The Opioid Epidemic is entering a new phase, having intensified during the Coronavirus Pandemic, with overdose deaths rising as job losses and stress from Covid-19 destabilize people struggling with addiction. https://www.wsj.com/articles/the-opioid-crisis-already-serious-has-intensified-during-coronavirus-pandemic-11599557401

Previously the overdose rate had steadied and even dipped throughout 2018 and early 2019, before resuming its rapid climb during the pandemic. The Opioid Epidemic began with the over-prescription of painkillers in the 1990s, but we are continuing to get increased overdose deaths even as different jurisdictions have had success in reducing the amount of opioid prescriptions.

Now is the time to launch a new dataset capturing data throughout 2020 and 2021. The hope is to seek to understand what the trends are, where they are located geographically and what factors (or "features") have impacted these trends.

These data come from a Vital Statistics Rapid Release (VSRR) from the National Vital Statistics System (NVSS) at the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Preventions (CDC). I will continue to update this data set as new information is released from the National Vital Statistics System. I will also continue to update either this dataset with new features, or create new datasets with new features, as Data Science Analysis reveals more about the causes of the epidemic. https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm

This is something I'm passionate about and I hope you will join me in seeking to deepen our understanding of the causes of the epidemic through the use of Data Science and Machine Learning.

Edit: I have updated the CSV file to change one of the columns from 'object' to 'float' to make it easier to work with.

US Opioid Overdose Deaths

1999-2014 Statistics and Trends

By Health [source]

About this dataset

This dataset contains information on the alarming rate of opioid overdose deaths in the United States. From 2000 to 2014, the rate of drug overdoses rose dramatically, increasing by 137%, and even more so for overdoses involving opioids - with an increase of 200%. This data was compiled by the Centers for Disease Control and Prevention's National Center for Health Statistics and includes year-by-year records of opioid death rates and population figures.

Opioids are highly addictive stimulants that act on opioid receptors to produce powerful pain relief but can have devastating physical, emotional, and social effects if misused. Commonly prescribed medications such as Oxycodone and Hydrocodone are opioids while Heroin is an illegal form of these substances. This dataset also includes information on the number of prescriptions dispensed by US retailers in that same year – a further indication of how the opioid crisis is affecting Americans both medically and directly.

The human cost has been high: We’re facing an epidemic with no easy way out involving grieving families turning to organ donation systems in hopes to help others from this tragedy; small-town cops learning first-hand how addiction ravages their communities; kids struggling at home with passed out parents who may not wake up from their high; waves of people overdosing from new drugs with unknown side effects slipping through our health care system; rising concerns about what appears once classified illnesses such as HIV becoming part of this larger puzzle.

These datasets can provide valuable insights into understanding how best to address this horrific trend, saving countless lives in its wake – help us make a difference today!

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How to use the dataset

This dataset includes information on opioid overdose deaths in the United States from 1999-2014. It includes death rates, population figures, and opioid prescriptions dispensed by US retailers. This data is valuable for understanding the prevalence of opioid overdose deaths in different parts of the US and for identifying trends over time.

The columns include: State, Year, Deaths, Population, Crude Rate and Prescriptions Dispensed by US Retailers in that year (millions). By examining this dataset you can compare a state's raw number of deaths as well as its death rate per 100,000 people to gain a better perspective on how severe an issue this is at state level. Additionally you can examine how many prescriptions are being dispensed each year to understand if there is cause for concern with regard to potential overprescribing.

Finally you can use this data to analyze changes or identify correlations between various factors such as population size, number of deaths and prescription numbers across states or years. This will enable you to gain deeper insights into the causes of opioid overdoses and form more informed opinions about what should be done next in order combat this issue effectively

Research Ideas

• Geographic Mapping: Generating visualizations 'heatmaps' to show the regional prevalence of both opioid overdose deaths and opioid prescriptions dispensed in order to compare with other regional population and health data to identify potential areas of need or at-risk groups.
• Resource Allocation & Program Development: Using the population and death rate information, city/state governments can better determine where resources need to be allocated for prevention programs, treatment programs, drug education outreach, harm reduction initiatives etc.
• Predictive Modeling/Analysis: Leveraging this dataset along with external datasets such as US census information, arrest/interdiction data, accessibility/availability variables etc., could potentially be used to create predictive models which can forecast areas in need of increased services or measures outside traditional healthcare approaches such as law enforcement interdiction efforts

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: Multiple Cause of Death, 1999-2014.csv | Column name | Description | |:---------------|:--------------------------------------------------------------------------------------...

Opioid Data Description

Land Area of County: factfinder.census.gov 2010 Census Summary 1890 counties are taken under consideration

Year: 2011- 2017

Population: https://www.census.gov/data/datasets/time-series/demo/popest/2010s-counties-total.html#par_textimage_70769902 Annual Estimates of the Resident Population for Counties: April 1, 2010 to July 1, 2018

Death by Opioid Type: https://wonder.cdc.gov/ The mortality data are based on information from all death certificates filed in the fifty states all sub-national data representing zero to nine (0-9) deaths are suppressed.

601 counties had the minimum mortality rate to be represented for analysis and were pulled from the WONDER database. These were the recommended codes to use when relating to Opioid deaths provided by the CDC.

Type of death: T40.0 (Opium) – No county reached the number of deaths above 9 per year to not be suppressed when finding specific cause T40.1 (Heroin) T40.2 (Other opioids) T40.3 (Methadone) T40.4 (Other synthetic narcotics) From the CDC Wonder Database. Type of death by county will not add up to total mortality due to the fact that low death rate of a county was withheld from data to protect privacy of individuals.

Non-US Born: factfinder.census.gov American Community Survey 5-Year Estimates The total number of Non-Us born citizens that reside in each county

Education: factfinder.census.gov American Community Survey 5-Year Estimates Categories Consist of: Less Than High School Degree Some College or Associate’s Degree Bachelor’s Degree Graduate or Professional Degree

Income by Household: factfinder.census.gov American Community Survey 5-Year Estimates Incomes given by the mean household income in that county

Transportation: Percentage of County that uses these means of transportation to get to work. American Community Survey 5-Year Estimates Categories Consist of: Commute Alone to work by driving Carpool Walk Public Transit Bike

Unemployment Rate by county collected from: https://catalog.data.gov/dataset?tags=unemployment-rate

GDP by county in regards to funds spent on healthcare, education, and social assistance as well as overall GDP collected from: https://www.bea.gov/data/gdp/gdp-county-metro-and-other-areas

Note on the data sets: 1) There will be initial issues with encoding so I used Chardet to fix this. Please use the below code in your notebooks:

import chardet # to help with encoding import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename))

with open('../input/drugrelated-deaths-in-scotland/drug-related-deaths-20-tabs-figs_1 - summary.csv', 'rb') as f: enc = chardet.detect(f.read()) opioid_data = pd.read_csv('../input/drugrelated-deaths-in-scotland/drug-related-deaths-20-tabs-figs_1 - summary.csv', encoding = enc['encoding'])

opioid_data.head(20)

2) There will need to be data cleaning due to the empty spaces in the data file. Running .head(20) will show this

The opioid epidemic is an international phenomenon. It began in the United States but has spread to other countries with similarly devastating effect. Here we have the drug-related deaths in Scotland, from the National Records of Scotland.

Here is the main data source https://www.nrscotland.gov.uk/statistics-and-data/statistics/statistics-by-theme/vital-events/deaths/drug-related-deaths-in-scotland/2020

Here is the news release on the drug-related deaths in 2020 with a 5% increase from 2019. Several key findings: - The number of drug-related deaths has increased substantially over the last 20 years – there were 4½ times as many deaths in 2020 compared with 2000. - Men were 2.7 times as likely to have a drug-related death than women, after adjusting for age. - After adjusting for age, people in the most deprived parts of the country were 18 times as likely to die from a drug-related death as those in the least deprived. - Scotland’s drug-death rate continues to be over 3½ times that for the UK as a whole, and higher than that of any European country. https://www.nrscotland.gov.uk/news/2021/drug-related-deaths-rise

These are similar patterns to what we see in the United States, with a rapid increase in the death rate over the past several decades, and hitting already struggling communities particularly hard.

Here are the key reports and analyses put out by the National Records of Scotland: - https://www.nrscotland.gov.uk/files//statistics/drug-related-deaths/20/drug-related-deaths-20-additional-analyses.pdf I'll highlight here: "one or more opiates or opioids (including heroin/morphine, methadone, codeine and dihydrocodeine) were implicated in 1, 192 drug-related deaths (89%)". So although Scotland's data set groups together all drug-related deaths, it is opioids in particular that are driving it. - and with graphs: https://www.nrscotland.gov.uk/files//statistics/drug-related-deaths/20/drug-related-deaths-20-pub.pdf

I previously published data sets on Opioids in the United States and Canada: https://www.kaggle.com/datasets/craigchilvers/opioids-vssr-provisional-drug-overdose-statistics https://www.kaggle.com/datasets/craigchilvers/opioids-in-the-us-cdc-drug-overdose-deaths https://www.kaggle.com/datasets/craigchilvers/opioids-in-the-us-cdc-nonfatal-overdoses https://www.kaggle.com/datasets/craigchilvers/opioids-in-canada

Data from surveillance reports provide information on opioid- and stimulant-related harms (deaths, hospitalizations, emergency department visits, and responses by emergency medical services) in Canada. The Public Health Agency of Canada (PHAC) works closely with the provinces and territories to collect and share accurate information about the overdose crisis in order to provide a national picture of the public health impact of opioids and other drugs in Canada and to help guide efforts to reduce substance-related harms.

This map shows the number drug poisoning deaths per 100,000 people in the U.S. The data comes from the County Health Rankings dataset.Drug overdose deaths are a leading contributor to premature death and are largely preventable. Currently, the United States is experiencing an epidemic of drug overdose deaths. Since 2000, the rate of drug overdose deaths has increased by 137% nationwide. Opioids contribute largely to drug overdose deaths; since 2000, there has been a 200% increase in deaths involving opioids (opioid pain relievers and heroin).Find strategies to address Drug Overdose DeathsThe data comes from County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. The layer used in the map comes from ArcGIS Living Atlas of the World, and the full documentation for the layer can be found here.County data are suppressed if, for both years of available data, the population reported by agencies is less than 50% of the population reported in Census or less than 80% of agencies measuring crimes reported data.

EMSIndicators:The number of individual patients administered naloxone by EMSThe number of naloxone administrations by EMSThe rate of EMS calls involving naloxone administrations per 10,000 residentsData Source:The Vermont Statewide Incident Reporting Network (SIREN) is a comprehensive electronic prehospital patient care data collection, analysis, and reporting system. EMS reporting serves several important functions, including legal documentation, quality improvement initiatives, billing, and evaluation of individual and agency performance measures.Law Enforcement Indicators:The Number of law enforcement responses to accidental opioid-related non-fatal overdosesData Source:The Drug Monitoring Initiative (DMI) was established by the Vermont Intelligence Center (VIC) in an effort to combat the opioid epidemic in Vermont. It serves as a repository of drug data for Vermont and manages overdose and seizure databases. Notes:Overdose data provided in this dashboard are derived from multiple sources and should be considered preliminary and therefore subject to change. Overdoses included are those that Vermont law enforcement responded to. Law enforcement personnel do not respond to every overdose, and therefore, the numbers in this report are not representative of all overdoses in the state. The overdoses included are limited to those that are suspected to have been caused, at least in part, by opioids. Inclusion is based on law enforcement's perception and representation in Records Management Systems (RMS). All Vermont law enforcement agencies are represented, with the exception of Norwich Police Department, Hartford Police Department, and Windsor Police Department, due to RMS access. Questions regarding this dataset can be directed to the Vermont Intelligence Center at dps.vicdrugs@vermont.gov.Overdoses Indicators:The number of accidental and undetermined opioid-related deathsThe number of accidental and undetermined opioid-related deaths with cocaine involvementThe percent of accidental and undetermined opioid-related deaths with cocaine involvementThe rate of accidental and undetermined opioid-related deathsThe rate of heroin nonfatal overdose per 10,000 ED visitsThe rate of opioid nonfatal overdose per 10,000 ED visitsThe rate of stimulant nonfatal overdose per 10,000 ED visitsData Source:Vermont requires towns to report all births, marriages, and deaths. These records, particularly birth and death records are used to study and monitor the health of a population. Deaths are reported via the Electronic Death Registration System. Vermont publishes annual Vital Statistics reports.The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) captures and analyzes recent Emergency Department visit data for trends and signals of abnormal activity that may indicate the occurrence of significant public health events.Population Health Indicators:The percent of adolescents in grades 6-8 who used marijuana in the past 30 daysThe percent of adolescents in grades 9-12 who used marijuana in the past 30 daysThe percent of adolescents in grades 9-12 who drank any alcohol in the past 30 daysThe percent of adolescents in grades 9-12 who binge drank in the past 30 daysThe percent of adolescents in grades 9-12 who misused any prescription medications in the past 30 daysThe percent of adults who consumed alcohol in the past 30 daysThe percent of adults who binge drank in the past 30 daysThe percent of adults who used marijuana in the past 30 daysData Sources:The Vermont Youth Risk Behavior Survey (YRBS) is part of a national school-based surveillance system conducted by the Centers for Disease Control and Prevention (CDC). The YRBS monitors health risk behaviors that contribute to the leading causes of death and disability among youth and young adults.The Behavioral Risk Factor Surveillance System (BRFSS) is a telephone survey conducted annually among adults 18 and older. The Vermont BRFSS is completed by the Vermont Department of Health in collaboration with the Centers for Disease Control and Prevention (CDC).Notes:Prevalence estimates and trends for the 2021 Vermont YRBS were likely impacted by significant factors unique to 2021, including the COVID-19 pandemic and the delay of the survey administration period resulting in a younger population completing the survey. Students who participated in the 2021 YRBS may have had a different educational and social experience compared to previous participants. Disruptions, including remote learning, lack of social interactions, and extracurricular activities, are likely reflected in the survey results. As a result, no trend data is included in the 2021 report and caution should be used when interpreting and comparing the 2021 results to other years.The Vermont Department of Health (VDH) seeks to promote destigmatizing and equitable language. While the VDH uses the term "cannabis" to reflect updated terminology, the data sources referenced in this data brief use the term "marijuana" to refer to cannabis. Prescription Drugs Indicators:The average daily MMEThe average day's supplyThe average day's supply for opioid analgesic prescriptionsThe number of prescriptionsThe percent of the population receiving at least one prescriptionThe percent of prescriptionsThe proportion of opioid analgesic prescriptionsThe rate of prescriptions per 100 residentsData Source:The Vermont Prescription Monitoring System (VPMS) is an electronic data system that collects information on Schedule II-IV controlled substance prescriptions dispensed by pharmacies. VPMS proactively safeguards public health and safety while supporting the appropriate use of controlled substances. The program helps healthcare providers improve patient care. VPMS data is also a health statistics tool that is used to monitor statewide trends in the dispensing of prescriptions.Treatment Indicators:The number of times a new substance use disorder is diagnosed (Medicaid recipients index events)The number of times substance use disorder treatment is started within 14 days of diagnosis (Medicaid recipients initiation events)The number of times two or more treatment services are provided within 34 days of starting treatment (Medicaid recipients engagement events)The percent of times substance use disorder treatment is started within 14 days of diagnosis (Medicaid recipients initiation rate)The percent of times two or more treatment services are provided within 34 days of starting treatment (Medicaid recipients engagement rate)The MOUD treatment rate per 10,000 peopleThe number of people who received MOUD treatmentData Source:Vermont Medicaid ClaimsThe Vermont Prescription Monitoring System (VPMS)Substance Abuse Treatment Information System (SATIS)

Over 50,000 people die annually from opioid overdoses in the United States leading to what has become known as the “opioid epidemic.” This is of heightened concern in states like Alabama that experience higher rates of overall drug use and overdose deaths. Thus, it is increasingly important for college students in Alabama to learn about how the opioid epidemic is affecting their communities. Previous studies have demonstrated that engaging non-majors in innovative active-learning oriented pedagogies like service-learning can enhance their understanding and awareness about contemporary societal issues. Despite its pedagogical potential, the impact of opioid-related service-learning, particularly for non-majors, continues to remain unexplored. In this study, we describe the implementation of a service-learning module centered on opioid addiction. Students in a non-major biology course learned the science behind opioids, had Naloxone training, and engaged in active discussions with an opioid researcher, physician, and former illicit opioid user. Our assessment of the thematic analysis of pre- and post-reflection free-write data from 87 consenting students revealed 10 categories that students reported in the post- but not pre-reflections (essay gain), pre- and post-reflections (neutral), and pre- but not post-reflections (essay loss). We found essay gains in students humanizing addiction and awareness of the cultural context of opioid addiction and essay losses from students indicating that non-major students had a low level of awareness related to these issues. Eight one-on-one, semi-structured interviews revealed that students were personally impacted by the epidemic and valued its curricular inclusion. Our data supports that service-learning can increase non-major biology student’s awareness and contextual understanding about the opioid epidemic, enabling much-needed advocacy to further enhance its awareness among the public.

Themes relating to the risk of opioid-related deaths during hospital admissions or shortly after hospital discharge.

This is a data set on the Opioid Epidemic that is designed to be easy to use. I took information from the Center for Disease Control and Prevention (CDC)'s Injury Center, which seems to me to be the most straight-forward data to analyse. I've joined this with data from the United States Census Bureau which I obtained via Wikipedia. I've selected the features that I think are most likely to affect trends in Overdose deaths. This allows us to analyse the trend in Overdose deaths over time, and how this correlates with each State's population, population density, income level, and level of economic inequality.

The aim here is to use data science to reveal what are the major factors influencing the epidemic. Data Science can show us what is influencing the epidemic in different regions of the United States, and at different times during the development of this epidemic. I hope that this data set and notebooks generated from it are useful to both data scientists and people involved in public health alike.

I created a different data set following the Opioid Epidemic at the following link: https://www.kaggle.com/craigchilvers/opioids-vssr-provisional-drug-overdose-statistics, which takes data from Vital Statistics Rapid Releases (VSRRs) by the National Vital Statistics System (NVSS). That data set has more recent data, including the recent wave of drug overdose deaths resulting (it would seem) from the covid lockdowns. That data set also has a breakdown of the overdose deaths by type of drug. So it is a very powerful and contemporary data set and I encourage people who are interested in analysing the data to also look at that data set.

I hope you will join me in this journey.

A note on running notebooks on this data: Running the data as is results in a UnicodeDecodeError. One way to resolve this is to add an encoding in the form: 'drug_overdose_data = pd.read_csv(drug_overdose_filepath, encoding = "ISO-8859-1")'. Here is an excellent notebook on the coding: https://www.kaggle.com/paultimothymooney/how-to-resolve-a-unicodedecodeerror-for-a-csv-file. Thank you to Karthik Vadlamudi for sharing that with me.

Content:

Over 93,000 people will die from drug overdoses in the United States in 2020, according to escalating death rates in recent years. Fentanyl and other synthetic opioids are a significant factor in the rise. The misuse of stimulants, benzodiazepines, and narcotic prescription drugs also contributes. A multimodal strategy is needed to address the problem, including better prescription drug monitoring schemes, more access to addiction treatment, and harm reduction tactics.

Context:

In recent years, the number of drug overdose deaths in the United States has become a significant public health concern. The misuse of prescription medications, the usage of synthetic opioids, and the lack of access to addiction treatment are a few of the causes contributing to the surge in drug overdose deaths. The problem emphasizes the requirement for successful treatments and preventative plans, as well as the necessity to deal with the social determinants of health that influence substance misuse.

Drugs prevention precautions:

Here are some drug prevention precautions that are important to keep in mind:

• Properly dispose of unused medication to prevent them from being misused.
• Keep prescription medication in a secure location and only take it as directed by a healthcare provider.
• Avoid using drugs, including prescription medication, that is not prescribed to you.
• Educate yourself and others on the risks and consequences of drug use.
• Seek help for substance abuse or addiction from a healthcare professional or addiction treatment provider.
• Practice harm reduction strategies, such as carrying naloxone for opioid overdoses.
• Address underlying mental health issues and social determinants of health that may contribute to substance abuse. # Acknowledgment: This Dataset was created from https://rb.gy/vk7kh/. if you want to learn more, you can visit the URL address. Cover Photo by https://wallpapercave.com/ # Dataset Glossary( Column-Wise) INDICATOR - name or code of the indicator PANEL - category or panel the indicator belongs to PANEL_NUM - numeric code for the panel UNIT - the unit of measurement for the indicator UNIT_NUM - numeric code for the unit of measurement STUB_NAME - name or code for the rows in the table STUB_NAME_NUM - numeric code for the row names STUB_LABEL - label or description for the row names STUB_LABEL_NUM - numeric code for the stub labels YEAR - year or time period for the data being measured YEAR_NUM - numerical representation of the year AGE - age group being measured AGE_NUM - numerical representation of the age group ESTIMATE - the estimated number of drug overdose deaths for the given year and age group FLAG - an indicator of data quality or reliability, such as a missing or suppressed estimate

All opioids are chemically related and interact with opioid receptors on nerve cells in the body and brain. Opioid pain relievers can be misused (taken in a different way or in a larger quantity than prescribed, or taken without a doctor’s prescription). Regular use - even as prescribed by a doctor - can lead to dependence and, when misused, opioid pain relievers can lead to addiction, overdose incidents, and deaths. The National Institute on Drug Abuse collects and analyzes data about deaths from opioid abuse. This data set reports on data from 1999-2019.

Data Dictionary

The COVID-19 pandemic is adding to the ongoing public health crisis related to high rates of opioid overdose and deaths, as well as acute substance use harms. These crises are made worse in communities where there is chronic overcrowding, including a shortage of housing or other shelters.

The following dataset is the World Drug Report 2021 produced by the United Nations Office on Drugs and Crime. https://www.unodc.org/unodc/en/data-and-analysis/wdr2021_annex.html

The Executive Summary: https://www.unodc.org/res/wdr2021/field/WDR21_Booklet_1.pdf

Special points of interest from the report: - Cannabis has come to be seen as less risky by adolescents from 1995 to 2019, but the herb potency has increased 4x in that time period. - Web-based sales have increased dramatically. - Number of drug users in Africa is projected to rise by 40 per cent by 2030, based on expected population growth in the 15-64 demographic. - Drug markets quickly recovered after the onset of the pandemic, but some trafficking dynamics have been accelerated during Covid-19 - Non-medical use of cannabis and sedatives has increased globally during the pandemic

On Opioids specifically: - The two pharmaceutical opioids most commonly used to treat people with opioid use disorders, methadone and buprenorphine, have become increasingly accessible over the past two decades. The amount available for medical use has increased sixfold since 1999, from 557 million daily doses in that year to 3,317 million by 2019. - The amounts of fentanyl and its analogues seized globally have risen rapidly in recent years, and by more than 60 per cent in 2019 compared with a year earlier. Overall, these amounts have risen more than twenty-fold since 2015. The largest quantities were seized in North America. - Elsewhere in the world, other pharmaceutical opioids (codeine and tramadol) predominate. Over the period 2015–2019, the largest quantities of tramadol seized were reported in West and Central Africa; they accounted for 86 per cent of the global total. Codeine was intercepted in large quantities in Asia, often in the form of diverted cough syrups. - Almost 50,000 people died from overdose deaths attributed to opioids in the United States in 2019, more than double the 2010 figure. By comparison, in the European Union, the figure for all drug-related overdoses (mostly relating to opioid use) stood at 8,300 in 2018, despite the larger population. - However, the opioid crisis in North America is evolving. The number of deaths attributed to heroin and the non-medical use of pharmaceutical opioids such as oxycodone or hydrocodone has been declining over the past five years. - The crisis is now driven mainly by overdose deaths attributed to synthetic opioids such as fentanyl and its analogues. Among the reasons for the large number of overdose deaths attributed to fentanyls is that the lethal doses of them are often small when compared with other opioids. Fentanyl is up to 100 times more potent than morphine. - The impact of fentanyl is illustrated even further by the fact that more than half of the deaths attributed to heroin also involve fentanyls. Synthetic opioids also contribute significantly to the increased number of overdose deaths attributed to cocaine and other psychostimulants, such as methamphetamine.

Characteristics of study participants; data from NPSAD 2010–2021.

The characteristics of people on opioids by jurisdiction.

BackgroundStructural racism continues to drive racial disparities in opioid-related deaths by creating inequitable access to healthcare, shaping prescription practices, limiting availability of culturally responsive care, and concentrating socioeconomic disadvantage in racial/ethnic minority communities. Emergency Medical Services (EMS) based interventions provide a critical opportunity to address these disparities at the frontlines of care, as minoritized communities often utilize EMS as their usual source of care. In King County, Washington, EMS has begun implementing several system changes aimed at reducing opioid overdose deaths, promoting harm reduction strategies, increasing access to overdose prevention resources, and improving outcomes for individuals who survive overdoses. The Overdose Response Centering Inequity and Diversity (ORCID) study will evaluate these EMS system changes to understand their impact on opioid-related outcomes differentially by race and ethnicity.MethodsThis study employs a mixed-methods, hybrid effectiveness-implementation design with three aims: (1) to understand experiences and outcomes for minoritized racial groups at the patient level using a prospective cohort study (n = 500) of non-fatal overdose survivors; (2) to evaluate EMS system changes’ implementation processes from the perspectives of Black, Hispanic/Latinx, and American Indian/Alaska Native non-fatal overdose survivors using in-depth interviews (n = 60); and (3) to examine population-level impacts of EMS system changes on racial disparities using secondary data from King County EMS. Utilizing an innovative community-based participatory approach, this study centers and incorporates individuals with lived and living experience of drug use as equal partners throughout the research process.DiscussionThrough a rigorous evaluation of EMS system changes in King County, this study will generate actionable insights for improving EMS responses to the opioid epidemic and addressing racial disparities both locally and nationally. As one of the first studies to track a longitudinal cohort of non-fatal overdose survivors, ORCID will provide critical data on both short- and long-term outcomes, informing future interventions focused on improving continuum of care for overdose survivors. By employing a community-engaged approach, the study centers the lived experiences of those most affected and enhances the relevance of the study findings. Potential limitations include the rapidly evolving landscape of EMS interventions and biases associated with non-random sampling.