Workplace drug testing in Italy: findings about second-stage testing.

Workplace Drug Testing (WDT) in Italy includes two levels of monitoring: a first stage concerning drug testing on urine samples and a second involving both urine and hair analysis. The second stage is performed only on workers who tested positive at the first level. We analyzed urine and hair specimens from 120 workers undergoing second-level testing between 2009 and 2012. Eighty percent of them had tested positive for cannabinoids during the first level analysis, and 15.8% for cocaine. Both urine and hair samples were analyzed in order to find the following drugs of abuse: amphetamines, buprenorphine, cannabinoids, cocaine, ecstasy, methadone, and opiates. Urine analyses were performed by immunological screening (EMIT); urine confirmatory tests and hair analyses were performed by gas chromatography-mass spectrometry (GC-MS). As regards second-stage testing on urine samples, 71.2% of workers were always negative, whereas 23.9% tested positive at least once for cannabinoids and 2.5% for cocaine. Hair analyses produced surprising results: 61.9% of hair samples tested negative, only 6.2% tested positive for cannabinoids, whereas 28.8% tested positive for cocaine. These findings confirm that second-level surveillance of WDT, which includes hair analysis, is very effective because it highlights drug intake - sometimes heavy - that cannot be revealed only through urine analyses. The employees for whom drug addiction is proved can begin rehabilitation, while keeping their job. Eventually, our results confirmed the widespread and undeclared use of cocaine in Italy.

Prevalence estimates of alcohol related problems in an area of northern Italy using the capture-recapture method.

BACKGROUND: The alcohol-related problems (ARPs) are a relevant issue in public health and contribute to premature deaths and avoidable disease burden. The capture-recapture (C-R) method can be a useful tool to provide reliable estimates for populations with hidden nature such as subjects with ARP. METHODS: C-R method was used to estimate the 'true count' of individuals with ARP using three independent health-related current databases in an area of northern Italy during 2007. To predict the frequency of unascertained cases, we constructed log linear models. The goodness-of-fit of a model was measured by the likelihood ratio test and the final model was selected using Akaike's Information Criterion. Confidence intervals (CIs) were calculated according to Hook and Regal. RESULTS: Altogether 1014 subjects with ARP were directly identified from the three sources using the C-R method the estimated unknown population was 2729 subjects, giving a total of 3743 subjects with ARP (95% CI 3148-4504) and a prevalence of 8.24 (95% CI 7.97-8.50) per 1000 inhabitants aged >15 years. The analyses stratified for gender estimated 12.31/1000 (95% CI 11.85-12.77) men and 4.86/1000 (95% CI 4.58-5.14) women with ARP. Besides, the analysis calculated a prevalence of 14.99 per 1000 (95% CI 14.29-15.69) for males <50 years (1731), corresponding to the majority of subjects with ARP. CONCLUSION: The C-R technique is useful to provide a more realistic picture of the size of ARP population. This has important implications both for future planning of service provision and for the way in which the impact of ARP interventions are evaluated.