Título A multi-level analysis of the relationship between spatial clusters of outpatient-treated depression, risk factors and mental health service planning in Catalonia (Spain)
Autores RODERO COSANO, MARÍA LUISA, SALINAS PÉREZ, JOSÉ ALBERTO, Luis Gonazlez-Caballero, Juan , GARCÍA ALONSO, CARLOS, Lagares-Franco, Carolina , Salvador-Carulla, Luis
Publicación externa No
Medio J. Affective Disord.
Alcance Article
Naturaleza Científica
Cuartil JCR 1
Cuartil SJR 1
Impacto JCR 3.43200
Impacto SJR 2.01600
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966341218&doi=10.1016%2fj.jad.2016.04.024&partnerID=40&md5=9ef6204e1176cda7df0566a8883321b4
Fecha de publicacion 01/09/2016
ISI 000377392900006
Scopus Id 2-s2.0-84966341218
DOI 10.1016/j.jad.2016.04.024
Abstract Background: Previous research identified high/low clusters of prevalence of outpatient-treated depression at municipal level in Catalonia (Spain). This study aims to analyse potential risk factors, both socioeconomic and related to the mental health service planning, which could influence the occurrence of hot/cold spots of depressed outpatients at two geographical levels: municipalities and service catchment areas.\n Method: Hot/cold spots were examined in relation to socioeconomic indicators at municipal level, such as population density, unemployment, university education, personal income, and also those related to service planning at catchment area level, such as adequacy of healthcare, urbanicity, accessibility and the availability of mental health community centres. The analysis has been carried out through multilevel logistic regression models in order to consider the two different scales.\n Results: Hot spots are related to high population density, unemployment, urbanicity, the adequacy of provision of mental health services, and accessibility to mental health community centres at both study levels. On the other hand, the multilevel model weakly explains cold spots, associating them with high personal incomes.\n Limitations: The dependent variables of the multi-level models are binary. This limits the interpretation of the results, since they cannot provide information about the variance of the dependent variables explained by the models.\n Conclusions: The results described diverse risk factors at two levels which are related to a high likelihood of hot and cold spots of depression. The findings show the relevance of health planning in the distribution of diseases and the utilisation of healthcare services. (C) 2016 Elsevier B.V. All rights reserved.
Palabras clave Depression; Spatial clustering; Multilevel model; Health planning; Risk factors
Miembros de la Universidad Loyola

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