A multi-level analysis of the relationship between spatial clusters of outpatient-treated depression, risk factors and mental health service planning in Catalonia (Spain)

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. 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. 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. 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. 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.