Prevalence, Incidence, and Sex Ratio of Transsexualism in the Autonomous Region of Madrid (Spain) According to Healthcare Demand.

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.

Emergency Department Visit Forecasting and Dynamic Nursing Staff Allocation Using Machine Learning Techniques With Readily Available Open-Source Software.

Although emergency department visit forecasting can be of use for nurse staff planning, previous research has focused on models that lacked sufficient resolution and realistic error metrics for these predictions to be applied in practice. Using data from a 1100-bed specialized care hospital with 553,000 patients assigned to its healthcare area, forecasts with different prediction horizons, from 2 to 24 weeks ahead, with an 8-hour granularity, using support vector regression, M5P, and stratified average time-series models were generated with an open-source software package. As overstaffing and understaffing errors have different implications, error metrics and potential personnel monetary savings were calculated with a custom validation scheme, which simulated subsequent generation of predictions during a 4-year period. Results were then compared with a generalized estimating equation regression. Support vector regression and M5P models were found to be superior to the stratified average model with a 95% confidence interval. Our findings suggest that medium and severe understaffing situations could be reduced in more than an order of magnitude and average yearly savings of up to €683,500 could be achieved if dynamic nursing staff allocation was performed with support vector regression instead of the static staffing levels currently in use.