Mental health (MH) has a relevant burden on the health of populations. Common MH disorders (anxiety and non-psychotic depression) are well associated to socioeconomic individual and neighborhood characteristics, but little is known about the influence of urban structure. We analyzed among a Turin (Northwest Italy) urban population the association at area level of different urban structure characteristics (density, accessibility by public transport, accessibility to services, green and public spaces) and consumption of antidepressants. Estimates were adjusted by individual socio-demographic variables (education, housing tenure, employment) and contextual social environment (SE) variables (social and physical disorder, crime rates). Data was extracted from the Turin Longitudinal Study (TLS)—a census-based cohort study following up prospectively the mortality and morbidity of the population. As expected, individual characteristics show the strongest association with antidepressant drug consumption, while among built environment (BE) indicators accessibility by public transport and urban density only are associated to MH, being slightly protective factors. Results from this study, in agreement with previous literature, suggest that BE has a stronger effect on MH for people who spend more time in the neighborhood. Therefore, this research suggests that good accessibility to public transport, as well as a dense urban structure (versus sprawl), could contribute to reduced risk of depression, especially for women and elderly, by increasing opportunities to move around and have an active social life.

Research has shown urban renewal policies have significant impacts on populations that are vulnerable and those that result in gentrification can result in negative health consequences for this population. A better understanding of this is needed to impact future policies and advocate for a community-participatory model that includes such populations in the early planning stages.

Objective: The Health in All Policies strategy aims to engage every policy domain in health promotion. The more socially disadvantaged groups are usually more affected by potential negative impacts of policies if they are not health oriented. The built environment represents an important policy domain and, apart from its housing component, its impact on health inequalities is seldom assessed. Methods: A scoping review of evidence on the built environment and its health equity impact was carried out, searching both urban and medical literature since 2000 analysing socio-economic inequalities in relation to different components of the built environment. Results: The proposed explanatory framework assumes that key features of built environment (identified as density, functional mix and public spaces and services), may influence individual health through their impact on both natural environment and social context, as well as behaviours, and that these effects may be unequally distributed according to the social position of individuals. Conclusion: In general, the expected links proposed by the framework are well documented in the literature; however, evidence of their impact on health inequalities remains uncertain due to confounding factors, heterogeneity in study design, and difficulty to generalize evidence that is still very embedded to local contexts.

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Several studies have recognized the health disadvantage of residents in socioeconomically deprived neighborhoods, independent of the influence of individual socioeconomic conditions. The effect of neighborhood socioeconomic deprivation on general mortality has appeared heterogeneous among the cities analyzed: the underlying mechanisms have been less empirically explored, and explanations for this heterogeneous health effect remain unclear. The present study aimed to: (1) analyze the distribution of socioeconomically disadvantaged persons in neighborhoods of 4 European cities-Turin, Barcelona, Stockholm and Helsinki-trying to measure segregation of residents according to their socioeconomic conditions. Two measuring approaches were used, respectively, through dissimilarity index and clustering estimated from Bayesian models. (2) Analyze the distribution of mortality in the above mentioned cities, trying to disentangle the independent effects of both neighborhood socioeconomic deprivation and neighborhood segregation of residents according to their socioeconomic conditions, using multilevel models. A significantly higher risk of death was observed among residents in more deprived neighborhoods in all 4 cities considered, slightly heterogeneous across them. Poverty segregation appeared to be slightly associated with increasing mortality in Turin and, among females and only according to dissimilarity, in Barcelona. Few studies have explored the health effects of social clustering, and results could inform urban policy design with regard to social mix.

The commentary from Kestens et al. [1] raises interesting issues about measuring contextual exposures and encourages new studies to incorporate them in their design: as a group of researchers, we strongly support their view and think that those useful reflections should be used as guidelines for future research.[...]

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