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.

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. In 2023 the survey was administered online for the first time, instead of paper-based surveys as in previous years. This move online also meant that completion of the survey could be managed through teacher-led sessions, rather than being conducted by external interviewers. The 2023 survey also introduced additional questions relating to pupils wellbeing. These included how often the pupil felt lonely, felt left out and that they had no-one to talk to. Results of analysis covering these questions have been presented within parts of the report and associated data tables. The report 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 in early 2025 (see link below).

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 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).

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.

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 ---

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?

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)

This pilot study was conducted in an attempt to better understand the jailed population in terms of the number of families at risk and the relationship between parental substance use and incarceration and its impact on the children of the incarcerated. The aim of the study was to describe the jailed population, their needs in relation to substance abuse and parenting issues, to explore children's risk factors resulting from having a parent with substance abuse and/or criminal justice involvement, and ultimately to offer a point of intervention for parents and children at risk. Participants included 229 men and 52 women aged 18 and older, who were in their first 48 hours of incarceration in the Santa Clara County Department of Corrections in August 2003 and who where voluntary participants in the National Institute of Justice's (NIJ) Arrestee Drug Abuse Monitoring (ADAM) Program (ARRESTEE DRUG ABUSE MONITORING (ADAM) PROGRAM IN THE UNITED STATES, 2003 [ICPSR 4020]). Male subjects were chosen through a random selection process, while female participants were taken from a convenience sample. The pilot study used a questionnaire completed as an addendum to the ADAM program main interview. Major types of variables included in this study are type and duration of alcohol/drug use, family history of incarceration, number and ages of children for whom the respondent was the primary caregiver, social consequences for the child due to the incarceration of the respondent, and if the child had any problems with drugs and/or alcohol.

The HealthLink BC Mental Health and Substance Use (MHSU) data set includes the following: Programs that offer early intervention, transitional care or other services that supplement and facilitate primary and adjunctive therapies; which offer community mental health education programs; or which link people who are in need of treatment with appropriate providers. Programs that provide preventive, diagnostic and treatment services in a variety of community and hospital-based settings to help people achieve, maintain and enhance a state of emotional well-being, personal empowerment and the skills to cope with everyday demands without excessive stress or reliance on alcohol or other drugs. Treatment may include emotional support, introspection and problem-solving assistance using a variety of modalities and approaches, and medication, as needed, for individuals who have a substance use disorder involving alcohol and/or other drugs or for people who range from experiencing difficult life transitions or problems in coping with daily living to those with severe, chronic mental illnesses that seriously impact their lives. Multidisciplinary programs, often offered on an inpatient basis with post-discharge outpatient therapy, that provide comprehensive diagnostic and treatment services for individuals who have anorexia nervosa, binge-eating disorder, bulimia or a related eating disorder. Treatment depends on the specific type of eating disorder involved but typically involves psychotherapy, nutrition education, family counseling, medication and hospitalization, if required, to stabilize the patient's health. Alliance of Information & Referral Systems (AIRS) / 211 LA County taxonomy is the data classification used for all HealthLink BC directory data, including this MHSU data set (https://www.airs.org/i4a/pages/index.cfm?pageid=1). AIRS taxonomy and data definitions are protected by Copyright by Information and Referral Federal of Los Angeles County, Inc (https://211taxonomy.org/subscriptions/#agreement)

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

These figures come from the annual health module of the Permanent Research LeefSituatie (POLS). Statistics Netherlands conducts this Health Survey with the aim of providing the most complete overview possible of developments in health, medical consumption, lifestyle and preventive behavior of the Dutch population. Surveyed the following types of drugs from individuals aged 15 to 65: marijuana/hashish, cocaine, amphetamines, ecstasy, heroin, performance-enhancing drugs, and LSD. People were asked if they had "ever" used drugs. The people who indicated that they had ever used drugs were then asked whether they had also used drugs in the "past 30 days". In the table, the data can be broken down into the following characteristics: - gender - age group - level of education - urbanity of place of residence Data available from: 2007 up to and including 2009 Status of the figures: final. Change as of January 10, 2017: Table has been discontinued. Change as of 15 June 2010: The figures for 2009 have been added. The 2007 and 2008 figures for "Drug use in the past 30 days" have been corrected due to a technical error in the programme. When will new numbers come out? Table has been discontinued.

The DC Metropolitan Area Drug Study (DCMADS) was conducted in 1991, and included special analyses of homeless and transient populations and of women delivering live births in the DC hospitals. DCMADS was undertaken to assess the full extent of the drug problem in one metropolitan area. The study was comprised of 16 separate studies that focused on different sub-groups, many of which are typically not included or are underrepresented in household surveys. The Homeless and Transient Population study examines the prevalence of illicit drug, alcohol, and tobacco use among members of the homeless and transient population aged 12 and older in the Washington, DC, Metropolitan Statistical Area (DC MSA). The sample frame included respondents from shelters, soup kitchens and food banks, major cluster encampments, and literally homeless people. Data from the questionnaires include history of homelessness, living arrangements and population movement, tobacco, drug, and alcohol use, consequences of use, treatment history, illegal behavior and arrest, emergency room treatment and hospital stays, physical and mental health, pregnancy, insurance, employment and finances, and demographics. Drug specific data include age at first use, route of administration, needle use, withdrawal symptoms, polysubstance use, and perceived risk.This study has 1 Data Set.

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

Content

This data contains 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 (see Technical notes) 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 (see Technical notes). Starting in June 2018, this monthly data release will include both reported and predicted provisional counts.

The provisional data include: (a) the reported and predicted provisional counts of deaths due to drug overdose occurring nationally and in each jurisdiction; (b) the percentage changes in provisional drug overdose deaths for the current 12 month-ending period compared with the 12-month period ending in the same month of the previous year, by jurisdiction; and (c) the reported and predicted provisional counts of drug overdose deaths involving specific drugs or drug classes occurring nationally and in selected jurisdictions. The reported and predicted provisional counts represent the numbers of deaths due to drug overdose occurring in the 12-month periods ending in the month indicated. These counts include all seasons of the year and are insensitive to variations by seasonality. Deaths are reported by the jurisdiction in which the death occurred.

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 (see Technical notes). 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 (see Technical notes). Provisional data will be updated on a monthly basis as additional records are received.

Technical notes

Nature and sources of data

Provisional drug overdose death counts are based on death records received and processed by the National Center for Health Statistics (NCHS) as of a specified cutoff date. The cutoff date is generally the first Sunday of each month. National provisional estimates include deaths occurring within the 50 states and the District of Columbia. NCHS receives the death records from state vital registration offices through the Vital Statistics Cooperative Program (VSCP).

The timeliness of provisional mortality surveillance data in the National Vital Statistics System (NVSS) database varies by cause of death. The lag time (i.e., the time between when the death occurred and when the data are available for analysis) is longer for drug overdose deaths compared with other causes of death (1). Thus, provisional estimates of drug overdose deaths are reported 6 months after the date of death.

Provisional death counts presented in this data visualization are for “12-month ending periods,” defined as the number of deaths occurring in the 12-month period ending in the month indicated. For example, the 12-month ending period in June 2017 would include deaths occurring from July 1, 2016, through June 30, 2017. The 12-month ending period counts include all seasons of the year and are insensitive to reporting variations by seasonality. Counts for the 12-month period ending in the same month of the previous year are shown for comparison. These provisional counts of drug overdose deaths and related data quality metrics are provided for public health surveillance and monitoring of emerging trends. Provisional drug overdose death data are often incomplete, and the degree of completeness varies by jurisdiction and 12-month ending period. Consequently, the numbers of drug overdose deaths are underestimated based on provisional data relative to final data and are subject to random variation. Methods to adjust provisional counts have been developed to provide predicted provisional counts of drug overdose deaths, accounting for delayed reporting (see Percentage of records pending investigation and Adjustments for delayed reporting).

Provisional data are based on available records that meet certain data quality criteria at the time of analysis and may not include all deaths that occurred during a given time period. Therefore, they should not be considered comparable with final data and are subject to change.

Cause-of-death classification and definition of drug deaths Mortality statistics are compiled in accordance with World Health Organization (WHO) regulations specifying that WHO member nations classify and code causes of death with the current revision of the International Statistical Classification of Diseases and Related Health Problems (ICD). ICD provides the basic guidance used in virtually all countries to code and classify causes of death. It provides not only disease, injury, and poisoning categories but also the rules used to select the single underlying cause of death for tabulation from the several diagnoses that may be reported on a single death certificate, as well as definitions, tabulation lists, the format of the death certificate, and regulations on use of the classification. Causes of death for data presented in this report were coded according to ICD guidelines described in annual issues of Part 2a of the NCHS Instruction Manual (2).

Drug overdose deaths are identified using underlying cause-of-death codes from the Tenth Revision of ICD (ICD–10): X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), and Y10–Y14 (undetermined). Drug overdose deaths involving selected drug categories are identified by specific multiple cause-of-death codes. Drug categories presented include: heroin (T40.1); natural opioid analgesics, including morphine and codeine, and semisynthetic opioids, including drugs such as oxycodone, hydrocodone, hydromorphone, and oxymorphone (T40.2); methadone, a synthetic opioid (T40.3); synthetic opioid analgesics other than methadone, including drugs such as fentanyl and tramadol (T40.4); cocaine (T40.5); and psychostimulants with abuse potential, which includes methamphetamine (T43.6). Opioid overdose deaths are identified by the presence of any of the following MCOD codes: opium (T40.0); heroin (T40.1); natural opioid analgesics (T40.2); methadone (T40.3); synthetic opioid analgesics other than methadone (T40.4); or other and unspecified narcotics (T40.6). This latter category includes drug overdose deaths where ‘opioid’ is reported without more specific information to assign a more specific ICD–10 code (T40.0–T40.4) (3,4). Among deaths with an underlying cause of drug overdose, the percentage with at least one drug or drug class specified is defined as that with at least one ICD–10 multiple cause-of-death code in the range T36–T50.8.

Drug overdose deaths may involve multiple drugs; therefore, a single death might be included in more than one category when describing the number of drug overdose deaths involving specific drugs. For example, a death that involved both heroin and fentanyl would be included in both the number of drug overdose deaths involving heroin and the number of drug overdose deaths involving synthetic opioids other than methadone.

Selection of specific states and other jurisdictions to report Provisional counts are presented by the jurisdiction in which the death occurred (i.e., the reporting jurisdiction). Data quality and timeliness for drug overdose deaths vary by reporting jurisdiction. Provisional counts are presented for reporting jurisdictions based on measures of data quality: the percentage of records where the manner of death is listed as “pending investigation,” the overall completeness of the data, and the percentage of drug overdose death records with specific drugs or drug classes recorded. These criteria are defined below.

Percentage of records pending investigation

Drug overdose deaths often require lengthy investigations, and death certificates may be initially filed with a manner of death “pending investigation” and/or with a preliminary or unknown cause of death. When the percentage of records reported as “pending investigation” is high for a given jurisdiction, the number of drug overdose deaths is likely to be underestimated. For jurisdictions reporting fewer than 1% of records as “pending investigation”, the provisional number of drug overdose deaths occurring in the fourth quarter of 2015 was approximately 5% lower than the final count of drug overdose deaths occurring in that same time period. For jurisdictions reporting greater than 1% of records as “pending investigation” the provisional counts of drug overdose deaths may underestimate the final count of drug overdose deaths by as much as 30%. Thus, jurisdictions are included in Table 2 if 1% or fewer of their records in NVSS are reported as “pending investigation,” following a 6-month lag for the 12-month ending periods included in the dashboard. Values for records pending investigation are updated with each monthly release and reflect the most current data available.

Percent completeness

NCHS receives monthly counts of the estimated number of deaths from each jurisdictional vital registration offices (referred to as “control counts”). This number represents the best estimate of how many deaths occurred in a given jurisdiction in each month. Death records in the NVSS database must have

Drug Information: The dataset likely includes the generic and brand names of medications, allowing researchers to analyze trends across different formulations.

Condition Specificity: While "Bladder Infection" might be a category, the data could contain more specific diagnoses like "Urinary Tract Infection (UTI)" or "Cystitis." This granularity allows for targeted analysis within conditions.
Sentiment Analysis: The text content of reviews can be analyzed to understand patient sentiment towards the medication. This goes beyond the rating by capturing positive experiences, concerns about side effects, and overall satisfaction.
Side Effect Reporting: Reviews often mention side effects experienced by patients. Analyzing this data can help identify common side effects and potential drug interactions.

Use Cases:

Comparative Effectiveness Research: By comparing patient experiences with different medications for the same condition, researchers can gain insights into their relative effectiveness and tolerability.
Patient-Centered Drug Development: Understanding patient perspectives on existing medications can inform the development of new drugs with improved side effect profiles and better patient experiences.
Pharmacovigilance: The dataset can be a valuable source of real-world data on medication safety, helping identify potential adverse effects that may not be captured in clinical trials.
Personalized Medicine: Analyzing patient reviews alongside their medical history could lead to the development of tools for personalized medicine, tailoring treatment plans based on individual responses to medications.
Natural Language Processing (NLP): Techniques like NLP can be used to extract insights from the text content. This could involve identifying patterns in patient experiences, summarizing common themes, or even building chatbots that answer patient questions about medications.

Limitations:

Data Accuracy: Patient reviews might not always be accurate or complete. Users might misreport side effects or have pre-existing biases.
Selection Bias: People with strong positive or negative experiences might be more likely to leave reviews, skewing the data towards extremes.
Anonymity: While anonymized, the data may not capture the full picture of a patient's medical history, which could influence their experience with a medication.

Overall, this patient review dataset offers a unique window into the real-world experiences of patients with various medications. By analyzing this data responsibly and considering its limitations, researchers and healthcare professionals can gain valuable insights to improve patient care and drug development.

A growing body of evidence suggests that news media which includes a sympathetic portrayal of a mother bereaved by substance use can increase public support for harm reduction initiatives. However, the extent to which such news media coverage occurs in Canada is unknown, and research has not documented how the news media in Canada covers such stories. We undertook a mixed-method secondary analyses of 5681 Canadian newspaper articles on harm reduction (2000-2016). Quantitative analyses described the volume and content of harm reduction reporting featuring a mother whose child’s death was related to substance use while qualitative thematic analysis provided in-depth descriptions of the discourses underlying such news reporting. Newspaper articles featuring a mother whose child’s death was related to substance use were rarely published (n = 63; 1.1% of total harm reduction media coverage during the study period). Deductive content analysis of these 63 texts revealed that coverage of naloxone distribution (42.9%) and supervised drug consumption services (28.6%) were prioritized over other harm reduction services. Although harm reduction (services or policies) were advocated by the mother in most (77.8%) of these 63 texts, inductive thematic analysis of a subset (n = 52) of those articles revealed that mothers’ advocacy was diminished by newspaper reporting that emphasized their experiences of grief, prioritized individual biographies over structural factors contributing to substance use harms, and created rhetorical divisions between different groups of people who use drugs (PWUD). Bereaved mothers’ advocacy in support of harm reduction programs and services may be minimized in the process of reporting their stories for newspaper readers. Finding ways to report bereaved mothers’ stories in ways that are inclusive of all PWUD while highlighting the role of broad, structural determinants of substance use has the potential to shift public opinion and government support in favour of these life-saving services.

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.

Source: Prescription Drug Monitoring Program (PDMP), Rhode Island Department of Health (RIDOH)Note: Data updated quarterly. On November 1, 2019, the PDMP data were revised to reflect updates to the PDMP data analysis protocol, including revised methods for removing veterinary prescriptions, matching patients, and querying drug types. Prescriptions for buprenorphine medication-assisted treatment for opioid use disorder are excluded from this measure. Data for overlapping opioid and benzodiazepine prescriptions are calculated differently by year or by quarter. Summing this quarterly data gives a higher number of people than the yearly prevention strategy metrics on the Track Our Action Plan data page.

Oral and written survey with standardized questionnaire