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422 DDD/1000 PDs for private hospitals) whereas, when expressed as DDD/100 admissions, there were significant differences according to the type of hospital (341 DDD/100 admissions for public vs. 212 DDD/100 admissions for private hospitals; p?Enzalutamide order the two types of hospital because characteristics were very different. For public hospitals, the univariate analysis showed that all the hospital characteristics investigated were significantly correlated with AbC when expressed as DDD per 1000 PDs, except the total number of beds. Total AbC was negatively correlated with the proportion of PDs in medium- and long-term care and psychiatry wards as well as length of stay. Conversely, total AbC was correlated positively and most strongly with the proportion of PDs in medical wards and VCLs (Table?3). When total AbC was expressed as DDD/100 admissions, it was associated with the length of stay and the number of beds, but these associations were weaker and thus the indicator expressed as DDD/1000 PDs was the most relevant Histone demethylase for multivariate analysis. In the resulting models, it was significantly associated with the proportion of PDs in surgery, intensive care and medical wards (Table?4). These characteristics explained 84% of the variation in AbC. A difference of 1% of PDs in intensive care between two public hospitals, adjusted with respect to the other variables, led to an increase of 29.3 DDD/1000 PDs Venetoclax (90% confidence intervals (CI), 14.9; 43.6). Few hospitals had an AbC predicted by this model that was far from the actual AbC (Fig.?2). For private hospitals, the AbC expressed as DDD/1000 PDs was only weakly associated with hospital characteristics, but when expressed as DDD/100 admissions, the association was stronger. Total AbC was positively correlated with the length of stay (r?=?0.81; p?