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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*
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]

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. MethodsLocal 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.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2010

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