Relative importance of perceived physical and social neighborhood characteristics for depression: a machine learning approach

This cross-sectional study reports on a nationally representative population sample in the Netherlands. In the course of the NEEDS project [24], an online survey was carried out with Statistics Netherlands between September and December 2018.

Participants needed to fulfill the following eligibility criteria: to be aged between 18 and 65 years, and living in a private household. Through systematic sampling with probabilities proportional to the target population size, sub-municipalities were first selected from each COROP region (i.e., a regional Dutch division). Next, from those regions individuals registered in the Dutch National Personal Records Database were randomly sampled. Incentives were offered to increase the response. Of those 45,000 invited people, 11,524 completed the questionnaire resulting in an overall response rate of 25.6%. We conducted a complete case analysis excluding those with any missing information. After exclusions due to incomplete variables (N = 2089), the final sample size was 9435. A full description of the study protocol [24] and the survey is available [25].

Ethical approval of the study design was obtained from the Ethics Review Board of the Faculty of Social and Behavioral Sciences of Utrecht University (FETC17–060). Informed consent was implied by conducting the questionnaire.

Our survey in the Dutch language comprised various modules including sociodemographics, mental health, and perception of the residential neighborhood. Other questions were asked but were not included here. The survey was further enriched with selected register data, namely urbanicity and income, available through Statistics Netherlands. If not mentioned otherwise, register data refer to 1st July 2018.

Of 11,524 participants, 9435 (81.8%) had complete data. The Mann–Whitney–Wilcoxon test confirmed that omitting survey respondents with incomplete information resulted in no significant differences (p = 0.333) in depression severity between the full and retained sample. Our sample had a mean PHQ-9 score of 4.857 with a standard deviation (SD) of 4.913. Table 1 summarizes the socio-demographic characteristics of our sample.

Figure 1 shows the cross-validated model fits. The median magnitudes of model performances were, independent of the fit measure, rather similar. More specifically, the lowest median MAE and RMSE were achieved by GBM, while GLM had the highest errors. GBM also achieved the highest R², while GLM had the lowest. The R²s were modestly high. Wilcoxon tests showed that the median performance of GBM was always significantly better than the one of GLM (p < 0.050). Generally, no significant difference in median performance were found for RF and NNET. The Table A2 in the supplementary materials lists the detailed test results.

The clustered heat map in Fig. 2 shows two groups of variables with different levels of importance. The three most important variables for predicting depression severity were social cohesion, age, and employment status. Of minor importance were urbanicity, ethnicity, and perceived distance to green and blue spaces. Only minor changes appeared across the models.

Correlations between PHQ-9 and the person-level and neighborhood characteristics are displayed in Fig. 3. As before, model differences were mostly small. Perceived safety and pleasantness were both negatively, and roughly linearly, associated with PHQ-9 scores. Social cohesion was inversely correlated with PHQ-9 scores. Perceived traffic was positively correlated with PHQ-9 scores. Both RF and GBM suggested that perceived distance to green space was positively correlated with PHQ-9 scores. Yet, the results for the distances > 5 km are inconclusive and diverge across the models. However, the category perceived distance to green space > 5 km was sparsely populated (Table 1). Blue space seemed to be uncorrelated with PHQ-9; as was urbanicity. Unemployed, female, and divorced people showed higher PHQ-9 scores. PHQ-9 scores were substantially higher also for low earners and lower educated people. No differences were observed across ethnicities.

Figure 4 shows pronounced overall variable interactions for social cohesion, age, employment, and education; neighborhood characteristics showed only little interaction.

Further, Figs. A1 and A2 detail with which variables social cohesion and age interact most (e.g., education, employment), which is the basis for Fig. 5 showing the bivariate interactions of these variables. The effect of employment status on the PHQ-9 scores varied over age, with unemployed persons always having a higher risk. Between 30 and 50 years of age, the differences were most pronounced; this gap in risk shrinks from 50 years onwards. Independent of the model, respondents that were less well educated and younger than 30 years old had pronounced PHQ-9 scores. With increasing age, the differences between lower and higher educated groups aligned each other.

Similar patterns were observed for social cohesion. High education and income were associated with lower PHQ-9 scores. However, while the differences in PHQ-9 scores between education groups decreased with social cohesion score, a decrease in the differences of the PHQ-9 scores between income groups was not always observable. For instance, there was always a notable gap in PHQ-9 scores between low and middle income groups, regardless of the social cohesion score. Moreover, when comparing the different models, it can be seen that for GBM, no decrease in PHQ-9 scores can be observed for a social cohesion score from 5 to 11.

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].

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.