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)

Epidemiol Psichiatr Soc. 2010 Oct-Dec;19(4):302-13.

García-Alonso CR, Salvador-Carulla L, Negrín-Hernández MA, and Moreno-Küstner B.

“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.”