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.

This data presents 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 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. 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. 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. Provisional data presented will be updated on a monthly basis as additional records are received. For more information please visit: https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm

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

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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

Annual number of deaths registered related to drug poisoning in England and Wales by sex, region and whether selected substances were mentioned anywhere on the death certificate, with or without other drugs or alcohol, and involvement in suicides.

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

Following on from my datasets on Drug Overdose deaths in the United States, https://www.kaggle.com/craigchilvers/opioids-vssr-provisional-drug-overdose-statistics and https://www.kaggle.com/craigchilvers/opioids-in-the-us-cdc-drug-overdose-deaths, here is a dataset on non-fatal overdoses. It is broken down by age and gender, and also by State. There are also breakdowns into overall drug overdoses, heroin overdoses, opioid overdoses and stimulant overdoses.

This data set is good for tracking progress or deterioration in states over time, especially through choropleth graphs.

Deaths related to drug poisoning in England and Wales by cause of death, sex, age, substances involved in the death, geography and registration delay.

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.

Source: Office of State Medical Examiners (OSME), Rhode Island Department of Health (RIDOH)Note: Rates are calculated using CDC WONDER single-race population estimates for each year (Obtained September 9, 2022) . 2021 rates are applied to 2022. The rate is the number of deaths, divided by the total population for each category, multiplied by 100,000. Hispanic or Latino includes people who identify as any race. All other racial and ethnic groups include people who identify as non-Hispanic ethnicity or have unknown ethnicity. People whose race was "Unknown" or "Asian" have been excluded. Data are limited to accidental drug overdose deaths pronounced in Rhode Island among Rhode Island residents. Some data have been suppressed due to unstable rates.

A. SUMMARY This dataset includes data on a variety of substance use services funded by the San Francisco Department of Public Health (SFDPH). This dataset only includes Drug MediCal-certified residential treatment, withdrawal management, and methadone treatment. Other private non-Drug Medi-Cal treatment providers may operate in the city. Withdrawal management discharges are inclusive of anyone who left withdrawal management after admission and may include someone who left before completing withdrawal management.

This dataset also includes naloxone distribution from the SFDPH Behavioral Health Services Naloxone Clearinghouse and the SFDPH-funded Drug Overdose Prevention and Education program. Both programs distribute naloxone to various community-based organizations who then distribute naloxone to their program participants. Programs may also receive naloxone from other sources. Data from these other sources is not included in this dataset.

Finally, this dataset includes the number of clients on medications for opioid use disorder (MOUD).

The number of people who were treated with methadone at a Drug Medi-Cal certified Opioid Treatment Program (OTP) by year is populated by the San Francisco Department of Public Health (SFDPH) Behavioral Health Services Quality Management (BHSQM) program. OTPs in San Francisco are required to submit patient billing data in an electronic medical record system called Avatar. BHSQM calculates the number of people who received methadone annually based on Avatar data. Data only from Drug MediCal certified OTPs were included in this dataset.

The number of people who receive buprenorphine by year is populated from the Controlled Substance Utilization Review and Evaluation System (CURES), administered by the California Department of Justice. All licensed prescribers in California are required to document controlled substance prescriptions in CURES. The Center on Substance Use and Health calculates the total number of people who received a buprenorphine prescription annually based on CURES data. Formulations of buprenorphine that are prescribed only for pain management are excluded.

People may receive buprenorphine and methadone in the same year, so you cannot add the Buprenorphine Clients by Year, and Methadone Clients by Year data together to get the total number of unique people receiving medications for opioid use disorder.

For more information on where to find treatment in San Francisco, visit findtreatment-sf.org. 

B. HOW THE DATASET IS CREATED This dataset is created by copying the data into this dataset from the SFDPH Behavioral Health Services Quality Management Program, the California Controlled Substance Utilization Review and Evaluation System (CURES), and the Office of Overdose Prevention.

C. UPDATE PROCESS Residential Substance Use Treatment, Withdrawal Management, Methadone, and Naloxone data are updated quarterly with a 45-day delay. Buprenorphine data are updated quarterly and when the state makes this data available, usually at a 5-month delay.

D. HOW TO USE THIS DATASET Throughout the year this dataset may include partial year data for methadone and buprenorphine treatment. As both methadone and buprenorphine are used as long-term treatments for opioid use disorder, many people on treatment at the end of one calendar year will continue into the next. For this reason, doubling (methadone), or quadrupling (buprenorphine) partial year data will not accurately project year-end totals.

E. RELATED DATASETS Overdose-Related 911 Responses by Emergency Medical Services Unintentional Overdose Death Rates by Race/Ethnicity Preliminary Unintentional Drug Overdose Deaths

F. CHANGE LOG

• 09/15/2025 - Data processing updated to capture new buprenorphine formulations.

Drug-related mortality is a complex phenomenon, which accounts for a considerable percentage of deaths among young people in many European countries. The EMCDDA, in collaboration with national experts, has defined an epidemiological indicator with two components at present: deaths directly caused by illegal drugs (drug-induced deaths) and mortality rates among problem drug users. These two components can fulfil several public health objectives, notably as an indicator of the overall health impact of drug use and the components of this impact, identify particularly risky patterns of use, and potentially identify new risks.

There are around 50 statistical tables in this dataset. Each data table may be viewed as an HTML table or downloaded in spreadsheet (Excel format).

This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances. REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

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

This data set depicts unintentional overdose deaths by county for Tennessee from 1999-2017.Data was compiled from the CDC Wonder database for each year and combined into a single spreadsheet. Each year has both a death field and a rate of fatalities per 100,000 people. The CDC does not publish the number of fatalities by county if the total is less than 10 in a given year. The CDC does not post a rate of fatalities if the total number of deaths per county is less than 20. The population field contains estimates from 2018 and is NOT the data used to generate the rates over time.The following details are copied directly from the CDC Wonder database text file. Note that the year is different for each data download from the original database."Dataset: Underlying Cause of Death, 1999-2017""Query Parameters:""Drug/Alcohol Induced Causes: Drug poisonings (overdose) Unintentional (X40-X44)""States: Tennessee (47)""Year/Month: 1999""Group By: County""Show Totals: True""Show Zero Values: False""Show Suppressed: False""Calculate Rates Per: 100,000""Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)""---""Help: See http://wonder.cdc.gov/wonder/help/ucd.html for more information.""---""Query Date: Aug 19, 2019 10:22:15 PM""1. Rows with suppressed Deaths are hidden, but the Deaths and Population values in those rows are included in the totals. Use""Quick Options above to show suppressed rows.""---"Caveats:"1. Data are Suppressed when the data meet the criteria for confidentiality constraints. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Assurance of Confidentiality.""2. Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Unreliable.""3. The population figures for year 2017 are bridged-race estimates of the July 1 resident population, from the Vintage 2017""postcensal series released by NCHS on June 27, 2018. The population figures for year 2016 are bridged-race estimates of the July""1 resident population, from the Vintage 2016 postcensal series released by NCHS on June 26, 2017. The population figures for""year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015 postcensal series released by NCHS""on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July 1 resident population, from the""Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for year 2013 are bridged-race""estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS on June 26, 2014. The""population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the Vintage 2012 postcensal""series released by NCHS on June 13, 2013. The population figures for year 2011 are bridged-race estimates of the July 1 resident""population, from the Vintage 2011 postcensal series released by NCHS on July 18, 2012. Population figures for 2010 are April 1""Census counts. The population figures for years 2001 - 2009 are bridged-race estimates of the July 1 resident population, from""the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012. Population figures for 2000 are""April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of July 1 estimates. Population""figures for the infant age groups are the number of live births.
Note: Rates and population figures for years 2001 -""2009 differ slightly from previously published reports, due to use of the population estimates which were available at the time""of release.""4. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the""resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/ucd.html#Age Group."

Total number of accidental overdose deaths in Pierce County

BackgroundPatients with opioid dependency prescribed opioid agonist treatment (OAT) may also be prescribed sedative drugs. This may increase mortality risk but may also increase treatment duration, with overall benefit. We hypothesised that prescription of benzodiazepines in patients receiving OAT would increase risk of mortality overall, irrespective of any increased treatment duration.Methods and findingsData on 12,118 patients aged 15–64 years prescribed OAT between 1998 and 2014 were extracted from the Clinical Practice Research Datalink. Data from the Office for National Statistics on whether patients had died and, if so, their cause of death were available for 7,016 of these patients. We identified episodes of prescription of benzodiazepines, z-drugs, and gabapentinoids and used linear regression and Cox proportional hazards models to assess the associations of co-prescription (prescribed during OAT and up to 12 months post-treatment) and concurrent prescription (prescribed during OAT) with treatment duration and mortality. We examined all-cause mortality (ACM), drug-related poisoning (DRP) mortality, and mortality not attributable to DRP (non-DRP). Models included potential confounding factors. In 36,126 person-years of follow-up there were 657 deaths and 29,540 OAT episodes, of which 42% involved benzodiazepine co-prescription and 29% concurrent prescription (for z-drugs these respective proportions were 20% and 11%, and for gabapentinoids 8% and 5%). Concurrent prescription of benzodiazepines was associated with increased duration of methadone treatment (adjusted mean duration of treatment episode 466 days [95% CI 450 to 483] compared to 286 days [95% CI 275 to 297]). Benzodiazepine co-prescription was associated with increased risk of DRP (adjusted HR 2.96 [95% CI 1.97 to 4.43], p

This dataset includes a subset of data collected through the Johns Hopkins University social network-based intervention study CHAMPS CONNECT conducted in Baltimore, Maryland. A total of 111 people who inject drugs (PWID) were recruited from an infectious disease clinic and community-based sites in Baltimore between 1/25/2018 and 1/4/2019. Index members were 18 years of age or older, English speaking, hepatitis C virus (HCV) antibody positive, and reported injecting drugs with another during the past year. Indexes were asked to recruit their injection drug network members for HCV testing and linkage to care. The primary objective of the secondary study was to analyze data from indexes and network participant members to assess psychological factors that may be significantly associated with self-reported number of lifetime drug overdoses. Variables in the dataset include demographics, employment, substance use history and treatment, mental health diagnoses and treatment, overdose, injection drug use, and questions from the Center of Epidemiologic Studies Depression Scale.

Data was compiled from the CDC Wonder database for each year and combined into a single spreadsheet. Each year has both a death field and a rate of fatalities per 100,000 people. The CDC does not publish the number of fatalities by county if the total is less than 10 in a given year. The CDC does not post a rate of fatalities if the total number of deaths per county is less than 20. The population field contains estimates from 2018 and is NOT the data used to generate the rates over time.The following details are copied directly from the CDC Wonder database text file. Note that the year is different for each data download from the original database."Dataset: Underlying Cause of Death, 1999-2017""Query Parameters:""Drug/Alcohol Induced Causes: Drug poisonings (overdose) Unintentional (X40-X44)""States: Tennessee (47)""Year/Month: 1999""Group By: County""Show Totals: True""Show Zero Values: False""Show Suppressed: False""Calculate Rates Per: 100,000""Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)""---""Help: See http://wonder.cdc.gov/wonder/help/ucd.html for more information.""---""Query Date: Aug 19, 2019 10:22:15 PM""1. Rows with suppressed Deaths are hidden, but the Deaths and Population values in those rows are included in the totals. Use""Quick Options above to show suppressed rows.""---"Caveats:"1. Data are Suppressed when the data meet the criteria for confidentiality constraints. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Assurance of Confidentiality.""2. Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Unreliable.""3. The population figures for year 2017 are bridged-race estimates of the July 1 resident population, from the Vintage 2017""postcensal series released by NCHS on June 27, 2018. The population figures for year 2016 are bridged-race estimates of the July""1 resident population, from the Vintage 2016 postcensal series released by NCHS on June 26, 2017. The population figures for""year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015 postcensal series released by NCHS""on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July 1 resident population, from the""Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for year 2013 are bridged-race""estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS on June 26, 2014. The""population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the Vintage 2012 postcensal""series released by NCHS on June 13, 2013. The population figures for year 2011 are bridged-race estimates of the July 1 resident""population, from the Vintage 2011 postcensal series released by NCHS on July 18, 2012. Population figures for 2010 are April 1""Census counts. The population figures for years 2001 - 2009 are bridged-race estimates of the July 1 resident population, from""the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012. Population figures for 2000 are""April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of July 1 estimates. Population""figures for the infant age groups are the number of live births.
Note: Rates and population figures for years 2001 -""2009 differ slightly from previously published reports, due to use of the population estimates which were available at the time""of release.""4. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the""resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/ucd.html#Age Group."

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.

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