Investigating wildfire smoke-related emergency department visits and syndromic surveillance in New Mexico 2019–2022

ObjectiveTo evaluate capacity of the BioSense ESSENCE platform and pre-defined overdose queries to identify emergency department admissions related to opioid overdose, in compliance with 2018 mandatory overdose reporting laws in IllinoisIntroductionAccuracy in identifying drug-related emergency department admissions is critical to understanding local burden of disease and assessing effectiveness of drug abuse prevention and overdose-reduction initiatives. In 2018 the Illinois Department of Public Health (IDPH) began implementation of a mandatory opioid overdose reporting law, applicable to all hospital emergency departments (ED). The mandate requires reporting of patient demographics, causal substance and antagonist ED administration within 48 hours of presentation. This reporting is not name-based.IDPH currently utilizes a near real-time syndromic surveillance (SyS) reporting system for all hospital ED, capturing most of the mandated criteria. Leveraging this existing system facilitates adherence to the mandate while imposing minimal additional burden of reporting on local hospitals. The Division of Patient Safety and Quality at IDPH has thus chosen to evaluate the completeness of overdose reporting and compliance with the opioid overdose mandate that have resulted from use of the current syndromic surveillance system.MethodsMulti-level internal and external validation methods are being employed to evaluate the accuracy of opioid overdose reporting through syndromic surveillance.An initial internal evaluation compared overdoses captured using hospital discharge data (HDD) and SyS data. This analysis compared daily overdose counts in the two datasets from 166 Illinois facilities, from admissions from April 1 through June 30 2017, inclusive. The opioid overdose query from HDD referenced ICD-10 poisoning codes; SyS utilized the preset Opioid Overdose Version 1 (v1.0) query in the ESSENCE Tool from the CDC’s National Syndromic Surveillance Program’s BioSense Platform. Daily and quarterly overdose counts by surveillance method were compared and visualized by facility.Three facilities were chosen for a secondary, case-level data comparison based on: magnitude of overdose discrepancies, overall overdose burden, and availability of linked data elements. Individual overdose visits were matched across SyS and HDD datasets based on: date of birth, sex and approximate date of admit. Cases identified in SyS but missing from (1) discharge diagnosis query and (2) discharge database overall were quantified. Cases identified by HDD that were (3) not identified in the SyS overdose query or (4) missing from the SyS database entirely were also counted.ResultsFrom April 1 to June 30 2017, among the 166 providers analyzed, the HDD query identified 2,998 opioid overdose-related visits; SyS identified 3,266 (268 cases or 8.9% difference) (Figure 1, r=0.724).A total of 25 (15%) of facilities had equivalent overdose visit counts between datasets: all were among those with low case burden (13 or fewer overdose visits per facility over the quarter). Among facilities with a higher number of overdose presentations, differences in quarterly case counts (SyS minus HDD) ranged from –56 to 120.Discrepant counts were found in 85% of centers (Figure 2). HDD captured a larger number of overdoses in 93 facilities (56%). SyS captured a larger number of overdoses in 48 facilities (29%). The ten facilities with highest syndromic caseload accounted for 33% of overall case burden (1069); the ten with the highest discharge counts accounted for 29% (897 cases). However, the top ten facilities by surveillance type were notably different: the 2nd and 3rd highest using syndromic surveillance ranked 30th and 41st using discharge surveillance over the same period. The center with 5th highest caseload by discharge criteria ranked 38th using syndromic surveillance.In secondary case-level analyses: across datasets from three facilities, both HDD and SyS captured 43.5% of overdoses, while 56% were only in SyS data and 0.5% were only in HDD. Discrepancies in the all-visit (“denominator”) datasets were found, requiring follow-up with facilities directly.ConclusionsNext steps in these evaluations include further characterization of cases missed differentially by syndromic and discharge surveillance. An external validation phase will engage facility staff to query the Electronic Medical Record directly. Hospital personnel will review and confirm opioid overdose events captured by SyS and hospital facilities will investigate and resolve discrepancies in data quality.These analyses have the potential to inform more accurate definitions for opioid-related overdose seen in emergency departments. Such improved surveillance can aid allocation of medication (naloxone and naltrexone), promotion of intervention (i.e. methadone-assisted treatment programs), and drug abuse prevention. Engagement of facility staff in public health surveillance has resulted in 187 hospital-registered users for the BioSense Platform to date, demonstrating the ability of surveillance improvement efforts to foster public health partnership. Finally, optimizations of automated hospital surveillance systems can help reduce the burden of reporting overdoses and ED morbidity in general, to encourage time spent on monitoring and response.

ObjectiveTo evaluate several non-infectious disease related syndromes that are based on chief complaint (cc) emergency department (ED) syndromic surveillance (SS) data by comparing these with the New York Statewide Planning and Research Cooperative System (SPARCS) clinical diagnosis data. In particular, this work compares SS and SPARCS data for total ED visits and visits associated with three non-infectious disease syndromes, namely asthma, oral health and hypothermia.IntroductionSyndromic surveillance data has predominantly been used for surveillance of infectious disease and for broad symptom types that could be associated with bioterrorism. There has been a growing interest to expand the uses of syndromic data beyond infectious disease. Because many of these conditions are specific and can be swiftly diagnosed (as opposed to infectious agents that require a lab test for confirmation) there could be added value in using the ICD9 ED discharge diagnosis field collected by SS. However, SS discharge diagnosis data is not complete or as timely as chief complaint data. Therefore, for the time being SS chief complaint data is relied on for non-infectious disease surveillance.SPARCS data are based on clinical diagnoses and include information on final diagnosis, providing a means for comparing the chief complaint (from SS) to a diagnosis code (from SPARCS), for evaluating how well the syndrome is captured by SS and for assessing if it would be advantageous to get SS ED diagnosis codes in a more timely and complete manner.MethodsSyndromes previously developed by the DOHMH were used for this work. Syndrome definitions are based on querying the cc field in SS data for terms associated with asthma, oral health and hypothermia. The asthma syndrome consists of search terms for ‘ASTHMA’, ‘WHEEZING’ and ‘COPD’. The oral health syndrome uses (‘TOOTH’ or ‘GUM’) and (‘ACHE’, ‘HURT’) and excludes visits resulting from trauma (e.g., ‘INJURY’, ‘ACCIDENT’). The hypothermia syndrome is limited to search for the word ‘HYPOTHERMIA’. For the purpose of comparison of the SS data with SPARCS data for the three syndromes, the following ICD9 diagnosis codes were considered in SPARCS: 493 for asthma, 521–523, 525, 528–529 for oral health and 991 for hypothermia.SS and SPARCS data for 2007 were used for this work as this was the most recent and complete SPARCS ED dataset that was available. Overall city-wide daily counts and hospital-level annual counts for total ED, asthma-, oral health- and hypothermia-related visits were computed for SS ED data and SPARCS ED data. A comparison of daily and hospital trends for SS and SPARCS for total and syndrome-related counts were conducted using correlation coefficients.ResultsThere is a high correlation between total ED SS and SPARCS daily counts (r=0.98, p-value<0.001). On average, SPARCS daily counts are higher by approximately 75 visits (range: −674, 591) per day. Correlations between SS and SPARCS daily counts for asthma, oral health and hypothermia were 0.96 (p-value<0.001), 0.66 (p-value<0.001) and 0.45 (p-value<0.001), respectively. Correlations between SS and SPARCS hospital-level annual counts for asthma, oral health and hypothermia were 0.89 (p<0.001), 0.87 (p<0.001) and 0.07 (p=0.61). In 2007, less than 8% of individual SS records had a discharge diagnosis, and this was found to vary between hospitals (0–69%); therefore, a comparison between SS discharge diagnosis and SPARCS diagnosis data was not possible.ConclusionsOverall, syndromic surveillance data was found to be a useful data source for public health surveillance of non-infectious disease. Total ED visits were found to be comparable between SS and SPARCS. While direct comparison of counts for syndromes is not possible, the daily syndrome counts between SS and SPARCS correlated well. However, the strength of correlation varied depending on the syndrome, with a better correlation for syndromes with larger volume of visits to the ED (e.g., asthma) and with more commonly used terms in the cc search (e.g., ‘tooth ache’) compared to syndromes with very specific search terms (e.g., ‘hypothermia’).In certain instances, it is hypothesized that SS discharge diagnosis would provide more reliable and representative estimates than cc for tracking non-infectious disease. Future work will consider a period with more complete SS ED discharge diagnosis data for further comparisons and to test the hypothesis that more complete and timely SS ED discharge diagnosis data could improve surveillance efforts.

Making Syndromic Surveillance Relevant and Valuable for Emergency Managers

Objective: This panel will:● Discuss the importance of identifying and developing success stories● Highlight successes from state and local health departments to show how syndromic surveillance activities enhance situational awareness and address public health concerns● Encourage discussion on how to further efforts for developing and disseminating success storiesIntroduction: Syndromic surveillance uses near-real-time emergency department and other health care data for enhancing public health situational awareness and informing public health activities. In recent years, continued progress has been made in developing and strengthening syndromic surveillance activities. At the national level, syndromic surveillance activities are facilitated by the National Syndromic Surveillance Program (NSSP), a collaboration among state and local health departments, the CDC, other federal organizations, and other organizations that enabled collection of syndromic surveillance data in a timely manner, application of advanced data monitoring and analysis techniques, and sharing of best practices. This panel will highlight the importance of success stories. Examples of successes from state and local health departments will be presented and the audience will be encouraged to provide feedback.Description: ●Success stories – acknowledging and informing syndromic surveillance practiceThis presentation will discuss the importance of success stories for NSSP focused on increasing syndromic surveillance representativeness, improving data quality, and strengthening syndromic surveillance practices among grant recipients and partners. From the beginning of the program, the identification of success stories has been an important part of the efforts to develop knowledge base that better guide syndromic surveillance program activities.●NJ and BioSense – Making The Connection The New Jersey Department of Health (NJDOH) uses Health Monitoring’s EpiCenter as its primary ED data for syndromic surveillance. This data is also submitted to CDC’s NSSP BioSense Platform. In April 2017, a spike in ED Visits of Interest was identified by a CDC NSSP subject matter expert and brought to the attention of NJDOH’s data analyst. Data showed an increase in “Exposure” and “School Exposure” chief complaints in two contiguous counties. News reports showed the visits resulted from a dormitory fire at a university in the area. The NSSP and NJDOH staff collaboration integrated data from both NJDOH’s EpiCenter and CDC’s BioSense Platform for further investigation. This activity shows BioSense Platform’s potential as an additional syndromic surveillance tool because of its different classifications and keyword groupings.●Evaluation and Performance Measures at the Utah Department of HealthSyndromic surveillance related evaluation activities at the Utah Department of Health requires collaboration between subject matter experts and system users from the UT-NSSP workgroup. The progress is examined quarterly and outcomes compared with the short-, mid-, and long-term outcomes listed in the NSSP logic model to ensure activities are in sync with the program’s overall goals. Throughout the budget year, a variety of tools were used to keep track of the progress. During this session, challenges and successes, lessons learned, and effective strategies will be discussed.●NSSP R tool Data Download Useful in NHThe New Hampshire Department of Health and Human Services (NH DHHS) uses the state-wide Automated Hospital Emergency Department Data (AHEDD) system as its primary syndromic surveillance system. A copy of this data is submitted to CDC’s NSSP BioSense Platform. In July of 2017, NH worked with the NSSP vendor, CDC staff, a jurisdictional expert, NH Division of Information Technology staff, and an external vendor to create an “R” software download in CSV format and home-based NSSP Cognos report. This allowed NH DHHS staff to compare these data to the home-based data and ultimately, it proved to be an important step in the NSSP data quality assessment process.●Achieving success to improve data quality through collaborative Community of Practice partnerships The Data Quality Committee is a forum to identify, discuss, and attempt to address syndromic surveillance data quality issues. Maintaining data quality for the chief complaint field is a priority as it can impact the creation and refinement in the successful application of a syndrome definition for one of the fundamental data elements. An issue was observed in the Arizona data in the BioSense Platform, where chief complaint was being truncated at 200 characters. Through efforts to build relationships from the committee in the Community of Practice, Arizona was able to discover the root causes for the issue, assess if it affected other jurisdictions, and work with the partners to find a feasible resolution. This talk will discuss how this collaborative approach helped improve data quality.How the Moderator Intends to Engage the Audience in Discussions on the Topic: The moderator will introduce the session and the panelists, and will invite questions and comments from the audience.

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.

ObjectiveTo assess the usefulness and acceptability of Maine’s syndromic surveillance system among hospitals who currently participate.IntroductionMaine has been conducting syndromic surveillance since 2007 using the Early Aberration Reporting System (EARS). An evaluation of the syndromic surveillance system was conducted to determine if system objectives are being met, assess the system’s usefulness, and identify areas for improvement. According to CDC’s Guidelines for Evaluating Public Health Surveillance Systems, a surveillance system is useful if it contributes to the timely prevention and control of adverse health events. Acceptability includes the willingness of participants to report surveillance data; participation or reporting rate; and completeness of data.MethodsA survey was created in 2012 to measure usefulness and acceptability among hospital partners who submit emergency department data to Maine CDC for syndromic surveillance. Currently, 24 of Maine’s 37 emergency departments collect syndromic surveillance data and 20 of those receive a weekly syndromic surveillance report from Maine CDC. The survey was included with the report on August 14, 2012, and hospitals were given two weeks for completion. The survey included questions about how useful hospitals find syndromic surveillance and how data is shared back with the hospitals; which syndromes are most and least useful; and chief complaint data collection at individual hospitals.ResultsThe survey was completed by 13 out of 22 emergency departments (59% participation rate), and six out of 13 respondents (46%) completed the entire survey. The factors reported as having an influence on a hospital’s decision to submit data for syndromic surveillance were: public health importance of events (6 respondents) and assurance of privacy/confidentiality (5 respondents). The majority of respondents (5 respondents) reported that there are no factors that limit their ability to send emergency department data. Among hospitals that did report factors that limit their ability to send data, lack of information technology support in the hospital (2 respondents) and manually entering data/lack of electronic health records (1 respondent) were the most frequently reported. Six out of seven hospitals who answered (86°%) reported the current method of sharing syndromic surveillance data on a weekly basis, including a statewide data summary, as useful. Respondents also recommended that data be shared back with participants using 30-day line graphs for each syndrome (4 respondents). The three syndromes respondents found most useful were influenzalike illness (7 respondents), gastrointestinal (5 respondents), and respiratory (5 respondents). The three syndromes respondents found least useful were the broad heat syndrome (4 respondents), the narrow heat syndrome (4 respondents), and the other syndrome that captures all visits not classified into any syndrome (4 respondents). Chief complaint data, which is used to classify emergency department visits into syndromes, is most often recorded by a drop-menu (4 respondents).ConclusionsWith a low survey completion rate, it is difficult to generalize responses to all hospitals who participate in syndromic surveillance. Hospitals that did not respond to or complete the survey will be followed up with to determine their reasons for not doing so, as this may be useful information. In general, those who responded have more factors that influence them to contribute to syndromic surveillance than factors that hinder them. Most hospitals find the current method of sharing data back with the hospitals useful. Also, it is advantageous to know which syndromes the hospitals find most useful, as they are the entities that collect and report the data. Opinions differ among system users, which is why it is important to evaluate a system throughout all points of interaction.

ObjectiveThe objective of this session is to discuss syndromic surveillance evaluation activities. Panel participants will describe contexts and importance of selected evaluation and performance measurement activities in NSSP. Discussions will explore ways to strengthen evaluation in syndromic surveillance activities in the future.IntroductionSyndromic surveillance uses near-real-time Emergency Department healthcare and other data to improve situational awareness and inform activities implemented in response to public health concerns. The National Syndromic Surveillance Program (NSSP) is a collaboration among state and local health departments, the Centers for Disease Control and Prevention (CDC), other federal organizations, and other entities, to strengthen the means for and the practice of syndromic surveillance. NSSP thus strives to strengthen syndromic surveillance at the national and the state, and local levels through the coordinated activities of the involved partners and the development and use of advanced technologies, such as the BioSense platform. Evaluation and performance measurement are crucial to ensure that the various strategies and activities implemented to strengthen syndromic surveillance capacity and practice are effective. Evaluation activities will be discussed at this session and feedback from audience will be sought with the goal to further strengthen evaluation activities in the future.DescriptionSyndromic surveillance practice among NSSP grant recipients: findings from a telephone based survey – S. Romano This presentation will highlight the development and implementation of a survey among the NSSP grant recipients about their syndromic surveillance practice. The objectives of the survey was to develop knowledge and understanding about: a) characteristics of syndromic surveillance practice at the state and local level among jurisdictions that are NSSP grant recipients; b) challenges encountered by these jurisdictions in conducting syndromic surveillance; and c) strategies that may help address these challenges. The objectives and methods of the survey will be described in detail. The survey is expected to be implemented before the end of this year. Preliminary findings will be presented if available. Lessons learned and strategies to consider for strengthening syndromic surveillance practice will be discussed.Defining a sustainable approach to syndromic surveillance through the AZ BioSense Workgroup Charter – K. Collier, S. Johnston The Arizona BioSense Workgroup has developed a five year charter outlining the method and measures used for implementation and adoption of syndromic surveillance in Arizona. Membership consists of clinicians, IT and public health. The mission and vision help to establish a foundation for building capacity and quality of the syndromic surveillance data, improved population health and emergency response through timely and effective use of the data. Cross-cutting topics resulted in a process for assessing training needs, establishing protocols and evaluation of use cases, shared plans for situational awareness and making public health decisions. This talk will discuss the collaborative approach and how lessons learned will inform future activities.User Acceptance Testing to inform development and enhancement of the BioSense Platform – C. Davis Between June, 2016 and January, 2017, NSSP operationalized an updated BioSense Platform for conducting syndromic surveillance. The platform included ESSENCE, a software that enables analysis and visualization of syndromic surveillance data and the Access Management Center, a tool that enables jurisdictions to manage access to data. The development of and transition to the updated platform was informed by a User Acceptance Testing (UAT) that examined the functionality and usability of the platform and associated tools After webinar based orientation UAT, participants were requested to carry out specific tasks using the updated platform and tools in development. This presentation will discuss the objectives and methods of implementation of the UAT, findings from the UAT, and how these guided transition activities and the refinement of the platform applications.A quantitative and qualitative assessment of user support provided by the NSSP Service Desk – H. Tesfamichael, S. Romano A principal component of NSSP is the BioSense platform that includes health care visits related information, particularly related to emergency department visits, from across the U.S. BioSense and its associated tools, including ESSENCE, the Access Management Center, and Adminer, enable state and local health departments, and other, as appropriate, to use syndromic surveillance data to implement surveillance and assessment activities. The NSSP Service Desk provides technical support to BioSense users to assist with the use of the BioSense platform and its tools Users submit support request tickets through an online application. An analysis of information related to these tickets, including the context of the requests and their resolution status, was conducted to better understand the support needs of users and how well these were being addressed. This presentation will discuss the assessment, findings, and conclusions.How the Moderator Intends to Engage the Audience in Discussions on the TopicThe moderator will introduce the session and the panelists. The moderator will also invite questions and comments from the audience, and will facilitate the discussions.

We assessed the local implementation of syndromic surveillance (SyS) as part of the European project 'System for Information on, Detection and Analysis of Risks and Threats to Health' in Santander, Spain. We applied a cumulative sum algorithm on emergency department (ED) chief complaints for influenza-like illness in the seasons 2010-11 and 2011-12. We fine tuned the algorithm using a receiver operating characteristic analysis to identify the optimal trade-off of sensitivity and specificity and defined alert criteria. We assessed the timeliness of the SyS system to detect the onset of the influenza season. The ED data correlated with the sentinel data. With the best algorithm settings we achieved 70/63% sensitivity and 89/95% specificity for 2010-11/2011-12. At least 2 consecutive days of signals defined an alert. In 2010-11 the SyS system alerted 1 week before the sentinel systemand in 2011-12 in the same week. The data from the ED is available on a daily basis providing an advantage in timeliness compared with the weekly sentinel data. ED-based SyS in Santander complements sentinel influenza surveillance by providing timely information. Local fine tuning and definition of alert criteria are recommended to enhance validity.

<h3>Statement of purpose</h3> Effective injury prevention efforts depend greatly on a surveillance system that monitors event occurrence. Traditionally, the Nebraska Department of Health and Human Services (NDHHS) has defined burden of such events through retrospective analysis of hospital discharge data (HDD). However, these data are limited by timeliness and availability of data elements. An emergency department (ED) based syndromic surveillance (SS) system could be used to enhance injury surveillance by allowing the timely detection of clusters, anomalies and trends. Our objective was to demonstrate the value of ED based SS system data to aid injury surveillance in Nebraska. <h3>Methods/Approach</h3> Syndromic Surveillance ED data from pilot Hospital A was analysed for ICD9-CM codes associated with MVC-related injuries (E810-E819), and suicide/self-inflicted injury (E950-E959). For MVC-related injury, time series graphs of weekly ED visits were created for years 2011–2014, and dates corresponding to relevant climate, sports and entertainment events were identified. For suicide/self-inflicted injury, the number of monthly ED visits was determined for years 2011–2014, and the distribution of ED visits was determined by sex, age group, discharge disposition, zip code and injury mechanism. <h3>Results</h3> The analysis of the 2011–2014 SS ED data for MVC-related injury indicates a possible temporal trend in the incidence of these injuries in NE. Results suggest that there is an association between weather, sports and entertainment events and spikes in MVC-related injuries. On the other hand, the analysis of the 2011–2014 ED SS data for suicide/self-inflicted injury, indicates a higher percentage of ED visits due to suicide/self-inflicted injury among females than males and among the 15–44 year age group. Most suicide/self-inflicted injury admissions were caused by poisoning. A map of incidence by zip code indicated areas with higher incidence. <h3>Conclusions</h3> Results of this pilot study suggests that SS ED data can be used for the timely identification of MVC-related injuries and suicide/self-inflicted injuries. Thus allowing the rapid identification of at risk populations and the timely deployment of prevention measures. <h3>Significance and contribution to the field</h3> An ED SS system could be incorporated to support an efficient and rapid prevention response to injuries. Thus allowing the rapid assessment of the burden of these events, and a more timely assessment of prevention efforts.

Objective: The objective is to develop a standard opioid overdose case definition that could be generalized nationallyIntroduction: Opioid ODs have been rising globally and nationally. The death rate from ODs in the United States has increased 137% since 2000, including a 200% increase of OD deaths involving opioids1. The pilot project, a collaboration across 3 states, allowed information sharing with Syndromic surveillance (SyS) partners across jurisdictions, such as sharing a standard SyS case definition and verifying its applicability in each jurisdiction. This is a continuation of the work from an initial pilot project presented during the ISDS Opioid OD Webinar series.Methods: Three regions (Colorado North Central Region [CO-NCR]), State of Nebraska [NE], and Marion County, Indiana) participated in the development and evaluation of the opioid OD case definition. Data sources included ESSENCE and 2015 hospital discharge data (HDD) for the first two jurisdictions. Work was conducted in 3 stages. Stage I and II consisted of the development and validation of an opioid misuse definition. In stage I, the percent of completeness of admission date, chief complaint (CC), and discharge diagnosis (DD) was assessed from January 2015 to August 2016 SyS emergency department (ED) data from each of the 3 participating jurisdictions. Data selected for the time period with the best completeness among all jurisdicions was utilized to develop a case definition. Completeness of ESSENCE data submission was assessed at all jurisdicions. The threshold for best data quality was 80% of completeness. SyS ED data was analyzed for the selection of CC search terms and ICD9/ICD102 DD codes, and the reported Chief Complaint-Discharge Diagnosis (CCDD) were validated by analyzing consistency between CC and DD. In stage II, the consistency of DD reporting corresponding to the opioid case definition was assessed for CO-NCR and NE data by performing Pearson Correlation analysis to compare the weekly counts of opioid misuse cases in 2015 SyS ED data to those obtained in HDD. Stage III consisted of the development of an opioid OD case definition that meets the DD code reporting requirements of the Centers for Disease Control and Prevention (CDC), Prescription Drug Overdose Prevention for States awardees. This definition consisted of an ESSENCE query containing CC, and CCDD components. For Stage III, SyS ED data was analyzed for the August 2016 to August 2017 time period. The case definition was evaluated by assessing the consistency between the CC and DD reported for each identified opioid OD possible case. Triage notes were used for case validation.Results: Stage I: Mean percent of completeness of DD codes for CO-NCR, NE and Marion County, IN, 2015 ED SyS data was ≥ 85%. In the CO-NCR, of 963 cases detected by the CC definition, 99.4% had an opioid misuse diagnostic code in the DD, while of 1,445 cases detected by the DD, 66.2 % had an associated opioid misuse in the CC search terms. In NE, of 6 cases detected by the CC definition, 33% identified opioid misuse DD. However, of 42 cases detected by the DD definition, only 5% identified opioid misuse CC search terms. In Marion County, IN, of 95 cases detected by the CC definition, 70% identified opioid related diagnosis codes. Of 191 cases detected by the DD definition, only 20% identified opioid-related CC search terms. Stage II: Results of the Pearson correlation analysis indicate statistically significant correlations between 2015 SyS and HDD data for the DD code based opioid definition for both CO (r = 0.92, p &lt; 0.0001), and NE (r = 0.63, p &lt; 0.0001). Stage III: In NE, 56% of the cases detected by the CC component, identified opioid OD DD codes, and only 8% of the cases detected by the DD component identified opioid OD search terms in the CC. Triage notes were consistent with opioid OD in 55% of the cases detected by the DD component. However, for CO-NCR, of 235 cases detected by the CC component, 215 identified opioid OD DD codes. Of 465 cases detected by the DD component, 46% identified opioid OD search terms in the CC field. Triage notes values were consistent with opioid OD reported DD codes in 80% of the cases.Conclusions: Results suggest that DD codes reported in SyS ED data correlated with HDD data. Indicators of opioid OD signs and symptoms were observed in CCDD. Therefore, the SyS case definition proposed through this pilot project may be applied by other states to support real-time monitoring of opioid OD related hospital ED visits, and consequences of opioid OD. Further study includes exploring how triage notes search terms may improve the identification of opioid OD related ED visits.

Using Syndromic Surveillance Data to Aid Public Health Actions in Tennessee

Introduction: According to the Portuguese clinical guidelines published in 1999, patients with traumatic brain injury and coagulopathies should remain in-hospital for 24 hours for clinical and image surveillance, despite having an admission computed tomography (CT) scan showing no intracranial lesions. Growing evidence suggests this practice is not only void of clinical relevance, but that it can also be potentially harmful for the patient. Nevertheless, upuntil now there is no published data concerning the economic impact of this clinical practice. Methods: A cost analysis compared retrospective data from patients admitted to our emergency department during 2022 with a hypothetical scenario in which a patient with an admission CT scan without traumatic lesions was discharged. Clinical data was also retrieved concerning the rate of a delayed intracranial bleeding on 24-hour CT scan and mortality at a six-month-period after discharge. Direct costs for the national health service were determined in terms of funding and time invested by medical teams.Results: From a sample of 440 patients, 436 remained in-hospital for a 24-hour clinical and image surveillance, of which only two (0.5%) showed a new intracranial lesion on the second CT-scan. Neither of these two patients required therapeutic measures to control bleeding and were discharged 36 hours after admission. Out of 440 patients, one patient (0.2%) died of cardiac arrest during the 24-hour surveillance period, despite having an initial normalCT scan showing no brain lesions. Our current surveillance practice directly amounted to €163 157.00, whereas the cost of our hypothetical scenario amounted to €29 480.00: a difference of €133 677.00. The application of our surveillance guideline also meant that nine emergency shifts were devoted to this task, compared to 4.6 hypothetical shifts if patients were discharged after an initial CT scan without traumatic intracranial lesions.Conclusion: In spite of apparently not adding any clinical value to our practice, our in-hospital surveillance may represent a significant financial and time-consuming burden, costing five times as much and demanding our medical teams twice as much work when compared to a scenario without clinical surveillance and 24-hour CT scans.

ObjectiveTo evaluate the readiness and timeliness of ED data submitted by hospitals following PHIN syndromic surveillance messaging guide and to evaluate the availability of minimum data elements. To validate the accuracy and completeness of data from ADT messages compared with data currently reported to the NY syndromic surveillance system.IntroductionThe final rules released by the Centers for Medicare and Medicaid Services specified the initial criteria for eligible hospitals to qualify for an incentive payment by demonstrating meaningful use of certified Electronic Health Record (EHR) technology. Syndromic surveillance reporting is one of three public health objectives that eligible hospitals can choose for stage 1. The PHIN messaging guide for syndromic surveillance was published for hospitals to construct emergency department data using Admit Discharge Transfer (ADT) messages, with the minimum dataset that is standard among hospitals and public health agencies.Currently New York hospitals are reporting emergency department (ED) visit data to the NY syndromic surveillance (SS) system. Patient chief complaint data are monitored for trends of illness at the community level in order to detect possible outbreaks and situational awareness.Methods12 hospitals using three EHR certified vendors pilot tested syndromic surveillance data for MU. Hospitals started to transmit ED data in HL7 v 2.5.1 to the NY pre-certification server beginning October 2011. The month of data from July 2012 was evaluated for availability by data elements listed in the implementation guide. The ADT message types were analyzed and the timeliness of reporting was calculated from visit date to report date of the first message type. The data from the pre-certification server was matched against data from the production SS system by medical record number and visit date to evaluate the data content.ResultsThere were 5 hospitals from vendor A, 3 from vendor B and 4 from vendor C participating in the pilot testing; 5854, 9882, and 13316 ED visits were reported from the three vendors respectively for the month of July. The type of first message by vendor is shown in Table 1. The availability of data elements is listed in Table 2. There were 79%, 82%, and 87% ED visit records received within 24 hours for vendor A, B, and C respectively. One hospital from vendor A, 3 hospitals from vendor B and 4 hospitals from vendor C also reported ED data to the production system, and their comparison with pilot testing data is shown in Table 3.ConclusionsThe types of ADT messages first reported varied by vendor and hospital. Not all data elements specified in the implementation guide were available or complete, and varied by vendor. An average 83% of first messages were received within 24 hours and the chief complaint from ADT messages did not match well with the current ED system in production. It is a very time consuming and resource demanding process to move a hospital from successful attestation stage to production and requires public health, EHR vendor, and hospital IT to work together. The learning experience from these three vendors in implementing syndromic surveillance for MU will help public health and EHR vendors to prepare for stage 2.

The purpose of this paper is to review new bioterrorist and emerging infectious threats to public health in Ontario, Canada, and to propose a means of integrating a telephone-based health information service and emergency department triage with a first-line real-time, 24-h a day syndrome surveillance system. This automated system could be beneficial in detecting a bioterrorist threat as well as in detecting and monitoring disease outbreaks such as influenza, Norwalk, West Nile virus, Escherichia coli 0157 or severe acute respiratory syndrome. The Medline PubMed database was searched for articles relating to bioterrorism and syndromic surveillance from 1997 onwards. The websites of the Ontario Ministry of Health and Long-Term Care, Ontario Ministry of Public Safety and Security, Centers for Disease Control and Canadian Population and Public Health Branch of Health Canada were searched for articles relating to bioterrorism and syndromic surveillance. Interviews were conducted with key informants from Telehealth staff, the public health services of Ontario and the Centers for Disease Control and Prevention, Atlanta, GA, USA. Real-time syndrome surveillance is a new means of detecting disease outbreaks or possibly acts of bioterrorism at the first contact with the healthcare system. It has been used successfully to detect influenza outbreaks at an early stage. The system that is proposed would be a province-wide integrated early warning system for both bioterrorist events and emerging infections. It would use clusters of symptoms tied to temporal, demographic and spatial data to increase sensitivity and specificity. Real-time syndrome surveillance is an evolving science. Emergency departments and Telehealth in Ontario lend themselves as first contacts to the healthcare system as excellent opportunities to perform syndrome surveillance. They offer the opportunity properly to identify at-risk patients for emerging infections by including contact and travel data into the symptom complex. This could identify at-risk patients early and lead to appropriate public health measures. The benefit of using Telehealth in Ontario is the provincial accessibility of Telehealth and the extensive data collected on one computerized system. Emergency departments should also have a uniform computerized triage data collection system to facilitate surveillance.

Objective: Provide justification for the collection and reporting of urgent care (UC) data for public health syndromic surveillance.Introduction: While UC does not have a standard definition, it can generally be described as the delivery of ambulatory medical care outside of a hospital emergency department (ED) on a walk-in basis, without a scheduled appointment, available at extended hours, and providing an array of services comparable to typical primary care offices.1 UC facilities represent a growing sector of the United States healthcare industry, doubling in size between 2008 and 2011.1 The Urgent Care Association of America (UCAOA) estimates that UC facilities had 160 million patient encounters in 2013.2 This compares to 130.4 million patient encounters in EDs in 2013, as reported by the National Hospital Ambulatory Medical Care Survey.3 Public Health (PH) is actively working to broaden syndromic surveillance to include urgent care data as more individuals use these services.4 PH needs justification when reaching out to healthcare partners to get buy-in for collecting and reporting UC data.Description: The International Society for Disease Surveillance (ISDS) Community of Practice (CoP) platform was used to host a webinar introducing the topic of urgent care participation in syndromic surveillance. This webinar provided a valuable opportunity to obtain insight from jurisdictions pursuing and using UC data. A workgroup was formed to create documentation justifying the collection and reporting of UC data. Using this forum, the workgroup brought together partners from various jurisdictions working with UC data to participate in a literature review of SCOPUS, PubMed, and the Online Journal of Public Health Informatics publications and to share their experiences. These two main sources of information – previous literature and jurisdictional experience – were combined and condensed to provide tangible justifications for the collection and use of UC data.While the workgroup found little in the literature to justify the collection of UC data as a part of syndromic surveillance, the shared experiences of the CoP jurisdictions working to onboard UC facilities provided valuable insight. From this collaborative response, three main reasons to collect UC data were identified.1) Healthcare reform is directing patients away from EDs and toward UC facilities. UC represents reduced cost and more efficient patient processing, thus easing the burden on both patient and healthcare system (according to a 2016 American Academy of Pediatrics article entitled “Urgent Care and Emergency Department Visits in the Pediatric Medicaid Population”). If syndromic surveillance does not adapt to include UC data, the potential exists to lose significant patient populations, which may lead to decreased situational awareness.2) According to the Centers for Medicare and Medicaid Services Stage 3 guidance, Meaningful Use (MU) will change the relationship between eligible professionals (EPs) and syndromic surveillance by restricting EPs to those who practice in a UC facility. This approach to EP participation simplifies the syndromic surveillance MU objective, thereby making it easier for PH jurisdictions to onboard UC facilities.3) Patients with certain conditions that are acute but non-emergent may report more frequently to an UC facility than to an ED. Broadening syndromic surveillance to include UC facilities may increase reporting of “rare event” encounters, which will lower the relative standard error for statistical calculation. Surveillance efforts for conditions like influenza-like illness and Zika virus may improve substantially with a larger data pool.How the Moderator Intends to Engage the Audience in Discussions on the Topic: The moderator will begin discussion with a brief presentation from the literature review and jurisdictional experience, highlighting three justifications for collecting and reporting UC data. The audience will be divided into 3 groups to discuss and validate 3 additional topics: creation of syndromic surveillance talking points to share with UC facility management, creation of jurisdictional UC facility listings, and UC onboarding best practices. Feedback from the 3 groups will be shared with the whole group, followed by a brief summary of the discussion and recommendations for next steps.