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. Author manuscript; available in PMC: 2026 Jun 2.
Published in final edited form as: SSM Ment Health. 2025 May 9;7:100452. doi: 10.1016/j.ssmmh.2025.100452

Community-based stress, social support, and depressive symptoms among black adults: A mediation analysis

James Huynh a,*, Dae-zhane Boland b, Cindy Le c, Aisha D Fletcher b, Lauren R Anderson b, Courtney Thomas Tobin b,1
PMCID: PMC13225855  NIHMSID: NIHMS2175011  PMID: 42232245

Abstract

Guided by social disorganization theory, this study examines the extent to which social support mediates the relationship between community-based stressors and depressive symptoms among Black Americans. Social support is a key mechanism linking psychosocial community-based stressors to racial disparities in mental health. However, little research has explored various sources of social support—family, friend, and church—mediates these relationships. Using data from the Nashville Stress and Health Study (N = 623), this study employs OLS regression and structural equation modeling to assess whether social support explains the associations between community-based stressors and depressive symptoms. Results show family support fully mediates the impact of perceived neighborhood disorder, highlighting the critical role of familial networks in shaping mental health. These findings underscore the importance of family-centered health interventions and culturally relevant community programs designed to strengthen social support and mitigate the adverse effects of community stressors on mental health disparities.

Keywords: Community stress, Social support, Depressive symptoms, Mediation analysis, Black americans, Social disorganization theory, Community-based stress, And depressive symptoms among black adults: a within-groups mediation analysis

1. Introduction

Epidemiologic research has demonstrated lower rates of psychiatric disorder among Black Americans relative to White Americans (Barnes and Bates, 2017; Erving et al., 2019). However, Black individuals report higher levels of non-specific psychological distress, including depressive symptoms (Barnes and &Bates, 2017; Mouzon, 2013; Thomas Tobin, 2021). In particular, rates of major depression are relatively low among Black Americans, yet studies show that they experience more chronic and severe depressive symptoms compared to other racial groups (Thomas Tobin, 2021; Walton and Shepard Payne, 2016). Since elevated depressive symptoms have also been linked to higher rates of physical morbidity and premature mortality in Black communities (Walton and Shepard Payne, 2016), identifying the social and structural factors that contribute to these disparities is critical for understanding racial inequities in mental health.

One explanation for these disparities is Black Americans’ disproportionate exposure to structural and environmental stressors that negative impact mental health. For instance, community-based psychosocial stressors such as neighborhood disorder and fear of crime have been identified as key determinants of psychological distress (Cutrona et al., 2006; Gilster, 2014; Latkin and Curry, 2003; Mair et al., 2010). At the same time, social support—particularly from family, friends, and religious communities—has been highlighted as a critical protective resource against the adverse effects of stress (Gariépy et al., 2016). However, research has largely examined these stressors and social resources in isolation, resulting in a limited understanding of how they function together to shape depressive symptoms within Black communities.

In this study, we consider how community-based stressors contribute to depressive symptoms among Black Americans. Drawing upon social disorganization theory Shaw and McKay (1972), we investigate whether different sources of social support mediate these relationships. By exploring these pathways, we aim to clarify how community-based stressors and protective resources individually and collectively shape mental health within Black communities.

2. Background

Black Americans experience disproportionate exposure to community-based psychosocial stressors that negatively impact mental well-being. Using Census 2000 tract-level data for U.S. metropolitan areas, Osypuk and colleagues (2009) found that, on average, Black people live in significantly worse neighborhood environments (i.e., impoverished neighborhoods) compared to other racial groups. In another study, Mujahid and colleagues (2011) found that 36 % of Black Americans reported experiencing high levels of community-based stressors (i.e., limited walkability, lack of aesthetic quality, heightened violence). Furthermore, some studies demonstrate that Black individuals, regardless of SES, are more likely to live in neighborhoods with higher social disorder, emphasizing the role of racial disparities at the community level in shaping health and well-being (Thomas, 2015; Turner et al., 2017). While each of these studies assessed single measures of community-based stressors, residents typically encountered multiple forms of social and physical stressors within their communities, including perceived neighborhood disorder and fear of crime (Barber et al., 2016, Turner and Avison, 2003).

While communities with high social disorder can damage health and well-being, they can also act as sources of social support that can play a protective role against stressors (Pickover et al., 2018). Social support refers to the interpersonal exchanges between people that offer instrumental, emotional, or informational help (House, 1983). A systematic review found that social support consistently plays an important protective role against depression (Gariépy et al., 2016). Receiving support from family, friends, and church members can offer hope, validation, a sense of belonging, and material (financial or instrumental) needs, which can mitigate the potentially harmful effects of community-based stressors on depressive symptoms (Chang et al., 2019; Taggart et al., 2023). For example, some studies indicate that Black Americans have more supportive family networks that tend to provide more instrumental support (Taylor et al., 2013). There is also well-documented literature on Black Americans’ greater involvement in their religious institutions and networks compared to general populations, suggesting that they may benefit from increased church support (Taylor et al., 2017).

Social disorganization theory (Shaw and McKay, 1972) posits that environmental stressors can undermine social relationships and therefore impact health. On the flip side, the theory articulates that socially cohesive communities can improve mental health through social support resources and shared values (Lin et al., 2009; Shaw and McKay, 1972). In other words, while much of the stress literature focuses on social support as a moderator (Lee and Liechty, 2015; Pickover et al., 2018; Stockdale et al., 2007), we draw on social disorganization theory to conceptualize social support as a mediator in the link between community-based stress and depressive symptoms. Some studies have shown that stressful community environments may significantly diminish the availability of social support (Giurgescu et al., 2015; Kim, 2010; Lin et al., 2009). The converse held true when Russell and colleagues (2018) found that greater relationship quality between community members was linked to lower levels of discrimination and depression. Collectively, these findings suggest that community-based stressors may disrupt the social support that community members can provide to one another. However, since prior studies examined only one form of social support at a time, it is unclear whether community-based stressors uniformly disrupt social support or have a distinct impact on these forms of support (Blair et al., 2014; Russell et al., 2018; Stockdale et al., 2007). Additional research is needed to clarify the relationships between different forms of community-based stressors and social support.

Studies also suggest that the role of social support in the associations between community-based stressors and depressive symptoms may operate differently for Black individuals (Gayman et al., 2014; Lincoln et al., 2005). Although growing evidence has challenged the “assumption of similarity,” which commonly presumed that these psychosocial processes are conceptually and functionally equivalent across racial groups (Hunt et al., 2000), few studies have evaluated the extent to which social support may explain the relationship between community-based stressors and depressive symptoms among Black individuals. Nevertheless, recent studies demonstrate that the determinants of social support are distinct for Black Americans (Thomas Tobin et al., 2021). In other words, different factors shape social support among this particular racial group.

Moreover, Gayman and colleagues (2014) also observed that racial differences in social support may “reflect cultural differences in the importance of and reliance on particular coping resources learned in the socialization process within race-ethnic groups.” Given these distinct social contexts, scholars have increasingly emphasized the importance of evaluating psychosocial pathways within racial groups, rather than assuming homogeneity across populations (Hunt et al., 2000; Whitfield et al., 2008). Despite this, fewer studies have explored how social support mediates the link between community-based stressors and depressive symptoms among Black individuals, limiting our understanding of how structural stressors shape mental health within this group.

2.1. The present study

Guided by social disorganization theory, the present study examines the extent to which social support mediates relationships between community-based stressors and depressive symptoms among Black Americans. In doing so, we seek to investigate how social support may be disrupted by community-based stressors, and how that contributes to mental health inequities. We address two aims: (1) assess the relationships between community-based stressors and sources of social support; and (2) determine whether and which sources of social support explain the link between community-based stressors and depressive symptoms distinctly among Black individuals. Given the substantial evidence documenting significant racialized inequities in stress exposure (Cutrona et al., 2006; Gilster, 2014; Mendez et al., 2014), social support (Gabriel et al., 2020; Griffin et al., 2006), and the associations between them (Giurgescu et al., 2015; Mair et al., 2010), provide a more nuanced understanding of how these pathways function within Black communities, acknowledging that prior research suggests these relationships may differ across racial groups. Furthermore, this study advances our understanding of within-group processes impacting depressive symptoms through examining two forms of community-based stressors (perceived neighborhood disorder and fear of crime) and three sources of social support (family support, friend support, and church support) (see Fig. 1 for conceptual model).

Fig. 1.

Fig. 1.

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Adapted conceptual model of Social Disorganization Theory (Shaw and McKay, 1972) linking community-based stressors, social support, and depressive symptoms.

3. Data and methods

3.1. Sample

The Nashville Stress and Health Study (NSAHS) is a population-based sample of Black and White adults ages 21 to 69 from the city of Nashville and surrounding areas within Davidson County, Tennessee. A random sample was obtained using a multistage, stratified sampling approach. Black households were oversampled to achieve a final sample with similar proportions of racial and sex groups, and a sampling weight allowed for generalizability of sample characteristics to the county population. Between 2011 and 2014, 1252 respondents were interviewed about their personal and family backgrounds, stress and coping experiences, health behaviors, and health histories during 3-h computer-assisted interviews with trained study staff of the same race. All participants provided informed consent. The NSAHS and all study procedures were approved by the Vanderbilt University Institutional Review Board and described in detail elsewhere (Brown et al., 2016). The present analyses included only Black respondents for whom 623 had complete data. Sample characteristics are described in Table 1.

Table 1.

Sample characteristicsa, Nashville stress and health study (2011–2014) (N = 623).

Depressive symptoms (range, 0-54) 14.32 (12.52)
Community Stressors
 Perceived neighborhood disorder (range, 0–31) 9.67 (8.09)
 Fear of crime (range, 0–30) 8.97 (11.79)
Social Support
 Family support (range, 0–24) 19.22 (7.46)
 Friend support (range, 0–24) 16.90 (4.91)
 Church support (range, 0–4) 1.68 (0.08)
Demographics
 Stress Exposure (range, −6.57-17.78) 0.70 (4.57)
 Age (range, 22–69) 43.55 (15.20)
 Gender
  Women (reference) 0.55
  Men 0.45
 Marital status
  Married (reference) 0.35
  Never married 0.39
  Other 0.25
 Socioeconomic statusb −0.47 (0.97)
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a

Weighted means and proportions are shown. For continuous variables, standard deviations are in parentheses.

b

Standardized.

3.2. Measures

Depressive Symptoms were assessed using the 20-item (Cronbach’s alpha = 0.89) Center for Epidemiologic Studies Depression Scale (CES-D) (Radloff, 1977). Respondents answered how often in the last month they had feelings or experiences such as “you could not shake off the blues,” “you felt lonely,” and “you had crying spells.” Responses were coded (0) not at all, (1) occasionally, (2) frequently, (3) almost all the time. Items were summed such that scores ranged from 0 to 60, with higher scores indicating higher depressive symptomatology.

3.2.1. Community-based stressors were assessed with two measures

Perceived neighborhood disorder was measured using 12 items (Cronbach’s alpha = 0.90) asking participants questions about the neighborhood in which they live. Examples include “My neighborhood is too polluted or noisy”, “There are a lot of abandoned buildings in my neighborhood”, “My neighborhood is pretty safe”, and “I can call on a neighbor for help if I needed it”. Responses options ranged from (0) strongly disagree to (3) strongly agree, and positive items (e.g., “In my neighborhood, people watch out for each other”) were reverse coded. Items were summed and scores ranged from 0 to 36, with higher scores indicating higher perceived neighborhood disorder.

Fear of crime was assessed with 10 items (Cronbach’s alpha = 0.95) asking participants how concerned they were about becoming a victim of crime within their community. Examples include “Being physically attacked”, “Having someone steal your wallet or purse when you are in a crowd”, and “How afraid you are of a family member or close friend being a victim of one of these crimes”. Responses were coded (0) not at all afraid, (1) mildly afraid, (2) moderately, (3) very afraid. Items were summed and scores ranged from 0 to 30. Higher scores indicated higher fear of crime.

3.2.2. Social support was measured from three sources

Family support (Turner and Marino, 1994) was assessed with 16 items (Cronbach’s alpha = 0.87) asking participants about their present relationship with family members other than their spouse/partner. Examples include “You feel very close to your family” and “You know that your family has confidence in you”. Response options ranged from (0) not true at all to (3) very true and negative items (e.g., “Your family is always pointing out mistakes you have made”) were reverse coded. Items were summed and scores ranged from 0 to 48, such that higher scores indicated higher levels of family support.

Friend support (Turner and Marino, 1994) was assessed with 8 items (Cronbach’s alpha = 0.91) asking participants about their relationship with their friends. Examples include “You feel very close to your friends”, “You feel that your friends really care about you”, and “Your friends often let you know that they think you are a worthwhile person”. Response options ranged from (0) not true at all. Items were summed and scores ranged from 0 to 24. Higher scores indicated higher levels of friend support.

Church support (Upenieks and Daniels, 2022) was assessed with 1 item asking participants how often people in their church (or place of worship) help them out. Responses were coded (0) never needed help, (1) never, (2) not too often, (3) fairly often, (4) very often, such that higher scores corresponded with greater church support. While prior research (e.g., Upenieks and Daniels, 2022) has examined this item alongside another item measuring frequency of contact with co-religionists, we opted to use only the support-related item. This decision was made to maintain consistency across our social support constructs, all of which focus on perceptions of tangible or emotional support rather than social contact or network engagement.

3.2.3. Other covariates

The following demographic characteristics were also included as covariates. Stress exposure was assessed with five dimensions of social stress: recent life events, chronic stress, trauma, everyday discrimination, and major discrimination (Thomas Tobin, 2021). To measure total stress exposure, each dimension was standardized (mean = 0, standard deviation = 1) and then summed to create the final stress exposure variable. The resulting variable has a mean of 0, while its standard deviation reflects the combined contributions of the standardized components. Age was measured continuously in years and ranged from 22 to 69. Gender was measured dichotomously as (0) women (reference category) and (1) men. Marital status was measured categorically as (0) married (reference category), (1) never married, (2) other (i.e., divorced, widowed, separated). Socioeconomic status (SES) was measured using a standardized index of educational attainment, annual household income, and level of occupational prestige. Education was measured by respondents’ highest level of education completed: (1) less than high school, (2) high school/GED, (3) some college, and (4) college graduate or higher. Respondents also provided information about their annual household income (0=<$20,000; 1 = $20,000-$34,999; 2 = $35,000-$54,999; 3 = $55,000-$74,999; 4 = $75,000-$94,999; 5 = $95,000+). Occupational prestige indicated individuals’ social class standing based on the perceived prestige of their job position as defined by the Nam-Powers-Boyd occupational status scale (Nam and Boyd, 2004). Scores ranged from 0 to 100, and higher scores denoted higher occupational prestige. To generate an SES score for each respondent, the three dimensions were standardized, summed, and divided by the number of dimensions on which data were available (Gayman et al., 2011). SES was measured continuously in the present analyses, with positive scores indicating above average SES levels and negative scores indicating below average SES levels.

3.2.4. Statistical analyses

There were several steps in this analysis. First, weighted means and proportions were estimated for all study variables (Table 1). Additionally, there was no multicollinearity between variables based on variance inflation factors. Second, to better understand the ways that exposure to community-based stressors may shape social support, we used weighted ordinary least squares (OLS) regression models, using the svy command, to examine the associations between community-based stressors and each dimension of social support (i.e., family, friend, and church support), accounting for differences in age, gender, marital status, and SES (Table 2). We then assessed the relationships between community-based stressors, social support, and depressive symptoms as the third step of the analysis (Table 3). Finally, we evaluated the extent to which each form of social support mediated the linkages between each community-based stressor (neighborhood disorder and fear of crime) and depressive symptoms among racial groups. Consistent with previous studies (Park et al., 2019), we used structural equation modeling (SEM) to calculate standardized direct, indirect, and total effect estimates. We also simulated 1000 bootstrap replications of the original sample to calculate bias-corrected 95 % confidence intervals (CIs) of the indirect effects of each stressor on depressive symptoms and indicate whether each form of social support significantly explained these associations (Li, 2011) (Table 4). SEM model convergence was achieved by changing the iterative maximization algorithm to options dfp and nr for 7 iterations per algorithm. Bias-corrected confidence intervals were used to estimate the significance of the indirect effects since the distribution was non-normal (MacKinnon et al., 2004). All statistical analyses were performed in STATA 16.

Table 2.

Associations between community stressors and social support among black adults, Nashville stress and health study (2011–2014) (N = 623)a.

Model 1
Family Support		 Model 2
Friend Support		 Model 3
Church Support
b (SE) b (SE) b (SE)
Community stressors
 Perceived neighborhood disorder −0.19** (0.06) −0.14* (0.07) −0.01 (0.01)
 Fear of crime −0.04 (0.03) −0.03 (0.05) 0.00 (0.01)
Intercept 20.56*** (1.08) 22.67*** (1.61) 2.21*** (0.35)
R2 0.14 0.05
0.02
F 15.23 6.96 3.40
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Note: Stress exposure (continuous), age, gender, marital status, and socioeconomic status are included as covariates in all models. Additionally, all models were conducted using weighted OLS regressions. Standard errors presented were estimated using the first-order Taylor linearization method.

a*

p<0.05,

**

p<0.01,

***

p<0.001 significant (two-tailed test).

Table 3.

Depressive symptoms regressed on community stressors and social support among black adults, Nashville stress and health study (2011–2014) (N = 623)a.

Model 1 Model 2 Model 3 Model 4
b (SE) b (SE) b (SE) b (SE)
Age −0.11** (0.04) −0.10** (0.04) −0.11** (0.04) −0.10** (0.04)
Gender (Reference = Women)
 Men −2.13* (0.98) −1.66 (1.05) −2.04* (0.97) −1.58 (1.04)
Marital status (Reference = Married)
 Never married 1.71* (0.81) 1.56* (0.76) 1.73* (0.82) 1.63* (0.75)
 Other 1.35 (1.02) 1.42 (0.96) 1.23 (1.09) 1.25 (1.05)
Socioeconomic status −2.16* (1.01) −2.43** (0.71) −2.48*** (0.61) −2.39*** (0.64)
Stress Exposure 1.43*** (0.08) 1.33** (0.10) 1.30*** (0.07) 1.22*** (0.09)
Community stressors
 Perceived neighborhood disorder 0.05* (0.06) −0.01 (0.06)
 Fear of crime 0.14*** (0.03) 0.14*** (0.03)
Social Support
 Family support −0.23** (0.08) −0.22** (0.08)
 Friend support −0.13 (0.07) −0.12 (0.07)
 Church support 0.34 (0.26) 0.32 (0.26)
Intercept 16.80*** (1.97) 14.51*** (2.37) 22.99*** (2.70) 21.01*** (3.15)
R2 0.37 0.39
0.40 0.41
F 68.32 110
89.14 133.62
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Note. All models were conducted using weighted OLS regressions. Standard errors presented were estimated using the first-order Taylor linearization method.

a*

p<0.05,

**

p<0.01,

***

p<0.001 significant (two-tailed test)

Table 4.

Test of the bootstrapped indirect effects from structural equation models (based on 1000 Bootstrap reps) (N=623)a.

95 % Bias-
Corrected CI.
Estimate Sig. Lower
2.5 %		 Upper
2.5 %		 Proportion of
total effect
mediateda
Indirect effects of perceived neighborhood disorder on CES-D
Perceived neighborhood disorder → Family Support → CES-D 0.028 * 0.002 0.064 0.288
Perceived neighborhood disorder → Friend Support → CES-D 0.014 0.000 0.045 0.153
Perceived neighborhood disorder → Church Support → CES-D
Indirect effects of fear of crime on CES-D 		−0.001 −0.024 0.004 0.019
Fear of crime →
Family Support →
CES-D		 0.000 −0.014 0.017 0.001
Fear of crime →
Friend Support →
CES-D		 0.003 −0.005 0.024 0.021
Fear of crime → Church Support → CES-D 0.001 −0.004 0.022 0.008
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Notes:

*

indirect effect is statistically significant (p < 0.05)

a

For each stressor, we calculated the indirect effect of the variable and the total effect of the variable on CES-D as mediated by a specific source of social support. To obtain the proportion of the total effect mediated, we divided the indirect effect of each independent variable by its total effect. This proportion tells us how much the total effect is due to the mediator. For each structural equation model, we test one stressor and mediator at a time while controlling for the other independent variables, mediators, and covariates (age, marital status, SES, gender, stress exposure).

4. Results

Overall, this sample reported a mean age of 43.55 years old, and the majority (55 %) were women (Table 1). Respondents reported above average stress exposure scores (m = 0.70, SD = 4.57) and below average socioeconomic status (m = −0.47, SD = 0.97). For depressive symptoms, respondents had an average score of 14.32 (SD = 12.52) falling below clinical significance (i.e., scores of 16). Respondents reported high exposure to community-based stressors, including perceived neighborhood disorder (m = 9.67, SD = 8.09) and fear of crime (m = 8.97, SD = 8.97). Regarding social support, respondents reported relatively high levels of family (m = 19.22, SD = 7.46) and friend support (m = 16.90, SD = 4.91) and moderate levels of church support (m = 1.68, SD = 0.08).

4.1. Community-based stressors and social support

To address our first aim of assessing the relationships between community-based stressors and sources of social support, we show their weighted unadjusted associations among Black respondents in Table 2. Model 1 indicated that perceived neighborhood disorder (b = −0.19, SE = 0.06, p < 0.01) was correlated with lower levels of family support. This was also true for the association between perceived neighborhood disorder and friend support (model 2, b = −0.14, SE = 0.07, p < 0.05). There was no statistically significant association for perceived neighborhood disorder and church support (model 3). Fear of crime was not statistically significantly associated with any of the social support variables. Overall, the unadjusted findings may indicate that links between community-based stress and social support vary by the type of stress and source of social support.

4.2. Community-based stress, social support, and depressive symptoms

Next, we addressed our second research aim by examining the weighted adjusted relationships between community-based stress and depressive symptoms with social support sources as mediators in a stepwise fashion (Table 3). Model 1 showed that age, gender, marital status, stress exposure, and SES were significant correlates of depressive symptoms. Specifically, younger age (p < 0.01), being a woman (p < 0.05), having never been married (p < 0.05), experiencing higher exposure to individual stress (p < 0.001), and having low SES (p < 0.05) were associated with greater depressive symptom levels among Black Americans. The impact of community-based stressors was examined in Model 2, and results indicated that both perceived neighborhood disorder (b = 0.05, p < 0.05) and fear of crime (b = 0.14, p < 0.001) were significantly associated with higher depressive symptoms. Model 3 examined the adjusted relationships of social support sources and showed that only family support was negatively associated with depressive symptoms while support from friends and church were not.

To aid in interpretation and contextualize the practical significance of these associations, we conducted supplemental analyses (not shown) estimating depressive symptom scores at different levels of perceived neighborhood disorder, fear of crime, and social support. Results indicated that individuals with high levels of perceived neighborhood disorder had depressive symptom scores that were 0.80 points higher than those with low levels, while individuals with high fear of crime had depressive symptom scores that were 2.92 points higher than those with low fear of crime. In contrast, greater family support was associated with lower depressive symptoms, with individuals reporting high family support experiencing a 3.44-point reduction in depressive symptoms compared to those with low family support. Friend support showed a smaller association with depressive symptoms, while church support had a minimal effect. These findings suggest that while community stressors are linked to greater depressive symptoms, family support may serve as a key protective factor in mitigating these effects.

Community-based stressors and social support were considered simultaneously in Model 4. Fear of crime continued to be significantly positively associated with depressive symptoms, even after accounting for social support (b = 0.14, p < 0.001). However, when controlling for social support, the relationship between perceived neighborhood disorder and depressive symptoms was no longer significant (b = −0.01, p = 0.91), which may indicate a mediation effect. To confirm the mediation effect, structural equation model results show that family support had a statistically significantly positive indirect effect (b = 0.028, p < 0.05) in the relationship between perceived neighborhood disorder and depressive symptoms (Table 4). Differences in family support mediated about 29 % of the perceived neighborhood disorder-depressive symptoms association among this group.

Taken together, these results demonstrate that both forms of community-based stress (perceived neighborhood disorder and fear of crime) are significantly linked to depressive symptoms among this group. However, the impacts of perceived neighborhood disorder may at least be partially explained by differences in family support. On the other hand, the relationship between fear of crime and depressive symptoms may be attributed to other factors that are beyond sources of social support among Black Americans.

5. Discussion

The purpose of this study was to assess whether various sources of social support mediated the relationship between community-based stressors and depressive symptoms among Black adults. While prior research has often examined social support and community-based stressors as independent predictors of depressive symptoms (Blair et al., 2014; Mair et al., 2010; Moore et al., 2016; Mujahid et al., 2011), social disorganization theory (Shaw and McKay, 1972) proposes that social support may instead mediate this relationship. Exposure to community-based stressors may undermine or diminish the availability of supportive relationships, thereby contributing to poorer mental health. These dynamics may vary depending on the type of stressor and source of support, particularly within Black communities, where distinct social and cultural contexts shape these interactions (Gayman et al., 2014). However, research has largely conceptualized social support as a moderator rather than exploring its mediating role. To address this gap, the present study aimed to: (1) assess the relationships between community-based stressors and sources of social support; and (2) determine whether and which sources of social support explain the link between community-based stressors and depressive symptoms. Several notable findings emerged.

5.1. Community-based stressors and social support

By conceptualizing social support as mediators, our findings highlight the ways in which community-based stressors may undermine social relationships. Despite a substantial literature documenting racial differences in community contexts (Beyer et al., 2016; Gerber and Sievert, 2018; Gilster, 2014; Kruger et al., 2007; Osypuk et al., 2009) and social support (Gayman et al., 2014; Mair et al., 2010; Mouzon, 2013), fewer studies have considered how these linkages may be distinct among Black individuals (Blair et al., 2014). Results showed nuanced patterns between each type of community-based stressor and each type of social support. Perceived neighborhood disorder contributed to lower family and friend support among Black respondents. On the other hand, fear of crime was unrelated to social support. Some qualitative studies found that relatively few participants perceived fear of crime to have serious mental health impacts, much less impacting the availability of social support one might receive (Lorenc et al., 2013). This also aligned with prior work among Black urban-dwelling older adults living in Flint, Michigan that social support was not significantly associated with fear of crime (Mathis et al., 2015). The lack of a significant relationship between fear of crime and social support may be attributable to broader structural inequities, such as systemic racism and discriminatory policing practices, which could disrupt typical pathways between fear of crime and social resources. In communities facing disproportionate over-policing, fear of crime may become normalized, lessening its direct impact on social support networks—an important consideration when examining mental health disparities through the lens of structural inequities.

These patterns highlight the significance of communities for shaping the availability of social support. While prior studies have examined how community stressors reduce social support in general (Beyer et al., 2016; Kruger et al., 2007), fewer studies have explored how these dynamics operate specifically among Black individuals. As Black Americans typically occupy distinctly racialized community contexts (Thomas Tobin et al., 2023; Gayman et al., 2014), it is important to evaluate how social support emerges in response to these stressors. This study contributes to this literature by demonstrating that social support not only functions as a protective resource but may also be shaped by exposure to neighborhood disorder, further reinforcing the need for within-group analyses. This consideration is crucial as our results demonstrated how perceived neighborhood disorder was fully mediated by family support, suggesting the critical need of focusing on family support, in particular. To successfully strengthen family support in communities with high levels of perceived neighborhood disorder, future efforts must take into account a given Black community’s geographic, ethnic, and cultural context to develop interventions that align with the local definition of family support.

5.2. Community-based stressors, social support, and depressive symptoms among black adults

Our second aim was to determine whether and which sources of social support explained the link between community-based stressors and depressive symptoms, which is the key pathway that social disorganization theory proposes in linking these constructs together. In our study, we found that social support helps explain the links between community-based stressors and depressive symptoms, reinforcing prior research suggesting that social support functions differently across racial groups (Gayman et al., 2014; Lincoln et al., 2005). While these pathways have been examined among White individuals (Mair et al., 2010; Stockdale et al., 2007), research has shown that Black Americans often rely more on family and church support rather than friend networks (Taylor et al., 2013, 2017). Findings from the present study further suggest that perceived neighborhood disorder is particularly relevant for shaping these relationships, as its effects were largely mediated by social support from family. This may be due to family members interacting within regular, multiple contexts (e.g., at home, church, and/or a relatives’ house, etc.), which can give way to more stressors experienced and undermine family relations (Pastrana Jr., 2016; Pollock et al., 2015). This underscores the importance of considering how neighborhood conditions shape access to key social resources in Black communities and thus lends evidence to social disorganization theory. Overall, these findings align with prior research that notes that a community with high levels of neighborhood disorder may shape the availability of support from family and friends, which can contribute to heightened depressive symptoms (Lin et al., 2009; Russell et al., 2018).

On the other hand, the relationship between fear of crime and depressive symptoms was not explained by social support. This important nuance highlights the linkages between different types of community-based stressors alongside specific sources of social support. Therefore, future studies may consider other community characteristics and structural factors, such as redlining and War on Crime policies, that could mediate the link between fear of crime and depressive symptoms (Rothstein, 2017; Simon, 2007).

5.3. Limitations

Results from the present study should be considered within the context of several limitations. For example, this study utilized cross-sectional data. Thus, we are unable to make conclusions about causality or temporal ordering (Brown et al., 2000). In addition, race relations in southern US cities are circumscribed by place-based experiences of racism (Thomas Tobin et al., 2021). Thereby, the regional sample of Black Americans in the NSAHS limits our ability to generalize these results to the broader population. Although these data provide insights into the links between community-based stressors, social support, and depressive symptoms among a socioeconomically diverse sample of Black people, future studies should consider other demographic differences within this population. Furthermore, studies should also examine the relationships between stressors, social support, and depressive symptoms using nationally representative longitudinal data to clarify the ways that exposure to community-based stressors shapes depressive symptoms over time. Finally, our study assesses two dimensions of community-based stressors, including perceived neighborhood disorder and fear of crime. While these are important aspects of communities that shape health and well-being, they focus on individuals’ perceptions of their neighborhoods rather than the built environment, institutional factors, and residential policies. As such, additional research is needed to evaluate the mechanisms through which both subjective and objective dynamics of communities contribute to health inequalities.

6. Conclusions

Despite these limitations, the present study enhances our understanding of the mechanisms through which community contexts may shape mental health within racial groups and ultimately contribute to inequalities. We draw on social disorganization theory and extend prior research by evaluating the mediating role of social support in the relationships between various community-based stressors and depressive symptoms. We used a novel mediation approach that combined stepwise OLS regression with structural equation modeling and have empirically shown that different sources of social support may be key to explaining why particular community-based stressors matter for risk of depressive symptoms for Black individuals. For broader public health implications, research and interventions should focus on bolstering social support, especially family support, among Black communities by attending to the ways that community-based stressors can disrupt those ties.

Funding

This project was supported by a seed grant from the Center for the Study of Racism, Social Justice & Health at the UCLA Fielding School of Public Health. Data collection for the Nashville Stress and Health Study was supported by a grant (R01-AG034067) from the Office of Behavioral and Social Science Research and the National Institute on Aging to R. Jay Turner.

We are grateful to the California Center for Population Research at UCLA (CCPR) for general support. CCPR receives population research infrastructure funding (P2CHD041022) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD).

Footnotes

CRediT authorship contribution statement

James Huynh: Writing – review & editing, Writing – original draft, Supervision, Software, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. Dae-zhane Boland: Writing – review & editing, Writing – original draft. Cindy Le: Writing – review & editing, Writing – original draft. Aisha D. Fletcher: Writing – review & editing, Writing – original draft. Lauren R. Anderson: Writing – review & editing, Writing – original draft, Project administration. Courtney Thomas Tobin: Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Courtney Thomas Tobin reports financial support was provided by National Institutes of Health Office of Behavioral and Social Sciences Research. Courtney Thomas Tobin reports administrative support was provided by National Institute of Child Health and Human Development. James Huynh reports financial support was provided by Robert Wood Johnson Foundation Health Policy Research Scholars. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Barber S, Hickson DA, Kawachi I, Subramanian SV, Earls F, 2016. Neighborhood disadvantage and cumulative biological risk among a socioeconomically diverse sample of african American adults: an examination in the jackson heart study. J. Racial Ethn. Health Dispar 3 (3), 444–456. 10.1007/s40615-015-0157-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Barnes DM, Bates LM, 2017. Do racial patterns in psychological distress shed light on the black–white depression paradox? A systematic review. Soc. Psychiatr. Psychiatr. Epidemiol 52 (8), 913–928. 10.1007/s00127-017-1394-9. [DOI] [PubMed] [Google Scholar]
  3. Beyer KM, Zhou Y, Matthews K, Bemanian A, Laud PW, Nattinger AB, 2016. New spatially continuous indices of redlining and racial bias in mortgage lending: links to survival after breast cancer diagnosis and implications for health disparities research. Health Place 40, 34–43. 10.1016/j.healthplace.2016.04.014. [DOI] [PubMed] [Google Scholar]
  4. Blair A, Ross NA, Gariepy G, Schmitz N, 2014. How do neighborhoods affect depression outcomes? A realist review and a call for the examination of causal pathways. Soc. Psychiatr. Psychiatr. Epidemiol 49 (6), 873–887. 10.1007/s00127-013-0810-z. [DOI] [PubMed] [Google Scholar]
  5. Brown TN, Williams DR, Jackson JS, Neighbors HW, Torres M, Sellers SL, Brown KT, 2000. “Being black and feeling blue”: the mental health consequences of racial discrimination. Race Soc. 2 (2), 117–131. 10.1016/S1090-9524(00)00010-3. [DOI] [Google Scholar]
  6. Brown TN, Turner RJ, Moore TR, 2016. The multidimensionality of health: associations between allostatic load and self-report health measures in a community epidemiologic study. Health Sociol. Rev 25 (3), 272–287. 10.1080/14461242.2016.1184989. [DOI] [Google Scholar]
  7. Chang EC, Chang OD, Rollock D, Lui PP, Watkins AF, Hirsch JK, Jeglic EL, 2019. Hope above racial discrimination and social support in accounting for positive and negative psychological adjustment in african American adults: is “knowing you can do it” as important as “knowing how you can”. Cognit. Ther. Res 43 (2), 399–411. 10.1007/s10608-018-9949-y. [DOI] [Google Scholar]
  8. Cutrona CE, Wallace G, Wesner KA, 2006. Neighborhood characteristics and depression: an examination of stress processes. Curr. Dir. Psychol. Sci 15 (4), 188–192. 10.1111/j.1467-8721.2006.00433.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Erving CL, Thomas CS, Frazier C, 2019. Is the black-white mental health paradox consistent across gender and psychiatric disorders? Am. J. Epidemiol 188 (2), 314–322. 10.1093/aje/kwy224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Gabriel AC, Bell CN, Bowie JV, LaVeist TA, Thorpe RJ, 2020. The role of social support in moderating the relationship between race and hypertension in a low-income, urban, racially integrated community. J. Urban Health 97, 250–259. 10.1007/s11524-020-00421-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Gariépy G, Honkaniemi H, Quesnel-Vallée A, 2016. Social support and protection from depression: systematic review of current findings in western countries. Br. J. Psychiatry 209 (4), 284–293. 10.1192/bjp.bp.115.169094. [DOI] [PubMed] [Google Scholar]
  12. Gayman MD, Brown RL, Cui M, 2011. Depressive symptoms and bodily pain: the role of physical disability and social stress. Stress Health 27 (1), 52–63. 10.1002/smi.1319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gayman MD, Cislo AM, Goidel AR, Ueno K, 2014. SES and race-ethnic differences in the stress-buffering effects of coping resources among young adults. Ethn. Health 19 (2), 198–216. 10.1080/13557858.2013.828827. [DOI] [PubMed] [Google Scholar]
  14. Gerber LM, Sievert LL, 2018. Neighborhood disorder, exposure to violence, and perceived discrimination in relation to symptoms in midlife women. Women’s Midlife Health. 4, 1–11. 10.1186/s40695-018-0043-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gilster ME, 2014. Neighborhood stressors, mastery, and depressive symptoms: racial and ethnic differences in an ecological model of the stress process in chicago. J. Urban Health 91 (4), 690–706. 10.1007/s11524-014-9877-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Giurgescu C, Misra DP, Sealy-Jefferson S, Caldwell CH, Templin TN, Slaughter-Acey JC, Osypuk TL, 2015. The impact of neighborhood quality, perceived stress, and social support on depressive symptoms during pregnancy in african American women. Soc. Sci. Med 130, 172–180. 10.1016/j.socscimed.2015.02.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Griffin ML, Amodeo M, Clay C, Fassler I, Ellis MA, 2006. Racial differences in social support: kin versus friends. Am. J. Orthopsychiatry 76 (3), 374–380. 10.1037/0002-9432.76.3.374. [DOI] [PubMed] [Google Scholar]
  18. House JS, 1983. Work Stress and Social Support. Addison-Wesley Pub. Co. [Google Scholar]
  19. Hunt MO, Jackson PB, Powell B, Steelman LC, 2000. Color-blind: the treatment of race and ethnicity in social psychology. Soc. Psychol. Q 63 (4), 352. 10.2307/2695845. [DOI] [Google Scholar]
  20. Kim J., 2010. Neighborhood disadvantage and mental health: the role of neighborhood disorder and social relationships. Soc. Sci. Res 39 (2), 260–271. 10.1016/j.ssresearch.2009.08.007. [DOI] [Google Scholar]
  21. Kruger DJ, Reischl TM, Gee GC, 2007. Neighborhood social conditions mediate the association between physical deterioration and mental health. Am. J. Community Psychol 40 (3–4), 261–271. 10.1007/s10464-007-9139-7. [DOI] [PubMed] [Google Scholar]
  22. Latkin CA, Curry AD, 2003. Stressful neighborhoods and depression: a prospective study of the impact of neighborhood disorder. J. Health Soc. Behav 44 (1), 34–44. [PubMed] [Google Scholar]
  23. Lee MJ, Liechty JM, 2015. Longitudinal associations between immigrant ethnic density, neighborhood processes, and latino immigrant youth depression. J. Immigr. Minority Health 17 (4), 983–991. 10.1007/s10903-014-0029-4. [DOI] [PubMed] [Google Scholar]
  24. Li SD, 2011. Testing mediation using multiple regression and structural equation modeling analyses in secondary data. Eval. Rev 35 (3), 240–268. 10.1177/0193841x11412069. [DOI] [PubMed] [Google Scholar]
  25. Lin J, Thompson MP, Kaslow NJ, 2009. The mediating role of social support in the community environment-psychological distress link among low-income african American women. J. Community Psychol 37 (4), 459–470. 10.1002/jcop.20307. [DOI] [Google Scholar]
  26. Lincoln KD, Chatters LM, Taylor RJ, 2005. Social support, traumatic events, and depressive symptoms among african Americans. J. Marriage Fam 67 (3), 754–766. 10.1111/j.1741-3737.2005.00167.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lorenc T, Petticrew M, Whitehead M, Neary D, Clayton S, Wright K, Thomson H, Cummins S, Sowden A, Renton A, 2013. Fear of crime and the environment: systematic review of UK qualitative evidence. BMC Public Health 13 (1), 496. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. MacKinnon DP, Lockwood CM, Williams J, 2004. Confidence limits for the indirect effect: distribution of the product and resampling methods. Multivariate Behav. Res 39 (1), 99–128. 10.1207/s15327906mbr3901_4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Mair C, Roux AVD, Morenoff JD, 2010. Neighborhood stressors and social support as predictors of depressive symptoms in the Chicago community adult health study. Health Place 16 (5), 811–819. 10.1016/j.healthplace.2010.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Mathis A, Rooks R, Kruger D, 2015. Neighborhood environment and self-rated health among urban older adults. Gerontology and Geriatric Medicine 1, 2333721415607314. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Mendez DD, Hogan VK, Culhane JF, 2014. Institutional racism, neighborhood factors, stress, and preterm birth. Ethn. Health 19 (5), 479–499. 10.1080/13557858.2013.846300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Moore KA, Hirsch JA, August C, Mair C, Sanchez BN, Roux AVD, 2016. Neighborhood social resources and depressive symptoms: longitudinal results from the multi-ethnic study of atherosclerosis. J. Urban Health 93 (3), 572–588. 10.1007/s11524-016-0042-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Mouzon DM, 2013. Can family relationships explain the race paradox in mental health? J. Marriage Fam 75 (2), 470–485. 10.1111/jomf.12006. [DOI] [Google Scholar]
  34. Mujahid MS, Roux AVD, Cooper RC, Shea S, Williams DR, 2011. Neighborhood stressors and race/ethnic differences in hypertension prevalence (the multi-ethnic study of atherosclerosis). Am. J. Hypertens 24 (2), 187–193. 10.1038/ajh.2010.200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Nam CB, Boyd M, 2004. Occupational status in 2000: over a century of census-based measurement. Popul. Res. Pol. Rev 23, 327–358. [Google Scholar]
  36. Osypuk TL, Galea S, McArdle N, Acevedo-Garcia D, 2009. Quantifying separate and unequal: racial-ethnic distributions of neighborhood poverty in metropolitan America. Urban Aff. Rev 45 (1), 25–65. 10.1177/1078087408331119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Park C, Nam S, Redeker NS, Shebl FM, Dixon J, Jung TH, Whittemore R, 2019. The effects of acculturation and environment on lifestyle behaviors in Korean immigrants: the mediating role of acculturative stress and body image discrepancy. Ethn. Health 27 (1), 157–173. 10.1080/13557858.2019.1658182. [DOI] [PubMed] [Google Scholar]
  38. Pastrana A Jr.. 2016. It takes a family: an examination of outness among black LGBT people in the United States. J. Fam. Issues 37(6), 765–788. doi: 10.1177_0192513X14530971. [Google Scholar]
  39. Pickover AM, Bhimji J, Sun S, Evans A, Allbaugh LJ, Dunn SE, Kaslow N, 2018. Neighborhood disorder, social support, and outcomes among violence-exposed african American women. J. Interpers Violence 36 (7–8), NP3716–NP3737. 10.1177/0886260518779599. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Pollock ED, Kazman JB, Deuster P, 2015. Family functioning and stress in african American families. J. Black Psychol 41 (2), 144–169. 10.1177/0095798413520451. [DOI] [Google Scholar]
  41. Radloff LS, 1977. The ces-D scale: a self-report depression scale for research in the general population. Appl. Psychol. Meas 1 (3), 385–401. 10.1177/014662167700100306. [DOI] [Google Scholar]
  42. Rothstein R., 2017. The Color of Law: A Forgotten History of How Our Government Segregated America. Liveright Publishing. [Google Scholar]
  43. Russell DW, Clavél FD, Cutrona CE, Abraham WT, Burzette RG, 2018. Neighborhood racial discrimination and the development of major depression. J. Abnorm. Psychol 127 (2), 150. 10.1037/abn0000336. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Shaw CR, McKay H, 1972. Juvenile Delinquency and Urban Areas. University of Chicago Press, Chicago. [Google Scholar]
  45. Simon J, 2007. Governing through Crime: How the War on Crime Transformed American Democracy and Created a Culture of Fear. Oxford University Press. [Google Scholar]
  46. Stockdale SE, Wells KB, Tang L, Belin TR, Zhang L, Sherbourne CD, 2007. The importance of social context: neighborhood stressors, stress-buffering mechanisms, and alcohol, drug, and mental health disorders. Soc. Sci. Med 65 (9), 1867–1881. 10.1016/j.socscimed.2007.05.045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Taggart T, Sawyer S, Andreou A, Kershaw T, Milburn NG, 2023. “But I Live Here Too”: social-structural stressors, racial discrimination, and resiliency among urban dwelling black emerging adult men. Am. J. Community Psychol 72 (1–2), 48–59. 10.1002/ajcp.12667. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Taylor RJ, Chatters LM, Woodward AT, Brown E, 2013. Racial and ethnic differences in extended family, friendship, fictive kin, and congregational informal support networks. Fam. Relat 62 (4), 609–624. 10.1111/fare.12030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Taylor RJ, Chatters LM, Lincoln KD, Woodward AT, 2017. Church-based exchanges of informal social support among african Americans. Race and Social Problems 9 (1), 53–62. 10.1007/s12552-017-9195-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Thomas CS, 2015. A new look at the black middle class: research trends and challenges. Sociol. Focus 48 (3), 191–207. 10.1080/00380237.2015.1039439. [DOI] [Google Scholar]
  51. Thomas Tobin CS, Erving CL, Barve A, 2021. Race and ses differences in psychosocial resources: implications for social stress theory. Soc. Psychol. Q 84 (1), 1–25. 10.1177/0190272520961379. [DOI] [Google Scholar]
  52. Thomas Tobin CS, 2021. Distinguishing distress from disorder: black-white patterns in the determinants of and links between depressive symptoms and major depression. J. Affect. Disord 279, 510–517. 10.1016/j.jad.2020.10.035. [DOI] [PubMed] [Google Scholar]
  53. Thomas Tobin CS, Huynh J, Farmer HR, Israel Cross R, Barve A, Robinson M, et al. , 2023. Perceived neighborhood racial composition and depressive symptoms among Black Americans across adulthood: evaluating the role of psychosocial risks and resources. J. Aging Health 35 (9), 660–676. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Turner RJ, Marino F, 1994. Social support and social structure: a descriptive epidemiology. J. Health Soc. Behav 35 (3), 193–212. 10.2307/2137276. [DOI] [PubMed] [Google Scholar]
  55. Turner RJ, Avison WR, 2003. Status variations in stress exposure: implications for the interpretation of research on race, socioeconomic status, and gender. J. Health Soc. Behav 44 (4), 488–505. 10.2307/1519795. [DOI] [PubMed] [Google Scholar]
  56. Turner RJ, Brown TN, Hale WB, 2017. Race, socioeconomic position, and physical health:A descriptive analysis. J. Health Soc. Behav 58 (1), 23–36. 10.1177/0022146516687008. [DOI] [PubMed] [Google Scholar]
  57. Upenieks L, Daniels CL, 2022. Unfair treatment by the police and race: is religiosity a protective resource for well-being. Soc. Curr 9 (4), 387–409. 10.1177/23294965211028847. [DOI] [Google Scholar]
  58. Walton QL, Shepard Payne J, 2016. Missing the mark: cultural expressions of depressive symptoms among african-American women and men. Soc. Work. Ment. Health 14 (6), 637–657. 10.1080/15332985.2015.1133470. [DOI] [Google Scholar]
  59. Whitfield KE, Allaire JC, Belue R, Edwards CL, 2008. Are comparisons the answer to understanding behavioral aspects of aging in racial and ethnic groups? J. Gerontol. B Psychol. Sci. Soc. Sci 63 (5), P301–P308. 10.1093/geronb/63.5.p301. [DOI] [PMC free article] [PubMed] [Google Scholar]

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