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.

In 2023, around 72,776 people in the United States died from a drug overdose that involved fentanyl. This was the second-highest number of fentanyl overdose deaths ever recorded in the United States, and a significant increase from the number of deaths reported in 2019. Fentanyl overdoses are now the driving force behind the opioid epidemic, accounting for the majority of overdose deaths in the United States. What is fentanyl? Fentanyl is an extremely potent synthetic opioid similar to morphine, but more powerful. It is a prescription drug but is also manufactured illegally and is sometimes mixed with other illicit drugs such as heroin and cocaine, often without the user’s knowledge. The potency of fentanyl makes it very addictive and puts users at a high risk for overdose. Illegally manufactured fentanyl has become more prevalent in the United States in recent years, leading to a huge increase in drug overdose deaths. In 2022, the rate of drug overdose death involving fentanyl was 22.7 per 100,000 population, compared to a rate of just one per 100,000 population in the year 2013. Fentanyl overdoses by gender and race/ethnicity As of 2022, the rate of drug overdose deaths involving fentanyl in the United States is over two times higher among men than women. Rates of overdose death involving fentanyl were low for both men and women until around the year 2014 when they began to quickly increase, especially for men. In 2022, there were around 19,880 drug overdose deaths among women that involved fentanyl compared to 53,958 such deaths among men. At that time, the rate of fentanyl overdose deaths was highest among non-Hispanic American Indian or Alaska Natives and lowest among non-Hispanic Asians. However, from the years 2014 to 2018, non-Hispanic whites had the highest fentanyl overdose death rates.

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

This data visualization presents county-level provisional counts for drug overdose deaths based on a current flow of mortality data in the National Vital Statistics System. County-level 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 (see Technical Notes). The provisional data presented on the dashboard below include reported 12 month-ending provisional counts of death due to drug overdose by the decedent’s county of residence and the month in which death occurred. Percentages of deaths with a cause of death pending further investigation and a note on historical completeness (e.g. if the percent completeness was under 90% after 6 months) are included to aid in interpretation of provisional data as these measures are related to the accuracy of provisional counts (see Technical Notes). Counts between 1-9 are suppressed in accordance with NCHS confidentiality standards. Provisional data presented on this page will be updated on a quarterly 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 the 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 and jurisdiction in which the death occurred. 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 due to the time often needed to investigate these deaths (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 2020 would include deaths occurring from July 1, 2019 through June 30, 2020. The 12 month-ending period counts include all seasons of the year and are insensitive to reporting variations by seasonality. 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. Cause of Death Classification and Definition of Drug Deaths Mortality statistics are compiled in accordance with the World Health Organizations (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 regul

From 1999 to 2023, the number of drug overdose deaths among U.S. females increased from ***** in 1999 to ****** in 2023. Globally, drug use is a general problem. As of 2021, there were an estimated *** million global drug consumers and **** million drug addicts. Opioid use in the United States Among many demographics, drug overdose deaths continue to rise in the United States. Opioids are the most commonly reported substance in drug-related deaths. The number of drug-related deaths in the U.S. due to opioids has dramatically increased since the early 2000s. In 2017, then-President Donald Trump declared a national emergency over the opioid crisis in the United States. Since then, there have been joint efforts among various governmental departments to address the opioid crisis through education and outreach. Substance use treatment Substance abuse treatment is vital in reducing the number of drug overdose deaths in the United States. As of 2020, the state of California had the largest number of substance abuse treatment facilities . However, many states in the U.S. have less than 100 substance abuse treatment facilities.

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.

Data on drug overdose death rates in the United States, by age, sex, race, Hispanic origin, and drug type. Data are from Health, United States. SOURCE: National Center for Health Statistics, National Vital Statistics System, Mortality File. Search, visualize, and download these and other estimates from a wide range of health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

This data presents counts of provisional drug overdose deaths by selected drugs and U.S. Department of Health and Human Services (HHS) public health regions, based on provisional mortality data from the National Vital Statistics System. This data is limited to drug overdose deaths with an underlying cause of death assigned to International Statistical Classification of Diseases, 10th Revision (ICD-10) code numbers X40-X44 (unintentional), X60-X64 (suicide), X85 (homicide), or Y10-Y14 (undetermined intent). Specific drugs were identified using methods for searching literal text from death certificates. The provisional data are based on a current flow of mortality data and include reported 12 month-ending provisional counts of drug overdose deaths by jurisdiction of occurrence and specified drug. Provisional drug overdose death counts presented on this page 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 2022 would include deaths occurring from July 1, 2021, through June 30, 2022. Evaluation of trends over time should compare estimates from year to year (June 2021 and June 2022), rather than month to month, to avoid overlapping time periods. It is important to note that the data represent counts of deaths, and not mortality ratios or rates, which are the standard measure used to compare groups, and therefore should not be used to determine populations at disproportionate risk of drug overdose death.

Context

Opioid addiction and death rates in the U.S. and abroad have reached "epidemic" levels. The CDC's data reflects the incredible spike in overdoses caused by drugs containing opioids.

Content

The United States is experiencing an epidemic of drug overdose (poisoning) deaths. Since 2000, the rate of deaths from drug overdoses has increased 137%, including a 200% increase in the rate of overdose deaths involving opioids (opioid pain relievers and heroin). Source: CDC

Acknowledgements

Retrieved from https://data.world/health/opioid-overdose-deaths Image by Dan Meyers on Unsplash

Citation for Opioid Prescription Data: IMS Health, Vector One: National, years 1991-1996, Data Extracted 2011. IMS Health, National Prescription Audit, years 1997-2013, Data Extracted 2014. Accessed at NIDA article linked (Figure 1) on Oct 23, 2016.

Data Use Restrictions: The Public Health Service Act (42 U.S.C. 242m(d)) provides that the data collected by the National Center for Health Statistics (NCHS) may be used only for the purpose for which they were obtained; any effort to determine the identity of any reported cases, or to use the information for any purpose other than for health statistical reporting and analysis, is against the law. Therefore users will: Use these data for health statistical reporting and analysis only. For sub-national geography, do not present or publish death counts of 9 or fewer or death rates based on counts of nine or fewer (in figures, graphs, maps, tables, etc.). Make no attempt to learn the identity of any person or establishment included in these data. Make no disclosure or other use of the identity of any person or establishment discovered inadvertently and advise the NCHS Confidentiality Officer of any such discovery. Eve Powell-Griner, Confidentiality Officer National Center for Health Statistics 3311 Toledo Road, Rm 7116 Hyattsville, MD 20782 Telephone 301-458-4257 Fax 301-458-4021

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This dataset, published by the National Center for Health Statistics (NCHS) and issued on 2021-06-16, provides drug overdose death rates for the United States covering the period 1999 through 2019. It contains tabulated estimates of mortality rates broken down by drug type, sex, age group, race, and Hispanic origin, and includes submeasures, units, numeric codes for categorical fields, and flags that document special conditions or footnoted limitations. Geographic coverage is national (United States). The data are public-domain (U.S. Government) and intended for research, surveillance, and policy analysis of overdose trends and disparities across demographic groups. For official context and source tables see the NCHS Health, United States pages and the data portal (provided in the dataset metadata).

Annual number of deaths registered related to drug poisoning, by local authority, England and Wales.

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

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

Deaths as a result of drug overdoses in Portugal amounted to ** in 2019, which is the second highest number of annual deaths reported in the provided time interval. In 2011, drug deaths fell to only **, before reaching ** just four years later. In 2020, drug-induced deaths were counted at **. In 2021, there were ** deaths by overdose, the highest value recorded. Low death rate compared to Europe When compared with the rest of Europe, Portugal has a fairly low incidence of drug deaths. A rate of ** drug deaths per million population (pmp) means that Portugal only had a higher drug death rate than a few countries in the continent, and a significantly lower rate than the ** deaths pmp in Norway, which is the highest in Europe. In 2001, Portugal became the first country in the world to decriminalize the consumption of drugs. The low amount of drug deaths in Portugal is usually attributed to this policy of decriminalization. Breakdown of drugs consumed The class of drugs that caused the highest share of individuals seeking treatment in Portugal, in 2021, were cannabis, with approximately ** percent of Portuguese drug treatment entrants seeking treatment primarily due to the use of this drug class. With a slightly lower share, opioids caused **** percent of drug treatment entries in Portugal. In 2022, Portugal had approximately ****** individuals in opioid substitution treatment, which was the sixth-highest in Europe.

In 2023, Estonia had the highest incidence of drug-induced deaths in Europe at *** per million population. This was followed by Latvia at *** deaths per million population, and ** deaths per million in Norway. On the other hand, in Romania, there were only * drug-induced deaths per million population in 2023. Number of drug-induced deaths There were nearly *** thousand drug-related deaths reported in the EU in 2022. There was a steady increase in the number of deaths in the EU from only *** thousand cases in 2013. When combined with Turkey and Norway, the number of drug-induced deaths in 2022 nearly reached ***** thousand. This was the highest number of drug-related deaths recorded in the given period. Drug deaths by gender and age In 2022, 77 percent of drug-induced deaths reported in the EU were attributed to men. Half of the deaths that occurred among men were among those aged between 25 and 44 years. Similarly, the largest share of female deaths due to drug use was also reported in the same age group.

This dataset provides model-based provisional estimates of the weekly numbers of drug overdose, suicide, and transportation-related deaths using “nowcasting” methods to account for the normal lag between the occurrence and reporting of these deaths. Estimates less than 10 are suppressed. These early model-based provisional estimates were generated using a multi-stage hierarchical Bayesian modeling process to generate smoothed estimates of the weekly numbers of death, accounting for reporting lags. These estimates are based on several assumptions about how the reporting lags have changed in recent months across different jurisdictions, and the resulting estimates differ from other sources of provisional mortality data. For now, these estimates should be considered highly uncertain until further evaluations can be done to determine the validity of these assumptions about timeliness. The true patterns in reporting lags will not be known until data are finalized, typically 11–12 months after the end of the calendar year. Importantly, these estimates are not a replacement for monthly provisional drug overdose death counts, or quarterly provisional mortality estimates. For more detail about the nowcasting methods and models, see:

Rossen LM, Hedegaard H, Warner M, Ahmad FB, Sutton PD. Early provisional estimates of drug overdose, suicide, and transportation-related deaths: Nowcasting methods to account for reporting lags. Vital Statistics Rapid Release; no 11. Hyattsville, MD: National Center for Health Statistics. February 2021. DOI: https://doi.org/10.15620/ cdc:101132

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.

In 2023, drug overdoses led to ** fatalities in Bulgaria. The trend of drug fatalities has generally decreased in the country over the provided time interval from a peak of ** deaths in 2008.

This file contains death counts and death rates for drug overdose, suicide, homicide and firearm injuries at the United States national level (additional datasets exist for other levels of geography). The data is grouped by 3 different time periods including monthly, yearly, and trailing twelve months. Please see data dictionary for intents and mechanisms included in each measure.