Abstract
Background
Black/African American men experience a disproportionate burden of type 2 diabetes (T2D) and high exposure to discrimination, yet it is unclear whether social networks buffer discrimination’s effects on mental health. This study tested moderation by network characteristics among Black/African American men with T2D.
Methods
In a cross-sectional online survey (February-June 2024), 1,225 Black/African American men with T2D completed validated measures of everyday discrimination and healthcare discrimination, mental health, and egocentric network inventories elicited via multiple name generators. Network indicators included mean support, percentage of very supportive members, general communication frequency, and diabetes-specific communication frequency. Multiple regressions with interaction terms estimated direct and moderating effects, controlling for demographics.
Results
Both perceived everyday (β = 0.72, 95% CI [0.67, 0.78], p < .001) and healthcare (β = 2.74, 95% CI [2.50, 2.99], p < .001) discrimination were associated with elevated levels of depressive symptoms, anxiety, and stress. Regarding network characteristics, a higher percentage of very supportive network members was associated with better mental health (everyday models: β=-4.97, 95% CI [-7.67, -2.28], p < .001; healthcare models: β=-5.00, 95% CI [-7.79, -2.20], p < .001). More frequent communication was associated with worse mental health: diabetes-specific communication (everyday models: β = 2.53, 95% CI [1.94, 3.13], p < .001; healthcare models: β = 2.28, 95% CI [1.65, 2.92], p < .001) and general communication (everyday models: β = 2.04, 95% CI [1.34, 2.74], p < .001; healthcare models: β = 1.94, 95% CI [1.20, 2.68], p < .001). Modest interactions emerged for everyday discrimination with diabetes-specific communication (β = 0.07, 95% CI [0.03, 0.11], p = .002) and with general communication (β = 0.05, 95% CI [0.00, 0.11], p = .04).
Conclusions
Perceived discrimination was consistently associated with poor mental health. Networks characterized by a higher proportion of very supportive alters were associated with better mental health, whereas greater communication frequency, including diabetes focused exchanges, was associated with poor mental health. These findings indicate that interventions should prioritize improving the quality and cultural responsiveness of support rather than increasing contact volume. Longitudinal studies are warranted to elucidate causal mechanisms and to clarify the content and context of network communication.
Keywords: Type 2 diabetes, Black/African american, Men, Mental health, Discrimination, Social network
Introduction
Type 2 diabetes (T2D) disproportionately affects Black/African American adults, with a prevalence rate of 12.9% compared to 8.9% among non-Hispanic White adults [1]. This 1.4-fold higher prevalence reflects persistent health disparities that extend beyond mere clinical presentation to encompass complex psychosocial factors, including the chronic stress of discrimination experiences [2]. The intersection of diabetes management with racial discrimination creates a particularly challenging context for Black/African American men, who must navigate both the medical demands of chronic illness and the psychological burden of systematic inequities [3, 4].
Discrimination as a social determinant of mental health
Discrimination represents a chronic psychosocial stressor that has been consistently linked to increased risk for depression, anxiety, and psychological distress among racial and ethnic minorities [3–6]. For Black/African American men, discriminatory experiences occur across multiple domains, including healthcare settings, employment contexts, and daily interpersonal interactions [7]. The intersection of racial discrimination with the challenges of managing a chronic illness creates a particularly complex stress environment that can undermine both physical and mental health outcomes [3, 4, 7]. The chronic nature of discriminatory stressors can lead to sustained activation of stress response systems, contributing to increased vulnerability to mood and anxiety disorders [8]. For individuals managing T2D, this additional psychological burden can interfere with self-management behaviors and create a cycle of worsening health outcomes [9].
Healthcare discrimination presents especially concerning implications for Black/African American men with T2D, as it can directly impact engagement with medical care and adherence to treatment recommendations [8, 10, 11]. Research has documented that Black/African American patients frequently report experiences of differential treatment, reduced provider empathy, and communication barriers within healthcare settings [12, 13]. These experiences can create distrust of healthcare systems and reluctance to seek necessary medical care, potentially exacerbating diabetes complications and associated psychological distress [14].
Social networks and social support as protective factors
Social support theory suggests that supportive social relationships can buffer the negative effects of stressful life experiences on mental health outcomes [15]. Further, Cohen and Wills’ work [16] on the stress-buffering hypothesis showed that social support moderates the stress–health relationship via two mechanisms: main effects that benefit health regardless of stress levels and buffering effects that provide particular protection during high-stress periods. Social support encompasses multiple functional dimensions including esteem support, informational support, social companionship, and instrumental support, each serving distinct roles in health maintenance [16]. For individuals managing T2D, social networks serve as critical resources for disease management through sharing knowledge and experiences, accessing and mediating resources, and providing ongoing support for self-management behaviors [17–20]. The social contexts of diabetes management extend beyond individual self-care to encompass shared activities, collective problem-solving, and mutual accountability that can enhance both physical and mental health outcomes [21].
Social networks among Black/African American men
The social network characteristics of Black/African American men reflect unique cultural patterns and historical experiences that shape relationship formation and utilization [22, 23]. Research has documented that Black/African American communities often maintain distinct patterns of extended family, friendship, and congregational support networks that differ from other racial and ethnic groups [24–26]. These networks frequently provide important resources for coping with adversity and maintaining cultural identity in the face of systemic challenges [26]. However, traditional masculine norms and cultural expectations can create barriers to effective utilization of social support resources among Black/African American men [8]. Research has shown that masculine identity concerns often conflict with help-seeking behaviors and emotional expression, potentially limiting the protective benefits of social relationships [11]. For Black/African American men with T2D, these dynamics may be further complicated by the stigma associated with chronic illness and the challenges of managing disease-related limitations [21]. The need to maintain independence and strength while managing a condition that requires ongoing self-care and medical monitoring can create tension within social relationships and limit opportunities for effective support utilization [27].
Gaps in current knowledge
Despite growing recognition of the importance of social determinants of health, limited research has examined how social network characteristics specifically influence the relationship between discrimination and mental health outcomes among Black/African American men with chronic illness [7, 9]. Most existing studies have focused on direct effects of social support on health outcomes rather than examining moderation processes that could illuminate how social resources provide protection against specific stressors [7]. Furthermore, the intersection of chronic disease management with discrimination experiences creates unique challenges that may require specialized forms of social support. Traditional support mechanisms may be insufficient for addressing the complex psychological and practical challenges faced by Black/African American men managing T2D while experiencing ongoing discrimination.
The measurement of social support in discrimination research has typically relied on global assessments of support availability or frequency of social contact, potentially missing important nuances in how social networks function during times of identity-based stress. The Network Episode Model suggests that individuals strategically activate different network resources in response to specific challenges, implying that effective support for discrimination-related distress may require particular types of relationships or support behaviors [28]. Social network analysis provides a sophisticated methodological approach for understanding how relationship characteristics influence health outcomes [29, 30]. Unlike traditional measures of social support that focus on global perceptions of support availability, social network analysis examines the structure, composition, and functional aspects of social relationships in detail [30, 31]. This approach can reveal important insights about how different types of relationships contribute to health outcomes and how network characteristics interact with other factors to influence well-being.
Study purpose and hypotheses
The present study examined whether social network characteristics moderate the relationship between discrimination experiences and mental health outcomes among Black/African American men with T2D. Specifically, we investigated whether various measures of social network support and communication patterns buffer the negative effects of everyday discrimination and healthcare-specific discrimination on depression, anxiety, stress, and overall psychological distress. Based on stress-buffering models of social support, we hypothesized that social network characteristics would moderate the relationship between discrimination and mental health outcomes, such that individuals with more supportive and engaged social networks would demonstrate weaker associations between discrimination experiences and psychological distress. We examined multiple operationalizations of social network support, including mean levels of support across network members, the percentage of highly supportive network members, and the frequency of both general and diabetes-specific communication within social networks. This research addresses critical gaps in understanding how social determinants of health interact to influence psychological well-being among Black/African American men with chronic illness.
Methods
Study design and participants
This cross-sectional study employed an online survey methodology to examine the moderating effects of social network characteristics on the relationship between discrimination experiences and mental health outcomes among Black/African American men with T2D. Data collection occurred between February and June 2024 using a web-based survey platform administered through Cloud Research, which provided access to a United States based sample of participants meeting the specified inclusion criteria. The study sample consisted of 1,225 Black/African American men living with T2D across the United States. Participants were recruited through Cloud Research, an online research platform that maintains panels of pre-registered participants who have agreed to participate in survey research. This recruitment approach enabled targeted selection of respondents who met the specific demographic and health criteria while ensuring participant anonymity to the research team. Inclusion criteria for study participation were: self-identification as Black or African American; male gender identity; age 21 years or older; self-reported medical diagnosis of T2D; and current residence in the United States.
Potential participants were directed to an internet-based Qualtrics survey through Cloud Research recruitment procedures. All participants viewed an Institutional Review Board (IRB)-approved informed consent sheet prior to beginning the survey, which detailed the study purpose, procedures, potential risks and benefits, and participants’ rights. Participation was entirely voluntary, and respondents could withdraw at any time without penalty. To ensure data quality and response validity, three attention check questions were embedded throughout the survey, and participants were required to pass all validity checks to be included in the final analytical sample. This study was approved by the referent IRB prior to data collection initiation and in compliance with the Helsinki Declaration.
Measures
Discrimination experiences
To evaluate experiences of everyday discrimination, the survey employed the Expanded Everyday Discrimination Scale (EDS), a widely recognized instrument that measures the frequency of routine discriminatory experiences encountered by racial and ethnic minority groups [32]. The EDS consists of 10 items (rated on a six-point Likert scale from “never” to “almost every day”) capturing respondents’ experiences of mistreatment in everyday life such as “you are treated with less respect than other people are.” Scale scores are calculated by summing responses across all items, which produces a score between 10 and 60, with higher scores indicating greater perception of discrimination in everyday settings. The EDS has demonstrated strong psychometric properties, with Cronbach’s alpha values ranging from 0.80 to 0.88 [32, 33].
Healthcare discrimination was evaluated using an adapted Discrimination in Medical Settings (DMS) Scale [34] with factors established in the Diabetes Care [35]. This adapted scale consisted of three items measuring the frequency (five-point Likert scale from “never” to “always”) of participants experiencing or perceiving discriminatory experiences in medical contexts. For example, one item asked, “How often are you treated with less respect than other people when you go to a doctor’s office or other health care provider?” Scale scores were calculated by summing responses across all items, which produces a score between 3 and 15, with higher scores indicating greater perception of discrimination in healthcare settings. The original DMS scale showed strong internal consistency, Cronbach’s alpha of 0.89 [34].
Social network characteristics
Social network characteristics were assessed using a comprehensive multiple name generator approach enabling detailed assessment of participants’ personal support networks by asking them to identify individuals who provide advice, serve as confidants, offer practical support, and potentially create challenges for diabetes management [18, 31]. Participants could list the same person across multiple support categories when applicable, resulting in a comprehensive roster of people who may impact their life in regard to diabetes management. For each person identified in their social network, participants provided detailed information about relationship characteristics and interaction patterns. Support quality was evaluated through multiple dimensions to capture the nuanced ways that social networks provide assistance [18]. Participants rated the supportiveness of each network member specific to diabetes management using a four-point scale ranging from “not at all supportive” to “very supportive.” Communication patterns within social networks were assessed through measures of both general and diabetes-specific interaction frequency. General communication frequency was measured using a six-point scale ranging from “several times daily” to “never,” providing information about the overall intensity of social relationships. Diabetes-specific communication frequency was assessed using a four-point scale to determine how often participants discussed diabetes-related topics with each network member, offering insight into disease-focused social support processes. Network-level variables were calculated by aggregating individual relationship data across all network members for each participant [18, 31]. These network composition variables have been associated with positive T2D health outcomes in previous studies [36–39] and included mean levels of support across the network, the proportion of highly supportive network members, and average communication frequencies for both general and diabetes-specific interactions.
Mental health outcomes
Mental health symptoms were assessed using the Depression, Anxiety, and Stress Scales (DASS-21), a widely validated instrument that evaluates psychological distress across three distinct but related domains [40, 41]. Depression subscale measures symptoms such as dysphoria, hopelessness, devaluation of life, self-deprecation, lack of interest, and anhedonia. The anxiety subscale assesses autonomic arousal, situational anxiety, and subjective experience of anxious affect. Stress subscale evaluates difficulty relaxing, nervous arousal, irritability, and impatience. Each subscale contains seven items rated on a four-point Likert scale reflecting the presence and severity of symptoms over the past week. Response options range from zero (“did not apply to me at all”) to three (“applied to me very much” or “most of the time”). Subscale scores were calculated by summing individual item responses, with possible scores ranging from zero to 21 for each domain. Higher scores indicate greater symptom severity within each mental health domain. A composite mental health score was created by summing all DASS-21 items to provide an overall measure of psychological distress. This approach enabled examination of both domain-specific effects and global mental health outcomes, providing comprehensive assessment of the psychological impact of discrimination experiences.
Demographic and health characteristics
Comprehensive demographic information was collected to characterize the study sample. Demographic information collected included age, educational attainment, employment, annual household income, and self-reported height and weight which was used to calculate body mass index.
Data analysis
To test whether social network characteristics moderated the association between everyday discrimination and mental health, we estimated a series of multiple linear regression models with interaction terms. All continuous predictors (everyday discrimination and social network variables) were mean-centered prior to analysis. In the first step, the mental health outcome was regressed on everyday discrimination. In the second step, one social network variable (e.g., diabetes-specific communication frequency, general health communication, or perceived network support) was added to the model. In the third step, we included the product term between everyday discrimination and the given social network variable (everyday discrimination x social network variable) to evaluate moderation. Significant interaction effects would indicate that social network characteristics moderate the relationship between discrimination and mental health outcomes. For interaction terms, the coefficient reflects the change in the association between discrimination and mental health per one-unit increase in the social network variable.
All continuous predictors were mean-centered prior to forming interaction terms. Thus, the main effect coefficients for discrimination and social network variables can be interpreted as the association with mental health when the interacting variable is at its sample mean. Unstandardized beta coefficients represent the expected change in the mental health score (e.g., DASS total) associated with a one-unit increase in the predictor (e.g., discrimination score). Statistical significance was evaluated using an alpha level of 0.05 for all analyses. Effect sizes were calculated and interpreted according to established conventions (approximately 0.10 = small, 0.30 = medium, 0.50 = large) [42] to determine the practical significance of observed relationships. All analyses were conducted using Stata version 29.0.
Results
Descriptive results
The study sample consisted of 1,225 Black/African American men with an average age of 41.9 years (SD = 14.5) and a mean Body Mass Index of 31.0 (SD = 9.2). Participants’ networks included 5.8 individuals on average (SD = 4.3). Regarding social network interactions, participants communicated with their network members an average of 3.2 times per week (SD = 1.0), while diabetes-specific conversations occurred less frequently at 2.4 times per week (SD = 1.2). Most network members were perceived as highly supportive, with 64.8% described as “very supportive” of participants’ diabetes management efforts. Overall social network support was rated favorably, with a mean score of 3.6 on a 4-point scale (SD = 0.6). Participants reported everyday discrimination scores averaging 25.0 (SD = 13.2) indicating experiencing these aspects between less than once a year and a few times a year. Participants reported healthcare discrimination scores averaging 5.9 (SD = 3.1), suggesting participants rarely felt discrimination in medical settings. According to DASS-21 reporting guidelines, participants reported mild depressive symptoms (M = 6.4; SD = 5.8), anxiety (M = 6.3; SD = 5.3), and stress (M = 6.9; SD = 5.6) as well as total distress (M = 19.6; SD = 15.9). See Table 1 for more information on participant demographics.
Table 1.
Participants’ characteristics
| Variables | Total (n = 1225) % or M (± SD) |
|---|---|
| Age (year) | 41.9 (± 14.5) |
| Education Level | |
| Less than High School | 1.6% |
| High School Graduate/GED | 21.5% |
| Some College/Associate’s Degree | 42.9% |
| Bachelor’s degree | 25.4% |
| Master’s/Doctoral degree | 8.5% |
| Marital Status | |
| Married/Partnered | 61.1% |
| Never Married | 27.6% |
| Divorced/Separated | 8.8% |
| Widowed | 2.5% |
| Employment | |
| A student | 1.9% |
| Employed | 78.2% |
| Disabled | 4.4% |
| Retired | 9.7% |
| Not Employed | 5.9% |
| Rurality | |
| Rural | 11.1% |
| Suburban | 36.1% |
| Urban | 52.4% |
| Other | 0.3% |
| Household Income | |
| Less than $24,999 | 11.4% |
| $25,000–49,999 | 26.4% |
| $50,000–74,999 | 24.7% |
| $75,00099,999 | 18.2% |
| $100,000124,999 | 8.9% |
| $125,000–149,999 | 4.2% |
| More than $150,000 | 6.0% |
| Number of Chronic Conditions | 2.5 (± 1.9) |
| Body Mass Index (kg/m 2) | 31.0 (± 9.6) |
| Network Interaction | |
| Mean communication frequency | 3.2 (± 1.0) |
| Mean T2D communication frequency | 2.4 (± 1.2) |
| Percent very supportive | 64.8% (± 35.2%) |
| Mean social network support | 3.6 (± 0.6) |
| Mental Health | |
| Depressive Symptoms | 6.4 (± 5.8) |
| Anxiety | 6.3 (± 5.3) |
| Stress | 6.9 (± 5.6) |
| Total | 19.6 (± 15.9) |
| Discrimination | |
| Everyday Discrimination | 25.0 (± 13.2) |
| Healthcare Discrimination | 5.9 (± 3.1) |
M Mean, SD Standard Deviation, GED General Educational Development, T2D Type 2 Diabetes
Direct effects of discrimination on mental health outcomes
Across all moderation analyses conducted, discrimination demonstrated robust and consistent positive associations with mental health symptoms, regardless of the specific discrimination measure or mental health outcome examined. Everyday discrimination showed direct effects on overall psychological distress (β ranging from 0.72 to 0.74, all p <.001), with similarly robust associations observed for individual symptom domains. Depressive symptoms consistently showed positive associations with discrimination (β ranging from 0.25 to 0.35, all p <.001), as did anxiety symptoms (β ranging from 0.21 to 0.29, all p <.001) and stress symptoms (β ranging from 0.24 to 0.32, all p <.001). Healthcare discrimination exhibited statistically significant direct effects, with particularly pronounced associations with overall mental health symptoms. Specifically, each additional discrimination experience was associated with a 2.74-point increase in overall mental health symptoms (95% CI [2.50, 2.99], p <.001).
Social network characteristic moderation of discrimination effects on mental health
The moderation analyses examined whether various social network characteristics buffer the relationship between discrimination and mental health outcomes among Black/African American men with T2D. Four distinct social network characteristics were tested as potential moderators: mean social network support, percentage of very supportive network members, diabetes-specific communication frequency, and general communication frequency. Each moderator was examined across multiple discrimination-mental health pathways. A summary of the moderation analyses can be found in Table 2. A forest plot of these analyses sorted by mental health outcome can be found in Fig. 1.
Table 2.
Results of social network characteristics moderating discrimination’s association with psychological distress
| Social Network Moderator | Discrimination Type | Mental Health Outcome | Discrimination → Mental Health | Social Network → Mental Health | Interaction Effect | ||||
|---|---|---|---|---|---|---|---|---|---|
| β (95% CI) | p | β (95% CI) | p | β (95% CI) | p | ||||
| Mean Social Network Support | Everyday Discrimination | DASS Total | 0.72 (0.67, 0.78) | < 0.001 | −0.94 (−2.27, 0.39) | 0.17 | −0.06 (−0.17, 0.04) | 0.26 | |
| Mean Social Network Support | Everyday Discrimination | DASS Depression | 0.26 (0.22, 0.30) | < 0.001 | −0.40 (−1.22, 0.42) | 0.11 | −0.02 (−0.06, 0.02) | 0.29 | |
| Mean Social Network Support | Everyday Discrimination | DASS Anxiety | 0.22 (0.18, 0.25) | < 0.001 | −0.17 (−0.88, 0.53) | 0.46 | −0.02 (−0.06, 0.02) | 0.26 | |
| Mean Social Network Support | Everyday Discrimination | DASS Stress | 0.25 (0.21, 0.29) | < 0.001 | −0.36 (−1.10, 0.38) | 0.14 | −0.02 (−0.06, 0.02) | 0.30 | |
| Very Supportive Members | Everyday Discrimination | DASS Total | 0.70 (0.59, 0.82) | < 0.001 | −4.97 (−8.50, −1.43) | < 0.001 | −0.08 (−0.28, 0.13) | 0.47 | |
| Very Supportive Members | Everyday Discrimination | DASS Depression | 0.25 (0.21, 0.30) | < 0.001 | −1.67 (−2.96, −0.37) | 0.001 | −0.04 (−0.12, 0.04) | 0.29 | |
| Very Supportive Members | Everyday Discrimination | DASS Anxiety | 0.21 (0.17, 0.25) | < 0.001 | −1.33 (−2.47, −0.19) | < 0.01 | −0.03 (−0.10, 0.05) | 0.47 | |
| Very Supportive Members | Everyday Discrimination | DASS Stress | 0.24 (0.20, 0.29) | < 0.001 | −1.93 (−3.16, −0.69) | < 0.001 | −0.01 (−0.08, 0.06) | 0.78 | |
| Mean Diabetes-Specific Talk | Everyday Discrimination | DASS Total | 0.70 (0.57, 0.83) | < 0.001 | 2.53 (0.99, 4.06) | < 0.01 | 0.07 (0.03, 0.11) | < 0.01 | |
| Mean Diabetes-Specific Talk | Everyday Discrimination | DASS Depression | 0.25 (0.21, 0.30) | < 0.001 | 0.75 (0.40, 1.11) | < 0.001 | 0.02 (0.01, 0.04) | < 0.01 | |
| Mean Diabetes-Specific Talk | Everyday Discrimination | DASS Anxiety | 0.21 (0.17, 0.25) | < 0.001 | 0.95 (0.62, 1.29) | < 0.001 | 0.03 (0.01, 0.04) | < 0.01 | |
| Mean Diabetes-Specific Talk | Everyday Discrimination | DASS Stress | 0.24 (0.20, 0.29) | < 0.001 | 0.85 (0.48, 1.21) | < 0.001 | 0.02 (0.00, 0.03) | 0.03 | |
| Mean General Talk Frequency | Everyday Discrimination | DASS Total | 0.71 (0.57, 0.85) | < 0.001 | 2.04 (0.62, 3.46) | < 0.001 | 0.05 (0.00, 0.11) | 0.04 | |
| Mean General Talk Frequency | Everyday Discrimination | DASS Depression | 0.25 (0.21, 0.30) | < 0.001 | 0.65 (0.32, 0.99) | < 0.001 | 0.02 (0.00, 0.04) | 0.03 | |
| Mean General Talk Frequency | Everyday Discrimination | DASS Anxiety | 0.21 (0.17, 0.25) | < 0.001 | 0.66 (0.35, 0.97) | < 0.001 | 0.02 (0.00, 0.04) | 0.04 | |
| Mean General Talk Frequency | Everyday Discrimination | DASS Stress | 0.24 (0.20, 0.29) | < 0.001 | 0.72 (0.39, 1.05) | < 0.001 | 0.01 (−0.00, 0.03) | 0.15 | |
| Mean Social Network Support | Healthcare Discrimination | DASS Total | 2.74 (2.08, 3.41) | < 0.001 | −1.17 (−2.58, 0.24) | 0.10 | 0.24 (−0.20, 0.68) | 0.29 | |
| Mean Social Network Support | Healthcare Discrimination | DASS Depression | 0.96 (0.73, 1.19) | < 0.001 | −0.50 (−1.05, 0.05) | 0.06 | 0.10 (−0.07, 0.26) | 0.26 | |
| Mean Social Network Support | Healthcare Discrimination | DASS Anxiety | 0.86 (0.65, 1.06) | < 0.001 | −0.20 (−0.69, 0.28) | 0.40 | 0.05 (−0.10, 0.20) | 0.52 | |
| Mean Social Network Support | Healthcare Discrimination | DASS Stress | 0.92 (0.70, 1.15) | < 0.001 | −0.47 (−0.99, 0.05) | 0.06 | 0.10 (−0.06, 0.26) | 0.21 | |
| Very Supportive Members | Healthcare Discrimination | DASS Total | 2.66 (2.13, 3.19) | < 0.001 | −5.00 (−8.05, −1.95) | < 0.001 | 0.58 (−0.31, 1.47) | 0.20 | |
| Very Supportive Members | Healthcare Discrimination | DASS Depression | 0.93 (0.73, 1.13) | < 0.001 | −1.71 (−2.80, −0.62) | 0.001 | 0.17 (−0.17, 0.50) | 0.32 | |
| Very Supportive Members | Healthcare Discrimination | DASS Anxiety | 0.83 (0.65, 1.01) | < 0.001 | −1.31 (−2.27, −0.34) | < 0.01 | 0.17 (−0.13, 0.47) | 0.26 | |
| Very Supportive Members | Healthcare Discrimination | DASS Stress | 0.89 (0.69, 1.09) | < 0.001 | −1.94 (−2.98, −0.90) | < 0.001 | 0.25 (−0.06, 0.57) | 0.11 | |
| Mean Diabetes-Specific Talk | Healthcare Discrimination | DASS Total | 2.59 (2.03, 3.14) | < 0.001 | 2.28 (1.05, 3.51) | < 0.001 | 0.09 (−0.10, 0.27) | 0.36 | |
| Mean Diabetes-Specific Talk | Healthcare Discrimination | DASS Depression | 0.92 (0.71, 1.12) | < 0.001 | 0.67 (0.21, 1.13) | < 0.001 | 0.04 (−0.03, 0.11) | 0.26 | |
| Mean Diabetes-Specific Talk | Healthcare Discrimination | DASS Anxiety | 0.80 (0.62, 0.99) | < 0.001 | 0.87 (0.45, 1.28) | < 0.001 | 0.04 (−0.02, 0.10) | 0.22 | |
| Mean Diabetes-Specific Talk | Healthcare Discrimination | DASS Stress | 0.88 (0.68, 1.07) | < 0.001 | 0.76 (0.33, 1.20) | < 0.001 | 0.01 (−0.06, 0.07) | 0.86 | |
| Mean General Talk Frequency | Healthcare Discrimination | DASS Total | 2.67 (2.07, 3.28) | < 0.001 | 1.94 (0.47, 3.41) | < 0.001 | 0.05 (−0.18, 0.27) | 0.67 | |
| Mean General Talk Frequency | Healthcare Discrimination | DASS Depression | 0.94 (0.72, 1.16) | < 0.001 | 0.63 (0.09, 1.17) | < 0.001 | 0.03 (−0.06, 0.11) | 0.53 | |
| Mean General Talk Frequency | Healthcare Discrimination | DASS Anxiety | 0.83 (0.64, 1.03) | < 0.001 | 0.62 (0.13, 1.12) | < 0.001 | 0.02 (−0.06, 0.09) | 0.68 | |
| Mean General Talk Frequency | Healthcare Discrimination | DASS Stress | 0.90 (0.70, 1.11) | < 0.001 | 0.68 (0.17, 1.20) | < 0.001 | 0.00 (−0.08, 0.08) | 0.97 | |
DASS Depression Anxiety and Stress Scale
Positive coefficient for the association between discrimination and mental health indicates that more discrimination is associated with worse mental health; Negative coefficient for the association between social support and mental health indicates that more social support is associated with better mental health; For interaction terms, a positive coefficient suggests that the protective effect of the moderator (such as social support) is weakened or that the harmful effect of the risk factor (such as discrimination) is amplified
Fig. 1.
Forest Plot Displaying Direct and Moderation Effects of Discrimination and Social Network Characteristics on Mental Health Outcomes among Black Men with Type 2 Diabetes Note: T2D – Type 2 Diabetes; DASS – Depression, Anxiety, and Stress Scale; SN – Social Network
Mean social network support
Direct effects analyses revealed that mean social network support showed consistently negative but often non-significant associations with mental health outcomes. For everyday discrimination models, mean social network support demonstrated non-significant direct effects on overall mental health symptoms (β = − 0.94, 95% CI [−2.27, 0.39], p =.17), depression symptoms (β = − 0.40, 95% CI [−0.90, 0.09], p =.11), anxiety symptoms (β = − 0.17, 95% CI [−0.63, 0.29], p =.46), and stress symptoms (β = − 0.36, 95% CI [−0.83, 0.11], p =.14). Healthcare discrimination models showed similar patterns, with mean social network support not significantly associated with overall mental health (β = −1.17, 95% CI [−2.55, 0.22], p =.10), depression (β = − 0.50, 95% CI [−1.02, 0.07], p =.06), anxiety (β = − 0.20, 95% CI [−0.67, 0.27], p =.40), or stress symptoms (β = − 0.47, 95% CI [−0.96, 0.04], p =.06). Further, all interaction effects were similarly not statistically significant for both everyday and healthcare discrimination.
Very supportive network members
The percentage of very supportive network members was examined as an alternative operationalization of social network support quality. For everyday discrimination models, very supportive network members demonstrated significant negative direct effects on overall mental health symptoms (β = −4.97, 95% CI [−7.67, −2.28], p <.001), depression symptoms (β = −1.67, 95% CI [−2.68, −0.66], p =.001), anxiety symptoms (β = −1.33, 95% CI [−2.26, −0.40], p =.005), and stress symptoms (β = −1.93, 95% CI [−2.88, −0.98], p <.001). Healthcare discrimination models revealed similar protective direct effects, with very supportive network members showing significant negative associations with overall symptoms (β = −5.00, 95% CI [−7.79, −2.20], p <.001), depression symptoms (β = −1.71, 95% CI [−2.68, −0.65], p =.001), anxiety symptoms (β = −1.31, 95% CI [−2.25, −0.36], p =.005), and stress symptoms (β = −1.94, 95% CI [−2.93, −0.98], p <.001). Stated simply, having a higher percentage of very supportive network members was associated with reduced psychological distress across all symptom domains. However, the percentage of very supportive network members did not significantly (statistically) moderate discrimination-mental health relationships in this study.
Diabetes-specific communication frequency
Direct effects of diabetes-specific communication frequency revealed significant positive associations with mental health outcomes in everyday discrimination models. These positive associations were observed for overall mental health symptoms (β = 2.53, 95% CI [1.94, 3.13], p <.001), depression symptoms (β = 0.75, 95% CI [0.52, 0.97], p <.001), anxiety symptoms (β = 0.95, 95% CI [0.65, 1.15], p <.001), and stress symptoms (β = 0.85, 95% CI [0.53, 1.06], p <.001). Healthcare discrimination models showed similar patterns, with diabetes-specific communication demonstrating positive direct effects on overall symptoms (β = 2.28, 95% CI [1.65, 2.92], p <.001), depression symptoms (β = 0.67, 95% CI [0.43, 0.91], p <.001), anxiety symptoms (β = 0.87, 95% CI [0.53, 1.08], p <.001), and stress symptoms (β = 0.76, 95% CI [0.53, 1.06], p <.001). Stated simply, increased frequency of diabetes-related discussions within social networks is associated with higher levels of psychological distress.
Diabetes-specific communication frequency within social networks yielded significant moderation effects for everyday discrimination, though not for healthcare discrimination. For everyday discrimination pathways, the interaction effects, while statistically significant, demonstrated modest effect sizes. The moderation was significant for overall mental health symptoms (β = 0.07, 95% CI [0.03, 0.11], p =.002), depression symptoms (β = 0.02, 95% CI [0.01, 0.04], p =.006), anxiety symptoms (β = 0.03, 95% CI [0.01, 0.04], p <.001), and stress symptoms (β = 0.02, 95% CI [0.00, 0.03], p =.03). Specifically, for each one-unit increase in discrimination experiences, individuals who engaged in more frequent diabetes-focused conversations showed smaller increases in mental health symptoms, approximately 0.07 points less increase in overall symptoms, 0.02 points less in depression, 0.03 points less in anxiety, and 0.02 points less in stress. Healthcare discrimination analyses with diabetes-specific communication frequency revealed no significant moderation effects across any mental health outcomes: overall mental health symptoms (β = 0.09, 95% CI [−0.10, 0.27], p =.36), depression symptoms (β = 0.04, 95% CI [−0.03, 0.11], p =.26), anxiety symptoms (β = 0.04, 95% CI [−0.03, 0.11], p =.22), or stress symptoms (β = 0.01, 95% CI [−0.06, 0.07], p =.86).
General communication frequency
Direct effects of general communication frequency within social networks showed significant positive associations with mental health outcomes across everyday discrimination models: overall mental health symptoms (β = 2.04, 95% CI [1.34, 2.74], p <.001), depression symptoms (β = 0.65, 95% CI [0.39, 0.91], p <.001), anxiety symptoms (β = 0.66, 95% CI [0.42, 0.90], p <.001), and stress symptoms (β = 0.72, 95% CI [0.47, 0.94], p <.001). Healthcare discrimination models demonstrated similar patterns, with general communication frequency showing positive direct effects on overall symptoms (β = 1.94, 95% CI [1.20, 2.68], p <.001), depression symptoms (β = 0.63, 95% CI [0.35, 0.91], p <.001), anxiety symptoms (β = 0.62, 95% CI [0.37, 0.88], p <.001), and stress symptoms (β = 0.68, 95% CI [0.42, 0.94], p <.001). These findings suggest that Black/African American men experiencing greater psychological distress may also reach out more frequently to their social networks for support, or alternatively, that more frequent communication occurs within networks where there may be elevated mental health concerns.
General communication frequency showed statistically significant but modest moderation effects for everyday discrimination. For each one-unit increase in discrimination experiences, individuals who communicated more frequently with their networks showed slightly smaller increases in mental health symptoms: overall symptoms (β = 0.05, 95% CI [0.00, 0.11], p =.04), depression (β = 0.02, 95% CI [0.00, 0.04], p =.03), and anxiety (β = 0.02, 95% CI [0.00, 0.04], p =.04), though not stress (β = 0.01, 95% CI [−0.01, 0.03], p =.15). While these interactions indicate that maintaining frequent contact with one’s social network may provide modest protection against everyday discrimination’s psychological toll, the small effect sizes suggest this buffering is limited. For healthcare discrimination, general communication frequency provided no protective benefit: overall symptoms (β = 0.05, 95% CI [−0.18, 0.27], p =.67), depression (β = 0.03, 95% CI [−0.06, 0.11], p =.53), anxiety (β = 0.02, 95% CI [−0.06, 0.09], p =.68), and stress (β = 0.00, 95% CI [−0.08, 0.08], p =.97). This pattern suggests that discrimination experienced in healthcare settings may be particularly pernicious, with the mental health consequences not meaningfully mitigated by general communication.
Discussion
The present study examined whether social network characteristics moderate the relationship between discrimination experiences and mental health outcomes among Black/African American men with T2D. While discrimination demonstrated robust and consistent associations with increased psychological distress across all models, the hypothesized buffering effects of social networks were largely not supported. These findings provide important insights into the complex relationships between social determinants of health and psychological well-being in this population, while highlighting critical areas for methodological refinement in social network research.
Discrimination effects on mental health
The consistent and robust associations between discrimination and mental health symptoms observed in this study align with extensive previous research documenting the psychological toll of discriminatory experiences among racial and ethnic minorities [4, 7, 9]. Both everyday discrimination and healthcare-specific discrimination demonstrated positive associations with depression, anxiety, stress, and overall psychological distress. These findings are particularly concerning given the dual burden faced by Black/African American men managing T2D, who must navigate both chronic disease management challenges and systematic discrimination across multiple life domains [8, 10]. The magnitude of these effects underscores the critical need for interventions that address discrimination as a fundamental determinant of mental health disparities in this population.
Limited evidence for social network moderation
Contrary to expectations based on stress-buffering models of social support [15, 16] social network characteristics generally failed to demonstrate significant moderating effects on discrimination-mental health relationships. Neither mean social network support nor the percentage of very supportive network members showed meaningful buffering effects against either everyday or healthcare discrimination. These null findings warrant careful interpretation, as they may reflect limitations in how social support processes are conceptualized and measured rather than evidence that social networks are ineffective in protecting against discrimination-related distress. The absence of significant moderation effects aligns with critiques of traditional social support measurement approaches that focus primarily on structural characteristics or global perceptions of support availability [28, 31, 43]. Contemporary social network theory suggests that the activation and utilization of social resources may be highly context-specific, varying based on the nature of the stressor, the characteristics of network members, and the specific types of support needed [44, 45]. The discrimination experiences examined in this study may require particular forms of support that were not adequately captured by the measures employed.
Communication patterns
Perhaps most intriguing were the findings regarding communication frequency within social networks. Both diabetes-specific communication and general communication frequency showed positive associations with mental health symptoms, suggesting that individuals experiencing greater psychological distress engage in more frequent social communication. These patterns may reflect several important dynamics. First, individuals experiencing greater discrimination-related distress may actively seek more social contact as a coping mechanism, resulting in higher communication frequency among those with worse mental health outcomes [9]. Second, the content and quality of these communications may be more critical than frequency alone. Previous research has demonstrated that social interactions can be sources of both support and additional stress, particularly when they involve discussions of sensitive topics such as discrimination or chronic illness management [17, 46]. The positive association between diabetes-specific communication frequency and psychological distress may indicate that such conversations can center on disease-related challenges, complications, or frustrations rather than providing emotional support or practical assistance [47]. This interpretation aligns with research suggesting that disease-focused social interactions can sometimes reinforce negative emotions or create additional burden when they emphasize limitations rather than empowerment [47].
Cultural and gender considerations
The lack of significant moderation effects may also reflect cultural and gender-specific factors that influence how Black/African American men utilize social support resources. Previous research has documented that traditional masculine norms often discourage help-seeking and emotional expression among men, particularly in response to stressors that may be perceived as threats to masculinity or competence [8, 11, 22, 23]. For Black/African American men, these dynamics may be further complicated by cultural expectations regarding strength and resilience in the face of adversity [10, 27]. The experience of discrimination may be particularly challenging to address through traditional social support mechanisms, as it involves identity-based threats that can undermine fundamental assumptions about fairness and belonging in society. Network members may lack the knowledge, experience, or resources necessary to provide effective support for discrimination-related distress, particularly if they have not experienced similar challenges themselves. Further, the gender and race/ethnicity of network members were not recorded in this study which may have influenced activation of network resources in the face of discrimination.
Implications for social support measurement and theory
These findings highlight critical gaps in current approaches to measuring and understanding social support processes, particularly in the context of discrimination experiences among marginalized populations. Future research should continue to consider more sophisticated approaches to characterizing social support processes. The Network Episode Model [28, 48] suggests that effective social support for distress may require network members who possess particular characteristics, such as shared experiences with discrimination, cultural understanding, or specific coping resources. Additionally, the content and quality of social interactions may be more predictive of mental health outcomes than structural characteristics or interaction frequency [28, 48]. Research examining the specific types of conversations that occur within social networks, the emotional tone of these interactions, and the degree to which they provide validation, problem-solving assistance, or emotional regulation support could provide more actionable insights for intervention development [17].
Clinical and intervention implications
These findings suggest that interventions aimed at improving mental health outcomes for Black/African American men experiencing discrimination should move beyond simply encouraging social connection or support-seeking. Instead, programs should focus on enhancing the quality and appropriateness of social support by training network members to provide effective assistance for discrimination-related distress. Clinical evaluations should include more detailed examination of the types of support available to patients and the specific ways that social networks are activated during times of stress. Community-based interventions might focus on developing culturally relevant support resources that specifically address discrimination experiences while respecting cultural values and gender norms. Such programs could include peer support groups, mentorship initiatives, or community education efforts that enhance collective capacity to respond to discrimination-related challenges.
Limitations and future directions
Several limitations should be considered when interpreting these findings. The cross-sectional design prevents determination of causal relationships between social network characteristics and mental health outcomes. Longitudinal research examining how social support processes evolve in response to discrimination experiences would provide valuable insights into the temporal dynamics of these relationships. Further, social desirability and recall bias may play a role in the measurement of social networks, mental health, and discrimination as these measures were self-reported. Future research should employ more comprehensive social network assessment approaches that examine the specific types of support provided, the contexts in which support is offered, and the potential effects of racial and gender concordance within networks. Additionally, the focus on structural and functional aspects of social networks may have overlooked important cultural and community-level factors that influence social support processes among Black/African American men. Research examining the role of cultural institutions, community organizations, and informal support systems in buffering discrimination effects could provide important insights for intervention development. Lastly, as the study population was limited to Black/African American men with T2D, this limits generalizability and created issues determining representativeness of this study sample. The sample described here is likely more well educated and have a higher household income as common with online survey samples [49]. Further, the relatively few discrimination experiences reported in this study may not be representative of the broad population.
Conclusion
While social networks did not demonstrate the expected buffering effects against discrimination-related psychological distress in this study, these findings should not be interpreted as evidence that social support is ineffective for Black/African American men with Type 2 diabetes. Instead, these results highlight the need for more sophisticated approaches to understanding and measuring social support processes that are sensitive to the specific challenges faced by this population. Future research should examine the content, timing, and cultural appropriateness of social support interventions while developing more nuanced theoretical models that account for the complex interplay between discrimination experiences, social resources, and mental health outcomes. Such advances in our understanding of social support mechanisms could inform more effective interventions for reducing mental health disparities among Black/African American men managing chronic illness in the context of ongoing discrimination.
Acknowledgements
Not applicable.
Authors’ contributions
TP conceptualized the study and prepared the draft as well as obtained funds. JHP analyzed the data and edited the draft. MSP helped conceptualize the study and edited the draft. LDS obtained funds for study and edited the draft. MLS conceptualized the study, provided supervision, and edited the draft. All authors read and approved the final manuscript.
Funding
The present study was supported by the National Institute of Minority Health and Health Disparities R21 grant (R21MD019048).
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Texas A&M University Institutional Review Board (#IRB2023-1311 M) and in compliance with the Helsinki Declaration. All participants provided informed consent prior to participation.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.