Spatial analysis to identify hotspots of prevalence of schizophrenia
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The geographical distribution of mental health disorders is useful information for epidemiological research and health services planning.
To determine the existence of geographical hotspots with a high prevalence of schizophrenia in a mental health area in Spain.
The study included 774 patients with schizophrenia who were users of the community mental health care service in the area of South Granada. Spatial analysis (Kernel estimation) and Bayesian relative risks were used to locate potential hotspots. Availability and accessibility were both rated in each zone and spatial algebra was applied to identify hotspots in a particular zone.
The age-corrected prevalence rate of schizophrenia was 2.86 per 1,000 population in the South Granada area. Bayesian analysis showed a relative risk varying from 0.43 to 2.33. The area analysed had a non-uniform spatial distribution of schizophrenia, with one main hotspot (zone S2). This zone had poor accessibility to and availability of mental health services.
A municipality-based variation exists in the prevalence of schizophrenia and related disorders in the study area. Spatial analysis techniques are useful tools to analyse the heterogeneous distribution of a variable and to explain genetic/environmental factors in hotspots related with a lack of easy availability of and accessibility to adequate health care services.
Keywordsspatial analysis schizophrenia mental health planning use of mental health services
This project was partly funded by the Andalusian Foundation for Social Integration of the Mentally ill Patients, the Spanish Ministry of Health (FIS: 98/087; redIAPP and SAMSERAP group:RD06/0018/0039) and the Andalusian Research Plan (PAI: CTS-01765 and CTS-587). We thank Francisco José Vázquez-Polo for his contribution to the Bayesian analysis and comments, also Carmen Rosales and José Alberto Salinas, geographers. We acknowledge all the staff of the South Granada Mental Health Area for help with data gathering. We would also like to thank Isolde Gornemann, Christina Emmett and Ian Johnstone for revision of the English text.
- 1.Anselin L (1995) Local indicators of spatial association: LISA. Geogr Anal 27(2):93–115Google Scholar
- 4.Bailey TC, Gatrell AC (1995) Interactive spatial data analysis. Longman Group Limited, EssexGoogle Scholar
- 5.Bamrah JS, Freeman HL, Goldberg DP (1991) Epidemiology of schizophrenia in Salford, 1974–84. Changes in an urban community over ten years. Br J Psychiat 159:802–810Google Scholar
- 6.Bernardinelli L, Pascutto D, Montomoli C, Wilks C (2000) Investigating the genetic association between diabetes and malaria: an application of Bayesian ecological regression models with errors in covariates. In: Elliot P, Wakefield J, Best NG, Briggs D (eds) Spatial epidemiology. Methods and applications. Oxford University Press, OxfordGoogle Scholar
- 9.Breslow N, Day N (1987) Statistical methods in cancer research, vol. 2: the design and analysis of cohort studies. International Agency of Research on Cancer, LyonGoogle Scholar
- 13.Elliot P, Wakefield J, Best N, Briggs D (2000) Spatial epidemiology: methods and applications. Oxford University Press, OxfordGoogle Scholar
- 14.Garrido M, Salinas JA, Almenara J, Salvador L (2007) Atlas de salud mental de Andalucía 2005. Servicio Andaluz de Salud, Junta de Andalucía. Sevilla http://www.aan.org.es/Atlas_SM.pdf. Accessed 17 December 2007
- 19.Hopper K, Wanderling J (2000) Revisiting the developd versus developing country distinction in course and outcome in schizophrenia: results from ISoS, the Who collaborative follow-up project. Schiz Bull 26:835–846Google Scholar
- 29.Ord JK, Getis A (1995) Local spatial autocorrelation statistics: distributional issues and an application. Geogr Anal 27:286–306Google Scholar
- 30.Pérez-Naranjo LM, García-Alonso CR (2005) Spatial income distribution of horticultural farms in Andalusia. Cuadernos Geográficos 37:41–58Google Scholar
- 33.Sabel C (1998) Peaks and troughs: space-time cluster detection in rare diseases. ESRI users conference. San Diego Convention Center 27–31 July 1998. http://gis.esri.com/library/userconf/proc98/PROCEED/TO250/PAP246/P246.HTM. Accessed 17 December 2007
- 35.Salvador-Carulla L, Tibaldi G, Johnson S, Scala E, Romero C, Munizza C, CSRP group, RIRAG group (2005) Patterns of mental health service utilisation in Italy and Spain—an investigation using the European Service Mapping Schedule. Soc Psychiatry Psychiatr Epidemiol 40(2):149–159Google Scholar
- 36.Salvador-Carulla L, Garrido M, McDaid D, Haro JM (2006) Financing mental health care in Spain: context and critical issues. Eur J Psychiatry 20(1):29–44Google Scholar
- 38.Sheskin DJ (2000) Handbook of parametric and non-parametric statistical procedures. Chapman & Hall/CRC, FloridaGoogle Scholar
- 39.Silverman BW (1986) Density estimation for statistics and data analysis. Chapman & Hall, LondonGoogle Scholar
- 40.Toral A (2001) Regional growth and convergence in the Spanish provinces. In: 41st congress of the European regional science association, Zagreb (Croatia)Google Scholar
- 42.Vázquez-Polo FJ, Negrin MA, Salvador-Carulla L, Cabasés JM, Sanchez E (2007) Geographical differences in cost of schizophrenia in Spain: a Bayesian mapping approach. J Ment Health Policy Econ 10(Suppl 1):s44Google Scholar
- 44.Williamson D, McLafferty S, Goldsmith V, Mollenkorpf J (1998) Smoothing crime incident data: new methods for determining the bandwidth in kernel estimation. http://gis.esri.com/library/userconf/proc98/proceed/to850/pap829/p829.htm. Accessed 17 December 2007
- 47.Zhu L, Gorman DM, Horel S (2006) Hierarchical Bayesian spatial models for alcohol availability, drug “hot-spots” and violent crime. Int J Health Geographics 2006, 5:54 http://www.ij-healthgeographics.com/content/5/1/54. Accessed 17 December 2007Google Scholar