Journal of Adolescence 37 (2014) 883e892

Contents lists available at ScienceDirect

Journal of Adolescence journal homepage: www.elsevier.com/locate/jado

Social consequences of early socioeconomic adversity and youth BMI trajectories: Gender and race/ethnicity differences Dayoung Bae*, K.A.S. Wickrama, Catherine Walker O'Neal Department of Human Development and Family Science, The University of Georgia, 107 Dawson Hall, 305 Sanford Drive, Athens, GA 30602, USA

a b s t r a c t Keywords: Early socioeconomic adversity BMI trajectories Young adult socioeconomic attainment

The present study investigated the mediating effects of adolescent BMI trajectories on socioeconomic continuity over the early life course using a nationally representative sample of 11,075 respondents. This study considered both the initial severity as well as change over time in BMI as psycho-physiological mediators. Consistent with the life course pathway model and the cumulative advantage and disadvantage principle, the results suggested that early socioeconomic adversity is associated with youth BMI trajectories over time, which in turn, impair young adult socioeconomic attainment. The results also revealed important gender and racial/ethnic differences in the hypothesized associations. These findings elucidate how early adversity exerts an enduring long-term influence on social attainment in young adulthood. Further, the findings suggest that effective obesity intervention and prevention programs should focus not only on the severity of obesity but also on growth in BMI over the early years. © 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

In the United States, national prevalence data show that approximately 34% of adolescents are overweight or obese (Ogden, Carroll, Kit, & Flegal, 2012). The high body mass of children and adolescents has grown into a global major public health concern because being overweight in one's early years is a robust predictor of obesity and negative social and health outcomes later in life (Ferraro & Kelley-Moore, 2003). Mountingevidence indicates that youth obesity or being overweight is, inpart, socially structured (Young & Nestle, 2002). Previous research has documented youth obesity or being overweight (as measured using body mass index, hereafter, BMI) is affected by community and family socioeconomic adversities and produces numerous health and socioeconomic problems in young adulthood (Burdette & Needham, 2012; Crosnoe, 2007; Merten, Wickrama, & Williams, 2008; Wickrama, O'Neal, & Lee, 2013). Other research has investigated the socioeconomic consequences of adolescent obesity. Obesity, especially when experienced early in the life course, is strongly linked to negative consequences in adulthood (Ferraro & Kelley-Moore, 2003). Particularly, adolescent obesity has a negative effect on young adult socioeconomic status as measured by income and occupation (Ball, Crawford, & Kenardy, 2004; Conley & Glauber, 2006). Thus, adolescent weight status appears to mediate the effect of childhood/adolescent socioeconomic experiences on socioeconomic attainment in young adulthood.

Abbreviations: BMI, body mass index; CAD, cumulative advantage and disadvantage principle; LGC, latent growth curve; SEM, structural equation modeling; FIML, full information maximum likelihood. * Corresponding author. Tel.: þ1 706 410 5036. E-mail addresses: [email protected] (D. Bae), [email protected] (K.A.S. Wickrama), [email protected] (C.W. O'Neal). http://dx.doi.org/10.1016/j.adolescence.2014.06.002 0140-1971/© 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

884

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892

However, previous research in this area is fragmented, focusing on either antecedents or consequences of youth obesity but not both. Moreover, most previous studies have used traditional mean comparisons with cross-sectional data or regression models, focusing on discrete obesity status at single points in time. Research that has examined multiple points in time has been limited to relatively short time spans. Therefore, less is known about the continuous life-course processes involving youth BMI trajectories stemming from early family socioeconomic circumstances and contributing to young adults' social and health outcomes. Drawing from the life course developmental perspective (Elder & Giele, 2009) and the existing research, we contend that socioeconomic adversity in early years initiates a chain of developmental successes or failures leading to impaired socioeconomic attainment in young adulthood (O'Rand & Hamil-Luker, 2005). In the present study, we will analyze a life-course pathway model to examine potential meditational process (Willson, Shuey, & Elder, 2007); more specifically, we examine if early socioeconomic adversity contributes to adverse BMI trajectories of youth and, in turn, if these trajectories impair young adult socioeconomic attainment. In addition, consistent with the life course cumulative advantage and disadvantage (CAD) principle (Dannefer, 2003), we expect that the influence of early socioeconomic adversity on youth BMI strengthens with age. In order to elucidate this cumulative disadvantage processes over the life course, we investigate how early adversity shapes youth BMI trajectories over time (Wickrama et al., 2013). With regard to these hypothesized associations, the present study also investigates potential gender and race/ethnicity differences. Existing research suggests that such differences are likely (S anchez-Vaznaugh, Kawachi, Subramanian, S anchez, & Acevedo-Garcia, 2009), but studies focusing on gender and race/ ethnicity differences in these associations are rare. Early family/community socioeconomic adversity and youth obesity Numerous studies have found that family economic hardship, low parental education, parents' marital history, and adverse community characteristics may impact youths' BMI (Burdette & Needham, 2012; Shin & Miller, 2012; Wickrama, Wickrama, & Bryant, 2006). Adolescents from poor families and single-parent families lack health resources, such as proper food, access to recreation facilities, proper housing, and health services (Fitzgibbon et al., 1998). Furthermore, less educated parents may lack the health knowledge and information necessary for healthy child rearing. Socioeconomically disadvantaged parents are also more likely than others to transmit their unhealthy behaviors and risky lifestyles (e.g., unhealthy eating behaviors, lack of exercise) to their offspring (Wickrama, Conger, Wallace, & Elder, 1999). In addition to family socioeconomic characteristics, there appears to be several adverse community processes that contribute to obesity. First, poor communities are unable to meet their residents' dietary and health-related needs (Story, Kaphingst, Robinson-O'Brien, & Glanz, 2008). For example, unaffordable prices can limit access to proper food in poor communities, and poor communities have a greater number of unhealthy fast food restaurants than do higher income communities (Kipke et al., 2007). In addition, lack of recreational activities, lack of community safety for physical activities, and lack of availability and accessibility of health care services in disadvantaged communities may also contribute to the higher prevalence of adolescent obesity in these communities (Wen & Maloney, 2011). In the present study, we will investigate individual trajectories of BMI to explain how socioeconomic adversity in early years influences not only early BMI levels (severity) but also the subsequent individual changes (deteriorations or improvements) in BMI over an extended period of time. Importantly, the influence of early adversity on health outcomes may gain momentum over time (cumulative effect of early adversity) with increasing age, which may be reflected in changes in BMI over time. Consistent with the life course cumulative advantage and disadvantage principle (Dannefer, 2003), this cumulative effect contributes to enlarging socioeconomic BMI inequality over time. Thus, as shown in Fig. 1, we expect early socioeconomic adversity, as captured by a composite index of family and community adverse characteristics in early years, to initiate and shape youth BMI trajectories from adolescence to young adulthood. Youth obesity and socioeconomic attainment in young adulthood Several studies have documented that young adults' labor market outcomes are influenced by high BMI or obesity. Obese employees experienced lower wages for the same job, fewer hiring opportunities in high-level positions, and lower

Fig. 1. Conceptual model.

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892

885

promotion prospects than their non-obese counterparts (Puhl & Brownell, 2001). Although both obese men and women face wage-related obstacles, obesity has a particularly negative effect on the hourly wages for women (Cawley, 2004; Han, Norton, & Powell, 2011). Work-related stereotypes, such as beliefs that overweight employees lack self-discipline, are lazy, less competent and emotionally unstable, could affect wages and promotion opportunities (Roehling, 1999). In addition to potential discrimination based on body size, one of the possible explanations for the association between obesity and negative labor market outcomes is that obesity adversely affects health, thereby reducing work productivity (Wada & Tekin, 2010). In addition, obese individuals may invest less in accumulating human capital because they may have lower expectations about their future (Baum & Ford, 2004) and lower self-esteem (Cawley, 2004; Wada & Tekin, 2010). Educational attainment in adulthood may also be associated with high BMI or obesity. Earlier studies have shown that obese students often reported lower academic performance and are less likely than their non-obese peers to graduate from high school (Classen, 2009), enter college after high school (Crosnoe, 2007), and attain a college degree (Fowler-Brown, Ngo, Phillips, & Wee, 2009). Several linking mechanisms explain the association between BMI and educational attainment. Obese students may experience negative social feedback and discrimination from their classmates and teachers, and internalize these negative social judgments into their self-concept. The social stigma attached to obesity may limit students' ability to reach their full educational potential (Crosnoe & Muller, 2004). It is also possible that parents may, purposely or subconsciously, invest less in post-secondary education for their obese children because of the expected lower return from future wages or a shorter lifespan to compensate the investment that they put into their children's education (Classen, 2009). In unraveling the hypothesized associations, a trajectory analysis allows for the examination of more dynamic life course processes; the influence of both the level and change in BMI on subsequent socioeconomic outcomes in young adulthood (Wickrama, Beiser, & Kaspar, 2002). A recent study has shown that BMI trajectories are better predictors of mortality than BMI at one point in time (Zheng, Tumin, & Qian, 2013). Therefore, in the present study, we expect both the initial level and rate of change of BMI trajectories to be associated with young adults' socioeconomic attainment, as measured by education, income, and financial problems. By investigating BMI trajectories, we will examine how rapid gains in BMI during this period may contribute to the adverse socioeconomic consequences of young adults.

Gender moderation Previous studies suggest that there are gender differences in the influence of early socioeconomic conditions on youth BMI. For example, Chang and Lauderdale (2005), using data over a span of more than 30 years, found that low-income White and Black women consistently experienced higher BMI than White and Black men. In regard to the socioeconomic consequences of obesity, McLaren (2007) found that the majority of studies examining women in industrialized countries concluded that, compared to smaller women, women with larger body size had lower socioeconomic status, but this association was less consistent for men. Several other developmental studies have also shown that obese female youth have lower status attainment as young adults than male youth and non-obese females (Merten et al., 2008; S anchez-Vaznaugh et al., 2009). To our knowledge, previous studies have primarily examined the association between the level of BMI and the socioeconomic characteristics of individuals, but these studies have failed to examine gender differences in the association between changes in BMI over time and the socioeconomic attainment of young adults, independent of the level of BMI. We expect gender differences in the associations between the antecedents and consequences of initial level and changes in BMI.

Race/ethnicity moderation In regard to the socioeconomic consequences of obesity, studies have documented that the consequences are greater for nchezWhites than members of minority racial/ethnic groups. For example, using a sample of 37,000 respondents, Sa Vaznaugh et al. (2009) found that the shape and the strength of the relationship between BMI and SES differed markedly by race/ethnicity with larger effects for Whites than Blacks and Hispanics. According to national data, obesity rates tended to increase with decreased income among women, but this trend was only significant for White women (Freedman, 2011; Ogden, Lamb, Carroll, & Flegal, 2010). In contrast to women and White men, Black and Mexican-American men with higher incomes had higher BMI levels than those with low income levels. Most of these findings were drawn from mean comparisons of BMI, which may not reveal the associations between the antecedents and consequences of individual BMI change, independent of the level of BMI. Thus, further investigation into racial/ethnic and gender differences in early socioeconomic adversity-BMI and BMI-socioeconomic attainment linkages is warranted, particularly with a focus on intra-individual changes in BMI. In the present study, we examine racial/ethnic differences in the influence of early socioeconomic conditions on both the level and rate of change in youth BMI and in the influence of both the level and rate of change in youth BMI on socioeconomic attainment in young adulthood.

886

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892

Methods Sample Data for this study came from a nationally representative sample of adolescents participating in the National Longitudinal Study of Adolescent Health (Add Health). In 1995, baseline (Wave 1) data were derived from a complex stratified clustersampling of middle and high school students, yielding 20,745 respondents (mean age ¼ 15.5 years; range ¼ 12e19 years at baseline) from 134 middle and high schools. To ensure diversity, the sample was stratified by region, urbanicity, school type (public vs. private), racial composition, and size. The second, third, and fourth waves of data were collected in 1996, 2001, and 2008 (N2 ¼ 14,738; N3 ¼ 15,100; N4 ¼ 15,701). We used inehome interview data from parents who responded to the parents' questionnaire in Wave 1 and adolescents who participated in all four waves. Thus, the study sample included 11,075 respondents. The final sample consisted of approximately 54% women, and 38% of respondents reported a minority racial/ ethnic status with the largest percentages reported for Black (16%) and Hispanic (13%). Attrition and missing data analysis showed that adolescents who participated in all four waves were slightly younger but otherwise confirmed that there was little difference between adolescents with missing data in our study sample and those with complete data. Measures Early socioeconomic adversity We constructed a composite index for cumulative early socioeconomic adversity by summing dichotomous indicators capturing different dimensions of adversity. These indicators included low parental education, high family economic hardship, low parental marital stability, and high community adversity. Except for marital stability (already a dichotomous measure), dichotomous indicators were created by mean splitting the following measures. Parental education. The responding parent reported both parents' highest level of education obtained at Wave 1 (1995). Responses ranged from: 1 ¼ never went to school to 10 ¼ professional training beyond four-year college or university degree. Mothers' and fathers' educational levels were summed to create an index of parental education. For single-headed families (n ¼ 79) with no available data from fathers, maternal education served as the indicator of parental education. Economic hardship. Five dichotomous items (0 ¼ no, 1 ¼ yes) assessed whether any member of the household received the following social service benefits: social security, supplemental security income, aid to families with dependent children, food stamps, or housing subsidies at Wave 1 (1995). Responses to these five items were summed to create an index of economic hardship with a range of 0e5. Parents' marital stability. A binary variable was used to differentiate parents who had been consistently married to their spouse (or in a marriage-like relationship) for at least 15 years (1) from other parents (0). Community adversity. The community adversity measure was generated by summing four indicators corresponding to census tract information from the 1990 U.S. Census. Those indicators included (a) the proportion of families living in poverty, (b) the proportion of single-parent families, (c) the proportion of adults employed in service occupations, and (d) the proportion of unemployed men (adapted from Wickrama & Bryant, 2003). This index had an internal consistency of .78. Body mass index/obesity Respondents' BMI, the ratio of weight to height squared ([lbs*703]/inches2), was used to assess their degree of being overweight. At Wave 2 (1995), BMI was calculated using respondents' self-reports of their height and weight. BMI values for Wave 3 and Wave 4 (2001 and 2008) were calculated from weight and height measurements obtained by trained interviewers. Young adult socioeconomic attainment Young adults' household income, educational attainment, and economic hardship at Wave 4 (2008) were used as multiple indicators of their socioeconomic attainment. Young adults' personal earnings. Young adults reported their average annual personal earnings before taxes and deductions (including wages/salaries, tips, bonuses, over-time pay, and self-employment income) for 2006, 2007, and 2008. Young adults' educational attainment. Young adults reported their highest level of education using an ordinal scale ranging from 1 ¼ completed 8th grade to 13 ¼ completed post baccalaureate professional education. Young adults' economic hardship. Economic hardship was measured by six dichotomous items (0 ¼ no, 1 ¼ yes) indicating whether any member of the household experienced difficulties meeting their basic needs in the previous 12 months. Example

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892

887

items include: could not pay the full rent or mortgage, had electricity/gas service turned off or heating oil not delivered, and worried whether food would run out. Responses to these seven items were summed to create an index of young adults' economic hardship ranging from 0 to 7, with higher scores reflecting greater economic hardship. Race/ethnicity At Wave 1 (1995), adolescents reported their race/ethnicity. Dichotomous variables were then created to assess Black, Hispanic, Asian, Native American, and White racial/ethnic statuses. The dichotomous variables for each of the minority statuses were included as independent variables in the regression equation resulting in regression coefficients that can be interpreted with reference to Whites. For multi-racial respondents, only their first choice of race/ethnicity category was considered. Gender Gender was coded as male (0) or female (1). Analysis strategy Most previous studies focusing on antecedents and consequences of obesity used traditional mean comparisons with cross-sectional data or regression models focusing on discrete obesity status at a single time point or multiple time points over a relatively short time span. Such analyses are unable to reveal antecedents and consequences of individual weight status and changes in weight status over time. Thus, we tested the theoretical model as a latent growth curve (LGC) in a structural equation modeling (SEM) framework to estimate individual trajectories using Mplus (version7) (Muthen & Muthen, 1998e2013). We used the TYPE ¼ COMPLEX command, which uses Huber/White correction, to adjust for potential bias in standard errors and chi-square computation due to the lack of independence between observations within schools in the Add Health data. Missing data were accounted for using the Full Information Maximum Likelihood (FIML) procedure. FIML does not impute missing values; rather, it estimates model parameters and standard errors from all available data, which minimizes potential age-related bias that would have influenced the results (Enders, 2001). Results Descriptive statistics Table 1 presents means, standard deviations, and ranges of the major study variables. On average, BMI levels increased with age. By Wave 4, the mean level of BMI was 29.04 (SD ¼ 7.46), which means that the average BMI level in our sample of young adults was close to the cut point for obesity (BMI of 30 or greater). This is similar with the national average (mean adult BMI is 28.7 for adult men and women; Flegal, Carroll, Kit, & Ogden, 2012). The mean yearly personal earnings of young adults was approximately $35,572 (SD ¼ 44,599), and they, on average, had some college education (M ¼ 5.73, SD ¼ 2.15). The mean for young adults' economic hardship experience was .72 (SD ¼ 1.27). Univariate growth curves To investigate individual changes, a univariate latent growth curve model was estimated for BMI from 1996 to 2008. Unstandardized coefficients and model fit indices for the univariate growth models are presented in Table 2. The total latent growth curve model (combining all race/ethnicities, men, and women) for BMI with three successive measurements showed acceptable fit with the data (c2ð1Þ ¼ 74.36; CFI ¼ .96; RMSEA ¼ .08). The mean initial BMI level was 23.20 (p < .001) with Table 1 Descriptive statistics of study variables. Variable

M (or %)

SD

Range

Early socioeconomic adversity (1995) BMI (Wave 2, 1996) BMI (Wave 3, 2002) BMI (Wave 4, 2008) Young adult personal earnings (2008) Young adult educational attainment (2008) Young adult economic hardship (2008) Gender (Female) Black Hispanic Asian Native American

1.58 23.32 26.58 29.04 35,572 5.73 .72 53.8% 15.8% 13.1% 6.0% 2.8%

1.11 4.57 6.14 7.46 44,599 2.15 1.27

.00e4.00 11.63e51.43 13.46e58.75 15.40e97.40 0e999, 995 1.00e13.00 .00e6.00

888

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892

Table 2 Estimates for univariate growth curve model for BMI. Total

Intercept Mean Variance Slope Mean Variance c2 (df) CFI RMSEA

Young men

Young women

White

Black

Hispanic

Asian

Male total

White

Black

Hispanic

Asian

Female total

23.20*** 19.90***

23.36*** 18.56***

23.81*** 17.99***

23.47*** 17.43***

22.77*** 17.16***

23.41*** 18.29***

22.67*** 19.69***

24.62*** 24.56***

23.84*** 26.36***

21.40*** 19.14***

23.10*** 21.41***

.49*** .18*** 74.36(1) .96 .08

.47*** .10*** 16.32(1) .96 .07

.44*** .14*** 4.30(1) .99 .07

.52*** .16*** 5.63(1) .98 .08

.42*** .09** 15.34(1) .94 .21

.48*** .11*** 25.69(1) .96 .07

.48*** .26*** 71.33(1) .96 .14

.62*** .20** .42(1) 1.00 .00

.52*** .26*** 22.10(1) .87 .17

.34*** .20*** 7.71(1) .99 .14

.50*** .25*** 64.41(1) .96 .10

Note: Unstandardized coefficients are shown. *p < .05,

**

p < .01,

***

p < .001.

significant variation (19.90, p < .001) indicating substantial differences among adolescents in their BMI levels at Wave 2 (1995). The average rate of change was .49 (p < .001) indicating a significant increase in BMI from 1996 to 2008. The significant variation in rates of change (.18, p < .001) showed that the rate of change for some adolescents was significantly steeper or flatter than the average rate of change for the sample as a whole. Table 2 also indicates the estimates for the univariate growth curve examined separately for men and women of each race/ethnicity. In all race/ethnicity groups, variation in the mean initial BMI level and the rate of change in BMI was greater for women than men, which indicated a larger variation in the individual BMI trajectories among women than men. Native American men (.58, p < .001) and Black women (.62, p < .001) showed the fastest growth in BMI while Asian men (.42, p < .001) and women (.34, p < .001) showed the slowest growth. Antecedents and consequences of youth BMI trajectories Having estimated univariate growth curves with three repeated BMI measures, the next step involved testing the conceptual model depicted in Fig. 1. This model contained cumulative early socioeconomic adversity as a predictor of growth parameters of BMI, which, in turn, were expected to predict young adults' socioeconomic attainment. Fig. 2 provides the standardized coefficients after controlling for gender and race/ethnicity. The model showed an adequate fit to the data (c2ð26Þ ¼ 474.39; CFI ¼ .94; RMSEA ¼ .04). Overall, the findings supported our hypotheses. Early socioeconomic adversity was positively associated with the mean level and rate of change in BMI (b ¼ .11, p < .001; b ¼ .06, p < .001, respectively). That is, adolescents who experienced higher adversity reported higher beginning levels of BMI at Wave 2, and greater increases in BMI from Wave 2 to Wave 4 than their peers who experienced less adversity. In turn, both the initial level and rate of change in BMI subsequently predicted lower socioeconomic attainment of young adults (b ¼ .06, p < .01). The total indirect effect of early socioeconomic adversity on young adults' socioeconomic attainment through both growth parameters of BMI was significant at p < .01 level. Thus, BMI trajectories served as a mediator between early socioeconomic adversity and socioeconomic attainment. In addition, the results showed that early socioeconomic adversity directly influenced young adults' socioeconomic attainment (b ¼ .54, p < .001); high early adversity predicted poor socioeconomic attainment at Wave 4. Thus, our hypothesized indirect paths through BMI trajectories did not completely capture the association between early adversity and later socioeconomic attainment as the direct effect of early adversity remained statistically significant.

Fig. 2. Linking early socioeconomic adversity to young adults' socioeconomic attainment. Notes: c2ð26Þ ¼ 474.39, CFI ¼ .94, RMSEA ¼ .04. Standardized coefficients are shown with unstandardized coefficients in parentheses. Gender and race/ethnicity were controlled (not shown) and had a statistically significant influence on the level and rate of change in BMI and socioeconomic attainment. **p < .01, ***p < .001.

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892

889

We hypothesized that young adults' socioeconomic attainment, as a consequence of their BMI in earlier years, would be moderated by gender and race/ethnicity. To test this hypothesis, multiple group analyses were conducted for each moderator, and the results are presented in Table 3. A significant reduction in chi-square between the constrained path and relaxed path indicates moderation effects and are shown in bold. The associations between the increase in early adversity and both level and growth of BMI were significantly stronger among women compared to men and among Whites compared to Asians, which indicated that women and Whites experienced higher initial levels and steeper increases in BMI when they were exposed to early socioeconomic adversity. In addition, the negative effect of BMI growth (increasing rate of change) on young adults' socioeconomic attainment was significantly stronger for women than men and for Whites than Blacks. This indicates that the socioeconomic attainment of women and Whites was more sensitive to BMI increases than men and Blacks. The negative effect of BMI level in adolescence (initial level) on socioeconomic attainment in young adulthood was significantly stronger for Asians than Whites; therefore, Asians' socioeconomic attainment was more sensitive to the initial level of BMI than Whites' socioeconomic attainment. For Whites, early adversity exerted a significantly stronger negative influence on socioeconomic attainment in young adulthood compared to Hispanics. Discussion The present study examined a dynamic life-course model to more fully understand how early socioeconomic adversity cumulatively influences youth BMI trajectories leading to young adult socioeconomic attainments. A key element in this model was the identification of BMI trajectories during this time period as the individual vulnerability mediating the socioeconomic continuity over early life course. In addition, the present study examined the gender and racial/ethnic differences in these processes and has produced important findings in regard to these differences. At a descriptive level, and consistent with expectations, the analyses showed that there is a linear increase in youth BMI over this period. The investigation of individual trajectories of BMI allowed us to preserve the continuity of change in BMI over the early life course. By doing so, we treated changes in BMI as a continuous process unfolding over time and examined the associations between different facets of BMI trajectories (the level and rate of change) and their antecedents and consequences. Consistent with the life-course path-dependent mechanism (Willson et al., 2007), early socioeconomic adversity exerts a persistent influence on young adult socioeconomic attainment over the early life-course indirectly through both the level and change in BMI trajectories. It seems that, in addition to its contemporaneous influence through early disadvantages (i.e., lack of health resources, educational, recreational and health facilities, and constant exposure to early stressful circumstances) that directly influence the initial level of adolescent BMI, early adversity may also lead to the exacerbation of adverse metabolic processes resulting in increased BMI with increasing age (Dowd, Simanek, & Aiello, 2009). The influence of early adversity on the growth rate in BMI is consistent with the notion that early socioeconomic adversity exerts a cumulative influence over the life course (posited by the cumulative advantage and disadvantage principle; Dannefer, 2003). Statistically, this cumulative effect corresponds to the interaction between early adversity and time. Although not explicitly tested in the current model, the cumulative influence of early adversity over the life course may be attributed to several mechanisms. In summary, these mechanisms may include (a) an increase in exposure to more stressors/disadvantages due to the proliferation of early stressors/disadvantages over the life course, (b) an increase in the susceptibility to stressors/ disadvantages over the life course (decrease in “biological robustness”), and (c) an intensification of the impact of early physiological damages in the later years. Future research should further elucidate these mechanisms. The results indicated that the severity (initial level) of adolescent BMI and also the amount of growth or decline (rate of change) in BMI independently contribute to the subsequent socioeconomic attainment of young adults. For example, the social consequences of an already obese adolescent who has experienced a sharp increase in BMI are different from the social consequences of an adolescent with an average BMI who has experienced the same amount of increase in BMI over the same period. In order to gain a deeper understanding about the mediational role of BMI trajectories, the different facets of change in BMI must be taken into account because both the level and change in BMI may be differentially influenced by early adversity as well as serve as independent predictors of risk for young adult socioeconomic failures. Thus, the findings of the present study would not have been revealed by traditional regression models and mean comparison analyses, which use discrete

Table 3 Standardized path coefficients of multiple group moderation analyses. Paths

Early ADV / BMI Level Early ADV / BMI Slope Early ADV / YA SES BMI Level / YA SES BMI Slope / YA SES

Gender

Race/Ethnicity

Female (male)

Black (White)

Hispanic (White)

Asian (White)

.12 .08 .52 .07 .13

.09 .04 .56 .15 .11

.09 .06 .43 .14 .06

.08 .04 .38 .19 .21

(.08) (.04) (.55) (.14) (.07)

(.17) (.08) (.53) (.06) (.10)

(.11) (.08) (-.54) (.06) (.10)

(.11) (.08) (.54) (-.05) (.10)

Note: Statistically significant differences are indicated in bold. The reference for gender is male and for minority races/ethnicities is White, and coefficients for the references are shown in parentheses. Early ADV ¼ Early socioeconomic adversity. YA SES ¼ Young adult socioeconomic status.

890

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892

weight status as predictors or outcomes. Particularly, it appears that the negative associations between adult BMI and adult socioeconomic status documented by previous studies may be attributed to the fact that higher growth in BMI during adolescence/emerging adulthood contributes to adverse socioeconomic attainment while early BMI level also continues to influence adult socioeconomic attainment. The present study found gender and race/ethnicity significantly moderated associations between BMI growth parameters and socioeconomic attainment in young adulthood. Consistent with the findings of previous studies (McLaren, 2007; nchez-Vaznaugh et al., 2009), the adverse socioeconomic consequences of increasing BMI from adolescence to young Sa adulthood were stronger for women than for men. This finding may be due to the fact that obese individuals experience more unfavorable labor market conditions (e.g., wages and promotions) than their non-obese peers, and this unfairness is exacerbated by gender discrimination. Also, heavier women may experience more stress and lower self-esteem than other women (Griffiths, Parsons, & Hill, 2010; Perrin, Boone-Heinonen, Field, Coyne-Beasley, & Gordon-Larsen, 2010) due to societal expectations of the ideal female body, which, in turn, may result in lower performance in the labor market. Conversely, women with higher social status may place more emphasis on conforming to societal expectations regarding body size and image nchez-Vaznaugh et al., 2009). In addition, the effect of increasing BMI on than men and may engage in a healthier lifestyle (Sa socioeconomic attainment was stronger for Whites than Blacks. This association may be attributed to Blacks being more culturally accepting of “large bodies” (Siegel, Yancey, Aneshensel, & Schuler, 1999) and reporting fewer weight-related concerns and behaviors than Whites (Neumark-Sztainer et al., 2002). Thus, Blacks may experience less pressure to conform to conventional societal norms for weight status. Also, this may be because Whites are often in a higher social class or in the process of attaining higher social status, and weight status is an important aspect of conforming to mainstream societal expectations. Or, due to discrimination and other hardships, Black youth, unlike White youth, may simply be more likely to engage in lower level occupations irrespective of their growing BMI over time. There were no gender differences in BMI growth parameters (the level and rate of change) suggesting that, overall, parallel BMI trajectories existed for men and women. However, the relationship between early adversity and youth BMI trajectories varied by gender. Compared to men, there was a stronger positive relationship between early adversity and the level and growth in BMI for women, irrespective of race/ethnicity. These findings suggest that, for all of the races/ethnicities examined, female adolescents are more susceptible to early socioeconomic adversity than male adolescents. It seems that early family and community adversities create more stressful situations for female adolescents, perhaps because female adolescent development is more stressful than male adolescent development (Ge, Lorenz, Conger, Elder, & Simons, 1994), exacerbating females' metabolic dysfunctioning and resulting in higher BMI. Furthermore, early socioeconomic adversity influences BMI levels and BMI growth for White adolescents (both male and females) more than their Asian peers. This may be due to White adolescents experiencing more early socioeconomic affluence, whereas minority adolescents are less likely to receive the benefits of early socioeconomic affluence, such as recreation and educational facilities. Independent of BMI trajectories, early socioeconomic adversity exerted “direct” influences on the socioeconomic attainment of young adults. Such strong direct effects are suggestive that there may be other indirect pathways that were not considered in the present study. These mediating pathways may include adolescent stressful life experiences, such as stressful transitions to young adulthood and lifestyle factors (e.g., alcohol use), which may also be influenced by early socioeconomic adversity (Wickrama & Baltimore, 2010). In addition, physiological and cognitive damages that occur during infancy, childhood, or adolescence may have long-term latent effects, which may manifest in the young adulthood years (Conroy, Sandel, & Zuckerman, 2010; Ivanovic et al., 2000; Scrimshaw, 1997). Several factors potentially limit the scope and the generalizability of the results. First, the present study used self-report measures of young adults' socioeconomic attainment. Replication using more independent reports (e.g., tax returns) would alleviate concerns regarding potential self-report biases. Second, we did not differentiate between Mexican, Puerto Rican, Cuban, and other Latino or Spanish origin Hispanics. This may yield mixed-group characteristics regarding personal earnings, educational attainment level, and economic hardship experiences among Hispanic ethnicity because some of these groups (e.g., Cuban) have been shown to have higher levels of income or education than other Hispanics or minority race/ethnicity groups (Bohon, Johnson, & Gorman, 2006; Williams, Mohammed, Leavell, & Collins, 2010). Third, we did not examine potential moderating effects of youth psychosocial resources and life transitions (e.g., “turning points”), which can protect youth from the negative influence of early socioeconomic adversity on BMI trajectories, as well as the influence of BMI trajectories on young adult socioeconomic attainment (Wheaton & Gotlib, 1997). Finally, individual genetic-make up has been shown to have additive and interactive influences on the study attributes, particularly early adversity and BMI trajectories (Wickrama et al., 2013). Thus, future investigations should be informed by individual genetic characteristics. Despite these limitations, the present study makes a valuable contribution to existing research by elucidating how early adversity initiates an adverse developmental process over the early life course leading to socioeconomic failures in young adulthood. BMI trajectories appear to be important mediators of the socioeconomic continuity over the early life course. Youth health programs and policies should promote youth competencies that aid to reduce adolescents' BMI and reduce BMI over-growth (i.e., obesity) stemming from early socioeconomic adversity. Acknowledgment This research used data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by Grant P01-

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892

891

HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from Grant P01-HD31921 for this analysis.

References Ball, K., Crawford, D., & Kenardy, J. (2004). Longitudinal relationships among overweight, life satisfaction, and aspirations in young women. Obesity Research, 12(6), 1019e1030. Baum, C. L., & Ford, W. F. (2004). The wage effects of obesity: a longitudinal study. Health Economics, 13(9), 885e899. Bohon, S. A., Johnson, M. K., & Gorman, B. K. (2006). College aspirations and expectations among Latino adolescents in the United States. Social Problems, 53(2), 207e225. Burdette, A. M., & Needham, B. L. (2012). Neighborhood environment and body mass index trajectories from adolescence to adulthood. Journal of Adolescent Health, 50(1), 30e37. Cawley, J. (2004). The impact of obesity on wages. Journal of Human Resources, 39(2), 451e474. Chang, V. W., & Lauderdale, D. S. (2005). Income disparities in body mass index and obesity in the United States, 1971e2002. Archives of Internal Medicine, 165(18), 2122e2128. Classen, T. J. (2009, January). Obesity and educational attainment. (ITP Working Paper). University of Wisconsin-Madison. Retrieved February, 2014 http://itp. wceruw.org/Fall%2010%20seminar/ClassenEDUObesityIV.pdf. Conley, D. R., & Glauber, R. (2006). Gender, body mass, and socioeconomic status: new evidence from the PSID. Advances in Health Economics and Health Services Research, 17, 253e275. Conroy, K., Sandel, M., & Zuckerman, B. (2010). Poverty grown up: how childhood socioeconomic status impacts adult health. Journal of Developmental & Behavioral Pediatrics, 31(2), 154e160. Crosnoe, R. (2007). Gender, obesity, and education. Sociology of Education, 80(3), 241e260. Crosnoe, R., & Muller, C. (2004). Body mass index, academic achievement, and school context: examining the educational experiences of adolescents at risk of obesity. Journal of Health and Social Behavior, 45(4), 393e407. Dannefer, D. (2003). Cumulative advantage/disadvantage and the life course: cross-fertilizing age and social science theory. Journal of Gerontology Series B: Psychological Sciences and Social Sciences, 58(6), S327eS337. Dowd, J. B., Simanek, A. M., & Aiello, A. E. (2009). Socioeconomic status, cortisol and allostatic load: a review of the literature. International Journal of Epidemiology, 38(5), 1297e1309. Elder, G. H., & Giele, J. Z. (Eds.). (2009). The craft of life course research. NY: Guilford Press. Enders, C. K. (2001). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling, 8(1), 128e141. Ferraro, K. F., & Kelley-Moore, J. A. (2003). Cumulative disadvantage and health: long-term consequences of obesity? American Sociological Review, 68(5), 707e729. Fitzgibbon, M. L., Spring, B., Avellone, M. E., Blackman, L. R., Pingitore, R., & Stolley, M. R. (1998). Correlates of binge eating in Hispanic, Black, and White women. International Journal of Eating Disorders, 24(1), 43e52. Flegal, K. M., Carroll, M. D., Kit, B. K., & Ogden, C. L. (2012). Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999e2010. Journal of the American Medical Association, 307(5), 491e497. Fowler-Brown, A. G., Ngo, L. H., Phillips, R. S., & Wee, C. C. (2009). Adolescent obesity and future college degree attainment. Obesity, 18(6), 1235e1241. Freedman, D. S. (2011). ObesityeUnited States, 1988e2008. Morbidity and Mortality Weekly Report, 60(Suppl.), 73e77. Ge, X., Lorenz, F. O., Conger, R. D., Elder, G. H., & Simons, R. L. (1994). Trajectories of stressful life events and depressive symptoms during adolescence. Developmental Psychology, 30(4), 467e483. Griffiths, L. J., Parsons, T. J., & Hill, A. J. (2010). Self-esteem and quality of life in obese children and adolescents: a systematic review. International Journal of Pediatric Obesity, 5(4), 282e304. Han, E., Norton, E. C., & Powell, L. M. (2011). Direct and indirect effects of body weight on adult wages. Economics & Human Biology, 9(4), 381e392. Ivanovic, D. M., Leiva, B. P., Perez, H. T., Inzunza, N. B., Almagi a, A. F., Toro, T. D., et al. (2000). Long-term effects of severe undernutrition during the first year of life on brain development and learning in Chilean high-school graduates. Nutrition, 16(11), 1056e1063. Kipke, M. D., Iverson, E., Moore, D., Booker, C., Ruelas, V., Peters, A. L., et al. (2007). Food and park environments: neighborhood-level risks for childhood obesity in east Los Angeles. Journal of Adolescent Health, 40(4), 325e333. McLaren, L. (2007). Socioeconomic status and obesity. Epidemiologic Reviews, 29(1), 29e48. Merten, M. J., Wickrama, K. A. S., & Williams, A. L. (2008). Adolescent obesity and young adult psychosocial outcomes: gender and racial differences. Journal of Youth and Adolescence, 37(9), 1111e1122. Muthen, L. K., & Muthen, B. O. (1998e2013). Mplus use' guide. (7th ed.). Los Angeles, CA: Muthen & Muthen. Neumark-Sztainer, D., Croll, J., Story, M., Hannan, P. J., French, S. A., & Perry, C. (2002). Ethnic/racial differences in weight-related concerns and behaviors among adolescent girls and boys: findings from Project EAT. Journal of Psychosomatic Research, 53(5), 963e974. O'Rand, A. M., & Hamil-Luker, J. (2005). Processes of cumulative adversity: childhood disadvantage and increased risk of heart attack across the life course. Journals of Gerontology Series B: Psychological Sciences & Social Sciences, 60B, 117e124. Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2012). Prevalence of obesity and trends in body mass index among US children and adolescents, 1999e2010. Journal of the American Medical Association, 307(5), 483e490. Ogden, C. L., Lamb, M. M., Carroll, M. D., & Flegal, K. M. (2010). Obesity and socioeconomic status in children and adolescents: United States 1998e1994 and 2005e2008. Hyattsville, MD: National Center for Health Statistics. NCHS data brief no 51. Perrin, E. M., Boone-Heinonen, J., Field, A. E., Coyne-Beasley, T., & Gordon-Larsen, P. (2010). Perception of overweight and self-esteem during adolescence. International Journal of Eating Disorders, 43(5), 447e454. Puhl, R., & Brownell, K. D. (2001). Bias, discrimination, and obesity. Obesity Research, 9(12), 788e805. Roehling, M. V. (1999). Weight-based discrimination in employment: psychological and legal aspects. Personnel Psychology, 52(4), 969e1016. nchez-Vaznaugh, E. V., Kawachi, I., Subramanian, S. V., S Sa anchez, B. N., & Acevedo-Garcia, D. (2009). Do socioeconomic gradients in body mass index vary by race/ethnicity, gender, and birthplace? American Journal of Epidemiology, 169(9), 1102e1112. Scrimshaw, N. S. (1997). The relation between fetal malnutrition and chronic disease in later life. BMJ: British Medical Journal, 315(7112), 825e830. Shin, S. H., & Miller, D. P. (2012). A longitudinal examination of childhood maltreatment and adolescent obesity: results from the National Longitudinal Study of Adolescent Health (AddHealth) Study. Child Abuse & Neglect, 36(2), 84e94. Siegel, J. M., Yancey, A. K., Aneshensel, C. S., & Schuler, R. (1999). Body image, perceived pubertal timing, and adolescent mental health. Journal of Adolescent Health, 25(2), 155e165. Story, M., Kaphingst, K. M., Robinson-O'Brien, R., & Glanz, K. (2008). Creating healthy food and eating environments: policy and environmental approaches. Annual Review of Public Health, 29, 253e272. Wada, R., & Tekin, E. (2010). Body composition and wages. Economics & Human Biology, 8(2), 242e254. Wen, M., & Maloney, T. N. (2011). Latino residential isolation and the risk of obesity in Utah: the role of neighborhood socioeconomic, built-environmental, and subcultural context. Journal of Immigrant and Minority Health, 13(6), 1134e1141.

892

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892

Wheaton, B., & Gotlib, I. H. (1997). Trajectories and turning points over the life course: concepts and themes. In I. H. Gotlib, & B. Wheaton (Eds.), Stress and adversity over the life course: Trajectories and turning points (pp. 1e25). Cambridge, U.K: Cambridge University Press. Wickrama, K. A. S., & Baltimore, D. L. (2010). Adolescent precocious development and young adult health outcomes. Advances in Life Course Research, 15(4), 121e131. Wickrama, K. A. S., Beiser, M., & Kaspar, V. (2002). Assessing the longitudinal course of depression and economic integration of south-east Asian refugees: an application of latent growth curve analysis. International Journal of Methods in Psychiatric Research, 11(4), 154e168. Wickrama, K. A. S., & Bryant, C. M. (2003). Community context of social resources and adolescent mental health. Journal of Marriage and Family, 65(4), 850e866. Wickrama, K. A., Conger, R. D., Wallace, L. E., & Elder, G. H. (1999). The intergenerational transmission of health-risk behaviors: adolescent lifestyles and gender moderating effects. Journal of Health and Social Behavior, 40, 258e272. Wickrama, K. A. S., O'Neal, C. W., & Lee, T. K. (2013). Early community context, genes, and youth body mass index trajectories: an investigation of genecommunity interplay over early life course. Journal of Adolescent Health, 53, 328e334. Wickrama, K. A. S., Wickrama, T., & Bryant, C. M. (2006). Community influence on adolescent obesity: race/ethnic differences. Journal of Youth and Adolescence, 35(4), 641e651. Williams, D. R., Mohammed, S. A., Leavell, J., & Collins, C. (2010). Race, socioeconomic status, and health: complexities, ongoing challenges, and research opportunities. Annals of the New York Academy of Sciences, 1186(1), 69e101. Willson, A. E., Shuey, K. M., & Elder, G. H. (2007). Cumulative advantage processes as mechanisms of inequality in life course health. American Journal of Sociology, 112(6), 1886e1924. Young, L. R., & Nestle, M. (2002). The contribution of expanding portion sizes to the US obesity epidemic. American Journal of Public Health, 92(2), 246e249. Zheng, H., Tumin, D., & Qian, Z. (2013). Obesity and mortality risk: new findings from body mass index trajectories. American Journal of Epidemiology, 178(11), 1591e1599.