Ranking the harm of non-medically used prescription opioids in the UK.

A panel of nine experts applied multi-criteria decision analysis (MCDA) to determine the relative overall harm to users and harms to others of street heroin (injected and smoked) and eleven non-medically used prescription opioids. The experts assessed harm scores for each of the 13 opioids on each of 20 harm criteria, weighted the criteria and explored the resulting weighted harm scores for each opioid. Both forms of heroin scored very high: overall harm score of 99 for injected heroin and 72 for smoked heroin on a scale of 0-100. The main feature that distinguishes both forms of street heroin use is that their harm to others is more than five times that of the other eleven opioids. The overall harm score of fentanyl (including injection of fentanyl extracted from patches) and diamorphine (medically prescribed form of heroin) was 54 and 51, respectively, whereas that of orally used opioids ranged from 32 (pethidine) to 11 (codeine-containing pharmaceuticals). Injected street heroin, fentanyl and diamorphine emerged as most harmful to users, with the latter two very low in harm to others. Pethidine, methadone, morphine and oxycodone are also low in harm to others, while moderate in harm to users. We conclude that the overall harms of non-medically used prescription opioids are less than half that of injected street heroin. These data may give a basis for precautionary regulatory measures that should be considered if the rising trend in non-medical use of prescription opioids were to become evident in the UK.

In common batch searching of illicit heroin samples--evaluation of data by chemometrics methods.

In this study three illicit heroin samples which belonged to three different batches were subdivided into eight samples each. To simulate the dealers' chain, and to check the influence (if any) of diluents on the analytical results, some of the samples were cut with the most frequently used cutting substances, whereas the others were left unchanged. Samples were analysed (within a 10-week period of time) by gas chromatography-mass spectrometry (GC-MS) and characterised by seven variables each. To recover batch links among investigated heroin samples three multivariate methods, i.e. hierarchical clustering (HCA), principal component analysis (PCA) and k-nearest neighbours (k-NN), were applied on to the normalised and scaled analytical dataset. The classification abilities of the HCA, PCA and k-NN were in the range from 95 to 100%. Disturbing effects due to the dilution of samples have not been observed.

Development of a predictive model for batch membership of street samples of heroin.

Street samples (n = 31) of heroin were analysed by gas chromatography with flame ionisation detection to determine opiate, noscapine and papaverine content. Using this data, the chromatograms obtained could be resolved into eight groups by visual examination of the data. The concentrations of opiates were significantly correlated (P < 0.05) with the exception of the pairs 6-O-monoacetylmorphine/noscapine and morphine/6-O-monoacetylmorphine. This precludes the use of simple cluster analysis for determining and predicting the relationship of different street samples. Application of Fisher's linear discriminant analysis to the data set indicated that 91.9% of the samples could be discriminated including pairs which could not be discriminated by eye. A blind trial (n = 2) resulted in the correct assignment to street sample. Application of such methods may provide, in the future, a powerful tool for the prediction of batch membership of drugs at the street level.

Persian heroin in the San Francisco Bay area: 1977-1980.

A new type of heroin has increased in frequency in the San Francisco Bay Area in the past three years. "Persian" heroin, coming to this country from Southwest Asia, differs from commonly-seen heroin in several ways: (1) distinctive marketing channels, (2) different routes of administration, (3) increased purity, (4) more severe withdrawal, and (5) different demographic characteristics of users. "Persian" heroin users tend to be younger and more likely to be White and unemployed as compared to other heroin users.