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Background Research into the relationship between gender identity disorder and psychiatric problems has shown contradictory results. Aims To investigate psychiatric problems in adults fulfilling DSM-IV-TR criteria for a diagnosis of gender identity disorder. Method Data were collected within the European Network for the Investigation of Gender Incongruence using the Mini International Neuropsychiatric Interview - Plus and the Structured Clinical Interview for DSM-IV Axis II Disorders (n = 305). Results In 38% of the individuals with gender identity disorder a current DSM-IV-TR Axis I diagnosis was found, mainly affective disorders and anxiety disorders. Furthermore, almost 70% had a current and lifetime diagnosis. Al! four countries showed a similar prevalence, except for affective and anxiety disorders, and no difference was found between individuals with early-onset and late-onset disorder. An Axis II diagnosis was found in 15% of all individuals with gender identity disorder, which is comparable to the general population. Conclusions People with gender identity disorder show more psychiatric problems than the general population; mostly affective and anxiety problems are found. [ABSTRACT FROM AUTHOR]

Background Finite mixture models posit the existence of a latent categorical variable and can be used for probabilistic classification. The authors illustrate the use of mixture models for dietary pattern analysis. An advantage of this approach is taking classification uncertainty into account. Methods Participants were a random sample of women from the European Prospective Investigation into Cancer. Food consumption was measured using dietary questionnaires. Mixture models identified latent classes in food consumption data, which were interpreted as dietary patterns. Results Among various assumptions examined, models allowing the variance of foods to vary within and between classes fit better than alternatives assuming constant variance (the K-means method of cluster analysis also makes the latter assumption). An eight-class model was best fitting and five patterns validated well in a second random sample. Patterns with lower classification uncertainty tended to be better validated. One pattern showed low consumption of foods despite being associated with moderate body mass index. Conclusion Mixture modelling for dietary pattern analysis has advantages over both factor and cluster analysis. In contrast to these other methods, it is easy to estimate pattern prevalence, to describe patterns and to use patterns to predict disease taking classification uncertainty into account. Owing to substantial error in food consumptions, any analysis will usually find some patterns that cannot be well validated. While knowledge of classification uncertainty may aid pattern evaluation, any method will better identify patterns from food consumptions measured with less error. Mixture models may be useful to identify individuals who under-report food consumption. [ABSTRACT FROM AUTHOR]