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The objective of this study was to describe a 12-year (1997-2008) observation of substance-related incidents occurring at rave parties in the Netherlands, including length of visits to first-aid stations, substances used, and severity of the incidents. During rave parties, specifically trained medical and paramedical personnel staffed first aid stations. Visitors were diagnosed and treated, and their data were recorded using standardized methods. During the 12-year period with 249 rave parties involving about 3,800,000 visitors, 27,897 people visited a first aid station, of whom 10,100 reported having a substancerelated problem. The mean age of these people was 22.3+/25.4 years; 52.4% of them were male. Most (66.7%) substancerelated problems were associated with ecstasy or alcohol use or both. Among 10,100 substance-related cases, 515 required professional medical care, and 16 of these cases were life threatening. People with a substance-related problem stayed 20 min at the first aid station, which was significantly longer than the 5 min that those without a substance-related health problem stayed. These unique data from the Netherlands identify a variety of acute health problems related to the use of alcohol, amphetamines, cannabis, cocaine, ecstasy, and GHB. Although most problems were minor, people using GHB more often required professional medical care those using the other substances. We recommended adherence to harm and risk reduction policy, and the use of first aid stations with specially trained staff for both minor and serious incidents. [ABSTRACT FROM AUTHOR]

Introduction: Cannabis is Europe's most commonly used illicit drug. Some users do not develop dependence or other problems, whereas others do. Many factors are associated with the occurrence of cannabis-related disorders. This makes it difficult to identify key risk factors and markers to profile at-risk cannabis users using traditional hypothesis-driven approaches. Therefore, the use of a data-mining technique called binary recursive partitioning is demonstrated in this study by creating a classification tree to profile at-risk users., Methods: 59 variables on cannabis use and drug market experiences were extracted from an internet-based survey dataset collected in four European countries (Czech Republic, Italy, Netherlands and Sweden), n = 2617. These 59 potential predictors of problematic cannabis use were used to partition individual respondents into subgroups with low and high risk of having a cannabis use disorder, based on their responses on the Cannabis Abuse Screening Test. Both a generic model for the four countries combined and four country-specific models were constructed., Results: Of the 59 variables included in the first analysis step, only three variables were required to construct a generic partitioning model to classify high risk cannabis users with 65-73% accuracy. Based on the generic model for the four countries combined, the highest risk for cannabis use disorder is seen in participants reporting a cannabis use on more than 200 days in the last 12 months. In comparison to the generic model, the country-specific models led to modest, non-significant improvements in classification accuracy, with an exception for Italy (p = 0.01)., Conclusion: Using recursive partitioning, it is feasible to construct classification trees based on only a few variables with acceptable performance to classify cannabis users into groups with low or high risk of meeting criteria for cannabis use disorder. The number of cannabis use days in the last 12 months is the most relevant variable. The identified variables may be considered for use in future screeners for cannabis use disorders.