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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: cgarcia@etea.com
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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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References

REFERENCES

Andrienko, N. & Andrienko, G. (Eds.) (2006). Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach. Springer Science, Business Media Inc.: Berlin.Google Scholar
Anselin, L. (1995). Local indicators of spatial association: LISA. Geographical Annals 27, 93–115.CrossRefGoogle Scholar
Anselin, L. (2002). Under the hood: Issues in the specification and interpretation of spatial regression models. Agricultural Economics 27, 247–267.Google Scholar
Asai, K. (1995). Fuzzy Systems for Information Processing. IOS Press: Amsterdam.Google Scholar
Asociación Española de Neuropsiquiatría. (2007). Análisis de la situación de la atención a la salud mental en las Comunidades Autónomas a diciembre del 2005. El Observatorio de Salud Mental de la Asociación Española de Neuropsiquiatría. Cuadernos técnicos [on-line] 7. Retrieved May 10, 2009, from: http://www.aen.es/web/docs/ObservatorioAEN_05.pdfGoogle Scholar
Assunçao, R., Costa, M., Tavares, A. & Ferreira, S. (2006). Fast detection of arbitrarily shaped disease clusters. Statistics in Medicine 25, 723–742.CrossRefGoogle ScholarPubMed
Ayuso-Mateos, J.L., Gutiérrez-Recacha, P., Haro, J.M., Chisholm, D. (2006). Estimating the prevalence of schizophrenia in Spain using a disease model. Schizophrenia Research 86, 194–201.Google Scholar
Baddeley, A., Gregori, P., Mateu, J., Stoica, R. & Stoyan, D. (Eds.) (2006). Case Studies in Spatial Point Process Modeling. Springer Science, Business Media Inc.: Berlin.Google Scholar
Beirlant, J., Goegebeur, Y., Segers, J. & Teugels, J. (2004). Statistics of Extremes: Theory and Applications. Wiley: West Sussex.CrossRefGoogle Scholar
Besag, J. & Green, P.J. (1993). Spatial statistics and Bayesian computation. Journal of the Royal Statistic Society, Series B 55, 25–37.Google Scholar
Besag, J., York, J. & Mollié, A. (1991). Bayesian image restoration with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43, 1–59.Google Scholar
Clayton, D.G., Bernardinelli, L. & Montomoli, C. (1993). Spatial correlation in ecological analysis. International Journal of Epidemiology 22, 1193–1202.Google Scholar
Coello-Coello, C.A. & Lamont, G.B. (2004). Applications of Multi-Objective Evolutionary Algorithms. Word Scientific: Singapore.CrossRefGoogle Scholar
Coello-Coello, C.A., Lamont, G.B. & Van Veldhuizen, D.A. (Eds.) (2007). Evolutionary Algorithms for Solving Multi-Objective Problems. Springer: New York.Google Scholar
Curtis, S. (2007). Socio-economic status and geographies of psychiatric inpatient service use. Places, provision, power and well-being. Epidemiologia e Psichiatria Sociale 16, 10–15.CrossRefGoogle ScholarPubMed
Das, I. & Dennis, J. (1997). A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Structural Optimization 14, 63–69.CrossRefGoogle Scholar
Elliot, P., Wakefield, J., Best, N. & Briggs, D. (Eds.) (2006). Spatial Epidemiology: Methods and Applications. Oxford University Press: Oxford.Google Scholar
Fischer, M.M. (2006). Spatial Analysis and Geocomputation. Springer: Berlin.Google Scholar
García-Alonso, C.R. (2008). Dealing with complexity in large scale and structured fuzzy systems. In 5th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'08), pp. 299–305. Institute of Electric and Electronic Engineers (IEEE): New Jersey.Google Scholar
García-Alonso, C.R., Guardiola, J. & Hervás-Martínez, C. (2009). Logistic evolutionary product-unit neural networks: innovation capacity of poor Guatemalan households. European Journal of Operational Research 195, 543–551.CrossRefGoogle Scholar
Garrido-Cumbrera, M., Salinas, J.A., Almenara, J. & Salvador-Carulla, L. (2007). Atlas de Salud Mental de Andalucía 2005. Consejería de Salud de la Junta de Andalucía [on-line] 2007. Retrieved January 20, 2009, from: http://www.aan.org.es/Atlas_SM.pdfGoogle Scholar
Garrido-Cumbrera, M., Almenara-Barrios, J., López-Lara, E., Peralta-Sáez, J.L., García-Gutiérrez, J.C. & Salvador-Carulla, L. (2008). Development and spatial representation of synthetic indexes of outpatient mental health care in Andalusia (Spain). Epidemiologia e Psichiatria Sociale 17, 192–200.CrossRefGoogle ScholarPubMed
Instituto Cartográfico de Andalucía ICA (2009). Retrieved June 8, 2009, from: http://www.juntadeandalucia.es/obraspublicasytrans-portes/cimfa/ica.htmGoogle Scholar
Kalyanmoy, D. (2004). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley: West Sussex.Google Scholar
Lawson, B.A. (2006). Statistical Methods in Spatial Epidemiology. John Wiley & Sons: West Suusex.CrossRefGoogle Scholar
Lee, M.A. & Esbensen, H. (1997). Fuzzy/Multiobjective genetic systems for intelligent systems design tools and components. In Fuzzy Evolutionary Computation (ed. Pedrycz, W.), pp. 57–80Kluwer Academic Publishers: Boston.CrossRefGoogle Scholar
Michalewicz, Z. & Schoenauer, M. (1996). Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation 4, 1–32.Google Scholar
Ministerio de Sanidad y Consumo (MSyC) (2007). Estrategia en Salud Mental del Sistema Nacional de Salud. Madrid: Ministerio de Sanidad y Consumo, 2007. Retrieved May 20, 2008, from: http://www.msc.es/organizacion/sns/planCalidadSNS/pdf/excelen-cia/salud_men-tal/ESTRATEGIA_SALUD_MENTAL_SNS_PAG_WEB.pdfGoogle Scholar
Mollié, A. (2006). Bayesian mapping of Hodgkin's disease in France. In Spatial Epidemiology Methods and Applications (ed. Elliot, P., Wakefield, J., Nicola, B. and Briggs, D.), pp. 267–287. Oxford University Press: Oxford.Google Scholar
Moreno-Küstner, B. (2008). Situación actual de los sistemas de infor-mación sobre salud mental en España. Monografías de Psiquiatría 20, 4–14.Google Scholar
Moreno-Küstner, B., Torres, González F., Rosales, Varo C. (2005). Prevalencia tratada de la esquizofrenia y trastornos afines en Granada Sur. In El Registro de Casos de Esquizofrenia de Granada (ed. Moreno-Küstner, B.), pp. 115–146. Asociación Española de Neuropsiquiatría: Madrid.Google Scholar
Moreno-Küstner, B., García-Alonso, C.R., Negrin, M.A., Torres-González, F. & Salvador-Carulla, L. (2008). Spatial analysis to identify hotspots of prevalence of schizophrenia. Social Psychiatry and Psychiatric Epidemiology 43, 782–791.CrossRefGoogle Scholar
Moscone, F. & Knapp, M. (2005). Exploring the spatial pattern of mental health expenditure. Journal of Mental Health Policy and Economics 8, 205–217.Google ScholarPubMed
Moscone, F. & Tosetti, E. (2009). A review and comparison of tests of cross-section independence in panels. Journal of Economic Surveys 23, 528–561.CrossRefGoogle Scholar
Ord, J.K. & Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical Annals 27, 286–306.CrossRefGoogle Scholar
Pérez-Naranjo, L.M. & García-Alonso, C.R. (2005). Spatial income distribution of horticultural farms in Andalusia. Cuadernos Geográficos 37, 41–58.Google Scholar
Salvador-Carulla, L., Haro, J.M. & Ayuso-Mateos, J.L. (2006a). A framework for evidence-based mental health care and policy. Acta Psychiatrica Scandinavica 114, Suppl. 432, S5-S11.CrossRefGoogle Scholar
Salvador-Carulla, L., Poole, M., González-Caballero, J.L., Romero, C., Salinas, J.A. & Lagares-Franco, C.M. (2006b). Development and usefulness of an instrument for the standard description and comparison of services for disabilities based on a mental healthcare assessment model (DESDE). Acta Psychiatrica Scandinavica 114, Suppl. 432, 19S-28S.Google Scholar
Salvador-Carulla, L., García-Alonso, C.R., Gonzalez-Caballero, J.L. & Garrido-Cumbrera, M. (2007) Use of an operational model of community care to assess technical efficiency and benchmarking of small mental health areas in Spain. Journal of Mental Health Policy and Economics 10, 87–100.Google ScholarPubMed
Sheskin, D.J. (2000). Handbook of Parametric and Non-Parametric Statistical Procedures. Chapman & Hall/CRC: Boca-Ratón.Google Scholar
Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall: London.Google Scholar
Smith, A.E. & Coit, D.W. (1997). Constrain handling techniques -penalty functions. In Handbook of Evolutionary Computation (ed. Bäck, T., Fogel, D.B. and Michalewicz, Z.), Chapter 5.2. Oxford University Press: Oxford.Google Scholar
Staal, S.J., Baltenweck, L., Waithaka, M., de Wolffa, T. & Njoroge, L. (2002). Location and uptake: integrated household and GIS analysis of technology adoption and land use with application to smallholder dairy farms in Kenya. Agricultural Economics 27, 295–315.CrossRefGoogle Scholar
Vázquez-Polo, F.J., Negrín, M.A., Cabasés, J.M. & Sánchez, E. (2005). Self-perceived health status of schizophrenic patients in Spain: analysis of geographic differences. Expert Review of Pharmacoeconomics & Outcomes Research 5, 531–540.Google Scholar
Vázquez-Polo, F.J., Negrín, M.A., Salvador-Carulla, L., Cabasés, J.M. & Sánchez, E. (2007). Geographical differences in cost of schizophrenia in Spain: A Bayesian mapping approach. Journal of Mental Health Policy and Economics 10, Suppl. 1, 44S.Google Scholar
Wakefield, J.C., Best, N.G. & Waller, L. (2006a). Bayesian approaches to disease mapping. In Spatial Epidemiology Methods and Applications (ed. Elliot, P., Wakefield, J., Nicola, B. and Briggs, D.), pp. 104–127. Oxford University Press: Oxford.Google Scholar
Wakefield, J.C., Kelsall, J.E. & Morris, S.E. (2006b). Clustering, cluster detection and spatial variation in risk. In Spatial Epidemiology Methods and Applications (ed. Elliot, P., Wakefield, J., Nicola, B. and Briggs, D.), pp. 128–152. Oxford University Press: Oxford.Google Scholar
Wang, G. & Terpenny, J.P. (2005). Interactive preference incorporation in evolutionary engineering design. In Knowledge Incorporation in Evolutionary Computation (ed. Jin, Y.), pp. 525–543. Springer: Berlin.CrossRefGoogle Scholar
Youssef, H.A., Kinsella, A. & Waddington, J.L. (1991). Evidence for geographical variations in the prevalence of schizophrenia in rural Ireland. Archives of General Psychiatry 48, 254–258.CrossRefGoogle ScholarPubMed
Zitzler, E. & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and strength Pareto approach. IEEE Transactions on Evolutionary Computation 3, 257–271.Google Scholar
Zitzler, E., Laumanns, M. & Thiele, L. (2001). SPEA2: Improving the Strength of Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology. Switzerland. Returved December 7, 210, from www.tik.ee.ethz.ch/sop/ publicationListFi-les/zlt2001a.pdfGoogle Scholar