Despite applying uniform methods, variations in the prevalence of antidepressant prescribing were obvious in the different populations. Database characteristics and clinical factors may both explain these variations.

GPs prescribe antidepressants predominantly for treating depression. However, using antidepressant drug as a proxy for identifying depressed patients in a prescription database should be done with caution and when possible in combination with clinical data.

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Purpose Studies on drug utilization usually do not allow direct cross-national comparisons because of differences in the respective applied methods. This study aimed to compare time trends in BZDs prescribing by applying a common protocol and analyses plan in seven European electronic healthcare databases. Methods Crude and standardized prevalence rates of drug prescribing from 2001-2009 were calculated in databases from Spain, United Kingdon (UK), The Netherlands, Germany and Denmark. Prevalence was stratified by age, sex, BZD type [(using ATC codes), i.e. BZD-anxiolytics BZD-hypnotics, BZD-related drugs and clomethiazole], indication and number of prescription. Results Crude prevalence rates of BZDs prescribing ranged from 570 to 1700 per 10 000 person-years over the study period. Standardization by age and sex did not substantially change the differences. Standardized prevalence rates increased in the Spanish (+13%) and UK databases (+2% and +8%) over the study period, while they decreased in the Dutch databases (À4% and À22%), the German (À12%) and Danish (À26%) database. Prevalence of anxiolytics outweighed that of hypnotics in the Spanish, Dutch and Bavarian databases, but the reverse was shown in the UK and Danish databases. Prevalence rates consistently increased with age and were two-fold higher in women than in men in all databases. A median of 18% of users received 10 or more prescriptions in 2008. Conclusion Although similar methods were applied, the prevalence of BZD prescribing varied considerably across different populations. Clinical factors related to BZDs and characteristics of the databases may explain these differences.