scholarly journals Development of Syndrome Definitions for Acute Unintentional Drug and Heroin Overdose

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Em Stephens
Keyword(s):  
Public Health ◽  
Chronic Pain ◽  
Real Time ◽  
Correlation Coefficient ◽  
Drug Overdose ◽  
Chief Complaint ◽  
Opioid Overdose ◽  
Free Text ◽  
Discharge Data ◽  
Heroin Overdose

ObjectiveTo develop and evaluate syndrome definitions for the identificationof acute unintentional drug overdose events including opioid, heroin,and unspecified substances among emergency department (ED) visitsin Virginia.IntroductionNationally, deaths due to opioid overdose have continuallyincreased for the past 15 years1. Deaths specifically related to heroinincreased more than four-fold between 2002 and 20142. Hospitalinpatient discharge data provide information on non-fatal overdoses,but include a significant lag in reporting time3. Syndromic ED visitdata provide near real-time identification of public health issues andcan be leveraged to inform public health actions on the emergingthreat of drug overdose.MethodsVirginia Department of Health (VDH) developed two syndromedefinitions in 2014 to capture acute unintentional drug overdoseevents among syndromic ED visit data. Syndrome 1 captured visitsfor overdose, whether or not a specific substance was mentioned.Syndrome 2 captured only visits for heroin overdose. Definitionswere based on free-text terms found within the chief complaintand standardized text or International Classification of Diseases(ICD) codes within the diagnosis field. In 2016, both definitionswere revised to identify additional inclusion and exclusion criteriaaccording to CDC guidance documentation and syndrome definitionsused by other state jurisdictions.Microsoft SQL was used to modify both definitions based on thenewly identified chief complaint and diagnosis criteria. Record leveldata were analyzed for their adherence to established criteria using aniterative evaluation process.The scope of Syndrome 1 (2016) was narrowed from the 2014version by excluding visits for non-opioid substances, heroin, andnon-acute indicators. It included chief complaint and diagnosisterms related to opioids, unspecified substance overdose, narcotics,and Narcan or naloxone, and excluded terms related to suicide,alcohol overdose alone, withdrawal, detoxification, rehab, addiction,constipation, chronic pain, and any specified non-opioid drug ormedication. Syndrome 2 (2016) included chief complaint or diagnosisterms mentioning heroin overdose and excluded suicide, withdrawal,detoxification, rehab, and addiction. Visits with mention of suicide,rehab, or addiction were identified during the evaluation process,resulting in the exclusion of these terms in the revised query.From January 1, 2015 to July 31, 2016, the number of visitscaptured by the revised syndrome definitions was compared to thenumber captured by the 2014 definitions. Correlation coefficientswere calculated using SAS 9.3.ResultsThe revised Syndrome 1 found 4296 fewer ED visits(29% decrease) for acute unintentional drug overdose betweenJanuary 1, 2015 and July 31, 2016 compared to the 2014 definition.Despite the drop in volume, the monthly trends were similar forthe 2014 and 2016 definitions (correlation coefficient = 0.95,p < 0.001). For the same time period, the revised Syndrome 2 definitionreturned 108 fewer visits (6% decrease) for acute unintentional heroinoverdose. The monthly trends were also similar for the 2014 and 2016definitions (correlation coefficient = 0.98, p < 0.001).ConclusionsBoth revised syndrome definitions improved specificity incapturing overdose visits as Syndrome 1 (2016) identified 29% fewervisits and Syndrome 2 (2016) identified 6% fewer visits found to beunrelated to the desired overdose criteria.When developing the revised syndrome definitions, VDH decidedto exclude non-acute drug-related visits. Terms such as addiction,detoxification, rehab, withdrawal, chronic pain, and constipation wereindicative of habitual drug use or abuse instead of acute overdose andwere thus excluded. In narrowing the scope of Syndrome 1, VDHalso identified and excluded visits for specified drug and medicationoverdose. Together, these expanded exclusion criteria resulted ingreater specificity with both updated syndromes.These revised syndrome definitions enable VDH to better trackopioid and heroin overdose trends in near real-time and overextended time periods which can be used to inform public healthactions. Limitations include the inconsistency of diagnosis codingamong syndromic data submitters, which may lead to geographicunderrepresentation of unintentional drug overdose visits based onthe location of health care systems. VDH will continue to evaluate andrefine these overdose syndrome definitions as this emerging healthissue evolves.

scholarly journals Tracking drug-related overdoses at the local level: Using Syndromic Surveillance in the CO-NCR

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Emery Shekiro ◽  
Lily Sussman ◽  
Talia Brown
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Real Time ◽  
Drug Overdose ◽  
Syndromic Surveillance ◽  
Case Definition ◽  
Chief Complaint ◽  
Discharge Diagnosis ◽  
Diagnosis Codes ◽  
Drug Overdoses

Objective: In order to better describe local drug-related overdoses, we developed a novel syndromic case definition using discharge diagnosis codes from emergency department data in the Colorado North Central Region (CO-NCR). Secondarily, we used free text fields to understand the use of unspecified diagnosis fields.Introduction: The United States is in the midst of a drug crisis; drug-related overdoses are the leading cause of unintentional death in the country. In Colorado the rate of fatal drug overdose increased 68% from 2002-2014 (9.7 deaths per 100,000 to 16.3 per 100,000, respectively)1, and non-fatal overdose also increased during this time period (23% increase in emergency department visits since 2011)2. The CDC’s National Syndromic Surveillance Program (NSSP) provides near-real time monitoring of emergency department (ED) events across the country, with information uploaded daily on patient demographics, chief complaint for visit, diagnosis codes, triage notes, and more. Colorado North Central Region (CO-NCR) receives data for 4 local public health agencies from 25 hospitals across Adams, Arapahoe, Boulder, Denver, Douglas, and Jefferson Counties.Access to local syndromic data in near-real time provides valuable information for local public health program planning, policy, and evaluation efforts. However, use of these data also comes with many challenges. For example, we learned from key informant interviews with ED staff in Boulder and Denver counties, about concern with the accuracy and specificity of drug-related diagnosis codes, specifically for opioid-related overdoses.Methods: Boulder County Public Health (BCPH) and Denver Public Health (DPH) developed a query in Early Notification of Community Based Epidemics (ESSENCE) using ICD-10-CM codes to identify cases of drug-related overdose [T36-T51] from October 2016 to September 2017. The Case definition included unintentional, self-harm, assault and undetermined poisonings, but did not include cases coded as adverse effects or underdosing of medication. Cases identified in the query were stratified by demographic factors (i.e., gender and age) and substance used in poisoning. The first diagnosis code in the file was considered the primary diagnosis. Chief complaint and triage note fields were examined to further describe unspecified cases and to describe how patients present to emergency departments in the CO-NCR. We also explored whether detection of drug overdose visits captured by discharge diagnosis data varied by patient sex, age, or county.Results: The query identified 2,366 drug-related overdoses in the CO-NCR. The prevalence of drug overdoses differed across age groups. The detection of drug overdoses was highest among our youth and young adult populations; 16 to 20 year olds (16.0%), 21-25 year olds (11.4%), 26-30 year olds (11.4%). Females comprised 56.1% of probable general drug overdoses. The majority of primary diagnoses (31.0%) included poisonings related to diuretics and other unspecified drugs (T50), narcotics (T40) (12.6%), or non-opioid analgesics (T39) (7.8%). For some cases with unspecified drug overdose codes there was additional information about drugs used and narcan administration found in the triage notes and chief complaint fields.Conclusions: Syndromic surveillance offers the opportunity to capture drug-related overdose data in near-real time. We found variation in drug-related overdose by demographic groups. Unspecified drug overdose codes are extremely common, which likely negatively impacts the quality of drug-specific surveillance. Leveraging chief complaint and triage notes could improve our understanding of factors involved in drug-related overdose with limitations in discharge diagnosis. Chart reviews and access to more fields from the ED electronic health record could improve local drug surveillance.

scholarly journals Tracking suspected heroin overdoses in CDC's National Syndromic Surveillance Program

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Alana M. Vivolo-Kantor ◽  
R. Matthew Gladden ◽  
Aaron Kite-Powell ◽  
Michael Coletta ◽  
Grant Baldwin
Keyword(s):  
Public Health ◽  
United States ◽  
Emergency Department ◽  
Drug Overdose ◽  
Syndromic Surveillance ◽  
Case Definition ◽  
Chief Complaint ◽  
Heroin Overdose ◽  
Ed Visits ◽  
Overdose Deaths

ObjectiveThis paper analyzes emergency department syndromic data in the Centers for Disease Control and Prevention’s (CDC) National Syndromic Surveillance Program’s (NSSP) BioSense Platform to understand trends in suspected heroin overdose.IntroductionOverdose deaths involving opioids (i.e., opioid pain relievers and illicit opioids such as heroin) accounted for at least 63% (N = 33,091) of overdose deaths in 2015. Overdose deaths related to illicit opioids, heroin and illicitly-manufactured fentanyl, have rapidly increased since 2010. For instance, heroin overdose deaths quadrupled from 3,036 in 2010 to 12,989 in 2015. Unfortunately, timely response to emerging trends is inhibited by time lags for national data on both overdose mortality via vital statistics (8-12 months) and morbidity via hospital discharge data (over 2 years). Emergency department (ED) syndromic data can be leveraged to respond more quickly to emerging drug overdose trends as well as identify drug overdose outbreaks. CDC’s NSSP BioSense Platform collects near real-time ED data on approximately two-thirds of ED visits in the US. NSSP’s data analysis and visualization tool, Electronic Surveillance System for the Notification of Community-based Epidemics (ESSENCE), allows for tailored syndrome queries and can monitor ED visits related to heroin overdose at the local, state, regional, and national levels quicker than hospital discharge data.MethodsWe analyzed ED syndromic data using ESSENCE to detect monthly and annual trends in suspected unintentional or undetermined heroin overdose by sex and region for those 11 years and older. An ED visit was categorized as a suspected heroin overdose if it met several criteria, including heroin overdose ICD-9-CM and ICD-10-CM codes (i.e., 965.01 and E850.0; T40.1X1A, T40.1X4A) and chief complaint text associated with a heroin overdose (e.g., “heroin overdose”). Using computer code developed specifically for ESSENCE based on our case definition, we queried data from 9 of the 10 HHS regions from July 2016-July 2017. One region was excluded due to large changes in data submitted during the time period. We conducted trend analyses using the proportion of suspected heroin overdoses by total ED visits for a given month with all sexes and regions combined and then stratified by sex and region. To determine significant linear changes in monthly and annual trends, we used the National Cancer Institute’s Joinpoint Regression Program.ResultsFrom July 2016-July 2017, over 72 million total ED visits were captured from all sites and jurisdictions submitting data to NSSP. After applying our case definition to these records, 53,786 visits were from a suspected heroin overdose, which accounted for approximately 7.5 heroin overdose visits per 10,000 total ED visits during that timeframe. The rate of suspected heroin overdose visits to total ED visits was highest in June 2017 (8.7 per 10,000) and lowest in August 2016 (6.6 per 10,000 visits). Males accounted for a larger rates of visits over all months (range = 10.7 to 14.2 per 10,000 visits) than females (range = 3.8 to 4.7 per 10,000 visits). Overall, compared to July 2016, suspected heroin overdose ED visits from July 2017 were significantly higher for all sexes and US regions combined (β = .010, p = .036). Significant increases were also demonstrated over time for males (β = .009, p = .044) and the Northeast (β = .012, p = .025). No other significant increases or decreases were detected by demographics or on a monthly basis.ConclusionsEmergency department visits related to heroin overdose increased significantly from July 2016 to July 2017, with significant increases in the Northeast and among males. Urgent public health action is needed reduce heroin overdoses including increasing the availability of naloxone (an antidote for opioid overdose), linking people at high risk for heroin overdose to medication-assisted treatment, and reducing misuse of opioids by implementing safer opioid prescribing practices. Despite these findings, there are several limitations of these data: not all states sharing data have full participation thus limiting the representativeness of the data; not all ED visits are shared with NSSP; and our case definition may under-identify (e.g., visits missing discharge diagnosis codes and lacking specificity in chief complaint text) or over-identify (e.g., reliance on hospital staff impression and not drug test results) heroin overdose visits. Nonetheless, ED syndromic surveillance data can provide timely insight into emerging regional and national heroin overdose trends.ReferencesWarner M, Chen LH, Makuc DM, Anderson RN, Minino AM. Drug poisoning deaths in the United States, 1980-2008. NCHS Data Brief 2011(81):1-8.Rudd RA, Seth P, David F, Scholl L. Increases in Drug and Opioid-Involved Overdose Deaths - United States, 2010-2015. MMWR Morb Mortal Wkly Rep 2016;65(5051):1445-1452.Spencer MRA, F. Timeliness of Death Certificate Data for Mortality Surveillance and Provisional Estimates. National Center for Health Statistics 2017.Richards CL, Iademarco MF, Atkinson D, Pinner RW, Yoon P, Mac Kenzie WR, et al. Advances in Public Health Surveillance and Information Dissemination at the Centers for Disease Control and Prevention. Public Health Rep 2017;132(4):403-410.

scholarly journals Using Syndromic Data for Opioid Overdose Surveillance in Utah

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Wei Hou ◽  
Elizabeth Brutsch ◽  
Angela C Dunn ◽  
Cindy L Burnett ◽  
Melissa P Dimond ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Real Time ◽  
Emergency Department Visit ◽  
Syndromic Surveillance ◽  
Chief Complaint ◽  
Surveillance Data ◽  
Urgent Care ◽  
Mortality Data ◽  
Opioid Overdose

Objective: To monitor opioid-related overdose in real-time using emergency department visit data and to develop an opioid overdose surveillance report for Utah Department of Health (UDOH) and its public health partners.Introduction: The current surveillance system for opioid-related overdoses at UDOH has been limited to mortality data provided by the Office of the Medical Examiner (OME). Timeliness is a major concern with OME data due to the considerable lag in its availability, often up to six months or more. To enhance opioid overdose surveillance, UDOH has implemented additional surveillance using timely syndromic data to monitor fatal and nonfatal opioid-related overdoses in Utah.Methods: As one of the agencies participating in the National Syndromic Surveillance Program (NSSP), UDOH submits de-identified data on emergency department visit from Utah’s hospitals and urgent care facilities in close to real-time to the NSSP platform. Emergency department visit data are available for analysis using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) system provided by NSSP. ESSENCE provides UDOH with patient-level syndromic data for analysis and early detection of abnormal patterns in emergency visits. A total of 38 out of 48 acute care hospitals and multiple urgent care facilities are enrolled in the system in Utah. More than 90% of these hospitals report chief complaint data, and discharge data are available from about 15% of the facilities. Data were analyzed by querying key terms in the chief complaint field including: any entry of: ‘overdose’, drug and brand names for opioids, street names, ‘naloxone’, and miss-spellings. Exclusion terms included any mention of: ‘denies’, ‘quit’, ‘refill’, ‘withdraw’, ‘dependence’, etc. Data containing any ICD entry of: T40.0-T40.4, T40.60, and T40.69 were included in the analysis.Results: Between September 1, 2016 and August 31, 2017, Utah Department of Health identified 4,063 opioid-related overdose emergency department (ED) visits through the ESSENCE system using both chief complaint and discharge diagnosis queries. Of these visits, 3,865 (95%) were identified using chief complaints alone and 198 (5%) visits were added by searching the discharge diagnosis field. Opioid-related visits comprised approximately 0.3% of the total ED visits (1,267,244) reported during this time (Graph 1). More than half of the opioid-related emergency visits were reported from just five facilities. Rate of opioid-related visits ranging from 0 to 292 visits per 100,000 population per year (median: 108 visits per 100,000 population per year), with an overall rate for the state of 129 visits per100, 000 population per year. The highest rate of opioid-related visits occurred among patients aged 18 to 24 (219 visits per 100,000 population per year), and 59% of all opioid-related patients in Utah were female.Conclusions: The results presented are estimates of opioid-related overdoses reported using close to real-time data. These results would not include visits with incomplete or incorrectly coded chief complaints or discharge codes, or cases of opioid overdose who do not present to an emergency department or urgent care facility. The results from using syndromic data are consistent with existing surveillance findings using mortality data in Utah. This suggests that syndromic surveillance data are useful for rapidly capturing opioid events, which may allow for a timelier public health response. UDOH is currently evaluating syndromic surveillance data versus hospital discharge data for opioid-related emergency department visits, which may further optimize queries in ESSENCE, in order to provide improved opioid surveillance data to local public health partners. This analysis demonstrates that using syndromic surveillance data provides a more time-efficient alternative, enabling more rapid public health interventions, which improved opportunities to reduce opioid-related morbidity and mortality in Utah.

Accuracy of Medical Examiner’s Assessment for Near–Real-Time Surveillance of Fatal Drug Overdoses, King County, Washington, March 2017–February 2018

2021 ◽  
pp. 003335492110084
Author(s):  
Kirsten Vannice ◽  
Julia Hood ◽  
Nicole Yarid ◽  
Meagan Kay ◽  
Richard Harruff ◽  
...  
Keyword(s):  
Public Health ◽  
Positive Predictive Value ◽  
Real Time ◽  
Negative Predictive Value ◽  
Drug Overdose ◽  
Predictive Value ◽  
Death Certificate ◽  
King County ◽  
Public Health Response ◽  
Drug Overdoses

Objectives Up-to-date information on the occurrence of drug overdose is critical to guide public health response. The objective of our study was to evaluate a near–real-time fatal drug overdose surveillance system to improve timeliness of drug overdose monitoring. Methods We analyzed data on deaths in the King County (Washington) Medical Examiner’s Office (KCMEO) jurisdiction that occurred during March 1, 2017–February 28, 2018, and that had routine toxicology test results. Medical examiners (MEs) classified probable drug overdoses on the basis of information obtained through the death investigation and autopsy. We calculated sensitivity, positive predictive value, specificity, and negative predictive value of MEs’ classification by using the final death certificate as the gold standard. Results KCMEO investigated 2480 deaths; 1389 underwent routine toxicology testing, and 361 were toxicologically confirmed drug overdoses from opioid, stimulant, or euphoric drugs. Sensitivity of the probable overdose classification was 83%, positive predictive value was 89%, specificity was 96%, and negative predictive value was 94%. Probable overdoses were classified a median of 1 day after the event, whereas the final death certificate confirming an overdose was received by KCMEO an average of 63 days after the event. Conclusions King County MEs’ probable overdose classification provides a near–real-time indicator of fatal drug overdoses, which can guide rapid local public health responses to the drug overdose epidemic.

scholarly journals Linking Emergency Medical Service Data to Death Records for Opioid Mortality Surveillance

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Joseph R. Tatar ◽  
Jennifer Broad
Keyword(s):  
Public Health ◽  
Emergency Medical Service ◽  
Cause Of Death ◽  
Emergency Response ◽  
Drug Overdose ◽  
Medical Service ◽  
Medical Examiner ◽  
Opioid Overdose ◽  
Naloxone Administration ◽  
Underlying Cause Of Death

ObjectiveTo identify the correlates of opioids as an underlying cause of death by linking coroner/medical examiner vital death records with emergency medical service (EMS) ambulance run data. By combining death data to EMS ambulance runs, the goal was to determine characteristics of the emergency response—particularly for opioid overdose events—that may connect to increased mortality.IntroductionOpioid abuse has increased exponentially in recent years throughout the United States, leading to an increase in the incidence of emergency response activities, hospitalization, and mortality related to opioid overdose. As a result, states that have been hit particularly hard during this period—such as Wisconsin—have allocated considerable resources to addressing this crisis via enhanced public health surveillance and outreach, procurement and administration of medical countermeasures, prescription drug monitoring programs, targeted preventive and acute treatment, first responder and hospital staff training, cross-agency collaboration, and Incident Management System activities. Central to these efforts is the identification of the primary drivers of opioid overdose and death to improve the precision and efficacy of targeted public health interventions to address the opioid crisis. The present study sought to accomplish this end by syncing rich data sources at the point of emergency response (EMS ambulance runs) to ultimate mortality outcomes (vital death records).MethodsIn the State of Wisconsin, data systems supporting the surveillance of EMS ambulance runs and coroner/medical examiner death records are both maintained under the Department of Health Services, enhancing the ability of public health researchers to connect these records using matched identifiers. Two years of EMS ambulance run data (2016-2017) were matched to three years of vital death records (2016-2018) that listed opioids as a contributing cause of death. Ambulance runs and death records for individuals aged 10 years or younger were removed from the data prior to matching and were not included in the final analytic set. Records between these two systems were matched using patient first and last name, gender, date of birth, and zip code. Ambulance runs for a suspected opioid overdose were identified by mining text fields from EMS primary and secondary impressions as well as incident narrative details that identified an opioid as a likely cause of the event. Ambulance runs resulting in Narcan/naloxone administration were also identified as opioid-related overdose. Coroner/medical examiner death records that identified opioids as a contributing cause were classified as an opioid-related death. Analyses examining correlates of deaths with opioids as a contributing cause focused on patient demographics, Narcan/naloxone administration rates and dosage, date and time of the ambulance run, lag between EMS response and time of opioid-related death, physical location and urbanicity of the incident, and the type of response by EMS personnel (i.e. treated and transported, treated and released, no treatment, patient refusal, DOA).ResultsFrom 2016-2017, there were over 800,000 emergency ambulance runs among those aged 11 years and older. Opioid overdose ambulance runs accounted for 1.1% (9,761) of those runs. There were over 100,000 deaths in Wisconsin and 1.7% (1,797) were related to opioids (i.e. opioids were a contributing cause). Linking resulted in 268 people with opioid overdose ambulance runs who had an opioid-related death. Of these, 34% died at the scene of the ambulance run, 12% died later that day, 16% died within a week of the ambulance run, and 37% died after a week. While all of these deaths had a contributing cause of opioids, 97% also had an underlying cause of death of drug overdose. Comparing those who died to those who didn’t die, those who died were more likely to be male, younger, and had the event occur on a Saturday. Additionally, while there were no differences in the likelihood of Narcan/naloxone receipt by opioid-related death, individuals who died were more likely to have received multiple Narcan/naloxone doses during the ambulance run than those who did not. Of those who died at the scene, the majority (32%) were aged 30 to 39 years. Of those who died later, the majority (32%) were aged 20 to 29 years. Also, for those who died at the scene, the majority of the events occurred from eight pm to midnight while for those who died later, the majority of events occurred from four to eight pm.ConclusionsThe majority of linked deaths to opioid ambulance runs were due to an underlying cause of drug overdose with opioids as a contributing cause. This demonstrates that the impressions of the EMS personnel were correct. The fact that so many of those who died did so at the scene highlights the continued need for community naloxone distribution. Additionally, there appear to be characteristic differences between those who died, those who died at the scene, and those who didn’t die. The results from this study highlight the benefits of connecting multiple sources of data to facilitate the identification of emergency health care drivers of opioid-related death, but there is still work to be done. Future analyses from this project will seek to link the existing data to hospitalization and post-discharge care records to capture a more complete picture of opioid-related deaths across the entire patient lifecycle. This future work will serve to fill key gaps in the surveillance process, particularly for instances opioid overdose and death where EMS was not called into service. 

Using Emergency Medical Services Data to Monitor Nonfatal Opioid Overdoses in Real Time

2021 ◽  
Vol 136 (1_suppl) ◽  
pp. 40S-46S
Author(s):  
Benjamin D. Hallowell ◽  
Laura C. Chambers ◽  
Jason Rhodes ◽  
Melissa Basta ◽  
Samara Viner-Brown ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Medical Services ◽  
Real Time ◽  
Rhode Island ◽  
Keyword Search ◽  
Case Definition ◽  
Outbreak Detection ◽  
Medical Services ◽  
Opioid Overdose ◽  
Emergency Medical

Objective No case definition exists that allows public health authorities to accurately identify opioid overdoses using emergency medical services (EMS) data. We developed and evaluated a case definition for suspected nonfatal opioid overdoses in EMS data. Methods To identify suspected opioid overdose–related EMS runs, in 2019 the Rhode Island Department of Health (RIDOH) developed a case definition using the primary impression, secondary impression, selection of naloxone in the dropdown field for medication given, indication of medication response in a dropdown field, and keyword search of the report narrative. We developed the case definition with input from EMS personnel and validated it using an iterative process of random medical record review. We used naloxone administration in consideration with other factors to avoid misclassification of opioid overdoses. Results In 2018, naloxone was administered during 2513 EMS runs in Rhode Island, of which 1501 met our case definition of a nonfatal opioid overdose. Based on a review of 400 randomly selected EMS runs in which naloxone was administered, the RIDOH case definition accurately identified 90.0% of opioid overdoses and accurately excluded 83.3% of non–opioid overdose–related EMS runs. Use of the case definition enabled analyses that identified key patterns in overdose locations, people who experienced repeat overdoses, and the creation of hotspot maps to inform outbreak detection and response. Practice Implications EMS data can be an effective tool for monitoring overdoses in real time and informing public health practice. To accurately identify opioid overdose–related EMS runs, the use of a comprehensive case definition is essential.

scholarly journals Assessment of the use of ED Chief Complaint Data for monitoring Chronic Diseases

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Megan T. Patel
Keyword(s):  
Emergency Department ◽  
Chronic Disease ◽  
Hospital Discharge ◽  
Pearson Correlation ◽  
Chief Complaint ◽  
Cook County ◽  
Free Text ◽  
Disease Category ◽  
Discharge Data ◽  
Predictive Values

ObjectiveTo create chronic disease categories for emergency department (ED) chief complaint data and evaluate the categories for validity against ED data with discharge diagnoses and hospital discharge data.IntroductionSyndromic Surveillance (SS), traditionally applied to infectious diseases, is more recently being adapted to chronic disease prevention. Its usefulness rests on the large number of diverse individuals visiting emergency rooms with the possibility of real-time monitoring of acute health effects, including effects from environmental events and its potential ability to examine more long-term health effects and trends of chronic diseases on a local level [1-3].MethodsEmergency department chief complaint (CC) data captured by the Cook County Department of Public Health local instance of ESSENCE from Jan 1, 2006 – Dec 31, 2013 was utilized to generate chronic disease categories for: CVD, AMI, ACS, angina, stroke, diabetes, hypertension, asthma, and COPD based on disease symptoms, natural language processing for free text chief complaints, and associated terms present in EMR system menus.A standard category was created for each chronic disease category based on discharge diagnoses (ICD-9 code), and their associated terms. The ICD-9 based categories were applied to the discharge diagnosis field within the ED data. The chief complaint based chronic disease category definitions were compared to the standard classification by determining the sensitivity, specificity, positive predictive value, and negative predictive value.The standard chronic disease categories created with ICD-9 codes for the chronic disease category validation were also applied to Illinois hospital discharge data for Cook County from Jan 1, 2006 – Dec 31, 2013. This data was compared to the chief complaint categories from the ED data for the same time period by visual analysis through time series and strength of correlation by Pearson correlation coefficient analysis. ESSENCE version 1.17 was utilized for the free-text query development and SAS 9.4 was utilized to perform the analyses.ResultsFor the validation analysis, 1,366,525 (24.76%) ED visits of individuals 40 years and older and 867,509 (15.72%) ED visits of individuals less than 18 years of age with a valid chief complaint and discharge diagnosis were included. Validation results are presented in Table 1. Specificity was generally high for most of the categories, with the narrow definitions having a higher specificity (Narrow AMI = 0.9996, Broad AMI = 0.9119). However, the loss in sensitivity is substantial in moving from the broader definition to the narrow definition (Broad AMI = 0.5444, Narrow AMI = 0.1040). The positive predictive values had a wide range from 0.0128 for the Broad ACS category to 0.7199 for the Narrow Asthma definition. The negative predictive values were high for all chronic disease categories ranging from 0.9501 for the Narrow CVD category to 0.9996 for Angina.The Pearson correlation coefficients are presented in Table 2. Graphs showing the comparisons of the chief complaint based ED data to the hospitalization data by chronic disease category definition are presented in Figure 1. Pearson correlations ranged from 0.9323 for Narrow Asthma to 0.1992 for Hypertension.ConclusionsBased on the high specificity and correlation coefficients in comparison to hospital discharge data, emergency department chief complaint data captured with syndromic surveillance could be utilized to examine chronic disease categories: asthma, COPD, CVD, AMI, ACS, stroke, and diabetes at a local, state or national level.References1. Bassil, K.L., et al., Temporal and spatial variation of heat-related illness using 911 medical dispatch data. Environ Res, 2009. 109(5): p. 600-6.2. Mathes, R.W., K. Ito, and T. Matte, Assessing syndromic surveillance of cardiovascular outcomes from emergency department chief complaint data in New York City. PLoS One, 2011. 6(2): p. e14677.3. Zanobetti, A. and J. Schwartz, Air pollution and emergency admissions in Boston, MA. J Epidemiol Community Health, 2006. 60(10): p. 890-5.

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

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Emilia S. Pasalic ◽  
Alana Marie Vivolo-Kantor ◽  
Pedro Martinez
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Data Sources ◽  
Opioid Overdose ◽  
Public Health Response ◽  
Discharge Data ◽  
Emergency Department Data ◽  
Health Response

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.References1. Pencheon, D. (2006). Oxford handbook of public health practice. 2nd ed. Oxford: Oxford University Press.2. Centers for Disease Control and Prevention (CDC) Evaluation Working Group on Public Health Surveillance Systems for Early Detection of Outbreaks. (May 7, 2004). Framework for Evaluating Public Health Surveillance Systems for Early Detection of Outbreaks. MMWR. Morbidity and Mortality Weekly Reports. Retrieved from: https://www.cdc.gov/mmwr/preview/mmwrhtml/rr5305a1.htm 

scholarly journals Enhanced Surveillance of Nonfatal Emergency Department Opioid Overdoses in California

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Natalie Demeter ◽  
Jaynia Angela Anderson ◽  
Mar-y-sol Pasquires ◽  
Stephen Wirtz
Keyword(s):  
Emergency Department ◽  
Drug Overdose ◽  
Health Planning ◽  
Age Groups ◽  
Emergency Department Visits ◽  
Public Health Issue ◽  
Opioid Overdose ◽  
Heroin Overdose ◽  
Drug Overdoses ◽  
Ed Visits

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. 

scholarly journals Enhancing TX SyS by integrating EMS and Poison Data for Opioid Overdose Surveillance

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jyllisa Mabion
Keyword(s):  
Public Health ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Data Sources ◽  
Opioid Overdose ◽  
Poison Center ◽  
State Health ◽  
Health Statistics ◽  
Time Data ◽  
Department Of State

ObjectiveTo improve Texas Syndromic Surveillance by integrating data from the Texas Poison Center and Emergency Medical Services for opioid overdose surveillance.IntroductionIn recent years, the number of deaths from illicit and prescription opioids has increased significantly resulting in a national and local public health crisis. According to the Texas Center for Health Statistics, there were 1340 opioid related deaths in 2015.1 In 2005, by comparison, there were 913 opioid related deaths. Syndromic surveillance can be used to monitor overdose trends in near real-time and provide much needed information to public health officials. Texas Syndromic Surveillance (TxS2) is the statewide syndromic surveillance system hosted by the Texas Department of State Health Services (DSHS). To enhance the capabilities of TxS2 and to better understand the opioid epidemic, DSHS is integrating both Texas Poison Center (TPC) data and Emergency Medical Services (EMS) data into the system.Much of the data collected at public health organizations can be several years old by the time it is released for public use. As a result, there have been major efforts to integrate more real-time data sources for a variety of surveillance needs and during emergency response activities.MethodsGuided by the Oregon Public Health Division’s successful integration of poison data into Oregon ESSENCE, DSHS has followed a similar path.2 DSHS already receives TPC data from the Commission on State Emergency Communication (CSEC), hence copying and routing that data into TxS2 requires a Memorandum of Understanding (MOU) with CSEC, which is charged with administering the implementation of the Texas Poison Control Network.EMS records are currently received by the DSHS Office of Injury Prevention (OIP) via file upload and extracted from web services as an XML file. Regional and Local Health Operations, the division where the syndromic surveillance program is located, and OIP, are both sections within DSHS. Therefore, it is not necessary to have a formal MOU in place. Both parties would operate under the rules and regulations that are established for data under the Community Health Improvement Division.CSEC and EMS will push data extracts to a DSHS SFTP folder location for polling by Rhapsody in Amazon Web Services. The message data will be extracted and transformed into the ESSENCE database format. Data are received at least once every 24 hours.ResultsTxS2 will now include TPC and EMS data, giving system users the ability to analyze and overlay real-time data for opioid overdose surveillance in one application. The integration of these data sources in TxS2 can be used for both routine surveillance and for unexpected public health events. This effort has led to discussions on how different sections within DSHS can collaborate by using syndromic surveillance data, and has generated interest in incorporating additional data streams into TxS2 in the future.ConclusionsWhile this venture is still a work in progress, it is anticipated that adding TPC and EMS data to TxS2 will be beneficial in surveilling not just opioid overdoses but other conditions and illnesses, as well as capturing disaster related injuries.References1. Texas Health Data, Center for Health Statistics [Internet]. Austin (TX): Department of State Health Services. Available from: http://healthdata.dshs.texas.gov/Opioids/Deaths2. Laing R, Powell M. Integrating Poison Center Data into Oregon ESSENCE using a Low-Cost Solution. OJPHI. 2017 May 1; 9(1).

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