Social Work Research

Hawkins, Robert L

doi: 10.1093/swr/svac034pmid: N/A

Moore, John Randolph; Eikenberry, Jacob; Fedina, Lisa; DeVylder, Jordan

doi: 10.1093/swr/svac033pmid: N/A

Some evidence suggests that the practice of solitary confinement in incarceration settings is linked to poor mental health outcomes; however, prior research has not yet examined associations between experiences of solitary confinement and psychosis symptoms. To address this gap, authors conducted a cross-sectional survey of 201 formerly incarcerated men and women in the United States to examine the relationship between solitary confinement and psychosis symptoms in the postrelease community setting. Results indicated that solitary confinement was significantly associated with higher levels of current psychosis symptoms after controlling for demographic factors and clinical characteristics. These findings highlight the need for short-term and long-term community-based mental health interventions and prevention efforts in the postrelease community setting. Social workers and practitioners in community mental health settings should be cognizant of patients’ histories with solitary confinement and consider how these experiences may present risks to current mental health symptoms (i.e., early onset psychosis). Future studies are needed on protective mechanisms that may buffer the effects of prior solitary confinement on psychosis.

Roll, Stephen; Despard, Mathieu; Grinstein-Weiss, Michal; Bufe, Sam

doi: 10.1093/swr/svac028pmid: N/A

Low-income households struggle to accumulate emergency savings, which increases economic vulnerability in the face of unexpected events like expensive car repairs. This vulnerability may be even greater among persistently low-income households, which might benefit most from building emergency savings using tax refunds. This study examined the effects of randomly assigned behavioral interventions that incorporated a choice architecture manipulation and savings-related messages aimed at encouraging refund saving and delivered through a free, online tax-filing software program. The study sample comprised 4,536 tax filers, including 1,235 with persistent low incomes. Using administrative tax data and data from a two-wave household financial survey, regression-adjusted treatment impacts were estimated using intent-to-treat analysis to examine whether filers had any of their tax refunds still in savings and how much of their refund they still had saved six months after filing their taxes. Results indicated directional but nonstatistically significant increases in these savings outcomes across three treatment groups for the full sample, yet statistically significant treatment effects among the persistently low-income subsample, effects that were moderated by the prefiling absence of emergency resources. These results suggested that tax-time savings interventions are most effective among low-income households with the greatest needs for emergency savings.

Veeh, Christopher A; Renn, Tanya; Moore, John

doi: 10.1093/swr/svac031pmid: N/A

State prisons incarcerate 1.06 million adults in the United States and, every year, release approximately 502,000 back to the community (Carson, 2021). Traumatic brain injury (TBI), defined as a head injury that disrupts normal function of the brain, is disproportionately experienced by individuals in prison, thus TBI is an important health concern for social workers who provide services to these adults. In fact, recent meta-analyses have found TBI prevalence to be approximately 51% in adults who are incarcerated versus 12% in the general population (Farrer & Hedges, 2011). TBI has important health consequences for returning citizens, since it is related to depression, anxiety, and substance abuse (McKinlay et al., 2014; Scholten et al., 2016). In addition to TBI, adults returning from prison suffer from a variety of other health problems. Infectious diseases such as hepatitis C are reported at levels five to eight times higher than among the general population (Varan et al., 2014), and chronic health conditions like diabetes and cardiovascular disease are also experienced at higher rates by adults in prison (Binswanger et al., 2009). Despite the health issues found disproportionately in adults who are incarcerated, there is limited research on how poor health impacts the transition from prison to the community. However, recent work by Link and colleagues (2019) introduced a health-based model of desistance that theorizes that an individual’s health can hamper one’s ability to overcome the numerous challenges of reintegration. Current best practice within correctional rehabilitation characterizes health as a minor risk for return to incarceration (Andrews & Bonta, 2010), but Link and colleagues demonstrate that poor health indirectly impacts success following incarceration. Analysis of 1,532 men released from incarceration found that physical limitations are directly associated with increased depression, which in turn increased the likelihood for financial hardship and reincarceration (Link et al., 2019). Therefore, a focus on physical health issues, specifically health issues found disproportionally among incarcerated individuals and that are a risk factor for depression, is vital for long-term success following release from incarceration. There is strong evidence that TBI is both found at elevated rates among incarcerated individuals and a risk factor for depression (see Farrer & Hedges, 2011; Scholten et al., 2016). Effective assessment of an individual’s self-perceived health status is their health-related quality of life (HRQoL), which measures an individual’s perception of how their health is impacting their physical, mental, and social functioning (Polinder et al., 2015). Individuals with TBI report lower levels of HRQoL compared with population norms, with past studies showing the largest deficits in mental HRQoL followed by physical HRQoL (Polinder et al., 2015). However, there is limited research on how specific health problems impact HRQoL of incarcerated individuals. For example, Thein and colleagues (2006) found no association between hepatitis C and HRQoL in men who are incarcerated in Australia. In more recent research, a study from the United Kingdom with men in prison reported those with comorbid ADHD and TBI showed significantly lower levels of HRQoL compared with individuals with TBI only (Young et al., 2018). Considering this final study was the only one we identified that looked at the relationship between TBI and HRQoL in any incarcerated population, there is a gap in the literature for this area. Informed by the health-based model of desistance, the current study undertook a preliminary investigation into whether TBI impacted levels of HRQoL in incarcerated adults who are preparing to be released from prison. The following research question guided the study: Do incarcerated adults with a lifetime history of TBI report lower HRQoL compared with incarcerated individuals without a TBI history? We hypothesized that TBI would have a negative association with an individual’s HRQoL. These study findings can help contribute further understanding into whether HRQoL is a factor to consider when tailoring interventions for individuals with TBI who are preparing for release back to the community. Method Participants and Procedures Participants were 80 adults incarcerated in a Midwest state prison system preparing for release in six to nine months. Individuals were recruited for a pilot trial of a prison reentry program. Trial eligibility was restricted to those releasing to three urban counties, 18 years of age or older, assigned to parole for at least 12 months, conversational in English, and cognitively able to understand study participation. Cognitive ability was determined by an individual’s capacity to correctly answer questions about the informed consent statement. Individuals with a sex offense or an unmanaged mental disorder were excluded. These eligibility criteria were used to identify a sample that would be able to participate in all facets of the pilot trial postrelease. Data come from the baseline interview of the efficacy trial. Recruitment, informed consent process, and interviews were conducted in a private room at each prison site. Research interviews were completed by a trained interviewer using computer-assisted personal interview software. Data was collected from March 2016 to May 2016. All trial procedures were approved by the institutional review board at a private Midwest research university. Measures Independent Variable TBI information was collected using two questions based on those used in the Pathways to Desistance Study (Center for Research on Health Care Data Center, n.d.). The first question asked about lifetime head injury that resulted in headaches, dizziness, irritability, extreme tiredness, difficulty sleeping, difficulty concentrating, anxiety, or memory difficulties. The second question asked about lifetime head injury that resulted in a loss of consciousness. For this study, a participant was indicated as positive for lifetime TBI if they answered in the affirmative for either question to capture a range of injury severity. Dependent Variable HRQoL was assessed with the RAND 36-Item Health Survey 1.0 (hereafter RAND Health Survey; Hays et al., 1993). The RAND Health Survey assesses eight domains of health: physical functioning (α = .85), bodily pain (α = .73), role limitation due to physical health (scale has no variance), role limitations due to emotional problems (scale has no variance), emotional well-being (α = .80), social functioning (α = .55), energy/fatigue (α = .80), and general health (α = .71). Control Variables Multivariate models included the following control variables: gender, race, education level, substance use disorder, mental health disorder, and childhood trauma (Maas et al., 2017; Polinder et al., 2015; Schofield et al., 2019). Gender was measured as either male or female. Race was coded with the three categories of Black, White, other persons of color. Education level was based on self-report of the highest grade completed. Substance use disorder and mental health disorder were assessed with the Mini Neuropsychiatric Interview (MINI; Sheehan et al., 1997). Participants were indicated for a mental health disorder if they reported symptoms consistent with major depressive episode, manic/hypomanic episode, social anxiety disorder, psychotic disorder, or generalized anxiety disorder. For substance use disorder, participants were coded as yes if they reported symptoms consistent with either alcohol use disorder or substance use disorder. The MINI has good test–retest and interrater reliability (Sheehan et al., 1997). Childhood trauma was measured with the Childhood Trauma Questionnaire (CTQ; Bernstein et al., 2003). The CTQ is a 28-item measure of physical abuse, sexual abuse, emotional abuse, physical neglect, and emotional neglect. The CTQ has strong interrater reliability and criterion-related validity (Bernstein et al., 2003). Data Analysis Analysis began with unadjusted bivariate comparisons of individuals with TBI to those without a TBI. Next, assumptions of normality, homogeneity of variance, heteroskedasticity, and missing data were investigated. Last, multivariate linear regression was used to investigate the association between TBI and HRQoL. All analyses used Stata version 15. Preliminary Analysis Normality, multicollinearity, and homogeneity of variance were all met. Multicollinearity was assessed with variance inflation factor (VIF), which found the mean VIF of 1.37 and no value above 2. Heteroskedasticity was assessed using the Cook–Wesiberg test, and physical functioning and general health perception were found significant (Long & Ervin, 2000). All analyses using these HRQoL domains used robust HC3 standard errors (Long & Ervin, 2000). Furthermore, both role limitation domains were dropped from analysis because of no score variation. Missing data were low with no variable exceeding 2.50%. Missingness was examined and the CTQ was missing not at random (MNAR). MNAR can be transformed to missing at random by using proxy indicators of the MNAR variable in the multiple imputation model (Lang & Little, 2018). Therefore, the Trauma History Questionnaire total score, a 24-item measure of lifetime trauma (Hooper et al., 2011), was used in multiple imputation of the CTQ total score. Results Over half of the respondents reported a lifetime TBI event (52.50%). Those with TBI were more likely to identify as either Black (42.86%) or a person of color (19.05%; p < .05), as well as screen for a mental health disorder (78.57% versus 57.89%; p < .05) and a substance use disorder (59.21% versus 34.21%; p < .05). No statistical difference was shown for gender, childhood trauma, and education level. Across the domains of HRQoL, individuals with TBI history reported significantly lower scores in physical functioning (88.33 versus 96.18; p < .05), energy/fatigue (62.26 versus 73.42; p < .05), and overall general health (65.71 versus 78.55; p < .01). The multivariate linear regression models are shown in Table 1. A TBI history was significantly related to lower scores in physical functioning (b = –6.31, p < .05) and general health (b = –5.13, p < .05). An effect size for the relationship between TBI and significant HRQoL domains was computed. For physical functioning, TBI had a medium to small effect (d = .46). In terms of overall general health, the effect size was small (d = .28). Table 1: Linear Regression Models for TBI and HRQoL . Emotional Well-Being Estimate [95% CI] . Social Functioning Estimate [95% CI] . Physical Functioninga Estimate [95% CI] . Energy/Fatigue Estimate [95% CI] . Pain Estimate [95% CI] . General Healtha Estimate [95% CI] . TBI –0.23 [–4.74, 4.29] 0.87 [–3.79, 5.53] –6.31* [–11.51, –1.10] –2.75 [–7.59, 2.08] –0.66 [–5.65, 4.32] –5.13* [–10.06, –0.21] Male –0.46 [–8.21, 7.30] 6.44* [0.36, 12.53] –0.82 [–7.61, 5.97] 1.50 [–4.81, 7.81] 3.03 [–3.47, 9.53] 2.16 [–5.48, 9.79] Race  Black (Reference group)  White –3.89 [–11.03, 3.25] 0.41 [–6.04, 6.86] 2.24 [–4.13, 8.62] 0.25 [–6.42, 6.93] –1.98 [–8.86, 4.89] –1.82 [–8.66, 5.03]  Other POC –4.17 [–13.22, 4.88] –2.23 [–8.90, 4.41] 3.85 [–1.79, 9.49] –1.79 [–8.69, 5.11] –0.58 [–7.70, 6.54] –0.77 [–8.86, 7.31] Mental health diagnosis –8.48** [–12.84, –4.11] –5.63* [–10.73, –0.53] –4.40 [–9.30, 0.50] –4.83 [–10.13, 0.46] –1.80 [–7.26, 3.67] –5.75* [–10.13, –1.39] Substance use history 1.02 [–3.90, 5.94] 1.07 [–3.94, 6.08] 0.89 [–4.67, 6.45] 0.37 [–4.83, 5.58] –2.28 [–7.65, 3.09] 1.33 [–3.83, 6.50] Childhood trauma –0.13 [–0.31, 0.04] –0.13 [–0.29, 0.02] 0.10 [–0.05, 0.24] –0.11 [–0.27, 0.05] –0.03 [–0.19, 13.00] –0.02 [–0.19, 0.16] Education level 2.13 [–2.98, 7.23] 3.67 [–1.23, 8.58] 0.55 [–3.99, 5.08] 1.68 [–3.41, 6.77] 0.97 [–4.28, 6.22] 0.75 [–4.79, 6.29] . Emotional Well-Being Estimate [95% CI] . Social Functioning Estimate [95% CI] . Physical Functioninga Estimate [95% CI] . Energy/Fatigue Estimate [95% CI] . Pain Estimate [95% CI] . General Healtha Estimate [95% CI] . TBI –0.23 [–4.74, 4.29] 0.87 [–3.79, 5.53] –6.31* [–11.51, –1.10] –2.75 [–7.59, 2.08] –0.66 [–5.65, 4.32] –5.13* [–10.06, –0.21] Male –0.46 [–8.21, 7.30] 6.44* [0.36, 12.53] –0.82 [–7.61, 5.97] 1.50 [–4.81, 7.81] 3.03 [–3.47, 9.53] 2.16 [–5.48, 9.79] Race  Black (Reference group)  White –3.89 [–11.03, 3.25] 0.41 [–6.04, 6.86] 2.24 [–4.13, 8.62] 0.25 [–6.42, 6.93] –1.98 [–8.86, 4.89] –1.82 [–8.66, 5.03]  Other POC –4.17 [–13.22, 4.88] –2.23 [–8.90, 4.41] 3.85 [–1.79, 9.49] –1.79 [–8.69, 5.11] –0.58 [–7.70, 6.54] –0.77 [–8.86, 7.31] Mental health diagnosis –8.48** [–12.84, –4.11] –5.63* [–10.73, –0.53] –4.40 [–9.30, 0.50] –4.83 [–10.13, 0.46] –1.80 [–7.26, 3.67] –5.75* [–10.13, –1.39] Substance use history 1.02 [–3.90, 5.94] 1.07 [–3.94, 6.08] 0.89 [–4.67, 6.45] 0.37 [–4.83, 5.58] –2.28 [–7.65, 3.09] 1.33 [–3.83, 6.50] Childhood trauma –0.13 [–0.31, 0.04] –0.13 [–0.29, 0.02] 0.10 [–0.05, 0.24] –0.11 [–0.27, 0.05] –0.03 [–0.19, 13.00] –0.02 [–0.19, 0.16] Education level 2.13 [–2.98, 7.23] 3.67 [–1.23, 8.58] 0.55 [–3.99, 5.08] 1.68 [–3.41, 6.77] 0.97 [–4.28, 6.22] 0.75 [–4.79, 6.29] Notes: TBI = traumatic brain injury; HRQoL = health-related quality of life; POC = people of color. Unstandardized coefficients from linear regression are presented with 95% CI. HC3 robust standard errors used in model estimation. * p < .05. ** p < .01. Open in new tab Table 1: Linear Regression Models for TBI and HRQoL . Emotional Well-Being Estimate [95% CI] . Social Functioning Estimate [95% CI] . Physical Functioninga Estimate [95% CI] . Energy/Fatigue Estimate [95% CI] . Pain Estimate [95% CI] . General Healtha Estimate [95% CI] . TBI –0.23 [–4.74, 4.29] 0.87 [–3.79, 5.53] –6.31* [–11.51, –1.10] –2.75 [–7.59, 2.08] –0.66 [–5.65, 4.32] –5.13* [–10.06, –0.21] Male –0.46 [–8.21, 7.30] 6.44* [0.36, 12.53] –0.82 [–7.61, 5.97] 1.50 [–4.81, 7.81] 3.03 [–3.47, 9.53] 2.16 [–5.48, 9.79] Race  Black (Reference group)  White –3.89 [–11.03, 3.25] 0.41 [–6.04, 6.86] 2.24 [–4.13, 8.62] 0.25 [–6.42, 6.93] –1.98 [–8.86, 4.89] –1.82 [–8.66, 5.03]  Other POC –4.17 [–13.22, 4.88] –2.23 [–8.90, 4.41] 3.85 [–1.79, 9.49] –1.79 [–8.69, 5.11] –0.58 [–7.70, 6.54] –0.77 [–8.86, 7.31] Mental health diagnosis –8.48** [–12.84, –4.11] –5.63* [–10.73, –0.53] –4.40 [–9.30, 0.50] –4.83 [–10.13, 0.46] –1.80 [–7.26, 3.67] –5.75* [–10.13, –1.39] Substance use history 1.02 [–3.90, 5.94] 1.07 [–3.94, 6.08] 0.89 [–4.67, 6.45] 0.37 [–4.83, 5.58] –2.28 [–7.65, 3.09] 1.33 [–3.83, 6.50] Childhood trauma –0.13 [–0.31, 0.04] –0.13 [–0.29, 0.02] 0.10 [–0.05, 0.24] –0.11 [–0.27, 0.05] –0.03 [–0.19, 13.00] –0.02 [–0.19, 0.16] Education level 2.13 [–2.98, 7.23] 3.67 [–1.23, 8.58] 0.55 [–3.99, 5.08] 1.68 [–3.41, 6.77] 0.97 [–4.28, 6.22] 0.75 [–4.79, 6.29] . Emotional Well-Being Estimate [95% CI] . Social Functioning Estimate [95% CI] . Physical Functioninga Estimate [95% CI] . Energy/Fatigue Estimate [95% CI] . Pain Estimate [95% CI] . General Healtha Estimate [95% CI] . TBI –0.23 [–4.74, 4.29] 0.87 [–3.79, 5.53] –6.31* [–11.51, –1.10] –2.75 [–7.59, 2.08] –0.66 [–5.65, 4.32] –5.13* [–10.06, –0.21] Male –0.46 [–8.21, 7.30] 6.44* [0.36, 12.53] –0.82 [–7.61, 5.97] 1.50 [–4.81, 7.81] 3.03 [–3.47, 9.53] 2.16 [–5.48, 9.79] Race  Black (Reference group)  White –3.89 [–11.03, 3.25] 0.41 [–6.04, 6.86] 2.24 [–4.13, 8.62] 0.25 [–6.42, 6.93] –1.98 [–8.86, 4.89] –1.82 [–8.66, 5.03]  Other POC –4.17 [–13.22, 4.88] –2.23 [–8.90, 4.41] 3.85 [–1.79, 9.49] –1.79 [–8.69, 5.11] –0.58 [–7.70, 6.54] –0.77 [–8.86, 7.31] Mental health diagnosis –8.48** [–12.84, –4.11] –5.63* [–10.73, –0.53] –4.40 [–9.30, 0.50] –4.83 [–10.13, 0.46] –1.80 [–7.26, 3.67] –5.75* [–10.13, –1.39] Substance use history 1.02 [–3.90, 5.94] 1.07 [–3.94, 6.08] 0.89 [–4.67, 6.45] 0.37 [–4.83, 5.58] –2.28 [–7.65, 3.09] 1.33 [–3.83, 6.50] Childhood trauma –0.13 [–0.31, 0.04] –0.13 [–0.29, 0.02] 0.10 [–0.05, 0.24] –0.11 [–0.27, 0.05] –0.03 [–0.19, 13.00] –0.02 [–0.19, 0.16] Education level 2.13 [–2.98, 7.23] 3.67 [–1.23, 8.58] 0.55 [–3.99, 5.08] 1.68 [–3.41, 6.77] 0.97 [–4.28, 6.22] 0.75 [–4.79, 6.29] Notes: TBI = traumatic brain injury; HRQoL = health-related quality of life; POC = people of color. Unstandardized coefficients from linear regression are presented with 95% CI. HC3 robust standard errors used in model estimation. * p < .05. ** p < .01. Open in new tab Discussion The role of health in the lives of adults preparing for release from incarceration is an important factor to consider for social work service providers assisting these individuals in the transition back to the community. TBI has received greater attention recently because of association with depression, anxiety, drug and alcohol use, and violent behavior. Current findings support the study’s general hypothesis and contribute to understanding the relationship TBI has with different domains of HRQoL. Therefore, there are implications that need to be considered and areas of future research. In contrast to prior research that found health conditions like hepatitis C to lack any relationship with HRQoL among adults who are incarcerated, TBI does appear to negatively impact physical functioning as well as general health status. In the context of Link and colleagues’ (2019) health-based model of desistance, TBI deserves attention since it negatively impacts HRQoL while also increasing likelihood for depression—a potentially potent combination that could substantively increase the likelihood for negative outcomes. Given these relationships with TBI, there is a need for investigations into how interventions can be tailored to this subgroup. Existing research has largely focused on a service referral approach to address TBI (Chitsabesan et al., 2015); however, we believe there is a need for innovations that address TBI sequelae in a reentry context. For example, evidence-based practices have shown cognitive–behavioral therapy (CBT) is effective for correctional rehabilitation (Andrews & Bonta, 2010). However, cognitive deficits associated with TBI can blunt any positive impact from CBT participation (Akerele et al., 2017; Gallagher, et al., 2019). Despite the impacts of a TBI history on CBT effectiveness, there has been limited work so far into how CBT-based interventions, particularly those targeting depression and health, can be effectively adapted to ensure individuals with TBI can receive the benefits from participation (Akerele et al., 2017). Wider adoption of well-being measures like HRQoL as an outcome to assess individual progress as well as program effectiveness is an important next step for the field of correctional rehabilitation. Current practices focus almost solely on measures of risk and recidivism without any regard for the well-being of adults undertaking the difficult transition from incarceration to the community (Pettus et al., 2021). Programs designed around improving an individual’s HRQoL that can help to build the capacity for resilience during community reintegration is an important area for social work research and practice. Individuals must want to engage with evidence-based practices, like CBT, at an adequate dosage to realize positive change in outcomes. Designing programs that center services around improving HRQoL has the potential to enhance the type of intrinsic motivation that is necessary for a participant’s long-term engagement. Additionally, given the high rate of TBI found among Black individuals and other persons of color, a vital next step is to investigate these racial inequities in health given the context of systemic racism that permeates throughout the criminal justice system (Jeffers, 2019). Interventions designed for individuals with TBI should prioritize a racial equity model as described by Bibbs (2019) where programming is specifically designed to be the most supportive of Black individuals’ agency and resilience in navigating the disproportionate challenges these individuals face in the community. Participatory research methods should be used to further elucidate the lived experience of Black individuals with TBI postincarceration to further inform how interventions can be designed to engage these individuals most effectively. Finally, future research should investigate mechanisms that may lead an individual with TBI to report lower physical functioning and overall health. In addition, the role of TBI as a factor within the health-based model of desistance should be further explored. For instance, do lower levels of HRQoL associated with TBI in turn lead to higher levels of depression within formerly incarcerated individuals in the community? Strengths and Limitations Study findings should be considered in light of strengths and limitations. For strengths, this is one of the first studies to include both a TBI and HRQoL measure completed by adults while incarcerated. An additional strength is that measures of trauma were used as controls in all statistical models. In terms of limitations, the sample size of 80 is not ideal, and therefore study results should be viewed as preliminary until a larger replication study can be conducted. Moreover, the current measure of TBI that utilized two yes/no questions can be improved. Future work should adopt the Ohio State University TBI Identification Method (OSU-TBI-ID) to ensure the most accurate measurement of an individual’s TBI history. The OSU-TBI-ID is a validated measure of lifetime TBI history with adults who are incarcerated (Bogner & Corrigan, 2009), thus large-scale adoption of the OSU-TBI-ID can provide social work researchers and practitioners with a more precise and reliable method to assess for TBI when working with incarcerated individuals. In sum, the present study’s preliminary findings are important given the disproportionate prevalence of TBI and adverse health conditions experienced by individuals in prison. These findings suggest that there is a potential need for social work researchers and practitioners who work with prison reentry programs to develop interventions that are appropriate for those with a history of TBI. 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