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Aims To determine the (1) incidence of adverse drug events (ADEs) in 10 emergency department (EDs) of general hospitals in the Regione Campania (southern Italy), (2) rate of ADE-related hospital admissions, (3) drug classes most frequently involved, and (4) the types of ADEs and their frequency. Methods We performed a cohort study of all patients attending the EDs. This study was carried out in two observational periods of 10 days each in 10 EDs. Demographic, clinical, and pharmacological data about all patients admitted to EDs were collected by trained and qualified monitors. Records related to ADEs were analyzed and validated by a specific scientific committee. Results Of 7,861 ED visits, 96 were ADE-related. The incidence of hospitalization was higher in patients who had taken medication than in patients with a negative drug history (24.9 vs. 16.4%). ADEs were significantly more frequent in women. Patients aged between 60 and 69 years and between 30 and 39 years were significantly more likely to experience an ADE. Serious ADEs were identified in 20 ED visits (20.8% of total sample). Antibiotics, NSAIDs, and agents acting on the renin-angiotensin system were the drugs most often involved in ADEs. In multivariate analyses, the adjusted odds ratio was 3.4 (95% CI: 1.07-2.84) for patients taking NSAIDs, 4.78 (95% CI: 2.26-10.12) for those taking β 2 -adrenergic-receptor agonists, and 6.20 (95%CI: 2.74-14.06) for those taking β-lactam antibiotics. Conclusion This study shows that ADEs are an important problem in industrialized countries. Moreover, it shows that ADEs affect hospital admission rates and reinforces the importance of drug-induced disease as a public health problem.

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In data mining it is usual to describe a group of measurements using summary statistics or through their empirical distribution functions. Each summary of a group of measurements is the representation of a typology of individuals (sub-populations) or of the evolution of the observed variable for each individual. Therefore, typologies or individuals are expressible through multi-valued descriptions (intervals, frequency distributions). Symbolic Data Analysis, a relatively new statistical approach, aims at the treatment of such kinds of data. In the conceptual framework of Symbolic Data Analysis, the paper aims at presenting new basic statistics for numeric multi-valued data. First of all, we propose how to consider all numerical multi-valued descriptions as special cases of distributional data, i.e. as data described by distributions. Secondly, we extend some classic univariate (mean, variance, standard deviation) and bivariate (covariance and correlation) basic statistics taking into account the nature, the source and the interpretation of the variability of such data. As opposed to those proposed in the literature, the novel statistics are based on a distance between distributions, the ℓ2 Wasserstein distance. Using a clinic dataset, we compare the proposed approach to the existing one showing the main differences in terms of interpretation of results.