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.

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.

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

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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

This dataset contains model-based county estimates for drug-poisoning mortality.

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 2011–2016 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.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8).

REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm.

1. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

2. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013.

3. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014.

4. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf.

5. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e.

6. National Center for Health Statistics. County geog

This dataset presents drug overdose death rates in the United States, categorized by drug type, sex, age group, race, and Hispanic origin. It provides comprehensive statistics on mortality rates attributed to various drugs, offering insights into the impact across different demographic segments. The data enables detailed analysis of trends and disparities in drug-related fatalities, crucial for public health research, policy development, and intervention strategies aimed at reducing overdose deaths.

Format: CSV

A brief description of each column: INDICATOR: The specific indicator or metric being measured (e.g., drug overdose death rates). PANEL: Indicates the panel or group within which the data is categorized or reported. PANEL_NUM: Numeric identifier for the panel or group. UNIT: Unit of measurement for the data (e.g., rates per 100,000 population). UNIT_NUM: Numeric identifier for the unit of measurement. STUB_NAME: Name or identifier for the stub variable, typically related to demographic categories (e.g., drug type, sex, age, race, Hispanic origin). STUB_NAME_NUM: Numeric identifier for the stub variable. STUB_LABEL: Label or description corresponding to the stub variable. STUB_LABEL_NUM: Numeric identifier for the stub label. YEAR: Year of the data observation or reporting. YEAR_NUM: Numeric identifier for the year. AGE: Age group of the population (e.g., 0-17, 18-34, 35-54, 55+). AGE_NUM: Numeric identifier for the age group. ESTIMATE: The numerical estimate or value corresponding to the indicator being measured (e.g., death rate per 100,000 population).

This dataset appears to be structured to facilitate detailed analysis of drug overdose death rates across various demographic dimensions over multiple years, providing essential information for public health research and policy formulation.

Overview

This dataset contains the number of yearly deaths due to Unintentional Drug Overdoses in the United States at a County Level between 2003-2015. To overcome the limitation of the original dataset, it is merged with population dataset to identify missing combinations and imputation is performed on the dataset taking into account the logical rules of the source dataset. Users can decide the proportion of the imputed values in the dataset by using the provided population and… See the full description on the dataset page: https://huggingface.co/datasets/revanth7667/usa_opioid_overdose.

DQS Drug overdose death rates, by drug type, sex, age, race, and Hispanic origin: United States from CDC WONDER

  Description

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 over 120 health topics with the NCHS Data Query… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/dqs-drug-overdose-death-rates-by-drug-type-sex-age.

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

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

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

This dataset contains age-adjusted statistical data on deaths caused by opioids overdose by state and the increase of deaths caused by opioids overdose.

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

This dataset includes the number and rate of drug overdose death among Virginians by year, drug class, and geography.

https://data.virginia.gov/dataset/vdh-pud-overdose-deaths-by-year

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 from 1999 to 2015.

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 2011–2015 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.

Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less.

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. Estimates should be interpreted with caution.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).

National provisional drug overdose deaths by month and 2013 NCHS Urban–Rural Classification Scheme for Counties. 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). Deaths are based on the county of residence in the United States. Death counts provided are for “12-month ending periods,” defined as the number of deaths occurring in the 12-month period ending in the month indicated. Estimates for 2020 are based on provisional data. Estimates for 2018 and 2019 are based on final data. For more information on NCHS urban-rural classification, see: https://www.cdc.gov/nchs/data/series/sr_02/sr02_166.pdf

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).

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

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This dataset tracks the updates made on the dataset "DEV DQS Drug overdose death rates, by drug type, sex, age, race, and Hispanic origin: United States from CDC WONDER" as a repository for previous versions of the data and metadata.

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.