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

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

The Estonian Drug Treatment Database is a state register which is kept on the people who have started drug treatment. The Drug Treatment Database started its work on January 1, 2008.

Collection and processing of data on these people is necessary for getting an overview on occurrence of mental and behavioural disorders related to drug use, as well as for organising of relevant health services and planning of drug abuse preventive actions. Health care institutions holding a psychiatry authorization in Estonia present data to the database if they are turned to by a patient who is diagnosed with a mental and behavioural disorder due to drug use.

On the basis of the database's data, an annual overview is compiled, giving information about drug addicts who have turned to drug treatment in the previous calendar year, about the health service provided, the patients' socio-economic background, drug use and the related risk behaviour.

The data on the Drug Treatment Database are also submitted to the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) and United Nations Office on Drugs and Crime (UNODC).

Statistical data on children living with parents who have substance use disorders, sourced from SAMHSA reports

Illicit Drug Use reports an estimated average percent of people who consumed illicit substances by type of use and by age range. Illicit drugs include marijuana or hashish (unless otherwise specified as 'Not Including Marijuana'), cocaine (including crack), heroin, hallucinogens (including phencyclidine [PCP], lysergic acid diethylamide [LSD], and Ecstasy [MDMA]), inhalants, or prescription-type psychotherapeutics used nonmedically, which include pain relievers, tranquilizers, stimulants, and sedatives, but does not include GHB (gamma hydroxybutyrate), Adderall, Ambien, nonprescription cough or cold medicines, ketamine, DMT (dimethyltryptamine), AMT (alpha-methyltryptamine), 5-MeO-DIPT (N, N-diisopropyl-5-methoxytryptamine, also known as 'Foxy'), and Salvia divinorum. Dependence is defined consistent with the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) definition as:Spending a lot of time engaging in activities related to substance useUsing a substance in greater quantities or for a longer time than intended. Developing tolerance (i.e., needing to use the substance more than before to get desired effects or noticing that the same amount of substance use had less effect than before)Making unsuccessful attempts to cut down on useContinuing substance use despite physical health or emotional problems associated with substance useReducing or eliminating participation in other activities because of substance useExperiencing withdrawal symptomsSimilarly, Abuse is also defined consistent with the DSM-IV definition as the following lifestyle symptoms due to the use of illicit drugs in the past 12 months: Experiencing problems at work, home, and schoolDoing something physically dangerousExperiencing Repeated trouble with the lawExperiencing Problems with family or friends

In this post, I'll give you all the social media addiction statistics you need to be aware of to moderate your social media use.

Teenagers are the 2nd largest group of people affected by social media addiction. Teens ages 13 to 18 years old spend a significant amount of their free time on social media with an average of 3 hours a day.

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

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

Over 210 million people worldwide suffer from social media addiction.

National Survey on Drug Use and Health (NSDUH) for Years 2015-2019

The National Survey on Drug Use and Health (NSDUH) is the "leading source of statistical information on the use of illicit drugs, alcohol, and tobacco and mental health issues in the United States" (SAMHSA). The abundance of Yes/No questions regarding the usage of illicit drugs make this dataset valuable for binary classification problems. During 2015, the survey received a partial redesign, creating "broken trends" from pre-2015 and post-2015. This is dataset contains every year of the NSDUH survey after the major restructuring in 2015.

Column Descriptions

All column names are identical to the Question Index found in the NSDUH documentation. The values in each column are codes that correspond to a particular answer in the survey. You can reference each question's meaning in the documentation, found here. Be sure to account for these codes before performing any analyses.

Additionally, some questions are not asked across ALL years, and will instead have an NA value.

Sources

All of the data used to create this dataset was obtained from the Substance Abuse & Mental Health Data Archive. You can access the data for separate years here.

The survey investigated the continuity of treatment of people with substance abuse problems as well as their readiness to change. The respondents were people with histories of alcohol and poly drug use who were being treated in an inpatient treatment facility for people with substance abuse problems. The dataset comprises four client samples. The respondents' readiness to change was charted with URICA (University of Rhode Island Change Assessment Scale) that had 32 statements and the respondents rated to what extent they agreed with each. The scale measured the respondents' State of Change with the help of four subscales: Precontemplation, Contemplation, Action and Maintenance. Statements presented in the scale included, among others: "As far as I'm concerned, I don't have any problems that need changing", "I've been thinking that I might want to change something about myself", "Even though I'm not always successful in changing, I am at least working on my problem", "It is frustrating, but I feel I might be having a recurrence of a problem I thought I had resolved", and "I may need a boost right now to help me maintain the changes I've already made". Background variables included, among others, the respondent's age, gender, economic activity and occupational status, total number of study years, marital status, number of children under R's guardianship living with R, housing tenure, employment status, substances used in the previous 12 months, substance most used in the previous 12 months and contacts with substance abusers.

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

The Subdirectorate General of Addictions of Madrid Salud has seven addiction care centers (CAD) distributed throughout the territory. Each CAD is assigned a few districts, although the choice of CAD corresponds to the person served. CAD offers interdisciplinary treatment to anyone with an addiction. In this data set are the number of people who have received treatment in the CAD of the Subdirectorate General of Addictions of Madrid Salud for a year. Those who were in treatment before 1 January of the reference year (history opened in the SUPRA application), new additions and readmissions (persons who were in treatment before the reflected date, their history was closed and reopened in the reflected year) are disaggregated by district of residence and differentiated.

90% of people aged 18-29 use social media in some form. 15% of people aged 23-38 admit that they are addicted to social media.

Einstellung zu Drogen. Themen: Präferierte Ansprechpartner für Informationen über illegale Drogen und Drogenkonsum; Informationsquellen für Informationen zu Auswirkungen und Risiken des Drogenkonsums; Konsum ´neuer psychoaktiver Substanzen (NPS)´ (´Legal Highs´), die die Wirkung illegaler Drogen imitieren, in den letzten zwölf Monaten; Kauf der neuen synthetischen Drogen von einem Freund, in einem Spezialgeschäft, im Internet bzw. von einem Drogendealer; Konsumsituation (allein, mit Freunden, während einer Party oder Veranstaltung bzw. im Alltag); Informationsquellen für erhaltene Informationen zu Auswirkungen und Risiken des Konsums neuer synthetischer Drogen; Einschätzung des Gesundheitsrisikos jeweils beim ein- oder zweimaligen Konsum und beim regelmäßigen Konsum von Cannabis, Ecstasy, Alkohol, Kokain sowie von neuen synthetischen Drogen, die die Wirkung illegaler Drogen imitieren; effektivste staatliche Maßnahmen zur Reduzierung der Drogenproblematik (Kampagnen zur Information und Vorbeugung, Behandlung und Rehabilitation von Drogenkonsumenten, strenge Maßnahmen gegen Drogendealer und Drogenhändler bzw. gegen Drogenkonsumenten, Drogen legalisieren, Reduzierung von Armut und Arbeitslosigkeit mehr Freizeitangebote für Jugendliche); Forderung nach einem (weiteren) Verbot oder einer gesetzlichen Regelung des Konsums ausgewählter Substanzen (Cannabis, Tabak, Ecstasy, Heroin, Alkohol, Kokain); geeigneter Umgang mit legalen neuen psychoaktiven Substanzen (Regulierung einführen, Verbot nur bei Gesundheitsrisiko, generelles Verbot, nichts tun); Beschaffungsmöglichkeit ausgewählter Substanzen innerhalb von 24 Stunden (Cannabis, Alkohol, Kokain, Ecstasy, Tabak, Heroin, neue psychoaktive Substanzen); Cannabiskonsum. Demographie: Alter; Geschlecht; höchster Bildungsabschluss; Beschäftigungsstatus und berufliche Stellung des Haupteinkommensbeziehers im Haushalt (falls Befragter Schüler oder Student); Beschäftigungsstatus und berufliche Stellung des Befragten; Region; Urbanisierungsgrad des Wohnortes; Mobiltelefonbesitz; Festnetztelefon im Haushalt; Anzahl der Personen im Haushalt ab 15 Jahren (Haushaltsgröße). Attitude towards drugs. Topics: Preferred contact for information about illicit drugs and drug use in general; information sources for information about the effects and risks of drug use of illicit drugs; consumption of new psychoactive substances (‘legal highs’) that imitate the effects of illicit drugs, in the last year; purchase of new substances by a friend, from a specialised shop, from the Internet or from a drug dealer; circumstances of use (alone, with friends, during a party or an event or during normal daily activities); information sources for information about the effects and risks of the use of new substances; assessment of the risk to a person’s health using cannabis, ecstasy, alcohol, cocaine, and new substances that imitate the effects of illicit drugs, once or twice and regularly; most effective ways for public authorities to reduce drugs problems (information and prevention campaigns, treatment and rehabilitation of drug users, tough measures against drug dealers and traffickers, as well as drug users, legalize drugs, reduction of poverty and unemployment, more leisure activities for young people); demand for (continued) banning or a legal regulation of the following substances (cannabis, tobacco, ecstasy, heroin, alcohol, cocaine); appropriate way to handle new psychoactive substances (introduce regulation, ban them only if they pose a risk to health, ban them under any circumstance, do nothing); possibility to obtain selected substances within 24 hours (cannabis, alcohol, cocaine, ecstasy, tobacco, heroin, new psychoactive substances); respondent has used cannabis. Demography: age; sex; highest education level; occupation and professional position of the main wage earner in the household (only full time students); occupation and professional position of the respondent; region; type of community; own a mobile phone and fixed (landline) phone in the household; number of persons aged 15 years and older in the household (household size). Telephone interview: CATI Bevölkerung der jeweiligen Nationalitäten der 28 Mitgliedsstaaten der EU, wohnhaft in den jeweiligen Mitgliedsstaaten im Alter zwischen 15 und 24 Jahren Die Umfrage umfast die nationale Bevölkerung der Bürger (in diesen Ländern) sowie die Bevölkerung der Bürger aller Mitgliedstaaten der Europäischen Union, die Bewohner dieser Länder sind und über ausreichende Kenntnisse der Landessprachen verfügen, um den Fragebogen zu beantworten. Population of the respective nationalities of the European Union Member States, resident in each of the 28 Member States and aged between 15 and 24 years old. The survey covers the national population of citizens (in these countries) as well as the population of citizens of all the European Union Member States that are residents in these countries and have a sufficient command of the national languages to answer the questionnaire.

Context

RxNorm is a name of a US-specific terminology in medicine that contains all medications available on US market. Source: https://en.wikipedia.org/wiki/RxNorm

RxNorm provides normalized names for clinical drugs and links its names to many of the drug vocabularies commonly used in pharmacy management and drug interaction software, including those of First Databank, Micromedex, Gold Standard Drug Database, and Multum. By providing links between these vocabularies, RxNorm can mediate messages between systems not using the same software and vocabulary. Source: https://www.nlm.nih.gov/research/umls/rxnorm/

Content

RxNorm was created by the U.S. National Library of Medicine (NLM) to provide a normalized naming system for clinical drugs, defined as the combination of {ingredient + strength + dose form}. In addition to the naming system, the RxNorm dataset also provides structured information such as brand names, ingredients, drug classes, and so on, for each clinical drug. Typical uses of RxNorm include navigating between names and codes among different drug vocabularies and using information in RxNorm to assist with health information exchange/medication reconciliation, e-prescribing, drug analytics, formulary development, and other functions.

This public dataset includes multiple data files originally released in RxNorm Rich Release Format (RXNRRF) that are loaded into Bigquery tables. The data is updated and archived on a monthly basis.

The following tables are included in the RxNorm dataset:

• RXNCONSO contains concept and source information

• RXNREL contains information regarding relationships between entities

• RXNSAT contains attribute information

• RXNSTY contains semantic information

• RXNSAB contains source info

• RXNCUI contains retired rxcui codes

• RXNATOMARCHIVE contains archived data

• RXNCUICHANGES contains concept changes

Update Frequency: Monthly

Fork this kernel to get started with this dataset.

Acknowledgements

https://www.nlm.nih.gov/research/umls/rxnorm/

https://bigquery.cloud.google.com/dataset/bigquery-public-data:nlm_rxnorm

https://cloud.google.com/bigquery/public-data/rxnorm

Dataset Source: Unified Medical Language System RxNorm. The dataset is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. This dataset uses publicly available data from the U.S. National Library of Medicine (NLM), National Institutes of Health, Department of Health and Human Services; NLM is not responsible for the dataset, does not endorse or recommend this or any other dataset.

Banner Photo by @freestocks from Unsplash.

Inspiration

What are the RXCUI codes for the ingredients of a list of drugs?

Which ingredients have the most variety of dose forms?

In what dose forms is the drug phenylephrine found?

What are the ingredients of the drug labeled with the generic code number 072718?

Contains a set of data tables for each part of the Smoking, Drinking and Drug Use among Young People in England, 2021 report

People with disabilities (PWD) are an overlooked health disparity population who routinely experience stigma, discrimination, ableism, and lower socioeconomic status. Barriers to health care are generally high for PWD, and despite over three decades of the Americans with Disabilities Act, many health care settings including addiction treatment are not fully accessible for PWD. The INROADS portfolio stems from two projects: (1) INROADS (NIDILRR 90DPGE0007) and (2) INROADS-Alcohol (NIAAA R01AA031236), each of which examine the intersection between substance use and risky use, addiction, disability, and addiction treatment services. Both studies focus on the broad population of PWD, as well as subpopulations such as those with vision or hearing impairments, mobility impairment or spinal cord injury, acquired brain injury including TBI, intellectual and developmental disabilities including those with autism, and serious mental illness. The original INROADS project, or Intersecting Research on Opioid Misuse, Addiction, and Disability Services, was a joint research program between Brandeis University’s Institute for Behavioral Health and its Lurie Institute for Disability Policy. It examined the intersection between addiction, disability, and service provision in an effort to address the rise of opioid use disorders (OUD) among people with disabilities. INROADS-A (INROADS-Alcohol) is an active joint research program between Brandeis University’s Institute for Behavioral Health and Boston University School of Public Health’s Department of Health Law, Policy and Management, which builds on the foundational INROADS research with a focus on alcohol use disorder. The original INROADS project laid a foundation for understanding the intersection of disability with opioid use disorder, and traumatic brain injury (TBI) with at-risk opioid use and consequences, and INROADS-A seeks to better understand alcohol use patterns, treatment needs, and alcohol use disorder (AUD) treatment access, quality and outcomes for people with disabilities.

Themes relating to the risk of opioid-related deaths during hospital admissions or shortly after hospital discharge.