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Development and spatial representation of synthetic indexes of outpatient mental health care in Andalusia (Spain)

Published online by Cambridge University Press:  11 April 2011

Marco Garrido-Cumbrera*
Affiliation:
Agéncia de Salut Pública de Barcelona, CIBER Epidemiologia y Salud Pública CIBERESP (Spain)
José Almenara-Barrios
Affiliation:
Área de Medicina Preventiva y Salud Pública (Bioestadìstica), Universidad de Cádiz, Cadiz (Spain) Asociación Científica Psicost, Jerez (Spain)
Enrique López-Lara
Affiliation:
Área de Análisis Geogràfico Regional, Universidad de Sevilla, Sevilla (Spain)
Juan Luis Peralta-Sáez
Affiliation:
Asociación Científica Psicost, Jerez (Spain) Departamento de Estadística e Investigación Operativa, Universidad de Cádiz, Cadiz (Spain)
Juan Carlos García-Gutierrez
Affiliation:
Grupo de Investigación en Medicina Psicosocial, Universidad de Cádiz, Cadiz (Spain)
Luis Salvador-Carulla
Affiliation:
Asociación Científica Psicost, Jerez (Spain) Grupo de Investigación en Medicina Psicosocial, Universidad de Cádiz, Cadiz (Spain)
*
Dr. M. Garrido Cumbrera, Departemento de Geografia, Universidad de Sevilla, C/Maria de Padilla s/n, Sevilla 1004 (Spain). E-mail: [email protected]

Summary

Introduction – There is a need to develop composite indicators to monitor mental health care in countries such as Spain, where there is wide variability of care systems in 17 different regions. The aim of this study is to generate and to test the usability of synthetic indexes in Andalusia (Southern Spain). Method – Seven mental health indicators were selected by expert opinion from a previous list of simple indicators used to compare mental health care systems across Spain (Psicost-74). A Geographical Information Systems (GIS) was used to delineate 71 sectors based on the catchment areas of the mental health centers in Andalusia. Synthetic indexes were obtained through linear combinations of simple indicators via Principal Components Analysis (PCA), using activity data from the Mental Health Information System of Andalusia (SISMA). Maps of these indexes were drawn for 71 catchment areas. Results – Two synthetic indexes were obtained and showed high consistency in the PCA. The Care Load Index (component 1) related to population size and total outpatient care provided within the area. The Case Load Index (component 2) related to assisted morbidity in relation to the population size. The care load index was higher in populated urban areas, whereas the case load was higher in rural areas. Discussion – Care and case load indexes show a different pattern in urban and rural areas. This may be related to a different underlying model of care related to the degree of urbanisation. Geographical Information Systems (GIS) improved recognition and assessment of the spatial phenomena related to the mental health care system, and support policy decision making process in mental health.

Declaration of Interest: None.

Type
Special Articles
Copyright
Copyright © Cambridge University Press 2008

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