Decrease the percentage of adults who have taken prescription drugs in the past 30 days without a doctor's prescription from 2.5% in 2013 to 2.1% by 2017.

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).

Maintain the percentage of youth ages 12-17 exposed to a substance abuse prevention message at 86.6% every year through 2018.

This report contains results from the latest survey of secondary school pupils in England in years 7 to 11 (mostly aged 11 to 15), focusing on smoking, drinking and drug use. It covers a range of topics including prevalence, habits, attitudes, and wellbeing. This survey is usually run every two years, however, due to the impact that the Covid pandemic had on school opening and attendance, it was not possible to run the survey as initially planned in 2020; instead it was delivered in the 2021 school year. In 2021 additional questions were also included relating to the impact of Covid. They covered how pupil's took part in school learning in the last school year (September 2020 to July 2021), and how often pupil's met other people outside of school and home. Results of analysis covering these questions have been presented within parts of the report and associated data tables. It includes this summary report showing key findings, excel tables with more detailed outcomes, technical appendices and a data quality statement. An anonymised record level file of the underlying data on which users can carry out their own analysis will be made available via the UK Data Service later in 2022 (see link below).

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

This dataset contains the estimated percentages of individuals or patients who used illicit drugs, alcohol, tobacco in the last month or year and respectively that need or have received specialized treatment or that have a mental condition due or not to substance dependence or abuse.

The National Household Survey on Drug Abuse (NHSDA) series measures the prevalence and correlates of drug use in the United States. The surveys are designed to provide quarterly, as well as annual, estimates. Information is provided on the use of illicit drugs, alcohol, and tobacco among members of United States households aged 12 and older. Questions include age at first use as well as lifetime, annual, and past-month usage for the following drug classes: marijuana, cocaine (and crack), hallucinogens, heroin, inhalants, alcohol, tobacco, and nonmedical use of prescription drugs, including psychotherapeutics. Respondents were also asked about personal and family income sources and amounts, substance abuse treatment history, illegal activities, problems resulting from the use of drugs, need for treatment for drug or alcohol use, criminal record, and needle-sharing. Questions on mental health and access to care, which were introduced in the 1994-B questionnaire (see NATIONAL HOUSEHOLD SURVEY ON DRUG ABUSE, 1994), were retained in this administration of the survey. Also retained was the section on risk/availability of drugs that was reintroduced in 1996, and sections on driving behavior and personal behavior were added (see NATIONAL HOUSEHOLD SURVEY ON DRUG ABUSE, 1996). The 1997 questionnaire (NATIONAL HOUSEHOLD SURVEY ON DRUG ABUSE, 1997) introduced new items that the 1998 NHSDA continued on cigar smoking, people who were present when respondents used marijuana or cocaine for the first time (if applicable), reasons for using these two drugs the first time, reasons for using these two drugs in the past year, reasons for discontinuing use of these two drugs (for lifetime but not past-year users), and reasons respondents never used these two drugs. Both the 1997 and 1998 NHSDAs had a series of questions that were asked only of respondents aged 12 to 17. These items covered a variety of topics that may be associated with substance use and related behaviors, such as exposure to substance abuse prevention and education programs, gang involvement, relationship with parents, and substance use by friends. Demographic data include sex, race, age, ethnicity, marital status, educational level, job status, income level, veteran status, and current household composition. This study has 1 Data Set.

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

Abstract copyright UK Data Service and data collection copyright owner.

The Smoking, Drinking and Drug Use among Young People surveys began in 1982, under the name Smoking among Secondary Schoolchildren. The series initially aimed to provide national estimates of the proportion of secondary schoolchildren aged 11-15 who smoked, and to describe their smoking behaviour. Similar surveys were carried out every two years until 1998 to monitor trends in the prevalence of cigarette smoking. The survey then moved to an annual cycle, and questions on alcohol consumption and drug use were included. The name of the series changed to Smoking, Drinking and Drug Use among Young Teenagers to reflect this widened focus. In 2000, the series title changed, to Smoking, Drinking and Drug Use among Young People. NHS Digital (formerly the Information Centre for Health and Social Care) took over from the Department of Health as sponsors and publishers of the survey series from 2005. From 2014 onwards, the series changed to a biennial one, with no survey taking place in 2015, 2017 or 2019.

In some years, the surveys have been carried out in Scotland and Wales as well as England, to provide separate national estimates for these countries. In 2002, following a review of Scotland's future information needs in relation to drug misuse among schoolchildren, a separate Scottish series, Scottish Schools Adolescent Lifestyle and Substance Use Survey (SALSUS) was established by the Scottish Executive.

Main Topics:

Smoking: smoking behaviour, attitudes to smoking, cigarette purchasing, administrative variables.
Drinking: drinking behaviour, drinking in last week, alcohol purchasing.
Drug use: awareness of drugs, availability of drugs, drug use.
Other information includes: classification variables, school data and health education.
Some administrative variables have been removed from the dataset to ensure respondent confidentiality is maintained - for example day of interview and day of date of birth.
Over 200 schools in England also completed a policy questionnaire which covered policy on adult (staff and visitors) smoking on school premises, disciplinary action for pupils found smoking, drinking or drugtaking on school premises, and policy for dealing with pupils not caught in the act of taking, but clearly under the influence of, drugs or alcohol at school.

The survey Epidemiological Survey on Substance Abuse in Germany 2018 (ESA) is a representative survey on the use and abuse of psychoactive substances among adolescents and adults aged 18 to 64 years, which has been conducted regularly nationwide since 1980. The data collection took place between March and July 2018 and was conducted by infas Institut für angewandte Sozialwissenschaft GmbH on behalf of the IFT, Institute for Therapy Research in Munich. The nationwide study was conducted in a mixed-mode design as a standardised telephone survey (CATI: Computer Assisted Telephone Interview), as a written-postal survey (PAPSI: Paper and Pencil Self Interview) and as an online survey. The study is financially supported by the Federal Ministry of Health. The survey covered 30-day, 12-month and lifetime prevalence of tobacco use (tobacco products as well as shisha, heat-not-burn products and e-cigarettes), alcohol, illicit drugs and medicines. For conventional tobacco products, alcohol, selected illicit drugs (cannabis, cocaine and amphetamines) and medications (painkillers, sleeping pills and tranquillisers), additional diagnostic criteria were recorded with the written version of the Munich Composite International Diagnostic Interview (M-CIDI) for the period of the last twelve months. Furthermore, a series of socio-demographic data, the physical and mental state of health, nutritional behaviour, mental disorders as well as modules on the main topics of children from families with addiction problems, reasons for abstinence in the field of alcohol and the perception or knowledge of the health risk posed by alcohol were recorded. Physical and mental health status: self-assessment of health status; self-assessment of mental well-being; chronic illnesses; frequency of physical problems or pain without clear explanation, anxiety attack / panic attack, frequent worries, strong fears in social situations, strong fears of public places, means of transport or shops, strong fears of various situations, e.g. use of lifts, tunnels, aeroplanes as well as severe weather, sadness or low mood, loss of interest, tiredness or lack of energy, unusually happy, over-excited or irritable, stressful traumatic events, psychiatric, psychological or psychotherapeutic treatment in the last 12 months; physical activity and diet in the last three months: frequency of physical activity (moving from place to place, recreational sports, work-related physical activity) per week; duration of physical activity; consumption of selected foods (low-fat dairy products, raw vegetables, fresh salads, herbs, fresh fruit, cereal products, herbal tea or fruit tea); illness caused by excessive alcohol consumption. 2. Medication use: type of medication use (painkillers, sleeping pills, tranquilizers, stimulants, appetite suppressants, antidepressants, neuroleptics and anabolic steroids) in the last 12 months; frequency of use of painkillers, sleeping pills, tranquilizers, stimulants, appetite suppressants, antidepressants and neuroleptics in the last 30 days and respective prescription by a physician; use of painkillers, sleeping pills or tranquilizers in the last 12 months; tendencies towards dependence: In the last 12 months, the following were asked: significant problems related to the use of painkillers, sleeping pills and tranquillisers (neglect of household and children, poor performance, injury-prone situations while under the influence of medication, unintentional injuries such as accidents or falls, legal problems, accusations from family or friends, broken relationship, financial difficulties, physically attacking or hurting someone, use in larger quantities or for longer periods than prescribed or intended by the doctor, discomfort when stopping the medication. discomfort when stopping the medication and then continuing to take the medication to avoid discomfort, higher doses required for desired effect or weakened effect, unsuccessful attempts to reduce or stop medication use, large amount of time required to obtain medication or recover from effects, restriction of activities, taking medication despite knowledge of harmful effects, craving for medication so strong that resisting or thinking otherwise was not possible. 3. Smoking: smoking status; smoking behaviour: smoked more than 100 cigarettes, cigars, cigarillos, pipes in total during lifetime; type of tobacco use (cigarettes, cigars, cigarillos, pipe); age of initiation of tobacco use; time of last tobacco use; specific number of days in the last month on which cigarettes (or cigars, cigarillos or pipes) were smoked and average number smoked per day; average daily consumption of 20 or more cigarettes (or 10 cigarillos, 7 pipes, 5 cigars) in the last 12 months; smoking behaviour in the last 12 months (had to smoke more than before to get the same effect, effect of smoking decreased, smoked much more than intended, tried unsuccessfully to cut down or quit smoking for a few days, chain smoker, gave up important activities because of smoking, continued to smoke during serious illness, smoking interfered with work, school or housework, smoked in situations where there was a high risk of injury, continued to smoke even though it made other people angry or unhappy, unable to resist strong cravings for tobacco, unable to think of anything else because of strong cravings for tobacco); physical or mental health problems in the last 12 months due to smoking; continued to smoke in spite of physical or mental health problems; health problems due to smoking cessation in the last 12 months (low mood, insomnia, irritability/annoyance, restlessness, difficulty concentrating, slow heartbeat, weight gain); started smoking again to avoid complaints; serious attempts to stop smoking in the last 12 months; successful attempt to quit smoking; ever used shisha (hookah), a Neat-Not-Burn product or an e-cigarette, e-shisha, e-pipe, e-cigar and time of last use; age at first use of e-cigarette/e-cigar/e-shisha/e-pipe and frequency of use in the last 30 days; use of e-cigarettes/e-cigars/e-shishas/e-pipes with or without nicotine. 4. Alcohol consumption: age at first glass of alcohol; alcohol consumption at least once a month; age of onset of regular alcohol consumption; alcohol excesses (binge drinking) in the past and frequency of alcohol excesses in the last 12 months; age at first alcohol excess; time of last alcohol consumption; total number of days with alcohol consumption in the last 30 days or 12 months; concrete information on the average amount of beer, wine/sparkling wine and mixed drinks containing alcohol (alcopops, long drinks, cocktails or punch) consumed in the last 30 days or 12 months. 12 months; concrete information on the average amount of beer, wine/sparkling wine, spirits and mixed drinks containing alcohol (alcopops, long drinks, cocktails or punch) consumed in the last 30 days or in the last 12 months; number of days with consumption of at least five glasses of alcohol in the last 30 days or 12 months; problems caused by alcohol in the last 30 days or 12 months; number of days with consumption of at least five glasses of alcohol in the last 30 days or 12 months. 12 months; problems caused by alcohol in the last 12 months (significant difficulties at work, school or home, situations involving risk of injury, trouble with the police, accusations from family or friends, broken relationship, financial difficulties, physically attacking or hurting someone); alcohol consumption behaviour in the last 12 months (had to drink more than before to get the same effect, effect of alcohol consumption decreased, drank much more than intended, tried unsuccessfully to drink less alcohol or to stop drinking altogether, drank a lot of alcohol over several days, been drunk or suffered from the effects of alcohol, gave up important activities because of alcohol, could not resist strong craving for alcohol, could not think of anything else because of strong craving for alcohol); symptoms after alcohol withdrawal (trembling, insomnia, anxiety, sweating, hallucinations (seizure), nausea, vomiting, urge to move, rapid heartbeat); drank alcohol to avoid such complaints; physical illnesses or mental problems related to alcohol in the last 12 months; alcohol consumption despite physical or mental problems; increased cancer incidence in the last 12 months; alcohol consumption in spite of physical or mental problems. increased cancer risk due to alcohol consumption (stomach cancer, ovarian cancer, breast cancer, mouth and oesophagus cancer, brain tumour, bowel cancer, liver cancer, bladder cancer); alcohol consumption in the last 30 days; personal reasons for abstaining from alcohol (alcohol causes people to lose control, condition of illness worsens due to alcohol, parents had an alcohol problem, family is against alcohol consumption, alcohol consumption is against my spiritual/religious attitude, I do not like the taste and/or smell of alcohol, own pregnancy or partner´s pregnancy). 5. Drug use: drug experience with cannabis (hashish, marijuana), stimulants, amphetamines, ecstasy, LSD, heroin, other opiates such as e.g. codeine, methadone, opium, morphine), cocaine, crack, sniffing substances and mushrooms as intoxicants or never tried any of these drugs before; ever used substances that imitate the effect of illegal drugs (legal highs, research chemicals, bath salts, herbal mixtures or new psychoactive substances (NPS); used such legal substances in the last 12 months; form of substances consumed (herbal mixtures for smoking, powders, crystals or tablets as well as liquids); generally tried drugs; frequency of drug use in total, in each case related to cannabis (hashish, marijuana), stimulants, amphetamines, ecstasy, LSD, heroin, other opiates, cocaine, crack cocaine, sniffing substances, mushrooms resp. Legal highs, research chemicals, bath salts, herbal mixtures, NPS; time of last use of any of the above drugs (in the

EMSIndicators:The number of individual patients administered naloxone by EMSThe number of naloxone administrations by EMSThe rate of EMS calls involving naloxone administrations per 10,000 residentsData Source:The Vermont Statewide Incident Reporting Network (SIREN) is a comprehensive electronic prehospital patient care data collection, analysis, and reporting system. EMS reporting serves several important functions, including legal documentation, quality improvement initiatives, billing, and evaluation of individual and agency performance measures.Law Enforcement Indicators:The Number of law enforcement responses to accidental opioid-related non-fatal overdosesData Source:The Drug Monitoring Initiative (DMI) was established by the Vermont Intelligence Center (VIC) in an effort to combat the opioid epidemic in Vermont. It serves as a repository of drug data for Vermont and manages overdose and seizure databases. Notes:Overdose data provided in this dashboard are derived from multiple sources and should be considered preliminary and therefore subject to change. Overdoses included are those that Vermont law enforcement responded to. Law enforcement personnel do not respond to every overdose, and therefore, the numbers in this report are not representative of all overdoses in the state. The overdoses included are limited to those that are suspected to have been caused, at least in part, by opioids. Inclusion is based on law enforcement's perception and representation in Records Management Systems (RMS). All Vermont law enforcement agencies are represented, with the exception of Norwich Police Department, Hartford Police Department, and Windsor Police Department, due to RMS access. Questions regarding this dataset can be directed to the Vermont Intelligence Center at dps.vicdrugs@vermont.gov.Overdoses Indicators:The number of accidental and undetermined opioid-related deathsThe number of accidental and undetermined opioid-related deaths with cocaine involvementThe percent of accidental and undetermined opioid-related deaths with cocaine involvementThe rate of accidental and undetermined opioid-related deathsThe rate of heroin nonfatal overdose per 10,000 ED visitsThe rate of opioid nonfatal overdose per 10,000 ED visitsThe rate of stimulant nonfatal overdose per 10,000 ED visitsData Source:Vermont requires towns to report all births, marriages, and deaths. These records, particularly birth and death records are used to study and monitor the health of a population. Deaths are reported via the Electronic Death Registration System. Vermont publishes annual Vital Statistics reports.The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) captures and analyzes recent Emergency Department visit data for trends and signals of abnormal activity that may indicate the occurrence of significant public health events.Population Health Indicators:The percent of adolescents in grades 6-8 who used marijuana in the past 30 daysThe percent of adolescents in grades 9-12 who used marijuana in the past 30 daysThe percent of adolescents in grades 9-12 who drank any alcohol in the past 30 daysThe percent of adolescents in grades 9-12 who binge drank in the past 30 daysThe percent of adolescents in grades 9-12 who misused any prescription medications in the past 30 daysThe percent of adults who consumed alcohol in the past 30 daysThe percent of adults who binge drank in the past 30 daysThe percent of adults who used marijuana in the past 30 daysData Sources:The Vermont Youth Risk Behavior Survey (YRBS) is part of a national school-based surveillance system conducted by the Centers for Disease Control and Prevention (CDC). The YRBS monitors health risk behaviors that contribute to the leading causes of death and disability among youth and young adults.The Behavioral Risk Factor Surveillance System (BRFSS) is a telephone survey conducted annually among adults 18 and older. The Vermont BRFSS is completed by the Vermont Department of Health in collaboration with the Centers for Disease Control and Prevention (CDC).Notes:Prevalence estimates and trends for the 2021 Vermont YRBS were likely impacted by significant factors unique to 2021, including the COVID-19 pandemic and the delay of the survey administration period resulting in a younger population completing the survey. Students who participated in the 2021 YRBS may have had a different educational and social experience compared to previous participants. Disruptions, including remote learning, lack of social interactions, and extracurricular activities, are likely reflected in the survey results. As a result, no trend data is included in the 2021 report and caution should be used when interpreting and comparing the 2021 results to other years.The Vermont Department of Health (VDH) seeks to promote destigmatizing and equitable language. While the VDH uses the term "cannabis" to reflect updated terminology, the data sources referenced in this data brief use the term "marijuana" to refer to cannabis. Prescription Drugs Indicators:The average daily MMEThe average day's supplyThe average day's supply for opioid analgesic prescriptionsThe number of prescriptionsThe percent of the population receiving at least one prescriptionThe percent of prescriptionsThe proportion of opioid analgesic prescriptionsThe rate of prescriptions per 100 residentsData Source:The Vermont Prescription Monitoring System (VPMS) is an electronic data system that collects information on Schedule II-IV controlled substance prescriptions dispensed by pharmacies. VPMS proactively safeguards public health and safety while supporting the appropriate use of controlled substances. The program helps healthcare providers improve patient care. VPMS data is also a health statistics tool that is used to monitor statewide trends in the dispensing of prescriptions.Treatment Indicators:The number of times a new substance use disorder is diagnosed (Medicaid recipients index events)The number of times substance use disorder treatment is started within 14 days of diagnosis (Medicaid recipients initiation events)The number of times two or more treatment services are provided within 34 days of starting treatment (Medicaid recipients engagement events)The percent of times substance use disorder treatment is started within 14 days of diagnosis (Medicaid recipients initiation rate)The percent of times two or more treatment services are provided within 34 days of starting treatment (Medicaid recipients engagement rate)The MOUD treatment rate per 10,000 peopleThe number of people who received MOUD treatmentData Source:Vermont Medicaid ClaimsThe Vermont Prescription Monitoring System (VPMS)Substance Abuse Treatment Information System (SATIS)

Analysis of ‘Drug Consumptions (UCI)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/obeykhadija/drug-consumptions-uci on 28 January 2022.

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

Context

Data Set Information:

Database contains records for 1885 respondents. For each respondent 12 attributes are known: Personality measurements which include NEO-FFI-R (neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness), BIS-11 (impulsivity), and ImpSS (sensation seeking), level of education, age, gender, country of residence and ethnicity. All input attributes are originally categorical and are quantified. After quantification values of all input features can be considered as real-valued. In addition, participants were questioned concerning their use of 18 legal and illegal drugs (alcohol, amphetamines, amyl nitrite, benzodiazepine, cannabis, chocolate, cocaine, caffeine, crack, ecstasy, heroin, ketamine, legal highs, LSD, methadone, mushrooms, nicotine and volatile substance abuse and one fictitious drug (Semeron) which was introduced to identify over-claimers. For each drug they have to select one of the answers: never used the drug, used it over a decade ago, or in the last decade, year, month, week, or day.

Detailed description of database and process of data quantification are presented in E. Fehrman, A. K. Muhammad, E. M. Mirkes, V. Egan and A. N. Gorban, "The Five Factor Model of personality and evaluation of drug consumption risk.," arXiv [Web Link], 2015 Paper above solve binary classification problem for all drugs. For most of drugs sensitivity and specificity are greater than 75%

Since all of the features have been quantified into real values please refer to the link to the original dataset to get more clarity on categorical variables. For example, for EScore (extraversion) 9 people scored 55 which corresponds to a quantified (real) value of in the dataset 2.57309. I have also converted some variables back into their categorical values which are included in the drug_consumption.csv file Original Dataset

Content

Feature Attributes for Quantified Data: 1. ID: is a number of records in an original database. Cannot be related to the participant. It can be used for reference only. 2. Age (Real) is the age of participant 3. Gender: Male or Female 4. Education: level of education of participant 5. Country: country of origin of the participant 6. Ethnicity: ethnicity of participant 7. Nscore (Real) is NEO-FFI-R Neuroticism 8. Escore (Real) is NEO-FFI-R Extraversion 9. Oscore (Real) is NEO-FFI-R Openness to experience. 10. Ascore (Real) is NEO-FFI-R Agreeableness. 11. Cscore (Real) is NEO-FFI-R Conscientiousness. 12. Impulsive (Real) is impulsiveness measured by BIS-11 13. SS (Real) is sensation seeing measured by ImpSS 14. Alcohol: alcohol consumption 15. Amphet: amphetamines consumption 16. Amyl: nitrite consumption 17. Benzos: benzodiazepine consumption 18. Caff: caffeine consumption 19. Cannabis: marijuana consumption 20. Choc: chocolate consumption 21. Coke: cocaine consumption 22. Crack: crack cocaine consumption 23. Ecstasy: ecstasy consumption 24. Heroin: heroin consumption 25. Ketamine: ketamine consumption 26. Legalh: legal highs consumption 27. LSD: LSD consumption 28. Meth: methadone consumption 29. Mushroom: magic mushroom consumption 30. Nicotine: nicotine consumption 31. Semer: class of fictitious drug Semeron consumption (i.e. control) 32. VSA: class of volatile substance abuse consumption

Rating's for Drug Use: - CL0 Never Used - CL1 Used over a Decade Ago - CL2 Used in Last Decade - CL3 Used in Last Year 59 - CL4 Used in Last Month - CL5 Used in Last Week - CL6 Used in Last Day

Acknowledgements

1. Elaine Fehrman, Men's Personality Disorder and National Women's Directorate, Rampton Hospital, Retford, Nottinghamshire, DN22 0PD, UK, Elaine.Fehrman@nottshc.nhs.uk

2. Vincent Egan, Department of Psychiatry and Applied Psychology, University of Nottingham, Nottingham, NG8 1BB, UK, Vincent.Egan@nottingham.ac.uk

3. Evgeny M. Mirkes Department of Mathematics, University of Leicester, Leicester, LE1 7RH, UK, em322@le.ac.uk

Inspiration

Problem which can be solved: - Seven class classifications for each drug separately. - Problem can be transformed to binary classification by union of part of classes into one new class. For example, "Never Used", "Used over a Decade Ago" form class "Non-user" and all other classes form class "User". - The best binarization of classes for each attribute. - Evaluation of risk to be drug consumer for each drug.

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

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.

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

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 presents the footprint of the percentage of tobacco use, alcohol risk and recent illicit drug use for people aged 14 years and over. The data spans the year of 2016 and is aggregated to Statistical Area Level 4 (SA4) geographic areas from the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the 2016 National Drug Strategy Household Survey (NDSHS). The NDSHS is the leading survey of licit and illicit drug use in Australia. The 2016 survey was the 12th conducted under the auspices of the NDS. Previous surveys were conducted in 1985, 1988, 1991, 1993, 1995, 1998, 2001, 2004, 2007, 2010 and 2013. The data collected through these surveys have contributed to the development of policies for Australia's response to drug-related issues.

The NDSHS data accompanies the National Drug Strategy Household Survey 2016: Detailed Findings Report.

For further information about this dataset, please visit:

• Australian Institute of Health and Welfare - National Drug Strategy Household Survey 2016 Data Tables.

• National Drug Strategy Household Survey 2016 - Data Quality Statement.

Please note:

• AURIN has spatially enabled the original data.

• The calculation of alcohol risk was updated in 2013. 'Abstainers' no longer equate to 'never' and 'ex-drinkers' combined because the calculation now excludes drinkers who did not indicate the quantity of alcohol they consumed. Trend data will not match the data presented in previous reports. Refer to technical notes for further details.

• Recent illicit drug use is defined as illicit drug use in the previous 12 months.

The complete list of public structures that deal with prevention, treatment and rehabilitation of people with problems of abuse and dependence on alcohol, drugs, drugs and compulsive behaviors such as pathological gambling

Geographic information on the locations of the Mental Health and Addiction Organization. This dataset uses information in the ConnexOntario database to produce directories of addiction and mental health services in the province. ConnexOntario operates three helplines (Drug and Alcohol Helpline, Ontario Problem Gambling Helpline, and Mental Health Helpline) and provides free and confidential health services information for people experiencing problems with gambling, drugs or alcohol and mental health.

Town level data on depicting the number of admissions, and individuals served per year in treatment programs funded or operated by the Department of Mental Health and Addiction Services, where the primary drug at admission was an opioid. Clients entering these programs were treated for their opioid related disorder as a result of it being the primary drug at the time of admission. This dataset does not reflect ALL individuals in Connecticut who have participated in an opioid related treatment program, but only service provided by the Department of Mental Health and Addiction Services, nor are these programs specifically designated as "opioid treatment."

* Values less than or equal to 5 and greater than 0 have been suppressed to protect the indentity of individuals.

The Population Assessment of Tobacco and Health (PATH) Study began originally surveying 45,971 adult and youth respondents. The PATH Study was launched in 2011 to inform Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who use or do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled Primary Sampling Unit (PSU)s and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the civilian, noninstitutionalized population at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort.Dataset 0001 (DS0001) contains the data from the Master Linkage file. This file contains 14 variables and 67,276 cases. The file provides a master list of every person's unique identification number and what type of respondent they were for each wave. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This second replenishment sample was combined for estimation and analysis purposes with Wave 7 adult and youth respondents from the Wave 4 Cohort who were at least age 15 and in the civilian, noninstitutionalized population at the time of Wave 7. This combined set of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Public-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts.Dataset 1001 (DS1001) contains the data from the Wave 1 Adult Questionnaire. This data file contains 1,732 variables and 32,320 cases. Each of the cases represents a single, completed interview. Dataset 1002 (DS1002) contains the data from the Youth and Parent Questionnaire. This file contains 1,228 variables and 13,651 cases.Dataset 2001 (DS2001) contains the data from the Wave 2 Adult Questionnaire. This data file contains 2,197 variables and 28,362 cases. Of these cases, 26,447 also completed a Wave 1 Adult Questionnaire. The other 1,915 cases are "aged-up adults" having previously completed a Wave 1 Youth Questionnaire. Dataset 2002 (DS2002) contains the data from the Wave 2 Youth and Parent Questionnaire. This data file contains 1,389 variables and 12,172 cases. Of these cases, 10,081 also completed a Wave 1 Youth Questionnaire. The other 2,091 cases are "aged-up youth" having previously been sampled as "shadow youth." Dataset 3001 (DS3001) contains the data from the Wave 3 Adult Questionnaire. This data file contains 2,139 variables and 28,148 cases. Of these cases, 26,241 are continuing adults having completed a prior Adult Questionnaire. The other 1,907 cases are "aged-up adults" having previously completed a Youth Questionnaire. Dataset 3002 (DS3002) contains the data from t