Int J Health Geogr - Identification and location of hot and cold spots of treated prevalence of depression in Catalonia (Spain).

Tópicos

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Resumo

CKGROUND: Spatial analysis is a relevant set of tools for studying the geographical distribution of diseases, although its methods and techniques for analysis may yield very different results. A new hybrid approach has been applied to the spatial analysis of treated prevalence of depression in Catalonia (Spain) according to the following descriptive hypotheses: 1) spatial clusters of treated prevalence of depression (hot and cold spots) exist and, 2) these clusters are related to the administrative divisions of mental health care (catchment areas) in this region.METHODS: In this ecological study, morbidity data per municipality have been extracted from the regional outpatient mental health database (CMBD-SMA) for the year 2009. The second level of analysis mapped small mental health catchment areas or groups of municipalities covered by a single mental health community centre. Spatial analysis has been performed using a Multi-Objective Evolutionary Algorithm (MOEA) which identified geographical clusters (hot spots and cold spots) of depression through the optimization of its treated prevalence. Catchment areas, where hot and cold spots are located, have been described by four domains: urbanicity, availability, accessibility and adequacy of provision of mental health care.RESULTS: MOEA has identified 6 hot spots and 4 cold spots of depression in Catalonia. Our results show a clear spatial pattern where one cold spot contributed to define the exact location, shape and borders of three hot spots. Analysing the corresponding domain values for the identified hot and cold spots no common pattern has been detected.CONCLUSIONS: MOEA has effectively identified hot/cold spots of depression in Catalonia. However these hot/cold spots comprised municipalities from different catchment areas and we could not relate them to the administrative distribution of mental care in the region. By combining the analysis of hot/cold spots, a better statistical and operational-based visual representation of the geographical distribution is obtained. This technology may be incorporated into Decision Support Systems to enhance local evidence-informed policy in health system research.

Resumo Limpo

ckground spatial analysi relev set tool studi geograph distribut diseas although method techniqu analysi may yield differ result new hybrid approach appli spatial analysi treat preval depress catalonia spain accord follow descript hypothes spatial cluster treat preval depress hot cold spot exist cluster relat administr divis mental health care catchment area regionmethod ecolog studi morbid data per municip extract region outpati mental health databas cmbdsma year second level analysi map small mental health catchment area group municip cover singl mental health communiti centr spatial analysi perform use multiobject evolutionari algorithm moea identifi geograph cluster hot spot cold spot depress optim treat preval catchment area hot cold spot locat describ four domain urban avail access adequaci provis mental health careresult moea identifi hot spot cold spot depress catalonia result show clear spatial pattern one cold spot contribut defin exact locat shape border three hot spot analys correspond domain valu identifi hot cold spot common pattern detectedconclus moea effect identifi hotcold spot depress catalonia howev hotcold spot compris municip differ catchment area relat administr distribut mental care region combin analysi hotcold spot better statist operationalbas visual represent geograph distribut obtain technolog may incorpor decis support system enhanc local evidenceinform polici health system research

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