Background
Depression contributes more to the global burden of mental and substance abuse disorders than any other single disorder [
1] and projections are that it will be the second leading cause of disability in the world by 2020 [
2]. There is a long tradition of inquiry from high-income countries (HIC) into the relationship between socioeconomic adversity and depression at the individual level. Variables such as low income [
3], unemployment, low education, social class [
4], and financial strain [
5] all show associations with depression. Conversely, financial and physical assets have been shown to safeguard against common mental disorders such as depression [
6]. More recently, evidence has begun to emerge on the nature of the ‘poverty–mental health’ relationship in low- and middle-income countries (LMIC) [
7].
Recent studies have begun to use the neighbourhood, rather than the individual, as a unit of exposure. At neighbourhood level, a promising body of empirical literature has begun to show significant associations between specific neighbourhood characteristics and depressive symptoms across a number of countries and socioeconomic groups [
8,
9]. For example, neighbourhood social disorder has been consistently found to be associated with depressive symptoms, and higher neighbourhood socioeconomic status (SES) to function as a protective factor against depression [
10]. Low neighbourhood SES and poor social cohesion have been shown to be significantly and independently associated with poor mental health status in the UK, after adjusting for individual SES [
11], while improvements in aspects such as community-based grassroots organisations and shared public spaces for recreation, have been associated with improved mental health outcomes in China [
12].
Neighbourhood characteristics can therefore function as either stressors or buffers that either increase or decrease the likelihood of depression [
13]. Features like lack of neighbourhood resources, violence or poor social cohesion could function as stressors, while physical and social characteristics of neighbourhoods like access to social support may function as buffers [
14].
Yet almost all the studies in this field have been conducted in high income countries (HIC) [
8,
10]. In one of the few studies of its kind in LMIC, Tomita and Burns [
15] addressed this gap with a study exploring correlations between neighbourhood social capital and depression in South Africa, and found that perception of social trust and the extent to which participants ‘preferred’ living in their neighbourhoods were significantly negatively associated with depression. A second study by Tomita, Labys and Burns [
16] found a positive and significant relationship between perceived neighbourhood social disorder (characterised by violent and threatening behaviour) and depressive symptomatology. However, both studies had a risk of same-source bias in that self-report data was used for both the outcome variable and the neighbourhood variable of interest.
South Africa presents a unique environment in which to further explore how neighbourhoods impact on mental health outcomes. Apartheid social planning not only conflated race and class but located inequality, poverty and exclusion geographically by neighbourhood, a legacy which persists [
17]. Deprivation in South Africa shows clear patterns with regard to it’s geographic location of deprivation: for instance, former Black homeland areas still remain the most deprived regions [
18].
This study therefore explores associations between neighbourhood-level deprivation and individual’s depressive symptoms in a nationally representative South African sample. It avoids the same-source bias problem by combining comprehensive individual-level depression and socioeconomic data with independently sourced neighbourhood-level deprivation data. This neighbourhood deprivation data can be analysed both as a composite index as well as along individual domains, allowing for a more focussed investigation of how specific area-level factors influence mental health.
Results
For the adult sample of the first wave of NIDS, 12,448 individuals were successfully mapped onto 417 datazones and had completed the CES-D10 portion of the individual adult questionnaire. Of these individuals, 11,955 had all the necessary data for incorporation into the full models: see Tables
1 and
2 for a description of this sample.
Table 1
Descriptives: Categorical socio-demographic variables (N = 11,995)
Female | 7473 (62.5%) |
Male | 4482 (37.5%) |
Race |
African | 9494 (79.41%) |
Coloured | 1568 (13.12%) |
Asian/Indian | 163 (1.36%) |
White | 730 (6.11%) |
Marital Status |
Married | 3516 (29.41%) |
Living with partner | 1050 (8.78%) |
Widow/widower | 1011 (8.46%) |
Divorced or separated | 351 (2.94%) |
Never married | 6027 (50.41%) |
Education |
Below Gr 9 | 5437 (45.48%) |
Above Gr 9 | 6518 (54.52%) |
Employment Status |
Not economically active | 4888 (40.89%) |
Unemployed discouraged | 781 (6.53%) |
Unemployed strict | 1482 (12.40%) |
Employed | 4804 (40.18%) |
Living Deprivation Status |
Not deprived | 5526 (46.22%) |
Deprived | 6429 (53.78%) |
Durable Goods |
No | 5265 (44.04%) |
Yes | 6690 (55.96%) |
Urban |
Rural/traditional | 6096 (50.99%) |
Urban | 5859 (49.01%) |
Negative Events |
None | 9470 (79.37%) |
One or more | 2466 (20.63%) |
Table 2
Descriptive statistics: Continuous Variables in sample (N = 11,955)
CES-D10 | 0–30 | 0–30 | 8.01 (4.76) | 7 | 5–11 |
SAIMD Composite Index | 0.1186/s12888-017-1561-24.08–368.23 | 0–400 | 145.59 (85.61) | 138.21 | 79.73–207.96 |
SAIMD Income and Material | 2.5–99.41 | 0–100 | 77.30 (24.49) | 87.99 | 67.67–94.18 |
SAIMD Employment | 5.23–87.08 | 0–100 | 43.348(21.41) | 44.68 | 25.05–61.05 |
SAIMD Education | 4.00–77.87 | 0–100 | 33.68 (16.06) | 34.02 | 22.31–44.74 |
SAIMD Living Environment | 1.35–99.91 | 0–100 | 68.05 (32.81) | 81.72 | 40.55–97.78 |
Age | 15–101 | – | 37.58 (17.05) | 35 | 23–50 |
Household Income | 0–136,968.7 | – | 4791.78 (8146.99) | 2327.71 | 1271.94–4837.72 |
We found significant bivariate associations (
p < 0.001) between the composite SAIMD and depression scores (see Table
3 and
4), as well as for all four individual domains. The positive coefficients indicate that individuals living in more deprived datazones reported significantly more symptoms of depression. The coefficients represent the change in the CES-D10 score per standard deviation increase in the deprivation variable.
Table 3
Weighted bivariate associations between 2008 CES-D10 scores and all area-level and individual-level independent variables
SAIMD Composite Index | 0.92 | 0.12 | <0.0001 | 3.5% |
SAIMD Income and Material | 0.88 | 0.10 | <0.0001 | 4.7% |
SAIMD Employment | 0.95 | 0.12 | <0.0001 | 3.6% |
SAIMD Education | 0.83 | 0.14 | <0.0001 | 2.8% |
SAIMD Living Environment | 0.91 | 0.12 | <0.0001 | 4.1% |
HH Income | −0.82 | 0.10 | <0.0001 | |
|
Unstandardised B | | | |
Age | 0.02 | 0.00 | <0.0001 | |
Male | −0.91 | 0.13 | <0.0001 | |
Race [African] |
Coloured | −1.57 | 0.38 | <0.0001 | |
Asian/Indian | −2.01 | 0.85 | 0.018 | |
White | −3.20 | −0.30 | <0.0001 | |
Marital status [Married] |
Living with partner | 1.50 | 0.26 | <0.0001 | |
Widow/widower | 2.36 | 0.28 | <0.0001 | |
Divorced/separated | 1.56 | 0.59 | 0.008 | |
Never married | 0.72 | 0.18 | <0.0001 | |
Gr 9 or more education | −1.81 | −0.18 | <0.0001 | |
Durable goods | −1.50 | 0.19 | <0.0001 | |
Living deprived | 1.28 | 0.25 | <0.0001 | |
Urban | 0.80 | 0.26 | 0.002 | |
Employment status [Employed] |
Not economically active | 0.77 | 0.16 | <0.0001 | |
Unemployed discouraged | 0.90 | 0.31 | 0.004 | |
Unemployed strict | 1.61 | 0.24 | <0.0001 | |
Negative life events reported | 0.09 | 0.21 | 0.677 | |
Table 4
Beta coefficients from linear regression models for 2008 CES-D10 depression scores across deprivation domains (N = 11,955)
Deprivation (Standardised β) | 0.31 (0.15)* | 0.36 (0.16)* | 0.38 (0.13)** | 0.28 (0.15) | 0.53 (0.16)** |
Age | 0.03 (0.01)*** | 0.03 (0.01)*** | 0.03 (0.01)*** | 0.03 (0.01)*** | 0.03 (0.01)*** |
Male | −0.69 (0.13)*** | −0.68 (0.13)*** | −0.68 (0.13)*** | −0.7 (0.13)*** | −0.66 (0.13)*** |
Marital status [married] |
Living with partner | 0.85 (0.26)** | 0.8 (0.26)** | 0.84 (0.26)** | 0.82 (0.26)** | 0.8 (0.26)** |
Widow/widower | 0.88 (0.28)** | 0.87 (0.28)** | 0.86 (0.28)** | 0.89 (0.28)** | 0.88 (0.28)** |
Divorced/separated | 1.8 (0.52)*** | 1.78 (0.53)*** | 1.76 (0.52)*** | 1.82 (0.52)*** | 1.79 (0.52)*** |
Never married | 0.48 (0.2)* | 0.45 (0.2)* | 0.44 (0.19)* | 0.47 (0.2)* | 0.44 (0.2)* |
Race [African] |
Coloured | −1.23 (0.34)*** | −1.13 (0.32)*** | −1.15 (0.34)*** | −1.32 (0.34)*** | −1.11 (0.32)*** |
Asian/Indian | −0.4 (0.93) | −0.36 (0.9) | −0.33 (0.94) | −0.48 (0.91) | −0.27 (0.91) |
White | −2.16 (0.39)*** | −1.95 (0.4)*** | −2.03 (0.4)*** | −2.18 (0.39)*** | −1.93 (0.38)*** |
Gr. 9 or more education | −0.95 (0.16)*** | −0.94 (0.16)*** | −0.97 (0.16)*** | −0.91 (0.16)*** | −0.95 (0.16)*** |
Employment status [employed] |
Not economically active | 0.19 (0.17) | 0.19 (0.17) | 0.16 (0.17) | 0.23 (0.17) | 0.2 (0.18) |
Unemployed discouraged | 0.34 (0.32) | 0.33 (0.32) | 0.31 (0.32) | 0.37 (0.32) | 0.34 (0.32) |
Unemployed strict | 1.11 (0.21)*** | 1.09 (0.21)*** | 1.07 (0.21)*** | 1.15 (0.21)*** | 1.08 (0.21)*** |
HH Income (Standardised β)
| −0.25 (0.12)* | −0.22 (0.12) | −0.24 (0.12)* | −0.24 (0.12)* | −0.23 (0.12) |
Durable goods | −0.61 (0.19)** | −0.62 (0.18)*** | −0.68 (0.19)*** | −0.61 (0.18)** | −0.61 (0.18)** |
Living environment deprived | 0.16 (0.3) | 0.16 (0.3) | 0.17 (0.29) | 0.2 (0.29) | −0.01 (0.3) |
Urban | 0.55 (0.27)* | 0.52 (0.26)* | 0.44 (0.25) | 0.57 (0.26)* | 0.73 (0.28)** |
Negative events | 0.14 (0.17) | 0.14 (0.17) | 0.16 (0.16) | 0.17 (0.18) | 0.2 (0.17) |
Negative events x deprivation | −0.35 (0.2) | −0.41 (0.2)* | −0.43 (0.21)* | −0.21 (0.2) | −0.3 (0.19) |
Constant | 7.29 (0.43)*** | 7.29 (0.43)*** | 7.44 (0.42)*** | 7.24 (0.44)*** | 7.22 (0.44)*** |
In terms of individual-level variables, a 1.00 standard deviation increase in household income elicits a 0.82 decrease in depression score. In the sample, being male is significantly associated with fewer depressive symptoms and a similar relationship holds for being younger. Coloured, Asian/Indian and White participants reported significantly fewer depressive symptoms when compared with Black African participants. Having been educated up to at least grade 9 and not being deprived of durable goods or in the living environment also functioned as protective factors against depressive symptoms. People living in urban environments displayed significantly more depressive symptoms than those who live in rural areas. Employed participants fared better than those in all the other employment categories. Those who had actively sought work without success showed significantly more depressive symptoms than the non-economically active participants. The category ‘unemployed strict’ denotes individuals who have actively sought out employment opportunities in the past four weeks, while ‘unemployed discouraged’ represents individuals who would like to have worked in the past month but have not actively searched for a job during that period. There is no evidence of an association between negative life events and reported depressive symptoms in the sample.
The full model for the composite deprivation index was significant: F(20,345) = 20.70, p < 0.0001 and had an R
2 = 0.113. The composite multiple deprivation coefficient’s significance remained at the 5% level after the inclusion of all the individual-level covariates B = 0.31 (0.15), p = 0.042. This indicates that a standard deviation increase in neighbourhood deprivation is associated with a 0.31 standard deviation increase in depression score, or approximately 1.5 points on the CESD-10 scale. Overall, the coefficients for the remaining explanatory variables remained quite similar across all the domains and resembled the original model closely. Of the four domains, Living Environment Deprivation was the most salient: B = 0.53 (0.16), p = 0.001. The Employment Deprivation domain was also a strong predictor of depression: B = 0.38(0.13), p = 0.004, while the Income and Material Deprivation domain was below the 5% level of significance: B = 0.35(0.16), p = 0.02. The Education Deprivation domain was the only non-significant area-level explanatory variables B = 0.28(0.15), p = 0.07.
The relationship between neighbourhood-level deprivation and depression is significant (higher deprivation leads to increases in depression scores for the composite and individual indices of deprivation), even after controlling for negative life events and the interaction of negative life events with deprivation. Reporting negative life events does not have an independent significant effect on depression scores for all indices of deprivation. For the composite index (as well as the Education and Living Environment indicies), the interaction of negative events with deprivation is also not a significant predictor of higher depression scores. That is, the effect of deprivation on depression scores is not mediated by the presence or absence of reporting a negative event. However, if we focus on the Income and Material, and the Employment indices respectively, the significant negative coefficient on the interaction between deprivation and negative events shows that for individuals that report negative life events (such as a death in the family), the effect of deprivation on depression is cancelled out.
Discussion
Results of this study suggest that individuals living in more deprived neighbourhoods in South Africa experience more depressive symptoms than those living in less deprived neighbourhoods and this effect persists after controlling for a number of individual-level covariates of depression. Over and above individual-level factors, mechanisms operating at the neighbourhood-level appear to affect residents’ depression levels. These could take the form of place-bound effects, effects of shared social backgrounds, or peer effects, to name a few [
22,
34].
Results from the income and material deprivation domain suggest that as the proportion of households with very low monthly incomes and few durable goods increases in neighbourhoods, individuals living in these neighbourhoods display more depressive symptoms. The aggregated effect of a community’s financial impoverishment has implications beyond those of individual circumstance. Future research should explore possible mechanisms whereby neighbourhood effects filter down to an individual level in the form of influential stressors or buffers that can affect mental well-being. Possibilities include neighbourhood quality and access to social resources [
36]. Such factors are more likely to be salient in neighbourhoods with high levels of poverty [
37] and, hence, residents experience unfulfilled needs and dissatisfaction that may be risk factors for depression. In situations of high material deprivation, cognitions relating to hopelessness, loss of control and helplessness have been linked to depression outcomes [
38].
The employment domain was the second most salient in the ‘effect’ it exerted on depression symptoms. Experiments in social psychology have shown that prolonged deprivation along with basic needs not being satisfied can lead to feelings of incompetence and inefficiency as well as powerlessness and helplessness [
39]. This in turn creates a perception of loss of control over one’s circumstances and often a sense of hopelessness [
39]. The concomitant effects of anxiety and relative helplessness on mental health outcomes in such milieus are well documented [
13,
40]. Another obvious outcome of highly concentrated levels of unemployment is that this facilitates delinquency, deviant peer affiliation (particularly among adolescents) and crime of various forms [
37]. In South Africa, areas characterised by high rates of economic exclusion and poverty, where prospects of upward mobility are small, are highly susceptible to gang-related activity [
41]. Reasons of despondency and disempowerment might also be help explain the association between unemployment and depression.
At an individual level, education has been consistently found to be a strong protective factor against depression [
28,
31]. However the results indicated that although it was significant at the individual level, it was not significant at the neighbourhood level. Ross [
13] reported a similar finding. This may be because individual-level education has more proximal and direct effects than the more distant neighbourhood-level education. The SAIMD definition of education deprivation is also a relatively crude measure and thus may be less sensitive to associations with depression.
For the composite index (as well as the Education and Living Environment domains), the interaction of negative events with deprivation does not have a significant effect on depression scores. That is, the effect of deprivation on depression scores is not mediated by the presence or absence of reporting a negative event. There were two exceptions to this result. For the Income and Material, and the Employment indices respectively, the significant negative coefficient on the interaction between deprivation and negative events shows that for individuals that report negative life events (such as a death in the family), the positive effect of deprivation on depression scores is cancelled out. A possible explanation for this could be that on the particular dimensions of income and material and the employment indexes of deprivation, the effect of neighbourhood deprivation on depression is attenuated in the presence of negative life events.
Various mechanisms could account for why neighbourhood level deprivation affects individual depression outcomes. It is likely that where a large percentage of houses lack basic amenities such as running water, toilets and electricity, this may serve as a proxy for deprivation of other resources and facilities within these communities. The more direct effect of this could relate to feelings of heightened insecurity, danger and humiliation among community residents. In areas with high population density and lack of resources such as toilets in houses and adequate lighting, the living environment lends itself to crime and violence [
42]. Robins [
43] highlights the “inextricably intertwined sanitation, safety and dignity issues” (p.494) that confront residents in a peri-urban settlement near Cape Town daily. Such characteristics are prevalent in many South African townships where levels of violent crime are very high [
41]. Other possible ameliorative factors like street lighting or proximity to public services such as police stations or health care facilities are also likely to be lacking in these neighbourhoods. Evidence from studies in the Northern Cape Province of South Africa indicated that women viewed having electricity and lighting in their neighbourhood as the most important factor in improving their living conditions as it reduced their susceptibility to crime, physical violence and sexual assault [
42,
44]. The appearance of a neighbourhood that constantly reminds its residents of pervasive poverty and a threatening environment is also likely to cause stress in the individuals living there and contribute to mental ill-health [
37].
There is a substantial body of sociological literature and theorising which may assist in elucidating some of the social processes and mechanisms that affect health outcomes like depression. Sampson and colleagues [
37] propose that two fundamental social processes operating at the neighbourhood level can be classified as ‘social capital’ and ‘norms and collective efficacy’. Social capital can be seen as consisting of cognitive, structural, bonding, bridging and linking mechanisms amongst social groups [
36]. Norms and collective efficacy can be seen in the degree of mutual trust in communities and the extent to which expectations about neighbourhoods are shared among residents [
37].
In essence, social cohesion in neighbourhoods regulates levels of informal social control and solidarity. High levels of neighbourhood social capital and its cognitive component, social cohesion, have consistently shown protective qualities for a variety of health outcomes, including common mental disorders and depression [
13,
45]. However, the results of the present study may indicate that concentrated levels of deprivation, disadvantage and economic exclusion make it very difficult to foster the social capital, cohesion and collective efficacy that can protect residents from the stressors associated with physical and mental ill-health [
37]. This is in line with the ‘social causation’ hypothesis which predicts that the accrual of multiple risk factors like lack of education, stress and lack of access to health care will increase vulnerability and could precipitate a mental illness like depression [
46].
Limitations
First, it must be acknowledged that this work refers to symptoms of depression rather than a formal diagnosis.
A second limitation is the lack of inclusion of a ‘neighbourhood tenure’ variable. Regrettably NIDS did not include a direct question about how long residents had been living in their particular neighbourhood. Without a tenure variable it is not possible to investigate cumulative exposures and lagged effects [
22].
Third, a cross-sectional analysis limits any discussion of causality. This area of research would benefit greatly from longitudinal studies that can address same-source bias, reverse causation issues and cumulative effects [
8]. Notwithstanding this limitation, it seems more likely that area-level deprivation leads to depression rather than the other way round, as it is unlikely that depressed individuals would drift into deprived areas on such a scale.
Fourth, the statistical method chosen used cluster corrected standard errors and survey regressions but was not a multilevel model. It was judged that this was an appropriate approach given the clustered nature of the data and the large number of primary sampling units (
n = 417). However, the strength of multilevel modelling is its ability to examine both the role of individual-level and group-level predictors on individual-level outcomes simultaneously [
47], and its use should be explored in future studies.
Fifth, while the datazone geographical units are by far the closest approximations of neighbourhoods available in South Africa, they are still slightly larger than equivalent units that have been used in other contexts [
48]. As such, we should be mindful of problems such as ecological fallacy [
49] and the modifiable areal unit problem [
50]. For example, it is possible that, especially in more rural areas, the superimposed datazones were not able to sufficiently capture the social homogeneity. Similarly, inferences about individuals should not be drawn from this analysis, as it looks at the effect of group-level characteristics on individual outcomes.
Sixth, the lower response rates from urban, and white households must be acknowledged. While these trends are commonly observed in South African survey research [
23], there is a possibility that the group that did answer, for example the 36% of white participants, systematically differed in some way from general population of white South Africans.
Finally, it must be acknowledged that even though multiple deprivation indices represent a far more sound proxy for relevant neighbourhood-level constructs than simple aggregated income proxies, they remain rather limited substitutes for the actual features of neighbourhoods, both physical and social, that are assumed to influence health outcomes [
22].
Future directions for research
A natural progression that has emerged from this particular study is to explore whether social capital and, more specifically, social cohesion modifies the association between neighbourhood deprivation and mental health. Theory and evidence suggest that socially cohesive neighbourhoods facilitate informal social control, which improves well-being of residents [
37]. There is also evidence that mental ill-health is associated with area-level deprivation, but that this relationship is modified in cases of high social cohesion in neighbourhoods [
11]. Recently, research using NIDS data found significant negative associations between neighbourhood social capital and depression [
15]. Following on from the significant association found between neighbourhood-level deprivation and depression in this study, it seems logical to explore how neighbourhood social capital and deprivation interact in relation to depression.
Implications for practice
Various forms of deprivation clearly have effects on mental health outcomes. Policy makers should incorporate this understanding into their intervention strategies. Strategies that have not traditionally been considered relevant for mental health, like housing and urban planning, are likely to have important mental health outcomes at a population level. Neighbourhoods are the units in which these strategies become actualised, in terms of the roll-out of integrated policies [
22]. This study indicates that providing neighbourhoods with amenities such as adequate street lighting, water, sanitation, electricity and access to public transport, as well as addressing neighbourhood level material deprivation and unemployment could have beneficial implications for promoting mental health.