Injury from poisoning exists under several Injury Intents: unintentional (accidental), intentional self-harm, assault, undetermined and adverse effect and underdosing. Only injuries in the first four categories are reported here combined. The data show rates per 100,000 people in order to standardize between areas with different population levels. Except for age specific rates, we use age-adjusted rates because they take into account where one age group dominates a population and thus are more representative. We use diagnosis by hospital records for non-fatal injury and cause of death from death certificates for fatal injury information.

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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For more datasets, click here.

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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 | |:---------------|:--------------------------------------------------------------------------------------...

Data on drug overdose death rates, by drug type and selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Vital Statistics System, numerator data from annual public-use Mortality Files; denominator data from U.S. Census Bureau national population estimates; and Murphy SL, Xu JQ, Kochanek KD, Arias E, Tejada-Vera B. Deaths: Final data for 2018. National Vital Statistics Reports; vol 69 no 13. Hyattsville, MD: National Center for Health Statistics.2021. Available from: https://www.cdc.gov/nchs/products/nvsr.htm. For more information on the National Vital Statistics System, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

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

A. SUMMARY This dataset includes data from the Office of the Chief Medical Examiner on the number of preliminary unintentional fatal drug overdoses per month.

B. HOW THE DATASET IS CREATED The Office of the Chief Medical Examiner releases a monthly report containing the previous month’s preliminary count of unintentional fatal drug overdoses. This dataset is manually updated based on that report.

The San Francisco Office of the Chief Medical Examiner (OCME) investigates any unknown cause of death for deaths that occur in San Francisco. OCME uses drug testing, death scene investigation, autopsy, medical record, and informant information to determine the cause of death. Preliminary determinations are generally based on drug testing and death scene investigations.

Preliminary deaths reported by the medical examiner consist of two categories: (a) cases that are still under investigation and involve suspected acute toxicity from opioids, cocaine, or methamphetamine; and (b) cases that have been finalized and were attributed to acute toxicity from any substance (including prescribed medication and over-the-counter medication).

C. UPDATE PROCESS This dataset is updated monthly following the release of the monthly accidental fatal drug overdose report from the Office of the Chief Medical Examiner. Department of Public Health staff manually copy data from the Office of the Chief Medical Examiner’s report to update this dataset.

D. HOW TO USE THIS DATASET This dataset is updated each month to include the most recent month’s preliminary accidental fatal drug overdose count. Counts from previous months are often also updated as it can take more than a month for the Office of the Chief Medical Examiner to finish reviewing cases.

E. RELATED DATASETS San Francisco Department of Public Health Substance Use Services Overdose-Related 911 Responses by Emergency Medical Services (EMS) Unintentional Drug Overdose Death Rate by Race/Ethnicity

To: State, territorial, tribal, and local policymakers and administrators of agencies and programs focused on child, youth, and family health and well-being

Dear Colleagues,

Thank you for your work to support children, youth, and families. Populations served by Administration for Children and Families (ACF)-funded programs — including victims of trafficking or violence, those who are unhoused, and young people and families involved in the child welfare system — are often at particularly high risk for substance use and overdose. A variety of efforts are underway at the federal, state, and local levels to reduce overdose deaths. These efforts focus on stopping drugs from entering communities, providing life-saving resources, and preventing drug use before it starts. Initiatives across the country are already saving lives: the overdose death rate has declined over the past year but remains too high at 32.6 per 100,000 individuals.

Fentanyl, a powerful synthetic opioid, raises the risk of overdose deaths because even a tiny amount can be deadly. Young people are particularly at risk for fentanyl exposure, driven in part by widespread availability of counterfeit pills containing fentanyl that are marketed to youth through social media. While overdose deaths among teens have recently begun to decline, there were 6,696 deaths among adolescents and young adults in 2022 (the latest year with data available)[1], making unintentional drug overdose the second leading cause of death for youth ages 15—19 and the first leading cause of death among young adults ages 20-24.[2]

Often these deaths happen with others nearby and can be prevented when opioid overdose reversal medications, like naloxone, are administered in time. CDC’s State Unintentional Drug Overdose Reporting System dashboard shows that in all 30 jurisdictions with available data, 64.7% of drug overdose deaths had at least one potential opportunity for intervention.[3] Naloxone rapidly reverses an overdose and should be given to any person who shows signs of an opioid overdose or when an overdose is suspected. It can be given as a nasal spray. Studies show that naloxone administration reduces death rates and does not cause harm if used on a person who is not overdosing on opioids. States have different policies and regulations regarding naloxone distribution and administration. Forty-nine states and the District of Columbia have Good Samaritan laws protecting bystanders who aid at the scene of an overdose.[4]

ACF grant recipients and partners can play a critical role in reducing overdose deaths by taking the following actions:

Stop Overdose Now

(U.S. Centers for Disease Control and Prevention)

Integrating Harm Reduction Strategies into Services and Supports for Young Adults Experiencing Homelessness (PDF) (ACF)

Thank you for your dedication and partnership. If you have any questions, please contact your local public health department or state behavioral health agency. Together, we can meaningfully reduce overdose deaths in every community.

/s/

Meg Sullivan

Principal Deputy Assistant Secretary

[1] Products - Data Briefs - Number 491 - March 2024

[2] WISQARS Leading Causes of Death Visualization Tool

[3] SUDORS Dashboard: Fatal Drug Overdose Data | Overdose Prevention | CDC

[4] Based on 2024 report from the Legislative Analysis and Public Policy Association

(PDF). Note that the state of Kansas adopted protections as well following the publication of this report.

Metadata-only record linking to the original dataset. Open original dataset below.

Source: Office of State Medical Examiners (OSME), Rhode Island Department of Health (RIDOH) Note: Data are limited to opioid-involved accidental drug overdose deaths. Counts may not add to annual totals due to missing case information. Percentages may not add to 100 due to rounding. Percentages are displayed as decimals. Prescription medications include prescription opioids such as oxycodone, hydrocodone, and benzodiazepines. Illicit drugs include substances such as heroin, illicit fentanyl, and cocaine.

This dashboard provides in-depth analysis surrounding events and characteristics of individuals who experienced non-fatal and/or fatal opioid overdoses in the District of Columbia. It includes data on ambulance transports for overdoses, fatalities, naloxone distribution, harm reduction efforts and the results of our used syringe testing. Data is aggregated at the neighborhood and ward levels. Data on fatal opioid overdoses will include deaths from 2021-2024. Data on non-fatal opioid overdoses will include incidents from 2021-2024. Note: Fatal opioid overdose data are delayed by approximately 90 days due to toxicological testing.

This data contains provisional counts for drug overdose deaths based on a current flow of mortality data in the National Vital Statistics System. Counts for the most recent final annual data are provided for comparison. National provisional counts include deaths occurring within the 50 states and the District of Columbia as of the date specified and may not include all deaths that occurred during a given time period. Provisional counts are often incomplete and causes of death may be pending investigation (see Technical notes) resulting in an underestimate relative to final counts. To address this, methods were developed to adjust provisional counts for reporting delays by generating a set of predicted provisional counts (see Technical notes). Starting in June 2018, this monthly data release will include both reported and predicted provisional counts.

The provisional data include: (a) the reported and predicted provisional counts of deaths due to drug overdose occurring nationally and in each jurisdiction; (b) the percentage changes in provisional drug overdose deaths for the current 12 month-ending period compared with the 12-month period ending in the same month of the previous year, by jurisdiction; and (c) the reported and predicted provisional counts of drug overdose deaths involving specific drugs or drug classes occurring nationally and in selected jurisdictions. The reported and predicted provisional counts represent the numbers of deaths due to drug overdose occurring in the 12-month periods ending in the month indicated. These counts include all seasons of the year and are insensitive to variations by seasonality. Deaths are reported by the jurisdiction in which the death occurred.

Several data quality metrics, including the percent completeness in overall death reporting, percentage of deaths with cause of death pending further investigation, and the percentage of drug overdose deaths with specific drugs or drug classes reported are included to aid in interpretation of provisional data as these measures are related to the accuracy of provisional counts (see Technical notes). Reporting of the specific drugs and drug classes involved in drug overdose deaths varies by jurisdiction, and comparisons of death rates involving specific drugs across selected jurisdictions should not be made (see Technical notes). Provisional data will be updated on a monthly basis as additional records are received.

Technical notes

Nature and sources of data

Provisional drug overdose death counts are based on death records received and processed by the National Center for Health Statistics (NCHS) as of a specified cutoff date. The cutoff date is generally the first Sunday of each month. National provisional estimates include deaths occurring within the 50 states and the District of Columbia. NCHS receives the death records from state vital registration offices through the Vital Statistics Cooperative Program (VSCP).

The timeliness of provisional mortality surveillance data in the National Vital Statistics System (NVSS) database varies by cause of death. The lag time (i.e., the time between when the death occurred and when the data are available for analysis) is longer for drug overdose deaths compared with other causes of death (1). Thus, provisional estimates of drug overdose deaths are reported 6 months after the date of death.

Provisional death counts presented in this data visualization are for “12-month ending periods,” defined as the number of deaths occurring in the 12-month period ending in the month indicated. For example, the 12-month ending period in June 2017 would include deaths occurring from July 1, 2016, through June 30, 2017. The 12-month ending period counts include all seasons of the year and are insensitive to reporting variations by seasonality. Counts for the 12-month period ending in the same month of the previous year are shown for comparison. These provisional counts of drug overdose deaths and related data quality metrics are provided for public health surveillance and monitoring of emerging trends. Provisional drug overdose death data are often incomplete, and the degree of completeness varies by jurisdiction and 12-month ending period. Consequently, the numbers of drug overdose deaths are underestimated based on provisional data relative to final data and are subject to random variation. Methods to adjust provisional counts have been developed to provide predicted provisional counts of drug overdose deaths, accounting for delayed reporting (see Percentage of records pending investigation and Adjustments for delayed reporting).

Provisional data are based on available records that meet certain data quality criteria at the time of analysis and may not include all deaths that occurred during a given time period. Therefore, they should not be considered comparable with final data and are subject to change.

Cause-of-death classification and definition of drug deaths
Mortality statistics are compiled in accordance with World Health Organization (WHO) regulations specifying that WHO member nations classify and code causes of death with the current revision of the International Statistical Classification of Diseases and Related Health Problems (ICD). ICD provides the basic guidance used in virtually all countries to code and classify causes of death. It provides not only disease, injury, and poisoning categories but also the rules used to select the single underlying cause of death for tabulation from the several diagnoses that may be reported on a single death certificate, as well as definitions, tabulation lists, the format of the death certificate, and regulations on use of the classification. Causes of death for data presented in this report were coded according to ICD guidelines described in annual issues of Part 2a of the NCHS Instruction Manual (2).

Drug overdose deaths are identified using underlying cause-of-death codes from the Tenth Revision of ICD (ICD–10): X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), and Y10–Y14 (undetermined). Drug overdose deaths involving selected drug categories are identified by specific multiple cause-of-death codes. Drug categories presented include: heroin (T40.1); natural opioid analgesics, including morphine and codeine, and semisynthetic opioids, including drugs such as oxycodone, hydrocodone, hydromorphone, and oxymorphone (T40.2); methadone, a synthetic opioid (T40.3); synthetic opioid analgesics other than methadone, including drugs such as fentanyl and tramadol (T40.4); cocaine (T40.5); and psychostimulants with abuse potential, which includes methamphetamine (T43.6). Opioid overdose deaths are identified by the presence of any of the following MCOD codes: opium (T40.0); heroin (T40.1); natural opioid analgesics (T40.2); methadone (T40.3); synthetic opioid analgesics other than methadone (T40.4); or other and unspecified narcotics (T40.6). This latter category includes drug overdose deaths where ‘opioid’ is reported without more specific information to assign a more specific ICD–10 code (T40.0–T40.4) (3,4). Among deaths with an underlying cause of drug overdose, the percentage with at least one drug or drug class specified is defined as that with at least one ICD–10 multiple cause-of-death code in the range T36–T50.8.

Drug overdose deaths may involve multiple drugs; therefore, a single death might be included in more than one category when describing the number of drug overdose deaths involving specific drugs. For example, a death that involved both heroin and fentanyl would be included in both the number of drug overdose deaths involving heroin and the number of drug overdose deaths involving synthetic opioids other than methadone.

Selection of specific states and other jurisdictions to report
Provisional counts are presented by the jurisdiction in which the death occurred (i.e., the reporting jurisdiction). Data quality and timeliness for drug overdose deaths vary by reporting jurisdiction. Provisional counts are presented for reporting jurisdictions based on measures of data quality: the percentage of records where the manner of death is listed as “pending investigation,” the overall completeness of the data, and the percentage of drug overdose death records with specific drugs or drug classes recorded. These criteria are defined below.

Percentage of records pending investigation

Drug overdose deaths often require lengthy investigations, and death certificates may be initially filed with a manner of death “pending investigation” and/or with a preliminary or unknown cause of death. When the percentage of records reported as “pending investigation” is high for a given jurisdiction, the number of drug overdose deaths is likely to be underestimated. For jurisdictions reporting fewer than 1% of records as “pending investigation”, the provisional number of drug overdose deaths occurring in the fourth quarter of 2015 was approximately 5% lower than the final count of drug overdose deaths occurring in that same time period. For jurisdictions reporting greater than 1% of records as “pending investigation” the provisional counts of drug overdose deaths may underestimate the final count of drug overdose deaths by as much as 30%. Thus, jurisdictions are included in Table 2 if 1% or fewer of their records in NVSS are reported as “pending investigation,” following a 6-month lag for the 12-month ending periods included in the dashboard. Values for records pending investigation are updated with each monthly release and reflect the most current data available.

Percent completeness

NCHS receives monthly counts of the estimated number of deaths from each jurisdictional vital registration offices (referred to as “control counts”). This number represents the best estimate of how many

Data Source: DC Office of the Chief Medical Examiner (OCME) and American Community Survey (ACS) 1-Year Estimates

Why This Matters

Opioid-related overdoses have been continuously rising since the late 1990s, with synthetic opioids (such as Tramadol or Fentanyl) being responsible for a sharp rise in opioid-related deaths since 2013.

Opioid Use Disorder (OUD) is treatable, and recovery is possible. Accessing treatment can help people regain their health and continue avoid the dangers associated with opioid misuse.

Several systemic inequities, including disparities in the treatment of mental health disorders, have led to Black individuals dying from opioid overdoses at a higher rate than white individuals.

The District Response

LIVE.LONG.DC (LLDC) is the District’s strategic plan to reduce opioid use, misuse, and related deaths. The plan provides a strategic framework that guides opioid work and investments.

The District does work to prevent, reduce the harm of, treat, and aid in the recovery of opioid use. This includes educational efforts, supplying Naloxone, no-cost rides to initial treatment appointments, and recovery support services.

The Interactive, Ward-specific map provides information about opioid use disorder and substance us disorder-related resources and services available in the District.

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.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F8a1e63df085793d18e2d1fa2109ebd44%2Fgrap%20video%201.gif?generation=1708634385396138&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F296225796c579724b56cb1d746475d93%2FToday%20(1).gif?generation=1708634392024756&alt=media" alt="">

Annual number of deaths in the United States from drug overdose per 100,000 people. Overdoses can result from intentional excessive use of a substance, but can also result from 'poisoning' where substances have been altered or mixed, such that the user is unaware of the drug's potency.

The data of this indicator is based on the following sources: US Centers for Disease Control and Prevention WONDER Data published by US Centers for Disease Control and Prevention WONDER

Retrieved from https://www.drugabuse.gov/related-topics/trends-statistics/overdose-death-rates How we process data at Our World in Data: All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline How to cite this data: In-line citation If you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Any opioids Deaths per 100,000 people attributed to any opioids.

Source US Centers for Disease Control and Prevention WONDER – processed by Our World in Data Unit deaths per 100,000

This dataset tracks the updates made on the dataset "Fatalities from Prescription Opioid Overdoses" as a repository for previous versions of the data and metadata.

View annual counts of Accidental or Undetermined overdose deaths for 2012 forward, including provisional estimates of annual counts of overdose deaths for recent years, as noted with an asterisk and the month the data was pulled. NOTE: Finalized death records for overdose deaths are often delayed by 3-6 months. Counties labeled “no value” have data suppressed because the counts are between 1 and 9. Dataset includes overdose deaths where the Manner of Death is Accidental or Undetermined. County complement counts file located here - https://data.pa.gov/Opioid-Related/Estimated-Accidental-and-Undetermined-Drug-Overdos/azzc-q64m Overdose Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Accidental and Undetermined drug overdose deaths are identified using underlying cause-of-death codes X40–X44, and Y10–Y14, and include - R99 when the Injury Description indicates an overdose death. - X49 when literal COD is Mixed or Combined or Multiple Substance Toxicity, as these are likely drug overdoses - X47 when substance indicated is difluoroethane, alone or in combination with other drugs Source Pennsylvania Prescription Drug Monitoring Program * * These data were supplied by the Bureau of Health Statistics and Registries, Harrisburg, Pennsylvania. The Bureau of Health Statistics and Registries specifically disclaims responsibility for any analyses, interpretations or conclusions. - Estimates are broken down by type of drugs involved in the overdose - Any Drug Overdose Death - all drug overdose deaths, regardless of type of drug involved, excluding alcohol only deaths - Opioid Overdose Death - any overdose death involving opioids, prescription or illegal

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

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.

Background: Fatal opioid overdose is a significant public health problem with increasing incidence in developed countries. This study aimed to describe demographic and service user characteristics of decedents of opioid overdose in Wales to identify possible targets for behaviour modification and life-saving interventions. Methods: A retrospective cross-sectional analysis was conducted of a census sample of opioid overdose-related deaths recorded between January 01, 2012, and October 11, 2018, in Wales. UK Office for National Statistics, Welsh Demographic Service, and National Health Service datasets were linked deterministically. Decedents’ circumstances of death, demographic characteristics, residency, and health service use were characterized over 3 years prior to fatal overdose using descriptive statistics. Results: In total, 638 people died of opioid overdose in Wales between January 01, 2012, and October 11, 2018, with an incidence rate of 3.04 per 100,000 people per year. Decedents were predominantly male (73%) and middle aged (median age 50 years). Fatal overdoses predominantly occurred in the community (93%) secondary to heroin (30%) or oxycodone derivative use (34%). In the 3 years prior to death, decedents changed address frequently (53%) but rarely moved far geographically. The majority of decedents had recently visited the emergency department (83%) or were admitted to the hospital (64%) prior to death. Only a minority had visited specialist drug services (32%). Conclusions: Deaths from opioid overdose typically occur in middle-aged men living peripatetic lifestyles. Victims infrequently visit specialist drug services but often attend emergency medical services. Emergency department-based interventions may therefore be important in prevention of opioid overdose fatalities in the community.

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

Key	List of...	Comment	Example Value
Year	Integer	The year for which the data is reported (1999-2019)	1999
Number.All	Integer	Total number of overdose deaths from all drugs	16849
Number.Opioid.Any	Integer	Total number of overdose deaths due to any Opioid drug	8050
Number.Opioid.Prescription	Integer	Total number of overdose deaths due to a prescription Opioid drug	3442
Number.Opioid.Synthetic	Integer	Total number of overdose deaths due to a synthetic Opioid drug (e.g. fentanyl)	730
Number.Opioid.Heroin	Integer	Total number of overdose deaths due to heroin	1960
Number.Opioid.Cocaine	Integer	Total number of overdose deaths due to cocaine	3822
Rate.All.Total	Float	The rate of overdose deaths due to all drugs per 100,000 people	6.1
Rate.All.Sex.Female	Float	The rate of overdose deaths among women due to all drugs per 100,000 people	3.9
Rate.All.Sex.Male	Float	The rate of overdose deaths among men due to all drugs per 100,000 people	8.2
Rate.All.Race.White	Float	The rate of overdose deaths among White non-Hispanic persons due to all drugs per 100,000 people	6.2
Rate.All.Race.Black	Float	The rate of overdose deaths among Black non-Hispanic persons from all drugs per 100,000 people	7.5
Rate.All.Race.Asian or Pacific Islander	Float	The rate of overdose deaths among Asian or Pacific Islander non-Hispanic persons from all drugs per 100,000 people	1.2
Rate.All.Race.Hispanic	Float	The rate of overdose deaths among Hispanic persons due to all drugs per 100,000 people	5.4
Rate.All.Race.American Indian or Alaska Native	Float	The rate of overdose deaths among American Indian or Alaska Native non-Hispanic persons due to all drugs per 100,000 people	6.0
Rate.Opioid.Any.Total	Float	The rate of overdose deaths due to any Opioid drug per 100,000 people	2.9
Rate.Opioid.Any.Sex.Female	Float	The rate of overdose deaths among women due to any Opioid drug per 100,000 people	1.4
Rate.Opioid.Any.Sex.Male	Float	The rate of overdose deaths among men due to any Opioid drug per 100,000 people	4.3
Rate.Opioid.Any.Race.White	Float	The rate of overdose deaths among White non-Hispanic persons due to any Opioid drug per 100,000 people	2.8
Rate.Opioid.Any.Race.Black	Float	The rate of overdose deaths among Asian or Pacific Islander non-Hispanic persons due to any Opioid drug per 100,000 people	3.5
Rate.Opioid.Any.Race.Asian or Pacific Islander	Float	The rate of overdose deaths among Black non-Hispanic persons due to any Opioid drug per 100,001 people	0.3
Rate.Opioid.Any.Race.Hispanic	Float	The rate of overdose deaths among Hispanic persons due to any Opi...

ObjectiveU.S. drug-related overdose deaths and Emergency Department (ED) visits rose in 2020 and again in 2021. Many academic studies and the news media attributed this rise primarily to increased drug use resulting from the societal disruptions related to the coronavirus (COVID-19) pandemic. A competing explanation is that higher overdose deaths and ED visits may have reflected a continuation of pre-pandemic trends in synthetic-opioid deaths, which began to rise in mid-2019. We assess the evidence on whether increases in overdose deaths and ED visits are likely to be related primarily to the COVID-19 pandemic, increased synthetic-opioid use, or some of both.MethodsWe use national data from the Centers for Disease Control and Prevention (CDC) on rolling 12-month drug-related deaths (2015–2021); CDC data on monthly ED visits (2019-September 2020) for EDs in 42 states; and ED visit data for 181 EDs in 24 states staffed by a national ED physician staffing group (January 2016-June 2022). We study drug overdose deaths per 100,000 persons during the pandemic period, and ED visits for drug overdoses, in both cases compared to predicted levels based on pre-pandemic trends.ResultsMortality. National overdose mortality increased from 21/100,000 in 2019 to 26/100,000 in 2020 and 30/100,000 in 2021. The rise in mortality began in mid-to-late half of 2019, and the 2020 increase is well-predicted by models that extrapolate pre-pandemic trends for rolling 12-month mortality to the pandemic period. Placebo analyses (which assume the pandemic started earlier or later than March 2020) do not provide evidence for a change in trend in or soon after March 2020. State-level analyses of actual mortality, relative to mortality predicted based on pre-pandemic trends, show no consistent pattern. The state-level results support state heterogeneity in overdose mortality trends, and do not support the pandemic being a major driver of overdose mortality.ED visits. ED overdose visits rose during our sample period, reflecting a worsening opioid epidemic, but rose at similar rates during the pre-pandemic and pandemic periods.ConclusionThe reasons for rising overdose mortality in 2020 and 2021 cannot be definitely determined. We lack a control group and thus cannot assess causation. However, the observed increases can be largely explained by a continuation of pre-pandemic trends toward rising synthetic-opioid deaths, principally fentanyl, that began in mid-to-late 2019. We do not find evidence supporting the pandemic as a major driver of rising mortality. Policymakers need to directly address the synthetic opioid epidemic, and not expect a respite as the pandemic recedes.

Age-adjusted rate of deaths from opioid overdose deaths excluding heroin among the Santa Clara County residents by total population and sex; trends if available. Source: California Department of Public Health. California Opioid Overdose Surveillance Dashboard. California Department of Public Health. https://discovery.cdph.ca.gov/CDIC/ODdash/METADATA:Notes (String): Lists table title, note and sourceYear (Numeric): Year of dataCategory (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and FemaleRate per 100,000 people (Numeric): Age adjusted rate of deaths from opioid overdoses among residents of Santa Clara County (rate per 100,000 people)