Influence of demographics and service provision, accessibility and adequacy in hot and cold spots of depression in Catalonia

Abstract

The study of spatial distribution of disease is an important research field today (Bithell, 2000). In previous research, using a multi-objective algorithm clusters of high and low values of prevalence or hot and cold spot of depression in the municipal level were detected (Salinas-Pérez et al., 2012). Once these patterns were detected, it is important to analyze the risk factors that explain this type of distribution, so this study pursues to find the possible relationships that can understand the appearance of these groups in parts of the region. Depression has been related to different socioeconomic indicators (Fryers et al., 2003, Fortney et al. 2009, Gabilondo et al, 2010; Sabes-Figuera et al, 2012.). Furthermore the research have also shown that quality indicators of heath service may be other factors involved (Fortney et al., 1999, Salvador-Carulla et al., 2008). Taking into account that health planning used unit small area of mental health attended by a Mental Health Center and the study of spatial distribution was carried out at municipal level, to study this type of data needed specific methodologies that allows us to analyze individual and group differences in the corresponding levels. Multilevel models are methods to study variables at different levels using submodels associated with these levels within the same model, and exploring the relationship between the observation units constituting the hierarchical structure (Raudenbush & Bryk, 2002; Goldstein, 2003). Using this methodology, the 39 municipalities that had been identified as a hot or cold spot have been related with the following risk factors: prevalence, population density, unemployment, income and educational level. In small areas of mental health included in these municipalities have been related with urbanicity, service availability, accessibility to care and adequacy or appropriateness. The results showed significant relationships of urbanity, population density and unemployment and accessibility with high prevalence of depression, however the relationship with low prevalence of depression was shown non-significant results. Based on these results it can be concluded that this study provided an opportunity that could help to planners and decision makers in their goal of efficiency, quality and equality in mental health care.