Enhanced State Opioid Overdose Surveillance Research Articles

A latent class analysis of drug and substance use patterns among patients treated in emergency departments for suspected drug overdose

IntroductionPolysubstance use and misuse can increase risks for nonfatal and fatal drug overdose. To categorize drugs used in combination in nonfatal overdoses, we analyzed data from emergency department (ED) overdose-related visits in 18 states funded by CDC’s Enhanced State Opioid Overdose Surveillance (ESOOS) program. MethodsFrom 2017 to 2018, 120,706 ED visits included at least one hospital discharge code indicating acute drug poisoning for opioids, stimulants, hallucinogens, cannabis, anti-depressants, sedatives, alcohol, benzodiazepines, or other psychotropic drugs. Latent class analyses were conducted to determine the groupings of drug combinations in overdose visits. ResultsLatent class analyses indicated a model of 5 classes – mostly heroin overdose (42.5% of visits); mostly non-heroin opioid overdose/use (27.3%); opioid, polysubstance (11.0%); female, younger (<25 years), other non-opioid drugs (10.5%); female, older (>55 years), benzodiazepine (8.0%). Findings indicated that heroin continues to be a large burden to EDs, yet EDs are also seeing overdose survivors with polydrug toxicity. ConclusionsMedication-assisted treatment could be initiated in the emergency department following overdose for patients with opioid use disorder, and post-overdose protocols, such as naloxone provision and linkage to treatment and harm reduction services, have the potential to prevent future overdose for those at risk.

U.S. Opioid Epidemic: Impact on Public Health and Review of Prescription Drug Monitoring Programs (PDMPs).

In recent years, the devastating effects of U.S. opioid epidemic has been making news headlines. This report explores background information and trends on opioid misuse, overdose fatalities and its impact on public health. In addition, various efforts to improve surveillance, timeliness of data and Prescription Drug Monitoring Program (PDMP) integration and interoperability are reviewed. PubMed and internet searches were performed to find information on the U.S. opioid epidemic. In addition, searches were performed to retrieve information about PDMPs and state-specific mandates along with presentation slides and learnings from the 2018 National Rx Drug Abuse & Heroin Summit in Atlanta, GA. It is clear that the U.S. opioid epidemic has a tremendous impact on public health including the next generation of children. Various data, surveillance & technology-driven efforts including CDC-Funded Enhanced State Opioid Overdose Surveillance Program (ESOOS) and use of telemedicine for opioid use disorder treatment aim to improve prevention, treatment and targeted interventions. In addition, PDMP integration and interoperability efforts are advancing to provide prescribers meaningful decision support tools. The opioid epidemic has a complex impact on public health intertwined with variable factors such as mental health and social determinants of health. Given the statistics and studies that suggest many of the illicit opioid users start with prescription opioids, continued advancement in the area of PDMP integration and interoperability is necessary. The PDMP integrated clinical decision support systems need to supply to healthcare providers access to complete, timely and evidence-based information that can meaningfully inform prescribing decisions and communication with patients that affect measurable outcomes. While Prescription Drug Monitoring Programs (PDMPs) are valuable tools for providers in making informed prescribing decisions, the variable state mandates and varying degrees of integration and interoperability across states may limit their potential as meaningful decision support tools. Sharing best practices, challenges and lessons learned among states and organizations may inform strategic and systematic use of PDMPs to improve public health outcomes.

Validating Syndromic Data for Opioid Overdose Surveillance in Florida

Validating Syndromic Data for Opioid Overdose Surveillance in Florida

Fatal Overdose Surveillance in the Commonwealth of Pennsylvania

ObjectiveSession objectives:Review strategies and policies employed to get independent C/MEs from a state with a de-centralized C/ME system to start reporting overdose death data to the Department of Health.Summarize flexibility needed to collect overdose death data from C/MEs with a variety of case management systems/methods.Preview how overdose death data is displayed on the Prescription Drug Monitoring Programs’ Interactive Data Report.IntroductionThe severity of the nationwide opioid epidemic necessitates a fully-informed and evidenced-based response on the part of public health organizations. To support that aim, Pennsylvania applied for and received the Center for Disease Control and Prevention's Enhanced State Opioid Overdose Surveillance (ESOOS) grant.MethodsToday’s poster presentation will outline issues with recruiting coroners/medical examiners (C/MEs) for participation who are unique to jurisdictions that utilize a decentralized C/ME system, and how those issues were addressed. Those issues include tension between state and county governments, time and staff concerns on the part of C/MEs, and the variety of case management systems that the C/MEs use.ResultsAt the beginning of this project, two out of 67 counties were submitting comprehensive toxicology and risk factor data to the Pennsylvania Department of Health. As of September 2018, thirty-eight out of 67 counties are submitting death data. The presentation will also discuss what data is collected and how it is reported.ConclusionsThe outreach strategy successfully increased the number of coroners and medical examiners that submit death data.

Enhanced Surveillance of Nonfatal Emergency Department Opioid Overdoses in California

ObjectiveTo track and monitor nonfatal emergency department opioid overdoses in California for use in the statewide response in the opioid epidemic.IntroductionThe opioid epidemic is a multifaceted public health issue that requires a coordinated and dynamic response to address the ongoing changes in the trends of opioid overdoses. Access to timely and accurate data allows more targeted and effective programs and policies to prevent and reduce fatal and nonfatal drug overdoses in California. As a part of a Centers for Disease Control and Prevention Enhanced State Opioid Overdose Surveillance grant, the goals of this surveillance are to more rapidly identify changes in trends of nonfatal drug overdose, opioid overdose, and heroin overdose emergency department visits; identify demographic groups or areas within California that are experiencing these changes; and to provide these data and trends to state and local partners addressing the opioid crisis throughout California. Emergency department (ED) visit data are analyzed on an ongoing quarterly basis to monitor the proportion of all ED visits that are attributed to nonfatal drug, opioid, and heroin overdoses as a portion of the statewide opioid overdose surveillance.MethodsCalifornia emergency department data were obtained from the California Office of Statewide Health Planning and Development. Data were (and continue to be) analyzed by quarter as the data become available, starting in quarter 1 (Q1) 2016 through Q1 2018. Quarters were defined as standard calendar quarters; January-March (Q1), April-June (Q2), July-September (Q3), and October-December (Q4). Counts of nonfatal ED visits for all drug overdoses, all opioid overdoses, and heroin overdoses were defined by the following ICD-10 codes in the principle diagnosis or external cause of injury fields respectively; T36X-T50X (all drug), T40.0X-T40.4X T40.6 and T40.69 (all opioid), and T40.1X (heroin). Eligible ED visits were limited to CA residents, patients greater than 10 years of age, initial encounters, and were classified as unintentional overdoses or overdoses of undetermined intent. Overdose ED visits are described by quarter, drug, sex, and age for Q1 2016 – Q1 2018.ResultsOn average, 6,450 emergency department visits in California are attributed to drug overdose every quarter. Between Q1 2016 and Q1 2018, on average 1,785 (range: 1,559-2,011 ED visits) of those visits were due to opioid overdoses and a further 924 (52%) of those ED visits were due to heroin overdoses. About 26-30% of all drug overdose ED visits were for opioid overdoses in California during Q1 2016 – Q1 2018. Quarterly, that is around 6.00-7.64 opioid overdose ED visits for every 10,000 ED visits (Table 1), with about half those (3.09-4.30 ED visits) being heroin overdose ED visits. Males accounted for approximately 52% of all drug overdose ED visits, 65% of all opioid overdose ED visits, and 76% of all heroin overdose ED visits per quarter. Across all quarters, 25-34 year olds had the highest proportion of emergency department visits attributed to opioid and heroin overdose compared to all other age groups. However, 11-24 year olds had the highest proportion of emergency department visits attributed to all drug overdoses compared to all other age groups for all quarters except one. Between Q1 2016 and Q1 2018, the proportion of emergency department visits attributed to all drug overdoses increased by 1.8%, all opioid overdoses increased 3.1%, and heroin overdoses increased by 13.5%.ConclusionsOverall trends for the proportion of all emergency department visits for all drug overdoses and all opioid overdoses are relatively stable over this time period, however the proportion of heroin overdose ED visits shows a more substantial increase between Q1 2016 and Q1 2018. In addition, heroin overdose ED visits account for over half of all opioid overdose ED visits during this time in California. Ongoing surveillance of drug, opioid, and heroin overdose ED visits is a crucial component of assessing and responding to the opioid overdose crisis in California and helps to better understand the demographics of those who could be at risk of a future fatal opioid overdose. Timely data such as these (in addition to prescribing, hospitalization, and death data) can inform local and statewide efforts to reduce opioid overdoses and deaths.

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

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

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

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

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

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

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

Suspected Heroin Overdoses in US Emergency Departments, 2017-2018.

To describe changes in suspected heroin overdose emergency department (ED) visits. Methods. We analyzed quarterly and yearly changes in heroin overdoses during 2017-2018 by using data from 23 states and jurisdictions (including the District of Columbia) funded by the Centers for Disease Control and Prevention Enhanced State Opioid Overdose Surveillance program. The analyses included the Pearson χ 2 test to detect significant changes. Results. Both sexes, all age groups, and some states exhibited increases from quarter 1 (Q1) 2017 to Q2 2017 and significant decreases in both quarters from Q3 2017 to Q1 2018 in heroin overdose ED visits. Overall, there was a significant yearly decline of 21.5% in heroin overdose ED visits. Three states had significant yearly increases (Illinois, Indiana, and Utah), and 9 states (Kentucky, Maryland, Massachusetts, New Hampshire, Ohio, Pennsylvania, Rhode Island, West Virginia, and Wisconsin) and the District of Columbia had significant decreases. Conclusions. We identified decreases in heroin overdose ED visits from 2017 through 2018, but these declines were not consistent among states. Even with the possibility of a stabilization or slowing of this epidemic, it is important that the field of public health and its partners implement strategies to prevent overdoses and target emerging hot spots.

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

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

Opioid Overdose Ambulance Runs: How Wisconsin Uses Free Text Data

Objective: 1. Develop an understanding of the benefits and challenges of analyzing free text fields on a population level.2. Observe how a complex surveillance definition can be created from free text fields.3. Observe how an ambulance data system can be used to describe the opioid epidemic.Introduction: In 2016, twelve states received Center for Disease Control and Prevention (CDC) Enhanced State Opioid Overdose Surveillance grants. The purpose of the grant is to explore enhanced data sources to track nonfatal opioid overdoses. One data source is ambulance runs. Wisconsin collects ambulance run information within the Wisconsin Ambulance Runs Data System (WARDS). Around 84% of all Wisconsin administrative services report into this electronic system. This is a timely, robust data system that has not been used previously to examine drug overdoses and presents an analytical challenge as it contains many free text fields.Methods: Wisconsin’s ambulance data system is robust, well-populated, and includes the majority of Emergency Medical Services (EMS) within the state. The analytic challenge with this data is that most of the reported fields are free text, which can be difficult to analyze on a population level. Wisconsin created a case definition using SAS regular expressions to take advantage of the free text fields. A combination of fields (chief complaint, secondary complaint, medications given, and the EMS narrative) were used to determine if the ambulance run was due to an opioid overdose. It was necessary to create a definition that used a diverse combination of phrases as free text fields are prone to spelling errors and there are many phrases used to identify opioid overdoses. It was also necessary to create a definition for unwanted phrases that signal a false positive, for example, “withdrawal”.Results: Wisconsin’s opioid definition uses regular expressions to search for the words “heroin”, “opioid”, “narcan”, or “methadone” (including various spellings). The overdose definition searches for words and phrases like “drug abuse”, “drug use”, “poisoning”, “drug ingestion”, and “overdose”. The medication administered fields are examined for “narcan”. In Wisconsin, the medication is listed every time it is used, so it is possible to determine the number of times Narcan was administered to a single person as well as how many ambulance runs used at least one dose of Narcan. False positives are identified with words and phrases like “withdrawal”, “detox”, and if Narcan was given but there is no indication that the ambulance run was due to drugs. From January 2016 – June 2017, Wisconsin had over 917,000 ambulance runs for people aged 11 years and older. We excluded non-emergency ambulance runs, like medical transports, and so our final denominator was 627,536 runs (32% of all runs were classified as non-emergencies). Suspected opioid overdoses were determined to be 1% of emergency ambulance runs. Narcan was administered in a total of 5,900 runs and the false positive flag picked up 10,399 runs that may not have been due to suspected opioid overdoses. Applying all of these components together, it was determined that in Wisconsin from January 2016 – June 2017, there were 4,041 emergency ambulance runs due to suspected, unintentional opioid overdoses for people 11 years and older (rate of 6 per 1,000 people).Conclusions: The use of regular expressions enables Wisconsin to extend analyses to data systems that contain robust information within free text fields. Within Wisconsin, this has been utilized to enhance opioid overdose surveillance with the use of a rapid data system previously not examined. Ambulance run information is a valuable resource to Wisconsin with the opioid epidemic. By creating case definitions with free text fields, we can quantify ambulance runs on a population level and create linkable analytic data sets to provide a more complete picture of the health of Wisconsin.

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

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

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

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

Deaths Involving Fentanyl, Fentanyl Analogs, and U-47700 - 10 States, July-December 2016.

Preliminary estimates of U.S. drug overdose deaths exceeded 60,000 in 2016 and were partially driven by a fivefold increase in overdose deaths involving synthetic opioids (excluding methadone), from 3,105 in 2013 to approximately 20,000 in 2016 (1,2). Illicitly manufactured fentanyl, a synthetic opioid 50-100 times more potent than morphine, is primarily responsible for this rapid increase (3,4). In addition, fentanyl analogs such as acetylfentanyl, furanylfentanyl, and carfentanil are being detected increasingly in overdose deaths (5,6) and the illicit opioid drug supply (7). Carfentanil is estimated to be 10,000 times more potent than morphine (8). Estimates of the potency of acetylfentanyl and furanylfentanyl vary but suggest that they are less potent than fentanyl (9). Estimates of relative potency have some uncertainty because illicit fentanyl analog potency has not been evaluated in humans. This report describes opioid overdose deaths during July-December 2016 that tested positive for fentanyl, fentanyl analogs, or U-47700, an illicit synthetic opioid, in 10 states participating in CDC's Enhanced State Opioid Overdose Surveillance (ESOOS) program.* Fentanyl analogs are similar in chemical structure to fentanyl but not routinely detected because specialized toxicology testing is required. Fentanyl was detected in at least half of opioid overdose deaths in seven of 10 states, and 57% of fentanyl-involved deaths also tested positive for other illicit drugs, such as heroin. Fentanyl analogs were present in >10% of opioid overdose deaths in four states, with carfentanil, furanylfentanyl, and acetylfentanyl identified most frequently. Expanded surveillance for opioid overdoses, including testing for fentanyl and fentanyl analogs, assists in tracking the rapidly changing illicit opioid market and informing innovative interventions designed to reduce opioid overdose deaths.