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Development of a new spatial analysis tool in mental health: Identification of highly autocorrelated areas (hot-spots) of schizophrenia using a Multiobjective Evolutionary Algorithm model (MOEA/HS)

Published online by Cambridge University Press:  11 April 2011

Carlos R. García-Alonso ,
Luis Salvador-Carulla ,
Miguel A. Negrín-Hernández  and
Berta Moreno-Küstner
Carlos R. García-Alonso*
Affiliation:
ETEA, Department of Management and Quantitative Methods, University of Córdoba, Córdoba (Spain)
Luis Salvador-Carulla
Affiliation:
Scientific Association PSICOST, Jerez (Spain)
Miguel A. Negrín-Hernández
Affiliation:
Department of Quantitative Methods, University of Las Palmas de Gran Canaria, Las Palmas (Spain)
Berta Moreno-Küstner
Affiliation:
Department of Personality, Evaluation and Psychological Treatment, University of Málaga, Málaga (Spain).
*
Address for correspondence: Professor C.R. Garcìa-Alonso, ETEA University of Córdoba, Escritor Castilla Aguayo 4, 14004 Córdoba (Spain). E-mail: [email protected]
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Summary

Aims — This study had two objectives: 1) to design and develop a computer-based tool, called Multi-Objective Evolutionary Algorithm/Hot-Spots (MOEA/HS), to identify and geographically locate highly autocorrelated zones or hot-spots and which merges different methods, and 2) to carry out a demonstration study in a geographical area where previous information about the distribution of schizophrenia prevalence is available and which can therefore be compared. Methods — Local Indicators of Spatial Aggregation (LISA) models as well as the Bayesian Conditional Autoregressive Model (CAR) were used as objectives in a multicriteria framework when highly autocorrelated zones (hot-spots) need to be identified and geographically located. A Multi-Objective Evolutionary Algorithm (MOEA) model was designed and used to identify highly autocorrelated areas of the prevalence of schizophrenia in Andalusia. Hot-spots were statistically identified using exponential-based QQ-Plots (statistics of extremes). Results — Efficient solutions (Pareto set) from MOEA/HS were analysed statistically and one main hot-spot was identified and spatially located. Our model can be used to identify and locate geographical hot-spots of schizophrenia prevalence in a large and complicated region. Conclusions — MOEA/HS enables a compromise to be achieved between different econometric methods by highlighting very special zones in complex areas where schizophrenia shows a high autocorrelation.

Declaration of Interest: This study was partly supported by the Andalusian Government, P05-TIC-00531, PAI:P06-CTS-01765, CTS-587, PI-338/2008]; the Ministry of Education and Science [TIN2005–08386-C05–02] and the Ministry of Health [PI08/90752]. No additional financial sources have been received. No involvements are in conflict with this paper.

Keywords

Multiobjective evolutionary algorithmsspatial analysisschizophreniahealth care
Type
Original Articles
Information
Epidemiology and Psychiatric Sciences , Volume 19 , Issue 4 , December 2010 , pp. 302 - 313
Copyright
Copyright © Cambridge University Press 2010

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