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2012 Marital Status Duration, Marital Transitions, and Sunshine M. Rote

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COLLEGE OF SOCIAL SCIENCES AND PUBLIC POLICY

MARITAL STATUS DURATION, MARITAL TRANSITIONS, AND HEALTH

By

SUNSHINE M. ROTE

A Dissertation submitted to the Department of Sociology in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded: Summer Semester, 2012

Sunshine Rote defended this dissertation on June 13, 2012.

The members of the supervisory committee were:

Jill Quadagno Professor Directing Dissertation

Frank Fincham University Representative

Terrence Hill Committee Member

John Taylor Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.

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TABLE OF CONTENTS

List of Tables ...... iv List of Figures ...... v Abstract ...... vi

1. INTRODUCTION ...... 1 2. , , AND ALLOSTATIC LOAD ...... 5 2.1 Introduction ...... 5 2.2 Methods...... 12 2.3 Results ...... 15 2.4 Discussion ...... 16 3. MARITAL LOSS, GENDER, AND MENTAL HEALTH...... 24 3.1 Introduction ...... 24 3.2 Methods...... 29 3.3 Results ...... 31 3.4 Discussion ...... 34 4. DISCUSSION ...... 52 4.1 Scope of the Study ...... 52 4.2 Main Findings ...... 53 4.3 Limitations ...... 54 4.4 Conclusion ...... 54 5. APPENDIX A. BRIEF DESCRIPTION OF BIOMARKERS USED IN MEASURING ALLOSTATIC LOAD ...... 55

6. APPENDIX B. HUMAN SUBJECTS COMMITTEE APPROVAL LETTER ...... 56

7. REFERENCES ...... 58

8. BIOGRAPHICAL SKETCH ...... 68

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LIST OF TABLES

1.1 Descriptive Statistics for Study Variables (N=1340) ...... 21

1.2 OLS Regression of Allostatic Load on Marital Status, Marital Duration, and Gender (N=1340) ...... 22

1.3 OLS Regression of Allostatic Load on Marital Status, Status Duration, and Gender (N=1340) ...... 23

1.4 OLS Regression of Allostatic Load on Current Marital Status, Number of Marital Transitions, and Gender (N=1340) ...... 24

2.1 Descriptive Statistics for Study Variables ...... 39

2.2 OLS Regression of Depressive Symptoms on Marital Status, Status Duration, and Gender (N=2700) ...... 40

2.3 OLS Regression of Anxiety Symptoms on Marital Status, Status Duration, and Gender (N=2504) ...... 41

2.4 Logistic Regression of Any Drinking Problems on Marital Status, Status Duration, and Gender (N=2292) ...... 42

2.5 Logistic Regression of Happiness on Marital Status, Status Duration, and Gender (N=2292)43

2.6 OLS Regression of Depressive Symptoms and Anxiety Symptoms on Current Marital Status, Number of Marital Transitions, and Gender ...... 44

2.7 Logistic Regression of Alcohol Problems and Happiness on Current Marital Status, Number of Marital Transitions, and Gender ...... 45

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LIST OF FIGURES

1.1 Predicted Probabilities of Any Drinking Problem by Gender for Adults Widowed 0 to 5 Years ...... 46

2.1 Predicted Probabilities of Any Drinking Problem by Gender for Adults Widowed 16 Years or More...... 47

3.1 Predicted Probabilities of Happiness by Gender for Divorced Adults ...... 48

4.1 Predicted Probabilities of Happiness by Gender for Never Married Adults ...... 49

5.1 Predicted Probabilities of Happiness by Gender for Adults Divorced 0 to 10 Years ...... 50

6.1 Predicted Probabilities of Happiness by Gender for Adults Divorced 21 to 30 Years ...... 51

7.1 Predicted Probabilities of Happiness by Gender for Adults Divorced with One Prior Marriage ...... 52

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ABSTRACT

Past research demonstrates a robust relationship between marital status and health, with married adults experiencing less disability and than unmarried adults (Goodwin, Hunt, Key, & Samet, 1987; Gordon & Rosenthal, 1995; Johnson, Backlund, Sorlie, & Loveless, 2000; Pienta, Hayward, & Rahrig, 2000; Hughes & Waite, 1999), and divorced and widowed adults displaying more depressive symptoms and physical strain than married adults (Johnson et al., 2000; Pienta et al., 2000; Umberson, Wortman, & Kessler, 1992). While these associations have been well-established, there is some debate as to whether the health advantage experience by married adults reflects the short-term consequences of marital loss or whether marital statuses influence health over duration in a particular status (Booth & Amato, 1991; Williams & Umberson, 2004). Additionally, it is unclear whether the number of past marital transitions along with one’s current marital status has implications for health. In the current study, I use a cumulative advantage framework to examine whether marital status durations and number of prior marital transitions is associated with physiological functioning (Chapter 2) and mental well-being (Chapter 3). I also see if gender conditions these relationships. Using the National Social Life, Health, and Aging Project (NSHAP), a nationally- representative sample of adults 57 years and older living in the U.S., I find that marital status duration has physical and mental health consequences and number of prior marital losses impacts well-being. In particular, married adults, on average, have less allostatic load than unmarried adults, and this reflects the advantage of adults who have been married the longest (46 years or more). Additionally, widowed and divorced adults have less allostatic load than married adults and this extends to adults who have been in these statuses the longest. For mental well-being, I find short-term and long-term effects of being widowed or divorced. Additionally, being currently widowed is associated with worse mental well-being than being married, regardless of past number of marital losses. Finally, gender interactions indicate that women who are divorced the longest and women who are currently widowed with two or more past marital transitions experience more allostatic load than similarly-situated men. While men tend to be more vulnerable to the mental health consequences of marital loss, there is one exception: women who have been widowed the longest are more likely to experience alcohol problems than similarly

vi situated men. Discussions of how marital loss and gain along with gender influence health are included.

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CHAPTER ONE

INTRODUCTION

Social gerontologists have argued that both the existence and evaluation of social bonds are important to physical health and mental well-being, especially in later life (Carstensen, 1992; Carstensen, Isaacowitz, & Charles, 1999; House, Landis, & Umberson, 1988). As adults transition to this life stage, they may begin a process of reevaluating their lives and increasing their emotional attachments to close, significant others. In doing so, they maximize relationships that are familiar, predictable, and supportive and minimize those with potential risk or strain (Carstensen, 1992; Carstensen et al., 1999). One social relationship that has received much attention in prior research is the marital bond. A majority of American adults will marry in their lifetime (Bramlett & Mosher, 2002; Goldstein & Kenny, 2001). However, researchers contend that in the U.S. traditional norms surrounding marriage are changing because: (a) alternative relationships-- such as cohabitation-- are becoming more common (Cherlin, 2004), (b) first are occurring at later ages (Fitch & Ruggles, 2000) and (c) 50% of married adults are expected to divorce in their lifetime (Raley & Bumpass, 2003; Schoen & Standish, 2001). These shifts may mark a broader array in marital histories as adults enter later life, and warrant an investigation of their influence for successful aging. While there has been some debate over the past decade as to whether marriage is becoming deinstitutionalized, public policy initiatives encouraging marriage have brought attention to expanding this research to understudied groups (see: Fincham & Beach, 2010). Being married or having experienced marital dissolution may be particularly important in later life, when adults are more likely to experience disability, chronic conditions, retirement, social isolation, and the loss of aging peers. Numerous studies also indicate that marriage has an influence on health and well-being. Specifically, the married fair better than the unmarried on many indicators of health including depressive symptoms (Johnson & Wu, 2002; Marks & Lambert, 1998; Mirowsky & Ross, 2003; Umberson, Chen, House, Hopkins, & Slaten, 1996; Williams, 2003), disability status (Johnson, Backlund, Sorlie, & Loveless, 2000; Pienta, Hayward, & Rahrig, 2000; Hughes & Waite, 1999), chronic conditions (Goodwin, Hunt, Key, & Samet, 1987; Gordon & Rosenthal, 1995), and even mortality risk (Rendall, Weden, Favreault, & Waldron, 2011). Research in this area is typically 1 guided by two models: (a) the marital resource model and (b) the marital strain model (Booth & Amato, 1991; Meadows, McLanahan, & Brooks-Gunn, 2008; Hill, 2012; Umberson, 1992; Williams & Umberson, 2004) Some attribute the positive health effects of being married to the economic (e.g. shared cost of living) and social resources (e.g. ) that marriages incur and a monitoring of health behaviors by spouses (Carr & Moorman, 2011; Umberson, Crosnoe, & Reczek, 2010; Waite, 1995; Waite, 2009). The loss of the spousal role through divorce or widowhood may indicate a depletion of such resources and a change in lifestyle and health behaviors. Indeed, widowhood and divorce have been shown to increase chronic conditions, disability (Johnson et al., 2000; Pienta et al., 2000), and depression (Umberson, Wortman, & Kessler, 1992), and result in a greater health decline over time (Stroebe & Stroebe, 1987; Williams & Umberson, 2004). Other researchers have argued that marital-status-based health differentials do not represent the resources conferred from marriage but the stress and strain associated with its loss (Booth & Amato, 1991; Williams & Umberson, 2004). This model brings to attention the methodological issues in comparing the married to the unmarried and calls for an examination of health differences by particular statuses, such as never married, divorced, widowed, and partnered, and not an aggregated “unmarried” category. Additionally, researchers have called for an examination of contextual factors surrounding the marital bond in terms of marital status durations and marital history (see: Umberson & Montez, 2010; Waite, 2009). If marriage benefits health and if the marital resource model is correct, then, perhaps greater exposure to a status may have health consequences. For example, being married longer may allow for the abovementioned resources to accumulate and this could be beneficial to health. Conversely, being widowed longer may indicate a depletion of these resources and could be deleterious to health. However, few have empirically examined whether there are marital status duration differences in health. Additionally, the number of past marital transitions an individual has experienced, either through divorce or widowhood, may impact health because it represents an accumulation of stressful life experiences. This accumulation may mark a change in resources over time which could be detrimental to health. Empirical examinations of health differences by past number of marital transitions are also uncommon in previous research.

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Researchers in this area have also called attention to possible gender differences in the marriage-health relationship. Marriage may confer different resources for men and women (Bernard, 1972; Gove, Hughes, & Style, 1983; Gove & Tudor, 1973). Researchers have argued husbands benefit more from the supportive functions and monitoring of health behaviors provided by their spouses (Waite & Gallagher, 2000) and wives from the increased economic stability provided by marriage (Lillard & Waite, 1995). In terms of physical health, most find that marriage in equally beneficial to men and women (see: Manzoli, Villari, Pirone, & Boccia, 2007) and some find that being married is more beneficial for men than women (Johnson et al., 2000). With respect to mental health, researchers find that both men and women benefit psychologically from being married (Williams, 2003). However, Simon (2002) found men and women vary in the ways they express mental distress upon exiting this role, with men displaying more externalizing emotional problems such as alcohol abuse and women displaying greater internalizing emotional problems such as anxiety or depression. In this dissertation, I address the above topics in two empirical papers. First, in Chapter 2, using insight from cumulative advantage theory I will examine whether marital duration influences physical health and whether past marital transitions may moderate or condition this relationship. Additionally, I consider whether marital duration and past losses similarly influences men and women’s health. My outcome measure is allostatic load or cumulative biological risk in order to assess whether these factors are associated with physiological functioning in later life. In Chapter 3, I will see whether duration in a marital status and past transitions influence well-being (in the forms of depressive symptoms, anxiety symptoms, any alcohol problems, and happiness), and whether this is similar for men and women. By understanding the mental and physical health consequences of these factors, I hope to contribute to research on marriage, health, and aging in three particular ways. First, by using a nationally-representative sample of adults 57 years and older, I aim to see if the salutary influences of being married or the deleterious influences of its loss extend to a relatively healthy sample of adults in later life. Understanding under what conditions marriage impacts health in later life will lend better insight to what aspects of this social bond are most effective for helping adults age successfully, and conversely, which aspects may lead to health decline and areas for intervention. Second, I hope to contribute to research in this area through my choice in outcome variables. In the past decade, researchers have been interested in how social relationships “get

3 under the skin” (McEwen & Stellar, 1993). In Chapter 2, I examine whether allostatic load, a cumulative measure of physiological functioning, is associated with marital duration and history, which has not been fully explored among U.S. adults in past research. For Chapter 3, I will employ multiple mental health outcomes to better understand different psychological pathways that marital relationships and social status can exert an influence on well-being. Third, by examining the role of gender I aim to see whether social status makes individuals more or less vulnerable to marital loss and, if so, whether through mental or physical health avenues. Overall, this may lend insight to how social relationships and gender work together to shape health and life course outcomes for men and women.

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CHAPTER TWO

MARRIAGE, GENDER, AND ALLOSTATIC LOAD

Introduction

An extensive body of work has emerged that finds the married fair better than unmarried on many indicators of health (see: Carr & Springer, 2010; Fincham & Beach, 2010; Waite, 2009). In particular, being married influences mortality risk (Johnson et al., 2000; Lillard & Waite, 1995; Ross, Mirowsky, & Goldsteen ,1990), the etiology of specific conditions such as heart disease (Maselko, Bates, Avendano, & Glymour, 2009; Zhang & Hayward, 2006) and (Goodwin, Hunt, Key, & Samet, 1987; Gordon & Rosenthal, 1995), and overall disease burden (Hughes & Waite, 2009). While this association has been established, there has been debate as to whether this relationship represents the benefits of marriage (i.e. the marital resource model) or the stressful experiences associated with marital loss (i.e. marital strain model). Both of these perspectives, while not mutually exclusive, highlight the importance of contextual aspects of the marital bond; most notably, duration in a status. It is somewhat unclear whether the advantage of married adults in terms of health is reflective of individuals who have been in this union the longest. Conversely, it is important to see whether the deleterious influence of marital loss reflects the strain individuals experience upon recently exiting this role or whether it has consequences for health over time in this role. In addition to duration, broader marital histories reflect what Waite (2009) describes as “the challenges—and the opportunities for recovery—that an individual faces over the course of his or her life” (p. 692). Marital duration and individuals’ broader marital history may have an impact on physical health, especially in later life, since it allows time and history in this role to accumulate. However, empirical examinations of these contextual factors on physical health are sparse. Recently, a number of researchers have also called for an examination of how social relationships especially the existence of the marital bond (Waite, 2009) influence physiological processes (Carr & Moorman, 2011; Umberson & Montez, 2010). Social relationships may influence cardiovascular, immune, endocrine functioning and cumulative biological risk or allostatic load (Seeman, McEwen, Rowe, & Singer, 2001). Few researchers, however, have

5 examined whether marital gain or loss is associated with allostatic load in a sample of U.S. adults (for an exception, see: Seeman, Singer, Ryff, Love, & Levy-Storms, 2002). In the current study, I use a cumulative advantage/disadvantage framework to frame my discussion of these contextual factors of marriage for allostatic load. Specifically, I examine whether (a) marital status, (b) marital status duration, and (c) individuals’ broader marital history is associated with allostatic load. Additionally, I see whether (d) gender, since men and women may contribute differently and receive different resources from the marital bond, conditions these relationships.

Background

Marital Status Durations

Researchers have attributed the health advantage experienced by married adults to two models: the marital resource model and the marital strain model (Booth & Amato, 1991; Meadows, McLanahan, & Brooks-Gunn, 2008; Hill, 2012; Umberson, 1992; Williams & Umberson, 2004). The marital resource model contends that much of the positive health effects of being married are attributed to increased resources gained from marriage and control of health behaviors by spouses (Carr & Moorman, 2011; Waite, 1995; Waite, 2009). For example, married adults tend to enjoy higher incomes (Mandara, Johnston, Murray, & Varner, 2008) and greater wealth accumulation (Schneider, 2011; Vespa & Painter, 2011) because, as Waite (2009) explains, spouses may combine economic resources and share living expenses. Additionally, married adults may benefit from the supportive functions provided by spouses (Mirowsky & Ross, 2003; Schieman, van Gundy, & Taylor, 2002; Waite & Gallagher, 2000). Spouses may help one another in times of duress. In addition, their relationship connects them to a broader base of friends and family which may provide additional social support (Mirowsky & Ross, 2003). Finally, being married may serve as a mechanism of social control, since spouses may monitor and encourage healthy behaviors such as exercise and going to the doctor on a regular basis and discourage risky behaviors such as excessive drinking and smoking (Schone & Weinick, 1998; Umberson et al. 2010; Waite, 1995). This perspective, therefore, highlights the resources or mechanisms linking marriage to health. While past research typically finds that the married fare better than the unmarried, Williams and Umberson (2004) found that married adults did not significantly differ from

6 unmarried adults on self-rated health over a five year period. They attribute the marriage-health connection to the stress and strain associated with marital loss and not necessarily to the resources provide by marriage. The marital crisis (or stress) model suggests that the deleterious short-term consequences of marital loss -- not the benefits of marriage-- account for marital status-based health differentials (Booth & Amato, 1991). The idea is that these differences represent the immediate toll spousal loss takes on individual health. Inherent to this argument is that individuals can recover from the stressful event of losing a spouse. Discussions of the model tend to highlight the resulting strain of marital dissolution on health (Williams & Umberson, 2004) rather than the resources conferred through marriage. Indeed, researchers find that divorced and widowed adults have worse physical health than married adults. For example, the currently divorced are more likely than currently married to develop cardiovascular disease, cancer, and functional limitations (Johnson et al. 2000; Pienta et al. 2000). Currently widowed adults experience a greater health decline over time than those who are married (Prigerson et al. 2000; Stroebe and Stroebe 1987; Williams & Umberson, 2004). It is important to note that some see marital loss as a chronic stressor that increases strain over time (Johnson & Wu, 2002), while others see it as a discrete life event or temporary crisis that exerts an immediate influence on an individuals’ health and well-being (Lorenz, Simons, Conger, Elder, Johnson, & Chao, 1997). One theory that may lend insight to under what temporal conditions marital statuses impact health in later life is cumulative advantage/disadvantage theory.

Cumulative Advantage Theory

Cumulative advantage theory was first developed by Robert Merton (1968, 1988) to describe how early opportunities among career scientists lead to further advancement and recognition in their profession. Following Merton’s insights, researchers expanded and applied cumulative advantage/disadvantage to the study of inequality, elaborating how one’s social position in society brought with it advantages or disadvantages that accumulate over time (Dannefer 1987, 1988, 2003; Ferraro, Shippee, & Schafer, 2009; O’Rand, 1996). This accumulation leads to differing life course trajectories wherein those with higher status or more advantage experience more opportunities and resources which results in more favorable outcomes (Ferraro et al., 2009), especially in later life (O’Rand, 1996; Ross & Wu, 1996; Settersten, 2003). Overall, the advantage of a set of individuals in terms of their status, roles, or 7 protective factors grows over time (Elder, 1995; DiPrete & Eirich, 2006; Hatch, 2005), and can result in differential outcomes, namely, health status (O’Rand & Henretta, 1999; Willson, Shuey, & Elder, 2007). DiPrete and Eirich (2006) review different empirical and theoretical models for applying cumulative advantage to research on inequality. The cumulative exposure model, which they identify as one of the most common models, states that long-term exposure to a particular status may have an effect on the accumulation process. This notion builds upon life course research that indicates that the temporal features of statuses such as their duration can influence life course trajectories and outcomes (O’Rand, 1996). The duration of exposure refers to the time the individual experiences opportunity for reward (Ferraro et al., 2009). Willson and colleagues (2007) note, however, that “we know relatively little about the cumulative effects of the duration of time spent in an advantaged or disadvantaged state as a mechanism generating health inequality across the life course” (p.1890).

Marital Transitions

Life course researchers have also highlighted the importance of transitions or “shocks” that can adversely influence health outcomes (DiPrete & Eirich, 2006; Elder & Shanahan, 2006; George, 2003). A transition or shock represents a change into or out of roles or statuses. These shocks or transitions may have persistent effects on health (Hatch, 2005) because the loss signifies a change in one’s position in the social structure (Thoits, 1991) and perhaps a deterioration of resources associated with that role. Researchers have called for the application of cumulative advantage/disadvantage theory and other life course perspectives to the study of social relationships (Umberson & Montez, 2010) and marital research (Waite, 2009) in particular.

Cumulative Advantage/Disadvantage and Durations and Transitions

If marriage benefits health and if the marital resources model is correct, longer exposure to the marital relationship should be especially beneficial to health. Working from a cumulative advantage perspective and the marital resource model, being married longer should result in the accumulation of the abovementioned resources that come from marriage; and, being in a marital status that is associated with worse health and a depletion of these resources will result in worse health outcomes, and more time in this status may be related to worse physical health.

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Additionally, marital loss may be a stressor that influences stress proliferation in that it may allow for other stressors to accumulate. For example, losing a spouse may result in economic losses and social isolation, which can influence physical health over time (Evans, Wethington, Coleman, Worms, & Frongillo, 2007). It is important to note that is in opposition to the marital crisis model. Similarly, marital shocks or transitions may result in worse health since they mark changes in identity and can results in a loss of financial, social, and psychological resources (Dupre & Meadows, 2007). Over time, the number of “shocks” or losses experienced may accumulate stress on the body’s regulatory systems. The few studies that examine marital status duration’s impact on physical heath tend to find a benefit derived from longer marriages. Lillard and Waite (1995) found that longer marriages are more protective against mortality risk than shorter marriages. Additionally, Dupre and Meadows (2007) using a sample of U.S. adults found that being married longer protected against health decline in the form of developing chronic conditions. There is also evidence that this relationship may be especially important in later life. Pienta and colleagues (2000) found that among married adults in their retirement years, being married 20 to 29 years was associated with less heart disease, stroke, and mobility loss than being in a shorter marriage. While little empirical research has addressed the role of duration in the widowhood or divorced status, researchers tend to find that there is a change in self-rated health (Williams & Umberson, 2004) and mortality risk (Thierry, 2000) immediately following a loss but these health differentials converge over time in the status. These findings give credence to the marital crisis model. However, Lorenz and colleagues (2006) found that the stably divorced had worse health than the stably married, but this differential became present ten years following the divorce, giving credence to a cumulative disadvantage perspective. In regards to shocks or transitions, most researchers find that second and third marriages are less beneficial to health than first marriages in terms of mental (Barrett, 2000) and physical health (Hughes & Waite, 2009; Zhang & Hayward, 2006). Researchers have also found that the impact of these losses varied by current marital status. In particular, Zhang and Hayward (2006) found that among the married ever experiencing a marital disruption was associated with cardiovascular disease. Hughes and Waite (2009) found similar results for self-rated health, chronic conditions, and functional limitations. Additionally, Barrett (2000) found that being currently married or currently widowed and experiencing additional past marital losses was

9 associated with worse mental health, than experiencing one; however, this relationship did not vary by the type (widowhood or divorce) of prior loss. Overall, there is evidence that the number of prior marital losses may influence physical health status.

Allostatic Load

One health outcome that has received attention in the past decade is the role of social relationships influencing biological risk or allostatic load. Allostasis theory is a modification of the homeostasis concept (Carlson & Chamberlain, 2005; Juster, McEwen, & Lupien, 2010; Sterling & Eyer, 1988). It suggests that in order to support homeostatic systems that are essential to living, physiological networks must adapt and change when faced with stressors (Carlson & Chamerlain, 2005; Evans, 2003), which are threats to the physiological or psychological integrity of individuals (McEwen, 2009). For example, when an individual experiences stress or stain, there is a physiological reaction and adjustment. Over time, if faced with increased demands or strain along with the inability to adapt to or modify the allostatic response can result in allostatic load (McEwen, 2003). Allostatic load predisposes organisms to disease (McEwen & Stellar, 1993). For example, it has been associated with hypertension, diabetes, autoimmune and inflammatory disorders, and cancer (Crews, 2007; Crimmins, Kim, & Seeman, 2009; McEwen & Wingfield, 2003). Additionally, allostatic load indices predict mortality and physical functioning (Karlamangla, Singer, & Seeman, 2006; Seeman et al., 2001), and may indicate frailty development (Gruenewald, Seeman, Karlamangla, & Sarkisian, 2009). Allostatic load, therefore, is a multidimensional construct and includes a number of pathways through which social relationships may “get under the skin” (McEwen & Stellar, 1993). These include cardiovascular, endocrine, immune, metabolic, and sympathetic nervous systems (Ryff & Singer, 2000; Seeman et al., 2002). Researchers have found that the influence of structural arrangements and social relationships may accumulate over time and influence physiological systems. For example, more supportive social interactions benefit health and reduce allostatic load while chronic strains or isolation take a toll over time and increase allostatic load (Seeman et al., 2002, 2004). While researchers have highlighted the importance of allostatic load as an outcome for marital research (see: Umberson & Montez, 2010; Waite, 2009), little empirical research has been conducted on the topic. Of the existing literature, Weinstein and colleagues (2003) using a sample of Taiwanese older adults found that being widowed was associated with more allostatic 10 load than being married. Gersten (2008) using the same data found that among both men and women being widowed was not associated with neuroendocrine allostatic load but living alone was for women. Seeman and colleagues found (2002) among older adults positive social relationships and allostatic load are associated among men but not women. However, Goldman and colleagues (2005) found that perceived stress and physiological response is stronger for women than men.

Gender

Researchers have also been interested in whether gender conditions the marriage-health relationship since researchers have argued that men benefit more from the marital bond than women (Bernard, 1972; Gove, 1972; Gove & Tudor, 1993). Research on mortality risk finds marriage is protective for both men and women (Lillard & Waite, 1995; Manzoli et al., 2007; Rendall et al., 2011), and that men experience a greater advantage from being married than women (Hemstrom, 1996; Johnson et al., 2000; Rendall et al., 2011). Additionally, marital loss has been found to influences men’s self-rated health to a greater extent than women’s health (Stroebe, Hansson, & Stroebe, 2001; Williams & Umberson, 2004). Most research indicates that gender conditions this relationship with men benefiting more than women. Gender may condition the relationship between marital status and health since women and men may receive different resources from marriage or experience different strains from its loss. In particular, researchers have argued that women benefit more from the increased economic support provided by marriage (Hill, 2012; Lillard & Waite, 1995); however, men tend to benefit more from the additional social support received from their spouses (Carr & Moorman, 2011), the monitoring of health behaviors such as discouraging alcohol use and encouraging adherence to medication regimens from their spouses (Umberson et al., 1996; Waite & Gallagher, 2000), and their wife’s household management (Lennon & Rosenfield 1994; South & Spitze, 1994). Past research has found women’s health benefits from their improved financial well-being of being married (Lillard & Waite, 1995). Similarly, there has been evidence that the gap between the married and never married in terms of self-rated health has converged over time for men but not women and that the adverse effects of marital dissolution have increase more for women than for men (Liu & Umberson, 2008). Perhaps since divorce entails a greater income gamble for women than for men (Light & Ahn, 2010) this has consequences for health over duration in that status. 11

Research addressing gender differences in the influence of marital duration has been sparse. Some evidence suggests that men benefit more than women in terms of longer marital duration and being in a marital loss status, especially in terms of widowhood (Dupre & Meadows, 2007; Williams & Umberson, 2004), is more damaging for men over time. Other researchers focusing their attention to samples of women have found that divorce is a chronic strain that increases stressful life events and has an influence on health over time (Lorenz et al., 2006). In contrast, Kiecolt-Glaser and colleagues (1987) found that being recently divorced is associated with impaired immune functioning for women; however, this did not vary years in this status. Overall, being married longer is associated with better physical health and marital loss may exert a greater effect on men’s physical health; however, some indicating long-term while others documenting only short-term effects of marital loss on health.

The Current Study

The above review has led to three research questions: 1. Is marital status duration associated with allostatic load? 2. Is marital history in the form of prior marital transitions associated with allostatic load? 3. Are these relationships the same for men and women?

Methods

Data

To address these questions, I will use data from the National Social Life, Health, and Aging Project (NSHAP). The NSHAP is a nationally-representative sample of individuals aged 57 to 85 years old living in the United States. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted interviews during 2005 and 2006, yielding a sample of 3,005 adults aged 57 to 85 (O’Muircheartaigh, Eckman, & Smith, 2009; Suzman, 2009). Data collection was conducted in face-to-face interviews in respondents' homes, in-home biomeasure collection, and a leave-behind questionnaire (Smith et al. 2009), yielding a 75.5% response rate (O’Muircheartaigh et al., 2009). One main goal of the NSHAP is to determine the importance of social relationships and health in older adults’ lives, therefore, questions concerning the marital bond are included making it an ideal source for the current investigation. Since not all respondents participated in the collection, the

12 analytic sample for this study will be 1,340 adults. All results will be weighted to account for non-response based on age and urbanicity.

Measures

Dependent Variable Allostatic Load. Allostatic load is assessed through eight biological markers. These include body mass index (BMI), systolic blood pressure, diastolic blood pressure, pulse rate, c- reactive protein (CRP), glycosylated hemoglobin (HbA1c), Epstein-Barr (EBV), and dehydroepiandrosterone (DHEA). BMI is calculated by taking the weight of the respondent (in pounds), dividing it by the square of the respondent’s height (in inches) and multiplying it by 703. Respondents’ systolic blood pressure, diastolic blood pressure, and pulse rate is based on the mean of two and, in cases where the first two readings varied greatly, three readings. CRP, HbA1c, and Epstein-Barr virus were measured through dried blood spots which were taken from the respondents’ finger with a lancet, applied to filter paper, and transported to laboratories for assessment (Williams & McDade, 2009). DHEA, which is obtained through salivary specimens provided by the respondent, is based on the mean of two readings and is reverse coded since it declines with age and represents a decline in physical functioning (Johnson, Bebb, & Sirrs, 2002). For more information on the quality control of these biomarkers see: Smith et al., 2009. Each of these biomarkers were standardized and combined to create a continuous measure of allostatic load (Juster et al., 2010). Therefore, higher scores indicate higher allostatic load or greater wear and tear on biological systems. See Appendix 1 for a detailed description of these biomarkers. Independent Variables Marital status. Respondents were asked their marital status and given the following options: married, living with a partner, divorced, separated, widowed or never married. For the current analyses, divorced and separated respondents are put together since the cell size was too small for participants who were both separated and participated in the biomarker collection. Marital status is coded as a multiple dummy, indicating married, partnered, divorced, widowed, and never married. Duration in status. Duration in status is calculated differently based on the respondent’s current marital status. For those who are currently married, timing in the status is determined by subtracting the year they were married to their current spouse from the year of the interview 13

(2005 or 2006). For those who are currently widowed, duration in status is taken from the year their partner died and subtracting it from the year of the interview. For those who are separated/divorced, this is based on subtracting the year in which they stopped living with their most recent partner from the year of the interview. For these analyses, I will distinguish among those married 0 to 15 years, 16 to 30 years, 31 to 45 years, and 46 years or more. Number of Marital Transitions. Number of Marital Durations is based on the past number of marriages that respondent has experienced prior to the interview. Respondents who are currently married or had experienced a divorce/separation or had been widowed were asked whether this marriage was their first marriage. Then they asked respondents “Altogether, how many times have you been married (IF CURRENTLY MARRIED: including your current marriage)?” I recoded this measure to distinguish among adults who have had one marriage, two marriages, or three or more marriages. Gender. Gender is coded 1 for female and 0 for males. Control Variables Age is a continuous variable, ranging from 57 to 85. Education is a multiple dummy indicating respondents with less than a high school degree, a high school degree or equivalent, some college (vocational certificate/associates/somecollege), and those with a bachelors or more. Income is based on household income per year. Originally 29.32 % of the sample was missing on this variable. Respondents were asked their yearly income and if they did not answer, they were probed with a number of questions such as “is your household income over or under 25,000,” “ under or over 50,000,” etc. Based on respondents’ answers to these questions, I used multiple imputation to retain a large part of the sample. For the remaining respondents missing on this measure (9.12%) I imputed the mean. Race and Ethnicity is based on respondents’ self-report of racial/ethnic identification and is coded as a multiple dummy variable indicating black, Hispanic, other, with white as the reference category. Number of children is a straight count of the respondent’s number of living daughters and sons.

Analytic Strategy

First, I present the descriptive statistics for all of the study variables including the range, mean/proportion, and standard deviation (Table 1). In Tables 2 and 3 I present a series of ordinary least squares (OLS) regressions to model the effects of allostatic load on marital status (Model 1), allostatic load marital status duration (Model 2), and gender by marital status and 14 gender by duration in status interactions to examine whether there are gender differences in these effects on allostatic load (Model 3 and 4). Finally, in Table 4 I present OLS regression results of allostatic load on marital history by marital status interactions to consider whether marital status may further vary as a function of marital history (Model 1), and gender interactions in these effects (Model 2). All of the regression analyses are performed with the statistical package STATA10.

Results

First, I present descriptive statistics for the study variables in Table 1.1. According to this table, the average respondent scores –0.11 on the allostatic load index. Most respondents are married (62%) and about half have been in their current marriage for more than 31 years. Widowed adults comprise 22% of the sample, while 12% are divorced, 2% are partnered, and 3% have never been married. About 46% of the analytic sample has been married for more than 30 years. Most respondents have experienced one marriage in their lifetime (68%) followed by those with two marriages (22%) and then those with three or more marriages (7%). In terms of background factors, the average respondent is 69 years old and about half of the sample is women (49%). The sample is comprised of non-Hispanic Whites (74%), Blacks (13%), Latinos (11%) and respondents of other race/ethnicities (2%). Education levels include less than a high school degree (22%), high school degree (27%), some college (28%) and a college degree or more (23%). Additionally, the average respondent has a household income of $49,584 per year and about three living children. Table 1.2 presents the regression analyses considering the significance of being married, marital duration, and gender for allostatic load. As anticipated, being married is significantly associated with lower allostatic load scores than not being currently married (b= –0.51; p <.05), holding constant age, gender, race/ethnicity, educational status, income, and number of living children (Model 1). Model 2 estimates the influence of marital duration for allostatic load. Here I find that being married 46 years or longer is significantly associated with less wear and tear on the body than not being married (b= –0.61; p <.05). Therefore, the health benefit of being married relative to unmarried is particularly salient at the longest marital duration. When I examine gender differences in these effects, I find that there are no significant gender variations in the influence of being married versus not or marital duration on allostatic load (Models 3 and 4). 15

Next, I examine whether being currently widowed, divorced, partnered, or never married is associated with more allostatic load than being currently married (Table 1.3). Model 1 indicates that currently widowed adults experience significantly more allostatic load than the currently married adults (b= 0.59; p <.05). It is important to note that divorced, partnered, and never married adults do not significantly differ from married adults in terms of allostatic load. In Model 2 I assess whether timing in these statuses is associated with allostatic load. These results suggest that adults who have been widowed for 16 years or more (b= 1.05; p <.01) and divorced for 31 years or more (b= 1.30; p <.01) have significantly more allostatic load than married adults. Gender interactions (Models 3 and 4) produce one significant finding-- that being divorced 31 or more years is significantly associated with greater allostatic load for women than for men (b=1.39; p<.05). Finally, I examine whether there are differences in allostatic load by number of prior marriages. I find that there are no significant differences between those who have experienced one, two, or three or more marriages and those who have never been married in terms of allostatic load (results not shown). Additionally, there were no gender differences in these effects (results not shown). Then, I examine the joint effects of current marital status and past number of marriages. These analyses distinguish between those who have had one marriage and those who have had two or more because cell sizes for gender by marital status and past number of losses interactions were too small for adults with three or more past marriages. In Table 1.4, I find that while there are no significant differences in allostatic load by current marital status and number of marriages (Model 1), there are gender differences in these effects. In particular, being currently widowed and having two or more previous marriages is significantly associated with greater allostatic load scores for women than for men (b= 3.10; p <.05).

Discussion

The present study addressed three questions: Are there marital status differences in allostatic load? Does marital status duration and past number of marriages influence these effects? And, do women and men differ in the extent to which these influence allostatic load? Results indicate that currently married older adults have lower allostatic load than unmarried adults. Additionally, this advantage extends to adults who have been married the longest (i.e. 46 years or longer). This is similar to past research on marital duration and health (Dupre & Meadows, 2007; Lillard & Waite, 1995; Pienta et al., 2000), and in congruence with 16 the cumulative exposure model, that long-term exposure to a particular role or may be especially beneficial to health. Results presented here indicate that perhaps it is not just the stress and strain associated with marital loss that accounts for the health advantage experienced by married adults as Williams and Umberson (2004) have argued, but the long term consequences of marital gain. Therefore, the advantage of married adults reflects the health advantage of adults who have been married the longest. In terms of marital loss, currently widowed adults have more allostatic load than currently married adults. Additionally, older adults who have been widowed or divorced for the longest amounts of time have more allostatic load than married adults. These results suggest that the persistent loss of marital resources influences physiological functioning, and not the short- term strain immediately following marital loss (as the marital crisis model contends). The reason for these results may reflect the nature of the outcome variable observed. In accordance with allostasis theory, stress and strain can accumulate and influence wear and tear on the body. Since allostatic load represents the inability to effectively cope with this stress or strain over time, it seems fitting that it would vary by marital status duration since both encompass time and accumulation processes. Perhaps widowed and divorced adults experience more stress and strain following marital loss- similar to the idea of chain of risk- wherein, this stressor sets into place other stressors (monetary losses) or losses in social (social support) and psychological resources (sense of control) that makes individuals more vulnerable to other subsequent stressors. Adults who remarry may, as Waite contends (2009) reestablish the benefits received from marriage. Adults who remain in these statuses, however, may experience strain and subsequent stressors (Lorenz et al., 2006). Additionally, while the results presented here for physical health do not support the marital crisis model, marital loss may exert an immediate influence on mental well-being, which could influence physiological functioning over time. This is an important avenue for future research. It is important to note that duration rather than number of prior marital transitions appears to be more pertinent for allostatic load. Since individuals with one past marriage or multiple prior marriages did not significantly differ in terms of allostatic load this suggests that individuals may recover from such losses or adverse shocks, at least in terms of physiological functioning. Additionally, it is important to note that while married adults fare better than unmarried adults, there were no significant differences between adults who are currently married and those who

17 have made it to this point in the life course unmarried. Carr and Moorman (2011) contend that similar health outcomes for married adults and adults who have been single their entire lives may represent selection of economically stable adults- especially women- with strong social and family support networks into this category. Gender interactions indicate that being divorced the longest (31 years or more) is more detrimental for physiological functioning for women than men. Additionally, being currently widowed and experiencing two or more marriages in the past is more detrimental for women than for men. These findings indicate that the influence of marital loss does vary by social position, and are in opposition to past research indicating that marital loss influences men’s self- rated health to a greater extent than women’s (Stroebe et al., 2001; Williams & Umberson, 2004). Results presented here indicate that women are more vulnerable to divorce over time and widowhood after experiencing multiple losses. As Strohschein and colleagues (2005) suggest, structural inequality which puts women at greater disadvantage than men, coupled with changes in marital relationship status may account for women’s vulnerability to these events. Additionally, other researchers have found divorce increases other stressors (Lorenz et al., 2006) such as income losses for women over time (Umberson, Wortman, & Kessler, 1992; Light & Ahn, 2010), which may account for women’s increased allostatic load following marital loss. However, as Carr (2004) notes, preloss marital functioning (in terms of support and dependence on a spouse) may also lend insight to how men and women experience life after widowhood. The loss of resources may be important mediators to this relationship, but so may be measures of preloss marital quality or marital conflict (Fincham & Beach, 1999) and preloss health of a spouse (Booth & Johnson, 1994). These may also be important mechanisms that account for more allostatic load experienced by women following marital loss, and an important avenue for future research. Some limitations are worth mentioning. First, this study does not follow the same individuals over time. While marital status duration has implications for health, it would be important to see whether these relationships hold across time for the same individuals. It is possible that these results reflect a selection bias wherein healthier adults select into longer marriages and unhealthy adults into longer marital loss durations. Second, the measure of allostatic load employed here takes into account three markers of cardiovascular functioning, but only one marker for each of the other systems (neurodendocrine, immune, and metabolic).

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Having multiple markers from each of these pathways would better encompass allostatic load and allostasic response, and examining aspects of the marital bond for each of these systems would be important for understanding how marital loss contributes to mortality risk. Third, results presented here may be an underestimation of the associations between marital status and health in later life since the sample is based on relatively healthy community-dwelling adults in the U.S. If adults from assisted-living facilities, who may be widowed and experiencing health complications, were included in the current analyses there may be greater discrepancies among married adults and widowed adults, and this may lend better insight to how social relationships impact health among adults in later life. Notwithstanding the above limitations, this research has built upon previous research on the marriage-health connection by incorporating key components of marital relationships-- their duration and history. Similarly, I focus my attention on allostatic load which has received much attention in the past decade, but has not been thoroughly examined in prior research on this topic. Additionally, I applied a life course framework, cumulative advantage theory, to the study of marriage and health to better understand how an individuals’ own history in a status and not just current status influences health. The next step would be to examine which mechanisms link marital status to allostatic load in later life. From the perspective of the marital resource model, perhaps the losses in social, economic, and psychological resources account for this relationship. For example, do losses in key sources of social support, income, or health behaviors following spousal loss explain some of this relationship? Additionally, how does marital functioning among the currently married, and preloss functioning among the formerly married influence these findings? By answering this question, we may better understand how marriage protects and marital loss makes older adults vulnerable to impaired physiological functioning which has implications for successful aging.

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TABLE 1.1 Descriptive Statistics for Study Variables (N=1340)

Range Mean/Proportion Standard Deviation

Outcome Variable Allostatic Load –15.36 – 13.33 –0.11 3.20

Marriage Variables Married 0.00 – 1.00 0.62 Married 0 to 15 years 0.00 – 1.00 0.08 Married 16 to 30 years 0.00 – 1.00 0.09 Married 31 to 45 years 0.00 – 1.00 0.25 Married 46 or more years 0.00 – 1.00 0.21 Widowed 0.00 – 1.00 0.22 Widowed 0 to 5 years 0.00 – 1.00 0.07 Widowed 6 to 10 years 0.00 – 1.00 0.05 Widowed 11 to 15 years 0.00 – 1.00 0.04 Widowed 16 or more years 0.00 – 1.00 0.06 Divorced 0.00 – 1.00 0.12 Divorced 0 to 10 years 0.00 – 1.00 0.03 Divorced 11 to 20 years 0.00 – 1.00 0.03 Divorced 21 to 30 years 0.00 – 1.00 0.03 Divorced 31 or more years 0.00 – 1.00 0.03 Partnered 0.00 – 1.00 0.02 Never Married 0.00 – 1.00 0.03 Number of Marital Transitions One Marriage 0.00 – 1.00 0.68 Two Marriages 0.00 – 1.00 0.22 Three + Marriages 0.00 – 1.00 0.07

Background Variables Age 57.00 – 85.00 69.39 7.73 Female 0.00 – 1.00 0.49 Black 0.00 – 1.00 0.13 Latino 0.00 – 1.00 0.11 Other 0.00 – 1.00 0.02 High School Degree 0.00 – 1.00 0.27 Some College 0.00 – 1.00 0.28 College Degree 0.00 – 1.00 0.23 Income 0.00 – 1,800,000 49584.14 71396.09 Number of Children 0.00 – 22.00 3.16 2.19 Notes: National Social Life, Health, and Aging Project (2005/2006)

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TABLE 1.2 OLS Regression of Allostatic Load on Marital Status, Marital Duration, and Gender (N=1340)

M1 M2 M3 M4

Married –0.51 * –0.12 (0.22) (0.34) Married 0 to 15 –0.39 0.18 (0.50) (0.75) Married 16 to 30 –0.28 0.09 (0.42) (0.58) Married 31 to 45 –0.51 –0.07 (0.33) (0.40) Married 46 or more –0.61 * –0.38 (0.24) (0.36) Female –0.16 –0.16 0.30 0.30 (0.20) (0.20) (0.34) (0.34) Female*Married –0.66 (0.45) Female* Married 0 to 15 –1.13 (0.82) Female* Married 16 to 30 –0.60 (0.74) Female* Married 31 to 45 –0.78 (0.55) Female* Married 46 or more –0.27 (0.54) Intercept 4.23 *** 3.98 *** 4.01 *** 3.69 *** Model F 9.88 *** 7.74 *** 8.86 *** 6.02 *** Adjusted R-squared 0.06 0.06 0.06 0.07 Notes: Shown are unstandardized coefficients with standard deviations in parentheses All models adjust for age, gender, race/ethnicity, education, income, and number of children. Reference group is unmarried adults. *p<0.05, **p<0.01, ***p<0.001

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TABLE 1.3 OLS Regression of Allostatic Load on Marital Status, Status Duration, and Gender (N=1340)

M1 M2 M3 M4 Widowed 0.59 * 0.06 (0.26) (0.52) Widowed 0 to 5 years –0.002 –0.11 (0.341) (0.58) Widowed 6 to 10 years 0.87 0.75 (0.51) (1.94) Widowed 11 to 15 years 1.18 0.33 (0.60) (0.81) Widowed 16 or more years 1.05 ** –0.10 (0.39) (1.52) Divorced 0.39 0.11 (0.30) (0.49) Divorced 0 to 10 years 0.02 –0.10 (0.50) (0.76) Divorced 11 to 20 years –0.07 0.49 (0.52) (0.84) Divorced 21 to 30 years 0.61 –0.26 (0.53) (1.08) Divorced 31 or more years 1.30 ** 0.31 (0.37) (0.52) Partnered 0.31 0.30 0.61 0.59 (0.68) (0.67) (0.91) (0.90) Never Married 0.85 0.90 –0.27 –0.26 (0.66) (0.66) (0.91) (0.91) Female –0.18 –0.24 –0.36 –0.37 (0.20) (0.21) (0.26) (0.26) Widowed*Female 0.77 (0.61) Widowed 0 to 5 years*Female 0.21 (0.72) Widowed 6 to 10 years*Female 0.22 (1.96) Widowed 11 to 15 years*Female 1.19 (1.13) Widowed 16 or more years*Female 1.32 (1.55) Divorced*Female 0.51 (0.63) Divorced 0 to 10 years*Female 0.27 (0.93) Divorced 11 to 20 years*Female –0.86 (1.21) Divorced 21 to 30 years*Female 1.31 (1.25) Divorced 31 or more years*Female 1.39 * (0.67) Partnered*Female –1.17 –1.13 (0.94) (0.93) Never Married*Female 1.77 1.75 (1.13) (1.12) Intercept 3.85 *** 4.19 *** 3.91 *** 4.30 *** Model F 8.69 *** 6.78 *** 7.05 *** 4.85 *** R-squared 0.06 0.07 0.07 0.08 Notes: Shown are unstandardized coefficients with standard deviations in parentheses

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All models adjust for age, gender, race/ethnicity, education, income, and number of children. *p<0.05, **p<0.01, ***p<0.001; Reference group is married adults.

TABLE 1.4 OLS Regression of Allostatic Load on Current Marital Status, Number of Marital Transitions, and Gender (N=1340)

M1 M2

Widowed with one marriage 0.14 0.26 (0.28) (0.63) Widowed with two or more marriages 0.60 –1.57 * (0.52) (0.69) Divorced with one marriage 0.48 0.09 (0.34) (0.57) Divorced with two or more marriages 0.33 0.38 (0.44) (0.74) Partnered 0.31 0.66 (0.71) (0.95) Never Married 0.71 –0.43 (0.65) (0.85) Female –0.11 –0.28 (0.21) (0.27) Female*Widowed with one marriage –0.06 (0.71) Female*Widowed with two or more marriages 3.10 ** (0.88) Female*Divorced with one marriage 0.69 (0.70) Female*Divorced with two or more marriages –0.003 (0.974) Female*Partnered –1.30 (1.00) Female*Never Married 1.79 (1.18) Intercept 0.06 0.09 Model F 4.15 *** 3.32 *** R-squared 0.05 0.06 Notes: Shown are unstandardized coefficients with standard deviations in parentheses All models adjust for age, gender, race/ethnicity, education, income, and number of children. Reference group is never married adults. *p<0.05, **p<0.01, ***p<0.001

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CHAPTER THREE

MARITAL LOSS, GENDER, AND MENTAL HEALTH

Introduction

Marital loss has been characterized as one of the most stressful events an individual can experience (Holmes & Rahe, 1967). Divorce and widowhood may result in a change in one’s self-concept or identity (Bachrach, 1975) and indicate the loss of social, economic, and psychological resources (Dupre & Meadows, 2007), which can influence mental well-being. While researchers have found that being currently divorced or widowed is associated with psychological distress (Johnson & Wu, 2002; Simon, 2002; for a review see: Carr & Springer, 2010), investigations of marital status durations and broader marital histories and how they impact well-being have been overlooked (for an exception, see: Barrett, 2000). Researchers have argued that marital loss is a discrete stressful life event that individuals can psychologically adapt to over time (Booth & Amato, 1991; Lorenz et al., 1997). Therefore, some suggest that the negative effects of marital dissolution dissipate over time, while others see it as having far-reaching health consequences. Additionally, the influence of marital loss on mental health may dissipate when an individual enters into another marital union. However, having more marital losses may be devastating over time, since they may represent increased exposure to stressful experiences. There is also evidence that the impact of these contextual aspects of marital statuses vary by gender, with researchers finding differences in the forms of emotional distress following marital loss (Simon, 2002) and differences in depletion of key resources following marital loss (Light & Ahn, 2010). In the current study, I use a nationally-representative sample of older adults in the U.S. to examine whether duration in a marital status and prior marital losses is associated with mental well-being in later life. In addition, I determine whether these influences are similar for men and women. I rely on a four indictors of mental well-being (depressive symptoms, anxiety symptoms, alcohol problems, and overall happiness). These multiple outcomes account for the different manifestations of marital loss and possible social group differences in the expressions of distress (Aneshensel, Rutter, & Lachenbruch, 1991; Horwitz, 2002). By examining whether these factors

24 influence mental health, we may better understand how the context of social relationships protects- or puts individuals at risk- for distress in later life.

Background

Marital Resource and Marital Strain Models

Being married rather than unmarried has been associated with less psychological distress (Johnson & Wu 2002), especially in terms of depression (Simon, 2002). This advantage has been accredited to (a) selection into marriage, (b) the resources marriage brings, or (c) the strain from marital loss. Some scholars attribute the mental health advantage of married adults to hardier and healthier adults selecting into marriage in the first place (Horwitz, White, & Howell-White, 1996). The idea is that adults who are happier and less depressed are more likely to marry. However, studies employing longitudinal data suggest than marriage and its loss tends to impact well-being and not the other way around (Lamb & DeMaris, 2003; Simon, 2002). The marital resource model argues that marriage provides individuals with economic (income), social (friendship and social support), and psychological (purpose, obligation, and belonging) resources (Cutrona, 1996; Umberson, 1987; Shieman, van Gundy, & Taylor, 2002; Waite, 1995). For example, married individuals tend to report higher incomes and more wealth than unmarried adults (Mandara et al., 2008). They also report larger and more supportive social networks (Mirowsky & Ross, 2003). Additionally, belonging to a social relationship that is built upon mutual responsibility and support may have benefits for mental health (Monin & Clark, 2011). While marriage provides these resources, it can also be seen as a mechanism of social control since spouses may encourage health lifestyles and behaviors such as regular exercise or doctor visits, and discourage risky behaviors such as excessive drinking or smoking (Umberson, 1987, 1992). The marital strain model, on the other hand, states that marital loss is a short-term experience and takes an immediate toll on mental well-being (Booth & Amato, 1991). However, adults can psychologically overcome this loss (Lorenz et al., 1997). This research tends to focus on stress literature and sees marital loss as a discrete stressful life event that immediately influences well-being and a change in health status. However, research is mixed as to whether marital loss is a discrete crisis or has far-reaching consequences for well-being (Amato, 2000).

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Marital Status Durations

The influence of marital disruption on health may vary by the duration of time spent in a marital loss status. Dupre and Meadows (2007), for example, argue that marital status durations indicate the accumulation of time spent within a status, and being exposed to a status longer can enhance stability. From a cumulative disadvantage standpoint, however, those with less advantage experience less opportunities and resources which results in less favorable outcome (Ferraro, Shippee, & Schafer, 2009), especially in later life (O’Rand, 1996; Ross & Wu, 1996; Settersten, 2003). In particular, the cumulative exposure model states that long-term exposure to a particular status may have an effect on the process of accumulating resources (DiPrete & Eirich, 2006). Perhaps long term exposure in the status of being widowed or divorced reflects a loss of the abovementioned resources and over time the loss of these resources influences health. Life events while they may take time to occur (Avison & Turner, 1988) have a clear beginning and end (Wheaton, 1999). Life events can be stressors that influence the stress proliferation process (Pearlin, Mullan, Semple, & Skaff, 1990; Strohschein et al., 2005). For example, losing a spouse may be preceded by caregiving demands and followed by social isolation and income losses that may impact health (Evans et al., 2007). Research on marital loss and mental health indicates that stably unmarried adults experience more depressive symptoms and alcohol problems than stably married adults (Simon, 2002). Similarly, Strohschein and colleagues (2005) found that over time those who remain married have less distress than those who are single, separated, or divorce; however, they also found that widowhood immediately increases distress but these effects do not persist the longer one is widowed. Lorenz and colleagues (2006), similarly, found support for the short-term impacts of marital loss on mental health. Johnson and Wu (2002), however, found that increases in psychological distress following a divorce did not improve until an individual remarried. For example, individuals who are not continuously married have less wealth than those who are continuously married (Wilmoth & Koso, 2002). Losing a spouse may represent a loss of a key source of social support, which can influence mental well-being. Researchers have argued that divorce may have a greater impact on social contact than widowhood (Dykstra & de Jong Gierveld, 2004) because network members may side with one partner in a divorce but family and friends usually come together to assist someone who has just been widowed (Milardo, 1987). Additionally, as people age and they experience the loss of aging peers and a deterioration in

26 health it may become more expected and less psychologically devastating to become widowed in later life (Dykstra & de Jong Gierveld 2004). Overall, research indicates both short-term and long-term effects of marital loss on mental health, but this may vary by type of loss.

Marital Transitions

In addition, Wilmoth and Koso (2002) argue that marital history can influence the cumulative advantage process. An individual’s broad marital history reflects the sequencing of life events (Wilmoth & Koso, 2002). Waite argues (2009) that divorce or widowhood may represent a loss of resources and changes in social networks and family dynamics. While remarriage may reestablish these resources, second or third marriage may be less advantageous to well-being because they are life transitions that may involve stepchildren and new daily routines and rules and a combining of families which may lead to more responsibilities and obligations (Dykstra & de Jong Gierveld, 2004; Henry & Lovelace, 1995). Similarly, experiencing a marital loss may be a negative shock that adversely influences mental well-being and adjusting to more than one marital dissolution over the life course may slow the cumulative advantage process and be less beneficial than having one continuous marriage. Researchers have found that marrying for the first time protects against depressive symptoms (Lamb et al., 2003; Marks & Lambert, 1998), and the number of prior marital losses is associated with distress and substance use (Kurdek, 1991; Warheit, Arey, & Holzer, 1976; Weingarten, 1980). Studies show that divorce leads to increased depression (Marks & Lambert, 1998; Simon, 2002) and alcohol abuse (Horwitz et al., 1996). Barrett (2000) found that being currently divorced and having experienced multiple prior losses rather than one loss is associated with more depressive symptoms, and that being currently widowed and experiencing being widowed previously rather than divorced is associated with more substance use.

Gender

Researchers have argued that marriage is more advantageous to men than women (Bernard, 1972). Marriage may be more advantageous to men in terms of general distress (Gove, 1972; Gove & Tudor, 1993) and alcohol problems (Simon, 2002) and to women in terms of depression (Simon, 2002). In terms of marital loss, some research suggests that women experience more distress than men when their marriages end (Horwitz et al., 1996; Marks & Lambert, 1998; Simon, 2002; Simon & Marcussen, 1999). However, after widowhood some find

27 that women fare better than men (Stroebe et al., 2001) while others find women are more vulnerable to distress (Marks & Lambert, 1998; Williams, 2003). In comparison, others find that both men and women experience short term increases in depressive symptoms (Lorenz et al., 2006). Strohschein and colleagues (2005) argue that over time structural disadvantage coupled with marital losses may be more detrimental for women than for men. Many processes within marriage and outside of marriage may account for gender differences in this relationship (Monin & Clark, 2011). First, men and women may benefit differently from the resources provided by marriage. Researchers have found that women benefit more from the increased monetary gain of being married (Lillard & Waite, 1995) and are more devastated economically by marital loss (Gerstel, Riessman, & Rosenfield, 1985; Peterson et al. 1996; Weiss, 1984). On the other hand, men may benefit more from the control of health behaviors, including healthier eating habits and more regular exercise and discouragement of heavy alcohol and smoking use (Umberson et al., 1996; Waite & Gallagher, 2000), and the supportive functions provided by marriage (Umberson et al., 1996). For instance, Monin and Clark (2011) argue that women have more options of expressing emotions outside of marriage to family and friends and have social ties that can provide similar supportive functions that marriages provide. Therefore, men may be more reliant on the support provided by spouses than women. Researchers have also called attention to how lifelong singlehood may influence aging processes and well-being (see: Carr & Moorman, 2011). Over the life course, the never married have become self-reliant (Dykstra & de Jong Gierveld, 2004) and older never married women tend to have distress levels similar to married adults (Pudrovska, Schieman, & Carr, 2006). Carr and Moorman (2011) suggest this represents selection of more educated and economically stable women with strong sources of friendship and family support into this status. For this reason, it is expected that never married adults will not differ from married adults on mental well-being but that never married women will fare better than their male counterparts. Given the above review, I ask the following questions: Are there mental health differences in marital loss status durations? Do past marital losses influence the association between current marital status and mental well-being? And, do women and men differ in the extent to which these contextual aspects of marriage have mental health consequences?

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Methods

Data

To address these questions, I use data from the National Social Life, Health, and Aging Project (NSHAP). The NSHAP is a nationally-representative sample of individuals aged 57 to 85 years old living in the United States. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted interviews during 2005 and 2006, yielding a sample of 3,005 adults aged 57 to 85 (O’Muircheartaigh, Eckman, & Smith, 2009; Suzman, 2009). Data collection was conducted in face-to-face interviews in respondents' homes, in-home biomeasure collection, and a leave-behind questionnaire (Smith et al., 2009), yielding a 75.5% response rate (O’Muircheartaigh, et al. 2009). One main goal of the NSHAP is to determine the importance of social relationships and health in older adults’ lives; therefore, questions concerning the marital bond are included. All results will be weighted to account for non-response based on age and urbanicity.

Measures

Dependent Variables Depressive symptoms. Depressive symptoms is based on a modified version of the CES- D scale and is comprised of the past week occurrence of 11 items of depressive symptoms including: did not feel like eating, everything was an effort, could not get going, felt depressed, sleep was restless, felt sad, felt lonely, felt people disliked me, people were unfriendly, enjoyed life, and felt happy. Each item was coded so that rarely or none of the time=0 and most of the time=3 except for the last two items which were reverse coded. Depressive symptoms range from 0 to 33. Since this measure is highly skewed, I will use a square root transformation in all analyses. Anxiety. Anxiety is based on 7 items experienced in the past week (felt tense or wound up, felt something awful was about to happen, worrying thoughts went through mind, felt butterflies in my stomach, felt restless, felt sudden feeling of panic, and could sit at ease and feel relaxed), and each item was coded so that rarely or none of the time=0 and most of the time=3 except for the last item which was reverse coded. Anxiety, therefore, ranges from 0 to 21. Since this measure is highly skewed, I will use a square root transformation in all analyses.

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Happiness. Happiness is based on the question “If you were to consider your life in general these days, how happy or unhappy would you say you are, on the whole?” Respondents then were asked to respond: extremely happy, very happy, pretty happy, unhappy sometimes, or unhappy usually. Since happiness is highly skewed towards positive responses I recode this measure into a dichotomous outcome, with 1= very happy or extremely happy and 0=pretty happy, unhappy sometimes, or unhappy usually. Alcohol problems. Alcohol problems is based on the Cut, Annoyed, Guilty, Eye Opener (CAGE) questionnaire (Ewing, 1984). This instrument includes responses to four statements: ever felt that you should cut down on drinking, people ever annoyed you by criticizing your drinking, you ever felt bad or guilty about drinking, or ever taken a drink first thing in the morning to steady your nerves or get rid of a hangover. Respondents, who endorsed any of these alcohol problems, were coded 1 for problem drinker and respondents who did not experience these problems or who never drank were coded 0. Independent Variables Marital status. In the NSHAP respondents are asked their marital status and given the following options: married, living with a partner, divorced, separated, widowed or never married. For the current analyses, divorced and separated respondents are put together since the percentage of respondents who were separated was very small (1.70%). Marital status is coded as a multiple dummy, therefore, indicating married, partnered, divorced, widowed, and never married. Duration in status. Duration in status is calculated differently based on the respondent’s current marital status. For those who are currently widowed, duration in status is taken from the year their partner died and subtracting it from the year of the interview. For those who are separated/divorced, this is based on subtracting the year in which they stopped living with their most recent partner from the year of the interview. For these analyses, I will be distinguishing among the following: widowed 0 to 5 years, widowed 6 to 10 years, widowed 11 to 15 years, and widowed 16 years or more, divorced 0 to 10 years, divorced 11 to 20 years, divorced 21 to 30 years and divorced 31 years or more. Number of Marital Transitions. Number of Marital Transitions is based on the past number of marriages that respondent has experienced prior to the interview. Respondents who are currently married or had experienced a divorce/separation or had been widowed were asked

30 whether this marriage was their first marriage. Then they asked respondents “Altogether, how many times have you been married (IF CURRENTLY MARRIED: including your current marriage)?” Though it is a count of previous marriages, I will divide this measure into: one marriage, two marriages, and three or more marriages. Gender. Gender is coded 1 for female and 0 for males. Control Variables Age is a continuous variable, ranging from 57 to 85. Education is a multiple dummy indicating respondents with less than a high school degree, a high school degree or equivalent, some college (vocational certificate/associates/somecollege), and those with a bachelors or more. Income is based on household income per year. Originally 29.32 % of the sample was missing on this variable. Respondents were asked their yearly income and if they did not answer, they were probed with a number of questions such as “is your household income over or under 25,000,” “ under or over 50,000,” etc. Based on respondents’ answers to these questions, I used multiple imputation based on their answers to these questions to retain a large part of the sample. For the remaining respondents missing on this measure (9.12%) I imputed the mean. Race and Ethnicity is based on respondents’ self-report of racial/ethnic identification and is coded as a multiple dummy variable indicating black, Hispanic, other, with white as the reference category. Number of children is a straight count of the respondent’s number of living daughters and sons. Self-rated physical health is based on respondent self-assessment of physical health status and varies from 0=poor and 4=excellent.

Results

Table 2.1 describes the study variables. In terms of the outcome variables, the average respondent scores 2.02 on the transformed depressive symptoms scale and 1.57 on the transformed anxiety scale. About 20% of respondents have ever experienced a drinking problem in their life and about half of respondents (55%) rate their lives as very or extremely happy. Most respondents are married (60%) follows by widowed (22%), divorced (12%), never married (4%) and partnered (2%) adults. Additionally, most have experienced one marriage in their lifetime (68%) followed by those who have experienced two marriages (22%) and then those with three or more marriages (7%). In terms of background factors, the average respondent is 69 years old and has a household income of $49,510.08. More than half of the respondents are female (52%), while 31

71% identify as white, 17% as Black, 10% as Latino, 2% as other race or ethnicity. For education, 24% of respondents have less than a high school degree, 26% have a high school degree, 28% have some college and 22% have a college degree or more. The average respondent has three children and rates their physical health as good (2.20). In the first step, I examine whether divorced, widowed, partnered, or never married adults vary from married adults on mental well-being. Model 1 in Table 2.2 shows that, as expected, currently widowed adults experience significantly more depressive symptoms (b= 0.32; p<.001) than currently married adults. Next, I assess whether duration in these statuses is associated with mental well-being (Model 2). I find that adults who have been widowed in the last five years, on average, experience significantly more depressive symptoms (b=0.46; p<.001) than married adults. Additionally, adults who divorced in the past 10 years, experience more depressive symptoms than unmarried adults (b=0.35; p<.001). Gender interactions while not significant for marital statuses (Model 3) are significant for status durations. Model 4 suggest that being widowed for 11 to 15 year and being divorced for 21 to 30 years is less distressing for women as it is for men (b= –0.49; p<.05; b= –0.57; p<.05). Table 2.3 presents similar regression models as Table 2.2 but with anxiety symptoms as the outcome variable. Surprisingly, Model 1 shows that currently partnered and divorced adults experience significantly less anxiety than currently married adults (b= –0.37; p<.05; b= –0.05; p<.05). Duration results in Model 2 indicate that adults who have been divorced the longest, for 31 years or more, experience less anxiety symptoms than married adults (b= –0.35; p<.05). Gender interactions (Models 3 and 4) present one significant finding, that women who have been widowed for 11 to 15 years are less anxious than similarly situated men (b= –0.24; p<.05). Table 2.4 presents the logistic regression results of any drinking problems on marital status, status duration, and gender. Here I find no significant status or duration differences in drinking problems. I do, however, find gender differences in these effects. Specifically, Model 4 shows gender conditions the relationship between being widowed in the past five years and drinking problems. The main effects coefficient is 0.25 (OR=1.29) and interaction coefficient is –1.31 (OR=0.27) making the partial slope for women –1.06 (OR=0.35), and the partial slope for men 0.25 (OR=1.29). This shows that being widowed in the past five years elevates the odds of drinking in men but not in women. Figure 1.1 graphically presents the

32 predicted probabilities of any drinking problem for men and women among adults widowed in the past five years. Additionally, when gender interactions are added in Model 4, adults widowed 16 years or more trends toward a lower odd of any drinking problem. This pattern is less pronounced among women and more pronounced among men. The main effect coefficient is –2.21, and interaction coefficient is 2.49. The partial slope for women is 0.28 (OR=1.32), and the partial slope for men is –2.21 (OR=.11). This shows that being widowed 16 years or more elevates the odds of drinking problems in women, but not men. See Figure 2.1 for a graphic representation of the predicted probabilities of these results. Next, in Table 2.5 I assess the influence of these contextual factors for overall happiness. Here I find that widowed and divorced adults are less likely than married adults to be happy (OR=0.47; p<.001; OR=0.66; p<.01). I also find that adults who have been widowed 0 to 5 years (OR=0.39; p<.001), widowed 11 to 15 years (OR=0.42; p<.01), widowed 16 years or more (OR=0.51; p<.01), divorced 0 to 10 years (OR=0.49; p<.05), or divorced 21 years to 30 years (OR=0.06; p<.05) are significantly less likely than married adults to be happy. Therefore, being widowed is associated with lower odds of being happy than being married, regardless of time spent in the status. Results from gender interactions from Table 2.5 which are presented in Model 3 and Figure 3.1 indicate that divorced adults tend to be less happy than married adults (OR=0.40), and that divorce decreases the odds of being happy more so for men than for women, with the partial slope for men being –0.92 (OR=0.40) and the partial slope for women being –0.04 (OR=0.96). There are also significant gender interactions for never married adults, which indicate that never married adults tend to be happier than married adults, and the being never married increases the odds of being happy more so for women than for men (Figure 4.1) Additionally, there is significant gender by duration interactions in Model 4. Divorcing within the past ten years decreases the odds of being happy in men but not in women. For example, the partial slope for men is –1.61 (OR=0.20) and the partial slope for women is 0.11 (OR=1.12). I also find that being divorced 21 to 30 years decreases the odds of being happy more for men than for women. The predicted probabilities for happiness by gender for these groups are displayed in Figures 5.1 and 6.1.

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In the next step, I assess whether the joint influence of current marital status and number of past marital transitions on mental well-being (Models 1), and gender differences in these effects (Models 2). Results from Table 2.6 indicate that regardless of the number of past marital losses, currently widowed adults experience significantly more depressive symptoms than those who are currently married (Model 1). For gender results for depressive symptoms, being divorced with one prior marital loss is associated with less depressive symptoms for women than for men (b=–0.37; p<.05). In terms of anxiety symptoms, widowed adults with two prior marriages have less anxiety than married adults (b=–0.17; p<.05); however, gender differences in these effects were not statistically significant. Table 2.7 presents logistic regression results for alcohol problems and happiness on current marital status, number of marital transitions, and gender. Results indicate that divorced adults with three or more prior marriages are more likely to experience alcohol problems (OR=2.67; p<.05) than married adults (Model 1). However, being divorced with three or more prior marriages does not differentially influence alcohol problems for men and women (Model 2). For overall happiness, being widowed regardless of number of marital transitions is associated with less happiness than being married, and adults who are divorced with one prior loss have a lower odds of being happy than married adults (OR=0.62; p<.01). Gender interactions (Model 2) indicate that being divorced with one prior marriage decreases the odds of being happy for men more than for women, with the partial slope for men being –1.08 (OR=0.34) and the partial slope for women being –0.09 (OR=0.91). Figure 7.1 presents the predicted probabilities of happiness by gender among divorced adults with one prior marriage.

Discussion

In the current study, I found that when holding constant physical health status and background characteristics, there are mental health differences by marital status duration and past number of marital losses, and these vary by mental health outcome observed. In particular, being currently widowed or divorced for a short or long time is associated with more depressive symptoms and less happiness than being currently married. Additionally, being widowed regardless of number of past marital transitions is associated with more depressive symptoms and a lower likelihood of being happy than being married, and being divorced with three past marital losses is associated with a greater likelihood of experiencing alcohol problems than being married. These findings suggest that widowhood is a stressor that influences well-being 34 regardless of past number of marital losses. Additionally, that divorce is a stressor that when compounded with three or more prior losses may put individuals at risk for experiencing alcohol problems, suggesting a cumulative impact of number of marital transitions for mental well-being. In the beginning of this paper, I describe two models that have been used in past research to discuss marital status differences in health: the marital resource model and the marital strain model. Taken together, the findings presented here lend partial support for the marital strain model—that short-term consequences of marital loss have implications mental well-being. However, the results presented here do not indicate that individuals overcome this loss in the long-term. In fact, being in a marital loss status for the longest time was associated with more psychological distress than being married. In light of these findings, I argue that these results lend partial support for the marital resource model. In particular, if the marital resource model is correct, than being in a marital loss status longer may indicate a loss of key resources provided by the marital bond over time and should be associated with mental duress. I argue that marital loss has a short-term impact on well-being and a cumulative influence on well-being over time in a status. It is important to note that I do not find consistent findings by mental health outcome observed. Marital status duration seems to influence positive and negative affect (overall life happiness and experiences with depressive symptoms) while number of prior marital transitions influences externalizing problems (alcohol problems). Interesting, I also find that partnered adults and adults who have been divorced for the longest time have less anxiety than married adults. Perhaps taking into account relationship functioning and relationship quality might lend insight to why partnered adults have less anxiety than married adults. Partnered adults may have recently entered into their relationships and be happy with this transition and this may help reduce anxiety symptoms. That adults divorced the longest can actually fare better than married adults in terms of anxiety suggest that individuals can overcome the impact of this loss, at least in terms of anxiety, lending additional support to the marital strain model. I also found gender differences in the effects of these contextual factors of marital loss on mental well-being. These results support prior research that marital loss is more detrimental for men than women (Horwitz et al., 1996; Marks & Lambert, 1998; Simon, 2002; Simon & Marcussen, 1999), and this extends prior work to show that the immediate toll of spousal loss and being divorced with one prior marriage is more detrimental for men than for women. These

35 results support the marital strain model more so for men than for women. While men are more vulnerable to the mental health influences of being widowed or divorced, there is one caveat-- that being widowhood for 16 years or more elevates the odds of experiencing drinking problems in women but not in men. As Gomberg (1994) suggests stressful events and how individuals cope with such events may lead to drinking problems. The reason these differences may not appear until longer widowhood durations is because disordered drinking such as abuse issues take time to occur. Without having a spouse to monitor health behaviors coupled with losses in financial security and social support may account for these gender differences. This would partially support the marital resource model and would be an important avenue for future research. There is also evidence that lifelong singlehood is more advantageous for women than for men in later life in terms of overall happiness. Carr and Moorman (2011) suggest that women who have been single their entire lives are more educated and self-reliant and have had time to build social networks that may produce similar supportive functions as marital relationships. Understanding how relationship functioning outside of the marital bond may provide the same supportive functions as a spouse among unmarried women would be an important area for future research. There are limitations to the current study that warrant discussion. First, the data is cross- sectional, therefore, untangling whether marital duration causes mental health issues or mental health issues influence marital status transitions cannot be fully examined. For example, while divorced adults on average experience more depressive symptoms and lower odds of happiness than married adults, this association could reflect adults who were unhappy or depressed selecting out of remarriage. I caution the assumption that these findings are purely reflective of selection issues, however, since I do find similar effects of marital status duration and number of marital transitions for each type of mental health outcome observed. If these results reflected selection issues, we would see similar results across the health outcomes observed. Second, the analyses presented here do not take into consideration the type of previous marital transition. Perhaps being currently widowed and experiencing widowhood previously is more distressing than experiencing a prior divorce. Indeed, Barrett (2000) found that being currently widowed and experiencing being widowed previously rather than divorced is associated with more substance use.

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Overall, results presented here build upon research on marriage, gender, and mental health by indicating both short-term and long-term influences of marital status duration on mental well-being and the health consequences of prior marital transitions. These results indicate that the contextual factors of relationships (duration and history) and gender work together to shape well-being in later life. Future research should determine whether the loss of key resources- as the marital resource model suggests- in terms of social, psychological and economic resources may account for these marital status and gender-based health differentials in later life. By understanding these particular mechanisms, we can better understand how adults can protect themselves from the deleterious influences of marital loss.

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TABLE 2.1 Descriptive Statistics for Study Variables

Range Mean/Proportion Standard Deviation N Outcome Variables Depressive Symptoms 0.00 – 5.66 2.02 1.21 2955 Anxiety 0.00 – 4.58 1.57 1.05 2756 Drinking Problems 0.00 – 1.00 0.21 2398 Happiness 0.00 – 1.00 0.55 2995

Marriage Variables Married 0.00 – 1.00 0.60 3005 Widowed 0.00 – 1.00 0.22 3005 Widowed 0 to 5 years 0.00 – 1.00 0.07 2996 Widowed 6 to 10 years 0.00 – 1.00 0.05 2996 Widowed 11 to 15 years 0.00 – 1.00 0.04 2996 Widowed 16 or more years 0.00 – 1.00 0.07 2996 Divorced 0.00 – 1.00 0.12 3005 Divorced 0 to 10 years 0.00 – 1.00 0.03 2996 Divorced 11 to 20 years 0.00 – 1.00 0.03 2996 Divorced 21 to 30 years 0.00 – 1.00 0.03 2996 Divorced 31 or more years 0.00 – 1.00 0.03 2996 Partnered 0.00 – 1.00 0.02 3005 Never Married 0.00 – 1.00 0.04 3005

Number of Marital Transitions One Marriage 0.00 – 1.00 0.68 2991 Two Marriages 0.00 – 1.00 0.22 2991 Three or More Marriages 0.00 – 1.00 0.07 2991

Background Variables Age 57.00 – 85.00 69.30 7.85 3005 Female 0.00 – 1.00 0.52 3005 Black 0.00 – 1.00 0.17 2993 Latino 0.00 – 1.00 0.10 2993 Other 0.00 – 1.00 0.02 2993 High School Degree 0.00 – 1.00 0.26 3005 Some College 0.00 – 1.00 0.28 3005 College Degree 0.00 – 1.00 0.22 3005 Income 0.00 – 1,800,000 49510.08 66195.55 3005 Number of Children 0.00 – 22.00 3.07 2.13 2782 Physical Health 0.00 – 4.00 2.20 1.11 2993 Notes: National Social Life, Health, and Aging Project (2005/2006)

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TABLE 2.2 OLS Regression of Depressive Symptoms on Marital Status, Status Duration, and Gender (N=2700)

M1 M2 M3 M4 Widowed 0.32 *** 0.46 *** (0.06) (0.11) Widowed 0 to 5 years 0.46 *** 0.50 *** (0.09) (0.13) Widowed 6 to 10 years 0.07 0.40 (0.13) (0.34) Widowed 11 to 15 years 0.29 * 0.67 *** (0.12) (0.18) Widowed 16 or more years 0.30 *** 0.08 (0.08) (0.24) Divorced 0.19 * 0.34 ** (0.07) (0.13) Divorced 0 to 10 years 0.35 * 0.44 (0.16) (0.24) Divorced 11 to 20 years 0.19 0.07 (0.14) (0.19) Divorced 21 to 30 years 0.17 0.54 * (0.13) (0.21) Divorced 31 or more years 0.04 0.29 (0.18) (0.35) Partnered –0.20 –0.20 –0.46 * –0.46 * (0.16) (0.16) (0.22) (0.22) Never Married 0.21 0.20 0.26 0.26 (0.17) (0.17) (0.26) (0.26) Female 0.10 * 0.11 * 0.15 ** 0.15 ** (0.05) (0.05) (0.05) (0.05) Widowed*Female –0.22 (0.12) Widowed 0 to 5 years*Female –0.07 (0.17) Widowed 6 to 10 years*Female –0.41 (0.34) Widowed 11 to 15 years*Female –0.49 * (0.24) Widowed 16 or more years*Female 0.22 (0.26) Divorced*Female –0.26 (0.17) Divorced 0 to 10 years*Female –0.19 (0.31) Divorced 11 to 20 years*Female 0.18 (0.28) Divorced 21 to 30 years*Female –0.57 * (0.28) Divorced 31 or more years*Female –0.39 (0.40) Partnered*Female 0.46 0.46 (0.34) (0.34) Never Married*Female –0.10 –0.10 (0.29) (0.29) Intercept 3.00*** 2.94*** 2.95*** 2.89*** Model F 41.24*** 32.06*** 33.49*** 23.60*** R-squared 0.18 0.18 0.18 0.19 Notes: Shown are unstandardized coefficients with standard deviations in parentheses. Reference group is married adults.

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All models adjust for age, gender, race/ethnicity, education, income, number of children, and physical health. * significant at .05; ** significant at .01; ***significant at.001.

TABLE 2.3 OLS Regression of Anxiety Symptoms on Marital Status, Status Duration, and Gender (N=2504)

M1 M2 M3 M4 Widowed –0.04 –0.07 (0.06) (0.10) Widowed 0 to 5 years –0.04 –0.12 (0.08) (0.13) Widowed 6 to 10 years –0.17 –0.03 (0.13) (0.35) Widowed 11 to 15 years 0.02 0.21 (0.11) (0.25) Widowed 16 or more years –0.001 –0.20 (0.110) (0.32) Divorced –0.05 * 0.006 (0.07) (0.103) Divorced 0 to 10 years 0.15 0.16 (0.15) (0.19) Divorced 11 to 20 years 0.008 –0.11 (0.118) (0.21) Divorced 21 to 30 years –0.06 –0.01 (0.13) (0.21) Divorced 31 or more years –0.35 * –0.04 (0.16) (0.28) Partnered –0.37 * –0.37 * –0.68 *** –0.68 *** (0.17) (0.17) (0.19) (0.18) Never Married –0.23 –0.23 –0.34 –0.34 (0.15) (0.15) (0.20) (0.20) Female 0.18 ** 0.19 *** 0.17 ** 0.17 ** (0.05) (0.05) (0.06) (0.06) Widowed*Female 0.04 (0.12) Widowed 0 to 5 years*Female 0.13 (0.17) Widowed 6 to 10 years*Female –0.16 (0.34) Widowed 11 to 15 years*Female –0.24 * (0.29) Widowed 16 or more years*Female 0.22 (0.31) Divorced*Female –0.09 (0.15) Divorced 0 to 10 years*Female –0.02 (0.28) Divorced 11 to 20 years*Female 0.18 (0.28) Divorced 21 to 30 years*Female –0.02 (0.28) Divorced 31 or more years*Female –0.43 (0.34) Partnered*Female 0.56 0.56 (0.37) (0.37) Never Married*Female 0.19 0.20 (0.25) (0.25) Intercept 2.61*** 2.56*** 2.59*** 2.54*** Model F 9.48*** 7.63*** 8.14*** 6.02***

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R-squared 0.07 0.07 0.07 0.07 Notes: Shown are unstandardized coefficients with standard deviations in parentheses. Reference group is married adults. All models adjust for age, gender, race/ethnicity, education, income, number of children, and physical health. * significant at .05; ** significant at .01; ***significant at.001.

TABLE 2.4 Logistic Regression of Any Drinking Problems on Marital Status, Status Duration, and Gender (N=2292)

M1 95% CI M2 95% CI M3 95% CI M4 95% CI Widowed 0.92 (0.96-2.10) 1.00 (0.61-1.64) Widowed 0 to 5 years 0.86 (0.57-1.30) 1.29 (0.76-2.18) Widowed 6 to 10 years 0.24 (0.09-0.65) 0.48 (0.11-2.03) Widowed 11 to 15 years 2.12 (0.97-4.66) 1.37 (0.33-5.66) Widowed 16 or more years 1.12 (0.56-2.25) 0.11 (0.01-1.07) Divorced 1.42 (0.96-2.10) 1.50 (0.89-2.54) Divorced 0 to 10 years 1.50 (0.75-3.01) 2.18 (0.93-5.09) Divorced 11 to 20 years 1.40 (0.72-2.72) 1.20 (0.52-2.80) Divorced 21 to 30 years 1.82 (0.94-3.49) 2.03 (0.67-6.13) Divorced 31 or more years 0.96 (0.45-2.05) 0.67 (0.18-2.50) Partnered 0.89 (0.45-1.79) 0.90 (0.45-1.80) 1.00 (0.37-2.69) 1.01 (0.38-2.70) Never Married 0.23 (0.41-2.01) 0.91 (0.41-2.03) 0.84 (0.34-2.05) 0.84 (0.34-2.09) Female 0.25** (0.18-0.36) 0.25***(0.18-0.35) 0.26*** (0.17-0.40) 0.26***(0.17-0.40) Widowed*Female 0.86 (0.41-1.80) Widowed 0 to 5 years*Female 0.27* (0.08-0.87) Widowed 6 to 10 years*Female 0.19 (0.02-1.65) Widowed 11 to 15 years*Female 1.71 (0.31-9.54) Widowed 16 or more years*Female 12.13* (1.20-122.33) Divorced*Female 0.87 (0.38-2.00) Divorced 0 to 10 years*Female 0.18 (0.02-1.79) Divorced 11 to 20 years*Female 1.36 (0.35-5.25) Divorced 21 to 30 years*Female 0.80 (0.17-3.85) Divorced 31 or more years*Female 1.82 (0.31-10.69) Partnered*Female 0.75 (0.15-3.70) 0.74 (0.15-3.65) Never Married*Female 1.24 (0.35-4.38) 1.24 (0.35-4.38) Model F 9.40*** 7.93*** 8.14*** 8.39*** Notes: Shown are odds ratios with confidence intervals in parentheses. Reference group is married adults. All models adjust for age, gender, race/ethnicity, education, income, number of children, and physical health. * significant at .05; ** significant at .01; ***significant at.001.

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TABLE 2.5 Logistic Regression of Happiness on Marital Status, Status Duration, and Gender (N=2292)

M1 95% CI M2 95% CI M3 95% CI M4 95% CI Widowed 0.47***(0.34-0.64) 0.42** (0.23-0.76) Widowed 0 to 5 years 0.39*** (0.24-0.64) 0.39* (0.17-0.90) Widowed 6 to 10 years 0.67 (0.41-1.08) 0.88 (0.32-2.40) Widowed 11 to 15 years 0.42** (0.25-0.73) 0.26 (0.07-1.03) Widowed 16 or more years 0.51** (0.33-0.78) 0.44 (0.15-1.27) Divorced 0.66** (0.49-0.87) 0.40*** (0.25-0.64) Divorced 0 to 10 years 0.46* (0.24-0.89) 0.20***(0.09-0.47) Divorced 11 to 20 years 0.69 (0.40-1.20) 0.86 (0.36-2.05) Divorced 21 to 30 years 0.60* (0.38-0.95) 0.25** (0.67-6.13) Divorced 31 or more years 1.03 (0.54-1.97) 0.69 (0.19-2.47) Partnered 0.92 (0.39-2.19) 0.92 (0.39-2.20) 0.43 (0.15-1.22) 0.43 (0.15-1.23) Never Married 0.66 (0.41-1.06) 0.67 (0.42-1.06) 4.75** (1.80-12.55) 0.29** (0.14-0.60) Female 0.78* (0.65-0.95) 0.77** (0.63-0.93) 0.26*** (0.17-0.40) 0.63** (0.49-0.82) Widowed*Female 1.27 (0.68-2.37) Widowed 0 to 5 years*Female 1.08 (0.44-2.62) Widowed 6 to 10 years*Female 0.78 (0.25-2.42) Widowed 11 to 15 years*Female 1.96 (0.49-7.93) Widowed 16 or more years*Female 1.30 (0.40-4.22) Divorced*Female 2.40* (1.24-4.65) Divorced 0 to 10 years*Female 5.59* (1.32-23.67) Divorced 11 to 20 years*Female 0.74 (0.24-2.26) Divorced 21 to 30 years*Female 3.80* (1.20-12.00) Divorced 31 or more years*Female 1.93 (0.48-7.80) Partnered*Female 4.13 (0.91-18.84) 4.13 (0.90-19.00) Never Married*Female 4.75** (1.80-12.55) 4.78** (1.81-12.61) Model F 10.44*** 7.42*** 9.62*** 7.11*** Notes: Shown are odds ratios with confidence intervals in parentheses. Reference group is married adults. All models adjust for age, gender, race/ethnicity, education, income, number of children, and physical health. * significant at .05; ** significant at .01; ***significant at.001.

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TABLE 2.6 OLS Regression of Depressive Symptoms and Anxiety Symptoms on Current Marital Status, Number of Marital Transitions, and Gender

Depressive Symptoms Anxiety Symptoms M1 M2 M1 M2 Widowed with one marriage 0.28*** 0.42*** –0.02 0.01 (0.07) (0.11) (0.06) (0.12) Widowed with two marriages 0.39** 0.52* –0.17* –0.33 (0.13) (0.24) (0.14) (0.23) Widowed with three or more marriages 0.53** 0.91** 0.04 –0.06 (0.18) (0.31) (0.22) (0.58) Divorced with one marriage 0.15 0.38** –0.05 0.08 (0.08) (0.13) (0.08) (0.12) Divorced with two marriages 0.33 0.20 –0.10 –0.31 (0.15) (0.25) (0.12) (0.19) Divorced with three or more marriages 0.21 0.49 0.03 0.57 (0.21) (0.34) (0.22) (0.30) Partnered –0.20 –0.46* –0.37* –0.68*** (0.16) (0.71) (0.17) (0.19) Never Married 0.21 0.26 –0.23 –0.34 (0.17) (0.26) (0.15) (0.20) Female 0.10* 0.15** 0.18** 0.17** (0.05) (0.05) (0.21) (0.06) Female*Widowed with one marriage –0.20 –0.04 (0.12) (0.14) Female*Widowed with two marriages –0.20 0.22 (0.27) (0.27) Female*Widowed with three or more marriage –0.48 0.11 (0.39) (0.62) Female*Divorced with one marriage –0.37* –0.19 (0.18) (0.16) Female*Divorced with two marriages 0.12 0.38 (0.29) (0.27) Female*Divorced with three or more marriages –0.59 –0.99 (0.53) (0.50) Female*Partnered 0.46 0.56 (0.34) (0.37) Female*Never Married –0.10 0.20 (0.29) (0.25) Intercept 2.99*** 2.91*** 2.62*** 2.54*** Model F 31.90 *** 23.77*** 7.56*** 5.75*** R-squared 0.18 0.18 0.07 0.07 Notes: Shown are unstandardized coefficients with standard deviations in parentheses. Reference group is married adults. All models adjust for age, gender, race/ethnicity, education, income, number of children, and physical health.

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* significant at .05; ** significant at .01; ***significant at.001.

TABLE 2.7 Logistic Regression of Alcohol Problems and Happiness on Current Marital Status, Number of Marital Transitions, and Gender

Alcohol Problems Happiness M1 95% CI M2 95% CI M1 95% CI M2 Widowed with one marriage 0.85 (0.41 – 1.99) 1.02 (0.58 – 1.79) 0.46*** (0.33 – 0.65) 0.44* (0.23 – 0.85) Widowed with two marriages 1.25 (0.63 – 2.48) 0.85 (0.29 – 2.50) 0.49* (0.28 – 0.86) 0.32* (0.12 – 0.84) Widowed with three or more marriages 0.69 (0.20 – 2.36) 1.11 (0.11 – 11.70) 0.46* (0.21 – 0.86) 0.53 (0.12 – 2.40) Divorced with one marriage 1.28 (0.75 – 2.17) 1.39 (0.69 – 2.83) 0.62** (0.44 – 0.88) 0.34*** (0.19 – 0.60) Divorced with two marriages 1.35 (0.69 – 2.65) 1.49 (0.66 – 3.34) 0.69 (0.38 – 1.23) 0.51 (0.21 – 1.28) Divorced with three or more marriages 2.67* (1.09 – 6.51) 2.04 (0.55 – 7.66) 0.82 (0.37 – 1.81) 0.42 (0.13 – 1.34) Partnered 0.89 (0.45 – 1.79) 1.00 (0.37 – 2.68) 0.92 (0.39 – 2.19) 0.37* (0.15 – 1.21) Never Married 0.90 (0.41 – 1.99) 0.83 (0.34 – 2.05) 0.66 (0.41 – 1.06) 0.28** (0.14 – 0.60) Female 0.25*** (0.18 – 0.36) 0.26** (0.17 – 0.40) 0.79* (0.65 – 0.96) 0.63** (0.49 – 0.82) Female*Widowed with one marriage 0.71 (0.31 – 1.60) 1.15 (0.61 – 2.18) Female*Widowed with two marriages 1.79 (0.41 – 7.81) 1.99 (0.64 – 6.22) Female*Widowed with three or more marriage 0.50 (0.02 – 10.49) 0.91 (0.22 – 3.75) Female*Divorced with one marriage 0.83 (0.30 – 2.34) 2.70** (1.38 – 5.27) Female*Divorced with two marriages 0.75 (0.17 – 3.27) 1.71 (0.47 – 6.25) Female*Divorced with three or more marriages 1.85 (0.28 – 12.29) 4.25 (0.94 – 19.23) Female*Partnered 0.76 (0.15 – 3.75) 4.15 (0.91 – 18.98) Female*Never Married 1.24 (0.35 – 4.38) 4.76*** (1.80 – 12.57) Model F 7.89 *** 6.19*** 8.66*** 7.96*** Notes: Shown are Shown are odds ratios with confidence intervals in parentheses. Reference group is married adults. All models adjust for age, gender, race/ethnicity, education, income, number of children, and physical health. * significant at .05; ** significant at .01; ***significant at

44

1 0.9 0.8 0.7 0.6 0.5 Women

Problems Problems 0.4 Men 0.3 0.2 0.1 Predicted Probability Anyof Drinking 0 Gender

Figure 1.1 Predicted Probabilities of Any Drinking Problem by Gender for Adults Widowed 0 to 5 Years

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1 0.9 0.8 0.7 0.6 0.5 Women

Problems Problems 0.4 Men 0.3 0.2 0.1 Predicted Probability Anyof Drinking 0 Gender

Figure 2.1 Predicted Probabilities of Any Drinking Problem by Gender for Adults Widowed 16 Years or More

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1 0.9 0.8 0.7 0.6 0.5 Women 0.4 Men 0.3 0.2

Predicted Probability Happiness of 0.1 0 Gender

Figure 3.1 Predicted Probabilities of Happiness by Gender for Divorced Adults

47

1 0.9 0.8 0.7 0.6 0.5 Women 0.4 Men 0.3 0.2

Predicted Probability Happiness of 0.1 0 Gender

Figure 4.1 Predicted Probabilities of Happiness by Gender for Never Married Adults

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1 0.9 0.8 0.7 0.6 0.5 Women 0.4 Men 0.3 0.2

Predicted Probability Happiness of 0.1 0 Gender

Figure 5.1 Predicted Probabilities of Happiness by Gender for Adults Divorced for 0 to 10 Years

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1 0.9 0.8 0.7 0.6 0.5 Women 0.4 Men 0.3 0.2

Predicted Probability Happiness of 0.1 0 Gender

Figure 6.1 Predicted Probabilities of Happiness by Gender for Adults Divorced 21 to 30 Years

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1 0.9 0.8 0.7 0.6 0.5 Women 0.4 Men 0.3 0.2

Predicted Probability Happiness of 0.1 0 Gender

Figure 7.1 Predicted Probabilities of Happiness by Gender for Divorced Adults with One Prior Marriage

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CHAPTER FOUR

DISCUSSION

Scope of the Study

The goal of the two empirical studies presented above is to provide a more comprehensive understanding of how contextual aspects of marriage influence physiological functioning and mental well-being in later life. In the reviews above, I have argued why we must go beyond the simple comparison of statuses and incorporate aspects of individual status duration, status history, and social position. First, life course researchers have argued that duration in a particular status may have consequences for health because it is a marker for exposure to advantage or disadvantage. In addition, the marital resource model, which highlights the social and economic resources provided by marriage, suggests that resources provided by marriage may also accumulate over time in a status. Second, there has some debate as to whether marital status-based health differentials reflect the immediate toll spousal loss takes on health or the impact of marital transitions takes time to develop (Booth & Amato, 1991; Meadows et al., 2008; Umberson, 1992; Williams & Umberson, 2004). Therefore, it is important to take into consideration these factors to see if the advantage of married adults relative to unmarried adults is simply a function of individuals with a particular duration in a status or reflective of individuals without multiple marital transitions. In the preceding chapter, I try to weigh in on this debate by examining the influence of these aspects for physiological functioning and mental well-being. In the preceding chapters, I also add to current research on marital status and health by examining cumulative biological risk or allostatic load (Chapter 2), and multiple aspects of mental well-being (Chapter 3). While there have been calls for the examination of marital status for allostatic load (Carr & Moorman, 2011; Umberson & Montez, 2010; Waite, 2009), empirical examples of differences among multiple marital status categories using samples of adults in the U.S. are sparse. I also rely on multiple indicator of mental health, in order to account for different manifestations of marital loss: positive affect (happiness), negative affect (depression/anxiety), and behavioral issues (alcohol problems).

52

Finally, there has been some discussion as to whether and under what conditions marriage benefits men and women’s health. Feminist scholars have argued that marriage is less beneficial to women (Bernard, 1972), since men tend to benefit in terms of power dynamics in relationships, inequitable distributions in unpaid household labor, and control of health behaviors. While researchers have found that men tend to benefit more from the marital bond in terms of health, few have examined whether marital status duration and prior marital losses influence men and women’s health to a similar extent. In the previous chapters, I add to research in this area by seeing if gender conditions these relationships.

Main Findings

Drawing from a cumulative advantage standpoint, I anticipated that martial gain and loss would have additional implications for health if individuals had been in this status the longest amount of time. I find that marital status duration has physical and mental health consequences and number of prior marital losses impacts well-being. In particular, married adults, on average, have less allostatic load than unmarried adults, and this reflects the advantage of adults who have been married the longest (46 years or more). Additionally, widowed and divorced adults have less allostatic load than married adults and this extends to adults who have been in these statuses the longest. I also found support for the short-term (in congruence with the marital crisis model) and long-term impacts of marital loss on mental well-being. I also found that these influences seem to be stronger for men for mental well-being and stronger for women in terms of physical well-being. Overall, I have highlighted important avenues for future research. Researchers should examine the potential mediators linking statuses, duration, and losses to physical and mental health. Using insight from the marital resource model, perhaps the social, psychological, and economic resources along with control of health behaviors that come from being married account for these differences. Additionally, perhaps certain mechanisms are more detrimental for men for mental well-being and for women for physical health. In congruence with past research, monetary strain may explain why women are more vulnerable to the physical health consequences of marital loss, and social support and changes in health behaviors following marital loss may account for men’s greater vulnerability in terms of mental well-being.

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Limitations

There are certain limitations worth mentioning. As indicated in Chapter 3, an alternative model to both the marital resources and marital strain models is the marital selection model. This states that married adults have better health because healthier adults select into marriage in the first place. In chapter 3, I control for physical health status to help account for some of the selection effects of adults with physical health problems out of marriage; however, selection issues in terms of the focal outcome, mental health, are still possible in the current analyses. Future research following individuals over time and assessing health status across multiple times in respondents’ marital history would help to unravel selection-causation issues. Second, it would be important to see if past number of marital transitions and marital status duration simultaneously had an influence on health. In particular, does the impact of status duration become more or less important when individuals have experienced more than one prior marital loss? Third, in Chapter 3 I note that I do not take into account the type of prior marital losses. Since widowhood and divorce may entail different losses in social support and financial resources, it would be important to see if having more than one type of marital loss or the compounding of the same type of marital loss has implications for physiological and psychological functioning.

Conclusion

Recent research has found that attitudes concerning entry into and exit out of marriage are changing. As individuals’ marital histories broaden, this may have important implications for health and well-being. Their influence may be particularly salient in later life since this point in the life course allows for time and history in marital statuses to accumulate. Additionally, the marital bond or its loss may be especially important at this point since individuals may be experiencing significant changes in work roles, friendship networks, and health status. In this dissertation, I have attempted to advance our understanding of marriage and health in later life by considering the impact of marital status duration, marital history, and gender.

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APPENDIX A

BRIEF DESCRIPTION OF BIOMARKERS USED IN MEASURING ALLOSTATIC LOAD

Biomarkers

BMI (Body Mass Index): Calculated by taking the weight of the respondent (in pounds) and dividing it by the square of respondent’s height (in inches) and multiplying it by 703. Proxy of body fat percentage.

Systolic Blood Pressure: Measured by sphygmomanometer. Force exerted by blood again the blood vessel walls when the left ventricle is contracting. Marker of cardiovascular functioning (Juster, McEwen, and Lupien 2010)

Diastolic Blood Pressure: Measured by sphygmomanometer. Force exerted by blood again the blood vessel walls when the left ventricle is relaxed. Marker of cardiovascular functioning (Juster, McEwen, and Lupien 2010)

Pulse Rate: Represents the number of palpitations made by the heart per minute. Marker of cardiovascular functioning.

C-Reactive Protein (CRP): Measured using dried blood spots. Acute phase protein that is a central component of the inflammatory response to injury or infection (Nallanathan et al. 2008)

Glycosylated Hemogloblin (HbA1c): Measured using dried blood spots. The ratio of glycosylated to nongylcosylated hemoglobin. Used to index the average glucose concentration over the past 3 to 4 months. Marker of risk of disease and metabolic functioning (Gomero et al. 2008)

Epstein-Barr Virus (EBV): Measured using dried blood spots. Member of the herpes virus family and indicates lower cell- mediated immune function (Mihai et al. 2008)

Dehydroepiandrosterone (DHEA): Obtained through salivary specimens. Androgen produced by the adrenal glands. HPA-axis antagonist. Marker of neuroendocrine functioning and has been indicated as a possible marker of physiologic aging. (Juster, McEwen, and Lupien 2010; Mendoza, Curran, and Lindau 2007)

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APPENDIX B

HUMAN SUBJECTS COMMITTEE APPROVAL LETTER

Office of the Vice President For Research Human Subjects Committee Tallahassee, Florida 32306-2742 (850) 644-8673, FAX (850) 644-4392

APPROVAL MEMORANDUM

Date: 1/26/2010

To: Sunshine Rote

Address: Florida State University, 526 Bellamy Building, Tallahassee, FL 32306 Dept.: SOCIOLOGY

From: Thomas L. Jacobson, Chair

Re: Use of Human Subjects in Research Racial/Ethnic Differences in the Mental Health Effects of Widowhood

The application that you submitted to this office in regard to the use of human subjects in the proposal referenced above have been reviewed by the Secretary, the Chair, and two members of the Human Subjects Committee. Your project is determined to be Exempt per 45 CFR § 46.101(b)4 and has been approved by an expedited review process.

The Human Subjects Committee has not evaluated your proposal for scientific merit, except to weigh the risk to the human participants and the aspects of the proposal related to potential risk and benefit. This approval does not replace any departmental or other approvals, which may be required.

If you submitted a proposed consent form with your application, the approved stamped consent form is attached to this approval notice. Only the stamped version of the consent form may be used in recruiting research subjects.

If the project has not been completed by 1/25/2011 you must request a renewal of approval for continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your expiration date; however, it is your responsibility as the Principal Investigator to timely request renewal of your approval from the Committee.

You are advised that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol

56 change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal Investigator promptly report, in writing any unanticipated problems or adverse events involving risks to research subjects or others.

By copy of this memorandum, the Chair of your department and/or your major professor is reminded that he/she is responsible for being informed concerning research projects involving human subjects in the department, and should review protocols as often as needed to insure that the project is being conducted in compliance with our institution and with DHHS regulations.

This institution has an Assurance on file with the Office for Human Research Protection. The Assurance Number is IRB00000446.

Cc: Jill Quadagno, Advisor [[email protected]] HSC No. 2009.3708

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BIOGRAPHICAL SKETCH

CURRICULUM VITAE Sunshine Rote July 18, 2012

Florida State University Department of Sociology Tallahassee, FL 32306-2270

EDUCATION

Ph.D. in Sociology from Florida State University, 2012 Expected Dissertation: Marital Status Duration, Marital Transitions, and Health M.S. in Sociology from Florida State University, 2009 B.A. in Sociology from The University of Texas at Austin, 2007

TEACHING AND RESEARCH INTERESTS

Medical Sociology, Family, Gender, Race, Religion, Statistics

TEACHING EXPERIENCE

Teaching Assistant: Deviance and Social Control (Summer 2010) Social Psychology of Groups (Summer 2009) Population and Society (Summer 2009) Aging Policies and Services (Spring 2009) Sociological Theory (Spring 2008; Fall 2008) Sociology of Law (Summer 2008) Family Problems and Social Change (Fall 2007) Teaching: Social Problems (Fall 2009) Social Statistics (Spring 2011) Methods of Social Research (Spring 2012)

PUBLICATIONS, REFEREED ARTICLES

Rote, Sunshine, Terrence D. Hill, and Christopher G. Ellison. forthcoming. “Religious Attendance and Loneliness in Later Life.” The Gerontologist

Rote, Sunshine and John Taylor. forthcoming. “Black/White Differences in Adolescent Drug Use: A Test of Six Hypotheses.” Journal of Child & Adolescent

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Rote, Sunshine and Jill Quadagno. 2011. “Depression and Alcohol Dependence among Welfare Recipients: Before and After the Personal Responsibility and Work Reconciliation Act of 1996.” Social Service Review 85(2): 229-245.

Rote, Sunshine M. and Brian Starks. 2010. “Racial/Ethnic Differences in Religiosity and Drug Use.” Journal of Drug Issues 40(4):729-753.

Ellison, Christopher G., Matt Bradshaw, Sunshine Rote, Jennifer Storch, and Marcie Trevino. 2008. “Religion and Alcohol Use Among College Students: Exploring the Role of Domain- Specific Religious Salience.” Journal of Drug Issues 38: 821-846.

BOOK REVIEWS

Rote, Sunshine. 2011. “RELIGION, FAMILIES, AND HEALTH: POPULATION-BASED RESEARCH IN THE UNITED STATES edited by Christopher G. Ellison and Robert A. Hummer” Journal for the Scientific Study of Religion 50(1): 223-225.

BOOK CHAPTERS

Quadagno, Jill and Sunshine Rote. 2011. “Gerontology” Oxford Bibliographies Online: Sociology. doi: 10.1093/obo/9780199756384-0023

ARTICLES UNDER REVIEW

Rote, Sunshine and Robyn Lewis Brown. 2011. “The Significance of Family Role Norms and Acculturation for Gender Differences in Substance Use Among Hispanic Adults.” (Revise and Resubmit, Social Science Research)

Miller, Byron, Sunshine Rote, and Verna Keith. 2011. “Income Differences in Discrimination and Depressive Symptoms among African Americans: Testing Exposure and Vulnerability Hypotheses” (Revise and Resubmit, Society & Mental Health)

PAPERS IN PROGRESS

Hill, Terrence, Sunshine M. Rote, Amy M. Burdette, and Christopher G. Ellison. “Religious Involvement and Biological Risk”

Rote, Sunshine. “Spouse’s Health, Marital Quality, and Well-Being in Later Life”

Rote, Sunshine. “Cumulative Advantage, Marital Duration, and Physical Health.”

PROFESSIONAL PRESENTATIONS

Rote, Sunshine. 2011. “Spouse’s Health, Marital Quality, and Well-Being” American Sociological Association annual meeting, Las Vegas, NV.

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Hill, Terrence D., Sunshine M. Rote, Amy M. Burdette, Christopher G. Ellison. 2011. “Religious Involvement and Biological Risk” American Sociological Association Annual Meeting, Las Vegas, NV.

Miller, Byron, Sunshine Rote, and Verna Keith. 2010. “Explaining the Moderating Effects of Poverty on the Relationship Between Discrimination and Mental Health among African Americans” The XII International Conference on Research, Portsmouth, New Hampshire.

Rote, Sunshine and John Taylor. 2010. “Black/White Differences in Adolescent Drug Use: A Test of Six Hypotheses” American Sociological Association annual meeting, Atlanta, GA.

Rote, Sunshine, Byron Miller, and Verna Keith. 2010. “Exploring the Effects of Income and Discrimination on the Mental Health of African Americans” Southern Sociological Society annual meeting, Atlanta, GA.

Rote, Sunshine and Robyn Lewis 2009. “Gender Differences in Alcohol and Drug Use Among Hispanic Adults: The Significance of Family Role Norms and Acculturation” Southern Sociological Society annual meeting, New Orleans, LA.

Starks, Brian and Sunshine Rote 2008. “Racial Differences in Drug Usage and Disorders” Southern Sociological Society annual meeting, Richmond, VA.

RESEARCH EXPERIENCE Research Assistant for Jill Quadagno (Fall 2011, Summer 2011, Fall 2010, Spring 2010), Analysis of Welfare Reform and its effect on Composition of Welfare Recipients using the National Survey of Drug Use and Health (NSDUH); Analysis of Aging, Marital Quality, and Health using the National Social Life, Health, and Aging Project (NSHAP) Research Assistant for Dan Tope (Fall 2008), Created a Bibliography and Literature Review on Labor Unions and Political Activism Research Assistant for Brian Starks (Fall 2007), Created a Bibliography and Literature Review on Religion and Substance Use

SERVICE

Sociology Graduate Student Union, Vice President, Department of Sociology, Florida State University (2010) Sociologists for Women in Society, Membership Committee (2009 to 2010) Departmental Meeting Committee, Department of Sociology, Florida State University (2009) Graduate Admission and Scholarships Committee, Department of Sociology, Florida State University (2009; 2011)

Paper Reviewer: Journal of Health and Social Behavior

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HONORS AND AWARDS

Nominated, Philanthropic Educational Organization (P.E.O.) Scholar Award 2010

PROFESSIONAL ASSOCIATION MEMBERSHIPS  American Sociological Association  Sociologists for Women in Society  Southern Sociological Society

ADDITIONAL TRAINING

Latent Growth Curve Analysis Workshop, Florida State University, led by Miles Taylor (Summer, 2010)

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