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.

Content:

Over 93,000 people will die from drug overdoses in the United States in 2020, according to escalating death rates in recent years. Fentanyl and other synthetic opioids are a significant factor in the rise. The misuse of stimulants, benzodiazepines, and narcotic prescription drugs also contributes. A multimodal strategy is needed to address the problem, including better prescription drug monitoring schemes, more access to addiction treatment, and harm reduction tactics.

Context:

In recent years, the number of drug overdose deaths in the United States has become a significant public health concern. The misuse of prescription medications, the usage of synthetic opioids, and the lack of access to addiction treatment are a few of the causes contributing to the surge in drug overdose deaths. The problem emphasizes the requirement for successful treatments and preventative plans, as well as the necessity to deal with the social determinants of health that influence substance misuse.

Drugs prevention precautions:

Here are some drug prevention precautions that are important to keep in mind:

• Properly dispose of unused medication to prevent them from being misused.
• Keep prescription medication in a secure location and only take it as directed by a healthcare provider.
• Avoid using drugs, including prescription medication, that is not prescribed to you.
• Educate yourself and others on the risks and consequences of drug use.
• Seek help for substance abuse or addiction from a healthcare professional or addiction treatment provider.
• Practice harm reduction strategies, such as carrying naloxone for opioid overdoses.
• Address underlying mental health issues and social determinants of health that may contribute to substance abuse. # Acknowledgment: This Dataset was created from https://rb.gy/vk7kh/. if you want to learn more, you can visit the URL address. Cover Photo by https://wallpapercave.com/ # Dataset Glossary( Column-Wise) INDICATOR - name or code of the indicator PANEL - category or panel the indicator belongs to PANEL_NUM - numeric code for the panel UNIT - the unit of measurement for the indicator UNIT_NUM - numeric code for the unit of measurement STUB_NAME - name or code for the rows in the table STUB_NAME_NUM - numeric code for the row names STUB_LABEL - label or description for the row names STUB_LABEL_NUM - numeric code for the stub labels YEAR - year or time period for the data being measured YEAR_NUM - numerical representation of the year AGE - age group being measured AGE_NUM - numerical representation of the age group ESTIMATE - the estimated number of drug overdose deaths for the given year and age group FLAG - an indicator of data quality or reliability, such as a missing or suppressed estimate

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Annual number of deaths in the United States from drug overdose per 100,000 people. Overdoses can result from intentional excessive use of a substance, but can also result from 'poisoning' where substances have been altered or mixed, such that the user is unaware of the drug's potency.

The data of this indicator is based on the following sources: US Centers for Disease Control and Prevention WONDER Data published by US Centers for Disease Control and Prevention WONDER

Retrieved from https://www.drugabuse.gov/related-topics/trends-statistics/overdose-death-rates How we process data at Our World in Data: All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline How to cite this data: In-line citation If you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Any opioids Deaths per 100,000 people attributed to any opioids.

Source US Centers for Disease Control and Prevention WONDER – processed by Our World in Data Unit deaths per 100,000

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

Following on from my datasets on Drug Overdose deaths in the United States, https://www.kaggle.com/craigchilvers/opioids-vssr-provisional-drug-overdose-statistics and https://www.kaggle.com/craigchilvers/opioids-in-the-us-cdc-drug-overdose-deaths, here is a dataset on non-fatal overdoses. It is broken down by age and gender, and also by State. There are also breakdowns into overall drug overdoses, heroin overdoses, opioid overdoses and stimulant overdoses.

This data set is good for tracking progress or deterioration in states over time, especially through choropleth graphs.

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

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

F. CHANGE LOG

• 09/15/2025 - Data processing updated to capture new buprenorphine formulations.

Content

Drug Use By Age

This directory contains data behind the story How Baby Boomers Get High. It covers 13 drugs across 17 age groups.

Source: National Survey on Drug Use and Health from the Substance Abuse and Mental Health Data Archive.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

Cover photo by Eric Muhr on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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).

Illicit Drug Use reports an estimated average percent of people who consumed illicit substances by type of use and by age range. Illicit drugs include marijuana or hashish (unless otherwise specified as 'Not Including Marijuana'), cocaine (including crack), heroin, hallucinogens (including phencyclidine [PCP], lysergic acid diethylamide [LSD], and Ecstasy [MDMA]), inhalants, or prescription-type psychotherapeutics used nonmedically, which include pain relievers, tranquilizers, stimulants, and sedatives, but does not include GHB (gamma hydroxybutyrate), Adderall, Ambien, nonprescription cough or cold medicines, ketamine, DMT (dimethyltryptamine), AMT (alpha-methyltryptamine), 5-MeO-DIPT (N, N-diisopropyl-5-methoxytryptamine, also known as 'Foxy'), and Salvia divinorum. Dependence is defined consistent with the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) definition as:Spending a lot of time engaging in activities related to substance useUsing a substance in greater quantities or for a longer time than intended. Developing tolerance (i.e., needing to use the substance more than before to get desired effects or noticing that the same amount of substance use had less effect than before)Making unsuccessful attempts to cut down on useContinuing substance use despite physical health or emotional problems associated with substance useReducing or eliminating participation in other activities because of substance useExperiencing withdrawal symptomsSimilarly, Abuse is also defined consistent with the DSM-IV definition as the following lifestyle symptoms due to the use of illicit drugs in the past 12 months: Experiencing problems at work, home, and schoolDoing something physically dangerousExperiencing Repeated trouble with the law

To: State, territorial, tribal, and local policymakers and administrators of agencies and programs focused on child, youth, and family health and well-being Dear Colleagues, Thank you for your work to support children, youth, and families. Populations served by Administration for Children and Families (ACF)-funded programs — including victims of trafficking or violence, those who are unhoused, and young people and families involved in the child welfare system — are often at particularly high risk for substance use and overdose. A variety of efforts are underway at the federal, state, and local levels to reduce overdose deaths. These efforts focus on stopping drugs from entering communities, providing life-saving resources, and preventing drug use before it starts. Initiatives across the country are already saving lives: the overdose death rate has declined over the past year but remains too high at 32.6 per 100,000 individuals. Fentanyl, a powerful synthetic opioid, raises the risk of overdose deaths because even a tiny amount can be deadly. Young people are particularly at risk for fentanyl exposure, driven in part by widespread availability of counterfeit pills containing fentanyl that are marketed to youth through social media. While overdose deaths among teens have recently begun to decline, there were 6,696 deaths among adolescents and young adults in 2022 (the latest year with data available)[1], making unintentional drug overdose the second leading cause of death for youth ages 15—19 and the first leading cause of death among young adults ages 20-24.[2] Often these deaths happen with others nearby and can be prevented when opioid overdose reversal medications, like naloxone, are administered in time. CDC’s State Unintentional Drug Overdose Reporting System dashboard shows that in all 30 jurisdictions with available data, 64.7% of drug overdose deaths had at least one potential opportunity for intervention.[3] Naloxone rapidly reverses an overdose and should be given to any person who shows signs of an opioid overdose or when an overdose is suspected. It can be given as a nasal spray. Studies show that naloxone administration reduces death rates and does not cause harm if used on a person who is not overdosing on opioids. States have different policies and regulations regarding naloxone distribution and administration. Forty-nine states and the District of Columbia have Good Samaritan laws protecting bystanders who aid at the scene of an overdose.[4] ACF grant recipients and partners can play a critical role in reducing overdose deaths by taking the following actions: Stop Overdose Now (U.S. Centers for Disease Control and Prevention) Integrating Harm Reduction Strategies into Services and Supports for Young Adults Experiencing Homelessness (PDF) (ACF) Thank you for your dedication and partnership. If you have any questions, please contact your local public health department or state behavioral health agency. Together, we can meaningfully reduce overdose deaths in every community. /s/ Meg Sullivan Principal Deputy Assistant Secretary [1] Products - Data Briefs - Number 491 - March 2024 [2] WISQARS Leading Causes of Death Visualization Tool [3] SUDORS Dashboard: Fatal Drug Overdose Data | Overdose Prevention | CDC [4] Based on 2024 report from the Legislative Analysis and Public Policy Association (PDF). Note that the state of Kansas adopted protections as well following the publication of this report. Metadata-only record linking to the original dataset. Open original dataset below.

This map shows the number drug poisoning deaths per 100,000 people in the U.S. The data comes from the County Health Rankings dataset.Drug overdose deaths are a leading contributor to premature death and are largely preventable. Currently, the United States is experiencing an epidemic of drug overdose deaths. Since 2000, the rate of drug overdose deaths has increased by 137% nationwide. Opioids contribute largely to drug overdose deaths; since 2000, there has been a 200% increase in deaths involving opioids (opioid pain relievers and heroin).Find strategies to address Drug Overdose DeathsThe data comes from County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. The layer used in the map comes from ArcGIS Living Atlas of the World, and the full documentation for the layer can be found here.County data are suppressed if, for both years of available data, the population reported by agencies is less than 50% of the population reported in Census or less than 80% of agencies measuring crimes reported data.

BackgroundPeople who inject drugs (PWID) experience elevated rates of premature mortality. Although previous studies have demonstrated the role of supervised injection facilities (SIFs) in reducing various harms associated with injection drug use, including accidental overdose death, the possible impact of SIF use on all-cause mortality is unknown. Therefore, we examined the relationship between frequent SIF use and all-cause mortality among PWID in Vancouver, Canada.Methods and findingsData were derived from 2 prospective cohort studies of PWID in Vancouver, Canada, between December 2006 and June 2017. Every 6 months, participants completed questionnaires that elicited information regarding sociodemographic characteristics, substance use patterns, social-structural exposures, and use of health services including SIFs. These data were confidentially linked to the provincial vital statistics database to ascertain mortality rates and causes of death. We used multivariable extended Cox regression analyses to estimate the independent association between frequent (i.e., at least weekly) SIF use and all-cause mortality. Of 811 participants, 278 (34.3%) were women, and the median age was 39 years (IQR 33–46) at baseline. In total, 432 (53.3%) participants reported frequent SIF use at baseline, and 379 (46.7%) did not. At baseline, frequent SIF users were on average younger than nonfrequent users, and a higher proportion of frequent SIF users than nonfrequent users were unstably housed, resided in the Downtown Eastside neighbourhood, injected in public, had a recent non-fatal overdose, used prescription opioids at least daily, injected heroin at least daily, injected cocaine at least daily, and injected crystal methamphetamine at least daily. A lower proportion of frequent SIF users than nonfrequent users were HIV positive and enrolled in addiction treatment at baseline. The median duration of follow-up among study participants was 72 months (IQR 24–123). In total, 112 participants (13.8%) died during the study period, yielding a crude mortality rate of 22.7 (95% CI 18.7–27.4) deaths per 1,000 person-years. The median years of potential life lost per death was 34 (IQR 27–42) years. In a time-updated multivariable model, frequent SIF use was inversely associated with risk of all-cause mortality after adjusting for potential confounders, including age, sex, HIV seropositivity, unstable housing, at least daily cocaine injection, public injection, incarceration, enrolment in addiction treatment, and calendar year of interview (adjusted hazard ratio 0.46, 95% CI 0.26–0.80, p = 0.006). The main study limitations are the limited generalizability of findings due to non-random sampling, the potential for reporting biases due to reliance on some self-reported information, and the possibility that residual confounding influenced findings.ConclusionsWe observed a high burden of premature mortality among a community-recruited cohort of PWID. Frequent SIF use was associated with a lower risk of death, independent of relevant confounders. These findings support efforts to enhance access to SIFs as a strategy to reduce mortality among PWID. Further analyses of individual-level data are needed to determine estimates of, and potential causal pathways underlying, associations between SIF use and specific causes of death.

Perception of Great Risk of Drug Use reports an estimated average percent of people who perceived great risk to themselves, physical or otherwise, when consuming certain drugs at various levels of frequency. 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.

The Opioid Epidemic is entering a new phase, having intensified during the Coronavirus Pandemic, with overdose deaths rising as job losses and stress from Covid-19 destabilize people struggling with addiction. https://www.wsj.com/articles/the-opioid-crisis-already-serious-has-intensified-during-coronavirus-pandemic-11599557401

Previously the overdose rate had steadied and even dipped throughout 2018 and early 2019, before resuming its rapid climb during the pandemic. The Opioid Epidemic began with the over-prescription of painkillers in the 1990s, but we are continuing to get increased overdose deaths even as different jurisdictions have had success in reducing the amount of opioid prescriptions.

Now is the time to launch a new dataset capturing data throughout 2020 and 2021. The hope is to seek to understand what the trends are, where they are located geographically and what factors (or "features") have impacted these trends.

These data come from a Vital Statistics Rapid Release (VSRR) from the National Vital Statistics System (NVSS) at the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Preventions (CDC). I will continue to update this data set as new information is released from the National Vital Statistics System. I will also continue to update either this dataset with new features, or create new datasets with new features, as Data Science Analysis reveals more about the causes of the epidemic. https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm

This is something I'm passionate about and I hope you will join me in seeking to deepen our understanding of the causes of the epidemic through the use of Data Science and Machine Learning.

Edit: I have updated the CSV file to change one of the columns from 'object' to 'float' to make it easier to work with.

Cocaine Use reports the estimated average percent of individuals who consumed cocaine in the past year 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 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.

EMSIndicators:The number of individual patients administered naloxone by EMSThe number of naloxone administrations by EMSThe rate of EMS calls involving naloxone administrations per 10,000 residentsData Source:The Vermont Statewide Incident Reporting Network (SIREN) is a comprehensive electronic prehospital patient care data collection, analysis, and reporting system. EMS reporting serves several important functions, including legal documentation, quality improvement initiatives, billing, and evaluation of individual and agency performance measures.Law Enforcement Indicators:The Number of law enforcement responses to accidental opioid-related non-fatal overdosesData Source:The Drug Monitoring Initiative (DMI) was established by the Vermont Intelligence Center (VIC) in an effort to combat the opioid epidemic in Vermont. It serves as a repository of drug data for Vermont and manages overdose and seizure databases. Notes:Overdose data provided in this dashboard are derived from multiple sources and should be considered preliminary and therefore subject to change. Overdoses included are those that Vermont law enforcement responded to. Law enforcement personnel do not respond to every overdose, and therefore, the numbers in this report are not representative of all overdoses in the state. The overdoses included are limited to those that are suspected to have been caused, at least in part, by opioids. Inclusion is based on law enforcement's perception and representation in Records Management Systems (RMS). All Vermont law enforcement agencies are represented, with the exception of Norwich Police Department, Hartford Police Department, and Windsor Police Department, due to RMS access. Questions regarding this dataset can be directed to the Vermont Intelligence Center at dps.vicdrugs@vermont.gov.Overdoses Indicators:The number of accidental and undetermined opioid-related deathsThe number of accidental and undetermined opioid-related deaths with cocaine involvementThe percent of accidental and undetermined opioid-related deaths with cocaine involvementThe rate of accidental and undetermined opioid-related deathsThe rate of heroin nonfatal overdose per 10,000 ED visitsThe rate of opioid nonfatal overdose per 10,000 ED visitsThe rate of stimulant nonfatal overdose per 10,000 ED visitsData Source:Vermont requires towns to report all births, marriages, and deaths. These records, particularly birth and death records are used to study and monitor the health of a population. Deaths are reported via the Electronic Death Registration System. Vermont publishes annual Vital Statistics reports.The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) captures and analyzes recent Emergency Department visit data for trends and signals of abnormal activity that may indicate the occurrence of significant public health events.Population Health Indicators:The percent of adolescents in grades 6-8 who used marijuana in the past 30 daysThe percent of adolescents in grades 9-12 who used marijuana in the past 30 daysThe percent of adolescents in grades 9-12 who drank any alcohol in the past 30 daysThe percent of adolescents in grades 9-12 who binge drank in the past 30 daysThe percent of adolescents in grades 9-12 who misused any prescription medications in the past 30 daysThe percent of adults who consumed alcohol in the past 30 daysThe percent of adults who binge drank in the past 30 daysThe percent of adults who used marijuana in the past 30 daysData Sources:The Vermont Youth Risk Behavior Survey (YRBS) is part of a national school-based surveillance system conducted by the Centers for Disease Control and Prevention (CDC). The YRBS monitors health risk behaviors that contribute to the leading causes of death and disability among youth and young adults.The Behavioral Risk Factor Surveillance System (BRFSS) is a telephone survey conducted annually among adults 18 and older. The Vermont BRFSS is completed by the Vermont Department of Health in collaboration with the Centers for Disease Control and Prevention (CDC).Notes:Prevalence estimates and trends for the 2021 Vermont YRBS were likely impacted by significant factors unique to 2021, including the COVID-19 pandemic and the delay of the survey administration period resulting in a younger population completing the survey. Students who participated in the 2021 YRBS may have had a different educational and social experience compared to previous participants. Disruptions, including remote learning, lack of social interactions, and extracurricular activities, are likely reflected in the survey results. As a result, no trend data is included in the 2021 report and caution should be used when interpreting and comparing the 2021 results to other years.The Vermont Department of Health (VDH) seeks to promote destigmatizing and equitable language. While the VDH uses the term "cannabis" to reflect updated terminology, the data sources referenced in this data brief use the term "marijuana" to refer to cannabis. Prescription Drugs Indicators:The average daily MMEThe average day's supplyThe average day's supply for opioid analgesic prescriptionsThe number of prescriptionsThe percent of the population receiving at least one prescriptionThe percent of prescriptionsThe proportion of opioid analgesic prescriptionsThe rate of prescriptions per 100 residentsData Source:The Vermont Prescription Monitoring System (VPMS) is an electronic data system that collects information on Schedule II-IV controlled substance prescriptions dispensed by pharmacies. VPMS proactively safeguards public health and safety while supporting the appropriate use of controlled substances. The program helps healthcare providers improve patient care. VPMS data is also a health statistics tool that is used to monitor statewide trends in the dispensing of prescriptions.Treatment Indicators:The number of times a new substance use disorder is diagnosed (Medicaid recipients index events)The number of times substance use disorder treatment is started within 14 days of diagnosis (Medicaid recipients initiation events)The number of times two or more treatment services are provided within 34 days of starting treatment (Medicaid recipients engagement events)The percent of times substance use disorder treatment is started within 14 days of diagnosis (Medicaid recipients initiation rate)The percent of times two or more treatment services are provided within 34 days of starting treatment (Medicaid recipients engagement rate)The MOUD treatment rate per 10,000 peopleThe number of people who received MOUD treatmentData Source:Vermont Medicaid ClaimsThe Vermont Prescription Monitoring System (VPMS)Substance Abuse Treatment Information System (SATIS)

This 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).

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).