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In recent years, different studies have provided estimates of the prevalence of transsexualism with very diverse results. The purpose of this study was to ascertain the prevalence, incidence, and sex ratio of transsexualism in the autonomous region of Madrid (Spain). A total of 1234 patients who attended from 2007 to the end of 2015 in the only Gender Identity Unit (GIU) in Madrid were analyzed. Sixty-three patients were excluded for various reasons; thus, 1171 could be included: 803 male-to-female (MtF) and 368 female-to-male (FtM) transsexual patients. Transsexualism was diagnosed based on the ICD-10, World Health Organization, 1992, and/or gender identity disorder based on the DSM-IV-TR, American Psychiatric Association, 2000. The demographic statistics were calculated on the basis of the population over 15 years old of Madrid. Based on healthcare demand, the prevalence of transsexualism was 22.1 in 100,000 inhabitants: 31.2 for MtF and 12.9 for FtM, making the MtF/FtM ratio approximately 2.2:1. The incidence rate was 2.5 in 100,000 inhabitants, representing an annual average of 130 demands. Although transsexualism occurs in all countries with different rates of prevalence, in our area, this prevalence was higher than reported from other European countries. We believe that two main circumstances might influence this high prevalence: the easy accessibility and the absence of a waiting list to the GIU, and the permissive social and legal climate and openness of Spain, especially in Madrid. [ABSTRACT FROM AUTHOR]

The usage of decision support tools in emergency departments, based on predictive models, capable of estimating the probability of admission for patients in the emergency department may give nursing staff the possibility of allocating resources in advance. We present a methodology for developing and building one such system for a large specialized care hospital using a logistic regression and an artificial neural network model using nine routinely collected variables available right at the end of the triage process.A database of 255.668 triaged nonobstetric emergency department presentations from the Ramon y Cajal University Hospital of Madrid, from January 2011 to December 2012, was used to develop and test the models, with 66% of the data used for derivation and 34% for validation, with an ordered nonrandom partition. On the validation dataset areas under the receiver operating characteristic curve were 0.8568 (95% confidence interval, 0.8508-0.8583) for the logistic regression model and 0.8575 (95% confidence interval, 0.8540-0. 8610) for the artificial neural network model. χ Values for Hosmer-Lemeshow fixed "deciles of risk" were 65.32 for the logistic regression model and 17.28 for the artificial neural network model. A nomogram was generated upon the logistic regression model and an automated software decision support system with a Web interface was built based on the artificial neural network model.