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

A. SUMMARY This dataset includes unintentional drug overdose death rates by race/ethnicity by year. This dataset is created using data from the California Electronic Death Registration System (CA-EDRS) via the Vital Records Business Intelligence System (VRBIS). Substance-related deaths are identified by reviewing the cause of death. Deaths caused by opioids, methamphetamine, and cocaine are included. Homicides and suicides are excluded. Ethnic and racial groups with fewer than 10 events are not tallied separately for privacy reasons but are included in the “all races” total. Unintentional drug overdose death rates are calculated by dividing the total number of overdose deaths by race/ethnicity by the total population size for that demographic group and year and then multiplying by 100,000. The total population size is based on estimates from the US Census Bureau County Population Characteristics for San Francisco, 2022 Vintage by age, sex, race, and Hispanic origin. These data differ from the data shared in the Preliminary Unintentional Drug Overdose Death by Year dataset since this dataset uses finalized counts of overdose deaths associated with cocaine, methamphetamine, and opioids only. B. HOW THE DATASET IS CREATED This dataset is created by copying data from the Annual Substance Use Trends in San Francisco report from the San Francisco Department of Public Health Center on Substance Use and Health. C. UPDATE PROCESS This dataset will be updated annually, typically at the end of the year. D. HOW TO USE THIS DATASET N/A E. RELATED DATASETS Overdose-Related 911 Responses by Emergency Medical Services Preliminary Unintentional Drug Overdose Deaths San Francisco Department of Public Health Substance Use Services F. CHANGE LOG 12/16/2024 - Updated with 2023 data. Asian/Pacific Islander race/ethnicity group was changed to Asian. 12/16/2024 - Past year totals by race/ethnicity were revised after obtaining accurate race/ethnicity for some decedents that were previously marked as “unknown” race/ethnicity.

This dataset describes drug poisoning deaths at the county 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).

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 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).

This dataset includes the count and rate per 100,000 Virginia residents for all-drug overdose deaths among Virginia residents by year and by city/county of the decedent. City/county localities are assigned using the patient's residence at time of death. Data set includes all-drug overdose death counts and rates for years 2018 through the most recent data year available. When data set is downloaded, the years will be sorted in ascending order, meaning that the earliest year will be at the top. To see data for the most recent year, please scroll down to the bottom of the data set.

These data show total annual Maryland drug intoxication deaths from 2007 to 2016, broken down by substance. Since an intoxication death may involve more than one substance, counts of deaths related to specific substances do not sum to the total number of deaths. Benzodiazepine deaths include deaths caused by benzodiazepines and related drugs with similar sedative effects.

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

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

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

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

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

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

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

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

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

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

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

The Mortality - Multiple Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the years 1999-2009. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, up to twenty additional multiple causes (Boolean set analysis), and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., region, state, and county), age group (including infants and single-year-of-age cohorts), race (4 groups), Hispanic ethnicity, sex, year of death, and cause-of-death (4-digit ICD-10 code or group of codes, injury intent and mechanism categories, or drug and alcohol related causes), year, month and week day of death, place of death and whether an autopsy was performed. The data are produced by the National Center for Health Statistics.

Title: Drug Overdose Deaths, Ages 15 to 34, Small Areas by Year, 1999 to 2011 - OD1534SAYR

Summary: Number of deaths and rates of deaths per 100,000 for persons age 15 to 34 due to Drug Overdose over the 13 years period; with person year and mean annual populations, for each year, for the total populations in each of 109 NM Small Area geographies. Includes trends in the death rates comparing 1999-2003 to 2007-2011 based on 68.2% confidence intervals (+/- 1 standard deviation).

Prepared by: T Scharmen, thomas.scharmen@state,nm.us

Includes ICD-10: X40-X44.9, X60-X64.9, X85-X85.9, Y10-Y14.9

Intentional and UN-intentional drug overdose deaths

ICD-10 list: http://apps.who.int/classifications/icd10/browse/2010/en#/X40

Data Sources: New Mexico Death Certificate Database, Office of Vital Records and Statistics, New Mexico Department of Health; Population Estimates: University of New Mexico, Geospatial and Population Studies (GPS) Program, http://bber.unm.edu/bber_research_demPop.html. Retrieved Wed, 22 August 2014 from New Mexico Department of Health, Indicator-Based Information System for Public Health Web site: http://ibis.health.state.nm.us

See Also NM Substance Abuse Epidemiology Report

https://ibis.health.state.nm.us/phom/Introduction.html

Shapefile:

Feature: http://nmcdc.maps.arcgis.com/home/item.html?id=ac726182c7574e64a3f5c68ecd814b58

Master File:

NM Data Variable Definition

999 SANo NM Small Area Number

NEW MEXICO SAName NM Small Area Name

67 D1999 Number of Drug Overdose Deaths, 1999

72 D2000 Number of Drug Overdose Deaths, 2000

58 D2001 Number of Drug Overdose Deaths, 2001

72 D2002 Number of Drug Overdose Deaths, 2002

95 D2003 Number of Drug Overdose Deaths, 2003

364 D9903 Number of Drug Overdose Deaths, 1999-2003

73 D2004 Number of Drug Overdose Deaths, 2004

85 D2005 Number of Drug Overdose Deaths, 2005

110 D2006 Number of Drug Overdose Deaths, 2006

121 D2007 Number of Drug Overdose Deaths, 2007

160 D2008 Number of Drug Overdose Deaths, 2008

134 D2009 Number of Drug Overdose Deaths, 2009

155 D2010 Number of Drug Overdose Deaths, 2010

152 D2011 Number of Drug Overdose Deaths, 2011

722 D0711 Number of Drug Overdose Deaths, 2007-2011

1484 D13YR Number of Drug Overdose Deaths, 1999-2011

500503 P1999 Population, Person-Years, 1999

503133 P2000 Population, Person-Years, 2000

508743 P2001 Population, Person-Years, 2001

514385 P2002 Population, Person-Years, 2002

520015 P2003 Population, Person-Years, 2003

2546779 P9903 Population, Person-Years, 1999-2003

509355.8 MAP9903 Mean Annual Population, Person-Years, 1999-2003

525660 P2004 Population, Person-Years, 2004

531294 P2005 Population, Person-Years, 2005

536930 P2006 Population, Person-Years, 2006

542573 P2007 Population, Person-Years, 2007

548210 P2008 Population, Person-Years, 2008

553846 P2009 Population, Person-Years, 2009

560941 P2010 Population, Person-Years, 2010

560779 P2011 Population, Person-Years, 2011

2766347 P0711 Population, Person-Years, 2007-2011

553269.4 MAP0711 Mean Annual Population, Person-Years, 2007-2011

6907010 P13YR Population, Person-Years, 1999-2011

531308.4615 MAP13YR Mean Annual Population, Person-Years, 1999-2011

13.4 R1999 Rate per 100,000 of Drug Overdose Deaths, 1999

14.3 R2000 Rate per 100,000 of Drug Overdose Deaths, 2000

11.4 R2001 Rate per 100,000 of Drug Overdose Deaths, 2001

14 R2002 Rate per 100,000 of Drug Overdose Deaths, 2002

18.3 R2003 Rate per 100,000 of Drug Overdose Deaths, 2003

14.3 R9903 Rate per 100,000 of Drug Overdose Deaths, 1999-2003

12.8 CIL9903 Rate per 100,000 of Drug Overdose Deaths, 1999-2003, 95% Confidence Interval Lower Limit

15.8 CIU9903 Rate per 100,000 of Drug Overdose Deaths, 1999-2003, 95% Confidence Interval Upper Limit

13.9 R2004 Rate per 100,000 of Drug Overdose Deaths, 2004

16 R2005 Rate per 100,000 of Drug Overdose Deaths, 2005

20.5 R2006 Rate per 100,000 of Drug Overdose Deaths, 2006

22.3 R2007 Rate per 100,000 of Drug Overdose Deaths, 2007

29.2 R2008 Rate per 100,000 of Drug Overdose Deaths, 2008

24.2 R2009 Rate per 100,000 of Drug Overdose Deaths, 2009

27.6 R2010 Rate per 100,000 of Drug Overdose Deaths, 2010

27.1 R2011 Rate per 100,000 of Drug Overdose Deaths, 2011

26.1 R0711 Rate per 100,000 of Drug Overdose Deaths, 2007-2011

24.2 CIL0711 Rate per 100,000 of Drug Overdose Deaths, 2007-2011, 95% Confidence Interval Lower Limit

28 CIU0711 Rate per 100,000 of Drug Overdose Deaths, 2007-2011, 95% Confidence Interval Upper Limit

21.5 R13YR Rate per 100,000 of Drug Overdose Deaths, 1999-2011

11.8 TrendDiff Difference in Drug Overdose Death Rate, 2007-2011 minus 1999-2003

INCREASE TrendSig Trend in Drug Overdose Death Rate Significance, 1999-2003 to.2007-2011

The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED) visits to hospitals since the early 1970s. First administered by the Drug Enforcement Administration (DEA) and the National Institute on Drug Abuse (NIDA), the responsibility for DAWN now rests with the Substance Abuse and Mental Health Services Administration's (SAMHSA) Center for Behavioral Health Statistics and Quality (CBHSQ). Over the years, the exact survey methodology has been adjusted to improve the quality, reliability, and generalizability of the information produced by DAWN. The current approach was first fully implemented in the 2004 data collection year.
DAWN relies on a longitudinal probability sample of hospitals located throughout the United States. To be eligible for selection into the DAWN sample, a hospital must be a non-Federal, short-stay, general surgical and medical hospital located in the United States, with at least one 24-hour ED. DAWN cases are identified by the systematic review of ED medical records in participating hospitals. The unit of analysis is any ED visit involving recent drug use. DAWN captures both ED visits that are directly caused by drugs and those in which drugs are a contributing factor but not the direct cause of the ED visit. The reason a patient used a drug is not part of the criteria for considering a visit to be drug-related. Therefore, all types of drug-related events are included: drug misuse or abuse, accidental drug ingestion, drug-related suicide attempts, malicious drug poisonings, and adverse reactions. DAWN does not report medications that are unrelated to the visit.
The DAWN public-use dataset provides information for all types of drugs, including illegal drugs, prescription drugs, over-the-counter medications, dietary supplements, anesthetic gases, substances that have psychoactive effects when inhaled, alcohol when used in combination with other drugs (all ages), and alcohol alone (only for patients aged 20 or younger). Public-use dataset variables describe and categorize up to 22 drugs contributing to the ED visit, including toxicology confirmation and route of administration. Administrative variables specify the type of case, case disposition, categorized episode time of day, and quarter of year. Metropolitan area is included for represented metropolitan areas. Created variables include the number of unique drugs reported and case-level indicators for alcohol, non-alcohol illicit substances, any pharmaceutical, non-medical use of pharmaceuticals, and all misuse and abuse of drugs. Demographic items include age category, sex, and race/ethnicity. Complex sample design and weighting variables are included to calculate various estimates of drug-related ED visits for the Nation as a whole, as well as for specific metropolitan areas, from the ED visits classified as DAWN cases in the selected hospitals.This study has 1 Data Set.

This dataset is comprised of data submitted to HCAI by prescription drug manufacturers for wholesale acquisition cost (WAC) increases that exceed the statutorily-mandated WAC increase threshold of an increase of more than 16% above the WAC of the drug product on December 31 of the calendar year three years prior to the current calendar year. This threshold applies to prescription drug products with a WAC greater than $40 for a course of therapy. Required WAC increase reports are to be submitted to HCAI within a month after the end of the quarter in which the WAC increase went into effect. Please see the statute and regulations for additional information regarding reporting thresholds and report due dates.

Key data elements in this dataset include the National Drug Code (NDC) maintained by the FDA, narrative descriptions of the reasons for the increase in WAC, and the five-year history of WAC increases for the NDC. A WAC Increase Report consists of 27 data elements that have been divided into two separate Excel data sets: Prescription Drug WAC Increase and Prescription Drug WAC Increase – 5 Year History. The datasets include manufacturer WAC Increase Reports received since January 1, 2019. The Prescription Drugs WAC Increase dataset consists of the information submitted by prescription drug manufacturers that pertains to the current WAC increase of a given report, including the amount of the current increase, the WAC after increase, and the effective date of the increase. The Prescription Drugs WAC Increase – 5 Year History dataset consists of the information submitted by prescription drug manufacturers for the data elements that comprise the 5-year history of WAC increases of a given report, including the amount of each increase and their effective dates.

There are 2 types of WAC Increase datasets below: Monthly and Annual. The Monthly datasets include the data in completed reports submitted by manufacturers for calendar year 2025, as of April 7, 2025. The Annual datasets include data in completed reports submitted by manufacturers for the specified year. The datasets may include reports that do not meet the specified minimum thresholds for reporting.

The Quick Guide explaining how to link the information in each data set to form complete reports is here: https://hcai.ca.gov/wp-content/uploads/2024/03/QuickGuide_LinkingTheDatasets.pdf

The program regulations are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/CTRx-Regulations-Text.pdf

The data format and file specifications are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/Format-and-File-Specifications-version-2.0-ada.pdf

DATA NOTES: Due to recent changes in Excel, it is not recommended that you save these files to .csv format. If you do, when importing back into Excel the leading zeros in the NDC number column will be dropped. If you need to save it into a different format other than .xlsx it must be .txt

DATA UPDATES: Annual datasets of reports from the preceding year are reviewed in the second half of the current year to identify if any revisions or additions have been made since the original release of the datasets. If revisions or additions have been found, an update of the datasets will be released. Datasets will be clearly marked with 'Updated' in their titles for convenient identification. Not all datasets may require an updated release. The review of previously released datasets will only be conducted once to determine if an updated release is necessary. Datasets with revisions or additions that may have been made after the one-time review can be requested. These requests should be sent via email to ctrx@hcai.ca.gov. Due to regulatory changes that went into effect April 1, 2024, reports submitted prior to April 1, 2024, will include the data field "Unit Sales Volume in US" and reports submitted on or after April 1, 2024, will instead include "Total Volume of Gross Sales in US Dollars".

Source: Office of State Medical Examiners (OSME), Rhode Island Department of Health (RIDOH)Note: Counts may not add to annual totals due to missing case information. Percentages may not add to 100 due to rounding. Percentages are displayed as decimals.

This project examined different aspects of campus crime -- specifically, the prevalence of crimes among college students, whether the crime rate was increasing or decreasing on college campuses, and the factors related to campus crime. Researchers made the assumption that crimes committed by and against college students were likely to be related to drug and alcohol use. Specific questions designed to be answered by the data include: (1) Do students who commit crimes differ in their use of drugs and alcohol from students who do not commit crimes? (2) Do students who are victims of crimes differ in their use of drugs and alcohol from students who are not victims? (3) How do multiple offenders differ from single offenders in their use of drugs and alcohol? (4) How do victims of violent crimes differ from victims of nonviolent crimes in their use of drugs and alcohol? (5) What types of student crimes are more strongly related to drug or alcohol use than others? (6) Other than drug and alcohol use, in what ways can victims and perpetrators of crimes be differentiated from students who have had no direct experiences with crime? Variables include basic demographic information, academic information, drug use information, and experiences with crime since becoming a student.

Data sets are extracted from the 2023 release of the e-Drug3D Database (2083 FDA-approved drug structures)

e-Drug3D_2083.zip (contains e-Drug3D_2083.sdf) - Chemical Structures - The e-Drug3D collection in SDF format file - one 3D conformer; ionization of carboxylic acid, phosphate, phosphonate, phosphonoamide, amidinium and guanidinium groups. The datablock contains the ID, name (INN), CAS number and Status.

e-Drug3D_2083_PK.csv - Pharmacokinetics - Column/field value is separated by a semicolon. It contains the e-Drug3D ID, INN (drug name), CAS number, year of approval, Status, is_or_has a metabolite, routes of administration, Volume of distribution (VD), Clearance (Cl), Plasma Protein Binding (PPB), Half-life (t1/2), Bioavailability (F), Cmax/Tmax, comment on solubility.

e-Drug3D_2083_PD.csv - Pharmacodynamics - Column/field value is separated by a semicolon. It contains the e-Drug3D ID, INN (drug name), CAS number, year of approval, Status, Primary target, ATC code(s), PDB codes and main list of drug targets.

e-Drug3D_2083_RD.csv - FDA Registration Data - Column/field value is separated by a semicolon. It contains the ID, name (INN), CAS number, First year of approval, Status, KNApSAcK or NPAtlas Id if natural product, all associated NDA numbers [FDA approval number, name of the label file in PDF format, company name, year of approval and commercial name of the drug] and the Indication/Therapeutic class information.

labels.tar.gz - The drug label files in PDF format (compressed directory). A label file is named with the NDA number. The NDA number is the approval number assigned by the FDA. A drug may possess several NDA numbers (see the above e-Drug3D-RD data set).

Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100 000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug–drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27 966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72–79% for the external test sets with a coverage of 81.36–100% when a conservative threshold for the model’s applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

The incident locations represented are approximated and not the actual location of the incident. Latitudinal and longitudinal coordinates have been truncate to 3 decimal points. The estimated location lies within approximately a 1/4 mile radius. This approximated location data is also shown on the dashboard.This feature layer supports the Opioid Abuse Probable EMS Call Dashboard. The following documents what data are collected and why they are being collected. Opioid Abuse ProbableA call may be coded as “opioid abuse probable” for many reasons, such asAre there are any medical symptoms indicative of opioid abuse?Are there physical indicators on scene (i.e. drug paraphernalia, pill bottles, etc.)?Are there witnesses or patient statements made that point to opioid abuse?Is there any other evidence that opioid abuse is probable with the patient?“Opioid abuse probable” is determined by Tempe Fire Medical Rescue Department’s Emergency medical technicians and paramedics on scene at the time of the incident. Narcan/Naloxone Given“Narcan/Naloxone Given” refers to whether the medication Narcan/Naloxone was given to patients who exhibited signs or symptoms of a potential opioid overdose or to patients who fall within treatment protocols that require Narcan/Naloxone to be given. Narcan/Naloxone are the same medication with Narcan being the trade name and Naloxone being the generic name for the medication. Narcan is the reversal medication used by medical providers for opioid overdoses.Groups“Groups” are used to determine if there are specific populations that have an increase in opioid abuse. The student population at ASU was being examined for other purposes to determine ASU's overall call volume impact in Tempe. Data collection with the university is consistent with Fire Departments who provide service to the other PAC 12 universities. Since this data set was already being evaluated, it was included in the opioid data collection as well.The Veteran and Homeless Groups were established as demographic tabs to identify trends and determine needs in conjunction with the City of Tempe’s Veterans and Homeless programs. Since these data sets were being evaluated already, they were included in the opioid data collection as well.The “unknown” group includes incidents where a patient is unable to answer or refuses to answer the demographic questions. GenderPatient gender is documented as male or female when crews are able to obtain this information from the patient. There are some circumstances where this information is not readily determined and the patient is unable to communicate with our crews. In these circumstances, crews may document unknown/unable to determine. Information about the data can be found at https://bit.ly/2xXbD20

record abstracts Several limitations to the data exist and should be noted: The number and client mix of TEDS records depends, to some extent, on external factors, including the availability of public funds. In states with higher funding levels, a larger percentage of the substance-abusing population may be admitted to treatment, including the less severely impaired and the less economically disadvantaged.; The primary, secondary, and tertiary substances of abuse reported to TEDS are those substances that led to the treatment episode, and not necessarily a complete enumeration of all drugs used at the time of admission. ; The way an admission is defined may vary from state to state such that the absolute number of admissions is not a valid measure for comparing states. ; States continually review the quality of their data processing. As systematic errors are identified, revisions may be enacted in historical TEDS data files. While this process improves the dataset over time, reported historical statistics may change slightly from year to year. ; States vary in the extent to which coercion plays a role in referral to treatment. This variation derives from criminal justice practices and differing concentrations of abuser subpopulations. ; Public funding constraints may direct states to selectively target special populations, for example, pregnant women or adolescents. ; TEDS consists of treatment admissions, and therefore may include multiple admissions for the same client. Thus, any statistics derived from the data will represent admissions, not clients. It is possible for clients to have multiple initial admissions within a state and even within providers that have multiple treatment sites within the state. TEDS provides a national snapshot of what is seen at admission to treatment, but is currently not designed to follow individual clients through a sequence of treatment episodes. ; TEDS distinguishes between "transfer admissions" and "initial admissions." Transfer admissions include clients transferred for distinct services within an episode of treatment. Only initial admissions are included in the public-use file. ; Some states have no Opioid Treatment Programs (OTPs) that provide medication-assisted therapy using methadone and/or buprenorphine. ; In 2012, a new variable was added that reports the number of times, if any, that a client was arrested in the 30 days preceding his or her admission into treatment. The variable is not on files prior to 2008. In 2012, changes were made to the full TEDS series. The changes consisted of the following: The recoding scheme of the variable DENTLF (Detailed Not in Labor Force Category) was changed. The cases for "Inmate of Institution" have been separated from "Other" and are now a standalone category. ; The recoding scheme of the variable DETCRIM (Detailed Criminal Justice Referral) was changed. The cases for "Prison" have been separated from "Probation/Parole" and are now a standalone category. The same was done for the cases for "Diversionary Program" which were previously combined with "Other". But the cases for "Other Recognized Legal Entity" previously combined with "State/Federal Court, Other Court" have now been combined with the "Other" category. ; In 2011, a change was made to the full TEDS series. All records for which the age is missing are now excluded from the dataset. In 2010, changes were made to the full TEDS series. The changes consisted of the following: Clients 11 years old and younger are excluded from the dataset. ; Puerto Rico now has its own category for Census Region and Division. Clients in Puerto Rico were formerly classified into the South Census Region and South Atlantic Census Division.; The state FIPS (STFIPS) variable is retained and a second state variable was dropped to reduce redundancy.; Value labels and question text are better aligned with the TEDS State Instruction Manual for Admissions Data.; The variable RACE is no longer recoded. Codes for "Asian" (code 13) and "Native Hawaiian or Pacific Islander" (code 23) are now retained. Previously these codes were combined into the single code "Asian or Pacific Islander" (code 3). Each state may report any of the three codes. Therefore, all three codes remain in the data, unchanged from the way they are collected by the states.; The categories and codes in this public-use file differ somewhat from those used by SAMHSA and those found in the TEDS Crosswalks and in other reports. This is a result of the recoding that was performed to protect client privacy in creating the public-use file. To further protect respondent and provider privacy, all Behavioral Health Services Information System (BHSIS) unique identification numbers have been removed from the public-use data. Therefore, no linkages are possible between the TEDS and the National Survey of Substance Abuse Treatment Services (N-SSATS) public-use files. The data are collected from the states by Synectics for Management De...

This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures:

2024: Most available figures are definite. Figures are provisional for: - causes of death; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university).

2023: Most available figures are definite. Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - hospital admissions by some diagnoses; - average period of hospitalisation; - supplied drugs; - AWBZ/Wlz-funded long term care; - physicians and nurses employed in care; - persons employed in health and welfare; - average distance to facilities; - profitability and operating results at institutions. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite, Figures are revised provisional for: - expenditures on health and welfare.f

2020 and earlier: All available figures are definite.

Changes as of 4 July 2025: More recent figures have been added for: - causes of death; - life expectancy; - life expectancy in perceived good health; - self-perceived health; - hospital admissions by some diagnoses; - sickness absence; - average period of hospitalisation; - contacts with health professionals; - youth care; - smoking, heavy drinkers, physical activity; - overweight; - high blood pressure; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - profitability and operating results at institutions.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

When will new figures be published? New figures will be published in December 2025.

The Treatment Episode Data Set -- Admissions (TEDS-A) is a national census data system of annual admissions to substance abuse treatment facilities. TEDS-A provides annual data on the number and characteristics of persons admitted to public and private substance abuse treatment programs that receive public funding. The unit of analysis is a treatment admission. TEDS consists of data reported to state substance abuse agencies by the treatment programs, which in turn report it to SAMHSA. A sister data system, called the Treatment Episode Data Set -- Discharges (TEDS-D), collects data on discharges from substance abuse treatment facilities. The first year of TEDS-A data is 1992, while the first year of TEDS-D is 2006. TEDS variables that are required to be reported are called the "Minimum Data Set (MDS)", while those that are optional are called the "Supplemental Data Set (SuDS)". Variables in the MDS include: information on service setting, number of prior treatments, primary source of referral, gender, race, ethnicity, education, employment status, substance(s) abused, route of administration, frequency of use, age at first use, and whether methadone was prescribed in treatment. Supplemental variables include: diagnosis codes, presence of psychiatric problems, living arrangements, source of income, health insurance, expected source of payment, pregnancy and veteran status, marital status, detailed not in labor force codes, detailed criminal justice referral codes, days waiting to enter treatment, and the number of arrests in the 30 days prior to admissions (starting in 2008). Substances abused include alcohol, cocaine and crack, marijuana and hashish, heroin, nonprescription methadone, other opiates and synthetics, PCP, other hallucinogens, methamphetamine, other amphetamines, other stimulants, benzodiazepines, other non-benzodiazepine tranquilizers, barbiturates, other non-barbiturate sedatives or hypnotics, inhalants, over-the-counter medications, and other substances. Created variables include total number of substances reported, intravenous drug use (IDU), and flags for any mention of specific substances.