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.

Objectives: Define the role of increasing cannabis availability on population mental health (MH).

Methods. Ecological cohort study of National Survey of Drug Use and Health (NSDUH) geographically-linked substate-shapefiles 2010-2012 and 2014-2016 supplemented by five-year US American Community Survey. Drugs: cigarettes, alcohol abuse, last-month cannabis use and last-year cocaine use. MH: any mental illness, major depressive illness, serious mental illness and suicidal thinking. Data analysis: two-stage and geotemporospatial methods in R.

Results: 410,138 NSDUH respondents. Average response rate 76.7%. When all drug exposure, ethnicity and income variables were combined in final geospatiotemporal models tobacco, alcohol cannabis exposure, and various ethnicities were significantly related to all four major mental health outcomes. Cannabis exposure alone was related to any mental illness (β-estimate= -3.315+0.374, P<2.2x10-16), major depressive episode (β-estimate= -3.712+0.454, P=3.0x10-16), serious mental illness (SMI, β-estimate= -3.063+0.504, P=1.2x10-9), suicidal ideation (β-estimate= -3.013+0.436, P=4.8x10-12) and with more significant interactions in each case (from β-estimate= 1.844+0.277, P=3.0x10-11). Geospatial modelling showed a monotonic upward trajectory of SMI which doubled (3.62% to 7.06%) as cannabis use increased. Extrapolated to whole populations cannabis decriminalization (4.35+0.05%, Prevalence Ratio (PR)=1.035(95%C.I. 1.034-1.036), attributable fraction in the exposed (AFE)=3.28%(3.18-3.37%), P<10-300) and legalization (4.66+0.09%, PR=1.155(1.153-1.158), AFE=12.91% (12.72-13.10%), P<10-300) were associated with increased SMI vs. illegal status (4.26+0.04%).

Conclusions: Data show all four indices of mental ill-health track cannabis exposure and are robust to multivariable adjustment for ethnicity, socioeconomics and other drug use. MH deteriorated with cannabis legalization. Together with similar international reports and numerous mechanistic studies preventative action to reduce cannabis use-exposure is indicated.

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 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).

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

This report presents results from the 2016 National Survey on Drug Use and Health (NSDUH) for people aged 12 or older regarding the perceived harmfulness of using cigarettes, alcohol, and specific illicit drugs as well as the perceived availability of substances. Estimates are presented for specific age groups. Estimates of the perceived great risk of harm associated with the use of marijuana, cocaine, alcohol, and cigarettes also are presented according to whether people initiated use of these substances in the past year.

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

Background: Whilst many studies have linked increased drug and cannabis exposure to adverse mental health (MH) outcomes their effects on whole populations and geotemporospatial relationships are not well understood. Objectives: Determine: (1) if cannabis use is associated with major MH outcomes ascross space and time, (2) if such impacts are robust to multivariable adjustment and (3) if the relationship is causal. Methods. Ecological cohort study of National Survey of Drug Use and Health (NSDUH) geographically-linked substate-shapefiles 2010-2012 and 2014-2016 supplemented by five-year US American Community Survey. Drugs: cigarettes, alcohol abuse, last-month cannabis use and last-year cocaine use. MH: any mental illness, major depressive illness, serious mental illness and suicidal thinking. Data analysis: two-stage, geotemporospatial, robust generalized linear regression and causal inference methods in R. Results: 410,138 NSDUH respondents. Average response rate 76.7%. When all drug exposure, ethnicity and income variables were combined in final geospatiotemporal models tobacco, alcohol cannabis exposure, and various ethnicities were significantly related to all four major mental health outcomes. Cannabis exposure alone was related to any mental illness (β-estimate= -3.315 (95%C.I. -4.04, -2.58, P<2.2x10-16), major depressive episode (β-estimate= -3.71 (-4.6, -2.82), P=3.0x10-16), serious mental illness (SMI, β-estimate= -3.063 (-4.05, -2.05), P=1.2x10-9), suicidal ideation (β-estimate= -3.01 (-3.87, -2.16), P=4.8x10-12) and with more significant interactions in each case (from β-estimate= 1.84 (1.30, 2.39), P=3.0x10-11). Geospatial modelling showed a monotonic upward trajectory of SMI which doubled (3.62% to 7.06%) as cannabis use increased. Extrapolated to whole populations cannabis decriminalization (4.26%, (4.18, 4.34%)), Prevalence Ratio (PR)=1.035(1.034-1.036), attributable fraction in the exposed (AFE)=3.28%(3.18-3.37%), P<10-300) and legalization (4.75% (4.65, 4.84%), PR=1.155 (1.153-1.158), AFE=12.91% (12.72-13.10%), P<10-300) were associated with increased SMI vs. illegal status (4.26+0.04%). Conclusions: Data show all four indices of mental ill-health track cannabis exposure across space and time and are robust to multivariable adjustment for ethnicity, socioeconomics and other drug use. MH deteriorated with cannabis legalization. Cannabis use-MH data are consistent with causal relationships in the forward direction and include dose-response relationships. Together with similar international reports and numerous mechanistic studies preventative action to reduce cannabis use is indicated.

Browse state-level population estimates based on the 2021-2022 National Surveys on Drug Use and Health (NSDUH). The 37 tables include estimates for 35 measures of substance use and mental health, by age group, along with 95% confidence intervals. The estimates are based on small area estimation (SAE) methods, in which state-level NSDUH data are combined with other data from smaller geographies. The combined data are used to create modeled state estimates of the civilian, noninstitutionalized population ages 12 and older, or adults 18 and older for mental health measures. Each table covers a single measure by state, region, and age group.The indicators are presented in the following 37 tables:Drug use and Perceived RiskIllicit Drug Use in the Past MonthMarijuana Use in the Past YearMarijuana Use in the Past MonthPerceptions of Great Risk from Smoking Marijuana Once a MonthFirst Use of Marijuana in the Past Year (among those at risk for initiation)Illicit Drug Use Other than Marijuana in the Past MonthCocaine Use in the Past YearPerceptions of Great Risk from using Cocaine Once a MonthHeroin Use in the Past YearPerceptions of Great Risk from Trying Heroin Once or TwiceHallucinogen Use in the Past YearMethamphetamine Use in the Past YearPrescription Pain Reliever Misuse in the Past YearOpioid Misuse in the Past YearAlcoholAlcohol Use in the Past MonthBinge Alcohol Use in the Past MonthPerceptions of Great Risk from Having Five or More Drinks of an Alcoholic Beverage Once or Twice a WeekAlcohol Use, Binge Alcohol Use in the Past Month, and Perceptions of Great Risk from Having Five or More Drinks of an Alcoholic Beverage Once or Twice a Week (among people aged 12 to 20)TobaccoTobacco Product Use in the Past MonthCigarette Use in the Past MonthPerceptions of Great Risk from Smoking One or More Packs of Cigarettes per DaySubstance Use DisordersSubstance Use Disorder in the Past YearAlcohol Use Disorder in the Past YearAlcohol Use Disorder in the Past Year (among people aged 12 to 20)Drug Use Disorder in the Past YearPain Reliever Use Disorder in the Past YearOpioid Use Disorder in the Past YearSubstance Use TreatmentReceived Substance Use Treatment in the Past YearClassified as Needing Substance Use Treatment in the Past YearDid Not Receive Substance Use Treatment in the Past Year among those Classified as Needing Substance Use TreatmentMental IllnessAny Mental Illness in the Past YearSerious Mental Illness in the Past YearReceived Mental Health Treatment in the Past YearMajor Depressive Episode in the Past YearSuicidalityHad Serious Thoughts of Suicide in the Past YearMade Any Suicide Plans in the Past YearAttempted Suicide in the Past YearThe tables are available in an Excel spreadsheet, a PDF file, or as a zip file of 37 CSV text files.

BackgroundPeripheral artery disease (PAD) is on the rise worldwide, ranking as the third leading cause of atherosclerosis-related morbidity; much less is known about its trends in hospitalizations among methamphetamine and cocaine users.ObjectivesWe aim to evaluate the overall trend in the prevalence of hospital admission for PAD with or without the use of stimulant abuse (methamphetamine and cocaine) across the United States. Additionally, we evaluated the PAD-related hospitalizations trend stratified by age, race, sex, and geographic location.MethodsWe used the National Inpatient Sample (NIS) database from 2008 to 2020. The Cochran Armitage trend test was used to compare the trend between groups. Multivariate logistic regression was used to examine adjusted odds for PAD and CLI hospitalizations among methamphetamine and cocaine users.ResultsBetween 2008 and 2020, PAD-related hospitalizations showed an increasing trend in Hispanics, African Americans, and western states, while a decreasing trend in southern and Midwestern states (p-trend <0.05). Among methamphetamine users, an overall increasing trend was observed in men, women, western, southern, and midwestern states (p-trend <0.05). However, among cocaine users, PAD-related hospitalization increased significantly for White, African American, age group >64 years, southern and western states (p-trend <0.05). Overall, CLI-related hospitalizations showed an encouraging decreasing trend in men and women, age group >64 years, and CLI-related amputations declined for women, White patient population, age group >40, and all regions (p-trend <0.05). However, among methamphetamine users, a significantly increasing trend in CLI-related hospitalization was seen in men, women, White & Hispanic population, age group 26–45, western, southern, and midwestern regions.ConclusionsThere was an increasing trend in PAD-related hospitalizations among methamphetamine and cocaine users for both males and females. Although an overall decreasing trend in CLI-related hospitalization was observed for both genders, an up-trend in CLI was seen among methamphetamine users. The upward trends were more prominent for White, Hispanic & African Americans, and southern and western states, highlighting racial and geographic variations over the study period.

Abstract The use of cocaine and its main derivative, crack, can cause some systemic effects that may lead to the development of some oral disorders. Objective To assess the oral health of people with a crack cocaine use disorder and identify salivary protein candidates for biomarkers of oral disorders. Methodology A total of 40 volunteers hospitalized for rehabilitation for crack cocaine addiction were enrolled; nine were randomly selected for proteomic analysis. Intraoral examination, report of DMFT, gingival and plaque index, xerostomia, and non-stimulated saliva collection were performed. A list of proteins identified was generated from the UniProt database and manually revised. Results The mean age (n=40) was 32 (±8.88; 18–51) years; the mean DMFT index was 16±7.70; the mean plaque and gingival index were 2.07±0.65 and 2.12±0.64, respectively; and 20 (50%) volunteers reported xerostomia. We identified 305 salivary proteins (n=9), of which 23 were classified as candidate for biomarkers associated with 14 oral disorders. The highest number of candidates for biomarkers was associated with carcinoma of head and neck (n=7) and nasopharyngeal carcinoma (n=7), followed by periodontitis (n=6). Conclusions People with a crack cocaine use disorder had an increased risk of dental caries and gingival inflammation; less than half had oral mucosal alterations, and half experienced xerostomia. As possible biomarkers for 14 oral disorders, 23 salivary proteins were identified. Oral cancer and periodontal disease were the most often associated disorders with biomarkers.

This report presents data from the 2015 National Survey on Drug Use and Health (NSDUH) regarding the perceived harmfulness of using cigarettes,alcohol, and specific illicit drugs and the perceived availability of substances. Estimates are presented for specific age groups. Estimates of the perceived great risk of harm associated with the use of marijuana, cocaine, alcohol, and cigarettes also are presented according to whether people initiated use of these substances in the past year. In addition, the report presents estimates for youth-specific protective factors, such as perceptions about parents strongly disapproving of youth substance use. Finally, this report presents the estimated numbers of individuals who initiated substance use in the past year and the average age at first use among people who initiated use in the past year (i.e., past year initiates). Statistically significant differences are noted for these various estimates.

This table contains 84 series, with data for years 1990 - 1998 (not all combinations necessarily have data for all years), and was last released on 2007-01-29. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Sex (2 items: Males; Females ...), Age group (2 items: 13 years; 15 years ...), Type of drug (7 items: Cocaine; Hashish or marijuana; Solvents; Heroin; opium or morphine ...), Frequency (3 items: 3 times or more; Once or twice; Never ...).

^ Any injection drug use includes injected cocaine, heroin, speedball, crystal meth, and painkillers.* Any crack/cocaine use includes cocaine that was injected, smoked (i.e., crack), or snorted. Any heroin use includes heroin that was injected, smoked, or snorted.Characteristics of participants and prevalence of oral HPV among 199 people who inject drugs in Baltimore City.

The impact of drugs on communities. Topics: assessment of people using or dealing drugs in the personal local area as a serious problem; problems associated with people using or dealing drugs in the personal local area: drugs being too easily available, people taking hard drugs, people smoking cannabis in public places, children and teenagers taking drugs, drug dealers or users being intimidating to local people, conflict and violence in the community related to drug use, domestic violence related to drug use, traffic accidents related to drug use, poverty and unemployment related to drug use; importance of drug use with regard to each of the following types of crime in the personal local area: youth crime, stealing / theft and burglary, violent crime and murder, corruption and lack of trust in public officials and institutions; development of the problems caused by drugs in the personal local area in the last few years; impact of the COVID-19 pandemic on the use of illegal drugs in the personal local area and on drug problems in the personal local area; attitude towards the following statements regarding the availability and the use of drugs: make respondent feel unsafe near his / her home or school or place of work, have a negative impact on personal relationships, have a negative impact on personal health and well-being, are central to reducing the overall quality of life in the personal local area; assessment of the impact of online drug sales on the problems in the personal local area; assessment of the severeness of health problems caused by cannabis; attitude towards the allowance of cannabis for: neither medical nor recreational use, medical use only under medical prescription, medical use only without prescription, both medical and recreational use for adults; personal use of cannabis; difficulty to obtain the following substances within 24 hours: cannabis, cocaine, MDMA (ecstasy), heroin, legal highs; attitude towards banning the sale of the aforementioned substances. Demography: age; sex; nationality; type of community; age at end of education; occupation; professional position; own a mobile phone and fixed (landline) phone; household composition and household size. Additionally coded was: respondent ID; country; type of phone line; region; nation group; weighting factor. Die Auswirkungen von Drogen auf Gemeinschaften. Themen: Bewertung des Drogenkonsums oder -handels im persönlichen Umfeld als ernstes Problem; Probleme im Zusammenhang mit Menschen, die in ihrem persönlichen Umfeld Drogen konsumieren oder damit handeln: zu leichte Verfügbarkeit von Drogen, harte Drogen konsumierende Menschen, an öffentlichen Plätzen Cannabis rauchende Menschen, Drogen konsumierende Kinder oder Teenager, Drogendealer oder -konsumenten mit einschüchterndem Verhalten, Konflikte und Gewalt im Zusammenhang mit Drogenkonsum, häusliche Gewalt im Zusammenhang mit Drogenkonsum, Verkehrsunfälle im Zusammenhang mit Drogenkonsum, Armut und Arbeitslosigkeit im Zusammenhang mit Drogenkonsum; Bedeutung des Drogenkonsums in Bezug auf jede der folgenden Arten von Kriminalität in der persönlichen Umgebung: Jugendkriminalität, Stehlen und (Einbruch-)Diebstahl, Gewaltverbrechen und Mord, Korruption und mangelndes Vertrauen in öffentliche Beamte und Institutionen; Entwicklung der Drogenproblematik im persönlichen Umfeld in den letzten Jahren; Auswirkungen der COVID-19-Pandemie auf den Konsum illegaler Drogen im persönlichen Umfeld und auf Drogenprobleme im persönlichen Umfeld; Einstellung zu den folgenden Aussagen über die Verfügbarkeit und den Konsum von Drogen: Befragte/r fühlt sich in der Nähe von Wohnung, Schule oder Arbeitsplatz unsicher, haben negative Auswirkungen auf die persönlichen Beziehungen, haben negative Auswirkungen auf persönliche Gesundheit und Wohlbefinden, sind von zentraler Bedeutung für die Verringerung der allgemeinen Lebensqualität im persönlichen Umfeld; Bewertung der Auswirkungen des Online-Drogenverkaufs auf die Probleme im persönlichen Umfeld; Bewertung der Schwere der durch Cannabis verursachten Gesundheitsprobleme; Einstellung zur Zulassung von Zulassung von Cannabis für: weder medizinische Zwecke noch für den Freizeitgebrauch, medizinische Verwendung nur auf ärztliche Verschreibung, medizinische Verwendung ohne ärztliche Verschreibung, sowohl medizinischen als auch Freizeitgebrauch für Erwachsene; bereits getätigter Cannabiskonsum; Schwierigkeit, die folgenden Substanzen innerhalb von 24 Stunden zu beschaffen: Cannabis, Kokain, MDMA (Ecstasy), Heroin, legale Rauschmittel; Einstellung zum Verbot oder zur gesetzlichen Regulierung der vorgenannten Substanzen. Demographie: Alter; Geschlecht; Staatsangehörigkeit; Urbanisierungsgrad; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Besitz eines Mobiltelefons; Festnetztelefon im Haushalt; Haushaltszusammensetzung und Haushaltsgröße. Zusätzlich verkodet wurde: Befragten-ID; Land; Interviewmodus (Mobiltelefon oder Festnetz); Region; Nationengruppe; Gewichtungsfaktor.

This data collection is part of the Monitoring the Future series that explores changes in important values, behaviors, and lifestyle orientations of contemporary American youth in eighth, tenth, and twelfth grades. The collection provides two datasets for each year since 1976 that are accessible only through the ICPSR Virtual Data Enclave VDE) and include original variables, including the unaltered weight variable, that in the public-use data were altered or omitted: one dataset without State, County, and Zip Code and one dataset including State, County, and Zip Code. Use of the geographic identifiers such as state, county, or zip code is limited and researchers interested in these variables are encouraged to read FAQs: About MTF Restricted-Use Geographic and Other Variables. Also included as part of each annual collection is a zip archive of the Monitoring the Future public-use data and documentation for each respective year. The basic research design used by the Monitoring the Future study involves annual data collections from eighth, tenth, and twelfth graders throughout the coterminous United States during the spring of each year. The 8th/10th grade surveys used four different questionnaire forms (and only two forms from 1991-1996) rather than the six used with seniors. Identical forms are used for both eighth and tenth grades, and for the most part, questionnaire content is drawn from the twelfth-grade questionnaires. Thus, key demographic variables and measures of drug use and related attitudes and beliefs are generally identical for all three grades. However, many fewer questions about lifestyles and values are included in the 8th/10th grade forms. Drugs covered by this survey include tobacco, smokeless tobacco, alcohol, marijuana, hashish, prescription medications, over-the-counter medications, inhalants, steroids, LSD, hallucinogens, amphetamines (stimulants), Ritalin (methylphenidate), Quaaludes (methaqualone), barbiturates (tranquilizers), cocaine, crack cocaine, ecstasy, methamphetamine, heroin, and GHB (gamma hydroxy butyrate). Other topics include attitudes toward religion, changing roles for women, educational aspirations, self-esteem, exposure to drug education, and violence and crime (both in and out of school).

Associations between cocaine use, gut integrity damage, microbial translocation and immune activation.

No description was included in this Dataset collected from the OSF

Participant’s characteristicsa.

This data set provides an estimate of the number of people living with Human Immunodeficiency Virus (HIV) Disease at the end of each year for 2012 through 2016 and the number of these persons who have injection drug use identified as the primary risk for having acquired the infection. The data sets also provides the number of new diagnoses of HIV Disease by county among all persons and among those with injection drug identified as the primary risk. These data are derived through HIV surveillance activities of the Pennsylvania Department of Health. Laboratories and providers are required to report HIV test results for all individuals with a result that indicates the presence of HIV infection. These include detectable viral load results and CD4 results below 200 cells. These data are reported electronically to the Pennsylvania National Electronic Disease Surveillance System. The most recent patient address information obtained from all reports (both HIV and non-HIV reports) is used to identify last known county of residence in 2016. Cases are also matched to lists that identify individuals who have been reported to be living outside of Pennsylvania by the US Centers for Disease Control and Prevention (CDC) to remove cases that are presumed to have moved from Pennsylvania. Address data for Philadelphia County cases are extracted from the Pennsylvania enhanced HIV/AIDS Reporting System.

IDU: use of non-prescribed injection drugs (e.g., heroin, fentanyl, cocaine, etc.)

HIV Disease: Confirmed infection with the Human Immunodeficiency Virus (HIV). Acquired Immunodeficiency Syndrome (AIDS) is a stage of HIV Disease marked by a low CD4 count and/or certain co-morbid conditions.