scholarly journals Naloxone Administration by Law Enforcement: Policy and Public Health Nursings Implications

2016 ◽  
Author(s):  
Thomas Stegnicki

<p>Opioid overdose has become a public health epidemic, and the use of naloxone by law enforcement personnel has recently become a controversial policy issue. This pilot research project addresses the question of attitudes regarding addiction, overdose, naloxone administration training, and the expanding role of law enforcement in naloxone administration by law enforcement personnel who have been trained in the administration of naloxone to those experiencing an opioid overdose. A comprehensive literature review was conducted relating to the topic of opioid use and overdose and the use of naloxone by law enforcement. The Theory of Planned Behavior was the theoretical framework chosen to guide this project. The methodology used was an exploratory qualitative approach with individual face-to-face interviews as the data collection method. The results are presented and analyzed including findings of a need for “hands-on” naloxone training, perception of empowerment by some officers since being trained to administer naloxone, and perception of empathy for those who overdose, especially toward the younger victims. Recommendations and implications for nursing practice, policy, research, and leadership are presented including a plan for dissemination to nursing, interprofessional stakeholders, and policy makers. </p>

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Howard Burkom ◽  
Joseph Downs ◽  
Raghav Ramachandran ◽  
Wayne Loschen ◽  
Laurel Boyd ◽  
...  

ObjectiveIn a partnership between the Public Health Division of the Oregon Health Authority (OHA) and the Johns Hopkins Applied Physics Laboratory (APL), our objective was develop an analytic fusion tool using streaming data and report-based evidence to improve the targeting and timing of evidence-based interventions in the ongoing opioid overdose epidemic. The tool is intended to enable practical situational awareness in the ESSENCE biosurveillance system to target response programs at the county and state levels. Threats to be monitored include emerging events and gradual trends of overdoses in three categories: all prescription and illicit opioids, heroin, and especially high-mortality synthetic drugs such as fentanyl and its analogues. Traditional sources included emergency department (ED) visits and emergency management services (EMS) call records. Novel sources included poison center calls, death records, and report-based information such as bad batch warnings on social media. Using available data and requirements analyses thus far, we applied and compared Bayesian networks, decision trees, and other machine learning approaches to derive robust tools to reveal emerging overdose threats and identify at-risk subpopulations.IntroductionUnlike other health threats of recent concern for which widespread mortality was hypothetical, the high fatality burden of opioid overdose crisis is present, steadily growing, and affecting young and old, rural and urban, military and civilian subpopulations. While the background of many public health monitors is mainly infectious disease surveillance, these epidemiologists seek to collaborate with behavioral health and injury prevention programs and with law enforcement and emergency medical services to combat the opioid crisis. Recent efforts have produced key terms and phrases in available data sources and numerous user-friendly dashboards allowing inspection of hundreds of plots. The current effort seeks to distill and present combined fusion alerts of greatest concern from numerous stratified data outputs. Near-term plans are to implement best-performing fusion methods as an ESSENCE module for the benefit of OHA staff and other user groups.MethodsBy analyzing historical OHA data, we formed features to monitor in each data source to adapt diagnosis codes and text strings suggested by CDC’s injury prevention division, published EMS criteria [Reference 1], and generic product codes from CDC toxicologists, with guidance from OHA Emergency Services Director David Lehrfeld and from Oregon Poison Center Director Sandy Giffen. These features included general and specific opioid abuse indicators such as daily counts of records labelled with the “poisoning” subcategory and containing “fentanyl” or other keywords in the free-text. Matrices of corresponding time series were formed for each of 36 counties and the entire state as inputs to region-specific fusion algorithms.To obtain truth data for detection, the OHA staff provided guidance and design help to generate plausible overdose threat scenarios that were quantified as realistic data distributions of monitored features accounting for time delays and historical distributions of counts in each data source. We sampled these distributions to create 1000 target sets for detection based on the event duration and affected counties for each event scenario.We used these target datasets to compare the detection performance of fusion detection algorithms. Tested algorithms included Bayesian Networks formed with the R package gRain, and also random forest, logistic regression, and support vector machine models implemented with the Python scikit-learn package using default settings. The first 800 days of the data were used for model training, and the last 400 days for testing. Model results were evaluated with the metrics:Sensitivity = (number of target event days signaled) / (all event days) andPositive predictive value (PPV) = (number of target event days signaled) / (all days signaled).These metrics were combined with specificity regarded as the expected fusion alert rate calculated from the historical dataset with no simulated cases injected.ResultsThe left half of Figure 1 illustrates a threat scenario along Oregon’s I5 corridor in which string of fentanyl overdoses with a few fatalities affects the monitored data streams in three counties over a seven-day period. The right half of the figure charts the performance metrics for random forest and Bayesian network machine learning methods applied to both training and test datasets assuming total case counts of 50, 20, and 10 overdoses. Sensitivity values were encouraging, especially for the Bayesian networks and even for the 10-case scenario. Computed PPV levels suggested a manageable public health investigation burden.ConclusionsThe detection results were promising for a threat scenario of particular concern to OHA based on a data scenario deemed plausible and realistic based on historical data. Trust and acceptance from public health surveillance of outputs from supervised machine learning methods beyond traditional statistical methods will require user experience and similar evaluation with additional threat scenarios and authentic event data.Credible truth data can be generated for testing and evaluation of analytic fusion methods with the advantages of several years of historical data from multiple sources and the expertise of experienced monitors. The collaborative generation process may be standardized and extended to other threat types and data environments.Next steps include the addition to the analytic fusion capability of report-based data that can influence data interpretation, including mainstream and social media reports, events in neighboring regions, and law enforcement data.References1. Rhode Island Enhanced State Opioid Overdose Surveillance (ESOOS) Case Definition for Emergency Medical Services (EMS), http://www.health.ri.gov/publications/guidelines/ESOOSCaseDefinitionForEMS.pdf, last accessed: Sept. 9, 2018.


2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Joseph R. Tatar ◽  
Jennifer Broad

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. 


2020 ◽  
Vol 14 ◽  
pp. 117822182095339
Author(s):  
Andrea J Yatsco ◽  
Rachel D Garza ◽  
Tiffany Champagne-Langabeer ◽  
James R Langabeer

Opioid overdoses continue to be a leading cause of death in the US. This public health crisis warrants innovative responses to help prevent fatal overdose. There is continued advocacy for collaborations between public health partners to create joint responses. The high correlation between persons with opioid use disorder who have a history of involvement in the criminal justice system is widely recognized, and allows for treatment intervention opportunities. Law enforcement-led treatment initiatives are still relatively new, with a few sparse early programs emerging almost a decade ago and only gaining popularity in the past few years. A lack of published methodologies creates a gap in the knowledge of applied programs that are effective and can be duplicated. This article seeks to outline an interagency relationship between police and healthcare that illustrates arrest is not the only option that law enforcement may utilize when encountering persons who use illicit substances. Program methods of a joint initiative between law enforcement and healthcare in a large, metropolitan area will be reviewed, supplemented with law enforcement overdose data and statistics on law enforcement treatment referrals.


2020 ◽  
Author(s):  
Anuwat Pengput ◽  
Peter Elkin

BACKGROUND Opioid analgesics are pain relievers. There are no better drugs than opioids for treating severe pain, however, opioids are the main drugs associated with overdose deaths. OBJECTIVE The study aimed to identify the distribution and clusters of opioid overdoses across New York. METHODS We used the deidentified hospital inpatient discharges datasets (SPARCS) from 2010 – 2015. ICD 9 and ICD 10 codes were used to identify and retrieve opioid overdose patients. We merged and aggregated SPARCS datasets to a geographic shapefile by all counties in New York. RESULTS More than half of the opioid overdose population (n = 235,178) were male (70%). Most patients were 30 - 49 years old (48.3%). Among patients, white non-Hispanics had the highest opioid overdose. Nearly all counties showed increasing rates of overdoses over six years. The high overdose clusters were identified in Niagara, Orleans, Genesee, Madison, Chenango, Delaware, and Sullivan counties (P < 0.05). The highest overdose rates were identified in the Central and Eastern New York regions. CONCLUSIONS The areas of highest overdose deaths among opioid use disorders were not necessarily the areas with the highest usage rates. This tells us that public health services may be lacking in these communities and this represents an opportunity for the New York Department of Public Health to improve our education and public health response in these communities. Opioid use and overdose rates do not always correlate well. This shows that special attention to counties with high overdose/user rates is warranted. The findings could inform health policy decisions at the county and state levels based on the geographic and demographic patterns to prevent and control opioid crises.


2021 ◽  
pp. 009145092110521
Author(s):  
Brandon del Pozo

From 2017 to early 2020, the US city of Burlington, Vermont led a county-wide effort to reduce opioid overdose deaths by concentrating on the widespread, low-barrier distribution of medications for opioid use disorder. As a small city without a public health staff, the initiative was led out of the police department—with an understanding that it would not be enforcement-oriented—and centered on a local adaptation of CompStat, a management and accountability program developed by the New York City Police Department that has been cited as both yielding improvements in public safety and overemphasizing counterproductive police performance metrics if not carefully directed. The initiative was instrumental to the implementation of several novel interventions: low-threshold buprenorphine prescribing at the city’s syringe service program, induction into buprenorphine-based treatment at the local hospital emergency department, elimination of the regional waiting list for medications for opioid use disorder (MOUD), and the de-facto decriminalization of diverted buprenorphine by the chief of police and county prosecutor. An effort by local legislators resulted in a state law requiring all inmates with opioid use disorder be provided with MOUD as well. By the end of 2018, these interventions were collectively associated with a 50% (17 vs. 34) reduction in the county’s fatal overdose deaths, while deaths increased 20% in the remainder of Vermont. The reduction was sustained through the end of 2019. This article describes the effort undertaken by officials in Burlington to implement these interventions. It provides an example that other municipalities can use to take an evidence-based approach to reducing opioid deaths, provided stakeholders assent to sustained collaboration in the furtherance of a commitment to save lives. In doing so, it highlights that police-led public health interventions are the exception, and addressing the overdose crisis will require reform that shifts away from criminalization as a community’s default framework for substance use.


CJEM ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 768-771
Author(s):  
Justin J. Koh ◽  
Michelle Klaiman ◽  
Isabelle Miles ◽  
Jolene Cook ◽  
Thara Kumar ◽  
...  

Deaths due to opioid overdose have reached unprecedented levels in Canada; over 12,800 opioid-related deaths occurred between January 2016 and March 2019, and overdose death rates increased by approximately 50% from 2016 to 2018.1 In 2016, Health Canada declared the opioid epidemic a national public health crisis,2 and life expectancy increases have halted in Canada for the first time in decades.3 Children are not exempt from this crisis, and the Chief Public Health Officer of Canada has recently prioritized the prevention of problematic substance use among Canadian youth.4


Author(s):  
Grant Baldwin ◽  
Jan L. Losby ◽  
Wesley M. Sargent ◽  
Jamie Mells ◽  
Sarah Bacon

Prescription drug monitoring programs (PDMPs) are secure, online, state-based databases that contain information about controlled substance prescriptions written by clinicians and dispensed by pharmacists within a jurisdiction. In this chapter, current and future trends impacting PDMPs are reviewed and the implication of these trends for the future development of even more effective PDMPs is discussed. Uses of PDMPs by public health partners are also reviewed. For example, law enforcement officials may use data collected by PDMPs when investigating unusual prescribing patterns. Law enforcement officials may also use PMDP data in drug courts and other criminal diversion programs. Medical licensing boards use PMDP data to assess aberrant prescribing practices. Health systems, insurers, and public health officials use aggregated PDMP data as part of their efforts to evaluate a quality improvement initiative, an opioid stewardship program to improve opioid prescribing system-wide, or broad changes to prescribing patterns across a city, county, or state.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yuchen Li ◽  
Ayaz Hyder ◽  
Lauren T. Southerland ◽  
Gretchen Hammond ◽  
Adam Porr ◽  
...  

Abstract Opioid use disorder and overdose deaths is a public health crisis in the United States, and there is increasing recognition that its etiology is rooted in part by social determinants such as poverty, isolation and social upheaval. Limiting research and policy interventions is the low temporal and spatial resolution of publicly available administrative data such as census data. We explore the use of municipal service requests (also known as “311” requests) as high resolution spatial and temporal indicators of neighborhood social distress and opioid misuse. We analyze the spatial associations between georeferenced opioid overdose event (OOE) data from emergency medical service responders and 311 service request data from the City of Columbus, OH, USA for the time period 2008–2017. We find 10 out of 21 types of 311 requests spatially associate with OOEs and also characterize neighborhoods with lower socio-economic status in the city, both consistently over time. We also demonstrate that the 311 indicators are capable of predicting OOE hotspots at the neighborhood-level: our results show code violation, public health, and street lighting were the top three accurate predictors with predictive accuracy as 0.92, 0.89 and 0.83, respectively. Since 311 requests are publicly available with high spatial and temporal resolution, they can be effective as opioid overdose surveillance indicators for basic research and applied policy.


2020 ◽  
Vol 132 (6) ◽  
pp. 1558-1568
Author(s):  
Antje M. Barreveld ◽  
Robert J. McCarthy ◽  
Nabil Elkassabany ◽  
Edward R. Mariano ◽  
Brian Sites ◽  
...  

Abstract Background A 6-month opioid use educational program consisting of webinars on pain assessment, postoperative and multimodal pain opioid management, safer opioid use, and preventing addiction coupled with on-site coaching and monthly assessments reports was implemented in 31 hospitals. The authors hypothesized the intervention would measurably reduce and/or prevent opioid-related harm among adult hospitalized patients compared to 33 nonintervention hospitals. Methods Outcomes were extracted from medical records for 12 months before and after the intervention start date. Opioid adverse events, evaluated by opioid overdose, wrong substance given or taken in error, naloxone administration, and acute postoperative respiratory failure causing prolonged ventilation were the primary outcomes. Opioid use in adult patients undergoing elective hip or knee arthroplasty or colorectal procedures was also assessed. Differences-in-differences were compared between intervention and nonintervention hospitals. Results Before the intervention, the incidence ± SD of opioid overdose, wrong substance given, or substance taken in error was 1 ± 0.5 per 10,000 discharges, and naloxone use was 117 ± 13 per 10,000 patients receiving opioids. The incidence of respiratory failure was 42 ± 10 per 10,000 surgical discharges. A difference-in-differences of –0.2 (99% CI, –1.1 to 0.6, P = 0.499) per 10,000 in opioid overdose, wrong substance given, or substance taken in error and –13.6 (99% CI, –29.0 to 0.0, P = 0.028) per 10,000 in respiratory failure was observed postintervention in the intervention hospitals; however, naloxone administration increased by 15.2 (99% CI, 3.8 to 30.0, P = 0.011) per 10,000. Average total daily opioid use, as well as the fraction of patients receiving daily opioid greater than 90 mg morphine equivalents was not different between the intervention and nonintervention hospitals. Conclusions A 6-month opioid educational intervention did not reduce opioid adverse events or alter opioid use in hospitalized patients. The authors’ findings suggest that despite opioid and multimodal analgesia awareness, limited-duration educational interventions do not substantially change the hospital use of opioid analgesics. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New


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