The light and dark sides of social capital: Depressive symptoms among incarcerated older adults

Abstract

Background:

Adults aged 55 or above represent a large and growing proportion of the US and international correctional populations and more physical and mental health problems than their non-incarcerated peers. Social capital represents the collective cognitive and network structure resources accessible through social relationships and may serve as a potential asset in carceral settings in the amelioration of depressive symptoms among older adults.

Method:

A sample of men drawn from a study of older adults in Kentucky prisons (n = 91) was used to meet the following aims: (1) explore relationships among cognitive and structural facets of social capital, chronic health conditions and depressive symptoms and (2) identify the role of social capital (viz. trust) alongside chronic health conditions as a determinant of depressive symptoms. We hypothesized that each indicator of social capital would relate negatively to depressive symptoms and that trust would emerge as most strongly associated with depressive symptoms.

Results:

Bivariate correlations between depression and social capital variables related as we hypothesized. However, our hypotheses were only partially supported. Higher trust was correlated to fewer depressive symptoms (r = .21, p < .05) revealing a lighter side of trust in the correctional milieu but was unrelated to depression when controlling for variables. An interaction term in the final model indicated a different role for trust. Factors accounted for 64% of variance in depressive symptoms among older adults in a state prison (F(10, 80) = 14.25, p < .001). In this model, higher trust was related to depressive symptoms when included alongside additional measures of social capital, indicating that a darker side of this facet may exist within correctional settings.

Conclusion:

Many indicators of social capital (e.g., network size) demonstrated potentially protective benefits against depression, while trust revealed a more complex role related to chronic health conditions. Limited measures and the agnostic behavior of trust require attention by future researchers.

Keywords

Older adults depression health social capital prisoners trust

Older adults are considered the fastest growing age group in prisons in the United States and other nations including Australia (Trotter & Baidawi, 2015) and the United Kingdom (A. J. Hayes, Burns, Turnbull, & Shaw, 2012; Senior et al., 2013). In less than 10 years, Canadian prison administrators saw an increase in the proportion of older adults in prisons from 9.3% in 1996 to 15.8% in 2005. In the United Kingdom, the number of older adults in prisons has more than doubled between 1999 and 2009 (A. J. Hayes et al., 2012; Senior et al., 2013). And in Australia, the population of older adults in prisons grew from 8.4% in 2002 to 12% in 2012 reflecting a 140% increase in a decade. The prison population is also aging in France, and older adults now represent 11% of the total prison population (Combalbert, Pennequin, Ferrand, Keita, & Geffray, 2019; Combalbert et al., 2016). In Japan, too, the older adult prison population swelled by 160% in just 6 years between 2000 and 2006.

In contrast, the population of older adults has grown by 282% in the United States between 1981 and 2011 (Fellner & Vinck, 2012) and by 400% in the 10 years between 1993 and 2013 (Carson & Sabol, 2016). And in 2013, the number of individuals aged 55 or above increased from 3% to 10% of the total state prison population (Carson & Sabol, 2016). By 2015 year end, an estimated 162,000 adults aged 55 and above were sentenced to more than 1 year in US prisons, constituting approximately 11% of the total prison population (Carson, 2018), and the number of individuals aged 55 or above is projected to reach 400,000 by 2030 (Fellner & Vinck, 2012). So while the issue is international in scope, the United States is uniquely situated in this area in part due to the War on Drugs and associated punitive practices reducing judicial discretion, increasing mandatory-minimum punishment, establishing truth-in-sentencing and reinvigorating the role of indeterminate sentencing (Blomberg & Lucken, 2010). Compared to the rest of the international community, the United States is an incarceration nation. Rates of incarceration for the United States hover near 700 per 100,000 persons with the next least NATO nation incarcerator (United Kingdom) placing only 139 per 100,000 persons behind bars annually. Other nations’ rates per 100,000 persons pale in comparison including Australia (167), Canada (114), France (102) and Japan (45; States of Incarceration: The Global Context, 2019).

Correctional health care planning and services are thus substantial concerns among administrators throughout the nation, as incarcerated persons already face physical and mental health concerns at higher rates than their non-incarcerated peers (James & Glaze, 2006; Maruschak, Berzofsky, & Unangst, 2015). Depression and chronic illnesses are common health conditions among older adults (Kok & Reynolds, 2017) and both influence their quality of life (QOL; Sprangers et al., 2000). Furthermore, there exists an increased risk for mortality when depression and other mental disorders are comorbid with chronic health conditions (Kessler & Bromet, 2013; Lawrence, Kisely, Pais, 2010). Identifying resources, then, that mitigate the intensity of depressive symptoms and other physical and mental health sequelae is critical – particularly in resource-limited environments. Social capital provides a link to understanding the physical and mental health of community-based populations (Moore & Kawachi, 2017) that may also apply to carceral settings such as prisons and jails.

Depression and chronic health conditions

An estimated 322 million people report living with depression (World Health Organization (WHO), 2017), making it the leading cause of disability worldwide (Friedrich, 2017). A person’s experience with depression is influenced by their social environment (Goldberg & Goodyer, 2005) with significant interpersonal problems and severe life events contributing to symptom chronicity (Harris, 2010). Furthermore, early mortality is risked when depression is comorbid with chronic health conditions (Scott et al., 2016)—physical health problems lasting longer than 3 months that require ongoing medical attention and/or limit activities of daily living (ADL; e.g., heart disease, cancer, stroke and diabetes). Chronic health conditions represent both discrete and recurrent stressors that relate to mental health (Schneiderman, Ironson, & Siegel, 2005). Among older adults, depression is correlated strongly with chronic health conditions (Fiske, Wetherell, & Gatz, 2009), each causing considerable functional impairment thereby influencing QOL (Sprangers et al., 2000). The synergistic effect between depression and chronic health conditions also increases health care use among older adults and is a driving force in socioeconomic burden (Han et al., 2018).

Older adults, carceral settings and social capital

Older adults who are incarcerated have higher rates of physical problems and disability compared to their non-incarcerated peers averaging three chronic health conditions including obesity, diabetes, heart attack, hypertension, cognitive impairment and liver disease (Binswanger, Krueger, & Steiner, 2009; Williams, Stern, Mellow, Safer, & Greifinger, 2012). Older adults may also suffer from elevated levels of psychological distress. Reported rates of mental health problems among older adults who are incarcerated are at least parallel to those of their younger adult peers (15%–20%), though some scholars suggest these rates are likely higher (Fazel, Hope, O’Donnell, Piper, & Jacoby, 2001; Williams et al., 2012) potentially due to under- and misdiagnosis.

Several factors increase the risk of physical and mental health conditions among older adults in prisons which may relate to pre-incarceration or the carceral space. Numerous pre-incarceration factors shape health during incarceration. However, the social conditions prior to incarceration are disparate internationally (viz., health care policies, levels of urbanization, collectivist vs. individualist culture). In the United States, Williams and Jackson (2005) note that segregation and the related inequities initiate and maintain racial disparities in health. For instance, limited access to or poor community health care is linked to health disparities between those who are and are not incarcerated (Møller, Gatherer, Jürgens, Stöver, & Nikogosian, 2007). Dumont, Allen, Brockmann, Alexander and Rich (2013) note that the paradox of more equitable mortality rates across racial groups seen among those in prisons is an indictment of public health. Food deserts also shape negative health outcomes in justice-involved persons (Testa, 2019). These are spaces where individuals face limited access to nutritious and affordable food and further are often located in areas of social deprivation (Barrenger & Draine, 2013; Wrigley, Sewell, & Margetts, 2003). Exposure to psychosocial stressors including racism among disadvantaged social groups also manifests as high levels of stress, thereby increasing the risk of numerous health problems (Williams & Jackson, 2005).

Carceral setting factors such as violence, overcrowding, limited airflow and sunlight and rampant communicable and acute diseases also shape chronic health conditions and psychological sequelae (Møller et al., 2007). These factors – combined with high physical and mental health comorbidities – have important implications for correctional health care and community health systems after incarceration. Despite many adverse effects, prison and jail settings may provide unconventional resources that may lead to better health outcomes (Haynie, Whichard, & Kreager, 2018) and reduced health disparities (Dumont et al., 2013), namely through the provision of health care through US Supreme Court dictate (see Estelle v. Gamble, 1976; Farmer v. Brennan, 1994).

Social capital as a health determinant

Social capital refers to the socio-structural resources (e.g., emotional or instrumental support) that accrue through shared norms and values within durable relationships (Bourdieu, 1986; Rosital, 2011). Social capital may be cognitive (e.g., trust) or linked to network structure (e.g., network size and composition). In tandem, these facets determine access to resources (e.g., health information and access to work; Villalonga-Olives & Kawachi, 2015). Of the many cognitive facets of social capital, trust has demonstrated a strong relationship with physical and mental health in social epidemiology (Kawachi, 2017). Importantly, a ‘dark side’ has been documented, whereby social networks have negative health-related consequences (Kawachi, Takao, & Subramanian, 2013). Context drives the relationship between trust and health – specific settings or environments may be instrumental in shaping the association between trust and individual health (Adjaye-Gbewonyo, Kawachi, Subramanian, & Avendano, 2018).

The relationship between health and social capital is not restricted to a uniform pathway and may be influenced by various aspects of a person’s social network (Villalonga-Olives & Kawachi, 2017). These pathways are ‘agnostic’, as they may promote or deteriorate health (Villalonga-Olives & Kawachi, 2017, p. 105). Closed social networks have ‘downsides’ relevant to health such as encouraging risky behavior, sharing incorrect information and excluding other members from accessing network resources (Campos-Matos, Subramanian, & Kawachi, 2015). Carceral settings represent unique structural environments that may emulate features of closed social networks, though the development and functions of social capital as a determinant of health among older adults in prisons and jails are unclear.

Trust, health and depression

Cognitive and structural aspects of social capital are often associated negatively with depressive symptoms and poorer health (Chen, Gao, Xu, Wang, & Li, 2018). Trust, specifically, supports the exchange of health-related information (Campos-Matos et al., 2015) and lower levels of trust link to higher rates of most major causes of death including those borne of chronic health conditions (Kawachi, Kennedy, Lochner, & Prothrow-Stith, 1997).

Trust is often associated with self-rated health and mental health more than other elements of social capital (Feng, Vlachantoni, Liu, & Jones, 2016). Among older adults, lower levels of trust and social reciprocity are associated with higher levels of depressive symptoms (Han et al., 2018). Despite the potential benefits of trust, cultural and environmental norms may influence the role that trust plays as a social resource. High levels of generalized trust have been found to associate positively with depressive symptoms when interactions were considered (Adjaye-Gbewonyo et al. 2018), revealing the potentially complex role of trust in health. Given these complex relationships, it important to explore how individual elements of social capital relate to depression and other chronic health conditions among older adults who are incarcerated.

The purpose of this study was to explore social capital within a carceral setting to understand its relationship to chronic health conditions and depressive symptoms among older adults. It was also important to explore how cognitive or network structure facets of social capital account for depressive symptom variance. We hypothesized that each indicator of social capital would be related negatively to depressive symptoms and that trust (viz. cognitive facet) would demonstrate a stronger inverse relationship to depressive symptoms than other indicators.

Methods

Participants and procedures

This study sample was drawn from a larger, mixed-method study of older adults in Kentucky prisons (N = 5) approved by both the University of Louisville Institutional Review Board and the Kentucky Department of Corrections. However, social capital data were only available at one institution at the time of this study. At this institution, 199 men were approached and asked to participate which represented approximately 16% of the total prison population at this institution. Men in this study (n = 91) represent voluntarily consented respondents from a single medium security prison in Kentucky interviewed in January and February of 2019. First, a census was obtained from institutional staff prior to data collection including all men aged 45 years or above who were able to understand and/or speak English. Individuals who are incarcerated often experience multiple chronic conditions and health-relative risks that have cumulative effects that accelerate aging (Aday, 2003; Williams, Goodwin, Baillargeon, Analt, Walter, 2012). This acceleration means that those individuals often experience physical and psychological conditions that are consistent with their non-incarcerated peers who are at least 10–15 years older. This acceleration has prompted recommendation to consider a younger chronical ages than those traditionally considered in the community to capture the experiences of older adults who are incarcerated (Greene, Ahalt, Stijacic-Cenzer, Metzger, & Williams, 2018). While not considering aging adults in the general population, men aged 45–54 years were included in the census based on accelerated aging (Aday & Krabill, 2012; Williams & Abraldes, 2007).

Those that were in isolation/seclusion/and/or on suicide watch and those who required translation or interpretation were excluded from the census. Intent to harm one’s self requires immediate notification of prison staff and termination of study participation regardless of the level of acuity or risk associated with the disclosure. Addressing suicidal ideations that represent imminent risk of harm to self or other is critical; however, seclusion for suicidal ideations observation may be accompanied with stigma that has implications for a person’s interaction with correction staff and peers if they are not a threat to themselves or others. All resulting men on the census were approached for participation in the larger study by trained research staff.

Research staff discussed study aims, risks, benefits and the nature of voluntary consent with potential respondents and also highlighted confidentiality exclusions linked to child or elder abuse allegations, threats to self or others, plans to escape or Prison Rape Elimination Act (PREA)-related concerns. All interviews were administered face-to-face by research staff using tablets to access electronic surveys embedded in the Qualtrics® offline application following the provisions of written informed consent. Any individual believed to not understand the essential elements of consent for any reason, at any time, was either excluded from participation or withdrawn administratively and their responses were not retained.

Measures

Control variables included age, years incarcerated, functional status and prison adjustment. Age and years incarcerated were obtained via self-report during the face-to-face interview and both were measured in years. Functional status was captured using the 20-item ADL Subscale of the Older Adults Resources and Services Multidimensional Functional Assessment Questionnaire (Fillenbaum, 1988). The ADL asks respondents to rate their ability to complete identified activities using one of three response options ranging from (0) I can do without help to (2) I am unable to do. Respondents were asked about a wide range of physical activities including their ability to feed themselves, manage their money, get in and out of bed and bathe. Scores are obtained by taking the sum of responses across the 20 items (possible range: 0–40) with higher scores relating to higher levels of impairment or disability. The ADL produced an acceptable level of reliability for the current sample (α = .86). A single item was used to assess respondents’ prison adjustment: ‘In the last two weeks, I have had a difficult time adjusting to prison’. Response options (7) ranged from (1) Strongly disagree to (7) Strongly agree.

Chronic health conditions

A list of chronic health conditions was developed using the most pervasive conditions identified by the Centers for Disease Control. Participants were asked to indicate whether they did or did not currently have any of the following 14 conditions: cancer/leukemia, stroke, Alzheimer’s disease, other dementia, heart disease, heart attack, high blood pressure, arthritis, osteoporosis, diabetes, lung disease, stomach/digestion problems, problems with walking/mobility or alcohol/drug problems. Scores were computed by obtaining the sum of all affirmative responses (possible range: 0–14).

Social capital

Several items were used to explore social capital. Generalized carceral trust was measured using a single item. Respondents reported their level of trust in other individuals with whom they were currently incarcerated using one of five options ranging from (1) All to (5) None. Participants were also asked to identify the total number of people with whom they are incarcerated that provide some form of support as a measure of respondents’ carceral network size. Response options (5) ranged from (1) Zero people to (5) 10 or more people. Finally, respondents were asked to evaluate their understanding of prison norms or ways of life. Response options for the item ranged from (1) Strongly disagree to (7) Strongly agree.

A modified version of the WHO QOL-BREF (WHOQOL-BREF) was used to measure satisfaction with social relationships. The WHOQOL-BREF is composed of four life domains (social relationships, environment, psychological and physical health). Only the three-item social relationships domain was included in this study as a measure of satisfaction with social relationships because of our focus on social capital. Queries regarding satisfaction with personal relationships and with support received by friends were asked. However, the third item regarding satisfaction with one’s sex life was excluded due to concerns surrounding potential PREA violations.¹ Participants were asked to rate their satisfaction levels over the past 2 weeks using one of five response options ranging from (1) not at all to (5) an extreme amount. Scores are obtained by summing the remaining two social relationship items and transforming these scores to a 100-point scale. These items demonstrated adequate levels of reliability for the current sample, α = .71.

Depressive symptoms

The Patient Health Questionnaire-8 (PHQ-8; Kroenke et al., 2009) was used to measure depressive symptoms. The PHQ-8 is an 8-item assessment tool comprised of Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria for major depression and other depressive disorders (Kroenke & Spitzer, 2002). Respondents are asked how often they have been bothered by problems over the last 2 weeks. Examples of problems include ‘little interest or pleasure in doing things’ and ‘feeling down, depressed or hopeless’. The ninth item is the least endorsed item of the PHQ-9, and removal is appropriate when adequate intervention cannot be provided or represents a risk to the population (Kroenke et al., 2009). Removal also has minimal impact on the scoring of the PHQ-8 relative to the PHQ-9 allowing for the scoring categories and interpretation to remain unchanged (Kroenke & Spitzer, 2002). Response options (4) ranged from (0) not at all to (3) nearly every day. Items are summed for a total score (possible range in current study: 0–24); severity levels have been established for the following scores: 5 (mild), 10 (moderate), 15 (moderately severe) and 20 (severe; Kroenke et al., 2009). The PHQ-8 demonstrated adequate levels of reliability for the current sample (α = .86)

Analyses

Bivariate correlations were used to examine initial relationships between social capital, chronic health conditions and depressive symptoms. After evaluating initial associations, a hierarchical multiple regression (HRM) model was used to test the study hypotheses. HRM controls for other variables in the model and estimates the unique amount of variance explained. In addition, HRM also allows for variables to be tested as direct effects before becoming simple effects following the entry of the moderated relationship (Jaccard & Turrisi, 2003).

Age, years incarcerated, functional status and prison adjustment were identified as controls, given their potential to influence depressive symptoms through social capital or chronic health conditions. These variables were entered along with social capital and chronic health conditions variable in the initial model. Incorporating an interaction term is often appropriate where an anticipated relationship does not emerge (Baron & Kenny, 1986). Trust is often a strong predictor but did not demonstrate a statistically significant relationship in the initial regression model. The lack of a statistically significant relationship with trust and depression prompted the use of an interaction term. An interaction term between general carceral trust and chronic health conditions was entered into the second model. This relationship was explored to determine whether generalized carceral trust could serve as a protective factor against depressive symptoms as physical vulnerabilities increase (see Figure 1 for a conceptual model) under different circumstances. Probing the interaction term helps to define the parameters of statistical significance while providing additional detail about how the slope changes across the continuum of values produced by the moderator (A. F. Hayes, 2013). To further explore the interaction term, PROCESS (A. F. Hayes, 2013) was entered into SPSS. PROCESS is a computational tool that can be entered as a macro through the custom macro function in SPSS allowing the program to test moderating and mediating relationships (A. F. Hayes, 2013).

Figure 1.

Conceptual model of the relationships between chronic health conditions, social capital and depressive symptoms among older adults incarcerated in a state prison setting.

PROCESS examines values of the moderator at the 16th, 50th and 85th percentiles (i.e., pick-a-point approach) to explore the interaction. Evaluating values at these percentiles indicates whether chronic health conditions were significantly related to depressive symptoms when moderated by particular levels of trust (e.g., trust all, trust some or trust few). The Johnson–Neyman technique is also available through PROCESS and was used to explore the interaction. The Johnson–Neyman technique examines the conditional effect of the focal predictor at multiple generated values across the moderator with individual p values to identify regions of significance for the interaction (A. F. Hayes, 2013). Exploring the moderator in this way allows us to determine which levels of trust (e.g., all or none), more specifically, are associated with the number of chronic conditions and depressive symptoms.

Results

Table 1 provides means, standard deviations and bivariate correlations between study variables. Depressive symptoms were related to all study variables but demonstrated the strongest relationships with functional status and the number of chronic conditions. Lower trust and smaller network size were related to higher depressive symptoms but did not relate to chronic health conditions or functional status. Carceral norms were related negatively to prison adjustment, functional status and depressive symptoms. Levels of generalized carceral trust and carceral network size were related to satisfaction with social relationships indicating these variables are consistent with supportive network features and social capital.

Table 1.

Means, standard deviations and bivariate correlations among chronic health, depressive symptoms and social capital among incarcerated older adults.

	Age	Years incarcerated	Prison adjustment	Carceral norms	Carceral network size	Generalized carceral trust	Satisfaction with social relationships	Chronic health conditions	Functional status	Depressive symptoms
Age	–
Years incarcerated	.09	–
Prison adjustment	–.07	.09	–
Carceral norms	–.07	.13	–.40**	–
Carceral network size	.00	.01	–.31**	.10	–
Generalized carceral trust^a	–.12	.08	.08	–.04	–.24*	–
Satisfaction with social relationships	.20*	.09	–.17	–.03	.29**	–.41**	–
Chronic health conditions	.03	–.33**	.14	–.16	.10	–.05	–.19*	–
Functional status	.06	–.02	.17	–.18*	.06	.12	–.19*	.47**	–
Depressive symptoms	–.21*	–.26**	.28**	–.27**	–.25**	.21*	–.45**	.59**	.48**	–
M	55.70	11.74	2.99	5.77	2.76	2.01	70.33	3.15	1.57	7.44
SD	7.48	9.55	1.97	1.52	1.55	.92	21.46	2.30	2.89	6.40

Levels of reported trust are 1 = All and 5 = None.

p < .05; **p < .01.

In the initial model, variables explained 62% of the variance in depressive symptoms (F(9, 81) = 14.58, p < .001; see Table 2). Prior to the entry of the interaction term, age (t = −2.80, p = .006), carceral norms (t = −2.09, p = .048), carceral network size (t = −2.85, p = .006), (t = −2.40, p = .019), chronic health conditions (t = 4.94, p < .001) and functional status (t = 2.90, p = .005) were statistically significant. The second model produced a slight increase in the amount of variance explained accounting for 64% of the variation in depression symptoms (F(10, 80) = 14.25, p < .001). The interaction term explained an additional 2.2% of the variance in depressive symptoms resulting in a statistically significant change (F(1, 80) = 4.91, p = .03). In the second model, age (t = −2.52, p = .011), carceral network size (t = −2.79, p = .007), social relationships QOL (t = −2.33, p = .022) and level of functioning (t = 3.42, p = .001) remained statistically significant. The entry of the interaction term changed the variables trust (t = 2.20, p = .031) and number of chronic conditions (t = 3.23, p = .002) from direct to simple effects which were statistically significant in the second model. The interaction term between trust and chronic conditions (t = −2.22, p = .030) was also statistically significant necessitating additional exploration (see Figure 2).

Table 2.

Hierarchical multiple regression results of the number of chronic conditions on depressive symptoms moderated by levels of trust in other individuals who are incarcerated (N = 91).

	Model 1		Model 2
	β	95% CI	β	95% CI
Age	–.20*	(–.29, –.05)	–.18*	(–.28, –.04)
Years incarcerated	–.08	(–.15, .05)	–.10	(–.17, .03)
Prison adjustment	–.01	(–.54, .50)	.02	(–.45, .58)
Carceral norms	–.16*	(–1.30, –.01)	–.10	(–1.09, .23)
Carceral network size	–.22**	(–1.55, –.28)	–.21**	(–1.50, –.25)
Generalized carceral trust^a	–.05	(–.75, 1.40)	–.31*	(.20, 4.09)
Satisfaction with social relationships	–.20*	(–.11, –.01)	–.19*	(–.10, –.01)
Chronic health conditions	.42**	(.70, 1.65)	1.28**	(1.37, 5.74)
Functional status	.24**	(.17, .89)	.28**	(.26, .99)
Chronic health conditions × generalized carceral trust			–.92*	(–1.15, –.06)
R ²	.62		.64

CI: confidence interval.

Levels of reported trust are 1 = All and 5 =None.

p < .05. **p < .01.

Figure 2.

Two-way effect for unstandardized variables between the level of carceral trust and the number of chronic conditions on depressive symptoms.

Examining the pick-a-point approach, the 16th and 50th percentile values for the moderator were significant while the 85th percentile value was not. The Johnson–Neyman technique was used to explore the interaction. Results from this approach appear in Table 3. One region of significance was identified where the values of the moderator no longer relate to a statistically significant relationship between the number of chronic conditions and depressive symptoms. Within the region of statistically significant values, moderating values associated with higher levels of trust produced larger effects for chronic conditions on depressive symptoms while lower levels of trust were associated with less of an effect of chronic conditions on depressive symptoms. These results indicate that trust may act as a potential vulnerability relative to other forms of social capital.

Table 3.

Conditional effect of chronic health conditions (IV) on depressive symptoms according to differences in generalized carceral trust (M) values using the Johnson–Neyman technique (N = 91).

	Trust values	Unstandardized effect	p value	LLCI	ULCI
All	1.00	2.94	<.001	1.29	4.60
	1.20	2.82	<.001	1.27	4.37
	1.40	2.70	<.001	1.25	4.15
	1.60	2.58	<.001	1.23	3.92
	1.80	2.46	<.001	1.22	3.70
Most	2.00	2.34	<.001	1.19	3.48
	2.20	2.22	<.001	1.17	3.26
	2.40	2.09	<.001	1.15	3.04
	2.60	1.97	<.001	1.12	2.83
	2.80	1.85	<.001	1.09	2.62
Some	3.00	1.73	<.001	1.05	2.41
	3.20	1.61	<.001	1.00	2.21
	3.40	1.49	<.001	.95	2.03
	3.60	1.37	<.001	.87	1.86
	3.80	1.25	<.001	.78	1.71
A few	4.00	1.12	<.001	.66	1.59
	4.20	1.00	<.001	.52	1.49
	4.40	.88	.001	.35	1.41
	4.60	.76	.013	.17	1.35
	4.77^a	.66	.050	.00	1.31
	4.80	.64	.060	–.03	1.30
None	5.00	.52	.173	–.23	1.27

LLCI: lower-limit confidence interval; ULCI: upper-limit confidence interval.

Region of significance.

Discussion

Older adults who were incarcerated experience depression and chronic health conditions at higher rates than their non-incarcerated peers. The associated costs with these conditions come at great expense to individuals attempting to serve their time and return to their communities, as well as the institutions charged with their care while they are incarcerated. The social environment in which individuals live and manage their physical and mental health plays a critical role in developing social capital. The purpose of this study was to explore whether social capital acts as a determinant of health among older adults in a carceral setting. Findings from this study partially support the hypotheses proposed and reveal both the light and dark sides of social capital related to depressive symptoms experienced by older adults in a carceral setting. Overall, many facets of social capital (e.g., carceral network size and satisfaction with social relationships) related negatively to depressive symptoms, indicating the potential protective benefits. However, results related to the interaction between chronic health conditions and generalized carceral trust indicate that thin trust may act as a potential vulnerability relative to other forms of social capital in prisons among older adults.

Prison as a source of community

Prisons are communities in that they represent a group of interacting persons in a bounded, geographical space, which share some commonalities (Brueggemann, 2006). Communities are often sources of health-harming or health-promoting behavior (McAlister, Perry, & Parcel, 2008). Thus, prisons can be instrumental to developing social resources that help older adults cope with depressive symptoms while incarcerated. Findings from this study indicate that older adults who were incarcerated may create and mobilize network resources within a carceral setting similar to those produced in other communities. Given the restricted access to resources, interpersonal relationships may provide greater protective benefits in institutions where other health-promoting mechanisms are absent.

Quality of social relationships

Social relationships provide emotional and instrumental resources that are critical in reducing health decline (Wyse, 2018) and serve as important indicators of successful aging (Rowe & Kahn, 1997; Steinkamp & Kelly, 1987). Although many positive relationships may develop among those who are incarcerated, many quality relationships are likely associated with pre-incarceration networks (e.g., family and friends). Yet, incarceration significantly harms familial and social relationships (Edin, Nelson, & Paranal, 2004; Lopoo & Western, 2005) particularly for older adults who may have a long history of criminal involvement, drug use or long prison sentences (Wyse, 2018). In this study, quality of relationships was significantly related to depressive symptoms and distinct from carceral network size. Significant individual hardships and institutional costs are incurred by the health conditions among older adults who were incarcerated. Supporting and strengthening quality social relationships while individuals are incarcerated may provide protective benefits that reduce physical and mental health decline. These relationships might be facilitated by removing or lessening the restrictions related to visitation or provided greater access to phone contact that does not add burden to older adults who were incarcerated or their family and friends.

In addition, programming that supports the development of healthy relationships may provide additional benefits that contact alone will not provide. However, elements of programming used to enhance relationships may not be appropriate, given the unique function of some aspects of social capital in carceral settings.

Trust within carceral networks

Trust is a component of social capital receiving increased attention as a potential health determinant (Campos-Matos et al., 2015). Within a carceral setting, trust is thought to facilitate relationships among people who are incarcerated, as well as between those incarcerated and staff (Harvey, 2012). However, trust is a complex dimension of social capital. And in such a milieu, trust may be rooted in suspicion of authority figures, concerns of safety and perceived vulnerability (Lafferty, Treloar, Butler, Guthrie, & Chambers, 2016). In this study, trust emerged as a risk factor increasing the potential influence of chronic health conditions on depressive symptoms. The increased vulnerability presented by trust reveals the darker side of social capital in carceral settings.

Strategies or approaches for building social capital should be aware of the function of trust in carceral settings. For example, therapeutic groups addressing experiences with depression and chronic health reliant on disclosure require trust which may be less effective than groups focused on providing information (e.g., psychoeducational groups) about depression and strategies for managing chronic health conditions in a carceral setting (e.g., information about scheduling medical appointments). Information-focused groups can support health promotion within prisons (Liau et al., 2004; Roe-Sepowitz, Bedard, Pate, & Hedberg, 2014). For example, one such strategy may target expanding carceral network size to provide channels for health-related information. These concomitant social efforts may yield the protective benefits that reduce depressive symptoms among older adults who were incarcerated with chronic health conditions.

Limitations

The use of self-reported health information represents a limitation to this study. In some circumstances, individuals may be poor historians of their health history and may not report this information accurately. In addition, respondents were only asked about the most commonly occurring chronic health conditions so less common chronic health conditions that related to depressive symptoms were not measured in this study. Measures of social capital were limited and only included single items. In addition, the specific level of impairment associated with each chronic condition was not measured, so it is unclear what exactly trust mitigates related to chronic conditions. Moreover, social capital encompasses a wide array of resources that may assist in reducing depressive symptoms beyond those captured here. Therefore, understanding which facets of social capital yield specific resources in carceral setting may be helpful to promote salutogenic rather than pathologic networks. In addition, experiences with social capital only reflect one institution, and social capital may fluctuate between prisons.

Implications for future research

This study explored social capital as a health determinant among older adults incarcerated in a state prison. The indicators used to represent trust, norms and network size were limited, and health condition symptom chronicity was not captured. Using psychometrically validated instruments with greater conceptual depth may provide information about the complex role of social capital components like trust in a carceral setting. Social capital may be an untapped institutional resource within these unique environments to reduce the impact of chronic health conditions and depression. Given the agnostic nature of social capital, researchers should be mindful that network connections and affiliated resources may promote health-damaging behaviors and thus are encouraged to investigate the dual nature of social networks within this and similar milieus.

Footnotes

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Adrian J Archuleta

Notes

References

Aday

R. H.

(2003). Aging prisoners: Crisis in American corrections. Westport, CT: Praeger Publishers.

Aday

R. H.

Krabill

J. J.

(2012). Older and geriatric offenders: Critical issues for the 21st century. In Gideon

(Ed.), Special needs offenders in correctional institutions (Vol. 1, pp. 203–233). London, England: SAGE.

Adjaye-Gbewonyo

Kawachi

Subramanian

S. V.

Avendano

(2018). High social trust associated with increased depressive symptoms in a longitudinal South African sample. Social Science & Medicine, 197, 127–135.

Baron

R. M.

Kenny

D. A.

(1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.

Barrenger

S. L.

Draine

(2013). ‘You don’t get no help’: The role of community context in effectiveness of evidence-based treatments for people with mental illness leaving prison for high risk environments. American Journal of Psychiatric Rehabilitation, 16, 154–178.

Binswanger

I. A.

Krueger

P. M.

Steiner

J. F.

(2009). Prevalence of chronic medical conditions among jail and prison inmates in the USA compared with the general population. Journal of Epidemiology & Community Health, 63, 912–919.

Blomberg

T. G.

Lucken

(2010). American penology: A history of control. New York, NY: Routledge.

Brueggemann

W. G.

(2006). The practice of macro social work. Belmont, CA: Thomson Brooks/Cole.

Bourdieu

(1986). The forms of capital. In Richardson

J. G.

(Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). Westport, CT: Greenwood Press.

10.

Campos-Matos

Subramanian

S. V.

Kawachi

(2015). The ‘dark side’ of social capital: Trust and self-rated health in European countries. European Journal of Public Health, 26, 90–95. https://doi.org/10.1093/eurpub/ckv089

11.

Carson

(2018, January). Prisoners in 2016 (NCJ #251149). Washington, DC: U.S. Department of Justice. Retrieved from https://www.bjs.gov/content/pub/pdf/p16.pdf

12.

Carson

Sabol

(2016, May). Aging of the state prison population, 1993-2013 (Special Report No. NCJ #248766). Washington, DC: Bureau of Justice Statistics, U.S. Department of Justice. Retrieved from https://www.bjs.gov/content/pub/pdf/aspp9313.pdf

13.

Chen

Gao

Wang

(2018). Association between personal social capital and depressive symptoms: Evidence from a probability sample of urban residents in China. International Journal of Social Psychiatry, 64, 767–777.

14.

Combalbert

Pennequin

Ferrand

Keita

Geffray

(2019). Effect of age, time spent in prison and level of education on the perceived health and quality of life of elderly prisoners. International Journal of Prisoner Health, 15, 168–180.

15.

Combalbert

Pennequin

Ferrand

Vandevyvère

Armand

Geffray

(2016). Mental disorders and cognitive impairment in ageing offenders. The Journal of Forensic Psychiatry & Psychology, 27, 853–866.

16.

Dumont

D. M.

Allen

S. A.

Brockmann

B. W.

Alexander

N. E.

Rich

J. D.

(2013). Incarceration, community health, and racial disparities. Journal of Health Care for the Poor and Underserved, 24, 78–88.

17.

Edin

Nelson

T. J.

Paranal

(2004). Fatherhood and incarceration as potential turning points in the criminal careers of unskilled men. In Patillo

M. E.

Weiman

D. F.

Western

(Eds.), Imprisoning America: The social effects of mass incarceration (pp. 46–75). New York, NY: SAGE.

18.

Fazel

Hope

O’Donnell

Piper

Jacoby

(2001). Health of elderly male prisoners: Worse than the general population, worse than younger prisoners. Age and Ageing, 30, 403–407.

19.

Fellner

Vinck

(2012). Old behind bars: The aging prison population in the United States. New York, N.Y: Human Rights Watch. Retrieved from https://www.hrw.org/sites/default/files/reports/usprisons0112webwcover_0.pdf

20.

Feng

Vlachantoni

Lui

Jones

(2016). Social trust, interpersonal trust and self-rated health in China: A multi-level study. International Journal of Equity in Health, 15, Article 180.

21.

Fillenbaum

G. G.

(1988). Multidimensional functional assessment of older adults: The duke older Americans resources and services procedures. Hillsdale, NJ: Lawrence Erlbaum.

22.

Fiske

Wetherell

J. L.

Gatz

(2009). Depression in older adults. Annual Review of Clinical Psychology, 5, 363–389.

23.

Friedrich

M. J.

(2017). Depression is the leading cause of disability around the world. Journal of the American Medical Association, 317(15), 1517.

24.

Goldberg

Goodyer

(2005). The origin and course of common mental disorders. London, England: Routledge.

25.

Greene

Ahalt

Stijacic-Cenzer

Metzger

Williams

(2018). Older adults in jail: High rates and early onset of geriatric conditions. Health Justice, 6(1), 3.

26.

Han

Shin

Jee

Yoon

. . . Kim

(2018). Social capital, socioeconomic status, and depression in community-living elderly. Journal of Psychiatric Research, 98, 133–140.

27.

Harris

(2010). A stress-vulnerability model of mental disorder: Implications for practice. In Webber

Nathan

(Eds.), Reflective practice in mental health: Advanced psychosocial practice with children, adolescents and adults (pp. 64–81). London, England: Jessica Kingsley.

28.

Harvey

(2012). Young men in prison: Surviving and adapting to life inside (2nd ed.). New York, NY: Routledge.

29.

Hayes

A. F.

(2013). Introduction to mediation, moderation and conditional process analysis: A regression-based approach (2nd ed.). New York, NY: The Guilford Press.

30.

Hayes

A. J.

Burns

Turnbull

Shaw

J. J.

(2012). The health and social needs of older male prisoners. International Journal of Geriatric Psychiatry, 27, 1155–1162.

31.

Haynie

D. L.

Whichard

Kreager

D. A.

(2018). Social networks and health in a prison unit. Journal of Health and Social Behavior, 59, 318–334.

32.

Jaccard

Turrisi

(2003). Sage university papers series on quantitative applications in the social sciences: Interaction effects in multiple regression (2nd ed.). Thousand Oaks, CA: SAGE.

33.

James

D. J.

Glaze

L. E.

(2006). Mental health problems of prison and jail inmates (Special Report No. NCJ #213600). Washington, DC: Bureau of Justice Statistics, U.S. Department of Justice. Retrieved from https://www.bjs.gov/content/pub/pdf/mhppji.pdf

34.

Kawachi

(2017). Trust and population health. In Uslaner

(Ed.), The Oxford handbook for social and political trust. New York, NY: Oxford University Press.

35.

Kawachi

Kennedy

B. P.

Lochner

Prothrow-Stith

(1997). Social capital, income inequality, and morality. American Journal of Public Health, 87, 1491–1498.

36.

Kawachi

Takao

Subramanian

S. V.

(2013). Global perspectives on social capital and health. New York, NY: Springer.

37.

Kessler

R. C.

Bromet

(2013). The epidemiology of depression across cultures. Annual Review of Public Health, 34, 119–138.

38.

Kok

R. M.

Reynolds

C. F

3rd . (2017). Management of depression in older adults: A review. Journal of the American Medical Association, 317, 2114–2122.

39.

Kroenke

Spitzer

R. L.

(2002). The PHQ-9: A new depression and diagnostic severity measure. Psychiatric Annuals, 32, 509–521.

40.

Kroenke

Strine

T. W.

Spitzer

R. L.

Williams

J. B. W.

Berry

J. T.

Mokdad

A. H.

(2009). The PHQ-8 as a measure of current depression in the general population. Journal of Affective Disorders, 114, 163–173.

41.

Lafferty

Treloar

Butler

Guthrie

Chambers

G. M.

(2016). Unlocking dimensions of social capital in the prison setting. Health and Justice, 4(9), 1–12.

42.

Lawrence

Kisely

Pais

(2010). The epidemiology of excess mortality in people with mental illness. Canadian Journal of Psychiatry, 55, 752–760.

43.

Liau

A. K.

Shively

Horn

Landau

Barriga

Gibbs

J. C.

(2004). Effects of psychoeducation for offenders in a community correctional facility. Journal of Community Psychology, 32, 543–558.

44.

Lopoo

L. M.

Western

(2005). Incarceration and the formation and stability of marital unions. Journal of Marriage and Family, 67, 721–734.

45.

Maruschak

L. M.

Berzofsky

Unangst

(2015). Medical problems of state and federal prisoners and jail inmates, 2011-2012 (Special Report No. NCJ #248491). Washington, DC: Bureau of Justice Statistics, U.S. Department of Justice. Retrieved from https://www.bjs.gov/content/pub/pdf/mpsfpji1112.pdf

46.

McAlister

A. L.

Perry

C. L.

Parcel

G. S.

(2008). How individuals, environments, and health behaviors interact: Social cognitive theory. In Glanz

Rimer

B. K.

Viswanath

(Eds.), Health behavior and health education: Theor, research, and practice (pp. 169–188). San Franciso, CA: Jossey-Bass.

47.

Møller

Gatherer

Jürgens

Stöver

Nikogosian

(Eds.) (2007). Health in prisons: A WHO guide to the essentials in prison health. Geneva, Switzerland: World Health Organization Regional Office Europe.

48.

Moore

Kawachi

(2017). Twenty years of social capital and health research: A glossary. Journal of Epidemiological Community Health, 71, 513–517.

49.

Roe-Sepowitz

D. E.

Bedard

L. E.

Pate

K. N.

Hedberg

E. C.

(2014). Esuba: A psychoeducation group for incarcerated survivors of abuse. International Journal of Offender Therapy and Comparative Criminology, 58, 190–208.

50.

Rosital

(2011). The facets of social capital. Journal for the Theory of Social Behavior, 41, 308–326.

51.

Rowe

J. W.

Kahn

R. L.

(1997). Successful aging. The Gerontologist, 37, 433–440.

52.

Senior

Forsyth

Walsh

O’Hara

Stevenson

Hayes

. . . Burns

(2013). Health and social care services for older male adults in prison: The identification of current service provision and piloting of an assessment and care planning model. Health Services and Delivery Research, 1(5), 1–137.

53.

Scott

K.M.

Lim

Alhamzawi

Alonso

Bruffaerts

Caldas-de-Almeida

J. M.

. . . Kessler

R. C.

(2016). Association of mental disorders with subsequent chronic physical conditions: World mental health survey from 17 countries. JAMA Psychiatry, 73(2), 150–158.

54.

Schneiderman

Ironson

Siegel

S. D.

(2005). Stress and health: Psychological, behavioral, and biological. Annual Review of Clinical Psychology, 1, 607–628.

55.

Sprangers

M. A.

de Regt

E. B.

Andries

van Agt

H. M.

Bijl

R. V.

de Boer

J. B.

. . . Haes

H. C.

(2000). Which chronic conditions are associated with better or poorer quality of life. Journal of Clinical Epidemiology, 53, 895–907.

56.

States of Incarceration: The Global Context 2018. (2019). Prison policy initiative. https://www.prisonpolicy.org/global/2018.html

57.

Steinkamp

M. W.

Kelly

J. R.

(1987). Social integration, leisure activity, and life satisfaction in older adults: Activity theory revisited. The International Journal of Aging & Human Development, 25, 293–307.

58.

Testa

A. M.

(2019). Access to healthy food retailers among formerly incarcerated individuals. Public Health Nutrition, 22, 672–680.

59.

Trotter

Baidawi

(2015). Older prisoners: Challenges for inmates and prison management. Australian & New Zealand Journal of Criminology, 48, 200–218.

60.

Villalonga-Olives

Kawachi

(2017). The dark side of social capital: A systematic review of the negative health effects of social capital. Social Science & Medicine, 194©, 105–127.

61.

Villalonga-Olives

Kawachi

(2015). The measurement of social capital. Gaceta Sanitaria, 29, 62–64.

62.

Williams

B. A.

Abraldes

(2007). Growing older: Challenges of prison and reentry for the aging population. In Greifinger

R. B.

(Ed.), Public health behind bars (pp. 56–72). New York, NY: Springer.

63.

Williams

B.A.

Goodwin

J.S.

Baillargeon

Ahalt

Walter

L.C.

(2012). Addressing the aging crisis in U.S. criminal justice health care. Journal of the American Geriatrics, 60(6), 1150–1156.

64.

Williams

B. A.

Stern

M. F.

Mellow

Safer

Greifinger

R. B.

(2012). Aging in correctional custody: Setting a policy agenda for older prisoner health care. American Journal of Public Health, 102, 1475–1481.

65.

Williams

D. R.

Jackson

P. B.

(2005). Social sources of racial disparities in health. Health Affairs, 24(2), 325–334.

66.

World Health Organization. (2017). Depression and other common mental disorders: Global health estimates. Geneva, Switzerland: Author.

67.

Wrigley

N.S.

Sewell

Margetts

(2003). Deprivation, diet, and food-retail access: Findings from the leeds ‘food deserts’ study. Environment and Planning A: Economy and Space, 35, 151–188.

68.

Wyse

(2018). Older men’s social integration after prison. International Journal of Offender Therapy and Comparative Criminology, 62(8), 2153–2173.