Antidepressant Use in Scandinavia

A Study of Population Characteristics and Drug Utilization Rates

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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 .

In 2021, it was estimated that around *** million people worldwide consumed illegal drugs such as cannabis, opioids, and cocaine. Furthermore, around **** million people were thought to be problem drug users or to have a drug use disorder. Although drug use varies from country to country, drug use remains a significant problem in many parts of the world. For example, the United States is currently experiencing an opioid epidemic, with drug overdose deaths reaching record levels over the past few years. What is the most used illicit drug worldwide? The most used illicit drug worldwide is cannabis, followed by opioids, and amphetamines. High estimates suggest that around *** percent of the global population consumed cannabis in the past year as of 2021. In comparison, around *** percent of people were thought to have consumed opioids in the past year, and less than *** percent were estimated to have used amphetamines. Drug use is generally more prevalent among men than women, but this distribution varies by drug. For example, around ** percent of cocaine users worldwide are men and ** percent are women, but women account for ** percent of amphetamine users. Cannabis uses In 2021, it was estimated that around *** million people worldwide consumed cannabis at least once in the past year. The highest number of past year cannabis users at that time was found in the Americas. This may be unsurprising since Canada and many U.S. states now allow the sale and use of recreational cannabis. The market for recreational cannabis is substantial in both countries. In the United States, sales of recreational cannabis reached **** billion U.S. dollars in 2021 and are expected to grow to some ** billion U.S. dollars by the year 2026. In 2020, there were thought to be around **** million adult consumers of cannabis in the United States, with this number expected to increase to just over *** million by 2025.

The data for this study were sourced from the crowd-sourcing project DrugRoutes, which we launched online on January 1, 2020. DrugRoutes was an online platform that gathered transaction data directly from individuals who had bought or sold drugs on cryptomarkets. The website, accessible via the clear web or the darknet, allowed users to anonymously share information regarding their latest cryptomarket transactions. The data gathered included the specific type of illicit drug involved, the quantity traded, the transaction amount, the transaction date, the countries of origin and destination, and confirmation of parcel receipt. To encourage participation, DrugRoutes openly shared the collected data, enabling cryptomarket users to identify the most popular routes. Consistent with previous studies, our methodology aimed to create a safe space for cryptomarket participants to contribute information for research purposes.Every submission to the project underwent moderation by the authors to filter out potential spam. Submissions deemed too deviant from the prevalent cryptomarket prices per unit at the time were labeled as spam and excluded from the dataset. The research team cross-referenced the price per unit from multiple listings on several cryptomarkets and calculated an average. A transaction price from the same origin country that deviated more than one standard deviation from the mean was regarded as spam and removed from the dataset. We also removed multiple submissions made within seconds of each other as potential spam. While DrugRoutes was one of the few crowd-sourcing initiatives collecting information on illicit drug transactions, it stands out as the only one incorporating successful delivery of illicit drugs. The research team advertised the crowd-sourcing platform on approximately 140 darkweb platforms, and the consent form and contact information were readily available on the website.In total, we collected 1,364 submissions between 2020 and 2022, all of which were confirmed to be authentic and genuine. As this paper is exclusively concerned with international transactions, the subsequent analyses will omit data that pertain strictly to domestic trade.This study views drug trafficking on cryptomarkets as a network of relationships between countries. This perspective aligns with previous literature analyzing drug trafficking across nations, and recent studies investigating the geographic structure of drug trafficking on cryptomarkets.We utilize data from DrugRoutes to identify relationships between countries. DrugRoutes solicited information from cryptomarket participants about their home country and the country with which they most recently transacted. Consequently, we establish a link from Germany to Spain if a participant based in Germany reports purchasing drugs from a dealer in Spain, or if a Spanish drug dealer declares having shipped drugs to Germany. Using this method, we identified a total of 731 different transactions involving 372 dyads across 42 pairs of countries.The network of drugs trafficked via cryptomarkets is characterized by two distinctive features. First, we only consider a connection if at least two submissions are reported for a pair of countries. For example, we dismissed the connection between Albania and Ireland since we have only one observation following this route. These connections are more likely to be random or sporadic links between countries and, therefore, are not included in our analysis. The final network is predicated on a total of 100 exchanges between any two countries.Secondly, we do not differentiate between substances. For example, a connection between Spain and Germany for cannabis is regarded in the same way as a connection between France and Germany for cocaine. Given that we have only a few transactions for most substances, creating individual networks for each illicit drug type would result in very small networks. As a result, we opted to group all drug types together to avoid information loss. More crucially, we anticipate the independent variables to exert a similar effect on cryptomarket transactions, irrespective of the drug type. This approach also enables us to compare our findings to previous studies that do not differentiate between substances .

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 ---

🚨 Read Attribute Information to understand the column values 🚨

🚨 Also check the starter notebook where I converted the column values into more meaningful values 🚨

Context

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. Database contains 18 classification problems. Each of independent label variables contains seven classes: "Never Used", "Used over a Decade Ago", "Used in Last Decade", "Used in Last Year", "Used in Last Month", "Used in Last Week", and "Used in Last Day".

Attribute Information

ID: ID is number of record in original database. Cannot be related to participant. It can be used for reference only.

Age: Age is the age of participant and has one of the values:

Gender: Gender is gender of participant:

Education: Education is level of education of participant and has one of the values:

Country: Country is country of current residence of participant and has one of the values:

Ethnicity: Ethnicity is ethnicity of participant and has one of the values:

Nscore: Nscore is NEO-FFI-R Neuroticism. Neuroticism is one of the Big Five higher-order personality traits in the study of psychology. Individuals who score high on neuroticism are m...

As of 2021, men represented around 75 percent of opiate users worldwide, while 45 percent of amphetamine users globally were women. This statistic shows the distribution of illicit drug users worldwide as of 2021, by gender and drug.

The Uniform Facility Data Set (UFDS) was designed to measure the scope and use of drug abuse treatment services in the United States. The survey collects information from each privately- and publicly-funded facility in the country that provides substance abuse treatment as well as from state-identified facilities that provide other substance abuse services. Data are collected on a number of topics including facility operation, services provided (assessment, therapy, testing, health, continuing care, special programs, transitional services, community outreach, ancillary), type of treatment, numbers of clients, and various client characteristics. The main objective of the UFDS is to produce data that can be used to assess the nature and extent of substance abuse treatment services, to assist in the forecast of treatment resource requirements, to analyze treatment service trends, to conduct national, regional, and state-level comparative analyses of treatment services and utilization, and to generate the National Directory of Drug and Alcohol Abuse Treatment Programs and its on-line equivalent, the Substance Abuse Treatment Facility Locator http://findtreatment.samhsa.gov/.This study has 1 Data Set.

Data Data comes from Organisation for Economic Cooperation and Development on https://data.oecd.org/healthres/pharmaceutical-spending.htm

It consists of useful information about the percent of health spending, percent of GDP and US dollars per capita for specific countries. Also, we added total spending by countries using their population data.

Population data comes from DataHub http://datahub.io/core/population since it is regularly updated and includes all country codes.

Preparation There are several steps that have been done to get the final data.

We extracted separately each resource by “percent of health spending”, “percent of GDP” and “US dollars per capita” We merged them into one resource and added nenew column “TOTAL_SPEND” “TOTAL_SPEND” is calculated using “US dollars per capita” and “population” data. Source for original pharmacy drug spending: https://stats.oecd.org/sdmx-json/data/DP_LIVE/.PHARMAEXP.../OECD?contentType=csv&detail=code&separator=comma&csv-lang=en.

Analysis of ‘Local Addiction and Drug Dependence Prevention Teams in the Basque Country’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-opendata-euskadi-eus-catalogo-equipos-locales-de-prevencion-de-adicciones-y-drogodependencias-en-euskadi- on 17 January 2022.

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

Technical teams for community prevention of addictions in local entities (city halls, partnerships), municipalities that without having technical equipment have some resource to work on addictions, and non-profit associations. They carry out projects for universal, targeted and targeted prevention of addictions and drug dependence, risk and harm reduction, care and reintegration, as well as the promotion of healthy behaviors.

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

The results might surprise you when looking at internet users that are active on social media in each country.

The Uniform Facility Data Set (UFDS), formerly the National Drug and Alcohol Treatment Unit Survey or NDATUS, was designed to measure the scope and use of drug abuse treatment services in the United States. The survey collects information from each privately- and publicly-funded facility in the country that provides substance abuse treatment as well as from state-identified facilities that provide other substance abuse services. Data are collected on a number of topics including facility operation, services provided (assessment, therapy, testing, health, continuing care, programs for special groups, transitional services, community outreach, ancillary), type of treatment, facility capacity, numbers of clients, and various client characteristics. The main objective of the UFDS is to produce data that can be used to assess the nature and extent of substance abuse treatment services, to assist in the forecast of treatment resource requirements, to analyze treatment service trends, to conduct national, regional, and state-level comparative analyses of treatment services and utilization, and to generate the National Directory of Drug and Alcohol Abuse Treatment Programs and its on-line equivalent, the Substance Abuse Treatment Facility Locator http://findtreatment.samhsa.gov. Additionally, the UFDS provides information that can be used to design sampling frames for other surveys of substance abuse treatment facilities.This study has 1 Data Set.

The dataset contains information about use of hypnotics in the Scandinavian countries, Denmark, Norway and Sweden. The categories of hypnotics described are: N05CH01 - Melatonine, N05CF* - Z drugs and R06AD* - H1 receptor antagonists.

The dataset consists of 2 files:

1. drug_use.csv
2. census.csv

Drug and census data were derived from the following national resources in the public domain:

Drug statistics data:

https://sdb.socialstyrelsen.se/if_lak/val.aspx (download date: 2020.06.15)

http://www.norpd.no/ (download date: 2020.12.06)

http://www.medstat.dk/ (download date: 2020.11.26)

Census data:

http://www.statistikdatabasen.scb.se (download date: 2020.06.12)

https://www.ssb.no/ (download date: 2020.06.15)

https://statistikbanken.dk (download date: 2020.06.12)

The source data owners take no responsibily for interpretation or analysis of data performed by third parties. Source data owners should be attributed when data are used. Consult data owners websites for details about attribution.

File descriptions:

drug_use.csv

This file contains aggregated information about the total number of unique users and the total amount of drug daily doses, by the categorical variables atc, year, country, sex and age. The categorical variables have the following valuesets

country (DK, NO, SE, SC), SC means Scandinavia and includes DK, SE and NO
year (2012 - 2019)
age (5-9, 10-14, 15-19, 20-24, 5-24)
sex (M,F, MF), MF means Male and Female

The variables prev_pr_1000 and ddd_pr_1000 are the results of dividing the variables n_users and ddd by the total population size (npop) in that country, year, agegroup and sex category. These census informations are also available in the census.csv file. Since ddd information is only available in Norway and Denmark, the ddd_pr_1000 for Scandinavia is based only on data from Denmark and Norway. The denominator for this calculation is in the variable called npop_excl_se.

census.csv

This file contains census data for each country grouped by sex and age in one year intervals. Use this file if you want to calculate population denominators for alternative aggregations of data in the drug_use.csv file

BackgroundThis study used data from the Global Burden of Disease Study (GBD) database to systematically assess the magnitude of drug use disorders (DUD) burden between 1990 and 2021.MethodsThis study used GBD data to analyze the trends in ASIR, DALYs and other DUD indicators from 1990 to 2021, and compared them among different regions and countries. The Estimated Annual Percentage Change (EAPC) and its 95% Confidence Interval (CI) were calculated to assess the temporal and geographical disparities. ASIR and DALYs were used to evaluate the burden of DUDs, and socio-demographic index (SDI) was used to measure the socio-economic development level of each country.ResultsThe global ASIR of DUDs showed a slight downward trend (EAPC = −0.26). The age-standardized DALY rate (per 100,000) significantly declined from 1990 to 2021 (EAPC = −1.44). Among the regions, the high SDI region exhibited the most substantial increase in ASIR (EAPC = 0.65). On a regional level, the high-income North America region had the highest EAPC for both age-standardized DALYs and ASIR (EAPC = 4.82, 1.02, respectively). Nationally, the United States of America reported the largest increase in age-standardized DALY rates and EAPC for ASIR (EAPC of 4.88, 1.05, respectively), while South Africa had the most significant decrease in EAPC (EAPC of −3.62, −1.52, respectively). In 2021, the highest ASIR was observed in high-income North America at 520.07; Central Asia had the highest age-standardized DALY rate. Globally, age-standardized DALYs and ASIR for DUDs were generally higher in men than in women, and the burden of DUDs decreased with age.ConclusionThe global burden of DUDs has shown complex and changing trends over the last decades, with large differences in burden between regions and countries. This highlights the need for targeted public health policies and interventions in High income North America region and Eastern Europe.

56.8% of the world’s total population is active on social media.

Information on the number of people entering treatment for a drug problem provides insight into general trends in problem drug use and also offers a perspective on the organisation and uptake of treatment facilities. ‘Treatment demand data’ come from each country with varying degrees of national coverage, principally from outpatient and inpatient centres’ treatment records. 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).

Over 210 million people worldwide suffer from social media addiction.

The 2019 National Drug Strategy Household Survey was conducted between April and September of 2019 and examines current awareness of attitudes and behaviours toward drugs and drug problems within the Australian community, focussing on respondents' personal attitudes toward drugs, knowledge of drugs and drug histories. The survey included questions regarding respondents' personal drug and alcohol experience and behaviour, opinions on drug policy and legislation, availability of drugs and alcohol, injury and harm from drugs and alcohol, and attitudes towards the use of alcohol and other drugs. The drugs covered included: tobacco/cigarettes, alcohol, pain-killers/pain-relievers and opioids, tranquillisers, heroin, methadone, inhalants, ketamine, GHB, ecstasy, hallucinogens, cocaine, meth/amphetamines, cannabis/marijuana, synthetic cannabis, other psychoactive substances, and steroids. Demographic and background variables included: state of residence, age, sex, marital status, self-assessed health status, sexuality, Indigenous status, country of birth, language spoken at home, employment status, occupation, level of education, income, index of socio-economic advantage and disadvantage, remoteness area and household composition.

Substance use refers that using psychoactive substances such as khat, alcohol, cigarette, and illicit drugs. Youth are more vulnerable to substance use than older people. Substance use has a different impact on the health as well as socio-economics of one country. Globally substance use was the major public health concern. Currently, substance use is a common public health concern among the youth of Ethiopia mainly in Jimma town. Therefore, this study aimed to explore the substance use and risk factors among the youth of Jimma town, 2019.

In this post, I'll give you all the social media addiction statistics you need to be aware of to moderate your social media use.

Health and social responses data reported here comprises information on the availability of different types of needle and syringe programmes (NSPs) in the country, including prison-, pharmacy- and non pharmacy-based programmes and on the number of syringes provided at these programmes.

There are approximately 20 statistical tables in this dataset. Each data table may be viewed as an HTML table or downloaded in spreadsheet (Excel format).