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

Analysis of ‘💉 Opioid Overdose Deaths’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/opioid-overdose-deathse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

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.

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

In-the-News:

• STAT: 26 overdoses in just hours: Inside a community on the front lines of the opioid epidemic
• NPR: Organ Donations Spike In The Wake Of The Opioid Epidemic, Deadly Opioid Overwhelms First Responders And Crime Labs in Ohio
• Scientific American: Wave of Overdoses with Little-Known Drug Raises Alarm Amid Opioid Crisis
• Washington Post: A 7-year-old told her bus driver she couldn’t wake her parents. Police found them dead at home.
• Wall Street Journal: For Small-Town Cops, Opioid Scourge Hits Close to Home
• Food & Drug Administration: FDA launches competition to spur innovative technologies to help reduce opioid overdose deaths

This data was compiled using the CDC's WONDER database. Opioid overdose deaths are defined as: deaths in which the underlying cause was drug overdose, and the ICD-10 code used was any of the following: T40.0 (Opium), T40.1 (Heroin), T40.2 (Other opioids), T40.3 (Methadone), T40.4 (Other synthetic narcotics), T40.6 (Other and unspecified narcotics).

Age-adjusted rate of drug overdose deaths and drug overdose deaths involving opioids
http://i.imgur.com/ObpzUKq.gif" alt="Opioid Death Rate" style="">
Source: CDC

What are opioids?
Opioids are substances that act on opioid receptors to produce morphine-like effects. Opioids are most often used medically to relieve pain. Opioids include opiates, an older term that refers to such drugs derived from opium, including morphine itself. Other opioids are semi-synthetic and synthetic drugs such as hydrocodone, oxycodone and fentanyl; antagonist drugs such as naloxone and endogenous peptides such as the endorphins.[4] The terms opiate and narcotic are sometimes encountered as synonyms for opioid. Source: Wikipedia

contributors-wanted See comment in Discussion

Footnotes

• The crude rate is per 100,000.
• Certain totals are hidden due to suppression constraints. More Information: http://wonder.cdc.gov/wonder/help/faq.html#Privacy.
• The population figures are briged-race estimates. The exceptions being years 2000 and 2010, in which Census counts are used.
• v1.1: Added Opioid Prescriptions Dispensed by US Retailers in that year (millions).

Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2014 on CDC WONDER Online Database, released 2015. Data are from the Multiple Cause of Death Files, 1999-2014, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Oct 19, 2016 2:06:38 PM.

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

This dataset was created by Health and contains around 800 samples along with Crude Rate, Crude Rate Lower 95% Confidence Interval, technical information and other features such as: - Year - Deaths - and more.

How to use this dataset

• Analyze Crude Rate Upper 95% Confidence Interval in relation to Prescriptions Dispensed By Us Retailers In That Year (millions)
• Study the influence of State on Crude Rate
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Health

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

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 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.

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 System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

This dataset tracks the updates made on the dataset "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.

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

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 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 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 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."

This file contains death counts and death rates for drug overdose, suicide, homicide and firearm injuries by county of residence (additional datasets exist for other levels of geography). The data is grouped by 2 different time periods including yearly and trailing twelve months. Please see data dictionary for intents and mechanisms included in each measure.

When there are 1-9 deaths in an area, CDC uses a Bayesian model to calculate rates. A Bayesian model is a type of statistical model often used in geographic analysis. This model can improve stability of the rates in lower population areas and protects privacy by taking into account information from neighboring areas.

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."

For current version see: https://data.sandiegocounty.gov/Health/2021-Behavioral-Health-Outcomes/id6m-2zrn

Basic Metadata Note: There are no deaths listed for Opioid Related Disorders prior to 2016. Starting in 2018, we were able to collect ICD-10 Mortality multiple cause of death codes, which translates to the 2016 data year. Opioid deaths may appear to be low; this is because our underlying cause of death is actually for Mental and Behavioral Disorders due to Use of Opioids. For the actual burden of deaths due to the use of opioids, use the Any Mention files.

*Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.

**Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native.

Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Code Source: ICD-9CM, ICD-10CM, ICD-10 Mortality - Combination of three definitions by CDPH and CDC. See links below. https://discovery.cdph.ca.gov/CDIC/ODdash/ https://www.cdph.ca.gov/Programs/CCDPHP/DCDIC/SACB/Pages/EpiCenter/OverviewofICD9and10codes.aspx https://www.cdc.gov/drugoverdose/pdf/pdo_guide_to_icd-9-cm_and_icd-10_codes-a.pdf

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

Overview

Opioids continue to remain a huge problem in many parts of the country. The Centers for Disease Control and Prevention recently analyzed opioid prescribing patterns in U.S. counties. Researchers found that while the amount of opioids prescribed dropped 18.2 percent nationally from 2010 to 2015, there was enormous variance in prescribing patterns by county.

The amount of opioids prescribed nationally peaked at the equivalent of 782 milligrams of morphine annually per capita in 2010 and fell to 640 in 2015. That’s an improvement, but it’s still three times higher than it was in 1999, Dr. Anne Schuchat, the CDC's principal deputy director, told the AP earlier this month.

Nearly half of all counties saw a significant decrease in prescription amounts from 2010 to 2015, but another 22.6 percent saw an increase of at least 10 percent during that time. The CDC analysis found that in 2015, the highest-prescribing counties had per-capita prescription amounts that were six times that of the lowest-prescribing counties.

The CDC analysis also found certain demographic and health characteristics were linked to -- but did not fully account for -- higher prescribing amounts. Counties with high prescribing often had these factors in common:

• Larger percentages of non-Hispanic white residents
• A higher prevalence of diabetes and arthritis
• Higher rates of unemployment
• Higher rates of Medicaid enrollment
• Were micropolitan areas (defined as counties with fewer than 50,000 people but with an urban cluster of at least 10,000 people)

The results of the CDC's look at possible contributing factors can be found here: https://www.cdc.gov/mmwr/volumes/66/wr/mm6626a4.htm?s_cid=mm6626a4_w#T2_down

The CDC produced a county-level map of the per-capita data here: https://www.cdc.gov/mmwr/volumes/66/wr/mm6626a4.htm?s_cid=mm6626a4_w#F2_down

The CDC also looked at other factors -- including the rate of prescriptions written (which dropped 13.1 percent nationally from 2012-2015); the number of high-dosage prescriptions written (which dropped 41.4 percent from 2010-2015); and the average daily milligrams of morphine equivalent per prescription, which dropped from 58.0 in 2010 to 48.1 in 2015. The one factor that rose was days' supply per prescription, which went up from 15.5 days' worth of medication in 2010 to 17.7 days in 2015.

CDC researchers point out that despite prescription amounts for legal drugs declining in many places, opiate-related deaths have continued to rise. Opioid overdoses -- from both legal and illegal drugs -- kill 91 people each day in the U.S. In 2015, roughly 15,000 people died from prescription opiate-related overdoses, according to CDC data.

The CDC researchers described their findings in detail in this report: http://bit.ly/2vH3AUW The report and data analysis will be used as a baseline to determine whether the CDC's 2016 guidelines for opioid prescribing (https://www.cdc.gov/mmwr/volumes/65/rr/rr6501e1.htm#B1_down) have been effective, a CDC spokeswoman said.

About This Data

This data was obtained by the CDC from QuintilesIMS Transactional Data Warehouse, which provides estimates of the number of opioid prescriptions dispensed in the United States based on a sample of approximately 59,000 pharmacies, representing 88 percent of prescriptions in the United States.

Prescriptions can vary widely by drug, dosage, and days' supply. Instead of merely counting the number of prescriptions written or pills dispensed, the CDC normalized the data to arrive at a single unit of measurement of opioids per capita for each county. The prescription amounts are measured in "Morphine Milligram Equivalents," or MMEs.

MMEs are a medically accepted method of measuring all the opioids a patient might be ingesting, so as to prevent overdoses and reduce the risk of addiction. In 2016, the CDC published guidelines recommending that clinicians use caution when increasing dosages past 50 MME a day, and to avoid reaching 90 MME a day except in the most extreme cases. General information about opioid dosing can be found here: https://www.cdc.gov/drugoverdose/pdf/calculating_total_daily_dose-a.pdf

The CDC placed each county into quartiles based on 2015 per-capita prescribing levels. In measuring change from 2010 to 2015, the CDC considered whether prescribing amounts had risen more than 10 percent ("Increased"), dropped more than 10 percent ("Decreased"), or stayed "Stable" (no change, or changes of less than 10 percent in either direction). These flags are included in the dataset.

The counties in the highest-prescribing quartile had an average of 1,319 MME per capita, while the counties in the lowest quartile had an average of 203 MME per capita.

Using This Data

According to the CDC's analysis, the national average daily MME per prescription in 2015 was 48.1. You can divide your county's annual per-capita MME by this number to find out the number of days' prescriptions per person in your county.

For Example: Surry County, N.C. has an annual 2015 per-capita MME of 2431.6. Divide that by 48.1 and you'll get 50.5.

This can be phrased as: "The prescription amounts in 2015 were the equivalent of a 50-day supply of opioids for every person in Surry County."

The CDC did similar calculations in a 2015 report, but instead of using the average daily MME prescription determined by this data, used as a basic guideline a 'typical' prescription of 5 mg of hydrocodone (5 MME) every 4 hours, for a total of 30 MME/day. Using this example, enough opioids were prescribed in Surry County, NC in 2015 to medicate every person in the county around the clock for 81 days.

You can also rank the counties in your state. To do this, click on "Rank Prescription Amounts in your state" under the 'Queries' tab in the upper right-hand bar on this page. Type the name of your state over the "STATE_NAME" placeholder text in the query. The resulting table will show you the counties in your state, ordered by 2015 MMEs. You can export this table. Keep in mind that the prescription data reflects where prescriptions were dispensed, not where recipients live.

Look for counties where prescription amounts have increased more than 10 percent since 2010. To do this, click on "Increasing prescription amounts" under the 'Queries' tab in the upper right-hand bar on this page. Type the name of your state over the "STATE_NAME". The resulting table will show you all the counties in your state that have seen prescription amounts increase by at least 10 percent, ordered by 2015 MMEs. You can export this table. Keep in mind that the prescription data reflects where prescriptions were dispensed, not where recipients live.

Data should be attributed to the CDC, based on raw prescription data obtained from QuintilesIMS, a pharmaceutical analytics company. Please give The Associated Press a contributing line on any story or graphic produced from this data distribution.

Caveats

• The county-level data reflects where an opioid is dispensed. Some of these prescriptions may have been obtained by people outside the county.

• Some counties did not have data robust enough for CDC to analyze. Of the 3,143 counties in the U.S., 180 counties did not have 2015 per-capita MME data that could be used. Still more counties did not have 2010 data. In all, the CDC was able to calculate a per-capita MME for both years in 2,734 counties.

• The data do not take into account illegal use of opiate drugs such as heroin.

• The data do not reflect drugs dispensed directly by a medical provider.

• Cold and cough products containing opioids and buprenorphine products indicated for conditions other than pain were excluded.

• The data does not include any details on the appropriateness of the prescriptions, or whether the opioids were dispensed for chronic, acute or end-of-life pain.

• The MME is calculated on an annual basis per capita. The CDC used American Community Survey data for population. Population estimates include all people in a county, including children.

AP Coverage of the Opioid Epidemic

The Associated Press has an ongoing series, Overcoming Opioids, running through this year, chronicling efforts to climb out of the worst drug epidemic in U.S. history. For earlier parts of this series, see: https://apnews.com/tag/OvercomingOpioids

If you have any questions about this data or its use, leave a comment in the discussion forum here or email Data Journalist Meghan Hoyer at mhoyer@ap.org

This file contains death counts and death rates for drug overdose, suicide, homicide and firearm injuries by census tract of residence (additional datasets exist for other levels of geography). The data is grouped by 2 different time periods including yearly and trailing twelve months. Please see data dictionary for intents and mechanisms included in each measure.

When there are 1-9 deaths in an area, CDC uses a Bayesian model to calculate rates. A Bayesian model is a type of statistical model often used in geographic analysis. This model can improve stability of the rates in lower population areas and protects privacy by taking into account information from neighboring areas.

Statistical output for the foundational ITS regression model of monthly opioid-related death rates, January 1999 to October 2022.

ObjectiveOur study analyzed the impact of civil commitment (CC) laws for substance use disorder (SUD) on opioid overdose death rates (OODR) in the U.S. from 2010–21.MethodsWe used a retrospective study design using the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) dataset to analyze overdose death rates from any opioid during 2010–21 using ICD-10 codes. We used t-tests and two-way ANOVA to compare the OODR between the U.S. states with the law as compared to those without by using GraphPad Prism 10.0.ResultsWe found no significant difference in the annual mean age-adjusted OODR from 2010–21 between U.S. states with and without CC SUD laws. During the pre-COVID era (2010–19), the presence or absence of CC SUD law had no difference in age-adjusted OODR. However, in the post-COVID era (2020–21), there was a significant increase in OODR in states with a CC SUD law compared to states without the law (p = 0.032). We also found that OODR increased at a faster rate post-COVID among both the states with CC SUD laws (p