Estadística hospitalaria andaluza : (E.E.S.R.I.) : [estadística de establecimientos sanitarios con régimen de internado : 1999]

Datos a 31 de diciembre del año 1999. Publicado en la página web de la Consejería de Salud: www.juntadeandalucia.es/salud (Consejería de Salud / Profesionales / Estadísticas Sanitarias / Estadísticas Hospitalarias / Estadísticas Hospitalarias de Andalucía

Interrupción voluntaria del embarazo : Andalucía 1997 : evolución de la incidencia de I.V.E. 1991-1998 : distribución de las I.V.E. en los distritos sanitarios 1998

En port.: Unidad Estadística. Publicado en la página web de la Consejería de Salud: www.juntadeandalucia.es/salud (Consejería de Salud / Profesionales / Estadísticas Sanitarias / Estadísticas de interrupción voluntaria del embarazo > Acceso a las Estadisticas de Interrupción Voluntaria del Embarazo)

Estadísticas vitales, distribución espacial y tendencia de la mortalidad por cáncer y otras causas : Andalucía, 1976-1996

En port.: Unidad Estadística

Interrupción voluntaria del embarazo : Andalucía 1996 : evolución de la incidencia de I.V.E. : años 1991-1997 : (1997 datos provisionales]

En port.: Unidad Estadística

Evaluation of Health-Related Quality of Life according to Carbohydrate Metabolism Status: A Spanish Population-Based Study (Di@bet.es Study)

Objective. To evaluate the association between diabetes mellitus and health-related quality of life (HRQOL) controlled for several sociodemographic and anthropometric variables, in a representative sample of the Spanish population. Methods. A population-based, cross-sectional, and cluster sampling study, with the entire Spanish population as the target population. Five thousand and forty-seven participants (2162/2885 men/women) answered the HRQOL short form 12 questionnaire (SF-12). The physical (PCS-12) and the mental component summary (MCS-12) scores were assessed. Subjects were divided into four groups according to carbohydrate metabolism status: normal, prediabetes, unknown diabetes (UNKDM), and known diabetes (KDM). Logistic regression analyses were conducted. Results. Mean PCS-12/MCS-12 values were 50.9 ± 8.5/47.6 ± 10.2, respectively. Men had higher scores than women in both PCS-12 (51.8 ± 7.2 versus 50.3 ± 9.2; P < 0.001) and MCS-12 (50.2 ± 8.5 versus 45.5 ± 10.8; P < 0.001). Increasing age and obesity were associated with a poorer PCS-12 score. In women lower PCS-12 and MCS-12 scores were associated with a higher level of glucose metabolism abnormality (prediabetes and diabetes), (P < 0.0001 for trend), but only the PCS-12 score was associated with altered glucose levels in men (P < 0.001 for trend). The Odds Ratio adjusted for age, body mass index (BMI) and educational level, for a PCS-12 score below the median was 1.62 (CI 95%: 1.2–2.19; P < 0.002) for men with KDM and 1.75 for women with KDM (CI 95%: 1.26–2.43; P < 0.001), respectively. Conclusion. Current study indicates that increasing levels of altered carbohydrate metabolism are accompanied by a trend towards decreasing quality of life, mainly in women, in a representative sample of Spanish population.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).