Stress processes and trajectories of depressive symptoms in early life: Gendered development

STRESS PROCESSES AND TRAJECTORIES OF DEPRESSIVE SYMPTOMS IN EARLY LIFE: GENDERED DEVELOPMENT$ Daniel E. Adkins, Victor Wang and Glen H. Elder Jr. ABSTRACT Despite considerable advances, significant gaps remain in our knowledge of how gender differences in depression develop over the life course. Applying mixed model growth curves to the National Longitudinal Survey of Adolescent Health, this study investigates gendered variation in the causes and course of depressive symptom trajectories across early life. Results show curvilinear trajectories, rising through adolescence, and falling in young adulthood, with female disadvantage persistent, but narrowing over time. The effects of stressful life events (SLEs) and social $

We are grateful to Amanda Byrd for comments and suggestions that contributed significantly to this chapter. This research uses data from the Add Health Study designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris and the Add Health Wave IV Program Project directed by Kathleen Mullan Harris (Grant 3P01 HD031921), funded by the National Institute of Child Health and Human Development with cooperative funding from 17 other agencies. We gratefully acknowledge support from NICHD to Glen H. Elder, Jr. and Michael J. Shanahan through their subproject to the Add Health Wave IV Program Project (Grant 3P01 HD031921).

Stress Processes across the Life Course Advances in Life Course Research, Volume 13, 107–136 Copyright r 2008 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1040-2608/doi:10.1016/S1040-2608(08)00005-1

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support on depressive symptoms are notably larger for females. Overall, results indicate that stress processes contributing to depression are highly gendered in early life with females generally experiencing higher levels of depressive symptoms and showing greater sensitivity to both the detrimental effects of SLEs and the buffering effect of social support.

The significant gender difference in depression among adults is one of the most robust findings in the mental health literature (Nolen-Hoeksema, 1990). Rates of depression are approximately two times higher among women than men cross-culturally, regardless of the diagnostic scheme or interview method (Culbertson, 1997). Research indicates that this gender differential emerges in early adolescence (e.g., Allgood-Merten, Lewinsohn, & Hops, 1990; Angold, Costello, & Worthman, 1998), with approximately half of age 15 adolescent girls experiencing weekly depressive symptoms compared to only a third of their male peers (Scheidt, Overpeck, Wyatt, & Aszmann, 2000). While scholars now have a fairly clear conception of gender differences in depression across adolescence and young adulthood, questions remain regarding gendered variation in causes of depression during this important developmental period. Indeed, gender differences in exposure and sensitivity to well-known predictors of depression are still debated for both adolescent and adult populations. For instance, gender variation in the influence of stress remains a contested topic, with researchers variously arguing for the influence of differential exposure and vulnerability. Thus, one prominent perspective suggests that women’s social roles expose them to more stress than men, asserting that women, like individuals with low socioeconomic status (SES), are often situated in social roles where they are expected to perform less desirable tasks with little recognition or reward (Turner & Lloyd, 1999; Turner & Avison, 1989). Another related perspective holds that regardless of gender differences in exposure, women are more vulnerable to the negative effects of stress on mental health (Ge, Lorenz, Conger, Elder, & Simons, 1994; Kessler, 1979). In response to this perspective, Aneshensel, Rutter, and Lachenbrach (1991) and others have argued that gender differences in stress reactivity are disorder specific, with women tending toward internalizing and men predisposed to externalizing reactions (Hagan & Foster, 2003). Even less is known regarding early life gender heterogeneity in the effects of other well-established predictors such as social support and SES. This study addresses these gaps in knowledge, employing the stress process framework to investigate gendered variation in the antecedents of and changes in depressive symptoms across early life. Over the past 25 years, the

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stress process has become the central sociological paradigm in explaining adult mental health disparities (see Pearlin, Menaghan, Lieberman, & mullan, 1981; Thoits, 1991; Turner & Lloyd, 1999). Stress process models are instructive from a developmental perspective because they elucidate the early social structuring of adversity and privilege that ultimately shape long-term patterns of psychological well-being. Indeed, numerous studies show that childhood SES influences mental health in early life (McLeod & Shanahan, 1993; Costello, Compton, Keeler, & Angold, 2003 [see Case 2004]), and though stressful life events (SLEs) are also known to be influential (e.g., Ge et al., 1994; Ge, Conger, & Elder, 2001; Ge, Natsuaki, & Conger, 2006; Meadows, Brown, & Elder, 2006), it is less clear how SES and SLEs exert their effects longitudinally. Recent research has sought to address this limitation by examining ‘‘depression trajectories’’ to better understand the development of negative affect along with its social etiology (e.g., Ge et al., 1994, 2006). Despite considerable advances, research has yet to fully integrate the principal components of the stress process with the life course construct of a depression trajectory. To investigate gendered variation in the trajectories of depressive symptoms across early life, this study employs Add Health, the largest nationally representative panel study of U.S. adolescents and young adults. This research is one of the first longitudinal analyses to test the major components of the stress process on age-based trajectories of early life depression by gender. The inquiry is guided by several key questions. First, how do patterns of depressive symptoms differ by gender as adolescents transition to young adulthood? Second, to what extent, and through what mechanisms, does the stress process function differently between genders? Specifically, do the various components of childhood SES have equal impacts on depressive symptoms in both males and females? Are gender differences in depressive symptom trajectories related to differential exposure and/or sensitivity to SLEs and social support? We conclude by discussing the implications of our findings for future research.

NORMATIVE DEVELOPMENT AND DEPRESSIVE SYMPTOMS Though relatively few trajectory analyses of the development of depression during adolescence and young adulthood have been conducted, there is mounting evidence, from both cross-sectional and longitudinal studies, of

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a normative curvilinear course of depressive symptoms through early life. This conclusion is supported by longitudinal research finding curvilinear trajectories in samples of individuals moving through adolescence and young adulthood, as well as by research in younger samples showing linear increase through middle adolescence and studies of young adult samples showing linear decrease or stability through the twenties. For instance, analyzing 11 waves of longitudinal data covering ages 12–23, Ge et al. (2006) found curvilinear trajectories of depressive symptoms, rising in early and middle adolescence and declining in late adolescence. Likewise, Wight, Sepu´lveda, and Aneshensel (2004) examined depressive symptoms in three datasets (one adolescent sample and two adult samples) and found increasing levels in the adolescent sample, while the adult samples showed both lower initial levels and a steady decline over time. Similar findings have been found in several other analyses (e.g., Wade, Cairney, & Pevalin, 2002; Hankin et al., 1998; Ge et al., 1994) and considered collectively, this literature offers strong support of an inverted-U curvilinear trajectory of depressive symptoms across adolescence and young adulthood for both genders. In addition to investigating the overall course of depressive symptoms across early life, researchers have also examined gender differentials across this period. Following early cross-sectional findings indicate that female disadvantage in depression emerges in early adolescence (e.g., Allgood-Merton et al., 1990; Nolen-Hoeksema, 1990), Ge and colleagues (1994) were among the first to apply the trajectory methods to examine the emergence of gender disparity in depression, tracing the origin of the disparity to ages 13–15. This finding has proven robust over the years, garnering support from numerous, methodologically diverse studies (see Hankin & Abramson, 2001, for review). Moving beyond the origins of the gender gap, research has recently begun tracing the gender gap in depression across late adolescence and young adulthood, with Ge, Conger, and Elder (2001) showing growth in the gap across adolescence and Ge et al. (2006) showing widening of the gap from early to late adolescence, and narrowing from the late teens to age 23. However, given the few methodologically rigorous analyses on the topic and the nonrepresentative nature of the samples analyzed, further research is clearly needed to better characterize variations in the gender gap across early life.

STRESS PROCESSES AND DEPRESSIVE SYMPTOMS A longstanding axiom in the sociological study of health is that much of the variation in health outcomes can be explained by differences in social

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experiences. This perspective asserts that structural dimensions (e.g., SES, race/ethnicity, and gender) position individuals in social locations more or less conducive to health. Stress process theory extends this logic, theorizing the mechanisms through which social structure impacts health. In the seminal statement of the theory, Pearlin and colleagues (1981) argue that stress exposure is a primary determinant of mental health. They develop a conceptual model distinguishing various types of stress exposure and theorizing that the impact of stress is mediated and/or moderated by buffering personal resources such as social support. In later work Pearlin (1989) explicitly contextualizes this stress process model, arguing that individuals’ exposure to stress and access to buffering resources is largely a function of their structural position in society. Here, we employ the stress process paradigm to conceptualize the social etiology of depression, dichotomizing the components as distal, structural, socioeconomic causes, and proximate factors including stressful events and social support.

Childhood Socioeconomic Status and Depressive Symptoms The influence of SES on mental health has also been the subject of extensive empirical investigation. Indeed, the significant positive correlation of SES and mental health is one of the most consistent empirical findings in the social sciences over the last 50 years (see Haas, 2006, for review). However, the causal direction of this effect has been the subject of considerable debate.1 While much of the research to date has used cross-sectional, observational data incapable of supporting strong causal inference, there are several studies employing methodologically rigorous designs indicating support for both social selection (health - status) and causation (status - health). For instance, Costello et al. (2003) examined data from the Great Smoky Mountains Study, in which a casino opened midway through the study giving every American Indian an income supplement. This exogenous shock raised 14% of sample families out of poverty, resulting in a significant reduction in emotional symptoms (i.e., depression and anxiety) for the children transitioning out of poverty (Costello et al., 2003 [see Case 2003]). This and other analyses using robust analytic approaches have indicated substantial social causation effects. While there is a substantial body of literature demonstrating the influence of childhood SES on depression, less research has considered gender differences in this effect. Of the few, mostly cross-sectional, studies to examine this issue, some have indicated that childhood SES, operationalized as parental occupation (Gilman, Kawachi, Fitzmaurice, & Buka, 2002),

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parental education, and income (Gore, Aseltine, & Colton, 1992), has a greater impact among females. Although the authors of these studies offer little theoretical interpretation for these findings, the empirical results are suggestive in this regard. For instance, Gilman and colleagues (2002) find that female disadvantage virtually disappears at the highest SES levels, while being quite large among low SES youth, indicating that when household resources (material and/or psychosocial) are abundant, children of both genders enjoy relatively low depression levels, but when resources are scarce, girls are disproportionately affected. However, given the sparseness of this evidence, it is also possible that there is no true gender difference and that these are chance findings based on relatively small samples. Support of the latter possibility is found in the one extant trajectory analysis examining this topic, which found no gender difference in the effect of income on trajectories of depressive symptoms across early life (Ge et al., 2006). Thus, the question of gender differences in the influence of childhood SES on depression across early life remains open and the large, nationally representative sample, multiple childhood SES indicators and longitudinal methods used here are well-suited to advance understanding on this issue.

Stressful Life Events, Social Support and Depressive Symptoms In the past 30 years many studies have examined the influence of recent SLEs on depression, providing consistent evidence of a significant effect (e.g., Paykel, 1978; Kendler, Karkowski, & Prescott, 1999; Ge et al., 2006). While most of this research has examined adult samples, consistent patterns have also been found among children and adolescents (Goodyer, Kolvin, & Gatzanis, 1985). For instance, using an index of 43 SLEs, Ge et al. (2001) found that SLEs were highly predictive of depressive symptoms in both genders across 7th to 12th grades. While the consistency of association between event accumulation and disorder clearly demonstrate that SLEs indices yield meaningful estimates of stress exposure (Turner & Wheaton, 1995), debate remains regarding gender differences in both sensitivity (Dornbush, Mont-Reynand, Ritter, Chen, & Steinberg, 1991; Ge et al., 1994; Aneshensel et al., 1991) and exposure (Turner, Wheaton, & Lloyd, 1995; Turner & Butler, 2003) to SLEs. Regarding gender differences in exposure to stressful events, research on adult populations has generally indicated that women experience a higher volume of SLEs than men (Turner et al., 1995). Early work in the area theorized the cause of this gender disparity to lie in the homemaker role, which

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researchers characterized as poorly rewarded, socially isolating, and generally unsatisfying (Gove & Tudor, 1973; Gove & Geerken, 1977). However, later research demonstrated the salience of factors beyond the homemaker status by showing higher levels of psychological distress for women even among the employed (Turner & Avison, 1989). Consequently, recent theoretical explanations have emphasized the stress entailed by the multiple role demands women often experience. For instance, researchers have pointed to the overload and role conflict experienced by employed women who also have primary responsibility for children and housework (e.g., Mirowsky & Ross, 1989). While the above theory may help explain gender differences in stress exposure in the latter, young adult, portion of the longitudinal sample examined here, it is less useful in understanding gender differences in stress exposure in early life, before females take on multiple roles. Addressing this topic, experts have suggested that the elevated volume of SLEs experienced by adolescent girls is primarily driven by stressful events in the domain of peer interpersonal relationships (see Hankin & Abramson, 2001). For instance, Gore and colleagues (1992) suggest that due to adolescent girls’ greater preoccupation with social standing and peer relationships, they tend to experience greater exposure to SLEs primarily through conflict in interpersonal friendships and peer rejections. However, considering elevated levels of academic problems and risk behaviors among adolescent boys (Crick & Zahn-Waxler, 2003, for review), a contrasting argument suggesting higher levels of SLEs among boys, at least in these domains, could easily be formulated. Furthermore, given that some studies of early life samples have failed to find gender differences in SLE exposure (e.g., Turner & Butler, 2003), further research into this topic is clearly needed. The literature on gender difference in sensitivity to SLEs in early life has also generally suggested female disadvantage, but researchers disagree as to why. Early research noting greater association between SLEs and depression among women theorized that women were globally more vulnerable to stress due to factors such as deficits in coping strategies (e.g., Kessler, 1979). Such explanations were later challenged by perspectives arguing that stress reactivity was likely to be disorder specific (e.g., Aneshensel et al., 1991). Recent research has generally supported the latter notion, showing that men and women tend to react to stress in different ways, with females tending toward internalizing reaction, such as depression, and males tending toward externalizing behaviors such as alcohol and substance abuse (Hagan & Foster, 2003). However, the issue is far from settled, with some findings indicating no gender differences in the effect of SLEs on depression (see Gore & Colten, 1991). Given this ambiguity, the current study has the potential to significantly advance

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understanding, particularly given the dynamic nature of the longitudinal sample examined, which spans both adolescence and young adulthood. Another central issue in investigations of the stress–depression relationship concerns moderation of the depressive effects of stress. Clearly, individuals differ substantially in how depressively they respond to stress, and social and personal resources, such as social support, have long been theorized as primary buffers moderating the stress–depression relationship (Pearlin et al., 1981; Pearlin, 1989). Empirical analyses have consistently supported this proposition, indicating social support to be among the strongest buffering resources typically examined (Turner & Lloyd, 1999). Beyond assessing the role of social support as a buffering resource in the population as a whole, advances have also been made in elucidating the social distribution of support. Thus, several studies have shown that women generally experience higher levels of social support than men (e.g., Gore et al., 1992; Turner & Marino, 1994). Indeed, gender differences in social support have been shown to be one facet of a more pervasive gender difference in interpersonal relationship styles. Thus, research has shown that women generally place more emphasis on intimacy, emotional disclosure and empathy in interpersonal relationships (Bell, 1981; Gilligan, 1982), while male norms tend to discourage emotional expressiveness and disclosure (Lowenthal & Haven, 1968). Furthermore, while no direct, empirical test has yet been conducted, this literature offers substantial indirect evidence that women are likely to exhibit greater sensitivity to variation in support. Specifically, several studies have shown that the mental health detriment associated with deficits in interpersonal relationships is greater among women. For instance, it has been shown that compared to men, women react to marital conflict with greater psychological distress (Turner, 1994) and also exhibit higher levels of emotional reliance on others (Turner & Turner, 1999). Cumulatively, this research suggests that women are more sensitive to the quality of their interpersonal relationships. Thus, while no direct empirical evidence is available, our reading of the literature suggests that the association of social support to depression is likely greater among women.

METHODS Sample and Procedures Data from the three waves of the National Longitudinal Study of Adolescent Health (Add Health) were used to develop our depressive

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symptom trajectory models. Add Health is a nationally representative, school-based sample of 20,745 adolescents in grades 7–12 surveyed during the 1994–1995 academic year. The sampling frame consisted of all high schools in the United States. A total of 80 high schools were selected with probabilities proportional to size and a sample of 52 feeder middle schools was attached to the sample of high schools. The response rate for the 134 participating schools was 78.9%. Of the over 90,000 students who completed the in-school survey in 1994 a baseline sample of 20,745 adolescents was selected for further data collection. The adolescents were interviewed three times during a 7-year period in 1994–1995, 1995–1996, and 2001–2002. The overall sample is representative of U.S. schools with respect to region of the country, urbanicity, school type (e.g., public, parochial, private non-religious, military, etc.), and school size. Members of ethnic minority groups were over-sampled. Further details regarding the sample are available at http://www.cpc.unc.edu/projects/addhealth/

Measures Depressive Symptoms The depressive symptoms scale is a 9-item derivative of the CES-D (Radloff, 1991, 1997). Previous research has shown the 20-item CES-D to cluster into four subfactors – somatic-retarded activity, depressed affect, positive affect, and interpersonal relationships. All four components are represented in the 9-item scale used here. Individual items are coded on a four-point scale, from never or rarely (0) to most or all of the time (3) and refer to feelings the respondent had in the past week. The CES-D 9-item scale is consistent across all three waves (a ¼ 0.79, wave one; a ¼ 0.80, wave two; a ¼ 0.80, wave three). The raw score means for the entire Add Health sample by wave are 5.66, 5.59, and 4.44, respectively. Parental Socioeconomic Status Variables measuring resident parent’s (generally, the mother’s) education originate in the student surveys. Each respondent reports on the highest level of education that his or her resident parent completed. From this information, the variable describing the mother’s educational attainment was derived. Additionally, mothers reported their education and that of their current partner, which were used to create a measure of father’s education. The household income measure was also taken from the parental questionnaire. Income was measured in thousands of dollars of household

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income in the previous year. Respondents are instructed to include their own income, the income of everyone else in their household, and income from welfare benefits, dividends, and all other sources. Bivariate correlations for the three SES indicators ranged 0.56–0.28, indicating collinearity was not problematically high SES indicators were mean-centered to aid in model interpretation.2 Stressful Life Events The index of SLEs presented in Appendix A is derived from the measures developed by Ge et al. (1994). A major challenge of developing the current measure of SLEs is to make it longitudinally accountable. As adolescents make the transition into adulthood, a number of stressors included in Add Health data become irrelevant (e.g., expelled from school), and a number of new stressors become appropriate (e.g., divorce, entering the military service). To ensure stress is appropriately measured at different life stages, we used a slightly different set of items for wave III to capture the different life experiences. Complying with the most common practice for comparability (Turner & Wheaton, 1995), the current study selected only the events that happened less than a year before the interview. Further, only acute events of sudden onset and of limited duration were included. Similar items (such as miscarriage and still birth, or dissolution of sexual nonromantic relationship, romantic relationship, cohabitation, marriage) were grouped together to avoid making the measurement overly specific, at the same time insuring a sufficient volume of events to form a relatively continuous measurement. Additive indices were then created with raw score means for the entire Add Health sample by wave equal to 2.37, 1.75, and 1.54, respectively. The index was standardized in the data analysis. Social Support The social support index shown in Appendix B is a composite measure of perceived social support. It assesses how the respondents feel about their relationship with their closest social ties such as family, teachers, and friends. Additive indices were created by summing the items, with raw score means for the entire Add Health sample by wave equal to 32.02 and 31.82,3 respectively. The index is mean-centered in the analysis. Race/Ethnicity Race/ethnicity was included as a control in all models. In keeping with the new census policy, Add Health respondents were allowed to mark as many race/ethnicity categories as they felt applied to them. Approximately

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4% of the sample identified as multi-racial/ethnic. Given this, we used the coding method used by the Add Health data manager as a way to obtain mutually exclusive race/ethnicity categories for the primary analysis. Thus, a single race is assigned to those reported multiple racial/ethnic backgrounds using the following criteria: if the respondent reported single race/ethnicity, he/she will be coded as is; if the respondent reported more than one race, only one race will be selected from the races the respondent reported in the following order: Hispanic, Black, Asian, and White. In a sensitivity analysis, a reduced sample composed of only individuals identifying as one race/ethnicity was used and results were compared for robustness.

Analytic Strategy While developmental theory posits age as the appropriate metric in the study of longitudinal change, Add Health data is not organized by age, but by wave. Thus, given the substantial age variation within each wave of Add Health (Table 1), it was necessary to reorganize the data from wave to age in order to address our research aims. While this approach is clearly indicated from a developmental perspective, it is not without potential weaknesses. Specifically, the method entails grouping individuals from different cohorts into the same synthetic cohort, leaving open the question of potential cohort effects. To address this possibility, sensitivity analyses were conducted and all substantive findings were robust to the control of cohort effects. To examine the development of depression across the ages 11–27 we employed individual growth curve modeling within a mixed model (i.e., hierarchical linear models, HLM) framework, which is a data analysis technique especially designed to explore longitudinal panel data (Goldstein, 1995; Bryk & Raudenbusch, 1992). Longitudinal panel data, such as in the present study, can be considered to be clustered or hierarchical data because repeated observations (first level) are nested within subjects (second level) (Willett, Singer, & Martin, 1998). We chose individual growth curve modeling over ordinary regression analysis, because the former method accounts for the dependency of the data owing to this clustering (Goldstein, 1995). Ordinary regression analysis would estimate a single equation for all data, whereas individual growth curve modeling fits a curve for each individual subject. These curves (i.e., depression development by age) are characterized by their intercept (or baseline level) and slope (rate of change). The addition of independent variables to the model, such as education level

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Table 1. Age

Frequency Distribution of Age, by Wave (Counts and Percentages).

Wave I (1995)

Wave II (1996)

Freq

Pct

Freq

Pct

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

6 292 1,224 1,422 1,870 1,927 1,811 1,217 156 14 3

0.06 2.94 12.31 14.3 18.81 19.38 18.22 12.24 1.57 0.14 0.03

5 351 1,054 1,251 1,593 1,561 1,139 400 67 5

0.07 4.73 14.19 16.85 21.45 21.02 15.34 5.39 0.9 0.07

Total

9,942

100

7,426

100

Wave III (2001–2002) Freq

Pct

78 790 1,104 1,293 1,465 1,427 1,148 332 39 4 1

1.02 10.29 14.37 16.83 19.07 18.58 14.95 4.32 0.51 0.05 0.01

7,681

100

and childbearing status, is aimed at explaining between-subject variation (in intercept and slope) of the depression growth curves. The method has a number of advantages over traditional statistical methods for analysis of quantitative longitudinal data. First, substantive questions can be addressed within the multilevel framework, e.g., whether some individuals experience faster rates of over time change in depression levels than others (Willett et al., 1998). Second, the method accounts for the dependency of observations caused by clustering (Goldstein, 1995). Third, any number of waves of data can be accommodated; the occasions of measurement need not be equally spaced; and data-collection schedules can be different for different individuals (Willett et al., 1998). Finally, the approach is particularly suitable for dealing with incomplete data (Diggle & Kenward, 1994). We began our investigation of gendered variation in trajectories of early life depression by modeling the unconditional (i.e., no predictors other than age) growth curve stratified by gender. Comparisons of various trajectory

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shapes (i.e., linear, quadratic, and cubic), indicated a quadratic growth curve characterized by random intercepts and random linear and fixed quadratic age slope components was the best fit to the data according to nested likelihood ratio tests (LRTs) of model fit.4 After determining the general modeling strategy of quadratic age-based growth curves stratified by gender, we then sequentially introduced groups of covariates in a nested fashion to investigate gender differentials in the influence of race/ethnicity, SES, SLEs, and social support. t-Tests were conducted to formally test gender difference in the model parameters (see Table 3 note). Finally, the last model presents the trimmed model in which only significant effects are retained. All analyses were conducted in Stata 9.2.

RESULTS Descriptive Statistics Table 2 presents the descriptive statistics for the analysis variables by gender. Although age is a continuous measure in our study, and depressive symptoms and SLEs are measured at each age, we present descriptive statistics for these variables consolidated into five age groups for the sake of concision. For both gender subgroups depressive symptoms show a pattern of moderate increase across the younger ages, peaking at ages 15–17 and declining relatively sharply from ages 18–27. There is a gender gap, with females having noticeably higher values. Furthermore, the depressive symptom means suggest a narrowing of the gender gap over time. The SLEs repeated measures show similarities to the depressive symptoms profile. On average, respondents start at relatively low levels of SLEs in the early teens, increase until ages 15–17 and then decline in young adulthood. On average, males show elevated levels of SLEs compared to females, particularly at younger ages. Values on SES variables and social support are generally comparable across gender in this sample.

Growth Curve Models of Depressive Symptoms To model trajectories of depressive symptoms, we begin by examining a series of unconditional trajectories by gender to identify the correct functional form of the growth curve. In preliminary analyses we compared various specifications including a simple linear model, and two polynomial

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Table 2.

Descriptive Statistics.

Variable

CES-D 11-14 CES-D 15-17 CES-D 18-20 CES-D 21-23 CES-D 23-28 SLEs 11-14 SLEs 15-17 SLEs 18-20 SLEs 21-23 SLEs 23-28 White Black Asian Hispanic Household income Mother’s education Father’s education Perceived social support

Male

Female

Mean (%)

Std. Dev.

Mean (%)

Std. Dev.

4.370 5.122 4.959 4.130 4.047 2.154 2.662 2.314 1.684 1.691 0.678 0.153 0.042 0.128 52.452 5.655 5.696 31.770

3.345 3.757 3.838 3.705 3.744 2.433 3.032 2.748 1.844 1.769 – – – – 50.016 2.376 2.243 4.392

5.506 6.467 5.715 4.751 4.436 1.435 1.884 1.566 1.460 1.310 0.666 0.171 0.035 0.128 53.442 5.625 5.650 32.023

4.274 4.518 4.428 4.347 3.935 1.860 2.217 1.773 1.554 1.409 – – – – 52.635 2.394 2.265 4.393

(i.e., quadratic and cubic) functions. These analyses showed that the quadratic model with random intercept and slope fit the data well and represented a superior balance of accuracy and parsimony. The results of the quadratic age-based growth curve are shown in Tables 3 (for males) and 4 (for females). As shown in model 1 of Tables 3 and 4, this quadratic model fit the data well with all fixed effects strongly significant. Thus, depressive symptoms in early life are well modeled as a curvilinear trajectory with values rising early in the trajectory, before declining in the mid and later sections. The mean trajectory is higher for females than males, with the difference primarily in intercept (b0 ¼ 5.739 and 4.271, respectively). However, as shown in Fig. 1, there is some evidence of convergence as female’s depressive symptom levels begin declining earlier and more steeply than males. While the results suggest narrowing of the gender gap in young adulthood, given that coefficient t-tests indicate that the gender differences in the age and age2 are not significantly different, we are hesitant to conclude they indicate convergence. Significant random effects for each gender indicate considerable variance around this mean trajectory, with greater variability in trajectory shapes among females than among males.

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Table 3. Trajectories of CES-D for Males Predicted by SES, SLE, Perceived Social Support, and Family Structure (N ¼ 4,992).

Fixed Effects Intercept Age Age2 White Black Asian Hispanic Log household income Mother’s education Father’s education Stressful life events Perceived social support

Model 1

Model 2

Model 3

Model 4

Model 5

4.271 0.289 0.026

3.962 0.296 0.026 – 0.762 0.882 1.045

4.035 0.301 0.027 – 0.699 1.003 0.668 0.002 0.078 0.134

4.270 0.252 0.023 – 0.527 1.036 0.500 0.002 0.056 0.122 0.697

4.331 0.205 0.021 0.679 0.981 0.577 0.001 0.061 0.116 0.513 0.242

Random Effects Level 1 residual Level 2 intercept Level 2 age Corr (intercept, age)

2.625 3.391 0.304 0.697

2.633 3.325 0.299 0.695

2.642 3.240 0.294 0.691

2.643 3.021 0.280 0.680

2.647 2.609 0.272 0.648

Log likelihood

30,461.3

30,414.8

30,358.8

30,142.7

29,828.8

Note: Bold letters indicate a statistically significant (po.05) difference between coefficient for compared groups (either male q and ffiffiffiffiffiffifemale, or White and each minority group) according to a t-test (one-tailed), z ¼ bx  by = s2bx þ s2by where bx and by are the coefficients and s2bx and s2by are the squared standard error of the coefficients for group 1 and 2, respectively (Clogg et al., 1995). po.05. po.01.

Next, as shown in model 2 of Tables 3 and 4, we introduce a battery of dummy variables to assess racial differences. Results indicate that all minority groups have significantly higher levels of depressive symptoms than the White reference group for both males and females. For both genders, Asians and Hispanics show the highest levels of depressive symptoms, with Blacks falling between these groups and Whites. The inclusion of race/ethnicity resulted in a significant improvement in model fit for both genders as measured by LRTs. Gender Differences in the Effects of SES, SLEs, and Social Support Having identified a well-fitting model of depressive symptom trajectories, we then move to examine gender differences in the effects of SES, SLEs, and

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Table 4. Trajectories of CES-D for Females Predicted by SES, SLE, Perceived Social Support, and Family Structure (N ¼ 4,950).

Fixed Effects Intercept Age Age2 White Black Asian Hispanic Log household income Mother’s education Father’s education Stressful life events Perceived social support

Model 1

Model 2

Model 3

Model 4

Model 5

5.739 0.250 0.030

5.459 0.250 0.030 – 0.642 1.158 1.040

5.540 0.251 0.030 – 0.527 1.394 0.686 0.002 0.138 0.068

5.987 0.167 0.026 – 0.299 1.465 0.629 0.001 0.104 0.064 1.383

5.931 0.162 0.026 0.219 1.235 0.551 0.000 0.080 0.069 1.095 0.313

Random Effects Level 1 residual Level 2 intercept Level 2 age Corr (intercept, age)

3.086 4.316 0.369 0.755

3.088 4.280 0.368 0.756

3.096 4.210 0.364 0.755

3.109 3.774 0.338 0.752

3.111 3.160 0.330 0.718

Log likelihood

34,104.7

34,071.5

34,030.2

33,654.8

33,244.7

Note: Bold letters indicate a statistically significant (po.05) difference between coefficient for compared groups (either male q and ffiffiffiffiffiffi female, or White and each minority group) according to a t-test (one-tailed), z ¼ bx  by = s2bx þ s2by where bx and by are the coefficients and s2bx and s2by are the squared standard error of the coefficients for group 1 and 2, respectively (Clogg et al., 1995). po.05. po.01.

social support. As shown in model 3 of Tables 3 and 4, childhood SES is operationalized as three variables – household income, mother’s education, and father’s education. While the effects of household income show little or no effects, both mother’s and father’s education exert strong, significantly negative effects on depressive symptom levels. There are also noteworthy gendered patterns to these SES effects, with father’s education showing stronger effects among males. The effects of age and age2 remain significant and continued to indicate a curvilinear, inverted U-shaped trajectory. LRTs indicate that the inclusion of childhood SES significantly improves model fit for both genders. Model 4 investigates gender differentials in the effects of SLEs on depression trajectories. As shown in Tables 3 and 4, the effects of SLEs are

123

3

Depressive symptoms 4 5 6

7

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11

16

21

26

Age Males

Fig. 1.

Females

Unconditional Trajectories of Depressive Symptoms by Gender.

large, positive, and highly significant for both males and females. There is also considerable gender difference in the effect of SLEs, illustrated in Fig. 2, with the effect for females approximately twice as large as that of males (bSLEs ¼ 1.383 and 0.697, respectively). Thus, females show greater vulnerability to the occurrence of such life events. Further, SLEs are shown to mediate the effects of childhood SES, with the effect of mother’s education dropping approximately 25% for both genders with the inclusion of SLEs. For both genders, the inclusion of SLEs resulted in a large improvement in model fit. In the final model we introduce perceived social support as a predictor of early life depression trajectories. As shown in Tables 3 and 4, the effects of perceived social support on the trajectories are large, negative, and highly significant for both males and females. However, similar to SLEs, perceived social support is found to exert a greater impact on depressive symptoms among females compared to males (bsupport ¼ 0.313 and 0.242, respectively) (Fig. 3). Also notable are the mediation effects of perceived social support on childhood SES and SLEs. Thus, with the inclusion of perceived social support the effects of both SES and SLEs declined

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DANIEL E. ADKINS ET AL. Female

2

Depressive symptoms 4 6

8

Male

11

16

21

26 11

16

21

26

Age High SLEs

Low SLEs

Note: High and Low SLEs defined as +/-1 SD from overal mean SLEs.

Gender Difference in the Effects of SLEs on Depressive Symptom Trajectories.

Fig. 2.

Female

0

Depressive symptoms 2 4 6

8

Male

11

16

21

26 11

16

21

26

Age High Social Support

Low Social Support

Note: High and Low social support defined as +/-1 SD from overal mean social support.

Fig. 3.

Gender Difference in the Effects of Social Support on Depressive Symptom Trajectories.

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considerably for both genders, particularly the effects of SLEs. Comparing the random effect parameters from the baseline model and the final model shows that cumulatively, race/ethnicity, childhood SES, SLEs, and social support explain much of the variability in trajectory shapes. For males, 23% of the variance in trajectory intercepts (Dm0 ¼ 2.609/3.391) and 11% in trajectory slopes5 (Dmslope ¼ 0.272/0.304) is explained by these covariates. Among females, the full model explains 27% and 11% of trajectory intercepts and slopes, respectively. Finally, for both genders, LRTs indicate the inclusion of social support results in an improvement in model fit.

DISCUSSION Research conducted from the stress process perspective has long indicated substantial gender differences in the social etiology of depression. While this literature has produced many findings of substantial practical and theoretical utility, several fundamental questions regarding gendered variation in the influence of the stress process on depression remain unanswered. Using Add Health, the largest nationally representative longitudinal sample of U.S. adolescents and young adults, this study addresses limitations in current knowledge regarding the course of gender disparity in early life depressive symptoms and in the role of stress, social support, and SES in explaining these gender differences. Employing mixed model growth curves, we examine gender differences in the effects of childhood SES and SLEs on trajectories of depressive symptoms across adolescence and young adulthood. Several major findings emerge from this analysis. First, depressive symptom trajectories are curvilinear for both gender groups, with trajectories first rising across most of adolescence, then declining in young adulthood. Second, although the basic shape of the trajectories is similar across gender, there are substantial differences – most notably, females evidence higher levels across the entire period examined (ages 11–28). Also notable is the fact that females appear to be relatively precocious in their development, peaking and declining at earlier ages than their male peers. Third, there is evidence of racial/ethnic disparity for both genders, with all minority groups having higher levels of symptoms than Whites. Fourth, although the overall explanatory power of SES is comparable for males and females, the influence of the individual indicators varied, with father’s education more important among males. Fifth, gender differences in exposure are evident, with males experiencing more SLEs. Finally, gender

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differences in sensitivity to SLEs and social support were also found, with females showing greater sensitivity to both factors. Our analyses show that trajectories of depressive symptoms are curvilinear across adolescence and young adulthood and that major gender differences exist across this period. Supporting other literature indicating that depression levels peak in mid to late adolescence (Wight et al., 2004; Ge et al., 2006), we found trajectory apices to generally occur around ages 16–17. These findings highlight the difficulties associated with adolescence and the beneficial effects of many of the events associated with the transition to adulthood, such as achieving independence, establishing stable relationships, and an increasing sense of control (Mirowsky & Ross, 1992; Schieman, Van Gundy, & Taylor, 2001). As expected, females were shown to have persistently higher levels of depressive symptoms than males. However, the suggestion of convergence observed between male and female depressive symptom trajectories in young adulthood was less expected. While some other studies have indicated slight narrowing of the gender gap in the transition to adulthood (Hankin et al., 1998; Ge et al., 2006), this is the first study to suggest such dramatic convergence. Although these findings should only be considered suggestive given the lack of formal statistical gender differences in the age slopes, given the relative superiority of the data employed here over that used in former examinations, we believe this anomalous finding deserves further investigation. That said, it is important to note that the trajectories presented here should not be extrapolated beyond the ages included in the sample. Given the overwhelming evidence of gender depression differentials across adulthood (e.g., Mirowsky, 1996), if any gender gap narrowing does occur, it must either stabilize or reverse at some point in early adulthood. Considering that female depression levels peak in mid adolescence, while males peak afterward in late adolescence, we suggest that a likely explanation for the pattern observed here is that females experience comparatively precocious social psychological development. Thus, we expect that as the female decline in depression levels peters out in adulthood, the male decline is apt to continue a bit longer, resulting in reinstatement of the gender gap at older ages. In sum, our findings regarding the gender gap support the longstanding finding of female disadvantage in depression (NolenHoeksema, 1990), but a more nuanced picture of fluctuation in the gender gap in depression across early life is revealed. While race/ethnicity was treated as a control in these analyses, the results indicating a general advantage for Whites of both genders are noteworthy given the lack of consensus on this issue in the literature. Former studies have

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alternately shown adolescent Blacks (Garrison, Jackson, Marsteller, McKeown, & Addy, 1990; Gore & Aseltine, 2003), Hispanics (Twenge & Nolen-Hoeksema, 2002), Asians (Greenberger & Chen, 1996), and Whites (Dornbusch, Mount-Reynand, Ritter, Chen, & Steinberg, 1991) each to exhibit higher rates of depression compared to other racial/ethnic groups. Given the exceptional size and quality of the Add Health data analyzed here, our finding of higher levels of depressive symptoms among minorities compared to Whites represents significant progress in the debate on this issue. Although empirical findings to date have been mixed, the minority disadvantage in early life depression observed here is generally consonant with leading theories of racial health disparities. For instance, the structural disadvantage perspective on racial health disparities suggests that minorities face pervasive, interlocking adversity from factors including low SES, discrimination, and neighborhood disadvantage (Vega & Rumbaut, 1991; Ross, 2000; Williams & Collins, 1995). Cumulatively, this pervasive structural disadvantage is theorized to exert a strong negative effect on psychological well-being (Vega & Rumbaut, 1991; Williams & Collins, 1995). Thus, our finding that racial/ethnic minority status was consistently associated with reduced psychological well-being, even after adjusting for childhood SES, is consistent with leading theoretical formulations (Williams, Neighbors, & Jackson, 2003). Future research should elaborate these findings, taking advantage of the rich behavioral and psychological data present in Add Health to empirically model the causes of these racial disparities in early life depression. Childhood SES was shown to be highly influential on depression trajectories for both genders. Given our exogenous conceptualization of socioeconomic environment as childhood SES, these findings provided strong evidence that the direction of the effect here is SES-depression, lending support to social causation theories of depression (e.g., Link & Phelan, 1995; Mirowsky & Ross, 2003). Furthermore, there were also substantial differences in the effect size and significance for the three indicators of SES. Contrary to former research indicating income as a stronger predictor of early life depression than parental education (Gore et al., 1992), we found the opposite, with household income showing nonsignificant effects, while both father’s and mother’s education were highly significant in all models. These findings debunk the common assumption in health research that education and income are interchangeable indicators of SES (see Braveman et al., 2005). In contrast, our results demonstrate that, though related, education and income represent different underlying concepts – schooling

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means something beyond simply economic status. As Mirowsky and Ross (1998) show, in addition to its income enhancing function, education promotes health by learned effectiveness – increasing exposure to healthy behaviors and lifestyles, and promoting the sense of control necessary to adopt and maintain these behaviors. Further, they demonstrate that highly educated parents not only reap the health benefits of increased knowledge and mastery, but they pass these benefits on to their children (Mirowsky & Ross, 1998). Thus, though lacking the data for an empirical test, we suspect that the relative importance of parental education over income points to the fact that, in an affluent society like contemporary America, the health behaviors, parenting knowledge, and sense of mastery engendered by education are of greater importance than material resources in promoting mental health in one’s children. Additionally, there were gender differences in the effects of the parental education, with males showing greater sensitivity to father’s education. These results contradict earlier findings that parental education is generally more influential on females (Gore et al., 1992). While no definitive interpretation is suggested by either our empirical results or extant theory, we suspect that this result may be explained by same-sex role modeling tendencies. Our interpretation here hinges on two facts: (1) children show a greater tendency to model the attitudes and behaviors of their same-sex parents (Bandura, 1977; Kohlberg, 1966); and (2) education may serve as a proxy for a variety of mechanisms promoting depression in adults (Mirowsky & Ross, 1998). Thus, we suggest that by modeling the depression-related attitudinal and behavior tendencies of their fathers, boys tend to reflect their father’s mental health profiles more so than their mothers. While obviously a tentative interpretation, given the fact that a similar, but not statistically significant, relationship was observed for mother–daughter dyads, we see this as a plausible explanation deserving future study. In line with stress process theory, the influence of childhood SES on trajectories of depressive symptoms were shown to be partially mediated by SLEs across the ages examined (Pearlin, 1989). We find that childhood SES has a large, protective, direct impact on depression trajectories and also a substantial indirect effect through reducing the likelihood of SLE occurrence. This corroborates research positing the occurrence of stressful events as primary paths through which poverty reduces psychological well-being (Turner & Lloyd, 1999; Turner & Butler, 2003). As alluded to above, for both gender groups SLEs were found to promote depressive symptoms. These effects were found to be consistent across age

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and to show strong evidence of gender disparity. Regarding differential exposure, SLEs occur less frequently among females. However, this is likely the result of limited coverage of the SLE measurement. Due to the limitation of the available information in Add Health data, our SLE measurement may not tap the full relevant range of events often experienced by the respondents, especially during youth adult ages. It includes a substantial portion of the events reflecting participation in delinquent or problem behaviors and/or associations with rule breaking network members. These events are potential ‘‘self-generated’’ stressors rather than ‘‘fateful’’ stressors (see Turner & Wheaton, 1995). This could potentially favor the types of stressors experienced by males as shown from their increasing representation among males at older ages. Regarding gender differences in sensitivity to stress, though occurring less frequently among females, the SLEs measured here were shown to have twice the impact on females compared to males. Thus, females were shown to exhibit substantially greater sensitivity to these SLEs than do males. This finding is consistent with former research showing increased sensitivity to stress among females (e.g., Ge et al., 1994; Kessler, 1979). However, in line with research showing gender differences in stress sensitivity to be disorder specific (Aneshensel et al., 1991; Hagan & Foster, 2003), we do not see this as evidence that females are generally more sensitive to the detrimental effects of stress. Instead, we interpret this finding as evidence of a gendered response to the stress process such that females are more likely to internalize stress and males are more likely to externalize it (see Meadows, 2007). Given that an explicit test of this hypothesis was beyond the scope of this article, we leave more detailed empirical investigations to future research. Similar to the results for SLEs, social support was found to have large, significant effects for both genders, but the effect size was approximately 30% larger for females. This finding fills an important gap in the literature, as no studies have yet explicitly examined gender differences in the effects of social support. But while no empirical work has been done explicitly on this topic, the results are consistent with theory. Specifically, given the common emphasis on relationships among women (Turner & Marino, 1994), it is not surprising that social support is more influential on depressive symptoms in this group. Although these analyses offer some of the first comprehensive trajectory models of depressive symptoms in early life for both genders, the study is nevertheless limited in several respects. First, additional waves of data would allow further refinement and extension of these findings. The present investigation was limited to the three waves of data currently available from

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the Add Health study. Further understanding of the process of depression would be facilitated through additional waves of data extending the age interval further into adulthood. Fortunately, such analyses will soon be possible using this dataset, as the fourth wave of data collection is now underway for Add Health (http://www.cpc.unc.edu/projects/addhealth/ design_focus/wave4). When released these data will allow the extension of the models presented here into the participants’ late 20’s and early 30’s. Another shortcoming of the current study was our partial conceptualization of the stress process. Here, we limited our modeling of the stress process to only childhood SES, SLEs, and social support. However, it has been demonstrated that other aspects of the stress process, including chronic stressors and self esteem, are also important components of the stress– depression relationship (Pearlin, 1989). Future research could improve the analysis presented here through a more exhaustive modeling of the stress process, including chronic stressors and other buffering psychological resources. Another potential improvement in the measurement of stress could be achieved through disaggregating the SLEs index into various domains (e.g., Ge et al., 2006). Despite these limitations, the present study improves our understanding of gender differences in early life trajectories of depressive symptoms and in the effects of childhood SES, SLEs, and social support. The results indicate that trajectories of depressive symptoms during adolescence and young adulthood follow a normative curvilinear pattern with rising levels across much of adolescence and declining levels in young adulthood. However, gender differences in trajectories were evident – females had persistently higher levels, but the gap showed some evidence of diminishing across young adulthood. Gendered variation in the function of the stress process was also shown with females responding far more depressively to SLEs and deficits in social support than their male counterparts, suggesting that the female disadvantage in early life depression may be a result of gender differences in response to adversity.

NOTES 1. Significant social selection effects have been shown in some mental health research (e.g., Costello, Keeler, & Angold, 2003; Miech et al., 1999; Dohrenwend et al., 1992) and unfortunately, the effects of selection and causation are notoriously hard to separate in non-experimental, survey research. However, in the current study we have largely avoided the risk of confounding social selection effects through focusing on parental SES during the subjects’ youth. Thus, social selection effects are

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likely to be minimized as the children’s mental health is generally unlikely to have a dramatic influence on their parents’ SES, particularly given that a major component of SES – parental education, was generally determined prior to the subjects’ births. 2. In mixed model growth curves, if all covariates are mean-centered the fixed effects of age describe the overall mean trajectory shape; if covariates are not meancentered the growth factor means represent the trajectory shape for cases with values of zero on all covariates – as this information is generally less substantively important, it is common practice to mean-center continuous covariates (Bollen & Curran, 2006). 3. Social support was only measured at waves 1 and 2. The measure used here is an average of the two. 4. Likelihood ratio tests were used to determine the significance of the fixed and random effects that were added to the model in each of the analysis steps. This test yields the deviance of the model which is defined as -2xloglikelihood. The deviance difference (between 2 models) is asymptotically w2 distributed, with the number of degrees of freedom equal to the difference in number of estimated parameters between the two models. To judge the significance of parameters in the full model, each parameter was removed from the model, and a likelihood ratio test with one degree of freedom was used to examine whether its effect was significant in this full model. 5. The random effect here refers only to the linear component of the age-based slope, as the quadratic component is modeled as fixed.

REFERENCES Allgood-Merten, B., Lewinsohn, P. M., & Hops, H. (1990). Sex-differences and adolescent depression. Journal of Abnormal Psychology, 99(1), 55–63. Aneshensel, C., Rutter, C., & Lachenbruch, P. (1991). Social-structure, stress, and mentalhealth – competing conceptual and analytic models. American Sociological Review, 56(2), 166–178. Angold, A., Costello, E. J., & Worthman, C. W. (1998). Puberty and depression: The roles of age, pubertal status, and pubertal timing. Psychological Medicine, 28, 51–61. Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall. Bell, R. R. (1981). Worlds of friendship. Beverly Hills, CA: Sage Publications. Bryk, A. S., & Raudenbusch, S. W. (1992). Hierarchical linear models: applications and data analysis methods. Newbury Park: Sage Publications. Costello, E. J., Compton, S. N., Keeler, G., & Angold, A. (2003). Relationships between poverty and psychopathology: A natural experiment. Journal of the American Medical Association, 290, 2023–2029. Crick, N. R., & Zahn-Waxler, C. (2003). The development of psychopathology in females and males: Current progress and future challenges. Development and Psychopathology, 15(3), 719–742. Culbertson, F. M. (1997). Depression and gender – an international review. American Psychologist, 52(1), 25–31. Diggle, P., & Kenward, M. (1994). Informative drop-out in longitudinal data analysis. Journal of the Royal Statistical Society, Series C, 43, 49–93.

132

DANIEL E. ADKINS ET AL.

Dohrenwend, B. P., Levav, I., Shrout, P. E., Schwartz, S., Navch, G., Link, B. G., Skodol, A. E., & Stueve, A. (1992). Socioeconomic status and psychiatric disorders: The causationselection issue. Science, 255, 946–952. Dornbusch, S. M., Mont-Reynand, R., Ritter, P. L., Chen, Z., & Steinberg, L. (1991). Stressful events and their correlates among adolescents of diverse backgrounds. In: M. E. Colten & S. Gore (Eds), Adolescent Stress: Causes and Consequences (pp. 111–130). New York: Aldine de Gruyter. Garrison, C. Z., Jackson, K. L., Marsteller, F., McKeown, R., & Addy, C. (1990). A longitudinal study of depressive symptomatology in young adolescence. Journal of the American Academy of Child and Adolescent Psychiatry, 29(4), 581–585. Ge, X., Conger, R. D., & Elder, G. H., Jr. (2001). Pubertal transition, stressful life events, and the emergence of gender differences in adolescent depression. Developmental Psychology, 37, 404–417. Ge, X. J., Lorenz, F. O., Conger, R. D., Elder, G. H., & Simons, R. L. (1994). Trajectories of stressful life events and depression during adolescence. Developmental Psychology, 30(4), 467–483. Ge, X. J., Natsuaki, M. N., & Conger, R. D. (2006). Trajectories of depressive symptoms and stressful life events among male and female adolescents in divorced and nondivorced families. Development and Psychopathology, 18(1), 253–273. Gilligan, C. (1982). In a different voice: Psychological theory and women’s development. Cambridge, MA: Harvard University Press. Gilman, S. E., Kawachi, I., Fitzmaurice, G. M., & Buka, S. L. (2002). Socioeconomic status in childhood and the lifetime risk of major depression. International Journal of Epidemiology, 31(2), 359–367. Goldstein, H. (1995). Multilevel statistical models. Wiley: New York. Goodyer, I., Kolvin, I., & Gatzanis, S. (1985). Recent undesirable life events and psychiatric disorder in childhood and adolescence. British Journal of Psychiatry, 147, 517–523. Gore, S., & Aseltine, R. H. (2003). Race and ethnic differences in depressed mood following the transition from high school. Journal of Health and Social Behavior, 44(3), 370–389. Gore, S., Aseltine, R. H., Jr., & Colton, M. E. (1992). Social structure, life stress and depressive symptoms in a high school-aged population. Journal of Health and Social Behavior, 33(33), 97–113. Gore, S., & Colten, M. E. (1991). Gender, stress, and distress: Social-relational influences. In: J. Eckenrode (Ed.), The social context of coping (pp. 139–163). New York: Plenum Press. Gove, W., & Geerken, M. R. (1977). The effects of children and employment on the mental health of married men and women. Social Forces, 56, 66–76. Gove, W., & Tudor, J. (1973). Adult sex roles and mental illness. American Journal of Sociology, 77(8), 12–35. Greenberger, E., & Chen, C. (1996). Perceived family relationships and depressed mood in early and late adolescence: A comparison of European and Asian Americans. Developmental Psychology, 32(4), 707–716. Haas, S. A. (2006). Health selection and the process of social stratification: The effect of childhood health on socioeconomic attainment. Journal of Health and Social Behavior, 47(4), 339–354. Hagan, J., & Foster, H. (2003). S/he’s a rebel: Toward a sequential stress theory of delinquency and gendered pathways to disadvantage in emerging adulthood. Social Forces, 82(1), 53–86.

Stress Processes and Trajectories of Depressive Symptoms in Early Life

133

Hankin, B., & Abramson, L. (2001). Development of gender differences in depression: An elaborated cognitive vulnerability-transactional stress theory. Psychological Bulletin, 127, 773–796. Hankin, B. L., Abramson, L. Y., Silva, P., McGee, R., Moffitt, T., & Angell, K. (1998). Development of depression from preadolescence to young adulthood: Emerging gender differences in a 10-year longitudinal study. Journal of Abnormal Psychology, 107(1), 128–140. Kendler, K., Karkowski, L. M., & Prescott, C. A. (1999). Causal relationship between stressful life events and the onset of major depression. American Journal of Psychiatry, 156, 837–841. Kessler, R. C. (1979). A strategy for studying differential vulnerability to the psychological consequences of stress. Journal of Health and Social Behavior, 20, 100–108. Kohlberg, L. (1966). A cognitive-development analysis of children’s sex-role concepts and attitudes. In: E. E. Maccoby (Ed.), The Development of Sex Differences. Stanford: Stanford University Press. Link, B., & Phelan, J. (1995). Social conditions as fundamental causes of disease. Journal of Health and Social Behavior, SI, 80–94. Lowenthal, M. F., & Haven, C. (1968). Interaction and adaptation: Intimacy as a critical variable. American Sociological Review, 33, 20–30. McLeod, J., & Shanahan, M. (1993). Poverty, parenting and children’s mental health. American Sociological Review, 58(3), 351–366. Meadows, S. O. (2007). Evidence of parallel pathways: Gender similarity in the impact of social support on adolescent depression and delinquency. Social Forces, 85(3), 1143–1167. Meadows, S. O., Brown, J., & Elder, G. (2006). Depressive symptoms, stress, and support: Gendered trajectories from adolescence to young adulthood. Journal of Youth and Adolescence, 35(1), 93–103. Mirowsky, J. (1996). Age and the gender gap in depression. Journal of Health and Social Behavior, 37(4), 362–380. Mirowsky, J., & Ross, C. (1989). Social causes of psychological distress. New York: Aldine de Gruyter. Mirowsky, J., & Ross, C. E. (1992). Age and depression. Journal of Health and Social Behavior, 33, 187–205. Mirowsky, J., & Ross, C. E. (1998). Education, personal control, lifestyle and health: A human capital hypothesis. Research on Aging, 20(4), 415–435. Mirowsky, J., & Ross, C. E. (2003). Social causes of psychological distress (2nd ed.). New York: Aldine de Gruyter. Nolen-Hoeksema, S. (1990). Sex differences in depression. Stanford, CA: Stanford University Press. Paykel, E. (1978). Contribution of life events to causation of psychiatric illness. Psychological Medicine, 8, 245–253. Pearlin, L. (1989). The sociological study of stress. Journal of Health and Social Behavior, 30(3), 241–256. Pearlin, L. E., Menaghan, M., Lieberman, M., & Mullan, J. (1981). The stress process. Journal of Health and Social Behavior, 22(4), 337–356. Radloff, L. (1991). The use of the center for epidemiologic studies depression scale in adolescents and young-adults. Journal of Youth and Adolescence, 20(2), 149–166.

134

DANIEL E. ADKINS ET AL.

Radloff, L. (1997). The CES-D scale for research in the general population. Applied Psychological Measurement, 1, 385–401. Scheidt, P., Overpeck, M., Wyatt, W., & Aszmann, A. (2000). Adolescents’ general health and wellbeing. In: W. Setterbulte (Ed.). Health and health behaviour among young people. WHO Policy Series: Health Policy For Children And Adolescents (Issue 1, pp. 24–38). Copenhagen. Schieman, S., Van Gundy, K., & Taylor, J. (2001). Status, role, and resource explanations for age patterns in psychological distress. Journal of Health and Social Behavior, 42, 80–96. Thoits, P. (1991). On merging identity theory and stress research. Social Psychology Quarterly, 54(2), 101–112. Turner, H., & Butler, M. (2003). Direct and indirect effects of childhood adversity on depression in young adults. Journal of Youth and Adolescence, 32, 89–103. Turner, H., & Turner, R. (1999). Gender, social status, and emotional reliance. Journal of Health and Social Behavior, 40, 360–373. Turner, H. A. (1994). Gender and social support: Taking the bad with the good? Sex Roles, 30, 521–541. Turner, R., & Wheaton, B. (1995). Checklist measures of stressful life events. In: S. Cohen, R. Kessler & L. Gordon (Eds), Measuring stress: A guide for health and social scientists (pp. 29–58). Oxford University Press. Turner, R., Wheaton, B., & Lloyd, D. (1995). The epidemiology of social stress. American Sociological Review, 60(1), 104–125. Turner, R. J., & Avison, W. R. (1989). Gender and depression: Assessing exposure and vulnerability to life events in a chronically strained population. The Journal of Nervous and Mental Disease, 177, 443–455. Turner, R. J., & Lloyd, D. A. (1999). The stress process and the social distribution of depression. Journal of Health and Social Behavior, 40, 374–404. Turner, R. J., & Marino, F. (1994). Social support and social structure: A descriptive epidemiology. Journal of Health and Social Behavior, 35, 193–212. Twenge, J., & Nolen-Hoeksema, S. (2002). Age, gender, race, socioeconomic status, and birth cohort differences on the children’s depression inventory: A meta-analysis. Journal of Abnormal Psychology, 111, 578–588. Vega, W., & Rumbaut, R. (1991). Ethnic minorities and mental health. Annual Review of Sociology, 17, 351–383. Wade, T., Cairney, J., & Pevalin, D. (2002). Emergence of gender differences in depression during adolescence: National panel results from three countries. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 190–198. Wight, R., Sepu´lveda, J., & Aneshensel, C. (2004). Depressive symptoms: How do adolescents compare to adults. Journal of Adolescent Health, 34, 314–323. Willet, J. B., Singer, J. D., & Martin, N. C. (1998). The design and analysis of longitudinal studies of development and psychopathology in context: Statistical models and methodological recommendations. Development and Psychopathology, 10, 395–426. Williams, D., & Collins, C. (1995). US socioeconomic and racial differences in health – patterns and explanations. Annual Review of Sociology, 21, 349–386. Williams, D., Neighbors, H., & Jackson, J. (2003). Racial/ethnic discrimination and health: Findings from community studies. American Journal of Public Health, 93(2), 200–208.

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APPENDICES Appendix A.

List of Stressful Life Event Items in Each Wave.

Items Available in All Three Waves Parent death Self attempted suicide resulting in injury Friend attempted suicide (unsuccessful) Friend attempted suicide (with success) Relative attempted suicide (unsuccessful) Relative attempted suicide (with success) Involving in fighting or violence Unwanted pregnancy (self or partner) Abortion, still birth, or miscarriage (self or partner) Having a child adopted Death of a child Romantic relationship ended Giving sex in exchange for drugs or money Contracted an STD Skipped needed medical care due to financial constraints Juvenile conviction Adult conviction Imprisoned Wave I and II Having a serious injury Expelled from school Ran away from home Parents received welfare Nonromantic sexual relationship ended Abuse in romantic or nonromantic sexual relationship Wave III Received welfare Baby having major health problems at birth Marriage dissolution Cohabitation dissolution Death of a romantic partner Eviction, cutoff service Entering full time active military duty Discharged from the armed forces Note: All items are coded as 1 year before the interview.

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Appendix B. 1. 2. 3. 4. 5. 6. 7. 8.

Social Support Scale.

How much do you feel that adults care about you? How much do you feel that your teachers care about you? How much do you feel that your parents care about you? How much do you feel that your friends care about you? How much do you feel that people in your family understand you? How much do you feel that you want to leave home? (Reverse coded for comparability) How much do you feel that you and your family have fun together? How much do you feel that your family pays attention to you?