The Need for Psychiatric Treatment among Polish Users of Psychoactive Substances Is Increasing: This and Other Results from the Newest PolDrugs Survey.
Gniewko WięckiewiczJulia MarekIga FlorczykSandra SzafoniSzymon PlutaKatarzyna SmukowskaGabriela ŻebrowskaMaciej StokłosaPiotr GorczycaRobert PudloPublished in: Medicina (Kaunas, Lithuania) (2023)
Background and Objectives : PolDrugs is the largest Polish naturalistic nationwide survey to present basic demographic and epidemiological data that could potentially prevent harm from illicit substances intake in drugs users. The most recent results were presented in 2021. The goal of this year's edition was to re-present the above data and compare it to the previous edition's data to identify and describe the differences. Materials and Methods : The survey included original questions about basic demographics, substance use, and psychiatric treatment. The survey was administered via the Google Forms platform and promoted via social media. The data was collected from 1117 respondents. Results : People of all ages use a variety of psychoactive substances in many situations. The three most commonly used drugs are marijuana, 3,4-methylenedioxymethamphetamine, and hallucinogenic mushrooms. The most common reason for seeking professional medical help was amphetamine use. A total of 41.7 percent of respondents were receiving psychiatric treatment. The three most common psychiatric diagnoses among the respondents were depressive disorders, anxiety disorders, and ADHD. Conclusions : Key findings include increases in the use of psilocybin and DMT, increases in the use of heated tobacco products, and a near doubling in the percentage of individuals receiving psychiatric help in the past two years. These issues are discussed in the discussion section of this paper, which also addresses the limitations to the article.
Keyphrases
- mental health
- social media
- healthcare
- big data
- cross sectional
- drinking water
- attention deficit hyperactivity disorder
- physical activity
- body mass index
- autism spectrum disorder
- working memory
- replacement therapy
- smoking cessation
- deep learning
- combination therapy
- data analysis
- machine learning
- single cell
- weight loss