Main findings in the context of available evidence
This study assessed how multiple physical and social neighborhood characteristics together are correlated with depression severity after adjusting for individual socio-demographic factors, using a ML approach. The four models fitted on our large-scale data resulted in robust evidence that demonstrates which perceived neighborhood characteristics are cross-sectionally correlated with depression severity. All ML models showed a better fit than basic regression, however, the differences were more of a statistical nature (Fig.
1).
We went a step further than previous studies [
19‐
21] in also assessing the relative importance of individuals’ perceptions of the physical and social neighborhood with respect to depression severity. Our models consistently showed that perceived physical neighborhood environment only played a minor role in explaining depression severity (Fig.
2). In contrast, social cohesion and safety were found to be important overall. Our result that the neighborhood social environment is of greater importance than the physical one replicates a study from the UK [
22].
In line with a systematic review [
42], we observed a negative relation between depression and age. It seems that older aged people’s susceptibility to depression declines which could result from diminishing emotional responsiveness or psychological immunization against stressful situations [
43]. Moreover, age was found to interact strongly with other variables, primarily personal-level (e.g., employment status [
44]) and to a minor extent with environmental ones (e.g., perceived green space) (Fig. A1), over the life span. Such a co-variation [
42] is not surprising because, for instance, unemployment may pose a higher risk for a young adult than someone close to retirement.
Perceived physical neighborhood characteristics including green and blue space, pleasantness, and urbanicity were found to be less important. This may partly be due to the way we assessed neighborhood features; some variables (e.g., green space) also showed limited variance. To circumvent methodological issues we employed, as frequently done [
22,
45], people’s neighborhood perceptions instead of geographic information system (GIS)-based measures per administrative area or buffer. Both ways cause spatial [
46] and temporal context uncertainties (i.e., temporally ill-aligned GIS and survey data) [
47] potentially translating into biased outcomes. Work undertaken in metropolitan Chicago found that perceived but not objectively measured neighborhood deterioration was correlated with higher depressive symptoms, which further supports our reasoning [
48].
Some neighborhood characteristics were identified as relevant, but not all turned out to be related to depression severity (Fig.
3). In what follows the neighborhood characteristics are discussed in accordance to their descending order of importance (Fig.
2). First, our study supports previous findings suggesting that pronounced neighborhood social cohesion seems to correlate with reduced depression severity [
45,
49]. It is assumed that in socially cohesive neighborhoods it is more likely that people help, support, and trust each other, and that a tightly knit social network may facilitate the spread of information among neighbors [
50]. Through such pathways living in a cohesive environment may promote mental health.
Second, neighborhood safety was confirmed in our study to be negatively associated with depression severity. Another Dutch cross-sectional study has concluded the same [
19], but overall findings are inconclusive [
51]. Among different conceivable mechanisms, we speculate that living in an unsafe neighborhood enhances experienced stress, which in turn is a depression risk factor [
52]. Alternatively, it has been theorized that a lack of safety limits social cohesion due to mistrusting others in the neighborhood [
50].
Third, perceived traffic appeared to be positively correlated with depression severity. While our data did not allow us to disentangle pollutants emitted from traffic, we believe that air pollution and noise are conceivable underlying pathways. This is underpinned by a meta-analysis on air pollution and risk of depression [
7], but contradicts a European multi-cohort study [
16]. Traffic noise is regarded as a psychosocial stressor causing annoyance and negative emotions [
53], and in a German study was significantly related to depressive symptoms [
8,
19].
Fourth, we found pleasantness was negatively correlated with depression severity. This is in line with previous research concerning neighborhood quality and depression. For example, walkable neighborhoods have previously been associated with reduced depressive symptoms [
54]. It is suggested that this is due to increased opportunity for social interaction, which in turn can improve depressive symptoms. Poor maintenance of buildings and incivilities in the street, or neighborhood social disorder, has been linked to increased risk of depression [
2]. This may be the result of reduced neighborhood satisfaction [
55], or via enhanced stress [
2].
Fifth, we found no indication that depression severity differed between urban and rural areas. While contradicting an international meta-analyses on mood disorders and urbanization [
56], our results confirm another Dutch study reporting an insignificant correlation [
19]. Further, in a recent analysis of eight Dutch cohort studies, inconsistent results were found for the effect of urbanization on depression severity [
57]. It is suggested this is due to the use of different research designs, measures of depression, and confounders.
Lastly, we could not confirm that blue space within people’s living environment is correlated with depression severity. This finding aligns with a series of others reporting insignificant associations on the 5% level [
19,
21]. However, our findings were suggestive for beneficial mental health effects of perceived closeness to green space, though no causality can be inferred. Similar results were reported elsewhere [
12,
20,
29]. The assumed mechanisms may operate through stress recovery, attention restoration, physical activity, and social interaction [
14].
Strengths and limitations
A number of key strengths of this study need to be emphasized. Our study is innovative in the way correlations were assessed. While earlier studies were limited to linear associations without examining variable interactions and non-linearities [
15,
19,
22], we put these challenges central and fitted flexible ML models in a data-driven manner. Our study also used a large nationally representative data set for the Netherlands. This produced a large sample size where our results are deemed to be robust. However, whether and how our findings can be generalized for a wider European or other cultural contexts needs further, ideally longitudinal [
58], exploration.
Despite these strengths, several limitations are recognized. The cross-sectional nature of the data has limited capability to establish causal links. We were unable to assess whether the social causation hypothesis or the social drift hypothesis applies [
59]. While the former posits that adversity linked with low socio-economic status contribute to depression, the latter argues that depressed people experience a downward drift towards neighborhoods with lower socio-economic status [
59,
60]. Our findings may also be biased because depressed people might be more likely to view their environment negatively [
11].
Our survey benefited from the inclusion of well-tested questionnaires (e.g., PHQ-9), which facilitates comparability with other studies, but they may be subject to self-reporting bias. We cannot eliminate that the perception of depressed people is impaired [
61]. As some survey questions relate to people’s living environment, ambiguities concerning the neighborhood size and the environmental perception may arise; which potentially have attenuated the relationships. Despite the fact we adjusted for several socio-economic characteristics, another final consideration is that we cannot rule out unmeasured and residual confounding. However, our findings were robust to adjustment for many potential confounding factors but some, for example people’s physical activity levels [
62], were not available to us on a personal level.