Cocaine contamination of banknotes: a review.

Background: The analysis of drug traces on banknotes with different validated techniques can provide important information about the types of substances that are used in a geographical region. The aim of our review was to investigate banknotes' contamination by cocaine, by its metabolite, but also by other drugs. Methods: A systematic literature search (English written literature) was conducted in MEDLINE, and Scopus, collecting studies from 1974 till 2017. The Key search terms included: 'banknote AND drug'; 'banknote AND cocaine'. Results: The literature search yielded 88 publications; 9 were included in our review. In six studies that showed banknotes' positivity to cocaine, the percentage ranged from 2.5% to 100%. The concentration of cocaine ranged from 0.09 ng/note to 889 µg/note. Benzoylecgonine was indentified only in three studies with a range from 0.71 to 130 ng/note. Other indentified drugs were: amphetamine derivatives, opiates, benzodiazepines. Conclusions: Circulating banknotes could be used to indicate substances used in a population, and those recently introduced in a geographical macro-area. The identification of very high amounts of cocaine can provide important information for the identification of banknotes used in illegal trafficking.

Predicting Healthcare-associated Infections: are Point of Prevalence Surveys data useful?

Introduction: Since 2012, the European Centre for Disease Prevention and Control (ECDC) promotes a point prevalence survey (PPS) of HAIs in European acute care hospitals. Through a retrospective analysis of 2012, 2015 and 2017 PPS of HAIs performed in a tertiary academic hospital in Italy, we developed a model to predict the risk of HAI. Methods: Following ECDC protocol we surveyed 1382 patients across three years. Bivariate logistic regression analyses were conducted to assess the relationship between HAI and several variables. Those statistically significant were included in a stepwise multiple regression model. The goodness of fit of the latter model was assessed with the Hosmer-Lemeshow test, ultimately constructing a probability curve to estimate the risk of developing HAIs. Results: Three variables resulted statistically significant in the stepwise logistic regression model: length of stay (OR 1.03; 95% CI: 1.02-1.05), devices breaking the skin (i.e. peripheral or central vascular catheter, OR 4.38; 95% CI: 1.52-12.63), urinary catheter (OR 4.71; 95% CI: 2.78-7.98). Conclusion: PPSs are a convenient and reliable source of data to develop HAIs prediction models. The differences found between our results and previously published studies suggest the need of developing hospital-specific databases and predictive models for HAIs.