Aging in Prison As a Collateral Consequence of Mass Incarceration

AGING IN PRISON AS A COLLATERAL CONSEQUENCE OF MASS INCARCERATION

A dissertation submitted To Kent State University in partial Fulfillment of the requirements for the Degree of Doctor of Philosophy

Meghan A. Novisky

August 2016

Except for previously published materials

Dissertation written by

Meghan A. Novisky

B.A., Kent State University, 2006

M.A. Kent State University, 2008

Ph.D., Kent State University, 2016

Approved by

Dr. Clare L. Stacey ,Chair, Doctoral Dissertation Committee

Dr. Shelley Johnson-Listwan ,Members, Doctoral Dissertation Committee

Dr. Robert L. Peralta

Dr. Lauren C. Porter

Dr. Eric Jefferis

Dr. Richard T. Serpe ,Chair, Department of Sociology

Dr. James L. Blank ,Dean, College of Arts and Sciences

TABLE OF CONTENTS...... iii

LIST OF TABLES……………………………………………………………………………….vii

ACKNOWLEDGMENTS……………………………………………………………………....viii

CHAPTERS

I. INTRODUCTION AND OVERVIEW…………………………………………...1

Mass Incarceration………………………………………………………...2

The Collateral Consequences of Mass Incarceration……………………...4

Aging Prisoners……………………………………………………………8

The Current Study………………………………………………………..10

Paper 1…………………………………………………………...11

Paper 2…………………………………………………………...12

Paper 3…………………………………………………………...14

Conclusion……………………………………………………………….15

References………………………………………………………………..17

II. RESEARCH METHODOLOGY………………………………………………...29

Setting and Participants…………………………………………………..29

Procedures………………………………………………………………..33

Measures and Materials – Quantitative Data…………………………….40

Measures and Materials – Qualitative Data……………………………...45

Data Analysis Plan……………………………………………………….46

iii

References………………………………………………………………..49

III. UNEMPLOYMENT, SOCIAL ISOLATION AND DEPRIVATION: AN EXAMINATION OF SECONDARY STRESSORS AND HEALTH AMONG ELDERLY PRISONERS……………………………………………...54

Introduction………………………………………………………………54

Review of Related Literature…………………………………….………55

Incarceration and Health………………………………………....55

Aging Prisoners…………………………………………………..56

Incarceration and Stress Proliferation Theory……………...……58

Methodology……………………………………………………………..65

Setting and Participants………………………………………..…65

Procedures………………………………………………………..66

Measures and Materials………………………………………….69

Results……………………………………………………………………73

Discussion………………………………………………………………..74

Conclusion……………………………………………………………….78

References………………………………………………………………..80

IV. ‘IF YOU DON’T KNOW, THEY TREAT YOU LIKE YOU DON’T KNOW: CHRONIC DISEASE MANAGEMENT AND THE ROLE OF CULTURAL HEALTH CAPITAL FOR OLDER INMATES……………...94

Introduction………………………………………………………………94

Review of Related Literature…………………………………………….95

The Aging Prisoner Population…………………………………..95

Chronic Disease Management…………………………………...97

Chronic Disease Management and Health Capital………………99

Methodology……………………………………………………………102

Research Design………………………………………………...102

Setting and Participants…………………………………………103

Procedures………………………………………………………104

Measures and Materials………………………………………...107

Data Analysis…………………………………………………...107

Results…………………………………………………………………..108

Theme 1: Controlling Food and Diet Options………………….108

Theme 2: Acquisition of Medical Knowledge………………….111

Theme 3: Health Advocacy…………………………………….115

Discussion………………………………………………………………120

Conclusion……………………………………………………………...123

References………………………………………………………………125

V. AN EXPLORATION INTO END OF LIFE PLANNING AMONG A SAMPLE OF OLDER MALE PRISONERS…………………………………..132

Introduction……………………………………………………………..132

Review of Related Literature…………………………………………...134

Health and Aging in Prisons……………………………………134

Prisons and End of Life Decision Making for Older Adults…...137

Methodology……………………………………………………………144

Setting and Participants…………………………………………144

Procedures………………………………………………………145

Measures and Materials………………………………………...148

Results…………………………………………………………………..153

Discussion………………………………………………………………156

Conclusion……………………………………………………………...159

References………………………………………………………………161

VI. SUMMARY AND POLICY IMPLICATIONS.……………………………….172

Summary of the Problem……………………………………………….172

Key Findings……………………………………………………………176

Overall Health…………………………………………………..176

Chronic Disease Management………………………………….177

End of Life Planning……………………………………………179 Limitations and Future Directions……………………………………...181 Policy Implications……………………………………………………..183 Conclusion……………………………………………………………...186 References………………………………………………………………188

APPENDICES

A. The Older Men’s Health Program and Screening Inventory (Loeb 2003)…………201

B. The Deprivation Scale/Prison Stresses (Rocheleau 2013)………………………….202

C. The Death Distress Scale (Abdel-Khalek 2011)……………………………………203

D. The Expectations Regarding Aging Survey ERA-12 (Sarkisian et al. 2005)………204

LIST OF TABLES

Table 1. Demographic Information by Institution……………………………………………….30

Table 2. Survey Research Response Rates………………………………………………………35

Table 3. Frequency Distribution (Demographic)………………………………………………...37

Table 4. Frequency Distribution (Offense and Sentencing)……………………………………..38

Table 5. Descriptive Statistics……………………………………………………………………39

Table 6. Sample vs. Sampling Frame Data………………………………………………………40

Table 7. Descriptive Statistics……………………………………………………………………69

Table 8. Frequency Distribution…………………………………………………………………70

Table 9. Binary Logistic Regression: Log Odds of Inmates Reporting Worse Health Ratings....73

Table 10. Frequency Distribution………………………………………………………………148

Table 11. Descriptive Statistics…………………………………………………………………149

Table 12. Binary Logistic Regression: Log Odds of Inmates Wanting Tube Feeding………....154

Table 13. Binary Logistic Regression: Log Odds of Inmates Wanting CPR…………………..155

vii

ACKNOWLEDGEMENTS

I would like to acknowledge the Graduate Student Senate of Kent State University for partially funding this research, and each of my committee members for offering feedback and encouragement from project development to defense.

I would also like to thank the state department of corrections for generously agreeing to provide me with research access, as well as the staff at each of the three state correctional institutions for accommodating my requests over a twelve month period. Most importantly, I would like to thank the 279 men incarcerated within the DOC who took the time to answer my questions and share their stories with me. I remain inspired to continue to find answers to the hardships you face.

Finally, this dissertation would not be possible without my parents, both of whom modeled for me the values of hard work, perseverance, and compassion.

viii

CHAPTER 1

INTRODUCTION

The correctional system in the United States is nearing a crisis. The U.S. has been

characterized by an era of mass incarceration since the 1970’s (see Austin and Irwin 2000;

Garland 2001; Travis et al. 2014). With decades of research now in place, it is clear that in

addition to the financial costs of housing so many men and women behind bars, incarceration

produces a multitude of collateral consequences tied to social standing, relationships,

neighborhoods, well-being, and health (see Western 2002; Pager 2003; Pettit and Western 2004;

Lopoo and Western 2005; Metraux and Culhane 2006; Western 2006; Clear 2007; Pager 2007;

Schnittker and John 2007; Massoglia 2008; Binswanger et al. 2009; Harris et al. 2010; Wakefield

and Uggen 2010; Wildeman 2010; Williams et al. 2010; Massoglia et al. 2011; Murray et al.

2012; Schnittker et al. 2012; Swisher and Roettger 2012; Carson 2014; Schnittker et al. 2014;

Turney 2015; Warner 2015). The prison population is also now aging at a rapid rate, leading to a

host of additional problems (see Aday 2003). Despite the collision of these factors, we know

surprisingly little about what it means to age in prison from an empirical standpoint. Thus, the

focus of my dissertation was to explore what it means to age in prison by addressing several components of aging – overall health, chronic disease management, and end-of-life planning – with a sample of older inmates.

Below, I outline the problem by explaining the extent of mass incarceration in the United

States, the assortment of collateral consequences that are tied to incarceration, and the complexities that come with a rapidly aging prisoner population. Following this explanation of the problem, I briefly introduce each of the three components of aging addressed in this dissertation.

Mass Incarceration At yearend 2014, more than 6.8 million people were under some form of correctional supervision in the United States and approximately one third of those 6.8 million people, or nearly 1 in every 100 U.S. adults, were serving time in either prisons or jails (Kaeble et al. 2015).

The reliance on incarceration for punishment has become so commonplace in the United States that mass incarceration is now characteristic of our country. The trend started in the 1970’s when incarceration rates began to increase dramatically and rates continued to climb each year for nearly 4 decades as a result of a variety of policy related changes, including mandatory sentencing guidelines, tough on crime politics, and increases in the use of life sentences (Austin and Irwin 2000; Garland 2001; Nellis and King 2009; Nellis 2013; Travis et al. 2014).

Growths in correctional budgets during this time period, as well as increases in the use of private prisons, mirror priorities the U.S. has placed on incarceration over the last several decades. For example, government budgets allocated a total of 9.7 billion dollars per capita for state correctional institution expenditures in 1982 and 37.2 billion dollars per capita in 2010, representing a nearly 400 percent increase in budgetary spending (Kyckelhahn 2012). The use of private prisons, once a rarity, saw 90 percent growth between 1999 and 2012 (Carson 2015).

It is important to note that incarceration rates of this magnitude are unique to the United

States. From an international perspective, the U.S. leads the world in incarceration rates

(Walmsley 2007; International Centre for Prison Studies 2015). For example, although the

United States only accounts for approximately 5 percent of the world’s population, we house roughly one-quarter of the world’s prisoners (Travis et al. 2014). As another example, approximately 1.6 million people were incarcerated in jails or prisons across all of Europe during

2014 (Aebi et al. 2015), yet 2.2 million people were incarcerated in the U.S. alone during that same time period (Kaeble et al. 2015).

Although recent years have been accompanied by small declines in the overall rates of correctional supervision in the United States, these declines are attributed largely to reductions in the use of community supervision rather than significant reductions in the use of incarceration

(see Kaeble et al. 2015). Over time, it has become apparent that the U.S. is ill-equipped to incarcerate at the rates we do. For example, in 2014, 18 states and the Federal Bureau of Prisons were operating above their maximum capacity, with ranges from 100.5 percent above capacity in

Montana to 150.4 percent above capacity in Illinois (Carson 2015).

Recent estimates also quote costs at 28,323 dollars per inmate per year in state custody

(Kyckelhahn 2012), which is becoming more and more difficult to sustain given the budgetary and healthcare crises the U.S. is facing. In addition these pressing problems, there are a host of inequalities that are perpetuated by our high rates of incarceration, not least of which involve inequalities in health. There has been a great deal of attention in recent years on identifying and explaining these collateral consequences from an empirical standpoint – a brief review of them is provided next.

The Collateral Consequences of Mass Incarceration The criminological literature documents an array of serious consequences associated with

incarceration, most of which focus on the ways that incarceration is stratifying to both

individuals and communities (Wakefield and Uggen 2010). For one, it is clear that incarceration

is concentrated among certain groups, most specifically among minority men with little

education. For example, in his analysis on punishment and inequality, Western (2006) reported

that by the time they reached their 40th birthdays, two times as many black men in his sample had prison records as college degrees.

According to the Bureau of Justice Statistics, at yearend 2013, black and Hispanic men were incarcerated at 6 and 2 times the rates of their white counterparts, respectively (Carson

2014). In their analysis of administrative, survey, and census data, Pettit and Western (2004) concluded that among black men born between 1965 and 1969, 1 in 5, or 20 percent, had served time in prison by the time they reached their 30’s. Comparatively, among white men born during the same time period, only 3 percent had gone to prison by the time they reached their 30’s, making black men in the sample nearly 7 times more likely to go to prison than white men.

These racial disparities perpetuate disadvantage that is already concentrated among minority groups.

Families are also negatively impacted by incarceration. Using data from the National

Longitudinal Study of Adolescent Health, Swisher and Roettger (2012) found that those who experienced their fathers being incarcerated during childhood or adolescence were at increased risk of developing depression. Children with an incarcerated parent also face heightened risks of physical aggression (Wildeman 2010), delinquency (Murray et al. 2012; Swisher and Roettger

2012; Clear 2007), behavioral issues (Geller et al. 2012), and lower educational achievement

(Hagan and Foster 2012). These negative implications are especially potent for children of color.

In fact, research has shown that black youth are more likely to have a mother who is incarcerated

than white children are to have a father who is incarcerated (Western and Wildeman 2009).

Moreover, Glaze and Maruschak (2008) reported that black and Hispanic youth were

approximately 7 times and 3 times more likely to have an incarcerated parent than white

children, respectively.

In addition to interruptions in the parent/child bond during incarceration and the negative

effects this has on children, research has found that incarceration is a major risk factor for

marriage or relationship dissolution (Lopoo and Western 2005; Massoglia et al. 2011; Turney

2015). Legal fines assessed during sentencing as an additional form of punishment, in

combination with a limited ability to pay off those fines, also create legal debt for inmates and

their families at a level that is difficult to overcome (Harris et al. 2010). Additionally, ex-inmates

are at risk for both unemployment and earning lower wages, both of which are challenging to

transcend due to the enduring effects of incarceration-related stigma as well as diminished

capacity to compete for jobs (Pager 2003; Pager 2007; Western 2002). With fractured

relationships between parents and increased risks of financial problems, major strains are placed

on the family unit as a result of incarceration.

Neighborhoods also face problems because of the far reaching effects of incarceration.

Approximately 500,000 people are released from prisons to rejoin communities each year

(Kaeble et al. 2015b) and there is a great deal of research which supports the conclusion that

incarceration weakens communities, especially communities that are already poor and

disadvantaged (Clear 2007; Morenoff and Harding 2014). Residential instability has strong ties

to incarceration, for example. Warner (2015) utilized a nationally representative dataset to

explore the relationship between residential mobility and incarceration. Warner concluded that

respondents with a history of incarceration were more likely to move after incarceration than

prior to incarceration, suggesting that incarceration contributes to residential instability. Other

scholars have reported that former inmates face disproportionate rates of homelessness, another

indicator of distress in neighborhoods (Metraux and Culhane 2006; Williams et al. 2010).

In addition to the collateral consequences outlined above, it is apparent that incarceration

has strong ties to health and well-being. There is a growing body of work that identifies

incarceration as a salient point of exposure to infectious disease and stress-related illness, for

example (Massoglia 2008). This is especially evident in that inmates have been shown to

contract Hepatitis C Virus at up to 20 times the rates of their community dwelling counterparts

(Macalino et al. 2004; Binswanger et al. 2009) and HIV at 2 to 5 times the rates of non-

incarcerated samples (Okie 2007; Wilper et al. 2009). Prisoners have also been found to have

1.5 times the risk of hypertension in comparison to those in the general population (Maruschak

and Berzofsky 2015).

In addition to heightened exposure to infectious disease and other stress-related illnesses,

there is evidence to suggest that incarceration has the potential to influence a variety of other

indicators of health. Schnittker and John (2007) analyzed a nationally representative dataset and

concluded that those with a history of incarceration were more than twice as likely to report

severe health impairments. Massoglia (2008b) found similar results, concluding that

incarceration was a significant predictor of physical health functioning at age 40, indicating long

term-effects of incarceration on health. Notably, the size of the effect of incarceration on

physical health functioning was even larger than other well-documented predictors of health such as cigarette use, exercise, and educational attainment. Other research points to incarceration as a

risk factor for mental health problems (Schnittker et al. 2012; Schnittker 2014), chronic illness

(Binswanger et al. 2009; Wilper et al. 2009; Harzke et al. 2010; Maruschak and Berzofsky

2015), increased body mass index (Houle 2014), and dangerous levels of sodium intake (Herbert et al. 2012).

Scholars have also shown how incarceration is tied to mortality. For example,

Bingswanger et al. (2007) reported increased mortality risks among those recently released from a period of incarceration in comparison to the general population. Likewise, Rosen et al. (2011) compared North Carolina Prison records with state death records between 1995 and 2005 and concluded that there were more deaths than expected from viral hepatitis, liver disease, cancer, chronic lower respiratory disease, and HIV among prisoners in comparison to non-incarcerated groups. Patterson (2013) echoed these findings, concluding that for each year of incarceration served, an individual’s life expectancy may be reduced by an average of 2 years. Other research offers support for an accelerated aging hypothesis, in that prisoners may physiologically age up to 10 years faster than their community dwelling counterparts (Dawes 2002; Aday 2003; Loeb et al. 2008; Chodos et al. 2014).

The findings explained above provide a brief outline of the range of collateral consequences associated with incarceration. These collateral consequences include, but are not limited to, the reproduction of racial inequality, damage to the family unit, damage to neighborhoods, and declines in health. This growing body of research is important, and criminologists must continue to explore the ways in which incarceration is stratifying to individuals and communities. One area that is particularly important, yet underdeveloped in the literature, involves understanding how incarceration is impacting a new and growing population of prisoners: the elderly.

Aging Prisoners

Older prisoners represent the fastest growing age group within our prison system today

(Aday 2003). For example, the number of state prisoners 55 years of age and up increased 400 percent between 1993 and 2013, growing from 3 percent of the state prison population in 1993 to

10 percent in 2013 (Carson and Sabol 2016). Given our trend of high incarceration rates, in addition to the U.S. Census Bureau’s estimates that nearly 20 percent of the population will be at least 65 years old by 2050 (Vincent and Velkhoff 2010), it is likely that the elderly prisoner population will continue to expand in the coming years.

This is problematic for a variety of reasons. For one, incarceration already represents a potent risk factor regarding a variety of negative health outcomes as outlined above. In addition, aging is accompanied by declines in health status and increased morbidity in general (Adams and

White 2004). Given that age and stress have an interactive effect on the immune system, prisoners are especially vulnerable to disease and premature mortality as they get older (Graham et al. 2006; Patterson 2013).

Maruschak and Berzofsky (2015) offer an example of increased risk with age, as 72 percent and 35 percent of prisoners 50 years of age and older reported having a chronic health condition or infectious disease in 2012, respectively, while 55 percent and 18 percent of prisoners 35 to 49 years of age reported the same. When compared to their counterparts in the community, prisoners 50 years of age and up are significantly more likely to have a disability

(Binswanger et al. 2009) and suffer from an average of 2 to 3 chronic health conditions at any given time, again highlighting older prisoners’ vulnerability to poor health outcomes (Aday

2003; Loeb and Steffensmeir 2006).

Another critical point is that prisons were not constructed or designed with the geriatric prisoner in mind (Aday 2003). Prisons generally serve as poor models in regards to the provision of care for older adults and this has serious implications. For example, older adults tend to be more sensitive to changes in temperatures, yet prisons are typically ill equipped to be responsive towards such needs (Reimer 2008). Temperatures are difficult to regulate and extra blankets are viewed as privileges or threats to security. Bunk beds are also commonly used for sleeping arrangements in prisons, which creates accessibility issues for older inmates who have a hard time climbing as well as increases the risk for sustaining serious injuries such as falls. The mattresses that are provided to inmates also tend to be very thin, and this can be particularly problematic for older inmates who suffer from chronic pain.

Moreover, research has shown that many prisoners, despite having a serious chronic physical illness, fail to receive medical care during incarceration (Wilper et al. 2009). Not receiving medical attention can exacerbate illness. We also know that there are problems with the provision of proper medications for inmates in prisons (Williams et al. 2010). Given the massive size of prison campuses, it can also be difficult to respond quickly to medical emergencies such as strokes and heart attacks. For example, the distance between the medical unit and each cell block within a prison can vary substantially. Since many prisons are also located in rural areas, commutes to the nearest hospital can be lengthy. These issues, perhaps less pressing for young and healthy prisoners, become more problematic as aging and the development of illness occur.

In combination, the aforementioned details are concerning for the future of corrections.

As they age growing numbers of incarcerated adults will require advanced medical care for chronic disease management and associated morbidity. Declines in health are likely to be

coupled with more extensive medication needs, for example. Basic tasks like walking, bathing, and getting dressed may also require assistance. Yet, these needs are difficult for prisons to meet

(Ahalt et al. 2013). Older inmates also cost approximately 3 times as much to incarcerate as younger prisoners, in large part because of medical expenses associated with aging (Williams et al. 2012). These expenses will continue to tax already strained correctional budgets during incarceration and expenses associated with declining health will then be passed along to communities and families upon release.

There will also be growing numbers of prisoners who require end of life care within prison walls. In 2013 for example, 57 percent of state prisoner deaths were among prisoners 55 years of age and up, as compared to 34 percent in 2001 (Noonan et al. 2015). The vast majority of those deaths, 80 percent, were due to illness. Yet, very few prisons have hospice or palliative care programs in place within their institutions (Aday 2003; Linder and Myers 2007).

The Current Study Despite the collision of the aforementioned factors, we lack comprehensive examination in the literature regarding a range of health related problems associated with aging in prison.

This is the case for a variety of reasons. For one, the problem has not always been as pressing as it is today. In the past, the average age of inmates was much younger and there was a smaller percentage of people in older age groups serving time in prisons (Aday 2003; Carson and Sabol

2016). Additionally, the process of obtaining human subjects approval when proposing original data collection with inmates can be cumbersome and the public has generally unsympathetic feelings towards inmates as a population (Williams et al. 2012). Prisoners also reside in a very hidden and difficult to access environment where outsiders are generally unwelcome. This

dissertation contributes to the literature by moving beyond these barriers and unpacking some of

the health related issues that older inmates face.

The focus of this dissertation was to answer the following overarching research question: what does it mean to age in prison? To address this question, data were collected that

concentrated on 3 different (but related) components of aging within the context of the prison

environment and an empirical chapter was written about each component. These 3 components

include: overall health, chronic disease management, and end-of-life planning. Below, each of the 3 chapters are briefly introduced.

Paper 1: Secondary Stressors as Predictors of Health for Elderly Offenders The first empirical paper addresses the overall health of older inmates. Specifically, this paper explores how health ratings among older inmates are related to three stressors tied to the incarceration experience: unemployment, social isolation, and deprivation. We know from the literature that incarceration is status stripping (Goffman 1963), depriving (Sykes 1958), and heavily structured by coercion (Colvin 1992). We also know from the literature that stress is tied to health and exposure to both primary and secondary stressors can fuel negative health outcomes (see Pearlin 1989). Given what it means to be incarcerated, incarceration itself can be seen as a primary stressor that also exposes its captives to a variety of secondary stressors, both of which have ties to health.

In recent years scholars have identified a number of secondary stressors that are linked to incarceration. For example, diminished social standing (Porter 2014), parenting problems

(Turney et al. 2012), unemployment (Pager 2003, Pager 2007), increased financial strife (Porter

2014), and relationship dissolution (Massoglia et al. 2011; Turney 2015) are all secondary stressors with strong links to incarceration. Given the documented connection between chronic

stress and poor health (Pearlin et al. 1981; Turner et al. 1995; Turner and Lloyd 1999;

Landsbergis et al. 2003; Turner and Avison 2003; Aboa-Eboule et al. 2007; Chida et al. 2008;

Nielsen et al. 2008; Block et al. 2009; Finlayson et al. 2010; Buyck et al. 2011; Lunau et al.

2013), the fact that incarceration exposes inmates to a variety of secondary stressors makes it an important area of inquiry.

However, thus far most research focuses on linking poor health to secondary stressors that are measured post-incarceration. This paper considers stressors that were measured during incarceration among a sample of older, incarcerated men. Estimates show that 1 in 9 prisoners are now serving sentences of life (Nellis et al. 2013), which makes it important to consider stressors that are experienced as regular components of prison life. Given the aging of the prisoner population (Aday 2003), it is also worthwhile to examine the impact of these stressors on aging inmates in particular. Data for this paper come from survey-led, quantitative interviews with a sample of older inmates. Respondents were asked to assess their own health statuses and provide demographic information about themselves. Data were also gathered to assess respondents’ employment statuses, degree of social isolation, and exposure to deprivation.

Paper 2: ‘If You Don’t Know, They Treat You Like You Don’t Know’: Chronic Disease Management and the Role of Cultural Health Capital for Older Inmates The second paper addresses the issue of chronic disease management among older inmates. The accelerated physiological aging hypothesis asserts that prisoners can be expected to age approximately 10 years faster than their community dwelling peers, which has led researchers to operationalize ‘older inmates’ as those who are 50 years of age and up (Dawes

2002; Aday 2003; Loeb and AbuDagga 2006). There is empirical support for this hypothesis when comparing inmate and community populations on the frequency and onset of conditions such as chronic lung disease and injuries such as falls (Chodos et al. 2014) as well as self-

reported health statuses (Loeb et al. 2008). Co-morbidity is also common among older prisoners.

Estimates tend to report that as a group, older inmates suffer from an average of 2 to 3 chronic health conditions at any given time (Aday 2003; Loeb and Steffensmeir 2006; Harzke et al.

2010).

It is clear in the literature that older inmates carry a high chronic disease burden. Yet, there are weaknesses in terms of health promotion and maintenance within prisons. For example, scholars have documented problems with prisoners not receiving medical attention since being incarcerated (Wilper et al. 2009), not receiving appropriate medications (Williams et al. 2010), and with consuming of excessive levels of sodium (Herbert et al. 2012). Prisoners also have to navigate some pretty serious incarceration-specific barriers as they manage their health.

Inmates cannot take walks to burn calories and increase movement whenever they would like, for example. They also generally cannot check their blood-sugar levels whenever they would like or prepare their own meals to target dietary restrictions specific to their diseases.

We know very little in the literature about how older inmates manage chronic illnesses within the depriving environments in which they live. It was important to explore chronic disease management among older inmates for this reason, especially because the problem of chronic disease management will only intensify as the prisoner population continues to age.

Data for this paper were taken from qualitative notes gathered during survey-led interviews with older inmates. These qualitative notes focused on accounts respondents offered about their experiences with incarceration and health care, the concerns they had about their health due to incarceration, and the strategies they employed in hopes of improving their health during incarceration.

Paper 3: An Exploration into End of Life Planning among a Sample of Older Male Prisoners

The third paper focuses on end-of-life planning. This component of aging was important

to explore because as discussed above, a growing number of prisoners are aging behind bars and

increasing numbers of prisoners are dying behind bars. In states like Pennsylvania, a life

sentence does not mean 25 years. Rather, a life sentence is essentially an arrangement for where

the sentenced will die: prison. Despite these facts, we know very little about end-of-life care in

prison settings, particularly from the perspective of older inmates themselves.

This gap in knowledge is important to address. We know from community settings that

when people do not decide early about what they want for end-of-life care, it costs more in

resources later and the quality of the care provided can become compromised. Moreover, if

preferences are not voiced in advance, there is a risk that preferences will not be honored in the

moment (Kelley et al. 2010). Given the uniquely depriving nature of the prison environment

(Skykes 1958), it is important that we have data to draw on from samples of incarcerated men

specifically when exploring the factors that might play a role in end-of-life decisions among

prisoners. Currently, there are only 2 empirical studies that offer such data and both involve

samples of 100 or fewer prisoners (Phillips et al. 2009; Phillips et al. 2011).

Data for this chapter come from survey-led quantitative interviews with inmates about

hypothetical illness scenarios, in which inmates were asked to imagine that their health reached

certain points of illness (i.e., severe stroke with no chance of recovery). Respondents were then

asked to consider what medical options (i.e., feeding tube, CPR) they thought they would want

given their state of health in each scenario. Five specific factors – race, death distress, age upon

release, deprivation, and social support – were explored to determine their potential association

with these preferences. Due to the particular dearth of information in the literature about this issue, this chapter was designed to be more exploratory and descriptive in nature.

Conclusion The use of mass incarceration in the United States has brought with it real and serious consequences. Incarceration is already tied to a host of collateral consequences, and growing numbers of people are facing these consequences as a result of their exposure to incarceration.

Given that the prisoner population is now aging, we are approaching a crisis in corrections.

Incarceration rates continue to be exorbitant and larger portions of the prisoner population are approaching old age, many with significant health problems (and some with terminal conditions).

The collision of these factors has brought some scholars to lead a charge towards understanding the problems facing older inmates and the correctional facilities that house them.

Ahalt et al. (2015), for example, completed an analysis of National Institute of Health (NIH) grants from 2008 to 2012 and reported that out of 250,000 NIH funded grants, less than .1% (n =

180) focused on criminal justice health research. The authors concluded that the current state of research funding towards addressing the health and health care needs of those involved with the criminal justice system, including incarcerated populations, is poor and more funded research is necessary.

Williams et al. (2012b) reviewed results from a meeting that convened 29 national experts in correctional health care, academic medicine, nursing, and civil rights in order to establish a policy agenda specifically targeted towards improving the medical care of older inmates. The authors described this as necessary due to the growing intensity of the problem of

housing so many older adults behind bars. Ahalt et al. (2013: 2014) also emphasized the importance of the problem by articulating the following:

“because older prisoners are generally in worse health, come into more contact with the correctional healthcare system, and generate higher healthcare costs than younger prisoners, they represent a critical population in which to optimize healthcare value, yet there remains a profound lack of data that can be used to evaluate and improve geriatric prison healthcare value” [emphasis added]. The research offered in this dissertation answers the calls outlined above by collecting more data to help explore the health-related issues that older inmates are facing and by doing so with a policy-driven focus. This dissertation also contributes to an emerging area of research that considers the collateral consequences of incarceration. Here, the difficulties of maintaining health and well-being while aging within the constraints of the prison environment are offered as an additional set of collateral consequences for scholars in corrections to contemplate. In the chapters that follow, I first offer a detailed explanation of the research methods utilized to collect the data, and subsequently present each of the three empirical papers introduced above.

References

Aboa-Eboule, Corine, Brisson, Chantal, Maunsell, Elizabeth, Masse, Benoit, Bourbonnais,

Renee, Vezina, Michel, Milot, Alain, Theroux, Pierre, and Gilles R. Dagenais. 2007. “Job

Strain and Risk of Acute Recurrent Coronary Heart Disease Events.” Journal of the

American Medical Association 298(14): 1652-1660.

Adams, Jean M. and Martin White. 2004. “Biological Ageing: A Fundamental Link Between

Socio-Economic Status and Health?” European Journal of Public Health 14: 331-334.

Aday, Ronald. 2003. Aging Prisoners: Crisis in American Corrections. Westport: Praeger.

Aebi, Marcelo F., Tiago, Melanie M., and Christine Burkhardt. 2015. “SPACE I: Council of

Europe Annual Penal Statistics – Prison Populations, Survey 2014.” Strasbourg: Council

of Europe.

Ahalt, Cyrus, Bolano, Marielle, Wang, Emily A. and Brie Williams. 2015. “The State of

Research Funding from the National Institutes of Health for Criminal Justice Health

Research.” Annals of Internal Medicine 162: 345-352.

Ahalt, Cyrus, Trestman, Robert L., Rich, Josiah, Greifinger, Robert B. and Brie A. Williams.

2013. “Paying the Price: The Pressing Need for Quality, Cost, and Outcomes Data to

Improve Correctional Health Care for Older Prisoners.” Journal of the American

Geriatrics Society 61: 2013-2019.

Austin, James and John Irwin. 2000. It’s About Time: America’s Imprisonment Binge. Belmont,

CA: Wadsworth.

Binswanger, Ingrid A., Krueger, Patrick M. and John F. Steiner. 2009. “Prevalence of Chronic

Medical Conditions among Jail and Prison Inmates in the USA Compared with the

General Population.” Journal of Epidemiological Community Health 63: 912-919.

Block, Jason P., He, Yulei, Zaslavsky, Alan M., Ding, Lin, and John Z. Ayanian. 2009.

“Psychosocial Stress and Change in Weight Among U.S. Adults.” American Journal of

Epidemiology 170: 181-192.

Buyck, Jean-Francois, Bonnaud, Sophie, Boumendil, Ariane, Andrieu, Sandrine, Bonenfant,

Sebastien, Goldberg, Marcel, Zins, Marie, and Joel Ankri. 2011. “Informal Caregiving

and Self-Reported Mental and Physical Health: Results from the Gazel Cohort Study.”

American Journal of Public Health 101(10): 1971-1979.

Carson, Anne E. 2015. “Prisoners in 2014.” U.S. Department of Justice, Bureau of Justice

Statistics (NCJ #: 248955). Washington, DC; U.S.

Carson, Ann E. 2014. “Prisoners in 2013.” U.S. Department of Justice, Bureau of Justice

Statistics (NCJ# 247282). Washington, DC: U.S.

Carson, Anne E. and William J. Sabol. 2016. “Aging of the State Prison Population, 1993-2013.”

U.S. Department of Justice, Bureau of Justice Statistics (NCJ#: 248766). Washington,

DC: U.S.

Chida, Yoichi, Hamer, Mark, Wardle, Jane, and Andrew Steptoe. 2008. “Do Stress-Related

Psychosocial Factors Contribute to Cancer Incidence and Survival?” Nature Clinical

Practice Oncology 5(8): 466-475.

Chodos, Anna H., Ahalt, Cyrus, Stijacic Cenzer, Irena, Myers, Janet, Goldenson, Joe and Brie A.

Williams. 2014. Older Jail Inmates and Community Acute Care Use. American Journal

of Public Health 104(9): 1728-1733.

Clear, Todd R. 2007. Imprisoning Communities: How Mass Incarceration Makes Disadvantaged

Neighborhoods Worse. Oxford: Oxford University Press.

Colvin, Mark. 1992. The Penitentiary in Crisis. Albany, NY: SUNY Press.

Dawes, J. 2002. “Dying with Dignity: Prisoners and Terminal Illness.” Illness, Crisis & Loss

10: 188-203.

Finlayson, Tracy L., Williams, David R., Siefert, Kristine, Jackson, James S. and Ruth Nowjack-

Raymer. 2010. “Oral Health Disparities and Psychosocial Correlates of Self-Rated Oral

Health in the National Survey of American Life.” American Journal of Public Health

Supplement 1, 100(S1): S246-S255.

Garland, David (ed). 2001. Mass Imprisonment: Social Causes and Consequences. Thousand

Oaks, CA: Sage.

Geller Amanda, Cooper Carey E., Garfinkel Irwin, Schwartz-Soicher, Ofira, and Ronald B.

Mincy. 2012. Beyond Absenteeism: Father Incarceration and Child Development.

Demography 49(1):49–76.

Glaze, Lauren E., and Laura M. Maruschak. 2008. “Parents in Prison and Their Minor Children.”

U.S. Department of Justice, Bureau of Justice Statistics (NCJ# 222984). Washington,

DC: U.S.

Goffman, Erving. 1963. Stigma: Notes on the Management of Spoiled Identity. Englewood

Cliffs, NJ: Prentice-Hall.

Graham, Jennifer E., Christian, Lisa M. and Janice K. Kiecolt-Glaser. 2006. “Stress, Age, and

Immune Function: Towards a Lifespan Approach.” Journal of Behavioral Medicine

29(4): 389-400.

Hagan John and Holly Foster. 2012. Intergenerational Educational Effects of Mass Imprisonment

in America.” Sociology of Education 85(3): 259–86

Harris Alexes, Evans Heather, and Katherine Beckett. 2010. “Drawing Blood from Stones: Legal

Debt and Social Inequality in the Contemporary United States.” American Journal of

Sociology 115(6):1753–1799.

Harzke, Amy J., Baillargeon, Jacques G., Pruitt, Sandi L., Pulvino, John S., Paar, David R. and

Michael F. Kelley. 2010. “Prevalence of Chronic Medical Conditions among Inmates in

the Texas Prison System.” Journal of Urban Health 87(3): 486-503.

Herbert, Katharine, Plugge, Emma, and Helen Doll. 2012. “Prevalence of Risk Factors for Non-

Communicable Diseases in Prison Populations Worldwide: A Systematic Review.”

Lancet 379: 1975-1982.

Houle, Brian. 2014. “The Effect of Incarceration on Adult Male BMI Trajectories, United States,

1981-2006. Journal of Racial and Ethnic Health Disparities 1(1): 21-28.

International Centre for Prison Studies. http://www.prisonstudies.org/highest-to-lowest/prison-

population-total?field_region_taxonomy_tid=All . Accessed: 1/26/16.

Kaeble, Danielle, Glaze, Lauren, Tsoutis, Anastasios, and Todd Minton. 2015. “Correctional

Populations in the United States, 2014.” U.S. Department of Justice, Bureau of Justice

Statistics (NCJ # 249513). Washington, DC: U.S.

Kaeble, Danielle, Maruschak, Laura M., and Thomas P. Bonczar. 2015b. “Probation and Parole

in the United States, 2014.” U.S. Department of Justice, Bureau of Justice Statistics

(NCJ# 249057). Washington, DC: U.S.

Kelley, Amy S., Wenger, Neil S. and Catherine A. Sarkisian. 2010. “Opiniones: End-of-Life

Care Preferences and Planning of Older Latinos.” Journal of the American Geriatric

Society 58: 1109-1116.

Kyckelhahn, Tracey. 2012. “State Corrections Expenditures, FY 1982-2010.” U.S. Department

of Justice: Bureau of Justice Statistics. (NCJ# 239672). Washington, DC: U.S.

Landsbergis, Paul A., Schnall, Peter, L., Pickering, Thomas G., Warren, Katherine, and Joseph

E. Schwartz. 2003. “Life-Course Exposure to Job Strain and Ambulatory Blood Pressure

in Men.” American Journal of Epidemiology 157: 998-1006.

Linder John F., Frederick J. Meyers. 2007. Palliative Care for Prison Inmates: ‘Don’t Let Me Die

in Prison.’” Journal of the American Medical Association 298:894–901.

Loeb, Susan J., Steffensmeier, Darrell, and Frank Lawrence. 2008. “Comparing Incarcerated and

Community-Dwelling Older Men’s Health.” Western Journal of Nursing Research 30(2):

234-249.

Loeb, Susan J. and Azza AbuDagga. 2006. “Health-Related Research on Older Inmates: An

Integrative Review.” Research in Nursing & Health 29: 556-565.

Loeb, Susan J. and Darrell Steffensmeier. 2006. “Older Male Prisoners: Health Status, Self-

Efficacy Beliefs, and Health Promoting Behaviors.” Journal of Correctional Health Care

12: 269-278.

Lopoo, Leonard M., and Bruce Western. 2005. “Incarceration and the Formation and Stability of

Marital Unions.” Journal of Marriage and Family 67(3): 721-734.

Lunau, Thorsten, Wahrendorf, Morten, Dragano, Nico, and Johannes Siegrist. 2013. “Work

Stress and Depressive Symptoms in Older Employees: Impact of National Labour and

Social Policies.” BMC Public Health 13: 1086-1103.

Macalino, Grace E., Vlahov, David, Sanford-Colby, Stephanie, Patel, Sarju, Sabin, Keith, Salas,

Christopher, and Josiah D. Rich. 2004. “Prevalence and Incidence of HIV, Hepatitis B

Virus, and Hepatitis C Virus Infections among Males in Rhode Island Prisons.” American

Journal of Public Health 94: 1218-1223.

Maruschak, Laura M. and Marcus Berzofsky. 2015. “Medical Problems of State and Federal

Prisoners and Jail Inmates, 2011-2012. U.S. Department of Justice: Bureau of Justice

Statistics. (NCJ# 248491). Washington, DC: U.S.

Massoglia, Michael, Remster, Brianna, and Ryan King. 2011. “Stigma or Separation?

Understanding the Incarceration Divorce Relationship.” Social Forces: 133-156.

Massoglia, Michael. 2008. “Incarceration as Exposure: The Prison, Infectious Disease, and Other

Stress-Related Illnesses.” Journal of Health and Social Behavior 49: 56-71.

Massoglia, Michael. 2008b. “Incarceration, Health, and Racial Disparities in Health.” Law &

Society Review 42(2): 275-306.

Metraux, Stephen and Dennis P. Culhane. 2006. “Recent Incarceration History among a

Sheltered Homeless Population.” Crime and Delinquency 52(3): 504-517.

Morenoff, Jeffrey D. and David J. Harding. 2014. “Incarceration, Prisoner Reentry and

Communities.” Annual Review of Sociology 40: 411-429.

Murray, Joseph, Loeber, Rolf, and Dustin Pardini. 2012. “Parental Involvement in the Criminal

Justice System and the Development of Youth Theft, Marijuana Use, Depression, and

Poor Academic Performance.” Criminology, 50(1), 255-302.

Nellis, Ashley. 2013. “Life Goes On: The Historic Rise in Life Sentences in America.” The

Sentencing Project, Washington DC: U.S.

Nellis, Ashley and Ryan S. King. 2009. “No Exit: The Expanding Use of Life Sentences in

America.” The Sentencing Project, Washington DC.

Nielsen, Naja Rod, Kristensen, Tage, S., Schnohr, Peter, and Morten Gronbaek. 2008.

“Perceived Stress and Cause-specific Mortality among Men and Women from a

Prospective Cohort Study.” American Journal of Epidemiology 168: 481-491.

Noonan, Margaret, Rohloff, Harley and Scott Ginder. 2015. “Mortality in Local Jails and State

Prisons, 2000–2013 - Statistical Tables.” U.S. Department of Justice, Bureau of Justice

Statistics: NCJ 248756.

Okie, Susan. 2007. Sex, drugs, prisons, and HIV. The New England Journal of Medicine 356:

105–108.

Pager, Devah. 2007. Marked: Race, Crime, and Finding Work in an Era of Mass Incarceration.

University of Chicago Press: Chicago.

Pager, Devah. 2003. “The Mark of a Criminal Record.” American Journal of Sociology 108:

937-975.

Patterson, Evelyn J. 2013. “The Dose–Response of Time Served in Prison on Mortality: New

York State, 1989–2003.” American Journal of Public Health 103(3): 523-528.

Pearlin, Leonard I. 1989. “The Sociological Study of Stress.” Journal of Health and Social

Behavior 30: 241-256.

Pearlin, Leonard. I., Menaghan, Elizabeth G., Lieberman, Morton A., and Joseph T. Mullan.

1981. “The Stress Process.” Journal of Health and Social Behavior 22(4): 337–356.

Pettit Becky, and Bruce Western. 2004. “Mass Imprisonment and the Life Course: Race and

Class Inequality in U.S. Incarceration.” American Sociological Review 69(2):151–69.

Phillips, Laura L., Allen, Rebecca S., Harris, Grant M., Presnell, Andrew H., DeCoster, Jamie,

and Ronald Cavanaugh. 2011. “Aging Prisoners’ Treatment Selection: Does

Prospect Theory Enhance Understanding of End-of-Life Medical Decisions.” The

Gerontologist 51(5): 663-674.

Phillips, Laura L., Allen, Rebecca S., Salekin, Karen L. and Ronald K. Cavanaugh. 2009. “Care

Alternatives in Prison Systems: Factors Influencing End-of-Life Treatment Selection.”

Criminal Justice and Behavior 36(6): 620-634.

Porter, Lauren C. 2014. “Incarceration and Post-release Health Behavior.” Journal of Health and

Social Behavior 55(2): 234-249.

Reimer, Glenda. 2008. “The Graying of the U.S. Prisoner Population.” Journal of Correctional

Health Care 14(3): 202-208.

Rosen, David L., Wohl, David A., and Victor J. Schoenbach. 2011. “All-Cause and Cause-

Specific Mortality Among Black and White North Carolina State Prisoners, 1995-2005.”

Annals of Epidemiology 21(10): 719-726.

Schnittker, Jason. 2014. "The Psychological Dimensions and the Social Consequences of

Incarceration." The ANNALS of the American Academy of Political and Social

Science 651(1): 122-138.

Schnittker, Jason, Michael Massoglia, and Christopher Uggen. 2012. “Out and Down:

Incarceration and Psychiatric Disorders.” Journal of Health and Social Behavior, 53,

448-464.

Schnittker, Jason and Andrea John. 2007. “Enduring Stigma: The Long-Term Effects of

Incarceration on Health.” Journal of Health and Social Behavior 48: 115-130.

Swisher, Raymond R. and Michael E. Roettger. 2012. “Father’s Incarceration and Youth

Delinquency and Depression: Examining Differences by Race and Ethnicity.” Journal of

Research on Adolescence 22(4): 597-603.

Sykes, Gresham. M. 1958. The Society of Captives: A Study of a Maximum Security Prison.

Princeton, NJ: Princeton University Press.

Travis, Jeremy, Western, Bruce and Steve Redburn, eds. 2014. The Growth of Incarceration in

the United States: Exploring Causes and Consequences. National Research Council: The

National Academies Press. Washington, D.C.

Turner, Jay and William Avison. 2003. “Status Variations in Stress Exposure: Implications for

the Interpretation of Research on Race, Socioeconomic Status, and Gender.” Journal of

Health and Social Behavior 44(4): 488-505.

Turner, R. Jay and Donald A. Lloyd. 1999. “The Stress Process and the Social Distribution of

Depression.” Journal of Health and Social Behavior 40(4): 374-404.

Turner, R. Jay, Wheaton, Blair, and Donald A. Lloyd. 1995. “The Epidemiology of Social

Stress.” American Sociological Review 60: 104-125.

Turney, Kristin. 2015. “Liminal Men: Incarceration and Relationship Dissolution.” Social

Problems 62(4): 499-528.

Turney, Kristin, Wildeman, Christopher, and Jason Schnittker. 2012. “As Fathers and Felons:

Explaining the Effects of Current and Recent Incarceration on Major Depression.”

Journal of Health and Social Behavior 53(4): 465-481.

Vincent, Grayson K. and Victoria A. Velkoff. 2010. “The Next Four Decades. The Older

Population in the United States: 2010 to 2050. Population Estimates and Projections.”

U.S. Department of Commerce, Economics and Statistics Administration: U.S. Census

Bureau. Number P25-1138.

Wakefield, Sara and Christopher Uggen. 2010. “Incarceration and Stratification.” Annual

Review of Sociology 36: 387-406.

Walmsley Roy. 2007. “World Prison Population List, 7th Edition.” International Centre for

Prison Studies, London: UK.

Warner, Cody. 2015. “On the Move: Incarceration, Race, and Residential Mobility.” Social

Science Research 52: 451-464.

Western Bruce. 2006. Punishment and Inequality in America. New York: Russell Sage Found.

Western, Bruce. 2002. “The Impact of Incarceration on Wage Mobility and Inequality.”

American Sociological Review 67(4): 526-546.

Western, Bruce and Christopher Wildeman. 2009. “The Black Family and Mass Incarceration.”

Annals of the American Academy of Political and Social Science 621 (1): 221–24.

Wildeman Christopher. 2010. “Paternal Incarceration and Children’s Physically Aggressive

Behaviors: Evidence from the Fragile Families and Child Wellbeing Study.” Social

Forces 89(1): 285–310

Williams, Brie A., Goodwin, James S., Baillargeon, Jacques, Ahalt, Cyrus, and Louise C.

Walter. 2012. “Addressing the Aging Crisis in U.S. Criminal Justice Health Care.”

Journal of the American Geriatrics Society 60: 1150-1156.

Williams, Brie A., Stern, Mark F., Mellow, Jeff, Safer, Meredith, and Robert B. Greifinger.

2012b. “Aging in Correctional Custody: Setting a Policy Agenda for Older Prisoner

Health Care.” American Journal of Public Health: 102(8): 1475-1481.

Williams, Brie A., McGuire, James, Lindsay, Rebecca G., Baillargeon, Jacques, Cenzer, Irena

Stijacic, Lee, Sei J., and Margot Kushel. 2010. “Coming Home: Health Status and

Homelessness Risk of Older Pre-release Prisoners.” Journal of General Internal

Medicine 25(10): 1038-1044.

Williams, Brie A., Baillargeon, Jacques G., Lindquist, Karla, Walter, Louise C., Covinsky,

Kenneth E., Whitson, Heather E. and Michael A. Steinman. 2010b. “Medication

Prescribing Practices for Older Prisoners in the Texas Prison System.” American Journal

of Public Health 100(4): 756-761.

Wilper, Andrew P., Steffie Woolhandler, J. Wesley Boyd, Karen E. Lasser, Danny McCormick,

David H. Bor, and David U. Himmelstein. 2009. “The Health and Health Care of US

Prisoners: Results of a Nationwide Survey.” American Journal of Public Health, 99, 666-

672.

CHAPTER 2

RESEARCH METHODOLOGY

Setting and Participants

This study was approved by the Kent State University institutional review board as well as the Research Review Committee within the state Department of Corrections (DOC).

Participants were recruited from three men’s State Correctional Institutions (SCI’s) within one state in the northeastern United States. The three SCI’s were stratified by security level: SCI 1 is a minimum security facility; SCI 2 is a medium security facility; and SCI 3 is a super maximum security facility. Table 1 highlights key descriptive information about each of the three institutions, which was gathered from close-out interviews with the superintendent’s assistant at each institution post-data collection as well as the state DOC’s website.

SCI 2 and SCI 3 were both established in 1993 and are two of five prototypical prisons dedicated within the state that year. By contrast, SCI 1 was established in 1978 and therefore has a less modern design. In addition to differences in design, SCI 1 is a designated minimum security facility, which gives the institution more of a campus feel than the other two institutions.

Many of the housing units actually resemble dormitories. Prized employment at SCI 1 involves working for the Community Work Program (CWP).

The CWP allows inmates who are close to completing their sentences and of the proper

clearance to perform labor in the community for minimum wage (currently $7.25). Other

programming also aids in the campus feel of the institution. The Corrections Adoptive Rescue

Endeavor (CARE) program, for example, involves pairing inmates with rescue dogs for several

weeks. During this time, inmates teach the dogs basic tricks and socialization skills that will

help make them more adoptable. The dogs reside with their assigned inmate owner during this

training period and are then sent back to the community animal shelter for adoption.

By contrast, SCI 2 and SCI 3 are medium and super-maximum security facilities, respectively. These facilities not only have a higher security level designation, but feel noticeably more restrictive. For one, both institutions enforce added security procedures at check in. For example, before a visitor can enter SCI 3, he or she must submit to drug testing,

which involves allowing an officer to rub a cloth over both of the visitor’s hands. There are also

noticeably more rows of barbed wire surrounding the gates at both institutions. Whereas only

46.81 percent of full time employees at SCI 1 were correctional officers at the time of data

collection, 60.37 percent of full time employees were correctional officers at SCI 2 and 67.09

percent were correctional officers at SCI 3. Importantly, only 5.84 percent of full time

employees at SCI 3 were female at the time of data collection, a stark contrast between 22.00

percent and 23.80 percent at SCI 1 and SCI 2, respectively.

The capacity for inmates placed in the restricted housing unit (RHU), previously referred

to as solitary confinement, is also greater at both SCI 2 and SCI 3. At SCI 1, the capacity is only

56 inmates whereas for SCI 3 the capacity is 384 inmates. SCI 3 also houses the majority of the

state’s capital case inmates, with 128 cells for these inmates alone. Prized programming at SCI 2

and SCI 3 primarily involves work assignments with Correctional Industries (CI). CI positions

typically offer higher pay than other jobs within the institution as well as the potential for

bonuses. SCI 2 houses the CI Commissary Distribution Center. Here, inmate employees (n =

90) process, pack and ship commissary orders to prisons throughout the state. Conversely, SCI 3 is home to the CI Garment Factory, where inmate employees (n = 56) make inmate clothing

items, hats, and laundry bags for the state.

A total of 5,504 adult men were housed across the three facilities at the time of data collection, with SCI 2 incarcerating the highest number of inmates (n = 2,301). The mean age of inmates incarcerated at each institution ranged from 38.00 (SCI 3) to 40.51 years (SCI 2). The percentage of the inmate population 50 years of age and older ranged from 20.39 (SCI 3) to

24.07 percent (SCI 2). By contrast, nationwide estimates for the percentage of state and federal

incarcerated men who are at least 50 years of age was 17.90 percent at year end 2013 (Carson

2014). Notably, despite the large populations incarcerated at these institutions and the percentage of those populations that were older, the percentage of full time employees designated as medical staff ranged from 5 percent at SCI 3 (n = 35) to 8 percent at SCI 2 (n =

43). Each facility also had a very limited number of beds available in the medical unit. Medical bed space ranged from 12 beds at SCI 1 to 19 beds at SCI 3. Notably, this would accommodate only .008 percent of the population at SCI 1 and .010 percent of the population at SCI 3 at any given time. Related to diet, staff reported that inmates were served an average of 2,000, 2,575, and 2,400 calories per day at SCI 1, SCI 2, and SCI 3, respectively.

Inclusion criteria for this study dictated that participants had to be at least 50 years of age in order to participate, as this is the most common lower limit age criterion used in studies of older inmates’ health (see Loeb and AbuDagga 2006). Participants were also required to be

English speaking because funding for a translator was not available. Participants were excluded from sampling if they: (a) had a sentence of death; (b) had an IQ score that fell 2 standard deviations below the mean; and (c) had a mental health classification within the state DOC that indicated (1) the respondent had a mental health history and required significant monitoring by the Psychiatric Review Team AND (2) the respondent was currently receiving treatment for a substantial disturbance of thought or mood which significantly impaired judgement, behavior, capacity to recognize reality, or cope with the ordinary demands of life. The IQ score and mental health classification parameters were set so as to exclude any participants who had cognitive or mental health impairment severe enough to potentially hinder their abilities to provide informed consent, which was an established priority given that that prisoners are a protected group in research. Based on the aforementioned inclusion and exclusion criteria, the

Research Review Committee chairperson at the DOC provided me with a computer-generated

listing of all eligible prisoners for recruitment. Of the 5,504 men housed across the three facilities at the time of the study, 1,270 (23 percent) were at least 50 years of age and 1,158 (21 percent) met recruitment criteria.

Procedures Data were collected over a 13 month period (October 2013 to November 2014) and were gathered in three phases. Each phase involved the same procedural steps but occurred at different points in time. Phase 1 was completed at SCI 2, Phase 2 was completed at SCI 1 and

Phase 3 was completed at SCI 3. At each SCI, I began by visiting the institution several weeks prior to data collection for pre-recruitment. During this visit, I accompanied religious staff throughout the day as they led worship services.

Prior to each scheduled worship service, staff allowed me to briefly introduce the research to any prisoners who were in attendance and explain that they may be getting a letter in the coming weeks inviting them to participate. At this time, attending prisoners were permitted to ask questions about the goals and procedures of the research design. Examples of questions asked included the following: “why do you have to be at least 50 years old to participate,” “how long does the survey take,” and “when will you be sending out the letters?” In an attempt to reach as many potential participants as possible, I attended a range of denominational worship services, including Protestant, Catholic, Islam, Native American, and Jehova’s Witness services.

Since all inmates attending religious worship would clearly not meet study inclusion criteria, I also encouraged those present to pass along information about the study to those who might be interested but were not present. In total, I had contact with approximately 215 prisoners across the three SCI’s at religious services during this phase of the research. A powerpoint slide

explaining the study was also placed on the rotation for the inmate run television channel at this time.

After the pre-recruitment phase, I created recruitment letters using the computer- generated list of eligible prisoners that the Research Review Committee chairperson at the DOC provided. These letters were addressed to each eligible prisoner by name and placed in each prisoner’s respective mail drawer. Within the letter, recruits were informed of the study’s purpose and told that participation was completely voluntary. The letter also explained to potential participants that choosing or not choosing to participate would have no impact on their parole status or privileges at the institution. If after reading the letter prisoners wished to participate, they were instructed to write a note to the point of contact the researcher had identified at the SCI (the superintendent’s acting assistant) explaining that they wished to participate and would like to be scheduled for an interview with the researcher. Requiring respondents to send in a note indicating that they would like to be scheduled for an interview provided some confidence to me that respondents were not being coerced to participate, as some initiative was required on their behalves.

The superintendent’s assistant then compiled a list of those prisoners who submitted letters and scheduled call-out lists for each day I would be conducting interviews. Designated interview days were negotiated between myself and the superintendent’s assistant, with priority given to days that were most convenient for the institution. I visited the SCI anywhere from 2-4 days per week until the list of prisoners who expressed a desire to participate had been exhausted. Prior to participating in the interview each potential respondent was given an informed consent document, which explained the study’s purpose, the voluntary nature of the study, and what the respondent would be asked to do. Before signing the informed consent

document, I walked through all components of the document and made sure the respondent

understood each item and had the opportunity to ask questions. Upon signing the informed

consent document, I conducted a survey-led interview with the respondent. Across the three

SCI’s, interviews lasted an average of 50 minutes.

In total, 1,158 prisoners were asked to participate across the three SCI’s, 374 submitted

notes expressing interest in participating, and 279 completed survey-led interviews, yielding an overall response rate of 24 percent. The 24 percent response rate for the surveys is on the lower end but comparable to other survey research with prisoners, which tends to be between 26 and 53 percent for general population inmates and 10 percent for inmates in administrative segregation

(see Table 2). A response rate of 24 percent is also in line with response rates among community populations (see Table 2).

Table 2. Survey Research Response Rates Response Rates Reported Prisoners - Gen Pop Blitz et al. (2008) 26.0% to 53.0% Pickett et al. (2014) 38.8% Rocheleau (2013) 52.0% Prisoners - RHU Wolf and Shi (2009) 10.0% Community Schrader et al. (2010) 21.0% Bookwala et al. (2001) 25.0% Musa et al. (2009) 39.6% Carr (2012) 53.0%

Those who submitted notes but ultimately did not participate (n = 95) were either scheduled to work on the day of the interview and did not want to miss their shifts, were too ill to attend, or changed their minds. All interviews were conducted one on one between myself and each respondent. Roughly 20 percent of interviews took place at tables inside the general population visiting room, which is similar to a cafeteria type setting, and the remaining 80

percent occurred in the no-contact visiting area at each facility. In an attempt to proactively address any reading, writing, or vision deficiencies on the part of inmates, as well as reduce the amount of missing data, all survey questions were read aloud by me and responses were transcribed directly onto the survey instrument for each interview.

In addition to the quantitative survey data gathered, I also took qualitative notes throughout the interviews when respondents wanted to elaborate on an answer or emphasize a point regarding their experiences with the prison health care system. Respondents were also asked the following questions at the survey’s conclusion: “is there anything else you would like to add today” and “do you have any other suggestions to help improve health care within the prison system?” The open-ended explanations and anecdotal accounts provided were transcribed directly onto each survey instrument and were coded either as paraphrased or direct quotes. Of the 279 respondents who completed interviews, 66 percent (n = 184) provided supplemental comments or anecdotal accounts of this nature. Thus, in addition to the quantitative sample of

279 cases, an idiographic data sample of 184 cases was collected for analysis.

In order to maintain confidentiality and protect respondents, the names of respondents were never recorded on their respective survey packets or attached to the qualitative data. Each survey packet was handled only by me and all information collected on the survey packets was protected by me and never shared with another party. At no point in time did SCI or DOC staff have access to completed surveys. Inmates never had access to the completed survey packets of other respondents either. Further, at no point in time were completed surveys left unattended or within plain view of staff or inmates.

Tables 3, 4 and 5 display key frequencies and descriptive statistics for those who participated in the research. Respondents were an average of 58 years, with the youngest

respondent being 50 years of age and the oldest respondent being 78. Forty-nine percent (n =

137) had completed high school or obtained a GED while 21.1 percent (n = 59) had less than 12 years of schooling. The racial distribution up of the sample was split between white and black respondents, with 61.1 percent of respondents (n = 168) reporting white for their race and 38.9 percent (n = 107) reporting black. The majority of respondents (77 percent) reported having at least one child, with respondents disclosing having an average of 2.5 children overall. Only 18.3 percent (n = 51) of the sample reported being married, with 39.8 percent (n = 111), 35.8 percent

(n = 100), and 6.1 percent (n = 17) designating themselves as divorced, never married, or

widowed, respectively. Overall, 60.6 percent (n = 169) were employed at the time of interview, while 39.4 percent (n = 110) were currently unemployed.

Most respondents were incarcerated at either SCI 3 (40.5 percent) or SCI 2 (40.1 percent), while 19.4 percent (n = 54) were incarcerated at SCI 1. The sample was fairly evenly divided between first and repeat prison terms. At the time of interview, 47.7 percent of respondents (n = 133) were serving their first prison term, while 52.3 percent (n = 146) had served time in a prison prior to this current period of incarceration. Most respondents (77.4 percent) were incarcerated for crimes of violence (sex offenses, homicide or manslaughter, robbery, or assault), but 22.6% (n = 63) were serving time for non-violent offenses such as property or drug crimes. At the time they were interviewed, respondents had served an average of 164.4 months (13.6 years) for their most recent crimes and 24.4 percent (n = 68) were serving sentences of life.

Table 4. Frequency Distribution (Offense and Sentencing) n % Prior Prison Term Served Yes 146 52.3 No 133 47.7 Conviction Violent 216 77.4 Sex 98 35.1 Homicide/Manslaughter 82 29.4 Robbery 26 9.3 Assault 10 3.6 Non-Violent 63 22.6 Property 17 6.1 Drug 34 12.2 Other 12 4.3 Sentence Life 68 24.4 Other 211 75.6

The majority of respondents (90 percent) were residing within the general population of prisoners at their respective institutions. However, a particularly unique characteristic of this sample is that 10 percent of respondents (n = 28) were being housed at the restricted housing unit

(RHU) within the super-maximum security institution (SCI 3). Those residing within the RHU are confined to single cells, prohibited from participating in any programming or employment, and are only able to leave their cells for 1 to 2 hours per day for physical activity. Respondents could be sent to the RHU for either punishment or for administrative custody, a strategy employed to help protect prisoners who may be at risk if living in the general population. Many of the respondents I interviewed with these living arrangements had been residing in the RHU for years. Due to the dearth of studies which examine the physical and mental health of prisoners residing in solitary confinement within supermax institutions (see Pizarro and Stenius

2004; Arrigo and Bullock 2008) this is one of the most exclusive characteristics of the sample.

Table 5. Descriptive Statistics M sd Range Demographic Age 58.3 6.4 50.0 - 78.0 Number of Children 2.5 2.5 0.0 - 14.0 Frequency of Prayer (per week) 10.6 10.5 0.0 - 56.0 Sentencing Time Served (months) 164.3 142.9 7.0 - 576 Sentence Length (months) 211.5 185.3 18.0 - 1140.0 Health # of Chronic Health Conditions 3.4 2 0.0 - 11.0 # of Medications 3.7 3.8 0.0 - 23.0

In terms of health, respondents disclosed suffering from an average of 3.5 chronic health conditions, with high blood pressure, high cholesterol, arthritis, diabetes, and other heart problems being the most common conditions reported. There was a fairly even divide between respondents’ ratings of their own health, with 50.9 percent of respondents reporting either

excellent (10 percent) or good health (40.9 percent) and 49.1 percent reporting either fair (33.7 percent) or poor health (15.4%). On average, respondents disclosed taking a total of 3.8 medications at the time of their interviews, with a range of 0 to 23 medications consumed per respondent.

Table 6 highlights available aggregate data. Among my sample, whites are overrepresented (61 percent in my sample versus 56 percent within the overall sampling frame), blacks are adequately represented (both at 39 percent), and Hispanics are underrepresented, as no

Hispanics are represented in my sample yet they comprise 5 percent of the overall sampling frame. Further, inmates with life sentences are overrepresented in my sample, as they make up

24 percent of my sample and only 12 percent of the overall sampling frame.

Table 6. Sample vs. Sampling Frame Data (sample) (sampling frame) n % n % Race White 168 61.1 619 55.8 Black 107 38.9 438 39.5 Hispanic 0 0.0 52 4.7 Sentence Life 68 24.4 136 12.2 Other 211 75.6 978 87.8 Institution SCI 1 (minimum) 54 19.4 306 27.5 SCI 2 (medium) 112 40.1 526 47.2 SCI 3 (super-max) 113 40.5 282 25.3

Measures and Materials – Quantitative Data Quantitative questions were prompted with a 35-item survey instrument developed in consultation with the literature. During the interview I gathered information about a range of demographic factors, conviction and sentencing information, extent of social support among family and friends, religiosity, trust in prison staff and the prison health care system, current

health, the extent of deprivation or hardships experienced while incarcerated, death distress,

expectations regarding aging, and preferences for medical treatment across several hypothetical

illness scenarios. Each measure is explained in more detail below.

A variety of questions were asked in order to capture respondents’ demographic

characteristics. Specifically, respondents were asked about their age, highest grade of education completed, race, state or country of birth, and marital status. Respondents were also

asked whether or not they had any children, and if so how many.

To record respondents’ incarceration histories, I asked if they had ever been

incarcerated in a prison prior to this current period of incarceration, how many months they had

been incarcerated for their most recent offense, what they were convicted of, and what sentence

they were given. For those who did not receive sentences of life, I also asked about their

expectations regarding parole.

To assess the extent of social support respondents had, I asked how many times per

month they are visited by a friend or family member, how many times per month they have a

phone call with a friend or family member, and how many times per month they receive a letter

or package from a friend or family member. I also asked respondents to specify how many

friends they have that they could count on, how many family members they have that they could

count on, and how many staff at the institution were supportive of their needs.

To collect data on religiosity, I asked respondents what their religious affiliations were

(if any), how many times per week they attended religious services, and how many times per

week they engaged in prayer. I also asked respondents to answer whether or not they believed in

an afterlife.

To understand respondents’ trust in prison medical staff and trust in the prison health

care system, I developed a scale to measure respondents’ beliefs about prison medical staff and the health care system within the prison. Respondents were asked to provide likert scale

responses (1 = definitely false…..2….3….4….5 = definitely true) for each of the following 6

statements (1) “the prison medical staff care about my needs,” (2) “if I have a medical problem, I

do not have to wait long to see a doctor;” (3) “if a [medical] staff member tells me he/she is

going to do something in regards to my medical care, he/she will do it;” (4) “if I become

terminally ill while incarcerated, my wishes regarding how/when I want my life to end will be

respected;” (5) “inmates receive the same quality of health care as those living in the

community;” and (6) “the prison [medical] staff want me to be as healthy as possible.” Those

with high scores on items 1, 3, and 6 on the scale have greater trust in medical staff, while those

with low scores on items 1, 3, and 6 have less trust in medical staff. Those with high scores on

items 2, 4, and 5 on the scale have greater trust in the prison health care system, while those with

low scores on items 2, 4, and 5 have less trust in the prison health care system. The only

research to date that has administered a scale to a sample of prisoners regarding trust and health

care merged questions about trust in physicians, trust in research and the medical community,

and perceptions about adequate health care (Phillips et al. 2011). As such, this scale will be a

new addition to the literature.

To capture respondents’ current health, I administered the Older Men’s Health Program

and Screening Inventory (Loeb 2003). This scale includes a total of fifteen chronic conditions

(i.e., high blood pressure, cancer, arthritis, diabetes, etc.) as well as the option to write in

additional chronic health conditions that do not appear on the list (i.e., hepatitis C, anxiety,

epilepsy, celiac disease, etc.). Upon completion, items were summed to produce a total number

of chronic health conditions for each respondent. The Older Men’s Health Program and

Screening Inventory (see Appendix A) has been widely used in both community (Loeb 2003)

and prison populations to assess health (Loeb, Steffensmeir and Kassab 2011; Loeb,

Steffensmeir and Priscilla 2007; Loeb and Steffensmeir 2006). Respondents were also asked to

choose one of four words that best described their health (excellent, good, fair, or poor) and

report the number of medications (excluding vitamins) they were currently taking.

To assess experienced deprivation or hardships while incarcerated, respondents were

asked to complete the Deprivation Scale (Rocheleau 2013), which was developed based on

Maitland and Sluder’s (1998) Prison Stress Scale. While the Deprivation Scale is a newer tool, the Prison Stress Scale has produced Cronbach’s alpha levels within acceptable limits for research in the social sciences (see Maitland and Sluder 1998). Respondents responded to 19 statements about difficulties they had experienced while incarcerated (i.e, “Missing family or friends,” “Conflicts with prisoners,” “Quality of medical care,” “Concerns about my safety,” etc.) with a number from a likert scale (1 = “this has not been hard at all for me,”…2….3….4....5

= “this has been very difficult for me”) and these responses were summed (see Appendix B).

Summed scores on the Deprivation Scale range from 19 to 95, with lower scores indicating a low level of personal difficulty regarding exposure to incarceration-specific deprivation or hardships and higher scores indicating a high level of personal difficulty regarding exposure to incarceration-specific deprivation or hardships. Although the Deprivation Scale has been administered in corrections populations (see Rocheleau 2013), this is the first study to utilize the

Deprivation Scale on a sample of older prisoners.

To collect data on death distress, respondents were asked to complete the Death Distress

Scale (DDS) (Abdel-Khalek 2011). Until now, prior studies have focused largely on death

anxiety by means of the Death Anxiety Scale (Templer 1970). The DDS, by contrast, has the

advantage of casting a wider net and captures three distinct constructs: death anxiety, death

depression, and death obsession. For the DDS, respondents were asked to respond to 24

statements about death (i.e, “I find it greatly difficult to get rid of thoughts about death,” “I fear

dying a painful death,” “I lose interest in caring for myself when I think about death,” etc.) with

a number from a likert scale (1 = “the statement doesn’t sound at all like me,”…2….3….4....5 =

“the statement sounds very much like me”) and these responses were summed (see Appendix C).

Summed scores on the DDS range from 24 to 120, with lower scores indicating low levels of

death anxiety, death depression and death obsession and higher scores indicating high levels of

death anxiety, death depression and death obsession. Although the DDS has been administered

in community populations (Abdel-Khalek 2011), this is the first study to utilize the DDS on a

sample of prisoners.

To capture respondents’ expectations regarding aging, respondents were asked to

complete the Expectations Regarding Aging Survey (ERAS-12) (Sarkisian et al. 2005). This tool asks respondents to consider how much they believe or do not believe 12 statements about aging (i.e., “When people get older, they need to lower their expectations of how healthy they can be, “It’s an expected part of aging to have trouble remembering names). For each statement, respondents had to respond with a number from a likert scale (1 = the statement is definitely true…2….3….4 = the statement is definitely false). Within the 12 items, statements are included to capture aspects of physical health, mental health, and cognitive health (see Appendix D). Low scores on the ERAS-12 indicate low expectations in terms of the body maintaining good physical, mental, and cognitive functioning as the individual ages. For example, those who score low on the scale are likely to believe that pain, depression, and forgetfulness are normal aspects

of getting older. Conversely, high scores on the ERAS-12 indicate high expectations in terms of the body maintaining good physical, mental, and cognitive functioning as the individual ages.

Although the ERAS-12 has been administered in community populations (Sarkisian et al. 2005;

Kim 2009; Davis et al. 2011; Meisner and Baker 2013), this is the first study to administer the

ERAS-12 on a sample of prisoners.

To collect data on respondents’ preferences for medical treatment, respondents were asked to complete the Life Support Preferences Questionnaire (LSPQ) (Ditto et al. 2001). This tool asks respondents to consider their preferences for antibiotics, cardio-pulmonary resuscitation

(CPR), tube feeding, and surgery options across a range of illness scenarios, including scenarios involving end-of-life. The LSPQ details 9 hypothetical illness scenarios, each of which varies in illness severity, prognosis, and level of pain. This measure has been used in community settings

(Bookwala et al. 2001; Ditto et al. 2001; Coppola et al. 1999) and has recently been used in prison settings as well (Phillips et al. 2011; Phillips et al. 2009). In alignment with prior work that incorporated only four of the nine original scenarios (Phillips et al. 2009; Phillips et al.

2011), I administered only six of the nine original scenarios in order to reduce respondent burnout and to save time: current health, Alzheimer’s disease, severe stroke with no chance of recovery, severe stroke with a slight chance of recovery, and terminal colon cancer with and without pain. Treatment options for each scenario included antibiotics, CPR, surgery, and tube feeding. For each treatment option, respondents selected a number between 1 and 5 to express their desire for treatment (1 = definitely do not want…2…3…4...5 = definitely want).

Measures and Materials – Qualitative Data Qualitative measures and materials were less structured than the quantitative measures and materials. There were only two established questions that were asked with the intention of

gathering qualitative data. At the end of the survey, I asked respondents these questions, “is

there anything else you would like to add today” and “do you have any other suggestions to help

improve health care within the prison system.” In addition to these questions, however,

respondents were also given the opportunity throughout the structured survey to add anecdotal

accounts or comments to explain their answers or emphasize a concern. Specifically, I informed

respondents at the beginning of the survey that if they had any questions or wanted to add

anything as we moved through the survey, that they were welcome to do that.

I had no pre-established hypotheses or agenda in terms of my recording of the qualitative

comments that were given. Rather, my intention was for respondents to feel empowered to raise

points either not covered in the survey or not covered in enough detail. Indeed, many respondents showed up to their interviews with their own notes or with concerns they wanted to raise. Some respondents even wanted to raise points in an altruistic sense for prisoners they knew who wanted to attend but could not or who they felt would have attended but were already deceased or too ill to sit through an interview. I feel that taking notes actually enhanced my rapport with respondents in a way that merely circling answers on the surveys would not have.

In fact, many respondents made comments throughout the interviews about how diligently I was

writing because they felt that was evidence I was taking their concerns seriously. As they made

points they felt were important, many respondents verbally told me, “write this down.” Thus,

this qualitative component of the research design offered methodological advantages over strictly

implementing the quantitative survey alone.

Data Analysis Plan The analysis plan consists of three separate analyses with three distinct research

questions, each focused on one of 3 aspects of aging in prison: overall health, chronic disease

management, and end of life planning. Each analysis and its associated research question occupies its own chapter in the body of this dissertation. It is important to note that Chapters 3 and 5 utilize the quantitative data while Chapter 4 focuses on the qualitative data. The data analysis plan for each is introduced briefly below, then discussed in detail within each respective chapter.

In Chapter 3 binary logistic regression analysis was utilized in order to understand the potential influence of 3 different stressors associated with prison life – unemployment, social isolation, and extent of exposure to deprivation – on the health ratings of older prisoners. Given that the outcome variable has two categories (worse health/better health) and the predictors are a combination of categorical and continuous variables, binary logistic regression was appropriate for analysis in this chapter. Data for Chapter 3 were entered using SPSS version 22 and then analyzed using the same software.

In Chapter 4, a modified grounded theory approach (Morse et al. 2009; Cutcliffe 2005) is utilized to analyze the qualitative data. Since no specific hypotheses were tested, but rather the data were analyzed with the intention of broadly exploring the concerns of older prisoners in regards to health care, a modified grounded theory approach was incorporated. While grounded theory (see

Glaser and Strauss 1967) directs the researcher towards respondents’ accounts fully guiding the research, modified grounded theory allows for the possibility that a researcher allows respondents’ accounts to influence the research while also bearing in mind that the data may have been collected with a broad research question in mind (i.e., “what are the health care related experiences of older prisoners?”) (see Cutcliffe 2005).

Data for Chapter 4 were first transcribed in Microsoft Word, which produced over 200 pages of text. These data were then coded using the qualitative data analysis program QSR

NVivo, version 10. Content analysis was performed initially to identify emerging patterns

within the data (see Loftland et al. 2006). After initial patterns were identified, a more in depth

analysis was then be performed and refined coding was used to eliminate and collapse the full

list of themes identified initially. After refined coding, themes that were connected by sharing

focus on respondent concerns towards the management of chronic disease specifically were

selectively coded (see Loftland et al. 2006).

Finally, in Chapter 5 quantitative data were utilized again in order to assess the potential

influences of 5 specific factors (race, death distress, age upon release, deprivation, and social

support) on two binary outcome variables: desire for CPR (yes/no) and desire for tube feeding

(yes/no) in one of the hypothetical illness scenarios selected from the LSPQ (Ditto et al. 2001).

The hypothetical illness scenario chosen for Chapter 5 reads as follows: “you have suffered a

severe stroke and have been in a coma for six weeks. In the opinion of your doctor, you have no

chance of regaining awareness or the ability to think, reason, and remember. Your current physical

condition is stable, but will slowly decline over time. You rely on others for help with feeding,

bathing, dressing, and toileting. You may live in this condition for several years.”

This particular scenario was chosen because it captures two common fears regarding end of

life: loss of cognition and loss of independence. This scenario also clearly states that the doctor

believes there is no chance of recovery. Therefore, respondents were asked to consider their

preference for treatment in a situation where the doctor did not offer hope in terms of a recovery.

As in Chapter 3, binary logistic regression was selected for analysis because this approach is commonly used for analyses involving a variety of predictor variables and a binary outcome variable.

References

Abdel-Khalek, Ahmed M. 2011. “The Death Distress Construct and Scale.” Journal on Death

and Dying 64(2): 171-184.

Arrigo, Bruce A. and Jennifer Leslie Bullock. 2008. “The Psychological Effects of Solitary

Confinement on Prisoners in Supermax Units: Reviewing What We Know and

Recommending What Should Change.” International Journal of Offender Therapy and

Comparative Criminology 52(6): 622-640.

Blitz, Cynthia L., Wolff, Nancy, and Jing Shi. 2008. “Physical Victimization in Prison: The Role

of Mental Illness.” International Journal of Law and Psychiatry 31: 385-393.

Bookwala, Jamila, Coppola, Kristen M., Fagerlin, Angela, Ditto, Peter H., Danks, Joseph H., and

William D. Smucker. 2001. “Gender Differences in Older Adults’ Preferences for Life-

Sustaining Medical Treatments and End of Life Values.” Death Studies 25: 127-149.

Carr, Deborah. 2012. “’I Don’t Want to Die Like That…’ The Impact of Significant Others’

Death Quality on Advance Care Planning.” The Gerontologist 52(6): 770-781.

Coppola, Kristen M., Bookwala, Jamila, Ditto, Peter H., Lockhart, Lisa Klepac, Danks, Joseph

H., and William D. Smucker. 1999. “Elderly Adults’ Preferences for Life-Sustaining

Treatments: the Role of Impairment, Prognosis, and Pain.” Death Studies 23: 617-634.

Cutcliffe, John R. 2005. “Adapt or Adopt: Developing and Transgressing the Methodological

Boundaries of Grounded Theory.” Journal of Advanced Nursing 51(4): 421-428.

Davis, Melinda M., Bond, Lynne A., Howard, Alan, and Catherine A. Sarkisian. 2011. “Primary

Care Clinician Expectations Regarding Aging.” The Gerontologist 51(6): 856-866.

Ditto, Peter H., Danks, Joseph H., Smucker, William D., Bookwala, Jamila, Coppola, Kriten M.,

Dresser, Rebecca, Fagerlin, Angela, Gready, Mitchell, Houts, Renate M., Lockhart, Lisa

K., and Stephen Zyzanski. 2001. “Advance Directives as Acts of Communication: A

Randomized Controlled Trial.” Arch Intern Med 161: 421-430.

Glaser, Barney G. and Anselm L. Strauss. 1967. The Discovery of Grounded Theory: Strategies

for Qualitative Research. Chicago: Adline Publishing.

Kim, Su Hyun. 2009. “Older People’s Expectations Regarding Ageing, Health-Promoting

Behaviour and Health Status.” Journal of Advanced Nursing 65(1): 84-91.

Loeb, Susan J. 2003. “The Older Men’s Health Program and Screening Inventory: A Tool for

Assessing Health Practices and Beliefs.” Geriatric Nursing 24(5): 278-285.

Loeb, Susan J. and Azza AbuDagga. 2006. “Health-Related Research on Older Inmates: A

Integrative Review.” Research in Nursing & Health 29: 556-565.

Loeb, Susan J. and Darrell Steffensmeier. 2006. “Older Male Prisoners: Health Status, Self-

Efficacy Beliefs, and Health Promoting Behaviors.” Journal of Correctional Health Care

12: 269-278.

Loeb, Susan J., Steffensmeier, Darrell, and Cathy Kassab. 2011. “Predictors of Self-Efficacy and

Self-Rated Health for Older Male Inmates.” Journal of Advanced Nursing 67(4): 811-

820.

Loeb, Susan J., Steffensmeier, Darrell, and Priscilla M. Myco. 2007. “In Their Own Words:

Older Prisoners’ Health Beliefs and Concerns for the Future.” Geriatric Nursing 28(5):

319-329.

Loftland, John, Snow, David A., Anderson, Leon, and Lyn Loftland. 2006. Analyzing Social

Settings: A Guide to Qualitative Observation and Analysis. Belmont: Wadsworth

Publishing.

Maitland, Angela S. and Richard D. Sluder. “Victimization and Youthful Prison Inmates: An

Empirical Analysis.” The Prison Journal 78(1): 55-73.

Meisner, Brad A. and Joseph Baker. 2013, “An Exploratory Analysis of Aging Expectations and

Health Care Behaviors among Aging Adults.” Psychology and Aging 28(1): 99-104.

Morse, Janice M., Noerager Stern, Phyllis, Corbin, Juliet, Bowers, Barbara, Charmaz, Kathy, and

Adele E. Clarke. 2009. Developing Grounded Theory: The Second Generation. Walnut

Creek, CA: Left Coast Press.

Musa, Donald, Schulz, Richard, Harris, Roderick, Silverman, Myrna, and Stephen B. Thomas.

2009. “Trust in the Health Care System and the Use of Preventive Health Services by

Older Black and White Adults.” American Journal of Public Health 99(7): 1293-1299.

NVivo Qualitative Data Analysis Software. QSR International Pty Ltd. Version 10, 2012.

Phillips, Laura L., Allen, Rebecca S., Harris, Grant M., Presnell, Andrew H., DeCoster, Jamie,

and Ronald Cavanaugh. 2011. “Aging Prisoners’ Treatment Selection: Does

Prospect Theory Enhance Understanding of End-of-Life Medical Decisions.” The

Gerontologist 51(5): 663-674.

Phillips, Laura L., Allen, Rebecca S., Salekin, Karen L. and Ronald K. Cavanaugh. 2009. “Care

Alternatives in Prison Systems: Factors Influencing End-of-Life Treatment Selection.”

Criminal Justice and Behavior 36(6): 620-634.

Pickett, Justin T., Falco Metcalfe, Christi, Baker, Thomas, Gertz, Marc and Laura Bedard. 2014.

“Superficial Survey Choice: An Experimental Test of a Potential Method for Increasing

Response Rates and Response Quality in Correctional Surveys.” Journal of Quantitative

Criminology 30: 265-284.

Pizarro, Jesenia. and Vanja M.K. Stenius. 2004. “Supermax Prisons: Their Rise, Current

Practices, and Effects on Prisoners.” The Prison Journal 84 (2): 248-264.

Rocheleau, Ann Marie. 2013. “An Empirical Exploration of the ‘Pains of Imprisonment’ and the

Level of Prison Misconduct and Violence.” Criminal Justice Review 00(0): 1-21.

Sarkisian, Catherine A., Steers, Neil, Hays, Ron D., and Carol M. Mangione. 2005.

“Development of the 12-Item Expectations Regarding Aging Survey.” The Gerontologist

45(2): 240-248.

Schrader, Susan L., Nelson, Margot L., and LuAnn M. Eidsness. 2010. “Dying to Know: A

Community Survey about Dying and End-of-Life Care.” Journal of Death and Dying

60(1): 33-50.

Templer, Donald I. 1970. “The Construction and Validation of a Death Anxiety Scale.” The

Journal of General Psychology 82: 165-177.

Wolf, Nancy and Jing Shi. 2009. “Type, Source, and Patterns of Physical Victimization: A

Comparison of Male and Female Inmates.” The Prison Journal 89(2): 172-191.

CHAPTER 3

UNEMPLOYMENT, SOCIAL ISOLATION AND DEPRIVATION: EXAMINING SECONDARY STRESSORS AND HEALTH AMONG ELDERLY PRISONERS

Introduction With more than 1.5 million men and women currently incarcerated in state and federal prisons throughout the United States (see Carson 2014) and nearly half a million individuals filtering out of prisons each year (Kaeble, Maruschak and Thomas 2015), it is clear that incarceration touches the lives of many adults in our country. However, the trend of exposing excessive numbers of adults to incarceration in the U.S. is not new. Incarceration rates climbed sharply roughly 40 years ago and have remained consistently high since that time (Travis,

Western, and Redburn 2014). After years of research, we also now know that incarceration is tied to a host of damaging consequences. For example, incarceration reproduces racial inequalities (Pettit 2012; Wakefield 2010) as well as a variety of health disparities (Massoglia and Pridemore 2015; Porter 2014; Wildeman and Muller 2012; Schnittker et al. 2012; Massoglia

2008; Massoglia 2008b; Schnittker and John 2007).

Something that currently lacks comprehensive examination in the literature is understanding the incarceration experiences of the fastest growing age group within the prison system today: older prisoners (Aday 2003; Chettiar et al. 2012). Indeed, the prisoner population

is changing, with the number of older prisoners tripling between 2000 and 2013 alone (see Beck and Harrison 2001 and Carson 2014). The risks for health declines that accompany aging in general, coupled with the health disparities that the incarcerated face, make it important to examine what it means to age in prison as our population of prisoners continues to get older.

This paper contributes to the literature by exploring the potential influence of 3 different stressors associated with prison life – unemployment, social isolation, and overall deprivation – on the health ratings of a sample of older inmates. Although exposure to incarceration undoubtedly carries with it an array of collateral consequences, data in this paper indicate that there are opportunities within the prison structure to diminish some of those consequences. For example, prisoners in this sample who were unemployed were more than twice as likely to report worse health outcomes than prisoners who were employed, which suggests that providing more employment opportunities may be one avenue for enhancing the health of prisoners.

Review of Related Literature

Incarceration and Health More than two million men and women are incarcerated in the United States and approximately 70 percent of those individuals are serving time in either federal or state prisons

(Glaze and Kaeble 2014). The fact is, for the last four decades, the criminal justice system has been overtaxed with inmates filtering in and out of prisons each year, which has generated a great deal of scholarly attention over the last several years. Of particular interest are the efforts scholars have made to identify and document an array of deleterious health outcomes that are associated with exposure to incarceration (Massoglia and Pridemore 2015). For those who have faced incarceration, negative outcomes include increased health risks in regards to infectious

disease and stress related illnesses (Bingswanger et al. 2009; Massoglia 2008), mental health problems (Schnittker 2014; Schnittker et al. 2012), increased body mass index (Houle 2014), and chronic illness (Wilper et al. 2009; Aday 2006; Aday 2003; Harzke et al. 2010).

Incarceration also poses a number of risks tied to overall health and mortality that have been identified in the literature. For example, Massoglia (2008b) documented lower self- reported health ratings among those who have faced incarceration and Bingswanger et al. (2007) found heightened mortality risks among those recently released from incarceration in comparison to the general population. Research has also shown that for each year of incarceration served, an individual’s life expectancy may be reduced by an average of 2 years (Patterson 2013).

Relatedly, there is evidence that indicates that prisoners are aging on average 10 years faster than their community dwelling peers (Aday 2003; Dawes 2002; Chodos et al. 2014; Loeb et al. 2008).

Aging Prisoners We not only know from the literature that incarceration is tied to a host of negative health outcomes, but that our prisoner population is now aging (Aday 2003). Between 2000 and 2013, the percentage of male prisoners at least 55 years of age more than tripled and by yearend 2013,

18 percent of the total male prisoner population was 55 years of age or older (Carson 2014). An aging prisoner population carries with it a variety of challenges. For one, prisoners are at risk for a variety of health related problems, as outlined above. Coupled with the declines in health and increases in morbidity that are associated with aging in general (Adams and White 2004), older inmates are likely to face a host of health issues at earlier ages than their peers living in the community (Aday 2003; Dawes 2002). In addition, many would agree that prison is a high stress environment. Since age and stress have been shown to have an interactive effect on the immune

system (see Graham et al. 2006; Patterson 2013), this places older inmates at increased risk for a variety of poor health outcomes tied to weakened immunity.

Given their health risks, as well as the fact that prisons were never built to accommodate the needs of geriatric populations, it is not surprising that older prisoners cost approximately 3 times as much to incarcerate as younger prisoners, which places significant financial strains on the correctional system (Aday 2003; Chettiar et al. 2012; Williams et al. 2012). Additionally, since many prisoners will eventually be released to re-join society, the financial costs to communities will be significant if those individuals leave prisons sicker and more disenfranchised (Schnittker et al. 2015). The costs of housing so many geriatric inmates transcend these financial implications, however. Given their already disadvantaged statuses, inequalities will become even further exacerbated if prisoners are not given adequate opportunities to monitor and protect their health during incarceration.

While research has documented a host of deleterious health outcomes associated with incarceration, as well as the rise in older prisoners, there remains a lack of attention to the identification of factors embedded within the prison structure that worsen the health of inmates.

Identifying these factors and their ties to prisoner health is important, because doing may shed light on interventions that can help reduce collateral health consequences during incarceration so that inmates can age with less morbidity. Using the framework of stress proliferation theory, this paper contributes to the literature by exploring how several stressors relevant to the structure of prison living are associated with the health of older inmates.

Incarceration and Stress Proliferation Theory For a variety of reasons, incarceration can be conceptualized as a stressful life event.

Prisoners are stripped of their prior statuses (Goffman 1963) and deprived of personal control,

prior relationships, and previously enjoyed goods and services (Sykes 1958). The lives of

prisoners are also heavily structured by coercion (Colvin 1992). We know from stress

proliferation theory, however, that the stress process is more complicated than the occurrence of

a single event such as incarceration. In particular, stress proliferation theory emphasizes the

importance of “casting a wider net” by considering both primary and secondary stressors when

examining the impacts of stress on the body (Pearlin 1989: 248).

Primary and secondary stressors refer mainly to an ordering of events. Primary stressors

occur first and secondary stressors occur later as a result of being exposed to the primary

stressor(s) (Pearlin 1989). Health research has shown that taking on caregiving responsibilities

(a primary stressor) may later lead to problems at work (a secondary stressor) (Pearlin et al.

1997). In corrections, a primary stressor may be incarceration, while a secondary stressor may be increased exposure to conflicts with others due to living conditions that involve high levels of stress, very close living quarters, and normative anti-social attitudes and beliefs among the population of inmates. In this way, stress proliferation theory offers a helpful framework for understanding how people touched by incarceration come to live with chronic strain by becoming exposed to a “clustering of stressors” or cumulative adversity over time (Pearlin 1989:

248).

Researchers have documented a host of secondary stressors that occur following incarceration. For example, using data from the National Longitudinal Study of Adolescent

Health, Porter (2014) showed how exposure to incarceration impacts physical health over time,

as formerly incarcerated individuals in their sample were more likely to consume fast food and engage in smoking than their never-incarcerated peers, in part because of the emergence of secondary stressors such as increased financial strife and diminished social standing. Using data from the Fragile Families and Child Wellbeing Study, Turney et al. (2012) showed how exposure to incarceration can impact mental health, as those currently and recently incarcerated were at increased risk for major depression, operating through secondary stressors involved with socioeconomic status and parenting. Likewise, Pager (2003, 2007) demonstrated how incarceration leads to the secondary stressor of unemployment, particularly for formerly incarcerated black men who face an added secondary stressor: racial discrimination. Researchers have also found that incarceration is a potent risk factor for relationship dissolution, due at least in part to the reduction of quality time spent together because of the incarceration sentence

(Massoglia et al. 2011; Turney 2015).

Inmates’ exposure to primary and secondary stressors is important because there is a solid line of research that documents a link between chronic stress and an array of negative health outcomes in community settings. For example, chronic stress is associated with poorer oral health (Finlayson et al. 2010), increases in body mass index (Block et al. 2009), lower cancer survival rates (Chida et al. 2008), increases in systolic blood pressure and heart problems

(Landsbergis et al. 2003; Aboa-Eboule et al. 2007), and higher mortality (Nielsen et al. 2008;

Chida et al. 2008). Chronic stressors are also strongly tied to depressive symptoms, another indicator of poor health (see Pearlin et al. 1981; Turner et al. 1995; Turner and Lloyd 1999;

Turner and Avison 2003; Buyck et al. 2011; Lunau et al. 2013).

As outlined above, it is clear that stress negatively impacts the body. The literature has also called attention to the salience of incarceration as a primary stressor, as well as the

implications of many secondary stressors being associated with incarceration. Yet, the literature

lacks an examination of how certain components of prison life, because of their potential to act

as secondary stressors, may be associated with prisoner health. Below, unemployment, social

isolation, and level of exposure to deprivation are explored as secondary stressors. Their

connection to the health ratings of a sample of older prisoners is then empirically tested.

Unemployment We know from the literature that unemployment is a stressor that has strong ties to health

in community populations (see Wanberg 2012). For example, Linn et al. (1985) conducted a

study in which a group of 300 men were assessed on a variety of health related outcomes every 6

months. Despite the fact that the men had a similar number of health problems, those who were

unemployed made significantly more appointments to see their doctors, spent more days in bed

due to illness, and took more medications than those who were employed. McKee-Ryan et al.

(2005) reported similar findings in their meta-analysis of 104 empirical studies, as those who were unemployed had reduced physical well-being in comparison to their employed peers.

Those facing unemployment are also at risk for lower self-reported health ratings (Fiorillo and

Sabatini 2011), reduced psychological well-being (Paul and Moser 2009; Artazcoz et al. 2004;

McKee-Ryan et al. 2005; Pharr et al. 2012) and suicide (Voss et al. 2004; Garacy and Vagero

2013).

Research investigating the potential association between unemployment and health for inmates is sparse. However, we know from qualitative work that underemployment is a problem in prisons. For example, deViggiani (2007) found that prisoners in his sample had a number of concerns regarding the levels of underemployment they faced, including idleness and boredom.

The unequal distribution of employment opportunities and pay also created to a hostile climate

within the prison. Loeb, Steffensmeier, and Kassab (2011), in their analysis of a sample of older prisoners, found that prisoners who reported being either unemployed or employed only part- time immediately preceding incarceration were more likely to report worse health than prisoners who reported full time employment.

In the prison environment in particular, employment offers a way for inmates to avoid boredom, isolation, excessive sedentary periods, and experience a greater sense of control over one’s life. Since jobs are not available to everyone in prison, yet benefits such as commissary items and phone calls require purchasing power for use, it is likely that employment is associated with status in prison and we know that perceived social standing or status is highly predictive of health outcomes in both prison (Friestad 2010) and community settings (Marmot 2005). Given what we know about unemployment as a stressor and its impacts on health in community populations, in addition to preliminary work with samples of prisoners, it is likely that prisoners who are unemployed will report worse health ratings in comparison to prisoners who are employed.

Social Isolation Social isolation is another stressor that is recognized in the literature as having strong connections to health among community samples, particularly among the elderly (see Tomaka,

Thompson, and Palacios 2006; Sintonen and Pehkonen 2014). Heffner et al. (2011) found that of the 2,321 adults in their sample, the most socially isolated individuals had more than twice the odds of suffering from coronary heart disease deaths in comparison to the most socially integrated individuals, while Locher et al. (2005) concluded that adults in their sample with more indicators of social isolation were at increased risk for nutritional problems. Pantell et al. (2013) documented complimentary findings, concluding that among the nearly 17,000 adults in their

nationally representative sample, social isolation was highly predictive of mortality.

Specifically, individuals who were socially isolated had increased risks of death in comparison to

those who were less socially isolated and these rates were similar to several well-established clinical risk factors, including smoking.

There are a variety of ways to capture social isolation, including number of social contacts, participation in organizations and activities, and living arrangements. One of the individual factors used to measure social isolation in Pantell et al.’s (2013) study was frequency of participation in religious services. Specifically, those who participated less frequently in religious services had higher risks of mortality than those who participated more. Other studies utilizing community populations report similar findings (see Oman and Reed 1998).

In prison settings, we know very little about how lack of participation in religious services may act as a catalyst for social isolation that has implications in regards to prisoner health. However, we do know that incarceration is isolating and there are few opportunities for quality social interaction (Sykes 1958). Given these circumstances, religious group participation is one of few opportunities inmates have on a predictable basis for social interaction, especially as the security level of the institution increases. This means that those who do not participate in religious services are likely to be more socially isolated than those who do. It is clear from the literature that religion and spirituality are important coping mechanisms in prison settings and can be predictive of mental health (Eytan 2011; Allen et al. 2013; Clear and Sumter 2002).

Given what we know in the literature with community samples about the health implications of religious group participation, as well as the potential religious group participation has to influence health in prison settings, it is likely that inmates who do not participate in

religious services during incarceration will report worse health ratings than inmates who do participate because inmates who do not participate will be more socially isolated.

Another way to measure social isolation is by looking at the availability of social support that respondents have. This measure is a well-established predictor of health in community populations (Holt-Lunstad et al. 2010). Research has consistently documented that those with less social support are at increased risk for a variety of negative health outcomes in comparison to those with more social support, including lower health ratings (White et al. 2009; Richmond et al. 2007; Melchoir et al. 2003; Montross et al. 2006), poorer cognitive aging (Seeman et al.

2001), and increased morbidity and mortality (Cacioppo and Cacioppo 2014; House et al. 1988).

Thus, being without social support is a major stressor that has serious implications for one’s health.

Given the context of incarceration, in which inmates become severed from prior established connections to friends and family (Comfort 2008; Sykes 1958), inmates are particularly at risk of exposure to a lack of availability of social support as a form of social isolation. For example, Duwe and Clark (2011), in their sample of over 16,000 felony offenders,

39 percent received no visits during incarceration. Incarcerated men are at particular risk of social isolation via poor social support, as men tend to receive fewer visits, phone calls, and letters during incarceration than incarcerated women (Jiang and Winfree 2006).

In corrections, the literature shows clear detriments when inmates have poor social support. Weak social support is tied to higher recidivism rates, prison violence, and escapes

(Spieldnes et al. 2012; Duwe and Johnson 2016; Colvin 2007; Colvin 1992). Yet, we know very little about how social support for inmates may be associated with their health. Given what we know from research in community settings about the link between social support and health, in

combination with the likely amplification of this stressor due to the context of the prison

environment, it is likely that inmates who do not have social support will report worse health

ratings than inmates who do have social support. In this study, social support is operationalized as whether or not prisoners have visits with loved ones.

Deprivation Prisoners face a range of stressors specific to the incarceration experience itself and research has shown that the extent of these stressors, known more formally as deprivations, have important impacts on health and well-being. For example, Johnson-Listwan et al. (2010) demonstrated that inmates in their sample who experienced more coercion, via perceiving the prison environment as threatening and hostile, witnessing victimization, and other such factors, were more likely to report symptoms of trauma such as sleep disturbance and sexual problems.

Slotboom et al. (2011) reported similar findings by showing that inmates who felt disrespected by prison staff or excluded by other inmates were more likely to experience depressive symptoms and self-harm, even after controlling for prior mental health problems. Researchers have also documented the negative effects of deprivation factors like loss of privacy and overcrowding on reduced self-esteem and self-efficacy (deViggiani 2007), anxiety (Marshall et al. 2000), and suicide (Wolff et al. 2016; Huey and McNulty 2005; Dye 2010).

Despite clear links being made between deprivation and psychological well-being, researchers have yet to explore the potential influence of deprivation on overall ratings of inmate health. Given what we know from the literature about the damaging effects of deprivation, it is likely that inmates who are more exposed to depriving aspects of the prison experience will report worse health than inmates who are less exposed to depriving aspects of the prison experience.

Methodology

Hypotheses Using self-reported health ratings as the outcome of interest, three specific hypotheses pertinent to the literature reviewed above are tested. The first hypothesis (H1) is that inmates who are unemployed will be more likely to report worse health than inmates who are not employed. The second hypothesis (H2) is that inmates who are more socially isolated, via (a) not participating in religious services and (b) not receiving visits from loved ones, will be more likely to report worse health than inmates who are less socially isolated. The third and final hypothesis (H3) is that inmates who experience higher levels of exposure to the deprivations of incarceration will be more likely to report worse health than inmates who experience lower levels of exposure to the deprivations of incarceration.

Setting and Participants This study was approved by the Kent State University institutional review board as well as the Research Review Committee within the state Department of Corrections (DOC) that collaborated on the project. Participants were recruited from three men’s State Correctional

Institutions (SCI’s) within one state in the northeastern United States. The three SCI’s were stratified by security level: SCI 1 is a medium security facility; SCI 2 is a minimum security facility; and SCI 3 is a super maximum security facility. A total of 5,504 adult men were housed across the three facilities at the time of data collection (October 2013 to November 2014).

Inclusion criteria dictated that participants had to be at least 50 years of age, as this is the most common lower limit age criterion used in studies of older inmates’ health (see Loeb and

AbuDagga 2006). Participants were also required to be English speaking because funding for a

translator was not available. Participants were excluded from sampling if they: (a) had a

sentence of death; (b) had an IQ score that fell 2 standard deviations below the mean; and (c) had

a mental health classification within the state DOC that indicated (1) the respondent had a mental

health history and required significant monitoring by the Psychiatric Review Team AND (2) the

respondent was currently receiving treatment for a substantial disturbance of thought or mood

which significantly impaired judgement, behavior, capacity to recognize reality, or cope with the

ordinary demands of life. The IQ score and mental health classification parameters were set so

as to exclude any participants who had cognitive or mental health impairment severe enough to

potentially hinder their abilities to provide informed consent. The Research Review Committee

chairperson at the DOC provided a computer-generated listing of all eligible prisoners for

recruitment. Of the 5,504 men housed across the three facilities at the time of the study, 1,270

were at least 50 years of age and 1,158 met recruitment criteria.

Procedures Data were gathered in three phases. At each SCI, the researcher began by visiting the

institution several weeks prior to data collection for pre-recruitment. During this visit, the researcher accompanied religious staff throughout the day as they led worship services. Prior to each scheduled worship service, staff allowed the researcher to briefly introduce the project to any prisoners who were in attendance and explain to prisoners that they may be getting a letter in the coming weeks inviting them to participate. Attending prisoners were also permitted to ask questions about the project. In an attempt to reach as many potential participants as possible, the researcher attended a range of denominational worship services, including Protestant, Catholic,

Islam, Native American, and Jehovah’s Witness services. In total, the researcher had contact with approximately 215 prisoners across the three SCI’s at religious services during this phase.

The intention of attending these services was not to recruit directly from the pool of attendees,

but to use religious services as a forum to spread interest about the research throughout the

prison. A powerpoint slide explaining the study was also placed on the rotation for the inmate

run television channel at this time.

After the pre-recruitment phase, the researcher created recruitment letters using the

computer-generated list of eligible prisoners that the Research Review Committee chairperson at

the DOC provided. These letters were personally addressed to each eligible respondent and

placed in each prisoner’s respective mail drawer. Within the letter, recruits were informed of the

study’s purpose and told that participation was completely voluntary. The letter also explained

that choosing or not choosing to participate would have no impact on their parole status or

privileges at the institution. If after reading the letter prisoners wished to participate, they were

instructed to write a note to the point of contact the researcher had identified at the SCI (the

superintendent’s acting assistant) explaining that they wished to participate and would like to be

scheduled for an interview with the researcher.

The superintendent’s assistant then compiled a list of those prisoners who submitted

letters and scheduled call-out lists for each day the researcher would be conducting interviews.

The researcher consistently visited the SCI anywhere from 2-4 days per week until the list of prisoners who expressed a desire to participate had been exhausted. Prior to participating in the interview each potential respondent was given an informed consent document by the researcher, which explained the study’s purpose, the voluntary nature of the study, and what the respondent would be asked to do. Before signing the informed consent document, the researcher walked through all components of the document and made sure the respondent understood each item and had the opportunity to ask questions. Upon signing the informed consent document, the

researcher conducted a survey-led interview with the respondent. On average, interviews lasted about 50 minutes. All interviews were conducted one on one between the researcher and the respondent in either the no-contact visiting area or at a table inside the general population visitation room.

Each completed interview document was handled by the researcher and the researcher only and all information collected was kept confidential. At no point in time did any SCI or

DOC staff have access to completed surveys. In total, 1,158 prisoners were asked to participate across the three SCI’s, 374 submitted notes expressing interest in participating, and 279 completed interviews. Those who submitted notes but ultimately did not participate (n = 95) were either scheduled to work on the day of the interview and did not want to miss their shift, were too ill to attend, or changed their mind. As a supplement to the quantitative survey data, qualitative notes were gathered during interviews as well.

Tables 7 and 8 show key descriptives and frequency distributions for those who participated in the research. Respondents were an average of 58 years old and 49 percent (n =

137) had completed high school or obtained a GED. At the time of interviews, respondents had served an average of 164 months (13.6 years) for their current offense(s) and 24 percent (n = 68) were serving life sentences. Sixty-one percent of the final sample was white (n = 168) and 39 percent was black (n = 107). The majority of respondents were divorced (n = 111) or never married (n = 100), with only 18 percent (n = 51) being married. Eighty percent of respondents were incarcerated at either the super-maximum (n = 113) or medium security (n = 112) state correctional institution, while 20 percent were incarcerated at the minimum security institution (n

= 54). The majority of respondents (77 percent) were also incarcerated for crimes of violence.

Measures and Materials Interview questions were prompted with a 35-item survey instrument developed by the researcher in consultation with the literature. The interview gathered information about a range of factors designed to answer questions for a larger project regarding inmates’ experiences with incarceration and health care. Data collected included demographic information, sentencing information, experience with incarceration, current health, religiosity, trust in prison staff and the prison health care system, extent of social support among family and friends, death distress, expectations regarding aging, perceptions regarding deprivation, and desire for various medical treatments across several hypothetical illness scenarios. The specific measures that are the focus of this study are explained more fully below.

Self-Rated Health: During interviews, respondents were asked to describe their current health status as either “poor,” “fair,” “good,” or “excellent.” For analyses, “poor” and “fair” were combined and coded as “1” to represent worse health, and “good” and “excellent” were combined and coded as “0” to represent better health. Self-reported health ratings have been

collapsed in this manner in other studies in order to make health ratings more meaningful during

analysis (see Chantelle et al. 2007; Carr 2012; Carr 2012b; Luth 2016).

Age: Age was measured by asking respondents to report their age at the time of interview.

Responses ranged from 50 to 78 years.

Race: Respondents were asked to select one or more racial categories to describe

themselves, including “American Indian or Alaskan Native,” “Asian,” “Native American or

Other Pacific Islander,” “Black or African American,” and “White.” However, 98% of

respondents (N = 275) selected either “Black or African American,” or “White.” For the sake of

coding and maintaining large enough numbers in each category to perform the analyses, the four

respondents who selected other categories were excluded from the analyses. For the analyses,

“0” was coded as Black and “1” was coded as White.

Time Served: Time served was measured by asking respondents to report how long they had been incarcerated for their most recent conviction. To increase accuracy, this information was recorded in months rather than years. Responses ranged from 7 months (less than a year) to

576 months (48 years).

Education: Education was measured by asking respondents to report the highest grade

they had completed in school, with responses ranging from “8th grade or less” to “college degree.” For the analyses, the original 7 response options were categorized and collapsed into 3:

1 = “11th grade or less,” 2 = 12th grade or GED, and 3 = “some college and beyond.”

Unemployment: Unemployment was measured by classification information provided for each inmate during scheduled interviews. Respondents either had a current job assignment listed

or were classified as presently unemployed. For the analyses, “1” was coded as unemployed and

“0” was coded as employed.

Social Isolation – No Religious Group Attendance: Religious group attendance was measured by asking respondents whether or not they attended religious services at the prison.

For the analyses, “1” was coded as does not attend services and “0” was coded as attends services.

Social Isolation – No Visits: Inmate visitation was measured by asking respondents how frequently they were visited by friends or family in an average month. For the analyses, responses were coded as “1” for respondents who reported no visits and “0” for respondents who reported at least 1 visit per month.

Deprivation: To measure inmates’ perceptions of deprivation while incarcerated, respondents were asked to complete the Deprivation Scale (Rocheleau 2013). The Deprivation

Scale was administered by asking inmates to respond to 19 statements about difficulties they have experienced while incarcerated (i.e, “conflicts with other prisoners,” “conflicts with staff,”

“concerns about my safety,” “overcrowding,” etc.) with a number from a likert scale (1 = “this has not been difficult at all for me,”…2….3….4....5 = “this has been very difficult for me”). At the end of the scale, all 19 item ratings were summed. Summed scores on the Deprivation Scale range from 19 to 95, with lower scores indicating a low level of exposure to deprivation or hardships associated with incarceration and higher scores indicating a high level of exposure to deprivation or hardships associated with incarceration.

Data were coded as described above. Given that the dependent variable (self-rated health) has a dichotomous outcome (1 = worse health; 0 = better health), binary logistic regression was employed for hypothesis testing.

Results Results of the binary logistic regression model are displayed in Table 9. The full model predicting worse health ratings among inmates included unemployment, no attendance at religious groups, no visits with loved ones, and level of exposure to deprivation. After controlling for age, race, time served, and education, this model was significant and two of the individual predictors had a significant association with inmate health. As predicted, there is a

statistically significant, positive relationship between respondents’ ratings of their health and

unemployment (β = .849; p < .01; OR = 2.337). Specifically, the model predicts that the odds of

reporting worse health are increased by a multiplicative factor of 2.337 for unemployed inmates.

Table 9. Binary Logistic Regression: Log Odds of Inmates Reporting Worse Health Ratings (N = 269)

Model 1 Model 2 β O.R. SE β O.R. SE Controls Age 0.023 0.978 0.020 0.061 1.062 ** 0.023 Black - 0.106 1.112 0.268 - 0.024 0.976 0.297 Time Served (in months) 0.000 1.000 0.001 - 0.001 0.999 0.001 Education (less than H.S./GED) 0.318 1.375 0.356 0.434 1.544 0.402 Education (H.S./GED) 0.440 1.553 0.290 0.676 1.966 * 0.320 Predictors Unemployment 0.849 2.337 ** 0.288 No Religious Group Attendance 0.119 1.126 0.281 No Visits 0.299 1.348 0.275 Deprivation 0.069 1.072 *** 0.013 Intercept - 1.646 - 8.238 Model Chi-Square 3.692 52.919 -2LL 369.128 319.901 Nagelkerke R Square 0.018 0.238 ***p≤.001, **p≤.01, *p≤.05

Also as predicted, there is a significant, positive relationship between respondents’

ratings of their health and exposure to deprivation (β = .069; p < .001; OR = 1.072). This model

predicts that the odds of reporting worse health are increased by a multiplicative factor of 1.072

for each increase on the deprivation scale. It is important to note that the social isolation measures (lack of religious group attendance and lack of visits with loved ones) failed to produce statistically significant associations with respondents’ ratings of their health. Implications regarding these findings are discussed in detail below.

Discussion

We know from the literature that incarceration is associated with a multitude of collateral consequences and deleterious health outcomes are amongst those consequences. However, we know very little about how stressors specific to prison life are tied to the health of older inmates.

This is important, as our prisoner population is graying. With nearly 1 in 5 male prisoners now being at least 55 years of age (Carson 2014), we can expect that a significant number of prisoners in the United States will approach old age during incarceration.

Findings demonstrate that at least two elements of the incarceration experience – inmate employment and level of exposure to the deprivations characteristic of imprisonment – are related to worse reported health ratings among older, incarcerated men. The fact that unemployed inmates were more than twice as likely to report worse health as employed inmates provides support for existing research which identifies unemployment as a stressor that has powerful ties to health in community settings (Wanberg 2012; Garacy and Vagero 2013; Pharr et al. 2012; Paul and Moser 2009; McKee-Ryan et al. 2005; Artazcoz et al. 2004; Voss et al. 2004;

Linn et al. 1985).

It is likely that the strains of unemployment are exacerbated in the prison environment, as many protective factors that are readily available in the community, such as social support, are

stripped from the individual upon entering prison (Sykes 1958; Comfort 2008). Additionally, we

know that perceived social standing is predictive of health among prisoners (Friestad 2010).

Living within an incredibly depriving environment, obtaining a job is one option that can help

inmates enhance their social standing, as doing so allows them to pay for commissary items and

phone calls with their friends and family members. Specifically related to health, inmates with

jobs can more easily afford to make medical appointments when they are sick, as the department

of corrections now requires a $5 fee for each medical appointment and a $5 fee for each

medication. Thus, inmates who have opportunities to work may enhance opportunities for aging

successfully by increasing their perceived social standing and increasing their ability to pay for

their medical needs. Qualitative data that were collected during the study support this

conclusion, particularly because some inmates reported that they would intentionally forgo

medical appointments and medications when sick due to their inability to afford them, which was

a major source of stress for them.

Prisoners in this study were also at significantly increased odds of reporting worse health

as level of exposure to deprivation increased. This finding offers support to an emergent area of

work that has started to identify a range of consequences that depriving aspects of incarceration

lead to, including increased recidivism rates (Johnson-Listwan et al. 2013), diminished psychological well-being (Johnson-Listwan et al. 2010; Slotboom et al. 2011; Marshall et al.

2000), increases in rule violations and violence (Rocheleau 2013), and increases in suicide

(Wolff et al. 2016; Huey and McNulty 2005; Dye 2010). Findings in this study contribute by concluding that perceived exposure to a range of incarceration-specific deprivations or stressors is related to prisoners’ ratings of their health. This is the case because inmates who reported

more extensive exposure to depriving elements of the prison environment were at significantly increased odds of reporting worse health.

Although support was found for hypotheses 1 and 3, support was not found for hypothesis 2. I predicted that prisoners who were more socially isolated would be at increased odds of reporting worse health. However, the data did not support this prediction, as inmates who did not attend religious services, as well as inmates who did not have visits with loved ones, were not at increased odds of reporting worse health. These findings may be the result of measures that do not fully capture the meaning of social isolation. Specifically, the measures at hand captured whether or not respondents attended religious services and whether or not respondents had visits with friends and family rather than capturing the quality of religious group participation and the quality of respondents’ relationships with friends and family. There is research in community samples that indicates that satisfaction with relationships is more predictive of health than the number of physical meetings someone has with friends and family, for example (see Fiorillo and Sabatini 2011).

The null finding regarding social isolation may also provide support for stigma as a fundamental cause of disease (Hatzenbuehlet, Plelan and Link 2013). Specifically, for inmates who have committed crimes of violence in particular, as most of the men in this sample did, interacting with friends and family may not offer the typical dose of health protection normally associated with social support. It is possible that while not seeing loved ones is a major stressor, it is also a stressor to see loved ones if seeing them is a reminder of the stigma attached to their crimes. It will be important for future researchers to collect additional measures of inmate visitation, such as the quality of visits and the quality of relationships with friends and family, as well as additional measures of inmate participation with religious services that incorporate

quality in order to tease this out further. More qualitative data is also needed to help untangle on whom exactly older men rely for social support while incarcerated. Given the divisive and isolating features of incarceration, social support may be more potent coming from sources within the prison environment rather than coming from sources external to the prison environment. For example, inmates may be relying more heavily on each other for social support than family members.

This study has limitations. Since convenience sampling was used, selection bias cannot be ruled out. It is possible that men who participated do not actually represent the average experience of older incarcerated men within the state department of corrections at hand. Yet, because inmates were interviewed across 3 different, varying security level state correctional institutions, this is unlikely. Another limitation is that the sample consists entirely of black and white men, which means other racial groups, as well as women, were excluded. This is especially problematic in regards to Hispanic men, as they make up a considerable portion of today’s prisoner population (Carson 2014). Future researchers will need to make concerted efforts to include this group in particular. Finally, since the data from this study were taken from one point in time only, time ordering cannot be established. As such, results must be seen as association specific rather than causal at this time, offering a starting point for future research to depart from.

Despite these limitations, this study offers important and new contributions to the literature. The costs of so many adults aging in our correctional facilities are extensive. Older inmates cost significantly more to incarcerate, which places tremendous strain on already burdened correctional budgets (Aday 2003; Chettiar et al. 2012; Williams et al. 2012). In addition, upon their release those costs will eventually be passed along to communities and

families (see Schnittker et al. 2015). Although incarceration is targeted towards punishment, we

must also remember that punishment must stay within the parameters of providing access to

basic human rights – such as opportunities to maintain health. Thus, it is beneficial for a variety

of reasons to identify opportunities for prisoners to age as successfully as possible while

incarcerated.

Conclusion

Results of this study, although preliminary, offer promise that there are opportunities

available within a correctional policy framework for prisoners to improve their health while not

interfering with the goals of punishment or security. For example, increasing the number of

available jobs for inmates could not only help improve inmate health, but offer additional

benefits such as inmates learning a trade and having increased opportunities to practice prosocial

skills and responsibility. If the number of paid positions cannot be increased, creating more

opportunities for volunteer work is another option. Here, inmates could volunteer their time and

earn credits within the institution that they could use to earn privileges like phone calls. Among

other benefits, research has shown promise that giving to others can help reduce mortality

(Poulin et al. 2013).

It will also be important for correctional administrators to pursue opportunities to reduce

the perceived deprivations associated with incarceration for inmates. Although certain

incarceration-specific deprivations are unavoidable, such as loss of freedom, others can be alleviated if efforts are made to incorporate promising programming ideas. For example,

policies that improve communication between managers and correctional officers can help

reduce correctional officer stress and burnout (Finney et al. 2013), which may in turn offer a

reduction in the number of conflicts between inmates and staff. Likewise, increasing employment and volunteer opportunities as described above offers potential for reducing isolation and boredom. These policies, as well as additional hypothesis testing focused on identifying other aspects of prison life that may have an impact on the health of inmates, are future directions that will be vital to pursue.

References

Aboa-Eboule, Corine, Brisson, Chantal, Maunsell, Elizabeth, Masse, Benoit, Bourbonnais,

Renee, Vezina, Michel, Milot, Alain, Theroux, Pierre, and Gilles R. Dagenais. 2007. “Job

Strain and Risk of Acute Recurrent Coronary Heart Disease Events.” Journal of the

American Medical Association 298(14): 1652-1660.

Adams, Jean M. and Martin White. 2004. “Biological Ageing: A Fundamental Link Between

Socio-Economic Status and Health?” European Journal of Public Health 14: 331-334.

Aday, Ronald. 2006. “Aging Prisoners’ Concerns toward Dying in Prison.” Journal of Death and

Dying 52(3): 199-216.

Aday, Ronald. 2003. Aging Prisoners: Crisis in American Corrections. Westport: Praeger.

Allen, Rebecca S., Harris, Grant, M., Crowther, Martha R., Oliver, JoAnn S., Cavanaugh,

Ronald, and Laura L. Phillips. 2013. “Does Religiousness and Spirituality Moderate the

Relations between Physical and Mental Health among Aging Prisoners?” International

Journal of Geriatric Psychiatry 28: 710-717.

Artazcoz, Lucia, Benach, Joan, Borrell, Carme, and Immaculada Cortes. 2004. “Unemployment

and Mental Health: Understanding the Interactions among Gender, Family Roles, and

Social Class.” American Journal of Public Health 94: 82-88.

Beck, Allen J. and Paige M. Harrison. 2001. “Prisoners in 2000.” U.S. Department of Justice,

Bureau of Justice Statistics (NCJ# 188207). Washington, DC: U.S.

Binswanger, Ingrid A., Krueger, Patrick M. and John F. Steiner. 2009. “Prevalence of Chronic

Medical Conditions among Jail and Prison Inmates in the USA Compared with the

General Population.” Journal of Epidemiological Community Health 63: 912-919.

Binswanger, Ingrid A., Stern, Marc F., Deyo, Richard A., Heagerty, Patrick J., Cheadle, Allen,

Elmore, Joann G., and Thomas D. Koepsell. 2007. Release from Prison: A High Risk of

Death for Former Inmates. New England Journal of Medicine. 356(2): 157–65.

Buyck, Jean-Francois, Bonnaud, Sophie, Boumendil, Ariane, Andrieu, Sandrine, Bonenfant,

Sebastien, Goldberg, Marcel, Zins, Marie, and Joel Ankri. 2011. “Informal Caregiving

and Self-Reported Mental and Physical Health: Results from the Gazel Cohort Study.”

American Journal of Public Health 101(10): 1971-1979.

Cacioppo, John T. and Stephanie Cacioppo. 2014. “Social Relationships and Health: The Toxic

Effects of Perceived Social Isolation.” Social and Personality Psychology Compass 8(2):

58-72.

Carr, Deborah. 2012. “Racial and Ethnic Differences in Advance Care Planning: Identifying

Subgroup Patterns and Obstacles.” Journal of Aging and Health 2:, 923–947.

Carr, Deborah. 2012b. “The Social Stratification of Older Adults’ Preparations for End-of-Life

Health Care.” Journal of Health and Social Behavior 53: 297–312.

Carson, Ann E. 2014. “Prisoners in 2013.” U.S. Department of Justice, Bureau of Justice

Statistics (NCJ# 247282). Washington, DC: U.S.

Chettiar, Inimai, Bunting, Will, and Geof. Schotter. 2012. At America’s Expense: The Mass

Incarceration of the Elderly. New York, NY: American Civil Liberties Union. Retrieved

from: http://aclu.org/elderlyprisoners .

Chida, Yoichi, Hamer, Mark, Wardle, Jane, and Andrew Steptoe. 2008. “Do Stress-Related

Psychosocial Factors Contribute to Cancer Incidence and Survival?” Nature Clinical

Practice Oncology 5(8): 466-475.

Chodos, Anna H., Ahalt, Cyrus, Stijacic Cenzer, Irena, Myers, Janet, Goldenson, Joe and Brie A.

Williams. 2014. Older Jail Inmates and Community Acute Care Use. American Journal

of Public Health 104(9): 1728-1733.

Clear, Todd R. and Melvina T. Sumter. 2002. “Prisoners, Prison, and Religion.” Journal of

Offender Rehabilitation 35: 125-156.

Colvin, Mark. 2007. “Applying Differential Coercion and Social Support Theory to Prison

Organizations: The Case of the Penitentiary of New Mexico.” The Prison Journal 87(3):

367-387.

Colvin, Mark. 1992. The Penitentiary in Crisis. Albany, NY: SUNY Press.

Comfort, Megan. 2008. Doing Time Together: Love and Family in the Shadow of Prison.

Chicago: University of Chicago Press.

Dawes, J. 2002. “Dying with Dignity: Prisoners and Terminal Illness.” Illness, Crisis & Loss

10: 188-203.

De Viggiani, Nick. 2007. “Unhealthy Prisons: Exploring Structural Determinants of Prison

Health.” Sociology of Health and Illness 29(1): 115-135.

Duwe, Grant, and Valerie Clark. 2011. “Blessed be the Social Tie That Binds: The Effects of

Prison Visitation on Offender Recidivism.” Criminal Justice Policy Review 24: 271-296.

10.1177/0887403411429724.

Duwe, Grant and Byron R. Johnson. 2016. “The Effects of Prison Visits from Community

Volunteers on Offender Recidivism.” The Prison Journal 96(2): 279-303.

Dye, Meredith Huey. 2010. “Deprivation, Importation, and Prison Suicide: Combined Effects of

Institutional Conditions and Inmate Composition.” Journal of Criminal Justice 38: 796-

806.

Eytan, Ariel. 2011. “Religion and Mental Health During Incarceration: A Systematic Literature

Review.” Psychiatric Quarterly 82: 287-295.

Finlayson, Tracy L., Williams, David R., Siefert, Kristine, Jackson, James S. and Ruth Nowjack-

Raymer. 2010. “Oral Health Disparities and Psychosocial Correlates of Self-Rated Oral

Health in the National Survey of American Life.” American Journal of Public Health

Supplement 1, 100(S1): S246-S255.

Finney, Caitlin, Stergiopoulos, Erene, Hensel, Jennifer, Bonato, Sarah, and Carolyn S. Dewa.

2013. “Organizational Stressors Associated with Job Stress and Burnout in Correctional

Officers: A Systematic Review.” BMC Public Health 13: 82-95.

Fiorillo, Damiano and Fabio Sabatini. 2011. “Quality and Quantity: The Role of Social

Interactions in Self-reported Individual Health.” Social Science & Medicine 73: 1644-

1652.

Friestad, Christine. 2010. “Socio-economic Status and Health in a Marginalized Group: The Role

of Subjective Social Status among Prison Inmates.” European Journal of Public Health

20(6): 653-658.

Garacy, Anthony M. and Denny Vagero. 2013. “Unemployment and Suicide During and After a

Deep Recession: A Longitudinal Study of 3.4 Million Swedish Men and Women.”

American Journal of Public Health 4: 1-8.

Glaze, Lauren E. and Danielle Kaeble. 2014. “Correctional Populations in the United States,

2013.” U.S. Department of Justice, Bureau of Justice Statistics: NCJ # 248479.

Goffman, Erving. 1963. Stigma: Notes on the Management of Spoiled Identity. Englewood

Cliffs, NJ: Prentice-Hall.

Graham, Jennifer E., Christian, Lisa M. and Janice K. Kiecolt-Glaser. 2006. “Stress, Age, and

Immune Function: Towards a Lifespan Approach.” Journal of Behavioral Medicine

29(4): 389-400.

Harzke, Amy J., Baillargeon, Jacques G., Pruitt, Sandi L., Pulvino, John S., Paar, David R. and

Michael F. Kelley. 2010. “Prevalence of Chronic Medical Conditions among Inmates in

the Texas Prison System.” Journal of Urban Health 87(3): 486-503.

Hatzenbuehler, Mark L., Phelan, Jo C., and Bruce G. Link. 2013. “Stigma as a Fundamental

Cause of Population Health Inequalities.” American Journal of Public Health online first:

e1-e9.

Heffner, Kathi L., Waring, Molly E., Roberts, Mary B., Eaton, Charles B., and Robert Gramling.

2011. “Social Isolation, C-reactive Protein, and Coronary Heart Disease Mortality among

Community Dwelling Adults.” Social Science & Medicine 72: 1482-1488.

Holt-Lunstad, Julianne, Smith, Timothy B., and J. Bradley Layton. 2010. “Social Relationships

and Mortality Risk: A Meta-analytic Review.” PLoS Med 7(7): 1-20.

Houle, Brian. 2014. “The Effect of Incarceration on Adult Male BMI Trajectories, United States,

1981-2006. Journal of Racial and Ethnic Health Disparities 1(1): 21-28.

House, J.S., Umberson, D., and K.R. Landis. 1988. “Structures and Processes of Social Support.”

Annual Review of Sociology 14: 293-318.

Huey, Meredith P. and Thomas L. McNulty. 2005. “Institutional Conditions and Prison Suicide:

Conditional Effects of Deprivation and Overcrowding.” The Prison Journal 85(4): 490-

514.

Jiang, Shanhe and L. Thomas Winfree, Jr. 2006. “Social Support, Gender, and Inmate

Adjustment to Prison Life.” The Prison Journal 86(1): 32-55.

Johnson-Listwan, Shelley, Sullivan, Christopher J., Agnew, Robert, Cullen, Francis T., and Mark

Colvin. 2013. “The Pains of Imprisonment Revisited: The Impact of Strain on Inmate

Recidivism.” Justice Quarterly 30(1): 144-168.

Johnson-Listwan, Shelley, Colvin, Mark, Hanley, Dena, and Daniel Flannery. 2010.

“Victimization, Social Support, and Psychological Well-Being: A Study of Recently

Released Prisoners.” Criminal Justice and Behavior 37(10): 1140-1159.

Kaeble, Danielle, Maruschak, Laura M., and Thomas P. Bonczar. 2015. “Probation and Parole

in the United States, 2014.” U.S. Department of Justice, Bureau of Justice Statistics

(NCJ# 249057). Washington, DC: U.S.

Kanfer Ruth, Wanberg Connie R., and Tracy M. Kantrowitz. 2001. “Job Search and

Employment: A Personality-Motivational Analysis and Meta-Analytic Review. Journal

of Applied Psychology 86(5): 837-855.

Landsbergis, Paul A., Schnall, Peter, L., Pickering, Thomas G., Warren, Katherine, and Joseph

E. Schwartz. 2003. “Life-Course Exposure to Job Strain and Ambulatory Blood Pressure

in Men.” American Journal of Epidemiology 157: 998-1006.

Linn, Margaret W., Sandifer, Richard, and Shayna Stein. 1985. “Effects of Unemployment on

Mental and Physical Health.” American Journal of Public Health 75: 502-506.

Locher, Julie L., Ritchie, Christine S., Roth, David L., Baker, Patricia Sawyer, Bodner, Eric V.,

and Richard M. Allman. 2005. “Social Isolation, Support, and Capital and Nutritional

Risk in an Older Sample: Ethnic and Gender Differences.” Social Science and Medicine

60: 747–761.

Loeb, Susan J. and Azza AbuDagga. 2006. “Health-Related Research on Older Inmates: An

Integrative Review.” Research in Nursing & Health 29: 556-565.

Loeb, Susan J., Steffensmeier, Darrell, and Cathy Kassab. 2011. “Predictors of Self-Efficacy and

Self-Rated Health for Older Male Inmates.” Journal of Advanced Nursing 67(4): 811-

820.

Loeb, Susan J., Steffensmeier, Darrell, and Frank Lawrence. 2008. “Comparing Incarcerated and

Community-Dwelling Older Men’s Health.” Western Journal of Nursing Research 30(2):

234-249.

Lunau, Thorsten, Wahrendorf, Morten, Dragano, Nico, and Johannes Siegrist. 2013. “Work

Stress and Depressive Symptoms in Older Employees: Impact of National Labour and

Social Policies.” BMC Public Health 13: 1086-1103.

Luth, Elizabeth A. 2016. “Future Time Perspective and End-of-Life Planning in Older Adults.”

Research on Aging 38(2): 178-201.

Marmot, M. 2004. The Status Syndrome: How Social Standing Affects Our Health and

Longevity. Owl Books: New York.

Marshall, Tom, Simpson, Sue and Andrew Stevens. 2000. Health Care in Prisons: a Health

Care Needs Assessment. Birmingham: University of Birmingham Press.

Massoglia, Michael. 2008. “Incarceration as Exposure: The Prison, Infectious Disease, and Other

Stress-Related Illnesses.” Journal of Health and Social Behavior 49: 56-71.

Massoglia, Michael. 2008b. “Incarceration, Health, and Racial Disparities in Health.” Law &

Society Review 42(2): 275-306.

Massoglia, Michael and William Alex Pridemore. 2015. “Incarceration and Health.” Annual

Review of Sociology 41: 291-310.

Massoglia, Michael, Remster, Brianna, and Ryan King. 2011. “Stigma or Separation?

Understanding the Incarceration Divorce Relationship.” Social Forces: 133-156.

McKee-Ryan, Frances M., Song, Zhaoli, Wanberg, Connie R., and Angelo J. Kinicki. 2005.

“Psychological and Physical Well-Being During Unemployment: A Meta-Analytic

Study.” Journal of Applied Psychology 90(1): 53-76.

Melchoir, Maria, Berkman, Lisa F., Niedhammer, Isabelle, Chea, Maline, and Marcel Goldberg.

2003. “Social Relations and Self-Reported Health: A Prospective Analysis of the French

Gazel Cohort.” Social Science & Medicine 56: 1817-1830.

Montross, Lori P., Depp, Colin A., Daly, John, Reichstadt, Jennifer, Golshan, Shahrokh, Moore,

David, Sitzer, David, and Dilip V. Jeste. 2006. “Correlates of Self-Rated Successful

Aging among Community Dwelling Older Adults.” American Journal of Geriatric

Psychiatry 14: 43–51.

Nielsen, Naja Rod, Kristensen, Tage, S., Schnohr, Peter, and Morten Gronbaek. 2008.

“Perceived Stress and Cause-specific Mortality among Men and Women from a

Prospective Cohort Study.” American Journal of Epidemiology 168: 481-491.

Oman, Douglas and Dwane Reed. 1998. “Religion and Mortality among the Community-

Dwelling Elderly.” American Journal of Public Health 88(10): 1469-1475.

Pager, Devah. 2007. Marked: Race, Crime, and Finding Work in an Era of Mass Incarceration.

University of Chicago Press: Chicago.

Pager, Devah. 2003. “The Mark of a Criminal Record.” American Journal of Sociology 108:

937-75.

Pantell, Matthew, Rehkopf, David, Jutte, Douglas, Syme, Leonard, Balmes, John, and Nancy

Adler. 2013. “Social Isolation: A Predictor of Mortality Comparable to Traditional

Clinical Risk Factors.” American Journal of Public Health 103(11): 2056-2062.

Patterson, Evelyn J. 2013. “The Dose–Response of Time Served in Prison on Mortality: New

York State, 1989–2003.” American Journal of Public Health 103(3): 523-528.

Paul Karsten I., and Klaus Moser. 2009. “Unemployment Impairs Mental Health: Meta-

Analyses. Journal of Vocational Behavior 74: 264-282.

Pearlin, Leonard I. 1989. “The Sociological Study of Stress.” Journal of Health and Social

Behavior 30: 241-256.

Pearlin Leonard I., Aneshensel Carol S,, and Allen J. LeBlanc. 1997. “The Forms Mechanisms

of Stress Proliferation: The Case of AIDS Caregivers.” Journal of Health and Social

Behavior 38: 223–236.

Pearlin, Leonard. I., Menaghan, Elizabeth G., Lieberman, Morton A., and Joseph T. Mullan.

1981. “The Stress Process.” Journal of Health and Social Behavior 22(4): 337–356.

Pharr, Jennifer R., Moonie, Sheniz, and Timothy J. Bungum. 2012. “The Impact of

Unemployment on Mental and Physical Access to Health Care and Health Risk

Behaviors.” ISRN Public Health 1-7.

Porter, Lauren C. 2014. “Incarceration and Post-release Health Behavior.” Journal of Health and

Social Behavior 55(2): 234-249.

Poulin, Michael J., Brown, Stephanie L., Dillard, Amanda J., and Dylan M. Smith. 2013.

“Giving to Others and the Association between Stress and Mortality.” American Journal

of Public Health 103: 1649-1655.

Richmond, Chantelle A.M., Ross, Nancy A., and Grace M. Egeland. 2007. “Social Support and

Thriving Health: A New Approach to Understanding the Health of Indigenous

Canadians.” American Journal of Public Health 97(9): 1827-1833.

Rocheleau, Ann Marie. 2013. “An Empirical Exploration of the ‘Pains of Imprisonment’ and the

Level of Prison Misconduct and Violence.” Criminal Justice Review 00(0): 1-21.

Schnittker, Jason. 2014. "The Psychological Dimensions and the Social Consequences of

Incarceration." The ANNALS of the American Academy of Political and Social

Science 651(1): 122-138.

Schnittker, Jason and Andrea John. 2007. “Enduring Stigma: The Long-Term Effects of

Incarceration on Health.” Journal of Health and Social Behavior 48: 115-130.

Schnittker, Jason, Uggen, Christopher, Shannon, Sarah K.S., and Suzy Maves McElrath. 2015.

“The Institutional Effects of Incarceration: Spillovers from Criminal Justice to Health

Care.” The Millbank Quarterly 93(3): 516-560.

Schnittker, Jason, Michael Massoglia, and Christopher Uggen. 2012. “Out and Down:

Incarceration and Psychiatric Disorders.” Journal of Health and Social Behavior, 53,

448-464.

Seeman, Teresa E., Lusignolo, Tina M., Albert, Marilyn, and Lisa Berkman. 2001. “Social

Relationships, Social Support, and Patterns of Cognitive Aging in Healthy, High-

Functioning Older Adults: MacArthur Studies of Successful Aging.” Health Psychology

20(4): 243–255.

Sintonen, Sanna and Aini Pehkonen. 2014. “Effect of Social Networks and Well-Being on Acute

Care Needs.” Health and Social Care in the Community 22(1): 87-95.

Slotboom, Anne-Marie. 2011. “Psychological Well-being of Incarcerated Women in the

Netherlands: Importation or Deprivation?” Punishment & Society 13(2): 176-197.

Spjeldnes, Solveig, Jung, Hyunzee, Maguire, Lambert, and Hide Yamatani. 2012. “Positive

Family Social Support: Counteracting Negative Effects of Mental Illness and Substance

Abuse to Reduce Jail Ex-Inmate Recidivism Rates.” Journal of Human Behavior in the

Social Environment 22: 130-147.

Sykes, Gresham. M. 1958. The Society of Captives: A Study of a Maximum Security Prison.

Princeton, NJ: Princeton University Press.

Tomaka, Joe, Thompson, Sharon, and Palacios, Rebecca. 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.

Travis, Jeremy, Western, Bruce and Steve Redburn, eds. 2014. The Growth of Incarceration in

the United States: Exploring Causes and Consequences. National Research Council: The

National Academies Press. Washington, D.C.

Turner, Jay and William Avison. 2003. “Status Variations in Stress Exposure: Implications for

the Interpretation of Research on Race, Socioeconomic Status, and Gender.” Journal of

Health and Social Behavior 44(4): 488-505.

Turner, R. Jay and Donald A. Lloyd. 1999. “The Stress Process and the Social Distribution of

Depression.” Journal of Health and Social Behavior 40(4): 374-404.

Turner, R. Jay, Wheaton, Blair, and Donald A. Lloyd. 1995. “The Epidemiology of Social

Stress.” American Sociological Review 60: 104-125.

Turney, Kristin. 2015. “Liminal Men: Incarceration and Relationship Dissolution.” Social

Problems 62(4): 499-528.

Turney, Kristin, Wildeman, Christopher, and Jason Schnittker. 2012. “As Fathers and Felons:

Explaining the Effects of Current and Recent Incarceration on Major Depression.”

Journal of Health and Social Behavior 53(4): 465-481.

Voss, Margaretha, Nylen, Lotta, Floderus, Birgitta, Diderichen, Finn, and Paul D. Terry. 2004.

“Unemployment and Early Cause-Specific Mortality: A Study Based on the Swedish

Twin Registry.” American Journal of Public Health 94(12): 2155-2161.

Wanberg, Connie R. 2012. “The Individual Experience of Unemployment.” Annual Review of

Psychology 63: 369-396.

White, Ann Marie, Philogene, Stephane, and Lawrence Fine. 2009. “Social Support and Self-

Reported Health Status of Older Adults in the United States.” American Journal of Public

Health 99: 1872-1878.

Wildeman, Christopher and Christopher Muller. 2012. “Mass Imprisonment and Inequality in

Health and Family Life.” Annual Review of Law and Social Science 8: 11-30.

Williams, Brie A., Goodwin, James S., Baillargeon, Jacques, Ahalt, Cyrus, and Louise C.

Walter. 2012. “Addressing the Aging Crisis in U.S. Criminal Justice Health

Care.” Journal of the American Geriatrics Society 60: 1150-1156.

Wilper, Andrew P., Steffie Woolhandler, J. Wesley Boyd, Karen E. Lasser, Danny McCormick,

David H. Bor, and David U. Himmelstein. 2009. “The Health and Health Care of US

Prisoners: Results of a Nationwide Survey.” American Journal of Public Health, 99, 666-

672.

Wolff, Hans, Casillas, Alejandra, Perneger, Thomas, Heller, Patrick, Golay, Diane, Mouton,

Elisabeth, Bodenmann, Patrick, and Laurent Getaz. 2016. “Self-harm and Overcrowding

among Prisoners in Geneva, Switzerland.” International Journal of Prisoner Health

12(1): 39-44.

CHAPTER 4

‘IF YOU DON’T KNOW, THEY TREAT YOU LIKE YOU DON’T KNOW’: CHRONIC DISEASE MANAGEMENT AND THE ROLE OF CULTURAL HEALTH CAPITAL FOR OLDER INMATES

Introduction Nearly 1.6 million men and women are currently incarcerated in federal and state prions throughout the U.S. (Carson 2014) and for nearly four decades the U.S. has heavily relied upon incarceration to address its crime problem. This dependence on incarceration is unique in comparison to other countries, as the U.S. leads the world in incarceration rates (International

Centre for Prison Studies 2015). Problems associated with high incarceration rates, such as overcrowding and violence, have burdened correctional facilities for years. Yet, these well- documented problems are now being met with another issue that requires attention: an aging prisoner population with significant health problems (Aday 2003; Chettiar, Bunting and Schotter

2012).

Over the last couple of decades the elderly prisoner population has grown tremendously and this population is currently the fastest growing age group within our prison system (Aday

2003). This alone is cause for concern, as aging is associated with declining health and increased morbidity in general (Adams and White 2004).

However, due to a variety of factors, prison administrators must be prepared to address

declining health among its population earlier in the life course than administrators in community

health settings. Prisoners have been hypothesized to experience accelerated physiological aging,

for example, which means that their health may decline up to 10 years earlier than their

community dwelling peers (Aday 2003; Dawes 2002). Additionally, older prisoners are

significantly more likely to have a disability or chronic health condition than those residing in

the community (Dawes 2002; Aday 2003; Binswanger, Krueger and Steiner 2009) and many

older inmates report declines in health since incarceration (Loeb, Steffensmeier and Kassab

2011).

Given the continued reliance on mass incarceration as a social control strategy, the aging

of the prisoner population, and the generally poor health of prisoners as a group, it is imperative

that efforts are made to examine health related issues for older, incarcerated adults. Mitka (2004:

423), explained the pressing nature of the problem by stating, “it’s clear that the [prison] system

is on the verge of a health care crisis.” One aspect of health that is especially important to

address among this population is chronic disease management. Expanding upon the theory of

cultural health capital (Shim 2010), this paper attempts to understand what specific health

promotion strategies are available to and utilized by older inmates to help them manage chronic

disease within the constraints of the prison environment.

Review of Related Literature

The Aging Prisoner Population According to the Bureau of Justice Statistics, 1 in every 110 U.S. adults are currently serving time in either jails or prisons across the country (Glaze and Kaeble 2014). With

approximately 1.6 million men and women incarcerated in state and federal prisons throughout the U.S. (Carson 2014), it is clear that incarceration is a lived reality for a significant portion of

Americans today and this has been the case since the 1970’s (Austin and Irwin 2000; Garland

2001). Relying on incarceration as a social control mechanism for such a long period of time carries with it certain problems. One such problem is the fact that the prisoner population is now aging.

In fact, the elderly prisoner population represents the fastest growing age group within our prison system today (Aday 2003). For example, the percentage of prisoners 55 years of age and up incarcerated at state or federal institutions more than tripled between 2000 and 2013

(Carson 2014). Given the U.S. Census Bureau’s estimates that nearly 20 percent of the population will be at least 65 years old by 2050 (Vincent and Velkhoff 2010), it is likely that the elderly prisoner population will continue to expand in the coming years. This is problematic for a variety of reasons, not least of which is the fact that that prisons were not originally constructed or designed with the geriatric prisoner in mind. Older adults tend to be more sensitive to changes in temperatures, for example, yet prisons are typically ill equipped to meet such needs (Reimer

2008). Bunk beds are also frequently used for sleeping arrangements in prisons, which creates accessibility issues for older inmates who have a hard time climbing as well as for those who have certain medical conditions, such as epilepsy.

In general, prisons are also poor environments for establishing and maintaining good health. Research has shown that an individual can expect to lose an average of 2 years of life for every year of incarceration served, for example (Patterson 2013). Stress related illnesses and infectious diseases are more likely to be contracted by prisoners than those living in the community (Massoglia 2008), as are mental (Schnittker, Massoglia and Uggen 2012), and

chronic illnesses (Aday 2006; Aday 2003; Loeb, Steffensmeier and Kassab 2011; Wilper et al.

2009). Some researchers have even hypothesized that prisoners physiologically age approximately 10 years faster than their peers living in the community (Aday 2003; Dawes

2002). In short, the negative impacts of incarceration on health are significant and have been shown to persist throughout the life course and after release (London and Myers 2006; Schnittker and John 2007). These circumstances highlight how important it is for prison administrators to be prepared to address chronic diseases among inmates at earlier stages in the life course.

Chronic Disease Management Over the last decade researchers have documented the prevalence of chronic health conditions among prisoners. For example, prisoners have been shown to have rates of Hepatitis

C Virus up to 20 times the rates of their community dwelling counterparts (Binswanger, Krueger and Steiner 2009; Macalino et al. 2004), while HIV rates among prisoners have been documented to be 2 to 5 times the rates of non-incarcerated samples (Wilper et al. 2009; Okie

2007). Prisoners have also been shown to have increased odds of arthritis, asthma, and hypertension compared to their non-incarcerated peers (Binswanger, Krueger and Steiner 2009).

Mortality rates among those who have experienced incarceration are also heightened when considering substance use, HIV, liver disease, liver cancer, and conditions related to smoking such as lung cancer and ischemic heart disease (Rosen, Schoenback and Wohl 2008; Binswanger et al. 2014).

The accelerated physiological aging hypothesis asserts that prisoners can be expected to age an average of 10 years faster than their community dwelling peers, which means that ‘older’ may be defined as early as 50 for incarcerated samples (Aday 2003; Dawes 2002; Loeb and

AbuDagga 2006). Empirical support has been found for this hypothesis. For example, inmates in

one sample, who had a mean age of 59 years, reported rates of chronic lung disease and recent

falls that were tantamount to community dwelling adults who were an average of 71 years of age

(Chodos et al. 2014). In another study, Loeb et al. (2008) found that the prisoners in their sample

reported health that was comparable to community populations of adults 15 years their senior. In

short, prisoners 50 years of age and up are particularly vulnerable to chronic disease and in

general can be expected to suffer from 2 to 3 chronic health conditions on average at any given

time, making co-morbidity quite common among this group (Aday 2003; Loeb and Steffensmeir

2006; Harzke et al. 2010).

The aforementioned body of work demonstrates that prisoners, and in particular older

prisoners, have significant health needs and are generally not a healthy group. Despite the

documentation of this problem, weaknesses in the promotion and maintenance of health among

this group exist within the prison population. For example, Herbert, Plugge and Doll (2012)

found that sodium intake among their sample of prisoners was 2 to 3 times the recommended

daily intake amount. Additionally, among state prisoners in Wilper et al.’s (2009) sample who

reported a persistent medical problem, 20 percent disclosed that they had received no medical

examination since being incarcerated. Loeb et al. (2008) also found that prisoners in their

sample participated in fewer health promotion behaviors and programs than their community

dwelling peers.

These weaknesses, in combination with the high chronic disease burden that older

prisoners endure, make it important to examine chronic disease management and health

promotion options for older inmates. However, little is known thus far about chronic disease

management and health promotion behaviors among this population. Existing literature to date

does indicate that older prisoners who have greater confidence in their abilities to manage their

health are more likely to report improved health since being incarcerated, participate in more

health-promoting activities, and report better health overall (Loeb and Steffensmeier 2006; Loeb,

Steffensmeier, and Kassab 2011). Research has also shown that prisoners who display low

confidence in their abilities to manage their health were likely to do so due to lack of resources

or due to perceptions that prison administrators were unresponsive to their health care needs

(Loeb, Steffensmeier, and Myco 2007). Although this line of work is a promising start, there

remains a gap of knowledge regarding the management of chronic disease among older,

incarcerated adults, particularly from a qualitative perspective (see Loeb, Steffensmeier, and

Lawrence 2008) and with attention to prisons of varying security levels (see Loeb, Steffensmeier

and Myco 2007).

Chronic Disease Management and Health Capital Pierre Bourdieu (1977) was a pioneer in explaining the importance of one’s social location in life and how this location determines access to various resources. For example, an individual living in a wealthy suburb will have access to more prestigious schools and educational opportunities than an individual living in a poor neighborhood in an inner city and these opportunities will propel that person towards subsequent opportunities later in life.

Inequalities are therefore both established and perpetuated based on one’s social location and exposure to resources (Bourdieu 1977).

Bourdieu (1986) specifically termed resources “capital” and articulated that capital may take three forms. Economic capital consists of income, property, and other material items.

Social capital is less tangible, but involves access to relationships that provide networking opportunities and allow a person to link him or herself to the opportunities that others may provide. Cultural capital, the third form of capital, involves skills or knowledge a person

develops over time such as the ability to speak multiple languages, understand art, or discuss food and wine competently (Bourdieu 1986). In general, the more access a person has to these forms of capital, the more power that person will have in establishing and maintaining enhanced life chances in comparison to those who do not have similar access.

An important point that Bourdieu makes is that having access to any of the three forms of capital provides a cumulative effect because the three forms of capital drive one another

(Bourdieu 1986). For example, if an individual has more cultural capital, that person will also have more social capital. Social and cultural capital in particular are important when looking at social location because they often translate into increased (or decreased) economic capital

(Bourdieu 1986). This means that having more social and cultural capital is often associated with having more economic capital. Bourdieu also coined the term “symbolic capital,” which again highlights the cumulative effect of the three forms of capital. Symbolic capital is accomplished when economic, social or cultural capital are converted into prestige or honor

(Bourdieu 1984). For example, a millionaire business person has economic capital due to the income he or she has made. However, the income he or she has made may also translate into symbolic capital if that business person is perceived as a worthy investment partner by others due to his or her successes.

Bourdieu’s (1986) theory provides a useful context for understanding inequalities and has been applied to understand a variety of them. Only in recent years, however, has the theory been expanded to include inequalities in health. Most commonly, Bourdieu’s (1986) theory of capital has been used to consider how health inequalities are perpetuated via social capital. These applications include access to safe drinking water and sanitation (Bisung and Elliott 2014), neighborhood environment and health (Browne-Yung, Ziersch and Baum 2013; Carpiano 2007),

100

food and other health choices (Kamphuis et al. 2015; Collyer et al. 2015), and perceptions of mental and physical health (Pinxten and Lievens 2014; Veenstra 2007).

A more under-developed extension of Bourdieu’s (1986) theory of capital as it relates to health is the idea of cultural health capital. Shim (2010) introduced the term, which involves skills and behaviors that can be used by patients or health care providers in health settings to improve outcomes. An example of cultural health capital would involve a patient’s understanding of his or her medications, what each medication targets, and what the expected side effects might be. This knowledge acts as a resource because ultimately, communication between the patient and his or her provider will be enhanced as a result (Shim 2010).

Researchers have started to apply this fourth form of capital specifically to better understand health inequalities. Dubbin, Chang, and Shim (2013) utilized the cultural health capital framework in order to explore how patient-centered care can be enhanced, for example.

One of their findings was that patients in their sample who were able to communicate their medical problems from a biomedical framework, such as being able to identify irregularities in one’s blood pressure, were held in higher regard by providers and received more satisfying patient-centered care.

Patients’ possession of cultural health capital is becoming more and more important, as patients are expected in modern day to not merely be consumers of health care, but to advocate for their health needs, be well informed about their conditions, follow prescribed courses of treatment, and make important choices such as selecting an appropriate insurance provider and coverage plan to meet their needs (Shim 2010). This means that without cultural health capital as a resource, patients today are at a disadvantage. For example, patients who do not understand that when a medical claim is denied by their insurance provider that they still have options are

101

likely to pay much more for their medical treatment or stack up unpaid medical claims that can

damage their credit. Conversely, patients who understand that they have the option to call their

insurance provider and inquire as to why the claim was denied, for example, and then call their

doctor and discuss re-coding the treatment so that the code is consistent with one’s insurance

coverage, have a distinct advantage in terms of protecting themselves from exorbitant medical

bills.

Thus far, cultural health capital has not been applied to the prison setting. Yet, cultural

health capital provides a promising framework to explain how inmates’ choices (or lack thereof)

regarding the management of their chronic illnesses are influenced by the prison environment.

Prisons provide a theoretically distinct environment for studying chronic disease management

because in many ways choices about health in prisons are different than choices about health in

the community. Meals are planned for inmates and inmates do not have a say in what the meals

will be or even what time they will be eating those meals, for example. Doctors are also selected

by the prison administration rather than by the receiver of care and second opinions are not

typically an option. This paper contributes to the literature by providing insight into how

inmates’ choices regarding the management of their health are driven by cultural health capital

and privilege, all in the context of a highly constrained prison environment.

Methodology

Research Design This research is part of a larger study that involved face-to-face, survey-led interviews with older incarcerated men for the purpose of understanding their experiences with incarceration and health care. The specific focus of this paper is on the qualitative data, which

102

was taken from open-ended explanations respondents gave for their survey responses as well as

anecdotal accounts respondents shared to help explain their concerns about health care. These

qualitative data were examined without a pre-conceived research question. Yet, the themes that emerged provided a shared focus towards explaining the specific health promotion strategies that

are available to and utilized by older prisoners and how these strategies help them manage

chronic disease within the constraints of the prison environment.

Setting and Participants This study was approved by the Kent State University institutional review board as well

as the Research Review Committee within the state Department of Corrections (DOC).

Participants were recruited from three men’s State Correctional Institutions (SCI’s) within one

state in the northeastern United States. The three SCI’s were stratified by security level: SCI 1 is

a medium security facility; SCI 2 is a minimum security facility; and SCI 3 is a super maximum

security facility. A total of 5,504 adult men were housed across the three facilities at the time of

data collection (October 2013 to November 2014).

Inclusion criteria dictated that participants had to be at least 50 years of age, as this is the most common lower limit age criterion used in studies of older inmates’ health (see Loeb and

AbuDagga 2006). Participants were also required to be English speaking because funding for a translator was not available. Participants were excluded from sampling if they: (a) had a sentence of death; (b) had an IQ score that fell 2 standard deviations below the mean; and (c) had a mental health classification within the state DOC that indicated (1) the respondent had a mental health history and required significant monitoring by the Psychiatric Review Team AND (2) the respondent was currently receiving treatment for a substantial disturbance of thought or mood

103