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Trajectories of childhood socioeconomic status and markers of cardiovascular health in adolescence Marin, Teresa J. 2006

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Trajectories o f Childhood Socioeconomic Status and Markers o f Cardiovascular Health in Adolescence by  Teresa J. M a r i n  A THESIS S U B M I T T E D IN P A R T I A L F U L F I L L M E N T OF T H E REQUIREMENTS FOR T H E D E G R E E OF  M A S T E R OF ARTS in  T H E F A C U L T Y OF G R A D U A T E STUDIES  (Psychology)  T H E UNIVERSITY OF BRITISH C O L U M B I A August, 2006  ©Teresa J . Marin, 2006  11  ABSTRACT Objective: The current study examined trajectories o f socioeconomic status (SES) throughout childhood and their relationship to markers o f cardiovascular health in adolescence. The goal was to determine whether early life S E S , current S E S , cumulative SES, and/or social mobility best explained the relationship between S E S experiences across an adolescent's lifespan and current blood pressure, heart rate ( H R ) , and body mass index ( B M i ) . Design: 102 medically healthy adolescents completed cardiovascular health assessments. Adolescents enrolled in the study with a parent.  Parents reported on family S E S ,  indicating the number o f bedrooms in the family home for each year o f the child's life. Main Outcome Measures:  Systolic blood pressure (SBP), Diastolic blood pressure  (DBP), H R , and B M I . Results: Using Jones, Nagin, and Roeder's (2001) semiparametric group-based method, four distinct trajectories o f childhood S E S were identified.  Trajectory groups were  differentially related to adolescents' S B P and D B P . Early life S E S explained trajectory group differences in adolescents' S B P and D B P .  Cumulative S E S also contributed to  differences in adolescents' D B P . Trajectories o f childhood S E S were unrelated to H R and B M I . Trajectory findings were confirmed in follow-up analyses. Conclusions:  O f the life-course models that we tested, an early life S E S model best  explained adolescents' current blood pressure. These findings point toward early life developmental processes as potential candidates for explaining the relationship between SES and risk factors related to C V D . Interventions designed to reduce S E S health disparities should take place early in a child's life.  iii T A B L E OF CONTENTS Abstract  ii  Table o f Contents  iii  List o f Tables  iv  List o f Figures  v  Acknowledgments  vi  Introduction  1  Method  5  Results  10  Discussion  18  References  25  iv LIST OF T A B L E S Table 1. Demographic and health characteristics o f the sample  31  Table 2. Indices o f socioeconomic status through child's life  32  Table 3. Selecting the number o f trajectories  33  Table 4. Model selection for the shape o f each trajectory  34  Table 5. Partial correlations between S E S indices and adolescents' biological outcomes  35  V  LIST OF FIGURES Figure 1. Estimates o f S E S trajectories  36  Figure 2. Estimates o f average S B P by trajectory group  37  Figure 3. Estimates o f average D B P by trajectory group  38  ACKNOWLEDGMENTS Thank you to Dr. Edith Chen and Dr. Gregory M i l l e r for their guidance and support throughout the preparation o f this manuscript.  1 INTRODUCTION Socioeconomic status (SES) is an important determinant o f health status at each phase o f the life-cycle.  Thus, among children, adults, and the elderly, individuals in lower S E S groups  experience higher rates o f morbidity and mortality due to a wide range o f medical conditions (Adler et al., 1994; Anderson & Armstead, 1995; Chen, Matthews, & Boyce, 2002). Life-course models of S E S have proposed various pathways through which S E S at different stages can influence health.  A t least four major life-course models exist: critical period, current S E S ,  cumulative S E S , and mobility.  The critical period and current S E S models emphasize the  importance o f timing o f SES exposure, whereas cumulative S E S and mobility models emphasize dynamic aspects o f S E S across time. According to critical period models, there is a window o f time i n which S E S exerts its most profound effects on the body. This critical period is thought to be in early childhood, when important developmental processes are underway.  Early life environmental conditions may  program a pattern o f biological and behavioural responses that have a long-term impact (Hertzman, 1999; Barker, 1992).  Research has demonstrated the significance o f early life  experiences in long-term health.  For instance, early childhood environments predict adult  cardiovascular disease ( C V D ) , stomach cancer, and hemorrhagic stroke (see Galobardes, Davey Smith, & Lynch, 2006; Galobardes, Lynch, & Davey Smith, 2004, for reviews).  Importantly,  these associations persist after accounting for adult S E S . In contrast, current S E S models specify paths through which socioeconomic experiences at any point in the life-cycle can influence concurrent health status. Such models suggest that the current living environment can have a relatively immediate impact on health.  For instance,  current living conditions may affect a person's access to healthcare or influence susceptibility to  2  acute medical conditions.  Although few studies have examined the impact o f current  socioeconomic circumstances independent o f the childhood environment, research evidence suggests that current  S E S is an important predictor o f self-reported  health (Laaksonen,  Rahkonen, Martikainen, & Lahelma, 2005) and C V D mortality (Pensola & Martikainen, 2003). Models o f cumulative risk focus on the additive effects o f S E S experience.  Thus,  individuals who are exposed to l o w S E S for longer durations are thought to be at greater risk. Indeed, research evidence has shown that the amount o f time spent i n l o w S E S is an important predictor o f mortality (Davey Smith & Hart, 2002; McDonough, Duncan, Williams, & House, 1997) and young adults' self-reported health (Power, Manor, and Matthews, 1999). Finally, mobility models propose that changes i n S E S over the life course w i l l affect health.  Markers o f S E S like family income can fluctuate from year to year (Duncan, 1988).  Thus, over any given period o f time, a person may experience upward mobility or downward mobility, and these changes i n social status may impact health. A few studies have examined these types o f relationships. Income fluctuations have been associated with morality risk among middle-income adults (McDonough, Duncan, Williams, & House, 1997).  More recently,  downward mobility i n adulthood has been associated with increased risk o f hypertension (Matthews, Kiefe, Lewis, L i u , Sidney, & Yunis, 2002) and poorer self-reported health (Virtanen, Vahtera, K i v i m a k i , Liukkonen, Virtanen, & Ferrie, 2005).  Conversely, upward mobility in  adulthood has been associated with cardiovascular risk reduction, but only among certain racegender groups (Karlamangla et al., 2005). Previous research on the longitudinal relationship between S E S and health has mainly focused on adult populations. Thus, we still know very little about S E S processes throughout childhood and adolescence. Findings from the mental health literature suggest that children and  adolescents are affected by both timing and dynamic S E S indicators (Duncan, Brooks-Gunn, & Klebanov, 1994; Duncan, Yeung, Brooks-Gunn, & Smith, 1998; M c l e o d & Shanahan, 1996). Specifically, early childhood experiences o f poverty influence future mental health outcomes; regardless o f subsequent changes in the child's S E S environment (Duncan, Yeung, BrooksGunn, & Smith, 1998).  Furthermore, the experience o f persistent poverty is associated with  worse mental health outcomes than the experience o f transient poverty or no poverty (Mcleod & Shanahan, 1996). Clearly, patterns o f S E S over time have implications for children's mental health; and it is also important to test whether these findings extend to markers o f children's physical health. This line o f research would have important implications for designing effective interventions to minimize health disparities among children. Recent evidence indicates that poor cardiovascular disease begins i n the early decades o f life.  Many young individuals have developed atherosclerotic plaque by the time they reach  adolescence (Berenson, Srinivasan, Bao, Newman, Tracy, & Wattigney, 1998; Berenson et al., 1992), and risk factors for C V D , including obesity, high blood pressure, and elevated lipids track across childhood and adolescence and into adulthood (Lauer, Burns, Clarke, & Mahoney, 1991; Mahoney et al., 1996).  Importantly, these risk factors create a long-term burden on the  cardiovascular system and are predictive o f later clinical outcomes such as morbidity and mortality.  Thus, it is important to identify environmental and psychosocial factors that  contribute to the early progression o f C V D (Matthews, 2005). In adults, the life-course approach has been used successfully to examine relationships between  childhood and adulthood  socioenvironmental circumstances and cardiovascular mortality (Davey Smith, Hart, Blane, & Hole, 1998; Frankel, Davey Smith, & Gunnel, 1999; Davey Smith & Hart, 2002).  4  The current article examines patterns o f SES through childhood and their association with systolic blood pressure (SBP), diastolic blood pressure ( D B P ) , heart rate ( H R ) , and body mass index ( B M I ) in adolescence. Both blood pressure and B M I in childhood and adolescence are important predictors o f C V progression in adulthood; higher levels o f these risk factors in early life are associated with premature onset o f preclinical atherosclerosis and coronary artery calcification (Raitakari et al., 2003; Mahoney et al., 1996). Our main objective was to determine which life-course model (or combination o f models) best explains the impact o f S E S throughout childhood on adolescent health.  We utilized a new statistical approach to identify common  trajectories o f family S E S across an adolescent's lifespan, and then used these trajectories to predict adolescent cardiovascular markers. W e expected to identify distinct trajectories o f family SES that would represent different combinations o f the timing and dynamics o f S E S experiences. For instance, one trajectory might show low S E S in the child's early life and upward mobility over time, whereas another might show high S E S in the child's early life that would persist over time. To the extent that distinct trajectories o f S E S could be identified, we expected that they would be differentially related to adolescents' S B P , D B P , H R , and B M I . W e expected that adolescents in families with persistently l o w and constantly fluctuating S E S would have the worst C V outcomes.  5  METHOD Participants Public high school students in the St. Louis area were recruited v i a school flyers, announcements, and classroom presentations.  Adolescents were eligible for the study i f they  were (a) between the ages o f 14 and 18, (b) fluent in the English language, (c) free o f chronic medical conditions and mental health problems, and d) not using medication that affected the cardiovascular system.  This study was approved by the Institutional Review Board at  Washington University. The final sample consisted o f 102 adolescents.  Ages ranged from 14-18 years (mean  15.61). Fifty-three percent were females; 75% were Caucasian, 24% were African American, and 1% "other."  Twenty-four percent o f the adolescents' parents had a high school diploma,  11% had some college, and 6 3 % had a college degree or higher. Students came from families where the mean family income category was 3.97 (category 3 corresponds to $50 000-74 999 and category 4 corresponds to $75 000-99 999). Students participated in the study with a parent. Eighty-three percent o f parents were mothers.  See Table 1 for a summary o f descriptive  information. Socioeconomic Status To capture S E S trajectories over time, parents were asked to indicate the number o f bedrooms in the family home during each year o f the child's life. Number o f bedrooms was used as a marker o f S E S for a number o f reasons.  First, retrospective recall o f home ownership is  more accurate than other asset variables such as family income (Cohen, Doyle, Turner, Alper, & Skoner, 2004). Second, housing is a more dynamic measure than other S E S indicators, such as  parental education.  In our sample, most families (68.6%) had lived in two or more homes  through the child's life, allowing us to capture changes in family S E S . Based on the number o f bedrooms in the family home during each year o f the child's life, we calculated trajectories o f S E S , using procedures described i n more detail below. Physiological Measures Blood pressure.  Systolic blood pressure (SBP) and diastolic blood pressure ( D B P ) were  monitored using a Dinamap Pro 100 automated blood pressure monitor (Critikon, Tampa, F L ) with a standard occluding cuff on the participant's right arm.  S B P and D B P measures were  taken 3 times during the last 5 minutes o f a 10-minute baseline rest period. The averages o f the three measures o f S B P and D B P were used in statistical analyses. The coefficient alpha was .96 for S B P and .94 for D B P . Heart rate.  Heart rate ( H R ) was measured through E K G monitoring. A n E K G signal  was transduced using two active Meditrace SF450 disposable silver/silver chloride electrodes (Kendall-LTP, Chicopee, M A ) placed on each side o f the abdomen and a ground electrode beside the navel. The E K G signal was filtered and amplified by the Biopac M P 100 system (Biopac Systems, Santa Barbara, C A ) . H R was monitored continuously during the last 5 minutes o f the 10-minute rest period. Body Mass Index. Height and weight were taken on a standard medical-grade balance beam scale and body mass ( B M I ) was computed from these two variables ( B M I = (Weight in Kilograms / ( Height i n Meters ) x (Height i n Meters )). For children and adolescents, the National Center for Health Statistics presents B M I by age and sex, using Z scores. These ageand sex-adjusted Z-scores were used i n this study.  7 Potential  Confounders.  W e measured a number o f processes that could provide  alternative explanations for relations between childhood S E S and biological outcomes. collected demographic information, including participant age and ethnicity.  We  Because the  majority o f the sample (99%) was o f Caucasian or African American descent, we created a dichotomous ethnicity variable coded as 1 for Caucasian and 2 for Other. Parents were asked whether there was a history o f heart disease among first-degree relatives o f the child. Answers were coded as 1 for "yes" and 0 for "no." We also asked parents to report on the number o f people living in the family home through the child's life. Procedures Upon arriving at our laboratory, a research assistant described the study procedures in detail. Written assent was obtained from adolescents and consent from parents.  Parents were  interviewed about family S E S . Adolescents were seated in an individual testing room, and the three E K G electrodes were applied. The blood pressure cuff was placed on the upper aspect o f the participant's right arm with the microphone placed above an area where the brachial artery could be palpated. After a 15 m i n adaptation period while machines were tested, the 10 min baseline period was begun, and blood pressure and heart rate were monitored and averaged during the final five minutes.  Adolescents then participated iii a videotaped 'survival task' with  parents, the results o f which are described elsewhere (Chen & Berdan, 2006). Statistical Analyses In the first wave o f analyses, we examined the distribution o f study variables and screened for outliers. In each o f the S B P and H R distributions, there was a score greater than 3 S D ' s from the sample mean.  These scores were replaced with the next highest score in their  respective distributions. In the second wave o f analyses, we conducted bivariate analyses to  8 assess the relationship between study variables and potential confounds.  In the third wave o f  analyses, we modeled trajectories o f S E S across childhood by using a S A S procedure called T R A J (Jones, Nagin, & Roeder, 2001) that separates individuals into trajectory groups. T R A J is a  semiparametric,  group-based  modeling strategy  that  identifies clusters  o f individual  trajectories. M o d e l estimation produces posterior probabilities o f membership in each trajectory group for each participant.  These probabilities are then used to assign individuals to the  trajectory group to which they have the highest probability o f belonging.  Differences in  outcomes by group membership can then be modeled (Jones & Nagin, submitted). In the first part of the analysis, we determined the number o f trajectories that best represented patterns o f SES in our sample. The Bayesian Information Criterion (BIC) was used to determine the optimal number o f trajectories, with higher values indicating a better fit. parameter estimates to determine the shape o f each trajectory.  Next, we looked at the  W e used these to determine  whether each trajectory was linear, quadratic, or cubic. Finally, we examined the relationship between trajectory group membership and adolescents' biological outcomes. After completing the group trajectory analyses, it was quite clear how the timing o f S E S exposure contributed to adolescents' cardiovascular health outcomes.  However, the trajectory  analyses did not provide a very direct test o f the dynamic models. Thus, the fourth wave o f analyses was done as a follow-up to the T R A J analyses.  Using specific measures o f early life  SES, current S E S , cumulative S E S , and social mobility, we were able to perform a head-to-head comparison o f the life-course models, which T R A J did not permit us to do - it only allowed indirect inferences about which model provides the best explanation. W e aimed to replicate the group trajectory findings and to further differentiate between the models o f S E S . First, we calculated the four specific indices o f SES:  (1) Early life SES was calculated by averaging the  9  number o f bedrooms in the family home across the first 3 years o f the child's life. (2) Current SES was the number o f bedrooms in the family's current home. (3) Cumulative SES was the average number o f bedrooms in the family home through the child's life. (4) Slope of SES (an indicator o f social mobility) was the slope o f the number o f bedrooms i n the family home through the child's life, calculated by regressing number o f bedrooms upon years o f life separately for each participant. See Table 2 for a summary and descriptive information. Second, we related early S E S , current S E S , cumulative S E S , and slope o f S E S to biological outcomes using Pearson correlations. Finally, we compared the relative magnitude o f each o f these effects by conducting multiple regression analyses in which multiple S E S measures were entered simultaneously predicting each biological outcome.  10  RESULTS Preliminary Analyses To identify potential confounders, correlations were computed between adolescents' demographic characteristics and study variables.  Family history o f heart disease was not  significantly associated with S E S indices or biological outcomes (p's>.10).  Each number o f  bedrooms S E S index was significantly related to the corresponding index o f number o f people in the family home; that is, families who lived in houses with more rooms tended to have more members (r's ranged from .35 to .64,/?<.001). Thus, we statistically controlled for the number o f people i n the house through childhood i n all analyses. Furthermore, we statistically controlled for adolescents' gender, race, and age in all analyses o f S B P , D B P , and heart rate, and adolescents' race in analyses o f B M I , given that B M I z-scores from the National Center for Health Statistics were already gender and age adjusted. Trajectories of SES and Biological Outcomes Model selection: Identifying the number and shape of trajectories. To test the hypothesis that we would identify distinct trajectories o f family S E S , we first modeled the trajectory patterns o f our sample. The B I C continued to increase as the number o f trajectories increased, indicating a better model fit with the addition o f each trajectory: The B I C was -1564.86 for three groups, -1406.64 for four groups, and -1310.65 for five groups.  To  prevent the trajectory group sizes from getting too small, we did not exceed the 5-group model. Both the 4 and 5-group models yielded similar information.  For parsimony, we retained the  four-trajectory model. The shape o f each trajectory was determined by initially including linear, quadratic, and cubic parameters for each trajectory, and then dropping the nonsignificant ones (See Table 2). In  11  Model 1, linear, quadratic, and cubic parameters were included for each o f the four trajectories. The cubic parameters were nonsignificant for each. Thus i n M o d e l 2, the cubic parameters were dropped from each trajectory. The quadratic parameter was nonsignificant for the first, second, and fourth trajectory.  Thus i n M o d e l 3, the quadratic parameters were dropped from these  trajectories. Because it had the highest B I C coefficient, we adopted M o d e l 3 as our final model. In this model, the first, second, and fourth trajectories were linear, and the third trajectory was quadratic. A s shown i n Figure 1, the first trajectory group, which comprised 33% o f the sample, showed the lowest S E S in the child's early life.  However, S E S increased steadily through  childhood and surpassed the next-lowest group by early adolescence. W e called this group " l o w increasing." The second trajectory group, accounting for 2 1 % o f the sample, showed moderate SES at the time o f the child's birth and did not change over the course o f childhood. We called this group "moderate-persistent."  Similar to the moderate-persistent group, a third trajectory  group, accounting for 36% o f the sample, showed moderate S E S at the child's birth; however, SES improved through childhood and leveled off around early adolescence. W e referred to this group as "moderate-increasing."  Finally, a fourth trajectory group, comprised o f 10% o f the  sample, showed the highest S E S at the time o f the child's birth and increased slightly with time. We called this group "high-increasing." It should be noted that trajectory group assignment is based on best fit; so, adolescents in each group do not follow exactly that trajectory. Relating the trajectories to biological outcomes. Next, we tested the hypothesis that SES trajectories would be differentially related to adolescent health outcomes. W e first created residualized outcome variables by separating out the shared variance between our outcomes and covariates. For example, we included age, race,  12  gender, and the average number o f people i n the family home through childhood in a regression equation predicting S B P . W e then used the residual scores from the regression analysis as our outcome variable. This variable represented S B P scores minus the shared variance between S B P and the covariates.  These steps were repeated to create D B P and H R . G i v e n that B M I Z-scores  had already been adjusted for age and gender, B M I was created by regressing B M I on race and average number o f people.  Scores for each outcome variable were standardized, and these  standardized, residualized scores were entered as outcome variables into the T R A J model. To test whether S B P differed by trajectory group, S B P was entered into the 4-group trajectory model specified above. This analysis yielded estimates o f the average S B P and their corresponding standard errors for each trajectory group. The estimated average S B P was 114.24 mmHg (££=1.91) for the low-increasing group, 106.80 m m H g ( £ £ = 1 . 5 7 ) for the moderatepersistent group, 108.34 m m H g ( £ £ = 1 . 4 8 ) for the moderate-increasing group, and 106.42 m m H g (££=2.88) for the high-increasing group (see Figure 2). Next, a W a l d test was used to test the equality o f the trajectory group S B P estimates. The W a l d test is a ^ - b a s e d test conducted on the variance estimates o f the estimated trajectory group averages (Jones & Nagin, submitted). We tested the contrast between the low-increasing and the moderate-persistent groups and found that the estimated average S B P was significantly higher for the low-increasing group, X\ =9.79, /?=.002.  Next, we contrasted the low-increasing group with the moderate-increasing and high-  increasing groups. Results indicated that the estimated average S B P was significantly higher for the low-increasing group compared to both the moderate-increasing group (jjfi =6.89,/»=.01) and 2  the high-increasing group (j£i =7.14, p=.007). 2  N o significant differences in the estimated  average S B P emerged among the other groups: moderate-persistent versus moderate-increasing (ji =.53, p>20), moderate-increasing versus high-increasing (%\ =.&9, p>.20), and moderate2  2  13  persistent versus high-increasing (%i =.22, p>.20). Thus, the low-increasing group had higher 2  S B P compared to the moderate-persistent, moderate-increasing, and high-increasing groups, all of which had similar S B P .  A comparison o f the trajectories suggests that early-life SES  contributes to group differences in S B P . Next, we assessed trajectory group differences in D B P by entering D B P into the 4-group trajectory model.  The estimated average D B P was 63.43 rnmHg ( £ £ = 1 . 3 4 ) for the low-  increasing group, 59.39 rnmHg (££=1.11) for the moderate-persistent  group, 60.17 rnmHg  (££=1.02) for the moderate-increasing group, and 56.44 rnmHg ( £ £ = 1 . 9 5 ) for the highincreasing group (see Figure 2). W e tested the contrast between the estimated average D B P for the low-increasing and the moderate-persistent  groups.  The W a l d test indicated that the  estimated D B P was significantly higher for the low-increasing group (%\ =5.96, /?=.015).  We  then compared the low-increasing group with the moderate-increasing and high-increasing groups.  Results indicated no significant difference between the low-increasing and moderate-  increasing groups (j£i =3.05, p>.05). However, the estimated average D B P was significantly 2  higher for the low-increasing group compared to the high-increasing group (x\ =9.45, p=.002). Next, we contrasted the moderate-persistent group with both the moderate-increasing and highincreasing groups and found no significant differences in the estimated average D B P {x\ =.90, p>.20 and %\ =1.64, p>.20, respectively). Finally, we tested the contrast between the moderate2  increasing and high-increasing groups.  Results indicated that the estimated average D B P was  significantly lower for the high-increasing group (jft =3.81, p=.05). 2  Thus, the low-increasing  group showed elevated D B P compared to the moderate-persistent and high-increasing groups. A comparison o f the trajectories suggests that early-life S E S contributes to these group differences. In addition, the high-increasing group had lower D B P compared to the moderate-increasing  14 group, but because the high-increasing group showed higher early-life, current, and cumulative SES, we cannot differentiate between the effects o f early-life, current, and cumulative S E S . Next, we tested trajectory group differences in H R and B M I . Results indicated no significant group differences in the estimated averages for these outcomes (p's>. 10). Life-Course Models of SES and Biological Outcomes Group trajectory analyses suggest that early life S E S , rather than current S E S , contributes to adolescents' S B P and D B P . However, the trajectory analyses did not provide a direct test o f the dynamic S E S models.  Thus, analyses using specific measures o f early life S E S , current  SES, cumulative S E S , and mobility were used to confirm the trajectory findings and to perform a head-to-head comparison o f each o f the life-course models.  These follow-up analyses were  intended to confirm our initial findings, and to clarify the role o f cumulative S E S and social mobility in adolescents' health outcomes. Correlations among SES indices. Early S E S , current S E S , and cumulative S E S were significantly associated with one another (r's ranging from .51 to .84, /?'s<.001).  Slope o f S E S was negatively related to early  SES (r=-.44, p<.001), positively related to current S E S (r=.49, p<.001), and unrelated to cumulative S E S (p>. 10). Correlations between SES indices and biological outcomes. We first tested each type o f S E S measure separately for its ability to predict biological outcomes. W e tested the critical period model by conducting Pearson correlations between early life S E S and biological outcomes.  Lower early life S E S was associated with higher S B P and  higher D B P (r=-.25,/K.01 and r=-.26,p<.05, respectively). Early life S E S was unrelated to H R and B M I ( r = . 1 0 , / » . 2 0 and r=-.09,/?>.20, respectively).  15 The second timing model proposes that current S E S is what is most important to current adolescent health.  Thus, we tested the associations between current S E S and biological  outcomes. Lower current S E S was associated with higher D B P (r=-.20, p<.05). Current S E S was unrelated to S B P , H R , and B M I (r's ranging from -.07 to .00, p's>.10). To investigate relationships between dynamic S E S and health, we first tested the model that cumulative S E S would predict biological outcomes.  Analyses indicated that lower  cumulative S E S was associated with higher S B P and D B P (r=-.20, p<.05 and r=-.26, p<.0\, respectively). Cumulative S E S was unrelated to H R and B M I (r=.00, p>.20 and r=-.08, p>.\0, respectively). A second conceptualization o f relationships between dynamic S E S and health is that change in SES w i l l affect adolescent health. To test this hypothesis, we correlated slope o f SES with adolescent health outcomes.  Analyses indicated that higher slope o f S E S was associated  with higher S B P and B M I (r=.23, p<.05 and r= 20, p<.05, respectively). Slope o f S E S was unrelated to D B P and H R (r=.05,p>.20 and r=-A3,p>.20, respectively). Simultaneous regression  analyses.  Simultaneous regression analyses were conducted to compare the effects o f early life SES, current S E S , cumulative S E S , and slope o f SES on S B P and D B P . B M I was not included in these analyses because it correlated with only one S E S index (slope o f SES). H R was not included in these analyses because there were no significant associations. When age, gender, and race were unrelated to the outcome variable, they were dropped from the regression model. Given that early S E S , cumulative S E S , and slope o f S E S were all significantly related to S B P , we tested whether one S E S index contributed to the prediction o f S B P over and above the others. Due to the number o f covariates entered in each regression analysis, we did not have the  16  statistical power to include all three predictors in a single equation. Thus, we compared two S E S indicators at a time.  First, we compared the effects o f early life S E S and cumulative S E S .  Results indicated that early life S E S predicted S B P over and above the effects o f cumulative S E S (P=-.34, f(94)=-1.99, p<.05). In contrast, cumulative S E S was not a significant predictor after controlling for early life S E S (P=.10, f(94)=.62, p>.20).  For early life S E S and slope o f S E S ,  early life S E S was marginally related to S B P after controlling for the effects o f slope (P=-.21, /(94)=-1.9, p=.06).  In contrast, slope o f S E S did not contribute to the prediction o f S B P after  controlling for early life S E S (P=.12, ?(94)=1.20,/?>.20). Finally, we compared slope o f SES and cumulative S E S simultaneously. Slope of SES contributed significantly to the prediction o f S B P above and beyond cumulative S E S (p=.219, r(95)=2.44, p<05).  Cumulative S E S was  marginally related to S B P after controlling for the effects o f slope (p=-.19, f(95)=-1.91,_p=06). These analyses indicate that early life S E S predicts S B P over and above the effects o f both cumulative S E S and slope o f S E S . Next, we compared the effects o f early S E S , current S E S , and cumulative S E S on D B P . First, we compared the effects o f early S E S and current S E S . Results showed that early S E S predicted over and above the effects o f current S E S (P=-.27, /(94)=-2.14, p<.05).  In contrast,  current S E S did not predict over and above the effects o f early S E S (p=-.05, r(94)=-.40, p>.20). We then entered early S E S and cumulative S E S into a regression equation predicting D B P . Results indicated that neither predictor was significant after controlling for the effects o f the other (P=-.23, t(93)=-l.2,p>.20  for early-life S E S and p=-.09, f(93)=-.48,p>.20 for cumulative  SES). Finally, we compared the effects o f current S E S and cumulative S E S . Neither predictor contributed significantly to D B P after controlling for the effects o f the other (p=.074, f(95)=.45, p>.20 for current S E S and p=-.32, r(95)=-1.85, p>.05 for cumulative S E S ) . These analyses  17 indicate that early life S E S predicts DBP independent o f current S E S . However, both early SES and cumulative S E S may play a similarly important role in adolescents' DBP.  18  DISCUSSION The current study examined patterns o f SES throughout childhood and their relationship to adolescents' S B P , D B P , H R , and B M I . The goal was to determine which life-course models (early life, current, cumulative, and/or mobility) best explained the relationship between S E S experiences across an adolescent's lifespan and markers o f current cardiovascular health. Our first objective was to determine whether distinct trajectories o f S E S would be evident in our sample o f middle-class families. Analyses revealed four distinct trajectories o f family SES:  low-increasing,  moderate-persistent,  moderate-increasing,  and  high-increasing.  Adolescents in the low-increasing group experienced low early life S E S and upward mobility through childhood. Adolescents i n the moderate-persistent group experienced moderate early life SES that remained stable over time.  Compared to adolescents i n other groups, these  adolescents experienced the least social mobility and the lowest current S E S . Adolescents in the moderate-increasing group experienced moderate early life S E S and upward mobility that leveled off in early adolescence. Finally, adolescents in the high-increasing group experienced high early life, cumulative, and current S E S . Our next goal was to determine whether these trajectories differentially related to markers of C V risk. In respect to S B P , adolescents in the low-increasing group had the highest S B P o f any o f the trajectory groups. The difference between the low-increasing group and each of the other groups was quite large (around 7 m m Hg). In adult populations, S B P differences o f this magnitude are clinically relevant. For instance, it has been estimated that a 7 m m H g drop in middle-aged men's S B P would be associated with a 16% reduction i n first occurrences o f major C V D over the following 10 years (Emberson, Whincup, Morris, Walker, Ebrahim, 2004). Which life-course model best explains this effect?  The low-increasing trajectory showed the  19  lowest S E S early i n life, but S E S increased over time so that it was comparable to the moderatepersistent and moderate-increasing groups by adolescence. This indicates that current SES does not differentiate adolescents' current S B P . In addition, the low-increasing trajectory showed similar cumulative S E S to the moderate-persistent group, indicating that group differences in S B P could not be predicted by cumulative S E S . However, trajectory group comparisons failed to differentiate the effects o f " l o w " versus "increasing" S E S .  Specifically, the low-increasing  group had higher S B P compared to the moderate-increasing and high-increasing groups, but because social mobility differed across groups, it was unclear whether group differences were due to early S E S or mobility.  However, follow-up analyses indicated that early life SES  predicted above and beyond slope o f S E S , and this strongly suggests that timing, rather than dynamics, is the critical factor. Collectively, these analyses indicate that o f the life-course SES models we studied, the critical period account best depicts S B P in adolescence. SES early in life also appears to play a role in adolescents' D B P . Adolescents in the lowincreasing group had higher D B P than the moderate-persistent or high-increasing groups. Given that the low-increasing and moderate-persistent trajectories showed similar current S E S , current SES does not best discriminate adolescents' D B P . In addition, the similar cumulative SES shown by the low-increasing and moderate-persistent trajectories indicates that cumulative SES does not fully explain group differences in D B P . Furthermore, the regression analyses showed that early life S E S predicted D B P over and above current S E S . Thus, there is evidence for the importance of early life S E S in predicting current adolescent D B P . However, there is also evidence that cumulative S E S contributes to adolescents' D B P . Specifically, the high-increasing group had the lowest D B P and was significantly different from both the moderate-increasing and low-increasing groups. The high-increasing group had higher early life, current, and cumulative  •  20  S E S ; so, it is difficult to discern which aspect(s) o f S E S account for the l o w D B P in this group. However, multiple regression analyses indicated that while early life S E S predicted over and above current S E S , it did not independently predict beyond cumulative S E S . These findings suggest that both early life and cumulative SES may play similarly important roles i n adolescent DBP.  Finally, trajectory analyses did not allow us to tease apart the effects o f " l o w " versus  "increasing" S E S .  Specifically, the low-increasing group had higher D B P compared to the  moderate-persistent group, but it was unclear whether this was due to early life S E S (low versus moderate) or mobility (increasing versus persistent). However, follow-up analyses indicated that slope o f S E S , was unrelated to D B P ; so, we were able to rule out mobility as a factor in adolescents' D B P . Taken together, these results point to early life S E S exposure as an important determinant o f adolescents' D B P , but there is also some evidence for cumulative S E S influences. In fact, it may be that timing matters at the low end o f SES (accounting for the high D B P in the low-increasing group), but that cumulative SES matters at the high end o f the spectrum (accounting for the low D B P i n the high-increasing group). These findings provide insights into how S E S in early life" comes to influence C V morbidity and mortality. They suggest that by the time children reach adolescence,  the  detrimental influence o f low early-SES on C V health are already apparent. It may be that low SES increases exposure to social and physical "pollutants" - such as crowding, violence, malnutrition, allergens - that have the capacity to program patterns o f biological functioning over the long-term (Meaney, 2004; M i l l e r & Chen, under review). In rodents this biological embedding o f social experience results in differential adulthood regulation o f stress-response systems such as the hypothalamic-pituitary-adrenocortical and sympathetic adrenal medullary axes (Hertzman, 1999; Meaney, Aitken, Bhatnagar, & Sapolsky, 1991; Meaney, Aitken, V a n  21  Berkel, Bhatnagar, & Sapolsky, 1998). Heightened activity o f these systems may increase vulnerability to cardiac disease over the lifespan. health behaviors through the life course.  Early life experiences may also influence  For instance, early life S E S is associated with  cardiovascular risk factors such as cigarette smoking, binge drinking, and obesity in adulthood (Lynch, Kaplan, & Salonen, 1997; Lawlor et al, 2005). Thus, adolescents who experienced low early life S E S may be more likely to engage i n poor health behaviors that have an adverse effect on the cardiovascular system. In contrast, current S E S was unrelated to adolescents' blood pressure after controlling for the effects o f early S E S experiences. This finding is consistent with past research showing that SES is unrelated to blood pressure among adolescents.  In general, studies in this area have  shown that S E S effects on blood pressure are evident in childhood and adulthood, but not in adolescence (Chen, Matthews, & Boyce, 2002). To explain this pattern o f relationships, West (1997) suggested that S E S differences in children's health diminish i n adolescence due to a common youth culture i n school. Youth culture is thought to cross family and neighborhood socioeconomic lines i n a way that reduces S E S health differences during this stage o f life. Although more research is needed to test these hypotheses directly, it appears that current SES models may be more useful for explaining the relationship between S E S and B P among populations o f children and adults, rather than among adolescents. In respect to dynamic aspects o f S E S , our findings indicate that cumulative S E S may play a role i n adolescents' D B P . Higher D B P among adolescents who experienced longer durations of low SES may reflect cumulative wear-and-tear on the body (i.e., allostatic load, M c E w e n , 1998). This is consistent with past research showing that the accumulation o f environmental and psychosocial risk associated with low S E S is related to children's blood pressure, adrenocortical  22  activity, and B M I (Evans, 2003).  In the present study, cumulative S E S was associated with  D B P , but not S B P . W e speculate that this is related to the way l o w - S E S children appraise their social environments.  Stressors can be appraised as threatening or challenging, and research  evidence suggests that these two responses to stressors have distinct physiological correlates (Tomaka, Blascovich, Kibler, & Ernst, 1997). Threat appraisals are characterized by increases in vascular reactivity, linked to D B P , whereas challenge appraisals are characterized by increases in cardiac reactivity, linked to S B P . Previous research has demonstrated that l o w - S E S children are more likely to interpret ambiguous social situations as threatening (Chen, Langer, Raphaelson, & Matthews, 2004). Thus, higher D B P among adolescents who experienced lower cumulative SES may reflect the accumulation o f threat appraisals over time. W e found no support for mobility as an important determinant o f B P . In the group trajectory analyses for S B P , the moderate-increasing and high-increasing groups differed from the low-increasing group, but did not differ from the moderate-persistent group. If mobility was an important predictor o f S B P , we would expect to see differences between "persistent" and "increasing" trajectory groups. Regression analyses confirmed these findings, as mobility was unrelated to S B P after controlling for the effects o f early life S E S .  W e reached similar  conclusions for D B P . Specifically, the moderate-increasing and high-increasing groups differed, whereas the moderate-increasing and moderate-persistent  groups did not, suggesting that  "persistent" versus "increasing" trajectories do not differentiate adolescents' D B P . Furthermore, correlational analyses showed no association between mobility and D B P . Collectively, these findings indicate that social mobility through childhood does not play a role i n adolescents' B P . However, our sample showed very little social mobility; so, this dimension might be more important i n populations with varying degrees of upward and downward mobility. In addition, it  23  may be important to use measures o f SES that are more sensitive to S E S fluctuations (e.g., family income), as well as dynamic measures o f health. For instance, changes in B P may occur closely in time with fluctuations i n S E S ; so, without a repeated-measures design, increases and decreases in B P would get washed out over time. Counter to our expectations, we found no association between trajectories o f S E S and adolescents' H R and B M I . These findings are inconsistent with past studies showing that lower SES is associated with higher B M I among adolescents (e.g., Evans, 2003; Goodman, 1999). Recent evidence suggests that family and neighborhood resources have independent effects on adolescents' B M I (Chen & Patterson, in press). Thus, it may be important to take neighborhood characteristics into account when examining the relationship between family housing and B M I . Given that H R and B P are both controlled by the autonomic nervous system, we would expect to find a similar pattern o f results for these outcomes. Thus, it is unclear why early-life SES would shape D B P and S B P , but not H R . There are a number o f limitations to this study that should be noted. First, assignment into trajectory groups is probabilistic i n nature. In each trajectory group, some adolescents had a high probability o f membership in that group, whereas others had a lower probability o f membership in that group.  However, the 95% confidence intervals calculated around the  trajectories show little overlap, indicating minimal classification error (See Figure 1). A l s o , errors in classification would only increase random error, making it more difficult to detect relationships between trajectories and outcomes like S B P and D B P .  Second, the study would  have benefited from a more thorough examination o f C V risk, including measures o f cigarette smoking, physical activity, cholesterol, and triglycerides. It is possible that the impact o f SES timing and dynamics depends on the C V risk factor in question, and this is an important area for  24  future study. Third, our outcome measures were limited by one-time lab assessment. Resting B P in the lab can be influenced by numerous factors, such as the white-coat-effect.  These  problems are mitigated to a large extent by ambulatory B P measures which are better predictors of clinical outcomes. For these reasons, ambulatory monitors should be used in the next wave o f studies. Fourth, there may be problems associated with the measurement o f S E S v i a number o f bedrooms.  First, parents' retrospective reports on housing may be unreliable.  Second, this  measure does not take into account whether families rent or own, which neighborhood they live in, or the costs o f renting versus owning i n their neighborhood. However, this imperfect indicator of S E S should only diminish power to detect relations with B P . Future studies that collect a broad array o f S E S data prospectively would help resolve these difficulties, especially i f they could be done from the time the child was born. Finally, our measure o f social mobility may also reflect the effects o f moving on children. Changing homes, neighborhoods, and schools can be stressful for children, and this may account for the association between upward mobility and higher B M I and S B P in this study. In summary, findings from the current study indicate that the timing o f S E S exposure plays an important role i n adolescents' blood pressure.  Specifically, S E S experiences early in  life were associated with adolescents' S B P and D B P , independent o f current S E S .  These  findings point toward early life developmental processes as potential candidates for explaining the relationship between S E S and risk factors related to C V D .  In addition, our results suggest  that interventions designed to reduce S E S health disparities would be most effective i f they took place within the first few years o f a child's life.  25  REFERENCES Adler, N . E . , Boyce, T., Chesney, M . A . , Cohen, S., Folkman, S., Kahn, R. L . , & Syme, S. L . (1994).  Socioeconomic status and health: The challenge o f the gradient.  Psychologist,  American  49, 15-24.  Anderson, N . B . , & Armstead, C . A .  (1995). Toward understanding the association o f  socioeconomic status and health: A new challenge for the biopsychosocial approach. Psychosomatic  Medicine, 57, 213-225.  Barker, D . J. P. (1992).  Fetal and infant origins of adult disease.  London: British Medical  Journal. Belsley, D . , K u h , E . , Welsch, R. (1980). Regression Diagnostics. N e w Y o r k : Wiley. Berenson, G . S., Srinivasan, S. R., Bao, W . , Newman, W . P., Tracy, R. E . , & Wattigney, W . A . (1998). Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults. New England Journal of Medicine, 338, 1650-1656. Berenson, G.S., Wattigney, W . A . , Tracy, R . E . , Newman, W.P., Srnivasan, S.R., Webber, L . S . , et al. (1992). Atherosclerosis o f the aorta and coronary arteries and cardiovascular risk factors i n persons aged 6 to 30 years and studied at necropsy (the Bogalusa Heart Study). American Journal of Cardiology, Chen, E . & Berdan, L . (2006).  70, 851-858.  Socioeconomic Status and Patterns o f Parent-Adolescent  Interactions. Journal of Research on Adolescence, Chen E . , & Patterson, L . Q. (in press).  16, 19-27.  Neighborhood, family, and subjective socioeconomic  status: H o w do they relate to adolescent health? Health  Psychology.  Chen, E . , Langer, D . , Raphaelson, Y . , & Matthews, K . (2004). Socioeconomic status and health in adolescents: The role o f stress interpretations. Child Development, 75, 1039-1052.  26 Chen, E . , Matthews, K . A . , & Boyce, W . T. (2002).  Socioeconomic differences in children's  health: H o w and why do these relationships change with age? Psychological  Bulletin,  128, 295-329. Cohen, S., Doyle, W . J., Turner, R. B . , Alper, C . M . , & Skoner, D . P. (2004). Childhood socioeconomic Psychosomatic  status  and  host  resistance  to  infectious  illness  in  adulthood.  Medicine, 66, 553-558.  Davey Smith, G . , & Hart, C . (2002).  Life-course socioeconomic and behavioral influences on  cardiovascular disease mortality: The collaborative study.  American  Journal of Public  Health, 92, 1295-1298. Duncan, G .  (1988). The volatility o f family income over the life course.  In P. Baltes, D .  Featherman, & R. M . Lerner (Eds.) Life-Span Development and Behavior,  Vol.  9 (pp.  317-358). Hillsdale, N J : Lawrence Erlbaum Associates. Duncan, G . , Brooks-Gunn, J., & Klebanov, P. (1994).  Economic deprivation and early  childhood development. Child Development, 65, 296-318. Duncan, G . , Yeung, W . J., Brooks-Gunn, J., & Smith, J. R. (1998). H o w much does childhood poverty affect the life chances o f children? American Sociological  Review, 63, 406-423.  Emberson, J., Whincup, P., Morris, R., Walker, M . , & Ebrahim, S. (2004).  Evaluating the  impact o f population and high-risk strategies for the primary prevention o f cardiovascular disease. European Heart Journal, 25, 484-491. Evans, G . W . (2003).  A multimethodological analysis o f cumulative risk and allostatic load  among rural children. Developmental  Psychology, 39, 924-933.  Frankel, S., Davey Smith, G . , & Gunnel, D . (1999).  Childhood socioeconomic position and  adult cardiovascular mortality: the B o y d Orr cohort. American Journal of  Epidemiology,  150, 1081-1084. Galobardes, B . , Davey Smith, G . , & Lynch, J. W . (2006). Systematic review o f the influence o f childhood socioeconomic circumstances on risk for cardiovascular disease in adulthood. Annals of Epidemiology,  16, 91-104.  Galobardes, B . , L y n c h , J. W . , & Davey Smith, G . (2004). circumstances  and  cause-specific  interpretation. Epidemiological Goodman, E . (1999).  mortality in adulthood:  Childhood socioeconomic Systematic  review and  Reviews, 26, 7-21.  The role o f socioeconomic status gradients i n explaining differences in  U S adolescents'health. American Journal of Public Health, 89, 1522-1528. Hertzman, A . (1999). The biological embedding o f early experience and its effects on health in adulthood. Annals of the New York Academy of Sciences, 896, 85-95. Jones, B . , & Nagin, D . Advances i n group-based trajectory modeling and a S A S procedure for estimating them. Submitted. Jones, B . , Nagin, D . , & Roeder, K . (2001). estimating developmental trajectories.  A S A S procedure based on mixture models for Sociological  Methods and Research,  29, 374-  393. Karlamangla, A . S., Singer, B . H . , Williams, D . R., Schwartz, J. E . , Matthews, K . A . , Kiefe, C . I., & Seeman,  T. E . (2005).  Impact  o f socioeconomic status on longitudinal  accumulation o f cardiovascular risk in young adults: the C A R D I A Study ( U S A ) . Science and Medicine, 60, 999-1015.  Social  28  Laaksonen, M . , Rahkonen, O., Martikainen, P., & Lahelma, E . (2005) Socioeconomic position and self-rated health: The contribution o f childhood socioeconomic circumstances, adult socioeconomic status, and material resources. American Journal of Public Health, 95, 1403-1409. Lauer, R. M , Burns, T. L . , Clarke, W . R., & Mahoney, L . T. (1991). Childhood predictors of future blood pressure. Hypertension,  18, 174-181.  Lynch, J. W . , Kaplan, G . A . , & Salonen, J. T. (1997).  W h y do poor people behave poorly?  Variation i n adult health behaviours and psychosocial characteristics by stages o f the socioeconomic lifecourse. Social Science and Medicine, 44, 809-819. Mahoney, L . T., Bruns, T. L . , Stanford, W . , Thompson, B . H . , Witt, J. D . , Rost, C . A . , & Lauer, R. M . (1996).  Coronary risk factors measured i n childhood and young adult life are  associated with coronary artery calcification in adulthood:  The Bogalusa Heart Study.  Journal of the American Medical Association, 290, 2271-2276. Matthews, K . A . (2005).  Psychological perspectives on the development o f coronary heart  disease. American Psychologist,  60, 783-79.  Matthews, K . A . , Kiefe, C . I., Lewis, C . E . , L i u , K . , Sidney, S., & Yunis, C .  (2002).  Socioeconomic trajectories and incident hypertension i n a biracial cohort o f young adults. Hypertension, 39, 772-776. McDonough, P., Duncan, G . J., Williams, D . , & House, J. (1997). Income dynamics and adult mortality in the United States, 1872-1989. American Journal of Public Health, 87, 14761483. M c E w e n , B . S. (1998).  Protective and damaging effects o f stress mediators.  Journal of Medicine, 338, 171-179.  New  England  Mcleod, J. D . , & Shanahan, M . J. (1996). Trajectories o f poverty and children's mental health. Journal of Health and Social Behavior, 37, 209-220. Meaney, M . J. (2004). The nature o f nurture: Maternal effects and chromatin remodeling. J. T. Cacioppo, & G . G . Berntson (Eds.)  Essays in social neuroscience.  In:  (pp. 1-14).  Cambridge, M A , U S : M I T Press. Meaney, M . J., Aitken, D . H . , Bhatnagar, S., & Sapolsky, R. M . (1991).  Postnatal handling  attenuates certain neuroendocrine, anatomical, and cognitive dysfunctions associated with aging in female rats. Neurobiology  of Aging, 12, 31-38.  Meaney, M . J., Aitken, D . H . , V a n Berkel, C . Bhatnagar, S., & Sapolsky, R. M . Effect o f neonatal handling on age-related impairments associated with the hippocampus. Science, 239, 766-768. Miller, G . E . , & Chen, E . (under review).  Early-Life Socioeconomic Conditions Predict  Expression o f Genes Regulating Inflammation During Adolescence. Pensola, T., & Martikainen, P. (2003). Life-courses experiences and mortality by adult social class among young men. Social Science and Medicine, 58, 2149-2170. Power, C , Manor, O., & Matthews, S. (1999). The duration and timing o f exposure: Effects o f socioeconomic environment on adult health.  American  Journal of Public Health, 89,  1059-1065. Raitakari, O. T., Juonala, M . , Kahonen, M . , Taittonen, L . , Laitinen, T., Maki-Torkko, N . , et al. (2003). Cardiovascular risk factors in childhood and carotid artery intima-media thickness in adulthood. Journal of the American Medical Association,  290, 2277-2283.  Tomaka, J., Blascovich, J., Kibler, J., & Ernst, J. M . antecedents o f threat and challenge appraisal.  (1997). Cognitive and physiological Journal  of Personality  and  Social  Psychology, 73, 63-72. Virtanen, P., Vahtera, J., K i v i m a k i , M . , Liukkonen, V . , Virtanen, M . , & Ferrie, J. (2005). Labor Market Trajectories and Health: A Four-Year Follow-up Study o f Initially Fixed-Term Employees. American Journal of Epidemiology,  161, 8 4 0 - 8 4 6 .  West, P. (1997). Health inequalities in the early years: Is there equalization in youth? Science and Medicine, 30, 665-673.  Social  Table 1 Demographic and Health Characteristics  of the Sample  Family Current Income (1-6 scale)  3.97±1.5  Parent Years o f Education  15.93±2.45  History o f Heart Disease  4%  Adolescents Age  15.61±1.05  Female  53%  Caucasian  75%  African American  24%  S B P (mmHg)  108.93±9.17  D B P (mmHg)  60.23±6.24  HR(bpm)  73.11±10.80  BMI  24.17±4.89  Note. For family income, category 3 corresponds to $50 000-74 999 and category 4 corresponds to $75 000-99 999.  Table 2 Indices of Socioeconomic SES Index  Status through Child's Life Computation  Mean±SD  Early Life  Average # bedrooms in family home from child's birth to age 3  3.07±0.85  Current  Current # bedrooms in family home  3.78±0.84  Cumulative  Average # bedrooms in family home from child's birth to present  3.46±0.72  Slope  Slope o f # bedrooms in family home from child's birth to present  0.053±0.064  Table 3 Selecting the number of trajectories. Number o f Groups 1 2 3 4 5  BIC  Change in B I C  -2161.97 -1616.40 -1564.86 -1406.64 -1310.65  545.57 51.54 158.22 95.99  Note. BIC=Bayesian Information Criterion  Table 4 Model Selection for the Shape of each Trajectory Trajectory 1  Trajectory 2  Trajectory 3  Trajectory 4 BIC  Model and Parameter  1  Function Tested  Parameter Estimate  Cubic  Function Tested  Parameter Estimate  Cubic  Function Tested  Parameter Estimate  Cubic  Function Tested  Parameter Estimate  -1406.64  Cubic  Intercept  1.84  2.85  3.25  4.31  Linear  0.11  0.039  0.13**  0.082  Quadratic  0.00  -.002  -.002  -.002  Cubic  0.00  0.00  0.00  0.00  2  Quadratic Intercept  Quadratic 1.83  Linear Quadratic 3  0.00 Linear  Intercept Linear Quadratic  Quadratic  -1392.00  2.85  3.23  4.32  0.036  0 15***  0.079**  -.001  -.006***  -.001  Linear 1.86  Quadratic  Quadratic  Linear  -1382.06  2.92  3.23  4.37  0.014**  0.15***  -.058***  -.006***  Note. BIC=Bayesian Information Criterion. Trajectory l=Low-increasing, Trajectory 2=Moderate-persistent, Trajectory 3=Moderate-increasing, and Trajectory 4=High-increasing. **p<.0\, ***p<.0  Table 5 Partial correlations between SES indices and adolescent biological  outcomes.  SBP  DBP  HR  BMI  Early life  -.25**  -.26*  .10  -.09  Current  -.07  -.20*  -.05  .00  Cumulative  -.20*  -.26**  .00  -.08  Slope  0.23*  .05  -.13  .20*  Note. Correlations control for age, race, gender, and the number o f people i n the family home. *jt?<.05. **p<.0l  36  6 i  0 "I  1  1  1  1  1  1  1  0  2  4  6  8  10  12  14  1 —  16  —  i  18  Age of Child (Years)  Figure 1. Estimates o f SES trajectories and their accompanying 95% confidence interval from the child's birth to current age.  37  • Low-increasing • Moderate-persistent O Moderate-increasing il High-increasing  Figure 2. Estimates o f average S B P by trajectory group.  The error bars represent  standard error o f the mean. The estimated average S B P for the low-increasing group is significantly higher than the estimated averages for the moderate-persistent, moderate increasing, and high-increasing groups.  38  Figure 3.  Estimates o f average D B P by trajectory group.  The error bars represent  standard error o f the mean. The estimated average D B P for the low-increasing group is significantly higher than the estimated averages for the moderate-persistent and highincreasing groups.  The estimated average D B P is significantly lower for the high-  increasing group compared to the moderate- increasing group.  

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