Academic literature on the topic 'Maine. Center for Disease Control and Prevention. Injury Prevention Program'

Ursula Bauer, PhD, MPH, currently directs the National Center for Chronic Disease Prevention and Health Promotion at the Centers for Disease Control and Prevention. She has also worked in the New York Department of Health as Director of the Division of Chronic Disease and Injury Prevention and as Director of the Tobacco Control Program. In this interview, she shares her perspectives on the importance of technical assistance in tobacco control.

Background: Few methods have been defined for evaluating the individual and collective impacts of academic research centers. In this project, with input from injury center directors, we systematically defined indicators to assess the progress and contributions of individual Injury Control Research Centers (ICRCs) and, ultimately, to monitor progress of the overall injury center program. Method: We used several methods of deriving a list of recommended priority and supplemental indicators. This included published literature review, telephone interviews with selected federal agency staff, an e-mail survey of injury center directors, an e-mail survey of staff at the Centers for Disease Control and Prevention, a two-stage Delphi process (e-mailed), and an in-person focus group with injury center directors. We derived the final indicators from an analysis of ratings of potential indicators by center directors and CDC staff. We also examined qualitative responses to open-ended items that address conceptual and implementation issues. Results: All currently funded ICRCs participated in at least one part of the process, resulting in a list of 27 primary indicators (some with subcomponents), 31 supplemental indicators, and multiple suggestions for using the indicators. Conclusion: Our results support an approach that combines standardized definitions and quantifiable indicators with qualitative reporting, which allows consideration of center distinctions and priorities. The center directors urged caution in using the indicators, given funding constraints and recognition of unique institutional environments. While focused on injury research centers, we suggest these indicators also may be useful to academic research centers of other types.

Abstract Background Antibiotic time out (ATO) policies have been proposed by the Centers for Disease Control and Prevention to limit unnecessary use of antibiotics. Critically ill patients are often treated empirically with MRSA-active agents for a prolonged duration. The objective of this study was to assess the impact of an ATO policy by targeting empiric gram-positive coverage. Methods Before this intervention, linezolid required pre-approval by the antimicrobial stewardship program or infectious diseases (ID) consult service before dispensing, and no automatic ATO policy was in place for any agent. In 2018, restriction of linezolid was modified to allow 72 hours of empiric use in the intensive care unit (ICU). This retrospective, single-center, pre- post-intervention study looked at eight ICUs at our institution from two equal periods. Adults (age ≥ 18 years) were included who received an IV gram-positive antibiotic (IVGP-AB), specifically linezolid or vancomycin, used for empiric therapy and were admitted to the ICU. The primary outcome was antimicrobial consumption of IVGP-AB defined as days of therapy (DOT) per patient. Secondary outcomes included in-hospital length of stay (LOS), ICU LOS, in-hospital mortality, 30-day readmission, and incidence of acute kidney injury (AKI). Figure 1. Flowchart of patient inclusion into the study Results 2718 patients met criteria for inclusion in the study. 1091 patients were included in the pre-intervention group and 1627 patients were included in the post-intervention group. Baseline characteristics between the two groups were similar, with ID consults being higher in the pre-intervention group. Total mean DOT of IVGP-AB in pre- and- post-intervention groups was 4.97 days vs. 4.36 days, p< 0.01. Secondary outcomes of in-hospital LOS, ICU LOS, and in-hospital mortality did not vary significantly between groups. Thirty-day readmission was lower in the post-intervention group (12.9% vs. 3.9%, p< 0.01). AKI did not differ significantly between groups, however the need for renal replacement therapy was higher in the pre-intervention group (1.2% vs. 0.2%, p< 0.01). Conclusion This study assessed the impact of an ATO policy allowing 72 hours of empiric linezolid in the ICU. We found a statistically significant reduction in days of therapy of IVGP-AB without increases in LOS, mortality, readmission, and AKI. Disclosures All Authors: No reported disclosures

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.7References1. Stone DM, Simon TR, Fowler KA, et al. Vital Signs: Trends in State Suicide Rates — United States, 1999–2016 and Circumstances Contributing to Suicide — 27 States, 2015. Morb Mortal Wkly Rep. 2018;67(22):617-624.2. CDCs National Center for Injury Prevention and Control. Web-based Injury Statistics Query and Reporting System (WISQARS). https://www.cdc.gov/injury/wisqars/index.html. Published 2018. Accessed September 1, 2018.3. Mercado M, Holland K, Leemis R, Stone D, Wang J. Trends in emergency department visits for nonfatal self-inflicted injuries among youth aged 10 to 24 years in the United States, 2005-2015. J Am Med Assoc. 2017;318(19):1931-1933. doi:10.1001/jama.2017.133174. Olfson M, Blanco C, Wall M, et al. National Trends in Suicide Attempts Among Adults in the United States. JAMA Psychiatry. 2017;10032(11):1095-1103. doi:10.1001/jamapsychiatry.2017.25825. Ikeda R, Hedegaard H, Bossarte R, et al. Improving national data systems for surveillance of suicide-related events. Am J Prev Med. 2014;47(3 SUPPL. 2):S122-S129. doi:10.1016/j.amepre.2014.05.0266. National Cancer Institute. Joinpoint Regression Software. https://surveillance.cancer.gov/joinpoint/. Published 2018. Accessed September 1, 2018.7. Centers for Disease Control and Prevention. Preventing Suicide: A Technical Package of Policy, Programs, and Practices.

ObjectiveTo evaluate four ICD-10-CM based case definitions designed to capture pedestrian and bicycle crash-related emergency department (ED) visits in North Carolina’s statewide syndromic surveillance system, NC DETECT.IntroductionOver the last few decades, the United States has made considerable progress in decreasing the incidence of motor vehicle occupants injured and killed in traffic collisions.1 However, there is still a need for continued motor vehicle crash (MVC) injury surveillance, particularly for vulnerable road users, such as pedestrians and bicyclists. In NC, the average annual number of pedestrian-motor vehicle crashes increased by 13.5 percent during the period 2011-2015, as compared to 2006-2010.2 Therefore, the Carolina Center for Health Informatics (CCHI), as part of a NC Governor’s Highway Safety Program-funded project to improve statewide MVC injury surveillance, developed and evaluated four ICD-10-CM based case definitions for use with NC DETECT, NC’s statewide syndromic surveillance system.MethodsWe created four pedestrian/bicycle crash injury case definitions based on ICD-10-CM transportation codes (“V-codes”): Traffic-Related Pedestrian Crashes, Traffic/Non-Traffic-Related Pedestrian Crashes, Traffic-Related Bicycle Crashes, and Traffic/Non-Traffic-Related Bicycle Crashes. These definitions were based on the Centers for Disease Control and Prevention (CDC) “ICD-10-CM External Cause of Injury Codes”.3 We then applied these pedestrian/bicycle crash case definitions to 2016-2017 NC DETECT ED visit data and data obtained from a single NC Level I Trauma Center. Next, we linked the two data sources using the variables date of visit, time of visit, and medical record number. Since trauma center data are collected and verified by a designated trauma registrar, we considered the data obtained from the Level I Trauma Center to be the “gold standard”.ResultsThe linkage between the two data sources was successful, with 99.5% of all Level I Trauma Center records linking to ED visits in NC DETECT. However, we found discrepancies in the assignment of codes between the ED visit and Trauma Center data. For example, 47.5% of NC DETECT ED visits that linked to a pedestrian/bicycle crash record in the Trauma Center data, were missing an ICD-10-CM injury mechanism code of any category. Historically, the proportion of injury-related ED visits that were missing corresponding injury mechanism codes was low (<15%). However, the transition from ICD-9-CM to ICD-10-CM increased the proportion of injury-related visits missing injury mechanism codes.4 Among the 92 NC DETECT ED visits missing injury mechanism codes, 35.9% contained a pedestrian/bicycle crash-related keyword in the Chief Complaint or Triage Note.Among the 100 linked records with valid ICD-10-CM injury mechanism codes, the percent agreement between the two data sources on whether the ED visit was a “pedestrian” or “bicycle” crash was 54.4% and 71.9%, respectively. Percent agreement decreased for “traffic” and “non-traffic” designations, however. The most common V-code assigned to misclassified pedestrian/bicycle crashes in the NC DETECT ED visit data was “V87.7XXA-Person injured in a collision between other specified motor vehicles (traffic)”.Although the linkage study used data obtained from only a single Level I Trauma Center and primarily a single facility in NC DETECT, we felt that the results of this limited linkage study were generalizable to statewide NC DETECT ED visit data. For example, many facilities in NC DETECT underreport injury mechanism codes. Therefore, we added pedestrian/bicycle crash injury-related keywords to the Traffic/Non-Traffic Pedestrian/Bicycle Crash Injury case definitions (Table 1). After inclusion of these keywords, the number of identified pedestrian and bicycle crash injury-related ED visits identified in NC DETECT increased by 16.9% and 57.9% from January-June 2018, respectively (Figure 1).ConclusionsPedestrian and bicycle crashes represent a major cause of MVC injury morbidity and mortality. Therefore, the development and evaluation of case definitions is key for the successful surveillance of these types of injuries. The inclusion of keywords can help account for some of the injury mechanism data missingness common to ED surveillance systems.References1.NHTSA. Traffic Safety Facts 2015. DOT HS 812 384. Washington, DC: US Department of Transportation; 2017. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812384. Accessed Sept 12, 2018.2.Thomas L, Vann, M, Levitt D. North Carolina Pedestrian Crash Trends and Facts 2011-2015. RP 2017-42. Chapel Hill, NC: University of North Carolina Highway Safety Research Center; 2018. http://www.pedbikeinfo.org/pbcat_nc/pdf/summary_ped_facts11-15.pdf. Accessed Sept 12, 2018.3.NCIPC. Help and Tools for Injury Data; Atlanta, GA: CDC 2018. https://www.cdc.gov/injury/wisqars/dataandstats.html. Accessed Sept 12, 2018.4.Harmon K, Barnett C, Marshall S, Waller A. Implementing the External Cause Matrix for Injury Morbidity – North Carolina Emergency Department Data – January 2015 – May 2015. Chapel Hill, NC: Carolina Center for Health Informatics and the Injury Prevention Research Center; 2016. https://ncdetect.org/files/2017/03/ICD10CCMExternalCauseMatrixImplementation_NCSQI_201607.pdf. Accessed Sept 12, 2018.

ObjectiveTo discuss the use of poison center (PC) data for public health (PH) surveillance at the local, state, and federal levels. To generate meaningful discussion on how to facilitate greater PC and PH collaboration.IntroductionSince 2008, poisoning is the leading cause of injury-related death in the United States; since 1980, the poisoning-related fatality rate in the United States (U.S.) has almost tripled1. Many poison-related injuries and deaths are reported to regional PCs which receive about 2.4 million reports of human chemical and poison exposures annually2. Federal, state, and local PH agencies often collaborate with PCs and use PC data for PH surveillance to identify poisoning-related health issues. Many state and local PH agencies have partnerships with regional PCs for direct access to local PC data which help them perform this function. At the national level, the National Center for Environmental Health (NCEH) of the Centers for Disease Control and Prevention (CDC) conducts PH surveillance for exposures and illnesses of PH significance using the National Poison Data System (NPDS), the national PC reporting database and real-time surveillance system.Though most PC and PH officials agree that PC data play an important role in PH practice and surveillance, collaboration between PH agencies and PCs can be hindered by numerous challenges. To address these challenges and bolster collaboration, the PC and PH Collaborations Community of Practice (CoP) has collaborated with members to provide educational webinars; newsletters highlighting the intersection of PH and PC work; and in-person meetings at relevant national and international conferences. The CoP includes over 200 members from state and local PH departments, regional PCs, CDC, the American Association of Poison Control Centers (AAPCC), and the U.S. Environmental Protection Agency (EPA).DescriptionThe panel will consist of 3 presenters and 1 moderator, who are members of the CoP. Each presenter will bring a unique perspective on the use of PC data for PH practice and surveillance. Dr. Prakash Mulay is the surveillance coordinator for chemical related illnesses and injuries in Florida. His primary focus is on carbon monoxide, pesticide, mercury, and arsenic poisoning. He also works as a liaison between the Florida Poison Information Centers and Department of Health. Dr. Mulay has a Medical Degree from India and a Masters of Public Health (MPH) in epidemiology from Florida International University, Miami. For the purpose of the panel discussion, Dr. Mulay will provide PC PH collaboration from the state perspective.Dr. Jay Schauben is the Director of the Florida/United States Virgin Islands Poison Information Center in Jacksonville, the Florida Poison Information Center Network Data Center, and the Clinical Toxicology Fellowship Program at University of Florida Health-Jacksonville Medical Center/University of Florida Health Science Center. He is board-certified in clinical toxicology and is a Fellow of the American Academy of Clinical Toxicology. In 1992, Dr. Schauben implemented the Florida Poison Information Center in Jacksonville and played a major role in crafting the Statewide Florida Poison Information Center Network. On the panel, Dr. Schauben will provide collaboration insight from the PC perspective.Dr. Royal Law is the surveillance and technical lead for the National Chemical and Radiological Surveillance Program, housed within the Health Studies Branch at the CDC. He received his PhD in Public Health from Georgia State University and his MPH at Emory University. Dr. Law will provide insight from the national level including CDC use of PC data for public health surveillance activities.How The Moderator Intends to Engage the AudienceAfter the panel members have been introduced and shared their contributions and experiences with PC PH collaboration the moderator will engage the audience by facilitating discussion of the successes and challenges to using PC data for PH practice and surveillance.Sample questions:What are your current capacities and collaborative activities between your state/local health department and your PC?What non-funding related barriers hinder the collaboration between your state/local health department and PC?If no increase in funding were available, how would you increase the level of interactivity with the PC and state/local health department? What if funding was available?References1Warner M, Chen LH, Makuc DM, Anderson RN, and Minino AM. Drug Poisoning Deaths in the United States, 1980–2008. National Center for Health Statistics Data Brief, December 2011. Accessed 8/29/2012.2Mowry JB, Spyker DA, Brooks DE, Zimmerman A, Schauben JL (2016) 2015 Annual Report of the American Association of Poison Control Centers’ National Poison Data Systems (NPDS): 33rd Annual Report, Clinical Toxicology, 54:10, 924-1109.

ObjectiveTo describe national-level trends in nonfatal self-harm and suicidal ideation among 10-19 year old youth from January 2016 through December 2017 and examine the impact of popular entertainment on suicidal behavior.IntroductionIn 2016, a half million people were treated in U.S. emergency departments (EDs) as a result of self-harm. 1 Not only is self-harm a major cause of morbidity in the U.S., but it is also one of the best predictors of suicide. Given that approximately 40% of suicide decedents visited an ED in the year prior to their death and that the majority of medically-serious self-harm patients are treated in EDs2, EDs serve as a critical setting in which to monitor rates and trends of suicidal behavior.To date, the majority of ED data for self-harm are generally two to three years old and thereby can only be used to describe historical patterns in suicidal behavior. Thus, in 2018, a syndrome definition for suicide attempts and suicidal ideation (SA/SI) was developed by the International Society for Disease Surveillance (ISDS) Syndrome Definition Committee in conjunction with Centers for Disease Control and Prevention (CDC) staff, allowing researchers to better monitor recent trends in medically treated suicidal behavior using data from the CDC’s National Syndromic Surveillance Program (NSSP). These data serve as a valuable resource to help detect deviations from typical patterns of SA/SI and can help drive public health response if atypical activity, such as geospatial or temporal clusters of SA/SI, is observed. Such patterns may be indicative of suicide contagion (i.e., exposure to the suicide or suicidal behavior of a friend or loved one, or through media content, that may put individuals at increased risk of suicidal behavior).Research has demonstrated that suicide contagion is a real phenomenon. 3 13 Reasons Why is a Netflix series focused on social, school, and family-related challenges experienced by a high school sophomore; each episode in the 13-episode series describes a problem faced by the main character, which she indicates contributed to her decision to die by suicide. The series premiered March 31, 2017 and is rated TV-MA by TV Parental Guidelines4 (may be unsuitable for those under age 18 years due to graphic content). Nevertheless, the series has become popular among youth under 18 years of age. Of note, in the final episode, the main character’s suicide by wrist laceration is graphically depicted. Following the premiere of the series, researchers and psychologists across the U.S. expressed concern that this graphic depiction of suicide could result in a contagion effect, potentially exacerbating suicidal thoughts and behavior among vulnerable youth viewers. To date, the only empirical data demonstrating the potential iatrogenic effects of this graphic portrayal of suicide comes from a study of Google Trends data demonstrating an increase in online suicide queries in the weeks following the show, with most of the queries focusing on suicidal ideation (e.g., “how to commit suicide,” “how to kill yourself”).5 However, there has been no study to examine changes in nonfatal self-harm trends following the series debut.MethodsNSSP data were aggregated at the national level from January 2016 through December 2017 to examine weekly trends in the percentage of ED visits that involved SA/SI among all ED visits for youth aged 10-19. Google Trends data were also used to examine suicide-related online searches conducted during this period. Additional sensitivity analyses to explore these findings will be conducted by HHS region using NSSP data.ResultsPreliminary results suggest an increase in ED visits due to SA/SI among 10-19 year old youth across the U.S. beginning about two weeks after the premiere of 13 Reasons Why (April 16, 2017) and lasting a total of six weeks before weekly percentages of SA/SI ED visits returned to their endemic levels during the week of May 28-June 3, 2017. The peak of the increase represented a 26% increase in SA/SI compared to the highest weekly percentage of these visits in the previous 15 weeks in 2017. Additionally, this peak coincided with marked peaks in online searches for phrases including “13 Reasons Why” from March 26-June 3, 2017, “how to kill yourself” from April 16-June 3, 2017, and “how to slit wrists” from April 2-June 3, 2017 as demonstrated by Google Trends data.ConclusionsThis study demonstrates the utility of syndromic surveillance data for tracking SA/SI at the national level and for detecting atypical fluctuations in trends over time. Using syndromic surveillance data for this purpose could help spark public health response to emerging health threats. However, it is important to note that NSSP data are subject to some limitations; for instance, although about 60% of ED visits in the U.S. are captured in NSSP, the system is not nationally representative and thus, these findings are not generalizable to areas not participating in NSSP. Additionally, our definition may under- or over-estimate SA/SI based on differences in chief complaints or discharge diagnosis data between jurisdictions. Further, hospital participation in NSSP can vary across months–a factor that could contribute to trends observed in NSSP data. Finally, these analyses explored the concurrent trends in SA/SI among youth and the popularity of only one television series. Although these analyses point to an association between the increases in SA/SI and the time period in which the series reached its peak popularity as evidenced by Google Trends, there may have been other sociocultural factors that impacted SA/SI trends during the study period. Still, preliminary findings suggest that media content containing graphic depictions of suicide viewed by youth audiences may contribute to increases in ED visits for self-harm and suicidal ideation, as well as greater interest in searching for information about suicidal behavior online. While it is impossible to assess causation, these results are consistent with the phenomenon of suicide contagion. It is also possible that the series or related media coverage during this time increased help-seeking among some youth or their families that contributed to the increases observed. Regardless of the underlying mechanism, entertainment content creators may consider referring to the Recommendations for Reporting on Suicide (www.reportingonsuicide.org), which can help reduce the risk of suicide among vulnerable individuals and avoid contributing to suicide contagion while promoting suicide prevention messages. Finally, ongoing surveillance of suicidal behavior using NSSP data could help reduce the burden of nonfatal self-harm by catalyzing the implementation of prevention efforts.Results1. Center for Disease Control and Prevention, National Center for Injury Prevention and Control. (2018). Web-based Injury Statistics Query and Reporting System (WISQARS). Available from www.cdc.gov/ncipc/wisqars. Accessed 10-3-2018.2. Ahmedani BK, Simon GE, Stewart C et al. (2014) Health care contacts in the year before suicide death. J Gen Intern Med, 29, 870-877.3. Gould, M., Jamieson, P., & Romer, D. (2003). Media contagion and suicide among the young. American Behavioral Scientist, 46(9), 1269-1284.4. The TV Parental Guidelines. (2018). Available from http://tvguidelines.org/. Accessed 10-3-2018.5. Ayers, J. W., Althouse, B. M., Leas, E. C., Dredze, M., & Allem, J. P. (2017). Internet searches for suicide following the release of 13 Reasons Why. JAMA internal medicine, 177(10), 1527-1529.

ObjectiveLink syndromic surveillance data for potential opioid-involved overdoses with hospital discharge data to assess positive predictive value of CDC Opioid Classifiers for conducting surveillance on acute drug overdoses.IntroductionThe opioid drug overdose crisis presents serious challenges to state-based public health surveillance programs, not the least of which is uncertainty in the detection of cases in existing data systems. New Jersey historically had slightly higher unintentional drug overdose death rates than the national average, but by 2001 dramatic increases in drug overdose deaths in states like West Virginia began to drive up the national rate (Figure 1). Although the rise in New Jersey’s fatal overdose rates has mirrored the national rate since 1999, the rate has dramatically increased since 2011- from 9.7 per 100,000 (868 deaths) to 21.9 per 100,000 in 2016 (1,931 deaths), an increase of 125% in five years.1The New Jersey Department of Health has been funded by the Centers for Disease Control and Prevention (CDC) to conduct surveillance of opioid-involved overdoses through the Enhanced Surveillance of Opioid-Involved Overdose in States (ESOOS) program, and to conduct syndromic surveillance through the National Syndromic Surveillance Program (NSSP); this has presented a collaboration opportunity for the Department’s surveillance grantee programs to use existing resources to evaluate and refine New Jersey’s drug overdose case definitions and develop new indicators to measure the burden of overdose throughout the state and to facilitate effective responses.MethodsThis work examined using probabilistic matching strategies to assess how accurately syndromic surveillance data identifies potential opioid-involved overdose patients by linking to hospital discharge records after subsequent treatment in an emergency department or inpatient setting for either a confirmed opioid-involved overdose or another condition(s).New Jersey syndromic surveillance data from NSSP’s ESSENCE system from December 2016 with either CDC’s CCDD Classifiers “CDC Opioid Overdose V1” or “CDC Heroin Overdose V3” were selected for inclusion (“NJ ESSENCE data”). NJ ESSENCE data were restructured to produce one record per patient visit, with each record assigned one or more overdose classifiers; these records were then matched to the universe of acute care hospital discharge billing records from the New Jersey Hospital Discharge Data System (“UB data”) from the same time period. Confirmed drug overdoses were flagged in the UB data by using the CDC’s baseline ESOOS case definition, which searches all diagnosis fields for ICD-10-CM codes indicating an unintentional or undetermined intent drug overdose, an opioid overdose, or a heroin overdose. Optionally, there are suggested codes for mental and behavioral health conditions that indicate opioid abuse or dependence with intoxication (Table 1).Using SAS® software and PROC SQL, data were matched using a three-round “blocking” strategy based on facility identifier and admission date, and combinations of date of birth, sex, patient ZIP code, and age. Concordance of ESSENCE opioid overdose classifiers with indicator categories used by CDC’s ESOOS was evaluated. Suspected opioid overdoses from NJ ESSENCE that matched to UB records for mental health conditions that were not also acute overdoses were reviewed.ResultsThere were 253 records in NJ ESSENCE data with either “CDC Opioid Overdose V1” or “CDC Heroin Overdose V3” CCDD classifiers; restructuring the data resulted in 149 unique records of potential opioid overdoses. Of these, 106 (71%) records from NJ ESSENCE were successfully matched to emergency department or inpatient records. Eighty (80) records (54%), were matched in the first round using facility identifier and date of admission, date of birth, sex, and patient’s home ZIP code. Of the 43 unmatched NJ ESSENCE records, 33 (77%) were patients missing age and date of birth.Of the 106 matched records (Table 2):● 74 opioid-involved overdoses in NJ ESSENCE matched to any drug overdose records in the UB data, for an overall PPV of 70%.● 69 opioid-involved overdoses in NJ ESSENCE matched to opioid-involved overdose records, for an opioid-involved PPV of 65%.● 54 heroin-involved overdoses in NJ ESSENCE matched to heroin-involved overdose records, for a heroin-involved PPV of 92%.32 matched records were NJ ESSENCE positive for opioids and UB negative, and 24 (75%) were classified as potential heroin overdoses.●18 records had at least one mental and behavioral health condition code as part of the final discharge record.● 3 were flagged with the mental and behavioral health conditions with opioid intoxication indicator.Only one record appeared to be a possible false positive, with an NJ ESSENCE record indicating a “suspected heroin overdose or an overdose by unspecified drugs and of undetermined intent”, but a discharge record indicated a primary diagnosis code of I46.9 (sudden cardiac arrest) and other systemic diagnoses but no poisoning or mental or behavioral health codes reported.ConclusionsNJ ESSENCE data with CDC Opioid or Heroin Overdose Classifiers was able to correctly identify opioid-involved overdoses in matched records for patients experiencing an acute overdose better than 2 out of 3 times. For patients experiencing an acute heroin overdose the PPV was over 90%. Cases with discordance in classification matched to records that may have been possible undetected drug intoxications or other mental and behavioral health conditions.This work does not confirm that the CDC Opioid or Heroin Overdose Classifiers accurately capture all or even most drug overdoses treated in New Jersey hospitals reported to NSSP ESSENCE as of December 2016. A total of 1,461 discharges for acute drug overdoses were identified in UB data using the ESOOS case definition; 1,069 were treated and released from the emergency department, and 392 were admitted for further inpatient care. The 106 matched records only represent 7% of total overdose records identified in the UB data.Further suggested work includes follow-up on possible data quality issues, pursuing a comprehensive project using all UB-identified overdoses matched to a broader selection of NJ ESSENCE data to examine what may be missed by the CDC’s NSSP overdose classifiers, and using more recent data to test improvements made to the system since the original data pull.References1. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Web-based Injury Statistics Query and Reporting System (WISQARS) [online]. (2005) [2018 Oct 1]. Available from URL: http://www.cdc.gov/injury/wisqars