to create an auto-generating QA report that runs weekly and e-mails for analyst review. Quality metrics included: % completeness of chief complaint and discharge diagnosis codes (overall and by facility)[Fig 1 & Fig 2]; visit trend by day of visit (with interactive spark lines)[Fig 2]; maximum date of message created, date message arrived at NSSP server, date of visit, and total messages[Fig 3]; message arrived trend (interactive sparklines)[Fig 3]; volume and type of error messages failing to process[Fig 4]; message volume by ADT type[Fig 5]; and volume of patient class by type by day[not shown]. Our SyS analyst reviews the report and delivers it to stakeholders with general comments about ongoing and newly emerging data quality concerns.Results: The report has proven to be beneficial in ongoing QA monitoring. The report is shared weekly with key stakeholders at the Kentucky Department for Public Health, Kentucky Health Information Exchange, NSSP, and regional ESSENCE users. Findings are reviewed at monthly SyS stakeholder meetings. The report has identified numerous errors, dead feeds, and other systems changes in near real-time; leading to corrective action and general data quality enhancement. Weekly monitoring of QA has improved data feed stability and communication of identified issue with key stakeholders.Conclusions: The R Studio Server provides a nimble platform to develop, refine, and automate a QA reporting system that can lead to improved SyS data quality. In Kentucky, in addition to improving overall data quality, these weekly reports and subsequent communication have help built relationships among key stakeholders and elevated the importance of syndromic surveillance data locally. Continual monitoring of data is critical to ensure quality and therefor the validity of the data.