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. Author manuscript; available in PMC: 2022 Apr 12.
Published in final edited form as: Clin Soc Work J. 2019 Oct 24;48(1):87–98. doi: 10.1007/s10615-019-00725-z

Objective and Subjective Social Isolation and Psychiatric Disorders among African Americans

Ann W Nguyen 1, Robert Joseph Taylor 2, Harry Owen Taylor 3, Linda M Chatters 4
PMCID: PMC9004793  NIHMSID: NIHMS1748603  PMID: 35418715

Abstract

Social isolation is a major problem in the United States that has adverse impacts on health and well-being. However, few studies investigate social isolation among African Americans or the impact of social isolation on psychiatric disorders. This study addresses this gap by investigating the impact of objective (absence of contact with others) and subjective (lacking feelings of closeness to others) social isolation on psychiatric disorders among African Americans. The sample includes 3,570 African Americans from the National Survey of American Life. Regression models were used to test the impact of objective and subjective isolation on 12-month MDD, any 12-month DSM disorder and number of 12-month DSM disorders. Analyses indicated that subjective isolation from family only, friends only, and both groups were associated with greater odds of meeting criteria for 12-month MDD, any 12-month disorder and number of 12-month DSM disorders. However, objective isolation was unrelated to either measure of psychiatric disorder. Study findings indicate that affective characteristics of social isolation (feelings of closeness with family and friends) are more significant for psychiatric disorders than are objective features (social contact). Our discussion notes that the connections between subjective and objective social isolation and psychiatric disorders are complex and potentially reciprocally associated with one another. Clinical practice should focus on both possible associations.

Keywords: social disconnectedness, African American mental health, social relationships, social networks

Social isolation has become a major public health crisis in many industrialized countries, including the United States. This is because social isolation is associated with many negative mental and physical health outcomes including mortality (Alcaraz et al., 2018; Holt-Lunstad, Smith, Baker, Harris, & Stephenson, 2015; Holt-Lunstad, Smith, & Layton, 2010), worse self-rated physical health (Cornwell & Waite, 2009; Coyle & Dugan, 2012; Miyawaki, 2015), and greater cognitive decline and impairment (Shankar, Hamer, McMunn, & Steptoe, 2013; Zunzunegui, Alvarado, Del Ser, & Otero, 2003). Given the numerous physical and mental health problems associated with social isolation, many prominent organizations are dedicated to preventing and mitigating its negative effects. This includes the AARP Foundation (Elder & Retrum, 2012; Li, Jiang, & Zhang, 2019), the American Public Health Association (Klinenberg, 2016), the Robert Wood Johnson Foundation, the National Academies of Engineering, Medicine, and Science (Institute of Medicine, 2014), and the World Health Organization (World Health Organization, 2007). In addition, the American Academy of Social Work and Social Welfare has declared the Eradication of Social Isolation as one of twelve Grand Challenges of Social Work (Lubben, Gironda, Sabbath, Kong, & Johnson, 2015).

The Eradicate Social Isolation Grand Challenge for Social Work (Lubben et al., 2015) clearly and succinctly lays out research that documents the importance of social isolation as a major risk factor for morbidity, mortality and mental disorders. Social isolation is commonly perceived as an issue that mostly concerns older adults. However, the Grand Challenges report documents its impacts on adults of all ages, children, and adolescents and underscores the importance of family and friend support networks and ties for understanding isolation. Despite these contributions, the report does not discuss social isolation among ethnic minorities, especially among African Americans, due to the lack of research in this area. Similarly, given the limited research and attention, the Eradicate Social Isolation Grand Challenge report does not discuss the relationship between social isolation and psychiatric disorders. To address these knowledge gaps, the goal of this paper is to examine the impact of social isolation on psychiatric disorders among African Americans.

Social Support and Social Isolation among African Americans

A long history of research, both ethnographic (Stack, 1972) as well as large scale quantitative (Nguyen, Chatters, & Taylor, 2016) studies, examines the informal social support networks of African Americans. This research generally finds that African Americans have strong and viable support networks comprised of friends, church members and extended family (R. J. Taylor, Chatters, Woodward, & Brown, 2013; R. J. Taylor, Mouzon, Nguyen, & Chatters, 2016). Research on African Americans also indicates that social support from family and friends is protective for depressive symptoms (R. J. Taylor, Chae, Lincoln, & Chatters, 2015), while negative interactions with family members, in the form of arguments and criticisms, are positively associated with mental health problems (R. J. Taylor et al., 2015); suicidal behaviors (Lincoln, Taylor, Chatters, & Joe, 2012) and psychiatric disorders (Lincoln et al., 2010). Similarly, this work indicates that, in some cases, social support protects against mental health problems, while negative interaction with family members is a consistent correlate of depressive symptoms, psychological distress, and psychiatric disorders (R. J. Taylor et al., 2015).

What has been missing from research on African Americans’ social support networks is an explicit focus on social isolation. That is, research that has a focus on the absence of social ties. Only recently has research in this area been conducted. Taylor et al.’s (2016) study of social isolation from extended family and friends among a national sample of African Americans found the vast majority of African Americans (3 out of 4) were not objectively isolated from family and friends. Among those who were isolated, there was a higher likelihood of being objectively isolated from friends than extended family. African American men were significantly more likely to be isolated than their female counterparts (R. J. Taylor, Taylor, et al., 2016).

A recent study of subjective social isolation among older African American, Caribbean Black and non-Hispanic White adults (Chatters, Taylor, Nicklett, & Taylor, 2018) did not find any racial or ethnic differences in the correlates of social isolation. Older men, however, regardless of race/ethnicity, were significantly more likely to be objectively isolated from both family and friends and from family members only than were older women.

Objective and Subjective Social Isolation

Research in this field differentiates between two components of social isolation --objective isolation and subjective isolation. Objective isolation is the tangible and quantifiable lack of connections with other people, while subjective isolation is the perceived lack of closeness between an individual and members of their social network. Objective social isolation is measured by the lack of contact/interaction with social network members, and subjective social isolation is typically measured by the lack of emotional closeness to network members. Although objective and subjective isolation are highly correlated (R. J. Taylor, Taylor, et al., 2016), they are different constructs. For example, a person may regularly attend family events due to their obligatory nature, but not feel emotionally close to their family members. This is consistent with research that finds that individuals are more likely to indicate that they are objectively isolated than subjectively isolated (Chatters et al., 2018; R. J. Taylor, Taylor, et al., 2016). It is important to remember that, overall, the vast majority of Americans are not socially isolated However, those who are socially isolated are at higher risk for serious mental and physical health problems (Lubben et al., 2015).

Psychiatric Disorders among African Americans

Approximately two out of five African Americans have experienced a psychiatric disorder in their lifetime (Breslau et al., 2006). Regarding specific categories of disorders, one in four African Americans have an anxiety disorder, and 16% have a mood disorder (Breslau et al., 2006). Generalized anxiety disorder (GAD), social phobia, posttraumatic stress disorder (PTSD), and panic disorder (PD) are the most prevalent anxiety disorders among African Americans. Lifetime prevalence rates for GAD range from 1.4% to 5.1% (Breslau et al., 2006; Himle, Baser, Taylor, Campbell, & Jackson, 2009), and the 12-month prevalence rate is 1.9% (Grant et al., 2005). Social phobia (4.6%) and PTSD (3.8%) are the second and third most common anxiety disorders among African Americans, respectively (Himle et al., 2009). Among mood disorders, major depressive disorder (MDD) is the most prevalent disorder. Lifetime prevalence rates for MDD range from 10.4% to 10.8% (Breslau et al., 2006; Williams et al., 2007), and the 12-month prevalence for MDD is 5.9% (Williams et al., 2007). Psychiatric disorders are associated with a range of functional impairments and high levels of disability (Hoffman, Dukes, & Wittchen, 2008; Revicki et al., 2012; Wittchen, 2002). For example, individuals with an anxiety disorder often report work and social impairments, role functioning impairments, more disability days, decreased work productivity, and lose on average 4.6 workdays per month due to disability (Hoffman et al., 2008; Revicki et al., 2012; Wittchen, 2002). According to the World Health Organization (WHO), depression is the leading cause of disability worldwide (WHO, 2018) and is associated with role functioning impairments, family, work and social impairments, and impaired memory (Kessler, 2012; Watkins & Teasdale, 2001). Although the prevalence rates of most psychiatric disorders among African Americans are similar to those of the general population and lower than those of the non-Hispanic White population, there are racial disparities in the course of illness, service use for mental health problems, and quality of care (APA, 2017; Breslau et al., 2006; Himle et al., 2009). For instance, depression is more persistent and severe among African Americans than Whites (Williams et al., 2007), and anxiety disorders are more persistent among African Americans than Whites (Breslau, Kendler, Su, Gaxiola-Aguilar, & Kessler, 2005). Moreover, only one third of African Americans who need mental health care receive it, and the mental health care that African Americans receive is often poorer in quality (APA, 2017). Those who do receive mental health care are less likely to receive guideline-consistent care, evidence-based care, and culturally competent care than are Whites (Alegría et al., 2008; APA, 2017).

Culturally competent care/practice is critical for addressing racial disparities in professional mental health service use and quality of mental healthcare. According to the National Association of Social Workers (NASW), culturally competent social work practice is characterized by “a heightened consciousness of how culturally diverse populations experience their uniqueness and deal with their differences and similarities within a larger social context” (NASW, 2015). The cultural competence framework developed by Lum (2011) indicates that culturally competent practice encompasses three distinct domains—cultural awareness, knowledge acquisition, and skill development. Cultural awareness refers to awareness of life experiences within cultural contexts of the clinician and of the client. Knowledge acquisition is the acquisition and organization of demographic information, theories, and issues relevant to specific cultural groups to further clinicians’ understanding of diverse clients. The skill development domain of Lum’s cultural competence framework refers to “the professional application of practice principles based on knowledge theory and research to working with a client” (Lum, 2011). Together, these three domains inform social work practice that is more sensitive to the unique experiences and needs of socially marginalized groups.

Focus of Present Study

The present study investigates the relationship between social isolation from family and friends and psychiatric disorders among African Americans. It has several notable strengths. First, the analysis is based on a national probability sample of African Americans. Second, it examines the impact of both objective and subjective social isolation. Third, this study examines several psychiatric disorders, including 12-month MDD, any 12-month DSM-IV disorder, and number of 12-month DSM-IV disorders, while the vast majority of studies on social isolation and mental health focus on some type of psychiatric symptoms checklist (e.g., CES-D).

Methods

Sample

The analytic sample for this analysis was drawn from the National Survey of American Life: Coping with Stress in the 21st Century (NSAL), which was collected by the Program for Research on Black Americans at the University of Michigan’s Institute for Social Research. A total of 6,082 interviews were conducted with persons aged 18 or older, including 3,570 African Americans, 891 non-Hispanic Whites, and 1,621 Blacks of Caribbean descent. Fourteen percent of the interviews were completed over the phone, and 86% were administered face-to-face in respondents’ homes. Respondents were compensated for their time. The data collection was conducted from February 2001 to June 2003. The African American sample is the core sample of the NSAL. Sixty-four primary sampling units (PSUs) comprised the core sample. The remaining eight primary areas were selected from the South to ensure representation of African Americans in the proportion in which they are nationally distributed. The African American sample is a nationally representative sample of households located in the 48 coterminous states with at least one Black adult aged 18 years or older who did not identify ancestral ties in the Caribbean. The overall response rate was 72.3%. Final response rates for the NSAL two-phase sample designs were computed using the American Association of Public Opinion Research (AAPOR) guidelines (for Response Rate 3 samples) (AAPOR, 2006) (see Jackson, Neighbors, Nesse, Trierweiler, & Torres, 2004 for a more detailed discussion of the NSAL sample). The NSAL data collection was approved by the University of Michigan Institutional Review Board. The current analysis was based on the African American subsample of the NSAL (N=3,570).

Measures

Objective social isolation.

Objective social isolation was created by combining frequency of contact with family members and frequency of contact with friends. Frequency of contact with family was assessed by the following item: “How often do you see, write or talk on the telephone with family or relatives who do not live with you? Would you say nearly everyday, at least once a week, a few times a month, at least once a month, a few times a year, hardly ever or never?” Frequency of contact with friends was assessed in the same manner as frequency of contact with family. Both frequency of contact items were recoded into two separate dichotomous variables—objective isolation from family and objective isolation from friends—by combining the following response categories: not objectively isolated (nearly every day, at least once a week, a few times a month) versus objectively isolated (at least once a month, a few times a year, hardly ever or never). The two dichotomous variables, objective social isolation from family and objective social isolation from friends, were then combined into a single variable. This resulted in a four category, objective isolation variable: (a) objectively isolated from family and friends, (b) objectively isolated from family, (c) objectively isolated from friends, and (d) not objectively isolated from either group (i.e., family and friends; reference category). This operationalization has the advantage of assessing interactions involving both family and friends (R. J. Taylor, Taylor, et al., 2016).

Subjective social isolation.

A similar coding strategy was used to create the variable representing subjective social isolation. Subjective social isolation was created by combining both subjective family closeness and subjective friend closeness. Subjective family closeness was assessed by the item, “How close do you feel towards your family members? Would you say very close, fairly close, not too close, or not close at all?” Subjective friend closeness was assessed in the same manner as subjective family closeness. Both subjective closeness items were recoded into two separate dichotomous variables—subjective isolation from family and subjective isolation from friends—by combining the following response categories: not subjectively isolated (very close and fairly close) versus subjectively isolated (not too close and not close at all). These two dichotomous variables were then combined to create a single four-category variable representing respondents who are (a) subjectively isolated from family and friends, (b) subjectively isolated from family, (c) subjectively isolated from friends, and (d) not subjectively isolated from either group (i.e., family and friends; reference category).

DSM-IV disorders.

The three dependent variables in this analysis—12-month MDD, any 12-month DSM-IV disorder and the number of 12-month DSM IV disorders—were assessed using the DSM-IV World Mental Health Composite International Diagnostic Interview (WMH-CIDI). The WMH-CIDI is a fully structured diagnostic interview (Kessler & Ustün, 2004). Any 12-month disorder includes panic disorder, social phobia, agoraphobia without panic, generalized anxiety disorder, major depressive disorder, dysthymia, bipolar disorder I, bipolar disorder II, and subthreshold bipolar disorder (bipolar disorders counted as one disorder). The number of 12-month DSM-IV disorders is a count of all previously mentioned disorders.

Covariates.

Multivariate analyses controlled for gender, age, education, family income, marital status, and number of chronic health conditions. Age, education, family income, and number of chronic health conditions were assessed continuously. Age and education were assessed in years. Family income was coded in dollars. Due to its skewed distribution, we used the log of family income. Missing data for family income and education were imputed using an iterative regression-based multiple imputation approach incorporating information about age, gender, region, race, employment status, marital status, home ownership, and nativity of household residents. Gender was coded as male=0 and female=1. Marital status differentiated between individuals who were married or cohabiting (reference category); separated, divorced, or widowed; and never married. Number of chronic health conditions was measured by reports from respondents of the number of physician-diagnosed physical conditions.

Analysis Strategy

We used logistic regression to test the associations between objective and subjective social isolation and 12-month MDD and any 12-month DSM-IV disorder. Due to the overdispersion of the 12-month DSM disorder count variable, negative binomial regression was used to estimate the association between objective and subjective isolation and number of 12-month DSM disorders. Regression analyses for 12-month MDD, any 12-month DSM disorder, and number of 12-month DSM disorders were modeled hierarchically. The first model estimated the association between objective social isolation and the outcome variable. In the second model, subjective isolation was added to test the joint influence of objective and subjective isolation on the outcome variable. All analyses were conducted using Stata 15, which uses the Taylor expansion approximation technique for calculating the complex design-based estimates of variance. All statistical analyses accounted for the complex multistage clustered design of the NSAL sample, unequal probabilities of selection, nonresponse, and poststratification to calculate weighted, nationally representative population estimates and standard errors.

Results

Table 1 presents descriptive statistics of study variables. The mean age of the sample was 42 years, and slightly over half of the sample were women (56%). On average, respondents reported just over 12 years of formal education. The average family income was $36,833. About two out of five respondents were either married or cohabiting, and close to a third of respondents had never married. The average number of chronic health conditions reported was 1.28. Slightly under 13% of respondents met criteria for any DSM-IV disorder in the previous 12 month period, and 6.7% met the 12-month criteria for MDD. The average number of DSM-IV disorders reported was .21. The majority of the sample reported not being objectively (75%) or subjectively (83%) isolated from either family or friends. Very few respondents reported complete social isolation; 4% reported being objectively isolated from both family and friends, and 2% reported being subjectively isolated from both family and friends. Close to 5% of the sample reported being objectively or subjectively isolated only from family members; and close to 12% of the sample reported being either objectively or subjectively isolated only from friends.

Table 1.

Demographic Characteristics of the Sample and Distribution of Study Variables

% N Mean S.D. Min Max
12 Month Major Depressive Disorder
 No 93.30 3206
 Yes 6.70 228
Any 12-Month DSM-IV Disorder
 No 87.11 2970
 Yes 12.89 460
Number of 12-Month DSM-IV Disorders 3430 0.21 0.55 0 3
Objective Social Isolation
 Objective Isolation from Both Family and Friends 4.24 146
 Objective Isolation from Family Only 6.05 191
 Objective Isolation from Friends Only 14.49 494
 Not Objectively Isolated from either group 75.22 2705
Subjective Social Isolation
 Subjective Isolation from Both Family and Friends 2.12 74
 Subjective Isolation from Family Only 4.34 150
 Subjective Isolation from Friends Only 10.81 388
 Not Subjectively Isolated from either group 82.74 2843
Age 3570 42.33 14.50 18 93
Gender
 Male 44.03 1271
 Female 55.97 2299
Education 3570 12.43 2.23 0 17
Family Income 3570 36832.66 33068.07 0 520000
Marital Status
 Married/Cohabiting 41.65 1220
 Separated/Divorced/Widowed 26.80 1163
 Never Married 31.55 1170
Number of Chronic Health Conditions 3437 1.28 1.40 0 13
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Percentages and N are presented for categorical variables and Means and Standard Deviations are presented for continuous variables. Percentages are weighted and frequencies are un-weighted.

Table 2 presents the weighted logistic regression results for the effects of objective and subjective isolation on 12-month MDD. In Model 1, respondents who were objectively isolated from both family and friends were more than twice as likely to meet criteria for MDD as respondents who were not objectively isolated from either group. With the addition of subjective isolation (Model 2), objective isolation from family and friends no longer achieved significance. However, subjective isolation from family and friends was associated with higher odds of meeting criteria for MDD.

Table 2.

Logistic Regression Analysis of Social Isolation and 12-Month DSM-IV Major Depressive Disorder among African Americans

Model 1 OR (95% CI) Model 2 OR (95% CI)
Objective Social Isolation
 Objective Isolation from Both Family and Friends 2.23 (1.14, 4.37)* 1.76 (0.85, 3.65)
 Objective Isolation from Family Only 1.52 (0.78, 2.98) 1.24 (0.56, 2.76)
 Objective Isolation from Friends Only 1.22 (0.78, 1.92) 1.12 (0.66, 1.89)
 Not Objectively Isolated from Either Groupa 1.00 1.00
Subjective Social Isolation
 Subjective Isolation from Both Family and Friends -- 2.43 (1.10, 5.35)*
 Subjective Isolation from Family Only -- 2.08 (0.82, 5.29)
 Subjective Isolation from Friends Only -- 1.15 (0.67, 1.98)
 Not Subjectively Isolated from Either Groupa -- 1.00
N 3356 3280
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a

Reference category

OR=odds ratio; CI=confidence interval.

Logistic regressions controlled for gender, age, education, family income, marital status and number of chronic health conditions.

*

p<.05,

**

p< .01,

***

p<.001

Table 3 presents the weighted logistic regression results for the effects of objective and subjective social isolation on any 12-month DSM-IV disorder. Model 1 indicated that objective social isolation was unrelated to the odds of meeting criteria for any DSM disorder in the previous 12 months. With the addition of subjective isolation to the model (Model 2), the data indicated that respondents who were subjectively isolated from both family and friends were over three times more likely to qualify for a DSM-IV diagnosis; respondents who were subjectively isolated from family only and friends only were also more likely to meet criteria for a DSM disorder.

Table 3.

Logistic Regression Analysis of Social Isolation and Any 12 month DSM-IV Disorder among African Americans

Model 1 OR (95% CI) Model 2 OR (95% CI)
Objective Social Isolation
 Objective Isolation from Both Family and Friends 1.52 (0.80, 2.87) 1.05 (0.51, 2.17)
 Objective Isolation from Family Only 1.39 (0.84, 2.32) 1.14 (0.60, 2.16)
 Objective Isolation from Friends Only 1.21 (0.87, 1.70) 1.08 (0.73, 1.60)
 Not Objectively Isolated from Either Groupa 1.00 1.00
Subjective Social Isolation
 Subjective Isolation from Both Family and Friends -- 3.24 (1.52, 6.91)**
 Subjective Isolation from Family Only -- 2.18 (1.02, 4.66)*
 Subjective Isolation from Friends Only -- 1.46 (1.03, 2.06)*
 Not Subjectively Isolated from Either Groupa -- 1.00
N 3352 3276
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a

Reference category

OR=odds ratio; CI=confidence interval.

Logistic regressions controlled for gender, age, education, family income, marital status and number of chronic health conditions.

*

p<.05,

**

p< .01,

***

p<.001

Table 4 presents the weighted negative binomial regression of the number of any 12-month DSM disorders and objective and subjective social isolation. With the inclusion of only objective social isolation in Model 1, the results indicated that respondents who were objectively isolated from both family and friends met criteria for more DSM disorders. With the addition of subjective isolation to the model (Model 2), objective isolation from family and friends no longer achieved statistical significance. However, the results showed that all categories of subjective social isolation were positively associated with number of any 12-month DSM disorders. That is, respondents who were subjectively isolated 1) only from family, 2) only from friends, and 3) from both family and friends met criteria for a greater number of 12-month DSM disorders than respondents in the reference group (those not subjectively isolated from family and friends).

Table 4.

Negative Binomial Regression Analysis of Social Isolation and Number of 12-month DSM-IV Disorders among African Americans

Model 1 b (SE) Model 2 b (SE)
Objective Social Isolation
 Objective Isolation from Both Family and Friends 0.61 (0.28)* 0.37 (0.32)
 Objective Isolation from Family Only 0.16 (0.22) −0.07 (0.25)
 Objective Isolation from Friends Only 0.28 (0.17) 0.25 (0.17)
 Not Objectively Isolated from Either Groupa 1.00 1.00
Subjective Social Isolation
 Subjective Isolation from Both Family and Friends -- 0.77 (0.26)**
 Subjective Isolation from Family Only -- 1.08 (0.38)**
 Subjective Isolation from Friends Only -- 0.37 (0.16)*
 Not Subjectively Isolated from Either Groupa -- 1.00
Intercept −1.06 (0.87) −1.77 (0.92)
F 18.03*** 17.04***
Complex Design df 34 34
N 3352 3276
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a

Reference category

b=unstandardized regression coefficient; SE=standardized error; df=degrees of freedom

Note: Degrees of freedom associated with F statistics are (x, x) for Model1 and (x,x) for Model 2.

Negative Binomial regressions controlled for gender, age, education, family income, marital status and number of chronic health conditions.

*

p<.05,

**

p< .01,

***

p<.001

Discussion

This study investigated the influence of objective and subjective social isolation on psychiatric disorders among African Americans. While very few respondents reported being completely socially isolated (i.e., isolated from both family and friends), one in four respondents reported some type of objective isolation and almost one in five reported some type of subjective isolation. Social isolation from friends only was the most prevalent type of isolation, with 14% of respondents reporting objective isolation from friends only and 11% of respondents reporting subjective isolation from friends only. Generally, rates of objective isolation were higher than rates of subjective isolation. The data indicated that although objective isolation from both family and friends was a risk factor for MDD and meeting criteria for more DSM disorders, when subjective isolation was accounted for, objective isolation no longer predicted MDD and number of DSM disorders. Objective isolation was unrelated to any 12-month DSM disorder. Respondents in all three categories of subjective isolation (i.e., isolated from family only, isolated from friends only, and isolated from both family and friends) were more likely to meet criteria for any DSM disorder and to meet criteria for a greater number of DSM disorders than respondents who were not subjectively isolated from either family or friends. Additionally, subjective isolation from both family and friends was associated with greater odds of meeting criteria for MDD.

These findings indicate that social isolation is detrimental to mental health, confirming prior research. Extant research has found that social isolation is a risk factor for a range of physical health problems, such as hypertension, heart disease, and stroke (Tomaka, Thompson, & Palacios, 2006). The connection between social isolation and mental health has received less research attention. However, accumulating evidence that social isolation is a risk factor for depressive symptoms, psychological distress, poor self-rated mental health, and other mental health problems (Cornwell & Waite, 2009; Coyle & Dugan, 2012; H. O. Taylor, Taylor, Nguyen, & Chatters, 2018) underscores the importance of social relationships and their role in the mental health of African Americans. Social relationships are critical sources of social support, which can protect against mental health problems and buffer against life stressors that could lead to mental health problems. Individuals who are socially isolated from their networks are unable to garner the benefits of their relationships with others, which leaves isolated individuals more vulnerable to the effects of stressors and compromised mental health.

The current findings demonstrate that the effects of subjective isolation are more detrimental to mental health than that of objective isolation. This indicates that perceptions of emotional distance and affective features of isolation are more important for mental health than quantifiable social interactions and objective features of social isolation. This is consistent with findings indicating that subjective assessments of relationships are more important for well-being as well as the studies that have differentiated between objective and subjective social isolation (Melrose, Brown, & Wood, 2015; H. O. Taylor et al., 2018). For example, Taylor et al. (2018) found among older adults that: 1) subjective isolation was predictive of higher levels of psychological distress, but objective isolation was unrelated to psychological distress; 2) objective isolation was associated with more depressive symptoms when subjective isolation was not accounted for; and 3) when subjective isolation was accounted for, objective isolation no longer predicted depressive symptoms, but subjective isolation predicted more depressive symptoms. Similarly, Cornwell and Waite (2009) found that among older adults, objective isolation was unrelated to depressive symptoms, while subjective isolation predicted more depressive symptoms. Together, these findings indicate that subjective isolation mediates the relationship between objective isolation and psychiatric disorders. That is, objective isolation does not have an independent effect on psychiatric disorders, but rather operates through subjective isolation to influence psychiatric outcomes. Thus, while subjective isolation has a direct effect on psychiatric outcomes, objective isolation indirectly impacts psychiatric outcomes via subjective isolation.

Social Work Practice Implications

Initial Assessment of Mental Health and Social Isolation.

This study found that approximately one out of every eight African American adults experienced at least one DSM-IV disorder in the previous year. In terms of social isolation, close to 25% of African American adults experienced some form of objective isolation and 20% experienced some form of subjective social isolation. These statistics confirm that social workers working with African American populations are likely to have clients with a DSM-IV diagnosis and/or are experiencing social isolation, and further, that it is important to screen African American clients for psychiatric disorders and social isolation during the initial assessment. In fact, in addition to using genograms and eco-maps to assess family and friend relationships and history, social work clinicians can use the questions and measures described in this study as screening tools for objective and subjective social isolation. Positive screens for objective isolation, subjective isolation, or both, indicate the need for a more in depth assessment and discussion of clients’ family and friend networks and history and their assessment of how these social relationships influence mood, thoughts, and behaviors. Considerations of family and friendship characteristics, history, and influences among African American clients is particularly important for culturally competent care, given the centrality of these social networks and informal social support in the lives of African Americans. Lastly, information from the initial assessment can be used to explore the major cause(s) of client’s social isolation.

Best Practices for Social Isolation-Objective and Subjective.

Our results found that subjective social isolation was the critical factor influencing mental health, even when accounting for objective social isolation. This is important to note given differences in current practice recommendations for objective isolation and subjective isolation. Best practices for objective social isolation involve group activities where group participants actively choose their own social activities and events (Dickens, Richards, Greaves, & Campbell, 2011; Findlay, 2003). In contrast, because subjective social isolation is more akin to loneliness, the best practice for alleviating subjective isolation involves addressing clients’ maladaptive cognitions (Masi, Chen, Hawkley, & Cacioppo, 2011), such as negative thoughts, negative social expectations and attitudes of others, and hyper-vigilance in social situations. Actively focusing on maladaptive cognitions is important because, if unaddressed, they have the potential to generate and intensify the experience of loneliness and subjective social isolation and are considered etiologically significant for psychiatric disorders (Cacioppo & Cacioppo, 2018; Cacioppo & Hawkley, 2009). Unaddressed maladaptive cognitions can also lead to greater objective social isolation. Individuals with maladaptive cognitions may avoid social situations to prevent triggering negative thoughts, feelings, and stress associated with social encounters. This can start a vicious spiral in which individuals who experience greater subjective social isolation could also be experiencing greater objective social isolation.

The goal of clinical therapy for social isolation is to interrupt negative thoughts and behaviors through the use of cognitive behavioral therapy or psychodynamic therapy. By interrupting maladaptive cognitions and introducing more positive ways of thinking and behaving, clients are able to form healthier relationships with family members and friends, improve objective social connections and decrease the likelihood of developing a mental health disorder. Reducing maladaptive cognitions may also enhance clients’ abilities to garner social support from family members and friends in the treatment of a diagnosed psychiatric disorder. Counseling efforts with family and friends should additionally include discussions of how maladaptive thoughts are associated with social isolation and provide coaching in supportive communication strategies. Lastly, given their interdependence, it is important that social isolation and psychiatric disorder are simultaneously addressed. This can be done, again, through cognitive behavioral therapy or psychodynamic therapy, in tandem with psychotropic medication, as appropriate.

Examples of Interventions.

There are several interventions that may be useful in helping prevent or substantially reduce social isolation. It is important to first note that social isolation interventions do not have to be expensive or complicated to have a successful impact. Many low-cost interventions are extremely effective. For instance, quilting or knitting circles are low-cost interventions in which individuals, usually women, meet every week as a group to quilt/knit. These group gatherings create social bonds, while also providing opportunities for cognitive stimulation and to spend time outside of one’s home. Churches in rural communities may be especially suited for knitting/quilting circles. Because rural areas have limited resources and institutional structures, churches are often a primary institution and may be especially suited for knitting/quilting circles.

Another relatively low-cost intervention was the Friendship Club organized and run by Mrs. Willie H. Taylor, a clinical social worker at the Community Health Center of Meharry Medical College during the 1970’s and 1980’s. African American older adults who resided in the local Nashville community would meet for several hours each week. Scheduled outings included weekly excursions to museums, amusement parks and other local activities. Friendship Club staff arranged travel and admission to these local activities. Once a month, the Friendship Club hosted a celebration for persons who had birthdays that month. The Friendship Club intervention had several notable strengths. First, it was low-cost and relatively easy to administer. Second, this intervention created new social networks and included structured activities that helped reduce social isolation. Third, due to weekly contact and their involvement in administering the program, clinical social worker staff were able to notice warning signs of dementia, depression or physical health problems early and intervene when necessary.

Home sharing programs, which build intergenerational relationships through shared housing, is another novel intervention for addressing social isolation. Home sharing programs match older adults (who may need companionship and assistance with household chores) with younger adults (who may need a roommate to help reduce the cost of rent). Nesterly is one such program located in Boston that began as a pilot program in 2017 by funding eight matches between older and younger adults (Tempera, 2019). Nesterly has been so successful that the Boston city government decided to invest additional funding to allow for up to 100 intergenerational matches. These intergenerational housing partnerships can help decrease both objective and subjective social isolation by promoting bonds between older and younger adults.

Considering Life Events and Chronicity of Social Isolation.

Two additional factors that are important to consider in relation to social isolation are whether social isolation is concurrent with a major life event or change and whether clients’ objective and/or subjective social isolation is chronic in nature. Some clients will experience social isolation after a major life changing event (e.g., death of a loved one, relocation, being diagnosed with an illness). Therefore, it is important to emphasize and bolster existing social support networks that are available to help clients through these transitions. Having access to and using strong social support networks can help establish relationships and norms for support that can be maintained over time and may prevent a reoccurrence of social isolation in response to future life events.

Clients who experience chronic social isolation and have been isolated for a significant portion of their lives will most likely require a different approach to alleviating their social isolation. Situations reflecting long-term social isolation include individuals who are reclusive, but nonetheless physically and psychologically healthy, as well as those for whom social isolation is associated with a mental disorder (e.g., agoraphobia, generalized anxiety disorder) or serious physical and functional limitations (e.g., hearing loss). It is natural to experience objective and subjective social isolation at some point in life. However, from a preventive standpoint, it is important to recognize and intervene in cases of chronic and persistent social isolation to avert the development and/or worsening of serious physical and mental health morbidities.

Concurrently Addressing Social Isolation and Mental Disorders.

Current research recognizes that social isolation both affects mental health status and is frequently co-occurring with some psychiatric disorders (Taylor et al., 2018). Given the reciprocal nature of social isolation and psychiatric disorders, it is important to concurrently address clients’ social isolation and mental health disorder. As suggested previously, understanding how clients became socially isolated and when they developed their mental illness (i.e., the temporal order of social isolation and illness) are critically important for developing a treatment/intervention plan. Given the cross-sectional design of this study, we were not able to determine the causal relationship between social isolation and psychiatric disorders. (That is, whether social isolation caused the individual to develop a psychiatric disorder, or if a psychiatric disorder caused the individual to become socially isolated.) However, by interrupting maladaptive cognitions and behaviors as described above, clients may be able to decrease their social isolation from family members and friends, thus improving their mental health. Another strategy to help prevent or mitigate clients’ social isolation is to conduct a psychoeducational intervention with clients’ close family members and friends. This would entail teaching family members and friends about the psychiatric disorder, so they would have a better understanding of their mental health symptoms and needs. Clients’ family and friends would then have the necessary knowledge and skills to assist them when they are experiencing symptoms. Additionally, this method could help enhance bonds between clients and their social network members, thereby decreasing the likelihood that they would experience social isolation in the future.

Limitations and Conclusions

This study’s findings should be interpreted within the context of several limitations. First, analyses were based on cross sectional data, which does not permit causal inference. Thus, we are unable to definitively conclude whether social isolation leads to mental illness or vice versa. Second, the NSAL surveyed community-dwelling adults; the current findings are not generalizable to institutionalized and homeless individuals, who may have higher rates of social isolation from family and friends and psychiatric disorders. Additionally, clinically institutionalized individuals were excluded due to the fact that only community-dwelling adults were sampled, which means that these findings are not generalizable to these individuals Third, all measures in this study were self-reported, which are susceptible to recall and social desirability biases.

Despite these limitations, this study makes several important contributions to the mental health and social isolation literatures. Because the majority of research on social isolation has focused on its effects on physical health in the general population, less is known about the relationship between social isolation and mental health, especially in racial and ethnic minority populations, which have traditionally been underserved and under-researched. The current analysis extends the literature by focusing specifically on the effects of social isolation on psychiatric disorders in a nationally representative sample of African Americans. Another important contribution this study makes is in its focus on the entire adult age span. Research on social isolation tends to focus on older adult populations, which results in a dearth of knowledge on the effects of social isolation across the entire adult life span. Research has identified age differences in the quantitative and qualitative characteristics of social networks, which suggest that social isolation and its effects may differ between age groups. Given these age differences, extant knowledge on social isolation and mental health in older adults may not be fully generalizable to adults outside of this developmental stage. By expanding the focus of the current analysis to individuals across the adult life span, this study bridges this critical knowledge gap. In distinguishing between objective and subjective social isolation, this study extends current understanding of social isolation, as most research on social isolation either focuses on objective isolation or do not make distinctions between objective and subjection isolation. The distinction between objective and subjective isolation is an important one, as they have differing effects on mental health, which this study has demonstrated. Additionally, this distinction holds significance for practice implications because interventions for objective social isolation target a different set of behaviors and cognitions than interventions for subjective social isolation. This study reinforces the importance of social ties, especially emotionally close ties, for mental health and contributed important and nuanced insights to the developing literature on social isolation and mental health among African Americans.

Funding/Support

The preparation of this manuscript was supported by grants from the National Institute on Aging to RJT (P30AG1528) and HOT (R36AG054647) The data collection on which this study is based was supported by the National Institute of Mental Health (NIMH; U01-MH57716) with supplemental support from the Office of Behavioral and Social Science Research at the National Institutes of Health (NIH) and the University of Michigan.

Footnotes

Conflict-of-Interest Statement

The authors declare that they have no conflict of interest.

Contributor Information

Ann W. Nguyen, Case Western Reserve University

Robert Joseph Taylor, University of Michigan.

Harry Owen Taylor, Washington University in St. Louis.

Linda M. Chatters, University of Michigan

References

  1. AAPOR. (2006). Standard definitions: Final dispositions of case codes and outcome rates for surveys (4th ed.). Lenexa, KS: American Association for Public Opinion Research. [Google Scholar]
  2. Alcaraz KI, Eddens KS, Blase JL, Diver WR, Patel AV, Teras LR, … Gapstur SM (2018). Social isolation and mortality in US Black and White men and women. American Journal of Epidemiology, 188(1), 102–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alegría M, Chatterji P, Wells K, Cao Z, Chen C. n., Takeuchi D, … Meng X-L (2008). Disparity in depression treatment among racial and ethnic minority populations in the United States. Psychiatric Services, 59(11), 1264–1272. doi: 10.1176/appi.ps.59.11.1264 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. APA. (2017). Mental health disparities: African Americans. In.
  5. Breslau J, Aguilar-Gaxiola S, Kendler KS, Su M, Williams D, & Kessler RC (2006). Specifying race-ethnic differences in risk for psychiatric disorder in a USA national sample. Psychol Med, 36(01), 57–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Breslau J, Kendler KS, Su M, Gaxiola-Aguilar S, & Kessler RC (2005). Lifetime risk and persistence of psychiatric disorders across ethnic groups in the United States. Psychol Med, 35, 317–327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cacioppo JT, & Cacioppo S (2018). Loneliness in the modern age: An evolutionary theory of loneliness (ETL). Advances in Experimental Social Psychology, 58, 127–197. [Google Scholar]
  8. Cacioppo JT, & Hawkley LC (2009). Perceived social isolation and cognition. Trends in Cognitive Sciences, 13(10), 447–454. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chatters LM, Taylor HO, Nicklett EJ, & Taylor RJ (2018). Correlates of Objective Social Isolation from Family and Friends among Older Adults. Paper presented at the Healthcare. [DOI] [PMC free article] [PubMed]
  10. Cornwell EY, & Waite LJ (2009). Measuring social isolation among older adults using multiple indicators from the NSHAP study. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 64(suppl_1), i38–i46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Coyle CE, & Dugan E (2012). Social isolation, loneliness and health among older adults. Journal of Aging and Health, 24(8), 1346–1363. [DOI] [PubMed] [Google Scholar]
  12. Dickens AP, Richards SH, Greaves CJ, & Campbell JL (2011). Interventions targeting social isolation in older people: a systematic review. BMC Public Health, 11(1), 647. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Elder K, & Retrum J (2012). Framework for isolation in adults over 50: AARP Foundation/isolation Framework Project. Retrieved from San Diego, CA: [Google Scholar]
  14. Findlay RA (2003). Interventions to reduce social isolation amongst older people: where is the evidence? Ageing & Society, 23(5), 647–658. [Google Scholar]
  15. Grant BF, Hasin DS, Stinson FS, Dawson DA, Ruan WJ, Goldstein RB, … Huang B (2005). Prevalence, correlates, co-morbidity, and comparative disability of DSM-IV generalized anxiety disorder in the USA: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Psychol Med, 35(12), 1747–1759. [DOI] [PubMed] [Google Scholar]
  16. Himle JA, Baser RE, Taylor RJ, Campbell RD, & Jackson JS (2009). Anxiety disorders among African Americans, blacks of Caribbean descent, and non-Hispanic whites in the United States. J Anxiety Disord, 23(5), 578–590. doi: 10.1016/j.janxdis.2009.01.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hoffman DL, Dukes EM, & Wittchen HU (2008). Human and economic burden of generalized anxiety disorder. Depress Anxiety, 25(1), 72–90. [DOI] [PubMed] [Google Scholar]
  18. Holt-Lunstad J, Smith TB, Baker M, Harris T, & Stephenson D (2015). Loneliness and social isolation as risk factors for mortality a meta-analytic review. Perspectives on Psychological Science, 10(2), 227–237. [DOI] [PubMed] [Google Scholar]
  19. Holt-Lunstad J, Smith TB, & Layton JB (2010). Social relationships and mortality risk: A meta-analytic review. Plos Medicine, 7(7). [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Institute of Medicine. (2014). Capturing Social and Behavioral Domains in Electronic Health Records: Phase 1. Washington, DC: The National Academies Press. [PubMed] [Google Scholar]
  21. Jackson JS, Neighbors HW, Nesse RM, Trierweiler SJ, & Torres M (2004). Methodological innovations in the National Survey of American Life. International journal of methods in psychiatric research, 13(4), 289–298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kessler RC (2012). The costs of depression. The Psychiatric Clinics of North America, 35(1), 1–14. doi: 10.1016/j.psc.2011.11.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kessler RC, & Ustün TB (2004). The world mental health (WMH) survey initiative version of the world health organization (WHO) composite international diagnostic interview (CIDI). International journal of methods in psychiatric research, 13(2), 93–121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Klinenberg E (2016). Social isolation, loneliness, and living alone: Identifying the risks for public health. American Journal of Public Health, 106(5), 786. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Li C, Jiang S, & Zhang X (2019). Intergenerational relationship, family social support, and depression among Chinese elderly: A structural equation modeling analysis. Journal of affective disorders, 248, 73–80. doi: 10.1016/j.jad.2019.01.032 [DOI] [PubMed] [Google Scholar]
  26. Lincoln KD, Taylor RJ, Bullard KM, Chatters LM, Woodward AT, Himle JA, & Jackson JS (2010). Emotional support, negative interaction and DSM IV lifetime disorders among older African Americans: Findings from the National Survey of American Life (NSAL). International Journal of Geriatric Psychiatry, 25(6), 612–621. doi: 10.1002/gps.2383 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lincoln KD, Taylor RJ, Chatters LM, & Joe S (2012). Suicide, negative interaction and emotional support among black Americans. Soc Psychiatry Psychiatr Epidemiol, 47(12), 1947–1958. doi: 10.1007/s00127-012-0512-y [DOI] [PubMed] [Google Scholar]
  28. Lubben J, Gironda M, Sabbath E, Kong J, & Johnson C (2015). Social isolation presents a grand challenge for social work. Retrieved from
  29. Lum D (2011). Culturally competent practice: A framework for understanding diverse groups and justice issues. Belmont, CA: Brooks/Cole. [Google Scholar]
  30. Masi CM, Chen H-Y, Hawkley LC, & Cacioppo JT (2011). A meta-analysis of interventions to reduce loneliness. Personality and Social Psychology Review, 15(3), 219–266. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Melrose KL, Brown GDA, & Wood AM (2015). When is received social support related to perceived support and well-being? When it is needed. Personality and Individual Differences, 77, 97–105. [Google Scholar]
  32. Miyawaki CE (2015). Association of social isolation and health across different racial and ethnic groups of older Americans. Ageing & Society, 35(10), 2201–2228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. NASW. (2015). Standards and indicators for cultural competence in social work practice. Retrieved from
  34. Nguyen AW, Chatters LM, & Taylor RJ (2016). African American extended family and church-based social network typologies. Family Relations, 65(5), 701–715. doi: 10.1111/fare.12218 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Revicki DA, Travers K, Wyrwich KW, Svedsater H, Locklear J, Mattera MS, … Montgomery S (2012). Humanistic and economic burden of generalized anxiety disorder in North America and Europe. Journal of affective disorders, 140(2), 103–112. doi: 10.1016/J.JAD.2011.11.014 [DOI] [PubMed] [Google Scholar]
  36. Shankar A, Hamer M, McMunn A, & Steptoe A (2013). Social isolation and loneliness: relationships with cognitive function during 4 years of follow-up in the English Longitudinal Study of Ageing. Psychosomatic Medicine, 75(2), 161–170. [DOI] [PubMed] [Google Scholar]
  37. Taylor HO, Taylor RJ, Nguyen AW, & Chatters LM (2018). Social isolation, depression, and psychological distress among older adults. Journal of Aging and Health, 1–18. doi: 10.1177/0898264316673511 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Taylor RJ, Chae DH, Lincoln KD, & Chatters LM (2015). Extended family and friendship support networks are both protective and risk factors for major depressive disorder and depressive symptoms among African-Americans and Black Caribbeans. J Nerv Ment Dis, 203(2), 132–140. doi: 10.1097/NMD.0000000000000249 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Taylor RJ, Chatters LM, Woodward AT, & Brown E (2013). Racial and ethnic differences in extended family, friendship, fictive kin, and congregational informal support networks. Family Relations, 62(4), 609–624. doi: 10.1111/fare.12030 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Taylor RJ, Mouzon DM, Nguyen AW, & Chatters LM (2016). Reciprocal family, friendship and church support networks of African Americans: Findings from the National Survey of American Life. Race and Social Problems, 8(4), 326–339. doi: 10.1007/s12552-016-9186-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Taylor RJ, Taylor HO, & Chatters LM (2016). Social isolation from extended family members and friends among African Americans: Findings from a national survey. Journal of Family Social Work, 1–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Tempera J (2019). Roxbury woman in her 60s and Boston University student in her 20s forge friendship through home sharing program. Retrieved from https://www.masslive.com/boston/2019/07/roxbury-woman-in-her-60s-and-boston-university-student-in-her-20s-forge-friendship-through-home-sharing-program.html
  43. Tomaka J, Thompson S, & Palacios R (2006). The Relation of Social Isolation, Loneliness, and Social Support to Disease Outcomes Among the Elderly. Journal of Aging and Health, 18(3), 359–384. doi: 10.1177/0898264305280993 [DOI] [PubMed] [Google Scholar]
  44. Watkins ED, & Teasdale JD (2001). Rumination and overgeneral memory in depression: effects of self-focus and analytic thinking. Journal of Abnormal Psychology, 110(2), 353. [DOI] [PubMed] [Google Scholar]
  45. WHO. (2018). Depression fact sheet. Retrieved from http://www.who.int/mediacentre/factsheets/fs369/en/
  46. Williams DR, Gonzalez HM, Neighbors HW, Nesse R, Abelson JM, Sweetman J, & Jackson JS (2007). Prevalence and distribution of Major Depressive Disorder in African Americans, Caribbean Blacks, and non-Hispanic Whites: Results from the National Survey of American Life. Archives of General Psychiatry, 64(3). [DOI] [PubMed] [Google Scholar]
  47. Wittchen HU (2002). Generalized anxiety disorder: Prevalence, burden, and cost to society. Depress Anxiety, 16(4), 162–171. [DOI] [PubMed] [Google Scholar]
  48. World Health Organization. (2007). Global age-friendly cities: A guide: World Health Organization. [Google Scholar]
  49. Zunzunegui M-V, Alvarado BE, Del Ser T, & Otero A (2003). Social networks, social integration, and social engagement determine cognitive decline in community-dwelling Spanish older adults. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 58(2), S93–S100. [DOI] [PMC free article] [PubMed] [Google Scholar]

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