María Paz García Portilla González

Evolución de la prevalencia administrativa de los trastornos mentales durante 13 años en Asturias (norte de España)

INTRODUCTION The study of administrative prevalence from cumulative psychiatric case registers allows the mental health state of the studied region and the functioning of its Health Services to be estimated. METHODS Data were extracted from the Asturias Cumulative Psychiatric Case Register (RACPAS) between January 1st 1998 and December 3 th 2010. Characteristics of the population of the catchment area were studied, and their relationship with the administrative prevalence was analyzed. RESULTS The mean population in the studied period was 1,078,406 inhabitants. The Fritz index and the Youth and replacement indices of the active population decreased throughout the period. There was no significant increase in the prevalence of organic mental disorders, psychosis, mood disorders, and substance use in males, or behavioral disorders associated with somatic factors and physiological dysfunctions in females. There were significant gender differences in the prevalence of all disorders, except for personality disorders and organic mental disorders. Population ageing had a significant influence on the increase in the prevalence of most mental disorders in both males and females. CONCLUSIONS A slight general increase in the administrative prevalence of mental disorders is observed during the studied period, and it was influenced by population ageing.

Vibration-Based Seismic Damage Identification in Buildings

This article deals with a method for seismic damage identification in buildings with steel moment-frame structure. The damage identification is based on artificial neural networks and natural frequencies. A simplified finite element model is used to obtain the data needed for training the nets. The method is simulated on a four-storey building under conditions as close as possible to reality. The robustness of the method and its sensitivity to the variations of the mass with time and the influence of the data errors is addressed. The statistical analysis of the results is successful, but it reveals that the predictions are quite sensitive to the data errors.