Search

Background: Neighbourhood social fragmentation and socioeconomic deprivation seem to be associated with suicide mortality. However, results are inconclusive, which might be because dynamics in the social context are not well-represented by administratively bounded neighbourhoods at baseline. We used individualized neighbourhoods to examine associations between suicide mortality, social fragmentation, and deprivation for the total population as well as by sex and age group. Methods: Using a nested case-control design, all suicides aged 18-64 years between 2007 and 2016 were selected from longitudinal Dutch register data and matched with 10 random controls. Indices for social fragmentation and deprivation were calculated annually for 300, 600, and 1,000 metre circular buffers around each subject's residential address. Results: Suicide mortality was significantly higher in neighbourhoods with high deprivation and social fragmentation. Accounting for individual characteristics largely attenuated these associations. Suicide mortality remained significantly higher for women living in highly fragmented neighbourhoods in the fully adjusted model. Age-stratified analyses indicate associations with neighbourhood fragmentation among women in older age groups (40-64 years) only. Among men, suicide risk was lower in fragmented neighbourhoods for 18-39-year-olds and for short-term residents. In deprived neighbourhoods, the suicide risk was lower for 40-64-year-old men and long-term residents. Associations between neighbourhood characteristics and suicide mortality were comparable across buffer sizes. Conclusion: Our findings suggest that next to individual characteristics, the social and economic context within which people live may both enhance and buffer the risk of suicide.

Exposure to green space seems to be beneficial for self-reported mental health. In this study we used an objective health indicator, namely antidepressant prescription rates. Current studies rely exclusively upon mean regression models assuming linear associations. It is, however, plausible that the presence of green space is non-linearly related with different quantiles of the outcome antidepressant prescription rates. These restrictions may contribute to inconsistent findings. Our aim was to assess antidepressant prescription rates in relation to green space, and to analyze how the relationship varies non-linearly across different quantiles of antidepressant prescription rates. We used cross-sectional data for the year 2014 at a municipality level in the Netherlands. Ecological Bayesian geoadditive quantile regressions were fitted for the 15, 50, and 85 percent quantiles to estimate green space-prescription rate correlations, controlling for confounders. The results suggested that green space was overall inversely and non-linearly associated with antidepressant prescription rates. More important, the associations differed across the quantiles, although the variation was modest. Significant non-linearities were apparent: The associations were slightly positive in the lower quantile and strongly negative in the upper one. Our findings imply that an increased availability of green space within a municipality may contribute to a reduction in the number of antidepressant prescriptions dispensed. Green space is thus a central health and community asset, whilst a minimum level of 28 percent needs to be established for health gains. The highest effectiveness occurred at a municipality surface percentage higher than 79 percent. This inverse dose-dependent relation has important implications for setting future community-level health and planning policies.

Studies on social networks have proved that endogenous and exogenous factors influence dynamics. Two streams of modeling exist on explaining the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In this interdisciplinary study we work to overcome a number of computational limitations within these current models. We employ a mean-field model which allows for the construction of a population-specific socially informed model for predicting links from both network and social properties in large social networks. The model is tested on a population of conference coauthorship behavior, considering a number of parameters from available Web data. We address how large social networks can be modeled preserving both network and social parameters. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks in terms of both network and social parameters. Additionally, we confirm that large social networks evolve through both network and social-selection decisions; asserting that the dynamics of networks cannot singly be studied from a single perspective but must consider effects of social parameters., Comment: Accepted to SocInfo 2012; full version including appendix