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 data presents 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 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. 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. 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. Provisional data presented will be updated on a monthly basis as additional records are received. For more information please visit: https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm

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.

This data presents counts of provisional drug overdose deaths by selected drugs and U.S. Department of Health and Human Services (HHS) public health regions, based on provisional mortality data from the National Vital Statistics System. This data is limited to drug overdose deaths with an underlying cause of death assigned to International Statistical Classification of Diseases, 10th Revision (ICD-10) code numbers X40-X44 (unintentional), X60-X64 (suicide), X85 (homicide), or Y10-Y14 (undetermined intent). Specific drugs were identified using methods for searching literal text from death certificates.

The provisional data are based on a current flow of mortality data and include reported 12 month-ending provisional counts of drug overdose deaths by jurisdiction of occurrence and specified drug. Provisional drug overdose death counts presented on this page 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 2022 would include deaths occurring from July 1, 2021, through June 30, 2022. Evaluation of trends over time should compare estimates from year to year (June 2021 and June 2022), rather than month to month, to avoid overlapping time periods. It is important to note that the data represent counts of deaths, and not mortality ratios or rates, which are the standard measure used to compare groups, and therefore should not be used to determine populations at disproportionate risk of drug overdose death.

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

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)

Drug-related mortality is a complex phenomenon, which accounts for a considerable percentage of deaths among young people in many European countries. The EMCDDA, in collaboration with national experts, has defined an epidemiological indicator with two components at present: deaths directly caused by illegal drugs (drug-induced deaths) and mortality rates among problem drug users. These two components can fulfil several public health objectives, notably as an indicator of the overall health impact of drug use and the components of this impact, identify particularly risky patterns of use, and potentially identify new risks.

There are around 50 statistical tables in this dataset. Each data table may be viewed as an HTML table or downloaded in spreadsheet (Excel format).

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

Source: Office of State Medical Examiners (OSME), Rhode Island Department of Health (RIDOH)Note: Rates are calculated using CDC WONDER single-race population estimates for each year (Obtained September 9, 2022) . 2021 rates are applied to 2022. The rate is the number of deaths, divided by the total population for each category, multiplied by 100,000. Hispanic or Latino includes people who identify as any race. All other racial and ethnic groups include people who identify as non-Hispanic ethnicity or have unknown ethnicity. People whose race was "Unknown" or "Asian" have been excluded. Data are limited to accidental drug overdose deaths pronounced in Rhode Island among Rhode Island residents. Some data have been suppressed due to unstable rates.

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,...

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).

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.

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

Heroin Use reports an estimated average percent of people who consumed heroin, by age range. These data are collected by the Substance Abuse and Mental Health Services Administration (SAMHSA) as part of the National Survey on Drug Use and Health (NSDUH), Substate Region Estimates by Age Group. This survey is conducted on a representative sample of U.S. civilian, non-institutionalized people ages 12 and older. Data are available for the state of Connecticut, substate regions within Connecticut, the Northeast region of the United States, and the Total United States.

This data set depicts unintentional overdose deaths by county for Tennessee from 1999-2017.Data was compiled from the CDC Wonder database for each year and combined into a single spreadsheet. Each year has both a death field and a rate of fatalities per 100,000 people. The CDC does not publish the number of fatalities by county if the total is less than 10 in a given year. The CDC does not post a rate of fatalities if the total number of deaths per county is less than 20. The population field contains estimates from 2018 and is NOT the data used to generate the rates over time.The following details are copied directly from the CDC Wonder database text file. Note that the year is different for each data download from the original database."Dataset: Underlying Cause of Death, 1999-2017""Query Parameters:""Drug/Alcohol Induced Causes: Drug poisonings (overdose) Unintentional (X40-X44)""States: Tennessee (47)""Year/Month: 1999""Group By: County""Show Totals: True""Show Zero Values: False""Show Suppressed: False""Calculate Rates Per: 100,000""Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)""---""Help: See http://wonder.cdc.gov/wonder/help/ucd.html for more information.""---""Query Date: Aug 19, 2019 10:22:15 PM""1. Rows with suppressed Deaths are hidden, but the Deaths and Population values in those rows are included in the totals. Use""Quick Options above to show suppressed rows.""---"Caveats:"1. Data are Suppressed when the data meet the criteria for confidentiality constraints. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Assurance of Confidentiality.""2. Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Unreliable.""3. The population figures for year 2017 are bridged-race estimates of the July 1 resident population, from the Vintage 2017""postcensal series released by NCHS on June 27, 2018. The population figures for year 2016 are bridged-race estimates of the July""1 resident population, from the Vintage 2016 postcensal series released by NCHS on June 26, 2017. The population figures for""year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015 postcensal series released by NCHS""on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July 1 resident population, from the""Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for year 2013 are bridged-race""estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS on June 26, 2014. The""population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the Vintage 2012 postcensal""series released by NCHS on June 13, 2013. The population figures for year 2011 are bridged-race estimates of the July 1 resident""population, from the Vintage 2011 postcensal series released by NCHS on July 18, 2012. Population figures for 2010 are April 1""Census counts. The population figures for years 2001 - 2009 are bridged-race estimates of the July 1 resident population, from""the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012. Population figures for 2000 are""April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of July 1 estimates. Population""figures for the infant age groups are the number of live births.
Note: Rates and population figures for years 2001 -""2009 differ slightly from previously published reports, due to use of the population estimates which were available at the time""of release.""4. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the""resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/ucd.html#Age Group."

This dataset includes a subset of data collected through the Johns Hopkins University social network-based intervention study CHAMPS CONNECT conducted in Baltimore, Maryland. A total of 111 people who inject drugs (PWID) were recruited from an infectious disease clinic and community-based sites in Baltimore between 1/25/2018 and 1/4/2019. Index members were 18 years of age or older, English speaking, hepatitis C virus (HCV) antibody positive, and reported injecting drugs with another during the past year. Indexes were asked to recruit their injection drug network members for HCV testing and linkage to care. The primary objective of the secondary study was to analyze data from indexes and network participant members to assess psychological factors that may be significantly associated with self-reported number of lifetime drug overdoses. Variables in the dataset include demographics, employment, substance use history and treatment, mental health diagnoses and treatment, overdose, injection drug use, and questions from the Center of Epidemiologic Studies Depression Scale.

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

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

Context

Data Set Information:

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

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

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

Content

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

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

Acknowledgements

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

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

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

Inspiration

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

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

We collect data and report statistics on opioid, stimulant, and other substance use and their impact on health and well-being.

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).

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