Influence of Individual and Contextual Perceptions and of Multiple Neighborhoods on Depression

Abstract

The risk of depression is related to multiple various determinants. The consideration of multiple neighborhoods daily frequented by individuals has led to increased interest in analyzing socio-territorial inequalities in health. In this context, the main objective of this study was (i) to describe and analyze the spatial distribution of depression and (ii) to investigate the role of the perception of the different frequented spaces in the risk of depression in the overall population and in the population stratified by gender. Data were extracted from the 2010 SIRS (a French acronym for “health, inequalities and social ruptures”) cohort survey. In addition to the classic individual characteristics, the participants reported their residential neighborhoods, their workplace neighborhoods and a third one: a daily frequented neighborhood. A new approach was developed to simultaneously consider the three reported neighborhoods to better quantify the level of neighborhood socioeconomic deprivation. Multiple simple and cross-classified multilevel logistic regression models were used to analyze the data. Depression was reported more frequently in low-income (OR = 1.89; CI = [1.07–3.35]) or middle-income (OR = 1.91; CI = [1.09–3.36]) neighborhoods and those with cumulative poverty (OR = 1.64; CI = [1.10–2.45]). In conclusion, a cumulative exposure score, such as the one presented here, may be an appropriate innovative approach to analyzing their effects in the investigation of socio-territorial inequalities in health.

Use of Geographic Information Systems to Explore Associations between Neighborhood Attributes and Mental Health Outcomes in Adults: A Systematic Review

“…The association between perceived traffic stress and depressive symptoms was related to increases in depressive symptoms for adults living in areas with a greater vehicular burden within a block group [52]. Individuals residing in poor or average income-level areas and people who frequented different types of neighborhoods or poor neighborhoods reported significantly higher levels of depressive symptoms [44]. The crime rate was significantly related to depressive symptoms (n = 3).…”

“…Thirty studies (93.8%) had high methodological quality (met 6-7 criteria), and two studies (6.2%) had moderate methodological criteria (met 4-5 criteria) [43,44] (Table 1). Fifteen studies (50%) met all applicable criteria.…”

“…One study [40] compared two different administrative/statistical units. Traoré et al's [44] study included residential, work-place, and frequented administrative/statistical units to measure outcomes. Eight studies used the buffer size of ≤500 m area as a geographical unit.…”

“…One study used global and local spatial autocorrelation, geostatistics, and spatial weights for modeling. For data presentation, mapping was used to represent the neighborhood attributes (n = 7) and statistical estimates (n = 3); two studies used mapping to compare datasets [48] or to present outcomes [44].…”

“…To find significant neighborhood attributes to aggregate the natural features, one study used a sensitivity analysis to compare image data linked to multiple datasets to evaluate data reliability [48]. The visualization of green spaces via mapping can present spatial patterns of neighborhood attributes, identify vulnerable places, and provide estimates of spatial statistical analysis quantifying these patterns [37,42,44,51].…”