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

Database of the nation''s substance abuse and mental health research data providing public use data files, file documentation, and access to restricted-use data files to support a better understanding of this critical area of public health. The goal is to increase the use of the data to most accurately understand and assess substance abuse and mental health problems and the impact of related treatment systems. The data include the U.S. general and special populations, annual series, and designs that produce nationally representative estimates. Some of the data acquired and archived have never before been publicly distributed. Each collection includes survey instruments (when provided), a bibliography of related literature, and related Web site links. All data may be downloaded free of charge in SPSS, SAS, STATA, and ASCII formats and most studies are available for use with the online data analysis system. This system allows users to conduct analyses ranging from cross-tabulation to regression without downloading data or relying on other software. Another feature, Quick Tables, provides the ability to select variables from drop down menus to produce cross-tabulations and graphs that may be customized and cut and pasted into documents. Documentation files, such as codebooks and questionnaires, can be downloaded and viewed online.

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.

Data from the Crime Survey for England and Wales (CSEW) on the extent and trends of illicit drug use.

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.

This data collection consists of the SPSS syntax used to recode existing variables and create new variables from the SURVEY OF INMATES OF LOCAL JAILS, 1996 [ICPSR 6858] and the SURVEY OF INMATES IN STATE AND FEDERAL CORRECTIONAL FACILITIES, 1997 [ICPSR 2598]. Using the data from these two national surveys on jail and prison inmates, this study sought to expand the analyses of these data in order to fully explore the relationship between type and intensity of substance abuse and other health and social problems, analyze access to treatment and services, and make estimates of the need for different types of treatment services in correctional systems.

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. In 2023 the survey was administered online for the first time, instead of paper-based surveys as in previous years. This move online also meant that completion of the survey could be managed through teacher-led sessions, rather than being conducted by external interviewers. The 2023 survey also introduced additional questions relating to pupils wellbeing. These included how often the pupil felt lonely, felt left out and that they had no-one to talk to. Results of analysis covering these questions have been presented within parts of the report and associated data tables. The report 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 in early 2025 (see link below).

1) Data Introduction • The Drug Dataset focuses on the medical and demographic characteristics of individuals collected to analyze drug use patterns and health conditions.

2) Data Utilization (1) Drug Dataset has characteristics that: • This dataset mainly covers factors such as age, gender, blood pressure (BP), cholesterol levels, sodium to potassium ratio (Na_to_K), and prescription drugs. (2) Drug Dataset can be used to: • Improving personalized healthcare strategies: Useful for improving personalized healthcare strategies by evaluating public health trends, designing personalized healthcare interventions, and identifying correlations between patient characteristics and drug selection. • Clinical studies: can be used to identify trends in drug effects, patient demographics, and underlying health factors.

Antidepressant Use in Scandinavia

A Study of Population Characteristics and Drug Utilization Rates

By [source]

About this dataset

This fascinating dataset examines the use of antidepressant medications among children and adolescents in Denmark, Norway, and Sweden from 2007 until 2017. Through a comprehensive exploration of drug usage along with population characteristics, we can uncover deeper insights into the prevalence of antidepressant use in this demographic and its potential causes. By carefully inspecting this data set which contains details about drug use, census data and associated drug names by code, we can shed light on an important issue with far reaching implications for public health worldwide

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How to use the dataset

This dataset offers an opportunity to analyze antidepressant use among children and adolescents in Denmark, Norway and Sweden from 2007 to 2017. To get started with your analysis, you'll need to familiarize yourself with the dataset. Below are some simple steps for getting acquainted with the available resources:

• Familiarize yourself with the column descriptions and data types. Each column contains meaningful information about drug use and population characteristics in the three countries during this window of time.
• Review the drug_names file contained in this dataset for a detailed list of drugs associated with each code represented in the main table. This is particularly important because ATC (Anatomical Therapeutic Chemical) codes provide an easy shorthand way of referring to individual medications without being too long-winded or cluttering up columns not relevant to your particular question or hypothesis
• Explore correlations between different parameters using crosstabs, scatterplots, or other common visualizations as necessary
• Use census data contained in census_data file as a reference when discussing population makeup within any given country during this period

With this approach, you will have all that's necessary to derive meaningful results out of this dataset! Good luck on your exploration!

Research Ideas

• Comparing the sex, age and population weights of those using different types of antidepressants in each country
• Tracking consumption trends across countries and between genders over time
• Correlating antidepressant use with national income indicators such as GDP per capita or overall Mental Health Index scores

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: census.csv | Column name | Description | |:--------------|:------------------------------------------| | year | Year of the data (Integer) | | sex | Gender of the population (String) | | age | Age group of the population (Integer) | | cnt | Number of people using the drug (Integer) | | country | Country of the population (String) |

File: drug_names.csv | Column name | Description | |:---------------|:------------------------------------------------------------------| | atc | Anatomical Therapeutic Chemical (ATC) code for the drug. (String) | | formalname | Formal name of the drug. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

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

This study investigated the frequency with which various nonnarcotic substances were used by male narcotic addicts and the relation of these substances to different types of criminal activity during periods of active addiction and periods of non- addiction. The variables were designed to facilitate an analysis of narcotic addicts as crime risks, patterns of nonnarcotic drug use, and the percentage of illegal income addicts obtained during periods of addiction compared with periods of nonaddiction. Information is included concerning types of narcotic drug use, crime patterns, and use of marijuana, cocaine, barbiturates, amphetamines, and Librium.

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 over 300 statistical tables in this dataset. Each data table may be viewed as an HTML table or downloaded in spreadsheet (Excel format).

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.

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...

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 22 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.

This data set includes the estimated number of individuals in Pennsylvania with Drug Use Disorder, which is an approximation for Opioid Use Disorder prevalence. The estimates are developed by applying mortality weights derived from the CDC’s National Center for Health Statistics to statewide illicit drug use estimates from the National Survey on Drug Use and Health (NSDUH, sponsored by the Substance Abuse and Mental Health Services Administration).

This dataset is deprecated and will be removed by the end of the calendar year 2024. Updated on 8/18/2024

Drug and alcohol-related Intoxication death data is prepared using drug and alcohol intoxication data housed in a registry developed and maintained by the Vital Statistics Administration (VSA) of the Maryland Department of Health and Mental Hygiene (DHMH). The methodology for reporting on drug-related intoxication deaths in Maryland was developed by VSA with assistance from the DHMH Alcohol and Drug Abuse Administration, the Office of the Chief Medical Examiner (OCME) and the Maryland Poison Control Center. Assistance was also provided by authors of a 2008 Baltimore City Health Department report on intoxication deaths. Data in this table is by incident location, where the death occurred, rather than by county of residence.

This dataset provides valuable insights for pharmaceutical agencies to track the effectiveness and sales of their drugs. It is a tabular dataset that has been collected over several years, containing key data points such as drug names, patient reviews, the drug's popularity, and its specific use cases. The primary purpose of this dataset is to facilitate the prediction of a drug's base score in various scenarios, supporting drug effectiveness analysis and market understanding.

Columns

• patient_id: A unique identifier for each patient.
• name_of_drug: The specific name of the pharmaceutical drug.
• use_case_for_drug: The disease or condition that the drug is intended to treat.
• review_by_patient: Detailed feedback or comments provided by the patient about the drug.
• effectiveness_rating: A numerical rating indicating how effective the drug is, typically on a scale from 1 to 10.
• drug_approved_by_UIC: The date on which the drug received approval from UIC.
• number_of_times_prescribed: The frequency or count of how many times the drug has been prescribed.
• base_score: A generated score that serves as the target variable for predictions within the dataset.

Distribution

The dataset is presented in a tabular format, typically provided as a CSV file. It comprises approximately 32,000 records.

Key distributions within the dataset include: * Drug Names: Notable drugs like Levonorgestrel and Etonogestrel each account for 2% of the data, with 96% categorised as 'Other' (representing 30,813 entries). * Use Cases: Birth Control is a use case for 18% of the data, Depression for 6%, and 76% are 'Other' (representing 24,579 entries). * Effectiveness Rating: Ratings between 9.55 and 10.00 are the most frequent, with over 10,000 occurrences. Ratings between 1.00 and 1.45 also show significant counts, exceeding 4,200. * Number of Times Prescribed: A large proportion of drugs have been prescribed between 0 and 38.55 times (over 24,000 instances), with fewer instances for higher prescription counts. * Base Score: The target variable has unique values up to 232,289.00.

Usage

This dataset is ideally suited for: * Predicting the base score of a specific drug to assess its overall performance and impact. * Analysing drug effectiveness and patient feedback. * Tracking drug popularity and market penetration for pharmaceutical agencies. * Developing machine learning models for drug performance prediction.

Coverage

The dataset offers a global geographic scope. The time range for the data collection spans from 24th February 2008 to 12th December 2017.

License

CCO

Who Can Use It

This dataset is particularly beneficial for: * Pharmaceutical agencies for internal drug performance tracking and strategic planning. * Data scientists and machine learning engineers building predictive models related to drug effectiveness and patient outcomes. * Market analysts in the healthcare sector to understand drug popularity and use case trends. * Researchers interested in patient review analysis and drug efficacy studies.

Dataset Name Suggestions

• Pharmaceutical Drug Effectiveness Dataset
• Patient Drug Review and Performance Data
• Drug Efficacy and Prescription Trends
• Healthcare Drug Outcomes Data
• Druggie Patient Review Dataset

Attributes

Original Data Source: 💊🩺Druggie⚕️💊

Since 1971, a national drug habit survey has been conducted among Swedish ninth graders. From 2004, annual survey are also being conducted among students in the second year of high school.

The Swedish Council for Information on Alcohol and Other Drugs (CAN) are responsible for the annual survey since 1986. CAN is a non-governmental organization and their main tasks are to follow the drug trends in Sweden and to inform the public and educate professionals on alcohol and other drugs.

The results from the survey are published in a report called “Skolelevers drogvanor” (Alcohol and Drug Use Among Students). CAN also conduct regional and local school surveys upon requests from municipalities and regions.

In 1983, Statistics Sweden assumed responsibility for the surveys. A major part of the questionnaire changed and it wasn't possible to make comparisons with previous studies.The 1983 survey was therefore carried out in two different versions. One (A) that could be linked to the old studies and one (B) containing the new questions.

Purpose:

The aim of the study is primarily to describe the development of drug habits and to study the differences between various groups.

One of two surveys in 1983 was answered by 1641 students in ninth grade (833 boys and 808 girls), which represented a response rate of 86 % in total. Data from this years survey is not electronic accessible.

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