Syndromic Surveillance Program Research Articles

Trends in Injection Opioid Use and Bloodborne Pathogen Related Diseases in New Jersey

ObjectiveTo utilize New Jersey’s syndromic surveillance data in the study and comparison of trends in injection opioid use and infection with selected bloodborne pathogens (BBPs) over the years 2013-2017.IntroductionWhen the opioid epidemic began in the early 1990s, pills such as oxycodone were the primary means of abuse. Beginning in 2010, injection use of, first, heroin and then synthetic opioids dramatically increased, which led the number of overdose deaths involving opioids to increase fivefold between 1999 and 2016.1 It would be expected that BBP rates would rise with this increase in injection use, and, nationally, there has been a rise in acute hepatitis C (HCV) rates, although the other two main BBPs, acute hepatitis B (HBV) and acute human immunodeficiency virus (HIV) have been flat and declining, respectively.2,3In this study, we compared New Jersey’s reported incidence of these three BBPs (acute HBV, acute HCV, and HIV) over five years (2013-2017) with syndromic surveillance data for opioid use over the same time period in order to test the hypothesis that emergency department (ED) visits for opioid use could be used as a predictor of BBP infection.MethodsTo indirectly track the number of injection opioid users, we wrote a custom classifier for EpiCenter, New Jersey's statewide syndromic surveillance system, to search ED chief complaints for the number of visits related to injection opioid usage. Our custom classifier creation started with the CDC’s National Syndromic Surveillance Program (NSSP) ESSENCE Chief Complaint Category classifier for opioid overdose.4 As we were looking to count not just overdoses but all visits likely to be associated with injection drug use, we chose not to omit the keyword “withdrawal,” differing from CDC’s classifier in which it is a negative indicator.Positive indicator keywords included “opioid,” “heroin,” “narcot,” “opiate,” “fentanyl,” “naloxo,” “narcan,” "ivdu," and the ICD-9 and ICD-10 codes e850.[0-2], 304, 305.5, f11, t40.[0-6], and 965. These keywords were used to target the chief complaints of people using injection opioids. Negative indicators included “patch,” “allerg,” and “med” to eliminate medical opioid use. Negative indicators also included “vicodin,” “tramadol,” “percocet,” “oral,” and t40.5 to filter out opioids most commonly used in pill form, as well as other drugs.Cases of acute HCV and acute HBV were totaled using CDRSS, New Jersey's Communicable Disease Reporting and Surveillance System. In order to maintain consistency, we used the respective 2012 case definition for each disease. Numbers of new HIV infections were accessed from NJ’s reportable disease list.5 All of the data sets followed the epidemiologic years 2013–2017 (based upon MMWR weeks).ResultsHIV diagnosis rates slightly decreased over time. HBV rates trend upwards, similar to the rates of injection drug use (IDU) for the first three years but start to drop after 2015. Aside from an unexplained dip in 2016, the HCV rates generally track the EpiCenter data for IDU (Figure 1). On a regional scale, NJ’s Northwest region had the highest rates per capita of the five NJ regions and the most similar trending between the HCV and EpiCenter data sets (Figure 2). This result follows the nationwide trend of the opioid epidemic occurring more widely in rural areas, as this region is the most rural region in New Jersey.6In figures 1 & 2, IDU (EpiCenter) and HIV are plotted on the primary (left) axis and HCV and HBV are plotted on the secondary (right) axis.ConclusionsBoth IDU related visits and cases of acute HCV show an ongoing upward trend. This result agrees with the initial hypothesis. However, the association between acute HBV cases and IDU wasn’t as strong. This finding can be attributed to the fact that while HBV is a BBP, it is most commonly transmitted vertically from an infected mother to her child at birth, whereas HCV is primarily transmitted through the sharing of needles or syringes.7,8 There is no apparent relationship between HIV rates and injection drug rates, likely because HIV has a 0.3% infection risk rate from a single infected needlestick versus the 1.8% risk of acquiring HCV and 22-31% risk of acquiring HBV.9

Forming Collaborations through the Data Quality Committee to Address Urgent Incidents

ObjectiveThe National Syndromic Surveillance Program (NSSP) Community of Practice (CoP) works to support syndromic surveillance by providing guidance and assistance to help resolve data issues and foster relationships between jurisdictions, stakeholders, and vendors. During this presentation, we will highlight the value of collaboration through the International Society for Disease Surveillance (ISDS) Data Quality Committee (DQC) between jurisdictional sites conducting syndromic surveillance, the Centers for Disease Control and Prevention’s (CDC) NSSP, and electronic health record (EHR) vendors when vendor-specific errors are identified, using a recent incident to illustrate and discuss how this collaboration can work to address suspected data anomalies.IntroductionOn November 20, 2017, several sites participating in the NSSP reported anomalies in their syndromic data. Upon review, it was found that between November 17-18, an EHR vendor’s syndromic product experienced an outage and errors in processing data. The ISDS DQC, NSSP, a large EHR vendor, and many of the affected sites worked together to identify the core issues, evaluate ramifications, and formulate solutions to provide to the entire NSSP CoP.DescriptionOn November 20, 2017, several sites participating in the NSSP reported anomalies in their syndromic data. Upon review, it was found that between November 17-18, an EHR vendor’s syndromic product experienced an outage and errors in processing data. The ISDS DQC, NSSP, a large EHR vendor, and many of the affected sites worked together to identify the core issues, evaluate ramifications, and formulate solutions to provide to the entire NSSP CoP.How the Moderator Intends to Engage the Audience in Discussions on the TopicFollowing presentation of this information, the presenters will lead a discussion on how to improve the response, provide resolution, communicate expectations, and decrease the time required to resolve issues should a similar event happen in the future. Participants from all three stakeholder groups, sites conducting syndromic surveillance, the NSSP, and vendor representatives, will be invited to share their experiences, successes, and concerns.

Using Syndromic Surveillance Data to Study the Impact of Media Content on Self-harm

Using Syndromic Surveillance Data to Study the Impact of Media Content on Self-harm

Use of N-grams and Term Relationship Graphs in the Syndrome Definition Development Process

ObjectiveTo describe the use of uni-grams, bi-grams, and tri-grams relationships in the development of syndromic categories.IntroductionThe use of syndromic surveillance systems has evolved over the last decade, and increasingly includes both infectious and non-infectious topic areas. Public health agencies at the national, state, and local levels often need to rapidly develop new syndromic categories, or improve upon existing categories, to enhance their public health surveillance efforts. Documenting this development process can help support increased understanding and user acceptance of syndromic surveillance. This presentation will highlight the visualization process being used by CDC’s National Syndromic Surveillance Program (NSSP) program to develop and refine definitions for syndromes of interest to public health programs.MethodsDevelopment of a syndromic definition is an iterative process that starts with an analyst testing how different terms, which are assumed to be associated with the topic of interest, and diagnostic codes are noted in the chief complaint and discharge diagnosis code fields. The analyst then manually scans through the resulting line list of patient chief complaint text and diagnostic codes to determine whether the query terms match the intended syndromic concept. Typically, more terms and diagnostic codes are then added to the query using Boolean operators, and other terms are negated and removed. To facilitate summarization of the resulting terms and diagnostic codes CDC’s NSSP program developed programs with R that extracted data using the ESSENCE application programming interface (API), and the chief complaint query validation data source (CCQV). We use N-gram analysis, which is extensively used in text mining, to show co-occurrences of words in a consecutive order. The co-occurrences of words can be a uni-gram which represents a single word, bi-gram for two words, and tri-grams for three words. The process tokenizes the chief complaint text and diagnosis code fields, with some pre-processing of the text and removal of stopwords. Uni-grams, bi-grams, and tri-grams are then calculated for the top 200 combinations along with term and diagnostic code co-occurrence. Other visualizations that can be used are network graphs, which show the connections between different chief complaints terms and also between discharge diagnosis codes and chief complaint terms. The use of these graphs provides an insight into the frequency and relationship between terms and codes.ResultsTo support the development of new syndrome definitions we used the R program to produce two time series graphs. The first time series graph is used to show the volume of visits over the user’s indicated time period and the second shows the median chief complaint compared over the user’s indicated time period. A series of histograms showing frequency of the uni-gram, bi-grams, and tri-grams are also used during the development process. Lastly, two network diagrams are used to show the co-occurrence between term and diagnostic codes. The use of this range of graphs during the syndrome definition development process provides multiple ways to view the characteristics of the chief complaint and discharge diagnosis fields.The sample graphs below can be used by the analyst to illustrate key information.ConclusionsThrough this development process and the use of graphs the relationship between the syndrome definition and search terms can be visualized. In addition when using this process, the analyst could be specific as to the syndrome of interest or be broad, allowing a generic trend series monitoring of the syndrome. The search words can also be based on specific local or regional terms and the relationship terms set to include or exclude certain terms. Use of this process for the development of syndrome definitions can support the use of syndromic surveillance and offer the opportunity to further refine the process. After the syndrome has been developed, the analyst can consider spatial or temporal analysis.

Trends in Suspected Opioid Overdoses from Emergency Departments in 11 States and DC

Trends in Suspected Opioid Overdoses from Emergency Departments in 11 States and DC

Evaluation of Syndromic Surveillance for Opioid Overdose Reporting in Illinois

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.

Monitoring suicide-related events using National Syndromic Surveillance Program data

ObjectiveTo describe epidemiological characteristics of emergency department (ED) visits related to suicidal ideation (SI) or suicidal attempt (SA) using syndromic surveillance data.IntroductionSuicide is a growing public health problem in the United States.1 From 2001 to 2016, ED visit rates for nonfatal self-harm, a common risk factor for suicide, increased 42%.2–4 To improve public health surveillance of suicide-related problems, including SI and SA, the Data and Surveillance Task Force within the National Action Alliance for Suicide Prevention recommended the use of real-time data from hospital ED visits.5 The collection and use of real-time ED visit data on SI and SA could support a more targeted and timely public health response to prevent suicide.5 Therefore, this investigation aimed to monitor ED visits for SI or SA and to identify temporal, demographic, and geographic patterns using data from CDC’s National Syndromic Surveillance Program (NSSP).MethodsCDC’s NSSP data were used to monitor ED visits related to SI or SA among individuals aged 10 years and older from January 1, 2016 through July 31, 2018. A syndrome definition for SI or SA, developed by the International Society for Disease Surveillance’s syndrome definition committee in collaboration with CDC, was used to assess SI or SA-related ED visits. The syndrome definition was based on querying the chief complaint history, discharge diagnosis, and admission reason code and description fields for a combination of symptoms and Boolean operators (for example, hang, laceration, or overdose), as well as ICD-9-CM, ICD-10-CM, and SNOMED diagnostic codes associated with SI or SA. The definition was also developed to include common misspellings of self-harm-related terms and to exclude ED visits in which a patient “denied SI or SA.”The percentage of ED visits involving SI or SA were analyzed by month and stratified by sex, age group, and U.S. region. This was calculated by dividing the number of SI or SA-related ED visits by the total number of ED visits in each month. The average monthly percentage change of SI or SA overall and for each U.S. region was also calculated using the Joinpoint regression software (Surveillance Research Program, National Cancer Institute).6ResultsAmong approximately 259 million ED visits assessed in NSSP from January 2016 to July 2018, a total of 2,301,215 SI or SA-related visits were identified. Over this period, males accounted for 51.2% of ED visits related to SI or SA, and approximately 42.1% of SI or SA-related visits were comprised of patients who were 20-39 years, followed by 40-59 years (29.7%), 10-19 years (20.5%), and ≥60 years (7.7%).During this period, the average monthly percentage of ED visits involving SI or SA significantly increased 1.1%. As shown in Figure 1, all U.S. regions, except for the Southwest region, experienced significant increases in SI or SA ED visits from January 2016 to July 2018. The average monthly increase of SI or SA-related ED visits was 1.9% for the Midwest, 1.5% for the West (1.5%), 1.1% for the Northeast, 0.9% for the Southeast, and 0.5% for the Southwest.ConclusionsED visits for SI or SA increased from January 2016 to June 2018 and varied by U.S. region. In contrast to previous findings reporting data from the National Electronic Injury Surveillance Program – All-Injury Program, we observed different trends in SI or SA by sex, where more ED visits were comprised of patients who were male in our investigation.2 Syndromic surveillance data can fill an existing gap in the national surveillance of suicide-related problems by providing close to real-time information on SI or SA-related ED visits.5 However, our investigation is subject to some limitations. NSSP data is not nationally representative and therefore, these findings are not generalizable to areas not participating in NSSP. The syndrome definition may under-or over-estimate SI or SA based on coding differences and differences in chief complaint or discharge diagnosis data between jurisdictions. Finally, hospital participation in NSSP can vary across months, which could potentially contribute to trends observed in NSSP data. Despite these limitations, states and communities could use this type of surveillance data to detect abnormal patterns at more detailed geographic levels and facilitate rapid response efforts. States and communities can also use resources such as CDC’s Preventing Suicide: A Technical Package of Policy, Programs, and Practices to guide prevention decision-making and implement comprehensive suicide prevention approaches based on the best available evidence.7

Using Evaluation to Inform the BioSense Platform: Results from a 2018 Survey

ObjectiveTo assess the present status of utility, functionality, usability and user satisfaction of the BioSense Platform.IntroductionSince 2015, CDC’s Division of Health Informatics and Surveillance staff have conducted evaluations to provide information on the utility, functionality, usability and user satisfaction associated with the National Syndromic Surveillance Program’s BioSense Platform tools. The BioSense Platform tools include: 1) Access and Management Center (AMC), a tool that enables site administrators to manage users and data permissions; 2) Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE), a software application that enables syndromic surveillance related data visualization and analysis; 3) Adminer, a tool that allows users to access site data on the datamart; and 4) Rstudio, an application that can be used for data analysis and visualization. The evaluation findings have informed activities that led to improvements in functionality, development or procurement of platform associated tools, and development of resource materials. In May 2018, NSSP conducted an evaluation with eight jurisdictions that participated in the first user acceptance testing (UAT) evaluations in 2015. The purpose of the evaluation was to assess the present status of utility, functionality, usability and user satisfaction of the tools on the BioSense Platform, and delineate progress since 2015.MethodsCDC’s evaluation framework and utilization-focused evaluation were used to inform and engage stakeholders, develop the evaluation questions, metrics, and methodology. Eight selected jurisdictions participated in an online, Epi-Info survey that captured quantitative and qualitative information. Prior to the survey, participants received a presentation about the evolution of the BioSense Platform since 2015, and were provided an overview of components to evaluate. The participants were asked to assess the following key areas based on use of the BioSense Platform within the past 30 days: 1) the utility, functionality and usability of the AMC, ESSENCE, Adminer and Rstudio; 2) how well the enhanced data flow has enabled them to conduct syndromic surveillance activities; 3) usefulness of the quick start guides. Additionally, participants were asked to provide suggestions for other improvements to the BioSense Platform, and to indicate their overall satisfaction. Descriptive statistics were generated and thematic analysis was conducted to identify themes from qualitative responses.ResultsOverall, participant’s responses remained positive about the utility, functionality, usability and overall satisfaction of the BioSense Platform. Participants indicated using the BioSense Platform regularly (e.g. daily, weekly and/or monthly) within those 30 days. Certain functions have been used more than others across the various tools to conduct syndromic surveillance, with at least 50% of participants reporting use. These included creating data access rules, viewing and verifying raw and processed data, running time series, conducting free-text queries, and assessing data details and total ER visit counts by hospital, county/region, or state. The challenges ranged from tool performance to user interpretation of the function. Participants reported that the enhanced data flow improved their data quality and helped identify issues. Although participants scored ESSENCE to have average usability per the system usability scale (SUS score=63.5 in 2018), the BioSense Platform and its tools were reported as useful by 88% of participants. Further, participants continue to be comfortable using the AMC, however creating data access rules that are outside of simple use cases continue to be a challenge. Participants comfort level with Adminer improved from 2016 to 2018 with all participants reporting comfortable in using the tool. The use of each tool’s quick start guides varied. Of those who used the guides, all of the participants agreed that the Adminer and Data Dictionary guides were useful. There was a smaller number of participants agreeing that the other guides were useful. Lastly, participants provided recommendations to improving the BioSense Platform. The most frequent recommendations were improving the data access control architecture, and sharing aggregate data with hospitals in their state.ConclusionsThe development and operationalization of the BioSense Platform and associated tools has been in an environment of continuing advancements in technology and changing public health needs and priorities. Up-to-date evaluation activities have helped to ensure that BioSense is best suited to address these challenges and meet the syndromic surveillance needs of users. Overall, the findings outlined above indicate that the functionality and utility of BioSense are well suited to meet user needs.ReferencesBangor A, Kortm P, Miller J. Determining What Individual SUS Scores Mean: Adding an Adjective Rating Scale. Journal of Usability Studies. 2009; 4:114-123.

Improved Identification and Assessment of Heat-Related Illnesses in Pinal County, AZ

Improved Identification and Assessment of Heat-Related Illnesses in Pinal County, AZ

Analysis of Emergency Department Visits for Motor Vehicle Injuries in Utah, 2016

ObjectiveTo describe the characteristics of emergency department (ED) visits for motor vehicle injuries in Utah using 2016 syndromic surveillance data.IntroductionMotor vehicle injury is the leading cause of death in injury category in the United States. In 2016, motor vehicle crashes were one of the main causes of death resulting from injury (8.8 per 100,000 population) in Utah. Motor vehicle crashes can lead to physical and economic consequences that impact the lives of individuals and their families. In addition, the treatment of injuries places an enormous burden on hospital Emergency Departments (EDs). Currently; there are no data sources other than syndromic data in the Utah Department of Health to monitor ED visits due to motor vehicle injuries in real time.MethodsUtah participates in the National Syndromic Surveillance Program (NSSP) to which all hospitals in the state submit ED visit data via the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). ESSENCE was used to analyze 2016 ED visit data. Total population data were obtained from Utah population estimates. Data from 2017 was not included due to major system changes at a major healthcare system that interrupted data feeds resulting in lower than expected data volume.Motor vehicle injury is defined by existing subsyndrome definition in the Centers for Disease Control and Prevention ESSENCE system. All ED visit data were analyzed by querying key terms in the chief complaint field including any mention of: vehicle, wheeler, motorcycle, motor scooter, motor cycle, motor cross, truck, motorbike etc. Exclusion terms included any mention of: car dealership, hit head and car door. Ages were divided into seven groups for data distribution and comparison: 0–17, 18–24, 25–34, 35–44, 45–54, 55–64 and ≥ 65 years.ResultsIn 2016, a total of 28,472 ED visits (2% of total visits) were identified using the motor vehicle injury query. The ED visit rate for motor vehicle injuries was highest among persons aged 18–24 years (1,682 per 100,000 population). Rates continued to decline with increasing age after 18–24 years. The rate of females visiting the ED was higher than males (1,040 versus 826 per 100,000 population respectively; p < 0.01) (Figure 1). The majority of injuries (11722(52%)) were reported between 10:00 a.m. and 5:59 p.m. Injuries were highest August-September (5913(22%)).ConclusionsSyndromic data is a robust source of data for analyzing ED visits due to motor vehicle injuries in real time, and providing information to injury prevention programs for targeting interventions. Our data suggest an increased risk of visiting an ED due to motor vehicle injuries by age group (18-24 year olds), sex (females), month (August-September), and time (10:00 a.m. to 5:59 p.m.). These results do not include visits with incomplete or incorrectly coded chief complaints or discharge codes, patients of motor vehicle injuries who do not present to the ED, or not classified as ‘emergency’ patient class.

Symptoms and characteristics of cancer patient visits to an emergency room.

e18306 Background: Cancer patients are increasingly likely to visit an emergency department (ED) for acute care compared to the general population. Cancer patients who visit the ED often have long wait times, increased exposure to infection, and lesser quality treatment. The Centers for Medicare & Medicaid Services (CMS) is encouraging efforts to decrease survivor acute care visits. The purpose of this study was to examine cancer-related ED visits using a national population-based sample to understand why they are seeking care in an ED. Methods: A retrospective cohort study of U.S. patients who visited EDs between June 1, 2017 and May 31, 2018 was conducted using the National Syndromic Surveillance Program (NSSP) BioSense Platform. Cancer patients were identified using International Classification of Diseases, 10th Revision (ICD-10) codes for any cancer type and specifically for cancers of the bladder, female breast, cervix, colon and rectum, kidney, liver, lung, ovary, pancreas, prostate, or uterus. Symptoms were identified using syndromic definitions and key-word queries. Significance testing (p-value ≤0.01), was used to assess differences in the prevalence of symptoms by cancer type. Results: There were 97 million visits to EDs during the study period, 710,297 (0.8%) were among cancer survivors. Slightly more were female (50.1%) than male (49.5%); more were aged 65 or older (53.6%) than 18-64 (1.4-35.3%). The most common symptoms were pain (19%), gastrointestinal (14%), respiratory (12%), neurologic (5%), fever (5%), and injury (4%). Prevalence of symptoms differed significantly by cancer type. Some symptoms were higher among those with specific cancer types compared to all cancers in aggregate: pain (cervical, liver, pancreas); gastrointestinal (pancreas, liver, colorectal); respiratory (lung); neurologic (liver, lung), fever (pancreas, liver), injury (prostate), and bleeding (colorectal, liver). Conclusions: Use of NSSP data enabled a descriptive characterization of more than half of the U.S. ED visits among cancer patients. These comprehensive findings inform best practices to reduce unplanned acute care and help inform possible modifications in treatment and care protocols among survivors of specific cancers.

Beginner R methods for syndromic surveillance data validation

ObjectiveTo share practical, user-friendly data validation methods in R that result in shorter validation time and simpler code.IntroductionThere are currently 123 healthcare facilities sending data to the Washington (WA) State syndromic surveillance program. Of these facilities, 30 are sending to the National Syndromic Surveillance Program’s (NSSP) production environment. The remainder are undergoing validation or in queue for validation. Given the large number of WA healthcare facilities awaiting validation, staff within the state syndromic surveillance program developed methods in R to reduce the amount of time required to validate data from an individual facility.MethodsThe dplyr package and R Markdown file format were used to more rapidly conduct syndromic data validation. Dplyr, written by Hadley Wickham, was created for easy data manipulation.<span style="font-size:10.8333px">1</span> The syntax of this package is user-friendly, providing a function for almost every common data manipulation task and utilizing the piping operator from the magrittr package. Data fields of interest for syndromic surveillance are classified as required (R), required but may be empty (RE), or optional (O). For R or RE data fields, dplyr can be used to check for patterns of missingness as well as verify that the correct value sets are being used for code fields. For character fields, dplyr can be used to pull samples of free-text, calculate word or character counts, or search for string patterns of interest.ResultsThe amount of time spent validating any single facility has decreased significantly. This has allowed the number of facilities undergoing data validation at one time to increase from 12 to 22. However, the length of time between beginning and completing data validation per facility has not decreased. While reporting data issues to facilities takes less time, the lag in the validation process still occurs while waiting for facilities to correct these issues at the feed origination.ConclusionsIn order to increase the number of healthcare facilities that are sending production quality data more quickly, more resources need to be directed at providing facilities with support on how to correct data issues rather than solely reporting the problems.

Developing and Validating a Fireworks-Related Syndrome Definition in Kansas

ObjectiveTo develop a syndrome definition and analyze syndromic surveillance data usefulness in surveillance of firework-related emergency department visits in Kansas. Introduction Across the U.S.A., multiple people seek treatment for fireworks-related injuries around the July 4th holiday. Syndromic surveillance in Kansas allows for near real-time analysis of the injuries occurring during the firework selling season. During the 2017 July 4thholiday, the Kansas Syndromic Surveillance Program (KSSP) production data feed received data from 88 EDs at excellent quality and timeliness. Previous and current firework safety messaging in Kansas is dependent on voluntary reporting from hospitals across the state. With widespread coverage of EDs by KSSP, data can be more complete and timely to better drive analysis and public information Methods:KSSP data was queried through the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) v.1.20 provided by the National Syndromic Surveillance Program. Data between June 12, 2017 and August 13, 2017 were queried. The first query (Query A, Table 1.) searched the Discharge Diagnosis History field for the “W39” ICD-10 Diagnosis code, “Discharge of firework.” These records were searched for common firework terms contained in the Chief Complaint History field. These firework-related free text terms (Query B, Table 1.) were then combined with other potential firework-related terms to create a preliminary free text query (Query C, Table 1.). This preliminary query was run on the Chief Complaint History field. Data were then searched for false positive cases and appropriate negation terms were included to accommodate this. The new query with negation terms (Query D, Table 1.) was run on the Chief Complaint History field, combined with the results from the Discharge Diagnosis History field, and then combined records were de-duplicated based on a unique visit identifier. The final data set was then classified by the anatomical location of the injury and the gender and age group of the patient. Results:The initial query (Query A, Table 1.) for the diagnosis code “W39” returned 101 unique ED visits. Of these 101 unique ED visits, the following terms were identified in the Chief Complaint History field: shell, artillery, bomb, sparkler, grenade, fire cracker, firework, and firework show. These key terms were translated into Query B, Table 1. Other key terms deemed likely to capture specific firework-related exposures were then included into Query C, Table 1. , including roman, candle, lighter, M80, and punk. Query C was then used to query the Chief Complaint History field, returning 144 unique ED visits. Cases captured by Query C were then reviewed by hand for false positives and the negation terms, lighter fluid, fish, nut, and pistachio, were incorporated the Query D, Table 1. The previous process for Query C was then repeated on Query D, leaving a remaining 136 unique cases. Query A’s 101 unique ED visits was then combined with the 136 unique ED visits captured by Query D and de-duplicated. The de-duplicated data set contained 170 unique ED visits which were then reviewed by hand for false positives. The final removal of false positives from the combined and de-duplicated data set left a remaining 154 unique ED visits for firework-related injuries during this time period.For these data, the most common victims of firework injuries were males, accounting for 65.5% of all firework related ED visits and children ages 0 to 19 accounting for 44.2% of these visits. At every age breakout, male injuries exceeded female injuries. The most common anatomical location of the injury was one or both hands with 38.3% of all injuries mentioned hands as their primary injury. Injuries to the eyes, face, and head accounted for the second most injuries (28.6% of all patients). Conclusions: The selling of fireworks will be a yearly occurrence of a specific exposure that can potentially lead to injuries. Utilizing syndromic surveillance to review the holiday firework injuries is a very rapid method to assess the impact of these injuries and may allow for future direction of public information during the holiday. Having a syndrome definition that builds on knowledge from previous years will allow for quicker case identification as well.State public information regarding firework safety can be significantly bolstered by accurate and rapid data assessment. Developing a firework injury syndrome definition that is accurate and returns information rapidly has allowed for increased buy-in to the Kansas Syndromic Surveillance Program from public information offices, fire marshal’s offices, and other program fields.

Syndromic Surveillance for Situation Awareness: Understanding Syndrome Performance

ObjectiveIn August 2017, a large influx of visitors was expected to view the total solar eclipse in Idaho. The Idaho Syndromic Surveillance program planned to enhance situation awareness during the event. In preparation, we sought to examine syndrome performance of several newly developed chief complaint and combination chief complaint and diagnosis code syndrome definitions to aid in interpretation of syndromic surveillance data during the event.IntroductionThe August 21, 2017 total solar eclipse in Idaho was anticipated to lead to a large influx of visitors in many communities, prompting a widespread effort to assure Idaho was prepared. To support these efforts, the Idaho Syndromic Surveillance program (ISSp) developed a plan to enhance situation awareness during the event by conducting syndromic surveillance using emergency department (ED) visit data contributed to the National Syndromic Surveillance Program’s BioSense platform by Idaho hospitals. ISSp sought input on anticipated threats from state and local emergency management and public health partners, and selected 8 syndromes for surveillance.Ideally, the first electronic message containing information on an emergency department visit is sent to ISSp within 24 hours of the visit and includes the chief complaint for the visit. Data on other variables, such as diagnosis codes, are updated by subsequent messages for several days after the visit. Chief complaint (CC) text and discharge diagnosis (DD) codes are the primary variables used for syndrome match; delay in reporting these variables adversely affects timely syndrome match of visits. Because our plan included development of new syndrome definitions and querying data within 24 hours of visits, earlier than ISSp had done previously for trend analysis, we sought to better understand syndrome performance.MethodsWe defined messages with completed CC and DD as the last message regarding a visit where term count increased from previous messages regarding that visit, indicating new information was added to the field. We retrospectively assessed the total number of ED visits and calculated the daily frequency of completed CC and DD by days since visit date for visits during June 1–July 31, 2017. Additionally, we calculated facility mean word count in CC fields by averaging the word count of parsed, complete CC fields for visits occurring June 1–July 31, 2017 for each facility.During July 10–24, 2017, we calculated the daily frequency of visits occurring in the previous 90 days for total ED visits and syndrome-matched visits for 8 selected syndromes (heat-related illness; cold exposure; influenza-like-illness; nausea, vomiting, and diarrhea; animal/bug bites and stings; drowning/submersion; alcohol/drug intoxication; and medication replacement). Syndrome-matched visits were defined as visits with CC or DD that match the syndrome definition. We calculated the percent of syndrome-matched visits by syndromes defined with CC or CC and DD combined (CCDD) over time. Syndromes with fewer than 5 matched visits were excluded from analysis.ResultsComplete CCs were received for 99.1% of visits and complete DDs were received for 89.8% of visits. Complete CCs were submitted for 58.2% of visits within 1 day of the visit, 88.9% of visits within 3 days, and 98.9% of visits within 7 days. In contrast, complete DDs were submitted for 24.3% of visits within 1 day, 38.7% of visits within 3 days, and 53.7% of visits within 7 days (Table 1).During the observation period, data submission from facilities representing approximately 33% of visits was interrupted for 5 (36%) of 14 days. Heat-related illness, cold exposure, and drowning/submersion, were excluded from syndrome-match analysis. During the 9 days of uninterrupted data submission, 100% syndrome-matched visits for syndromes defined by CC alone and 69.1% syndrome-matched visits for syndromes defined by CCDD were identified within 6–7 days of initial visit. Facilities with interrupted data submission contributed 75% of CC syndrome-matched visits and 33% of CCDD syndrome-matched visits. The facility mean word count in CC fields from these facilities was >15 compared with 2–4 from other facilities.ConclusionsExamination of syndrome performance prior to a known event quantitated differences in timeliness of CC and DD completeness and syndrome match. CCs and DDs in visit messages were not complete within 24 hours of initial visit. CC completion was nearly 34 percentage points greater than DD completeness 1 day after initial visit and did not converge until ≥15 days after initial visit. Higher percentages of syndrome match within 6–7 days of initial visit were seen by CC alone than CCDD defined syndromes. Facilities using longer CCs contributed disproportionately to syndrome matching using CC, but not CCDD syndrome definitions. Syndromic surveillance system characteristics, including timeliness of CCs and DDs, length of CCs, and characteristics of facilities from which data transmission is interrupted should be considered when building syndrome definitions that will be used for surveillance within 7 days of emergency department visits and when interpreting syndromic surveillance findings.

Syndromic Surveillance – Reports of Successes from the Field

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.

Effect of the Work Week on Demographics of Heat-Related Illness Patients in Syndromic Surveillance

ObjectiveTo describe the differences in patient populations between those who seek care for heat exposure during the work week and those who seek care during the weekend.IntroductionAs global temperatures increase, so too does interest in the effect of climate change on the population’s health. 2016 represented the hottest year on record globally and well above the 20th century average in Virginia.1,2 With large-scale climate change comes an increase in severe weather patterns, including heat waves.3 Heat waves can have immense health impacts on a community, including heat stroke, heat exhaustion, and dehydration.Previous analyses of emergency department (ED) data indicate that certain populations – specifically males and rural residents – are more at risk for heat-related illness.4,5 None of these studies, however, looked for temporal relationships between the population seeking care and the day of the week. Syndromic surveillance data can be used to further describe those communities affected by heat exposure as well as identify any temporal patterns in visits.MethodsThe Virginia Department of Health (VDH) receives data from 148 EDs and urgent care centers (UCCs) as part of its syndromic surveillance program. During regular surveillance of a heat wave, it was observed that males made up a larger proportion of heat-related visits during the week than they did over the weekend. Data received on visits between January 1, 2015 and July 31, 2017 were used for a retrospective, cross-sectional analysis of demographic risk factors for heat-related illness. During this time frame, 6,739 visits were identified using the September 2016 Council for State and Territorial Epidemiologists (CSTE) syndrome definition for heat-related illness.6The effect of various demographics and visit factors on weekday heat exposure was measured using chi-squared tests. The variables in question included sex, race, ethnicity, rural vs. urban residence, and age group. Odds ratios, 95% confidence intervals, and p-values were reported for these analyses. Analyses were conducted using SAS 9.3 with a significance level of 0.05.ResultsOf the total 6,739 visits identified for heat-related illness, 4,782 (71.0%) occurred during the work week and 1,957 (29.0%) occurred on the weekend. The odds of seeking care for heat-related illness on a weekday were 1.84 times higher for males than for females, p < 0.001, 95% CI [1.65, 2.06]. Blacks or African Americans were more likely to seek care than whites during the work week with an odds ratio of 1.38, p < 0.001. 95% CI [1.20, 1.57]. Adults aged 18-64 years were more likely to seek care during the work week than both children aged 0-17 years (OR = 1.61, p < 0.001, 95% CI [1.37, 1.89]) and adults aged 65 years or older (OR = 1.36, p < 0.001, 95% CI [1.17, 1.58]). No significant relationship between ethnicity or rural vs. urban residence and work week visits for heat-related illness was observed.ConclusionsThe patient population that seeks care for heat-related illness differs between the work week and the weekend. These data suggest the presence of potential mediators or confounders that make males, blacks or African Americans, and adults aged 18-64 more likely to suffer from heat-related illness during the week. Collecting data on patients’ health behaviors, risk factors, and occupation could further elucidate this relationship. Syndromic surveillance, however, does not include the level of detail needed to investigate anything beyond basic demographics.With an increase in the intensity and frequency of heat waves on the horizon, the issue of heat-related illness is one of growing public health concern. Syndromic surveillance data can be used to describe patterns in the patient population most at risk. Public health action is then needed to protect these communities while further research explores the relationships in greater depth.References1 Nuccitelli, D. (2017, July 31). 2017 is so far the second-hottest year on record thanks to global warming. The Guardian. Retrieved from http://bit.ly/2vkPZpg2 Boyer, J. (2017, January 18). 2016 was the planet’s warmest year in modern records, but it wasn’t for Richmond or even Va. Richmond Times-Dispatch. Retrieved from http://bit.ly/2jptCKg3 Duffy, P. B. (2012, January 21). Increasing prevalence of extreme summer temperatures in the U.S. Climate Change, 111(2), 487-495. https://doi.org/10.1007/s10584-012-0396-64 Hess, J. J., Saha, S., & Luber, G. (2014 November). Summertime Acute Heat Illness in U.S. Emergency Departments from 2006 through 2010: Analysis of a Nationally Representatitve Sample. Environmental Health Perspectives 122(11), 1209. http://dx.doi.org.proxy.library.vcu.edu/10.1289/ehp.13067965 Sanchez, C. A., Thomas, K. E., Malilay, J., & Annest, J. L. (2010, January). Nonfatal natural and environmental injuries treated in emergency departments, United States, 2001-2004. Family & Community Health 33(1), 3-10. doi:10.1097/FCH.0b013e3181c4e2fa6 Berisha, V., Braun, C. R., Cameron, L., Hoppe, B., Lane, K., Mamou, F., Menager, H., Roach, M., White, J. R., Wurster, J. (2016, September). Heat-Related Illness Syndrome Query: A Guidance Document for Implementing Heat-Related Illness Syndromic Surveillance in Public Health Practice. Retrieved from http://bit.ly/2w884aJ

Comparison of National and Local Syndromic Surveillance Data - Cook County, IL, 2017

ObjectiveThis analysis was undertaken to determine how the data completeness, consistency, and other attributes of our local syndromic surveillance program compared to the National Syndromic Surveillance Platform.IntroductionIn 2005, the Cook County Department of Public Health (CCDPH) began using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) as an emergency department (ED)-based local syndromic surveillance program (LSSP); 23 (100%) of 23 hospitals in suburban Cook County report to the LSSP. Data are transmitted in delimited ASCII text files (i.e., flat files) and contain a unique patient identifier, visit date and time, zip code, age, sex, and chief complaint. Discharge diagnosis and disposition are optional data elements. Prior to 2017, the Illinois Department of Public Health placed facilities participating in the Cook LSSP in a holding queue to transform their flat file submissions into a HL7 compliant message; however as of 2017, eligible hospitals must submit HL7 formatted production data to IDPH to fulfill Meaningful Use. The primary syndromic surveillance system for Illinois is the National Syndromic Surveillance Program (NSSP), which transitioned to an ESSENCE interface in 2016. As of December 2016, 20 (87%) of 23 hospitals reporting to the LSSP also reported to IDPH and the NSSP. As both syndromic surveillance systems aim to collect the same data, and now can be analyzed with the same interface, CCDPH sought to compare the LSSP and NSSP for data completeness, consistency, and other attributes.MethodsOur comparison of NSSP to the LSSP focused on data completeness for key demographic and medical variables and consistency in total visit counts. Analysis of completeness utilized data from December 2016 for 20 hospitals contributing HL7 production data to IDPH at that time. Total visit counts in both systems were compared for the same 20 hospitals from February 5th-11th 2017, a randomly chosen time period. A target threshold of less than 3% difference in total visit counts was set by the CCDPH system users. Analysis was completed in Microsoft Excel 2010. Other attributes of the surveillance systems were qualitatively assessed by the primary system users at CCDPH.ResultsAll variables required by the LSSP had 98-100% completeness in both the LSSP and NSSP (unique patient identifier, age, sex, zip code, visit time and date, and chief complaint). However, the LSSP optional data elements, discharge diagnosis and discharge disposition, were less complete in the LSSP, compared to the NSSP (Diagnosis: 56% versus 83%, Disposition: 66% versus 80%). Among variables required for NSSP reporting but not reported to the LSSP, completeness ranged from 100% (race, ethnicity) to 82% (county). Optional data elements within NSSP ranged in completeness from 73% (initial pulse oximetry) to 0% (initial blood pressure, insurance coverage). Of the 20 hospitals evaluated for visit counts, only one hospital had <3% difference in visit counts in the LSSP and NSSP for all 7 days assessed. Ten hospitals had >3% difference in visit counts on all seven days. Average seven day differences for hospitals ranged from 0% to 54%. Eighteen (90%) of 20 hospitals were reporting larger numbers of visits to NSSP than to the LSSP.ConclusionsOverall completeness of data was similar between the national and our local ESSENCE systems with most required variables having over 98% completeness. NSSP had higher completeness over the LSSP for discharge diagnosis and disposition. Additional data elements required by NSSP, but unavailable in the LSSP, had similarly high completeness but optional NSSP variables of interest showed greater variability in reporting. Differences in visit counts were higher than expected. An ongoing exploration of these differences has shown they are multifaceted and require hospital-specific interventions. There are strengths and limitations to both the NSSP and LSSP. CCDPH has direct control over data sharing between jurisdictions in the LSSP and there has historically been less system “down time” in the LSSP compared to the NSSP; however, the use of flat files instead of HL7, as well as having fewer incentives for hospital participation (e.g. Meaningful Use) after 2016, results in limited data collection and stagnant growth compared to the NSSP. Jurisdictions using their own LSSPs should consider analyzing their data completeness, consistency, and quality compared to the NSSP.

Comparing and Contrasting Two ESSENCE Syndrome Definition Query Methods

Comparing and Contrasting Two ESSENCE Syndrome Definition Query Methods

User Generated SQL Queries Inform Evaluation of NSSP ESSENCE

Objective: The objective of this work is to describe the use and performance of the NSSP ESSENCE system by analyzing the structured query language (SQL) logs generated by users of the National Syndromic Surveillance Program’s (NSSP) Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE).Introduction: As system users develop queries within ESSENCE, they step through the user-interface to select data sources and parameters needed for their query. Then they select from the available output options (e.g., time series, table builder, data details). These activities execute a SQL query on the database, the majority of which are saved in a log so that system developers can troubleshoot problems. Secondarily, these data can be used as a form of web analytics to describe user query choices, query volume, query execution time, and develop an understanding of ESSENCE query patterns.Methods: ESSENCE SQL query logs were extracted from April 1, 2016 to August 23th, 2017. Overall query volume was assessed by summarizing volume of queries over time (e.g., by hour, day, and week), and by Site. To better understand system performance the mean, median, and maximum query execution times were summarized over time and by Site. SQL query text was parsed so that we could isolate, 1) Syndromes queried, 2) Sub-syndromes queried, 3) Keyword categories queried, and 4) Free text query terms used. Syndromes, sub-syndromes, and keyword categories were tabulated in total and by Site. Frequencies of free text query terms were analyzed using n-grams, wordclouds, and term co-occurrence relationships. Term co-occurrence network graphs were used to visualize the structure and relationships among terms.Results: There were a total of 354,101 SQL queries generated by users of ESSENCE between April 1, 2016 and August 23rd, 2017. Over this entire time period there was a weekly mean of 4,785 SQL queries performed by users. When looking at 2017 data through August 23rd this figure increases to a mean of 7,618 SQL queries per week for 2017, and since May 2017 the mean number of SQL queries has increased to 10,485 per week. The maximum number of user generated SQL queries in a week was 29,173. The mean, median, and maximum query execution times for all data was 0.61 minutes, 0 minutes, and 365 minutes, respectively. When looking at only queries with a free text component the mean query execution time increases slightly to 0.94 minutes, though the median is still 0 minutes. The peak usage period based on number of SQL queries performed is between 12:00pm and 3:00pm EST.Conclusions: The use of NSSP ESSENCE has grown since implementation. This is the first time the ESSENCE system has been used at a National level with this volume of data, and number of users. Our focus to date has been on successfully on-boarding new Sites so that they can benefit from use of the available tools, providing trainings to new users, and optimizing ESSENCE performance. Routine analysis of the ESSENCE SQL logs can assist us in understanding how the system is being used, how well it is performing, and in evaluating our system optimization efforts.

Improving Timeliness of Georgia Emergency Room Data

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