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

Analysis of ‘Accidental Drug Related Deaths in Connecticut’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/accidental-drug-related-deaths-in-connecticute on 13 February 2022.

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

About this dataset

This dataset contains the list of each accidental death associated with a drug overdose in the state of Connecticut from 2012 to 2018. Deaths are grouped by age, race, ethnicity, and gender and by the types of drugs detected post-death.

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For subscribing to a commercial license for John Snow Labs Data Library which includes all datasets curated and maintained by John Snow Labs please visit https://www.johnsnowlabs.com/marketplace.

This dataset was created by John and contains around 0 samples along with County, City, technical information and other features such as: - Is Hydrocodone - Age - and more.

How to use this dataset

• Analyze Race in relation to Is Methadone
• Study the influence of Is Heroin on Is Oxymorphone
• More datasets

Acknowledgements

If you use this dataset in your research, please credit John

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

Collection of village histories for 50 villages in Nimroz province, Afghanistan. These village histories capture the changing social and political conditions, the drug economy and the presence of armed groups in the borderlands of Afghanistan, from the perspective of people who live there. Village histories were captured through group interviews with elder people knowledgeable about the village, using a standard list of questions, and cover the history since the 1970s through different political periods, wars and turbulent times. These village histories are complemented by a household survey carried out in the same villages (UKDA-SN-855856). They form part of wider research to develop a robust and dynamic understanding of the actors, commodities and events that shape the borderlands of Afghanistan, in particular with regards to opium production, trade and use, and associated insecurity and conflicts.Drugs & (dis)order is a Global Challenges Research Fund (GCRF) project generating new evidence on how to transform illicit drug economies into peace economies in Afghanistan, Colombia and Myanmar. By 2030, more than 50% of the world’s poor will live in fragile and conflict-affected states. And many of today’s armed conflicts are fuelled by illicit drug economies in borderland regions. Trillions of dollars have been spent on the War on Drugs, but securitised approaches have failed. In fact, they often increase state fragility and adversely affect the health and livelihoods of communities and households. In light of these failures, there’s increasing recognition that drug policies need to be more pro-poor and aligned with the Sustainable Development Goals (SDGs). But the evidence base for this policy reform is patchy, politicised and contested. Drugs & (dis)order is helping to generate pro-poor policy solutions to transform illicit economies into peace economies. To do this we will: (1) Generate a robust evidence base on illicit drug economies and their effects on armed conflict, public health and livelihoods. (2) Identify new approaches and policy solutions to build more inclusive development and sustainable livelihoods in drugs affected contexts. (3) Build a global network of researchers and institutions in Afghanistan, Colombia, Myanmar and the UK to continue this work. Village histories were captured through group interviews. One group interview was held per village, with 3-5 elder people knowledgeable about the village, that were contacted through the Community Development Council (CDC) or shura. Interviews used a standard list of questions / topics. Questions focused on village history governance since the 1970s through different political periods; the history of agricultural production; the history of manufacturing, factories and industries; cross-border trade affecting the village; main development programmes affecting the community; major population movements, events and natural disasters affecting the community; central state intervention in the community in different periods; and the history of militia and tribal self-defence groups in the village. Twelve or 13 villages were selected in each of four districts of Nimroz province. Half were chosen randomly, the other half based on criteria for population, geographical location, agriculture and drug history to ensure variation.

These figures come from the annual health module of the Permanent Research Live Situation (POLS). CBS conducts this health survey with the aim of provide a complete overview of developments in the health, medical consumption, lifestyle and preventive behaviour of the Dutch population. The following types of drugs were requested from persons aged 15 to 65: marijuana/hash, cocaine, amphetamines, ecstasy, heroin, performance enhancing agents and LSD. It was asked if people had ever used drugs. The people who reported that they had ever used drugs were then asked if they had taken the “last 30 days” drugs.

In the table, the data can be broken down to the following features: — gender — age class — level of education — urbanity of residence

Data available from: 2007 to 2009

Status of the figures: it’s definitely.

Change as of 10 January 2017: Table has been discontinued.

Amendment as of 15 June 2010: The 2009 figures have been added. The 2007 and 2008 figures on “drugs used in the past 30 days” have improved due to a technical error in the programme.

When are new figures coming?
Table has been discontinued.

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

The Drug Use Forecasting (DUF) Program measures levels of and trends in drug use among persons arrested and booked in the United States. The data address the following topics: (1) types of drugs used by arrestees (based on self-reports and urinalysis), (2) self-reported dependency on drugs, (3) self-reported need for alcohol/drug treatment, (4) the relationship between drug use and certain types of offenses, and (5) the relationship between self-reported indicators of drug use and indicators of drug use based on urinalysis. Participation in the project is voluntary, and all information collected from the arrestees is anonymous and confidential. The data include the arrestee's age, race, gender, educational attainment, marital status, and the charge at the time of booking. The recently modified DUF interview instrument (used for part of the 1995 data and all of the 1996 and 1997 data) also collected information about the arrestee's use of 15 drugs, including recent and past use (e.g., 3-day and 30-day drug use) of each of these drugs, age at first use, and whether the arrestee had ever been dependent on drugs. In the original DUF interview instrument (used for the 1987 to 1994 data and part of the 1995 data), the information collected was the same as above except that the use of 22 drugs was queried, and the age at which the arrestee first became dependent on the drug was included. Arrestees also were questioned in the original instrument about their history of intravenous drug use, whether the consideration of AIDS influenced whether they shared needles, history of drug and alcohol treatment, their past and current drug treatment needs, and how many persons they had sex with during the past 12 months. Finally, arrestees were asked to provide a urine specimen, which was screened for the presence of ten drugs, including marijuana, opiates, cocaine, PCP, methadone, benzodiazepines (Valium), methaqualone, propoxyphene (Darvon), barbiturates, and amphetamines (positive test results for amphetamines were confirmed by gas chromatography). The Gun Addendum Data (Parts 27, 35, and 37) contain variables on topics such as arrestees' encounters with guns, whether they agreed or disagreed with statements about guns, gun possession, how they obtained handgun(s), whether they were armed with a gun at their arrest or during crimes, and if they had ever used a gun against another person. The Heroin Addendum Data, 1995 (Part 29) contains information that was formerly covered in the main annual file in 1992-1994, but in 1995 was revised and prepared as a separate dataset.

The Risk Factors for AIDS among Intravenous Drug Users study is an ongoing series of cross-sectional studies that recruits participants from a storefront research site and from one of New York City's largest detoxification facilities. The goal of the study was to assess the potential effectiveness of HIV interventions by examining participants' drug use, risk behavior, and AIDS prevention knowledge and activities. The dataset combines survey responses taken from interviews conducted at the Bellevue Methadone Maintenance Treatment Program, the Beth Israel Medical Center and from a high drug use area in Lower East Side of Manhattan. All participants were at least 18 years of age or older. Participants from the Beth Israel Medical Center and the Lower East Side were given face-to-face interviews based on a World Health Organization Multi-Centre questionnaire. Data from the Bellevue Methadone Maintenance Treatment Program were extracted from patients' clinical files. Minimal demographic and HIV risk behavior were included in the methadone patient responses in these data to protect their anonymity. Blood samples were taken from participants to test for HIV. These data also contain information on topics including participant demographics, alcohol use, drug use, substance abuse treatment, needle sharing habits, sexual behavior, social networks, HIV testing services, as well as mental and physical health. Drugs use explored in this study includes heroin, cocaine, crack, methadone, amphetamines, ice, tranquilizers, barbiturates and other drugs.There are 2,907 respondents and 906 variables in the dataset.

This indicator includes unintentional overdoses, homicides, and suicides from drug overdose. Death rate has been age-adjusted to the 2000 U.S. standard population. ICD-10 codes used to identify drug overdose related deaths are X40-X44, X60-X64, X85, and Y10-Y14.Drug overdose deaths have increased dramatically in the US over the past two decades. The first wave of deaths in the 1990s largely involved prescription opioids and was a consequence of increased prescribing of these drugs by medical providers. In the second wave that began in 2010, there was a rapid increase in the number of deaths involving heroin and, in the current wave that started in 2013, there has been a rise in the number of overdose deaths involving synthetic opioids, particularly illicitly manufactured fentanyl, which can be found in combination with heroin, counterfeit pills, cocaine, and other drugs. In Los Angeles County in recent years, the vast majority of all drug overdose deaths have involved fentanyl. Important inequities have been noted by sociodemographic characteristics, with low-income and Black individuals found to have the highest overdose death rates. Cities and communities can take an active role in preventing overdose deaths by promoting primary prevention and supporting evidence-based harm reduction and treatment strategies.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

According to the World Drug Report 2020, cocaine and ecstasy are the most consumed stimulant drugs, with 19 and 27 millions estimated users in 2018. Unsurprisingly, large efforts are being made to design fast and cost effective analytical methods to track and monitor the distribution networks of these synthetic drugs. Here we share two datasets of ecstasy pills seized in the north-east of Switzerland between 2010 and 2011. The first contains 621 forensic grade images of pills, while the second one consists of 486 mIR spectra. While both sets are not covering the same seizure, both provide high quality data with orthogonal information to evaluate clustering and dimension reduction methods.

This project involved an experiment conducted in three Drug Use Forecasting (DUF) [DRUG USE FORECASTING IN 24 CITIES IN THE UNITED STATES, 1987-1997 (ICPSR 9477)] program sites to determine whether using a more detailed informed consent procedure and/or altering the sequence of the interview and urine specimen collection could enhance the validity of arrestees' self-reports of drug use without adversely affecting study response rates. A 2x2 factorial design was used to assess the effects of the two manipulations. The first two experimental conditions involved administering either the standard DUF informed consent or an enhanced consent that told the arrestees more about the confidential nature of the research and the capabilities of urinalysis. The second two conditions involved collecting the urine specimen either before or after the interview was administered. The experiment included 2,015 adult arrestees from Cleveland, Ohio, Detroit, Michigan, and Houston, Texas, who were randomly assigned to one of the four experimental conditions. The experiment was designed so that the only variability across the interviews was the manipulation of informed consent and the sequencing of the urine specimen request. All other procedures of a standard DUF collection were followed. Data were collected in Cleveland between July 8 and August 22, 1997, in Detroit from August 4 to September 27, 1997, and in Houston from October 17 to November 1, 1997. Variables specific to this project include the experimental condition to which the respondent was assigned, follow-up questions asking whether the arrestee would have responded differently if assigned to the other conditions, and several dummy variables on length and type of drug use. Data from the DUF interview provided detailed information about each arrestee's self-reported use of 15 drugs. For each drug type, arrestees were asked whether they had ever used the drug, the age at which they first used the drug, whether they had used the drug within the past three days, how many days they had used the drug within the past month, whether they had ever needed or felt dependent on the drug, and whether they were dependent on the drug at the time of the interview. Data from the DUF interview instrument also included alcohol/drug treatment history, information about whether arrestees had ever injected drugs, and whether they were influenced by drugs when the crime that they were charged with was committed. The data also include information about whether the arrestee had been to an emergency room for drug-related incidents and whether he or she had had prior arrests in the past 12 months. Urine tests screened for the presence of ten drugs, including marijuana, opiates, cocaine, PCP, methadone, benzodiazepines (Valium), methaqualone, propoxyphene (Darvon), barbiturates, and amphetamines (positive test results for amphetamines were confirmed by gas chromatography). Demographic data include the age, race, sex, educational attainment, marital status, employment status, and living circumstances of each respondent.

Database of development aid interventions and investments for Nangarhar, Nimroz and Badakshan provinces for period 2002-2018. Data have been compiled from existing paper reports, digital reports and digital databases obtained from donor agencies, ministries, local authorities and NGOs.

The purpose of compiling the database was to gather evidence on whether or not interventions and developments influence the drugs economy. There is a belief that tackling drugs and promoting development are mutually reinforcing, assuming that development will generate viable alternative livelihood options for those engaged in drug economies. Official and NGO strategies thus follow the assumption that if lack of development fosters illicit activities such as drug crops, this can be alleviated by economic development interventions and state-building projects.

Aim was to record all development organizations active in the borderland areas and record the scope, scale, location and monitory investment of programmes, projects and interventions they fund ‒ not just drug control interventions but also mainstream development interventions in general ‒ for the period 2002 to 2018.

Drugs & (dis)order is a Global Challenges Research Fund (GCRF) project generating new evidence on how to transform illicit drug economies into peace economies in Afghanistan, Colombia and Myanmar. By 2030, more than 50% of the world’s poor will live in fragile and conflict-affected states. And many of today’s armed conflicts are fuelled by illicit drug economies in borderland regions. Trillions of dollars have been spent on the War on Drugs, but securitised approaches have failed. In fact, they often increase state fragility and adversely affect the health and livelihoods of communities and households. In light of these failures, there’s increasing recognition that drug policies need to be more pro-poor and aligned with the Sustainable Development Goals (SDGs). But the evidence base for this policy reform is patchy, politicised and contested. Drugs & (dis)order is helping to generate pro-poor policy solutions to transform illicit economies into peace economies. To do this we will: (1) Generate a robust evidence base on illicit drug economies and their effects on armed conflict, public health and livelihoods. (2) Identify new approaches and policy solutions to build more inclusive development and sustainable livelihoods in drugs affected contexts. (3) Build a global network of researchers and institutions in Afghanistan, Colombia, Myanmar and the UK to continue this work.

This study is a process evaluation of a Residential Substance Abuse Treatment (RSAT) program at the South Idaho Correctional Institution (SICI), addressing the following research questions: (1) Did the SICI RSAT program as delivered conform with its stated goals and objectives? (2) Did the program result in reduced recidivism, abstinence from drug and alcohol use, and reduced costs of incarceration? (3) Did the referral process identify the targeted population? (4) Would the SICI RSAT data, management, staffing, and design be suitably established within two years to allow for a full outcome evaluation? (5) Were there communication issues among the IDOC, Parole Commission, and contract providers that might interfere with program implementation and delivery? and (6) Were there any cooperative remedies that had been, or might be developed to address implementation and delivery difficulties? Researchers conducted field observations (Part 1, Observational Data) of program delivery by program leaders using both the Cognitive Change Program Module and the Minnesota Model-Based Chemical Dependency Treatment Modules in each of the three phases of the therapeutic community environment. Researchers administered questionnaires to inmates (Part 2, Inmate Interview Data) and staff (Part 3, Staff Interview Data) regarding their perceptions of program operations. Variables for Part 1 include the date and time of observation, nature of observation, clarity, organization, and substance of program delivery, the program leader's involvement and the quality of that involvement with inmates, how prepared the program leader was, and the general therapeutic atmosphere of the program. Demographic variables for Part 2 include the race, age, ethnicity, and level of education of each inmate. Other variables include use of alcohol and illegal drugs prior to incarceration, inmates' perceptions of the treatment personnel, their levels of involvement with the group meetings and cognitive self-change groups, the atmosphere of therapy, ratings of communication and delivery of treatment, quality of service, and the strengths and weaknesses of the RSAT program. Variables for Part 3 include staff's perceptions of the RSAT program and whether they felt the program content and delivery were well organized and easy to understand, perceptions of the program leader's preparation and involvement, perceptions of communication and consistency issues, the quality of service, and the strengths and weaknesses of the RSAT program.

Cannabis (hemp and marijuana) is an iconic yet controversial crop. On the one hand, it represents a growing market for pharmaceutical and agricultural sectors. On the other hand, plants synthesizing the psychoactive THC produce the most widespread illicit drug in the world. Yet, the difficulty to reliably distinguish between Cannabis varieties based on morphological or biochemical criteria impedes the development of promising industrial programs and hinders the fight against narcotrafficking. Genetics offers an appropriate alternative to characterize drug vs. non-drug Cannabis. However, forensic applications require rapid and affordable genotyping of informative and reliable molecular markers for which a broad-scale reference database, representing both intra- and inter-variety variation, is available. Here we provide such a resource for Cannabis, by genotyping 13 microsatellite loci (STRs) in 1 324 samples selected specifically for fibre (24 hemp varieties) and drug (15 marijuana varieties) production. We showed that these loci are sufficient to capture most of the genome-wide diversity patterns recently revealed by NGS data. We recovered strong genetic structure between marijuana and hemp and demonstrated that anonymous samples can be confidently assigned to either plant types. Fibres appear genetically homogeneous whereas drugs show low (often clonal) diversity within varieties, but very high genetic differentiation between them, likely resulting from breeding practices. Based on an additional test dataset including samples from 41 local police seizures, we showed that the genetic signature of marijuana cultivars could be used to trace crime scene evidence. To date, our study provides the most comprehensive genetic resource for Cannabis forensics worldwide.