A chemical analysis examining the pharmacology of novel psychoactive substances freely available over the internet and their impact on public (ill) health. Legal highs or illegal highs?

Objectives: Public Health England aims to improve the nation's health and acknowledges that unhealthy lifestyles, which include drug use, undermine society's health and well-being. Recreational drug use has changed to include a range of substances sold as ‘research chemicals’ but known by users as ‘legal highs’ (legal alternatives to the most popular illicit recreational drugs), which are of an unknown toxicity to humans and often include prohibited substances controlled under the Misuse of Drugs Act (1971). Consequently, the long-term effects on users' health and inconsistent, often illegal ingredients, mean that this group of drugs presents a serious risk to public health both now and in the future. Therefore, the aim of this study was to ascertain what is in legal highs, their legality and safety, while considering the potential impact, these synthetic substances might be having on public health. Design: A total of 22 products were purchased from five different internet sites, 18 months after the UK ban on substituted cathinones, like mephedrone, was introduced in April 2010. Each substance was screened to determine its active ingredients using accepted analytical techniques. Setting: The research was conducted in Leicestershire but has implications for the provision of primary and secondary healthcare throughout the UK. Results: Two products, both sold as NRG-2 from different internet suppliers, were found to contain the banned substituted cathinones 4-methylethcathinone (4-MEC) and 4-methylmethcathinone (4-MMC), the latter being present in much smaller quantities. Although sold as research chemicals and labelled ‘not for human consumption’, they are thinly disguised ‘legal highs’, available online in quantities that vary from 1 g to 1 kg. Conclusions: Despite amendments to legislation, prohibited class B substances are still readily available in large quantities over the internet. The findings suggest that these prohibited substances are being manufactured or imported into the UK on a large scale, which has serious implications for public health and clinicians who are ill equipped to deal with this newly emerging problem

Linking solved and unsolved crimes using offender behaviour

Offender behaviour is used to distinguish between crimes committed by the same person (linked crimes) and crimes committed by different people (unlinked crimes) through behavioural case linkage. There is growing evidence to support the use of behavioural case linkage by investigative organisations such as the police, but this research is typically limited to samples of solved crime that do not reflect how this procedure is used in real life. The current paper extends previous research by testing the potential for behavioural case linkage in a sample containing both solved and unsolved crimes. Discrimination accuracy is examined across crime categories (e.g. a crime pair containing a car theft and a residential burglary), across crime types (e.g. a crime pair containing a residential burglary and a commercial burglary), and within crime types (e.g. a crime pair containing two residential burglaries) using the number of kilometres (intercrime distance) and the number of days (temporal proximity) between offences to distinguish between linked and unlinked crimes. The intercrime distance and/or the temporal proximity were able to achieve statistically significant levels of discrimination accuracy across crime categories, across crime types, and within crime types as measured by Receiver Operating Characteristic (ROC) analysis. This suggests that behavioural case linkage can be used to assist the investigation, detection and prosecution of prolific and versatile serial offenders

A Comparison of Logistic Regression and Classification Tree Analysis for Behavioural Case Linkage

Much previous research on behavioural case linkage has used binary logistic regression to build predictive models that can discriminate between linked and unlinked offences. However, classification tree analysis has recently been proposed as a potential alternative owing to its ability to build user-friendly and transparent predictive models. Building on previous research, the current study compares the relative ability of logistic regression analysis and classification tree analysis to construct predictive models for the purposes of case linkage. Two samples are utilised in this study: a sample of 376 serial car thefts committed in the UK and a sample of 160 serial residential burglaries committed in Finland. In both datasets, logistic regression and classification tree models achieve comparable levels of discrimination accuracy, but the classification tree models demonstrate problems in terms of reliability or usability that the logistic regression models do not. These findings suggest that future research is needed before classification tree analysis can be considered a viable alternative to logistic regression in behavioural case linkage