For those interested in data on student drug addiction in 2024, several sources offer valuable datasets and statistics.

1. Kaggle Dataset: Kaggle hosts a specific dataset on student drug addiction. This dataset includes various attributes related to student demographics, substance use patterns, and associated behavioral factors. It's a useful resource for data analysis and machine learning projects focused on understanding drug addiction among students【5†source】.

2. National Survey on Drug Use and Health (NSDUH): This comprehensive survey provides detailed annual data on substance use and mental health across the United States, including among students. It covers a wide range of substances and demographic details, helping to track trends and the need for treatment services【6†source】【8†source】.

3. Monitoring the Future (MTF) Survey: Conducted by the National Institute on Drug Abuse (NIDA), this survey tracks drug and alcohol use and attitudes among American adolescents. It provides annual updates and is an excellent source for understanding trends in substance use among high school and college students【7†source】.

4. Australian Institute of Health and Welfare (AIHW): For those interested in a more global perspective, the AIHW offers data from the National Drug Strategy Household Survey, which includes information on youth and young adult drug use in Australia. This can be useful for comparative studies【10†source】.

For detailed datasets and further analysis, you can explore these resources directly:

• Kaggle Student Drugs Addiction Dataset
• NSDUH Data
• Monitoring the Future
• AIHW Drug Survey

The National Survey on Drug Use and Health (NSDUH) provides national and state-level data on the use of tobacco, alcohol, illicit drugs (including non-medical use of prescription drugs) and mental health in the United States. This annual survey involves interviews with approximately 70,000 randomly selected individuals aged 12 and older. NSDUH is sponsored by the Substance Abuse and Mental Health Services Administration (SAMHSA), an agency of the U.S. Public Health Service in the U.S. Department of Health and Human Services (DHHS).

The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED) visits to hospitals since the early 1970s. First administered by the Drug Enforcement Administration (DEA) and the National Institute on Drug Abuse (NIDA), the responsibility for DAWN now rests with the Substance Abuse and Mental Health Services Administration's (SAMHSA) Center for Behavioral Health Statistics and Quality (CBHSQ). Over the years, the exact survey methodology has been adjusted to improve the quality, reliability, and generalizability of the information produced by DAWN. The current approach was first fully implemented in the 2004 data collection year.
DAWN relies on a longitudinal probability sample of hospitals located throughout the United States. To be eligible for selection into the DAWN sample, a hospital must be a non-Federal, short-stay, general surgical and medical hospital located in the United States, with at least one 24-hour ED. DAWN cases are identified by the systematic review of ED medical records in participating hospitals. The unit of analysis is any ED visit involving recent drug use. DAWN captures both ED visits that are directly caused by drugs and those in which drugs are a contributing factor but not the direct cause of the ED visit. The reason a patient used a drug is not part of the criteria for considering a visit to be drug related. Therefore, all types of drug-related events are included: drug misuse or abuse, accidental drug ingestion, drug-related suicide attempts, malicious drug poisonings, and adverse reactions. DAWN does not report medications that are unrelated to the visit.
The DAWN public-use dataset provides information for all types of drugs, including illegal drugs, prescription drugs, over-the-counter medications, dietary supplements, anesthetic gases, substances that have psychoactive effects when inhaled, alcohol when used in combination with other drugs (all ages), and alcohol alone (only for patients aged 20 or younger). Public-use dataset variables describe and categorize up to 16 drugs contributing to the ED visit, including toxicology confirmation and route of administration. Administrative variables specify the type of case, case disposition, categorized episode time of day, and quarter of year. Metropolitan area is included for represented metropolitan areas. Created variables include the number of unique drugs reported and case-level indicators for alcohol, non-alcohol illicit substances, any pharmaceutical, non-medical use of pharmaceuticals, and all misuse and abuse of drugs. Demographic items include age category, sex, and race/ethnicity. Complex sample design and weighting variables are included to calculate various estimates of drug-related ED visits for the Nation as a whole, as well as for specific metropolitan areas, from the ED visits classified as DAWN cases in the selected hospitals.This study has 1 Data Set.

1. Government Websites: Many government health departments or agencies collect data on drug use among students. For example, the National Institute on Drug Abuse (NIDA) in the United States often conducts surveys and publishes reports on drug use among various demographics, including students.

2. Research Institutions: Universities and research institutions often conduct studies on drug addiction, including among student populations. These studies may include survey data, clinical data, or experimental data.

3. Public Health Organizations: Organizations like the World Health Organization (WHO) may also collect and publish data on drug addiction among students on a global scale.

4. Online Data Repositories: Websites like Kaggle, UCI Machine Learning Repository, or Data.gov sometimes host datasets related to drug addiction and student populations.

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.

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).

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

According to Cognitive Market Research, The Drug Abuse Testing Market will be USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031. North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX % from 2024 to 2031. The Europe region is the fastest growing market with a CAGR of XX% from 2024 to 2031 and it is projected that it will grow at a CAGR of XX% in the future. Asia Pacific accounted for a market share of over XX% of the global revenue with a market size of USD XX million. Latin America had a market share for more than XX% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031. The Drug Abuse Testing Market held the highest market revenue share in 2024.

Market Dynamics of The Drug Abuse Testing Market

Key Drivers for The Drug Abuse Testing Market

Increase in road accidents due to alcohol consumption growing the market for drug abuse testing.

The number of people killed and injured in traffic accidents, whether they occur right away or 30 days later, are the metrics used to assess these incidents. Alcohol and other substances impair reflexes, judgment, decision-making, impulse control, and motor function, which increases the risk of traffic accidents and frequently results in fatalities. The World Health Organization (WHO) estimates that between 20 and 50 million people get non-fatal injuries and 1.3 million people die in traffic accidents each year. Rising road accidents through alcohol consumption have increased drug abuse testing due to this the market is growing. For instance, according to the 2021 National Survey on Drug Use and Health (NSDUH), in 2021, around 57.8% of people aged 12 years and more used tobacco, alcohol, and illicit drugs. Further, 47.5% of them were alcohol consumers, 19.5% took tobacco, and 14.3% used illicit drugs. Additionally, it is well-known that men are more likely to consume drugs and alcohol than women. Source:(https://www.samhsa.gov/data/sites/default/files/2022-12/2021NSDUHFFRHighlights092722.pdf) For instance, in October 2023, In India, Kerala police launched a rapid drug screening system that uses saliva samples and gives results in five minutes. The hand-held device, the SoToxa Mobile Test System, has been deployed on a trial basis in Thiruvananthapuram city, and depending on its reliability and accuracy, the system will be expanded to other parts of the state as well. Over the past two days, the police have booked 11 people for narcotic abuse through a roadside drive using this device that helps in identifying those who took drugs even two days before Source:(https://www.onmanorama.com/news/kerala/2023/10/08/police-launch-new-device-detect-drug-use-in-5-minutes.html) After alcohol, marijuana is most frequently detected in the blood of drivers who have been in accidents. The mind-altering component of marijuana, delta-9-tetrahydrocannabinol (THC), which is found in blood, sometimes takes center stage during an inquiry. Furthermore, compared to when the substances are taken separately, marijuana may increase the risk of car accidents when combined with cocaine, alcohol, or benzodiazepines. Therefore, in the future, the frequency of drug misuse testing will be determined by the rising incidence of such adverse occurrences.

The increasing prevalence of drug abuse treatment drives growth in the drug abuse testing market. 

The increasing prevalence of drug abuse has led to a rising demand for drug abuse treatment, consequently driving growth in the drug abuse testing market. With a greater focus on identifying and addressing substance abuse issues, there's a corresponding need for accurate and efficient testing methods to monitor and manage treatment progress effectively. The growing consumption of illicit drugs by individuals needs to have drug abuse treatment which in turn increases the prevalence of drug abuse treatment hence it has led to drive the market growth of drug abuse testing. For instance,...

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)

A. SUMMARY This dataset includes data on a variety of substance use services funded by the San Francisco Department of Public Health (SFDPH). This dataset only includes Drug MediCal-certified residential treatment, withdrawal management, and methadone treatment. Other private non-Drug Medi-Cal treatment providers may operate in the city. Withdrawal management discharges are inclusive of anyone who left withdrawal management after admission and may include someone who left before completing withdrawal management.

This dataset also includes naloxone distribution from the SFDPH Behavioral Health Services Naloxone Clearinghouse and the SFDPH-funded Drug Overdose Prevention and Education program. Both programs distribute naloxone to various community-based organizations who then distribute naloxone to their program participants. Programs may also receive naloxone from other sources. Data from these other sources is not included in this dataset.

Finally, this dataset includes the number of clients on medications for opioid use disorder (MOUD).

The number of people who were treated with methadone at a Drug Medi-Cal certified Opioid Treatment Program (OTP) by year is populated by the San Francisco Department of Public Health (SFDPH) Behavioral Health Services Quality Management (BHSQM) program. OTPs in San Francisco are required to submit patient billing data in an electronic medical record system called Avatar. BHSQM calculates the number of people who received methadone annually based on Avatar data. Data only from Drug MediCal certified OTPs were included in this dataset.

The number of people who receive buprenorphine by year is populated from the Controlled Substance Utilization Review and Evaluation System (CURES), administered by the California Department of Justice. All licensed prescribers in California are required to document controlled substance prescriptions in CURES. The Center on Substance Use and Health calculates the total number of people who received a buprenorphine prescription annually based on CURES data. Formulations of buprenorphine that are prescribed only for pain management are excluded.

People may receive buprenorphine and methadone in the same year, so you cannot add the Buprenorphine Clients by Year, and Methadone Clients by Year data together to get the total number of unique people receiving medications for opioid use disorder.

For more information on where to find treatment in San Francisco, visit findtreatment-sf.org. 

B. HOW THE DATASET IS CREATED This dataset is created by copying the data into this dataset from the SFDPH Behavioral Health Services Quality Management Program, the California Controlled Substance Utilization Review and Evaluation System (CURES), and the Office of Overdose Prevention.

C. UPDATE PROCESS Residential Substance Use Treatment, Withdrawal Management, Methadone, and Naloxone data are updated quarterly with a 45-day delay. Buprenorphine data are updated quarterly and when the state makes this data available, usually at a 5-month delay.

D. HOW TO USE THIS DATASET Throughout the year this dataset may include partial year data for methadone and buprenorphine treatment. As both methadone and buprenorphine are used as long-term treatments for opioid use disorder, many people on treatment at the end of one calendar year will continue into the next. For this reason, doubling (methadone), or quadrupling (buprenorphine) partial year data will not accurately project year-end totals.

E. RELATED DATASETS Overdose-Related 911 Responses by Emergency Medical Services Unintentional Overdose Death Rates by Race/Ethnicity Preliminary Unintentional Drug Overdose Deaths

Prescription Drug Misuse reports the prevalence of the misuse of prescription pain killers by age range.

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).

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

The Drug Abuse Warning Network (DAWN) survey is designed to capture data on emergency department (ED) episodes that are induced by or related to the use of an illicit, prescription, or over-the-counter drug. For purposes of this collection, a drug "episode" is an ED visit that was induced by or related to the use of an illegal drug or the nonmedical use of a legal drug for patients aged six years and older. A drug "mention" refers to a substance that was mentioned during a drug-related ED episode. Because up to four drugs can be reported for each drug abuse episode, there are more mentions than episodes in the data. Individual persons may also be included more than once in the data. Within each facility participating in DAWN, a designated reporter, usually a member of the emergency department or medical records staff, was responsible for identifying drug-related episodes and recording and submitting data on each case. An episode report was submitted for each patient visiting a DAWN emergency department whose presenting problem(s) was/were related to their own drug use. DAWN produces estimates of drug-related emergency department visits for 50 specific drugs, drug categories, or combinations of drugs, including the following: acetaminophen, alcohol in combination with other drugs, alprazolam, amitriptyline, amphetamines, aspirin, cocaine, codeine, diazepam, diphenhydramine, fluoxetine, heroin/morphine, inhalants/solvents/aerosols, LSD, lorazepam, marijuana/hashish, methadone, methamphetamine, and PCP/PCP in combination with other drugs. The use of alcohol alone is not reported. The route of administration and form of drug used (e.g., powder, tablet, liquid) are included for each drug. Data collected for DAWN also include drug use motive and total drug mentions in the episode, as well as race, age, patient disposition, reason for ED visit, and day of the week, quarter, and year of episode.This study has 1 Data Set.

This submission includes publicly available data extracted in its original form. If you have questions about the underlying data stored here, please contact the SAMHSA Webmaster: webmaster@samhsa.hhs.gov. If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. N-SUMHSS releases yearly public-use files (PUFs). These are full datasets treated with confidentiality protections. The PUFs contain facility data on mental health and substance use treatment services. This includes location, characteristics, service provision and utilization of substance use and mental health treatment facilities. Note, the codebooks may not contain all variables. [Quote from https://www.samhsa.gov/data/data-we-collect/n-sumhss-national-substance-use-and-mental-health-services-survey/datafiles] The PUFs available each year have varied over time. This dataset is believed to contain a union of all the available PUFs. They include the N-SUMHSS files of the dataset title (2021 to 2023) but also N-MHSS files (2010 to 2020), N-SSATS files (1997-2020), and UFDS files (1997-1998). Most data is available in a variety of formats, including Delimited (tab or comma), R, SAS, SPSS, and Stata. Files are organized here in zip files by year. Within each year are one or more collection folders with names like: collection_1283-N-SUMHSS where the collection number was a value used in the web page dropdown control and the more meaningful abbreviation was derived from the actual filenames within the collection.

The Treatment Episode Data Set -- Admissions (TEDS-A) is a national census data system of annual admissions to substance abuse treatment facilities. TEDS-A provides annual data on the number and characteristics of persons admitted to public and private substance abuse treatment programs that receive public funding. The unit of analysis is a treatment admission. TEDS consists of data reported to state substance abuse agencies by the treatment programs, which in turn report it to SAMHSA. A sister data system, called the Treatment Episode Data Set -- Discharges (TEDS-D), collects data on discharges from substance abuse treatment facilities. The first year of TEDS-A data is 1992, while the first year of TEDS-D is 2006. TEDS variables that are required to be reported are called the "Minimum Data Set (MDS)", while those that are optional are called the "Supplemental Data Set (SuDS)". Variables in the MDS include: information on service setting, number of prior treatments, primary source of referral, sex, race, ethnicity, education, employment status, substance(s) abused, route of administration, frequency of use, age at first use, and whether methadone was prescribed in treatment. Supplemental variables include: diagnosis codes, presence of psychiatric problems, living arrangements, source of income, health insurance, expected source of payment, pregnancy and veteran status, marital status, detailed not in labor force codes, detailed criminal justice referral codes, days waiting to enter treatment, and the number of arrests in the 30 days prior to admissions (starting in 2008). Substances abused include alcohol, cocaine and crack, marijuana and hashish, heroin, nonprescription methadone, other opiates and synthetics, PCP, other hallucinogens, methamphetamine, other amphetamines, other stimulants, benzodiazepines, other non-benzodiazepine tranquilizers, barbiturates, other non-barbiturate sedatives or hypnotics, inhalants, over-the-counter medications, and other substances. Created variables include total number of substances reported, intravenous drug use (IDU), and flags for any mention of specific substances.This study has 1 Data Set.

The Arrestee Drug Abuse Monitoring (ADAM) Program/Drug Use Forecasting (DUF) Series is an expanded and redesigned version of the Drug Use Forecasting (DUF) program, which was upgraded methodologically and expanded to 35 cities in 1998. The redesign was fully implemented beginning in the first quarter of 2000 using new sampling procedures that improved the quality and generalizability of the data. The DUF program began in 1987 and was designed to estimate the prevalence of drug use among persons in the United States who are arrested and booked, and to detect changes in trends in drug use among this population. The DUF program was a nonexperimental survey of drug use among adult male and female arrestees. In addition to supplying information on self-reported drug use, arrestees also provide a urine specimen, which is screened for the presence of ten illicit drugs. Between 1987 and 1997 the DUF program collected information in 24 sites across the United States, although the number of data collection sites varied slightly from year to year. Data collection took place four times a year (once each calendar quarter) in each site and selection criteria and catchment areas (central city or county) varied from site to site. The original DUF interview instrument (used for the 1987-1994 data and part of the 1995 data) elicited information about the use of 22 drugs. A modified DUF interview instrument (used for part of the 1995 data and all of the 1996-1999 data) included detailed questions about each arrestee's use of 15 drugs. Juvenile data were added in 1991. The ADAM program, redesigned from the DUF program, moved to a probability-based sampling for the adult male population during 2000. The shift to sampling of the adult male population in 2000 required that all 35 sites move to a common catchment area, the county. The ADAM program also implemented a new and expanded adult instrument in the first quarter of 2000, which was used for both the male and female data. The term "arrestee" is used in the documentation, but because no identifying data are collected in the interview setting, the data represent numbers of arrests rather than an unduplicated count of persons arrested. Funding The National Institute of Justice (NIJ) initiated ADAM in 1998 to replace DUF. In 2007, the Office of National Drug Control Policy (ONDCP) initiated ADAM II.

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.

record abstracts Several limitations to the data exist and should be noted: The number and client mix of TEDS records depends, to some extent, on external factors, including the availability of public funds. In states with higher funding levels, a larger percentage of the substance-abusing population may be admitted to treatment, including the less severely impaired and the less economically disadvantaged.; The primary, secondary, and tertiary substances of abuse reported to TEDS are those substances that led to the treatment episode, and not necessarily a complete enumeration of all drugs used at the time of admission. ; The way an admission is defined may vary from state to state such that the absolute number of admissions is not a valid measure for comparing states. ; States continually review the quality of their data processing. As systematic errors are identified, revisions may be enacted in historical TEDS data files. While this process improves the dataset over time, reported historical statistics may change slightly from year to year. ; States vary in the extent to which coercion plays a role in referral to treatment. This variation derives from criminal justice practices and differing concentrations of abuser subpopulations. ; Public funding constraints may direct states to selectively target special populations, for example, pregnant women or adolescents. ; TEDS consists of treatment admissions, and therefore may include multiple admissions for the same client. Thus, any statistics derived from the data will represent admissions, not clients. It is possible for clients to have multiple initial admissions within a state and even within providers that have multiple treatment sites within the state. TEDS provides a national snapshot of what is seen at admission to treatment, but is currently not designed to follow individual clients through a sequence of treatment episodes. ; TEDS distinguishes between "transfer admissions" and "initial admissions." Transfer admissions include clients transferred for distinct services within an episode of treatment. Only initial admissions are included in the public-use file. ; Some states have no Opioid Treatment Programs (OTPs) that provide medication-assisted therapy using methadone and/or buprenorphine. ; In 2012, a new variable was added that reports the number of times, if any, that a client was arrested in the 30 days preceding his or her admission into treatment. The variable is not on files prior to 2008. In 2012, changes were made to the full TEDS series. The changes consisted of the following: The recoding scheme of the variable DENTLF (Detailed Not in Labor Force Category) was changed. The cases for "Inmate of Institution" have been separated from "Other" and are now a standalone category. ; The recoding scheme of the variable DETCRIM (Detailed Criminal Justice Referral) was changed. The cases for "Prison" have been separated from "Probation/Parole" and are now a standalone category. The same was done for the cases for "Diversionary Program" which were previously combined with "Other". But the cases for "Other Recognized Legal Entity" previously combined with "State/Federal Court, Other Court" have now been combined with the "Other" category. ; In 2011, a change was made to the full TEDS series. All records for which the age is missing are now excluded from the dataset. In 2010, changes were made to the full TEDS series. The changes consisted of the following: Clients 11 years old and younger are excluded from the dataset. ; Puerto Rico now has its own category for Census Region and Division. Clients in Puerto Rico were formerly classified into the South Census Region and South Atlantic Census Division.; The state FIPS (STFIPS) variable is retained and a second state variable was dropped to reduce redundancy.; Value labels and question text are better aligned with the TEDS State Instruction Manual for Admissions Data.; The variable RACE is no longer recoded. Codes for "Asian" (code 13) and "Native Hawaiian or Pacific Islander" (code 23) are now retained. Previously these codes were combined into the single code "Asian or Pacific Islander" (code 3). Each state may report any of the three codes. Therefore, all three codes remain in the data, unchanged from the way they are collected by the states.; The categories and codes in this public-use file differ somewhat from those used by SAMHSA and those found in the TEDS Crosswalks and in other reports. This is a result of the recoding that was performed to protect client privacy in creating the public-use file. To further protect respondent and provider privacy, all Behavioral Health Services Information System (BHSIS) unique identification numbers have been removed from the public-use data. Therefore, no linkages are possible between the TEDS and the National Survey of Substance Abuse Treatment Services (N-SSATS) public-use files. The data are collected from the states by Synectics for Management De...

A. SUMMARY This dataset includes unintentional drug overdose death rates by race/ethnicity by year. This dataset is created using data from the California Electronic Death Registration System (CA-EDRS) via the Vital Records Business Intelligence System (VRBIS). Substance-related deaths are identified by reviewing the cause of death. Deaths caused by opioids, methamphetamine, and cocaine are included. Homicides and suicides are excluded. Ethnic and racial groups with fewer than 10 events are not tallied separately for privacy reasons but are included in the “all races” total.

Unintentional drug overdose death rates are calculated by dividing the total number of overdose deaths by race/ethnicity by the total population size for that demographic group and year and then multiplying by 100,000. The total population size is based on estimates from the US Census Bureau County Population Characteristics for San Francisco, 2022 Vintage by age, sex, race, and Hispanic origin.

These data differ from the data shared in the Preliminary Unintentional Drug Overdose Death by Year dataset since this dataset uses finalized counts of overdose deaths associated with cocaine, methamphetamine, and opioids only.

B. HOW THE DATASET IS CREATED This dataset is created by copying data from the Annual Substance Use Trends in San Francisco report from the San Francisco Department of Public Health Center on Substance Use and Health.

C. UPDATE PROCESS This dataset will be updated annually, typically at the end of the year.

D. HOW TO USE THIS DATASET N/A

E. RELATED DATASETS Overdose-Related 911 Responses by Emergency Medical Services Preliminary Unintentional Drug Overdose Deaths San Francisco Department of Public Health Substance Use Services

F. CHANGE LOG

• 12/16/2024 - Updated with 2023 data. Asian/Pacific Islander race/ethnicity group was changed to Asian.
• 12/16/2024 - Past year totals by race/ethnicity were revised after obtaining accurate race/ethnicity for some decedents that were previously marked as “unknown” race/ethnicity.