Pathways Linking Childhood SES and Adult Health Behaviors and Psychological Resources in Black and White Men

Pathways Linking Childhood SES and Adult Health Behaviors and Psychological Resources in Black... Abstract Background Exposure to low socioeconomic status (SES) in childhood predicts increased morbidity and mortality. However, little prospective evidence is available to test pathways linking low childhood SES to adult health. Purpose In the current study, indirect effects through positive parenting in adolescence and adult SES were tested in the association between childhood SES and adult health behaviors and psychological resources. Methods Men (n = 305; 53% Black) were followed longitudinally from ages 7 to 32. SES was measured annually in childhood (ages 7–9) and again in adulthood (age 32) using the Hollingshead index. Parenting was assessed annually (ages 13–16) using caregivers’ and boys’ self-report of supervision, communication, and expectations for their son’s future. Health behaviors (cigarette and alcohol use, fruit and vegetable consumption, and physical activity) and psychological resources (optimism, purpose in life, self-mastery, and self-esteem) were assessed in adulthood (age 32). Results Structural equation modeling showed that higher childhood SES was associated with more positive parenting in adolescence and higher adult SES. Higher childhood SES was indirectly associated with healthier behaviors and higher psychological resources in adulthood through pathways involving positive parenting during adolescence and SES in adulthood. Findings were consistent in both racial groups. Conclusions Positive parenting in adolescence was an important pathway in understanding associations among childhood SES and health behaviors and psychological resources in adulthood. Low childhood SES was prospectively associated with healthier behaviors and greater psychological resources in part through more positive parenting in adolescence. Socioeconomic status, Childhood, Parenting, Health behaviors, Psychological resources Introduction The socioeconomic environment in early life can have lasting influences on morbidity and mortality decades later [1–3]. Individuals from families of lower socioeconomic status (SES) have higher rates of cardiovascular disease, diabetes, cancer, and asthma, among other adverse health outcomes as compared to individuals raised in more advantaged environments [4–6]. Understanding the pathways from SES in childhood to health in adulthood is complicated, given a multitude of psychosocial, behavioral, and biological processes that underlie associations and unfold over several decades (reviewed in [1]). In the current study, we consider both the parent–child relationship in adolescence and SES in adulthood as mediators of links between childhood SES and adult health behaviors and psychological resources, respectively, using data from a prospective, longitudinal study of Black and White men followed from ages 7 to 32. Behavioral factors and psychological resources are considered outcomes in the present research. Health behaviors, such as cigarette and alcohol use, fruit and vegetable consumption, and physical activity, are important mediators of the association between childhood SES and morbidity and mortality [7, 8]. Adults from lower SES backgrounds report more smoking, more sedentary behavior and less physical activity, poorer dietary habits, and more drug use, especially in the context of current unemployment [5, 9–12]. These health behaviors can affect physical health directly via physiological pathways, and they may also promote obesity, which has independent, negative health consequences [13]. Psychological resources, here defined to include purpose in life, mastery, self-esteem, and optimism, are important components of mental health, reflecting the presence of positive attributes and not simply the absence of depression and anxiety [14]. In the Reserve Capacity Model [15], such resources mitigate against stressful events and help explain how lower SES may lead to increased morbidity and mortality. The psychological resource composite utilized in the current study focuses on intrapersonal resources thought to develop in childhood and also is akin to eudaimonic well-being, which is multidimensional in nature and captures psychological flourishing and self-realization with important linkages to health (e.g., [16]). Such resources are uniquely predictive of physical health, including better self-rated health, lower rates of disease and increased longevity as well as more favorable profiles of biological risk factors [16–20]. Associations between resources and health may be stronger among lower, as compared to higher, SES individuals [21–24]. Intervention efforts focused on enhancing psychological well-being in primarily clinical populations have demonstrated concomitant improvements in self-reported mental and physical health outcomes [25–29]. Including both behavioral and psychological factors as outcomes offers a holistic window into health and well-being among a diverse sample of relatively young adults. Childhood SES and Adult Health Behaviors and Psychological Resources The socioeconomic environment in childhood predicts adult health behaviors, such as smoking, alcohol consumption, and physical activity [4, 30–33]. For example, childhood socioeconomic position was associated with smoking, physical activity, and body mass index, but not diet in adulthood among African Americans in the Jackson Heart Study [30]. Developmental data support the idea that psychological factors in adulthood also vary as a function of childhood SES, such that men with lower childhood SES had higher scores on measures of hostility, hopelessness, and depressive symptoms [31, 34]. Relatively less attention has been paid to associations between childhood SES and positive measures of psychological resources in adulthood. Explanations for the associations between low SES in early life and unhealthy behaviors involve more than personal lifestyle choices, and include social, structural, and economic circumstances that differ systematically by SES. For example, lower SES environments have easier access to tobacco, alcohol, and fast food and less availability of affordable fresh food and safe places for physical activity (e.g., green space, sidewalks; [35]). Further, unhealthy behaviors are often sources of stress-relief and mood regulation, which may offset the burden of chronic stress disproportionately affecting socioeconomically disadvantaged individuals [7, 36, 37]. There are important socioeconomic differences in attitudes and beliefs about healthy behaviors as well, such that lower SES individuals are more fatalistic about their ability to reduce health risks and have stronger beliefs in the influence of chance factors affecting health [7, 38]. Family and peers are also influential in the adoption and maintenance of health behaviors [39]. Such factors contribute to the aforementioned differences in health behavior profiles and psychological dispositions as a function of childhood SES. A key limitation in the literature on childhood SES and adult health behaviors and psychological factors, however, is that childhood SES is often assessed retrospectively, which is subject to recall bias and measurement error [40]. Associations between childhood SES and adult health are typically stronger in prospective studies that do not rely on adult recall of childhood SES [3]. Further, childhood and adult SES are correlated [41], and childhood SES may be associated with adult health outcomes at least in part because it is highly correlated with adult SES. Thus, longitudinal data, such as those utilized in the present study, are necessary to discern the shared and independent associations of SES and health behaviors at different points across the life course. There is also a notable lack of diversity in sample populations of the extant literature on childhood SES, and health behaviors and psychological factors, primarily relying on White Americans and Western European samples (cf., [30, 42]). This study seeks to address this shortcoming by including both White and Black men. Parenting and Adult Health Behaviors and Psychological Resources The family context is critical for understanding how childhood SES affects adult health behaviors and psychological dispositions. Parents typically provide the most proximal social environment for children and can shape adult psychological and behavioral factors [43]. During adolescence, there are notable changes in the parent–child relationships, including parents granting greater autonomy and adolescents beginning to shape their own social environment. However, parents remain a key influence for adolescent development, and the establishment of lasting health behaviors typically occurs during this developmental period as well [1, 44, 45]. In the current study, multiple aspects of parenting during adolescence are highlighted, including communication styles marked by warmth, consistency of supervision, and positive expectations for the future. Factors such as parental expectations for achievement and discipline strategies stand out as important parental and home factors that link socioeconomic factors to achievement in school [46]. In particular, parenting that involves close supervision, consistency, future orientation, and warmth is especially important among socioeconomically disadvantaged children living in urban environments [47–53]. In a 12-year longitudinal study of positive youth development, factors that distinguished among rural African American males classified as high versus low risk for substance use and sexual risk behaviors included exposure to harsh and inconsistent parenting, lower future orientation, and more deviant peers [53]. Further, randomized trials designed to improve parental monitoring and communication with adolescents have led to reductions in childhood and adolescent obesity through improvements in health-related behaviors and decreases in depressive symptoms [54–56]; reduced inflammation, particularly in adolescents from low-SES families [57]; and fewer increases in risky behaviors (e.g., marijuana and alcohol use, sexual risk behaviors) in emerging adults, especially among those reporting high stress [58, 59]. In each of these trials, effects on health were at least partially mediated by changes in parenting and in the parent–adolescent relationship following the intervention. Several theoretical models inform the ways in which parenting practices may differ by early life SES and affect health and well-being in childhood, adolescence, and into adulthood. The Family Stress Model emphasizes that socioeconomic disadvantage and accompanying economic stress contribute to family distress and dysfunction, which are linked to parental depressive symptoms [60], hostility among family members, and maladaptive parenting practices. The key parental practices associated with more socioeconomic disadvantage are insufficient supervision, a harsher, more authoritarian style, a lack of warmth and support, and inconsistency [61, 62]. These parenting practices in turn affect child and adolescent adjustment [63]. Similarly, the Risky Families Model posits that a negative family social environment, including dimensions of conflict, neglect, and a lack of warmth and support, can affect emotional, behavioral, and biological processes throughout childhood and adolescence, contributing to mental and physical health problems in adulthood [64]. Parenting practices of monitoring, modeling, discipline, communication, and affection have further been identified as key pathways linking marital conflict and dissolution to emotional dysregulation, emotional insecurity, and ultimately physical health problems [65]. While these respective models have garnered considerable empirical support, linkages to adult outcomes are rare, given the paucity of relevant prospective, longitudinal data over several decades (cf., [66–69]). Study Aim and Hypotheses Using a prospective, longitudinal design of Black and White men starting at age 7 and into their early 30s, the current study addresses several shortcomings of prior research by testing a model with direct and indirect effects from SES in childhood to parenting factors in adolescence to adult SES and to health behaviors and psychological resources in adulthood. We examined the following hypotheses: 1. Positive health behaviors and psychological resources in adulthood are predicted by higher childhood SES, more positive parenting in adolescence, and higher adult SES. 2. Childhood SES is linked with adult health behaviors and psychological resources in part through its association with positive parenting in adolescence. 3. Childhood SES is linked with adult health behaviors and psychological resources in part through its association with adult SES. Methods Sample Data for the current study came from the youngest cohort of the Pittsburgh Youth Study (PYS), a longitudinal study of boys initially recruited from a pool of first graders enrolled in the Pittsburgh Public Schools in 1987–1988 (N = 503). The sample was recruited from an original pool of 1,165 boys registered to attend the first grade. From that pool, 849 were randomly chosen to undergo a multi-informant (i.e., parent, teacher, child report) screening that assessed early conduct problems (e.g., fighting, stealing). Boys identified at the top 30% on the screening risk measure (N = 256), and a roughly equal number of boys randomly selected from the remainder (N = 247), were selected for longitudinal follow-up (total N = 503). At screening, the mean age was 6.2 years, and the sample was predominately White (40.6%) and Black (55.7%). The PYS sample was followed at least annually until age 19 with reports from the boys, their primary caretaker, and teachers. Further detail about the PYS is available elsewhere [70]. Men in the PYS were contacted to participate in the current study focused on cardiovascular health and risk for cardiovascular disease in adulthood (mean age = 32 years; range 30–34 years). At the time of the current study, 18 men were deceased, 44 had previously dropped out of the PYS, 4 were severely mentally disabled, and 42 were incarcerated. Of the remaining 395 men, 312 participated (79%). Among those eligible but who did not participate, 27% (n = 22) declined participation, 23% (n = 19) failed to respond to contact or missed appointments, and 51% (n = 42) could not be located. The current sample did not differ from the initial PYS sample on race, risk for conduct problems, reported overweight, childhood SES, or number of health problems in childhood, ps > .05 [71]. However, comparing the analytic sample to those with adolescent data who did not participate in the most recent follow-up (n = 169), revealed less supervision and lower expectations for the future among those who did not participate in adulthood as compared to those in the analytic sample, but no differences in parent–child communication. It is worth noting that these differences in parenting were nonsignificant when we compared the analytic sample to those with adolescent data who did not participate in follow-up in adulthood, excluding those incarcerated at follow-up. This study was approved by the Institutional Review Board at the University of Pittsburgh, and all men provided written, informed consent. Measures Socioeconomic status SES was measured annually via the two-factor Hollingshead index [72], which incorporates parental educational attainment and occupational status as reported by the boy’s primary caretaker (childhood SES) or by the participant (adult SES). For childhood SES, the higher of the two parents was used for two-parent families. The mean Hollingshead SES across six occasions between ages 7 and 9 was used as the overall index of childhood SES. Adult SES was only measured on one occasion. Positive parenting Positive parenting in adolescence was assessed as a latent variable with scales of parental supervision, expectations for the future, and parent–child communication as the indicator variables [70]. Dimensions of positive parenting were self-reported annually by the boy’s primary caretaker (86.3% were biological mother) when participants were between the ages of 13 and 16. These scales were initially developed based on pilot research conducted at the Oregon Social Learning Center [73], including literature reviews of the impact of parenting on childhood outcomes, items from existing scales adapted for an urban sample with a substantial minority membership and a range of SES (e.g., Family Environment Scale; [74]), and the Family Assessment Measure [75], followed by detailed psychometric analyses. Parental supervision and involvement was assessed with four items (e.g., “Do you know who your son’s companions are when he is not at home?”) on a 3-point scale (1 = almost never, 2 = sometimes, 3 = almost always). Internal consistency for the supervision and involvement scale ranged from .63 to .74 (mean = .70) across three annual assessments (supervision and involvement was not collected at one of the annual waves). Expectations for the future were assessed by primary caretaker’s indicating how likely it was that their son would achieve 17 different goals or activities related to money, hard work, family life, and legal issues (e.g., “have a well-paying job,” “have a happy family life”) on a 4-point scale (1 = very likely, 4 = not likely at all). Internal consistency for expectations for the future was .64 at both of the two annual assessments (expectations for the future was not collected at two of the annual waves). Parent–child communication was measured with 37 items regarding the parent–child relationship (e.g., “Do you openly show affection to your son?”, “Do you think that your son feels close to you?”) on a 3-point scale (1 = almost never, 2 = sometimes, 3 = almost always). Internal consistency for the communication scale ranged from .58 to .68 (mean = .62) across the four annual assessments and across informants. For parent–child communication, the primary caretaker and the participant both completed the instrument, and these ratings were averaged on an annual basis, which is a commonly accepted method for combining multiple-informant ratings in the developmental literature [76]. The correlation between parent-reported communication and child-reported communication was .34. An overall score was calculated for each dimension by averaging each year’s assessment within the 3-year window. Item scores were re-coded so that higher scale scores reflected more supervision and communication, and higher expectations, respectively. An initial principal components analysis was conducted on the three parenting scales. Using an eigen value greater than one criterion and via inspection of the scree plot, one factor was extracted, explaining 66.3% of the variance. Factor loadings for the three parenting scales ranged from .80 to .84 (median factor loading = .81), indicating that each individual scale was highly correlated with the extracted factor. Health behaviors Health behaviors in adulthood were self-reported by the men and included assessments of cigarette smoking, fruit and vegetable intake, alcohol consumption, and physical activity. Cigarette smoking was treated as a three-level ordinal variable (never smoker or not in past year, less than or equal to 10 cigarettes/day in past year, and greater than 10 cigarettes/day in past year). Fruit and vegetable intake was measured as the average weekly intake of fruits and vegetables. Data on fruit and vegetable consumption were collected using six items from the Behavioral Risk Factor Surveillance System fruit and vegetable module [77]. Participants indicated how often they consumed fruit, fruit juice, lettuce salad, fried potatoes, other kinds of potatoes, and vegetables other than lettuce salads and potatoes on a daily, weekly, or monthly basis. Alcohol consumption was scored as total drinks consumed in an average week. Physical activity was measured using the Paffenbarger Physical Activity Questionnaire and treated as a continuous variable in analyses [78]. Four items assessed the general levels of exercise in terms of kilocalories expended weekly. Participants reported on their regular activity, the amount of walking and stairs they climb each day, and any sports or recreational activities they participated in during the past week and how much time they spent in each activity. Psychological resources Psychological resources in adulthood were assessed with a latent variable with scales of optimism, purpose in life, mastery, and self-esteem as the indicator variables. Optimism was evaluated via the Life Orientation Test-Revised [79]. This scale has six items (e.g., “In uncertain times, I usually expect the best”), and ratings are made on a 4-point scale (1 = strongly disagree, 4 = strongly agree; alpha = .80). Purpose in life was measured with the 6-item Life Engagement Test [80]; participants rate the extent of their agreement to statements such as, “I have lots of reasons for living,” on a 4-point scale (1 = strongly disagree, 4 = strongly agree; alpha = .82). The Pearlin Mastery Scale assessed the extent to which individuals perceive they are in control of forces that significantly affect their lives [81]. The Pearlin Mastery Scale has seven items (e.g., “I can do just about anything I really set my mind to do”), and participants rate the extent to which they agree with the items on a 5-point scale (1 = strongly disagree, 5 = strongly agree; alpha = .80). Finally, self-esteem was evaluated using the Rosenberg Self-Esteem Scale [82]. Participants rated the extent to which they agreed with 10 items (e.g., “I feel that I have a number of good qualities”) on a 4-point scale (1 = strongly disagree, 4 = strongly agree; alpha = .85). For all measures, higher scores reflect greater psychological resources. An initial principal components analysis was conducted on the four psychological resources scales. Using an eigen value greater than one criterion and via inspection of the scree plot, one factor was extracted, explaining 72.7% of the variance. Factor loadings for the four psychological resources scales ranged from .84 to .87 (median factor loading = .85), indicating that each individual scale was highly correlated with the extracted factor. Covariates Race (0 = Black, 1 = White) was self-reported at age 32 and was included as a covariate in all analyses. Cigarette and alcohol use in adolescence (ages 13–16) were also included as covariates in order to address whether these factors may confound any observed associations between parenting in adolescence and health behaviors in adulthood. Cigarette use in adolescence was treated as a binary variable reflecting daily or near daily smoking (smoking on 312 days per year or more) versus not. Alcohol use in adolescence was characterized using two dummy coded variables to reflect never drinking as compared to light users (less than or equal to 10 drinks/week) and never drinking as compared to heavy users (greater than 10 drinks/week). Statistical Analysis Structural equation modeling was used to examine hypothesized associations among childhood SES, positive parenting in adolescence, adult SES, and health behaviors and psychological resources in adulthood. Indirect effects between childhood SES and the outcomes were tested via: (i) positive parenting, (ii) adult SES, or (iii) positive parenting to adult SES [83, 84]. All outcome variables were allowed to correlate with each other, with the exception of smoking as it was run in a separate model (see the following paragraphs). Model fit was assessed using χ2 tests, the root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), and the comparative fit index (CFI). Good fit is indicated by χ2 tests with p > .05, RMSEA less than .05, SRMR less than .08, and CFI greater than .95 [85, 86]. Maximum likelihood estimation was used to produce model parameters for continuous outcomes (all outcomes except smoking), whereas robust weighted least squares (WLSMV) estimation was used to produce model parameters for the smoking outcome. Maximum likelihood estimation is not appropriate for mediation modeling with an ordinal dependent variable (i.e., smoking in adulthood, categorized as never smoker or not in the past year; ≤10 cigarettes/day in past year, >10 cigarettes/day in past year). In both ML and WLSMV models, indirect effects were estimated using bootstrapping procedures with 5,000 resamples [87]. In order to achieve normal distributions, alcohol consumption and physical activity were natural log transformed, and fruit and vegetable consumption was square-root transformed prior to analyses. All parenting and psychological resource variables were standardized (z-scored) prior to analyses. Preliminary analyses determined metric invariance in both latent factors (positive parenting and psychological resources) across race (following procedures outlined in [88]). Further, there was no evidence of moderation by race as model fit was not significantly improved when factor loadings and path coefficients were freed (v. fixed) across race. Therefore, all participants were examined in the same model. Race and adolescent cigarette and alcohol were included as covariates in all models. Analyses were performed with Mplus, version 7.3. Results Table 1 presents descriptive information on all study variables in childhood, adolescence, and adulthood for the full sample. Table 2 presents bivariate correlations among study variables. Lower childhood SES was significantly correlated with poorer parent–child communication, less supervision, and lower parental expectations in adolescence, as well as lower SES and less physical activity in adulthood. Table 1 Descriptive statistics of study variables Total (N = 312) M (SD) or % Range Childhood (ages 7–9)  Family SES 36.8 (10.8) 6–66 Adolescence (ages 13–16)  Poor parent–child communication 52.4 (6.9) 38–72  Low parental supervision 5.9 (1.2) 4–10  Low parental expectations 27.9 (6.4) 17–53  Smoking: never or infrequent 95.4%   Daily or near daily 4.6%  Alcohol: never 34.7%   ≤10 Drinks/week 43.2%   >10 Drinks/week 22.1% Adulthood (mean age 32; range 30–34)  Adult SES 32.2 (15.3) 6–66  Life engagement 19.5 (3.0) 10–24  Optimism 17.1 (3.2) 7–24  Self-mastery 26.9 (4.6) 9–35  Self-esteem 22.7 (5.2) 0–30  Alcohol use (drinks/week) 5.3 (7.5) 0–39  Smoking: never 42.3%   ≤10 Cigarettes/day 38.8%   >10 Cigarettes/day 18.9%  Physical activity 1,470 (1,919) 0–14,716  Fruit/vegetable consumption 16.9 (12.8) 0–85 Total (N = 312) M (SD) or % Range Childhood (ages 7–9)  Family SES 36.8 (10.8) 6–66 Adolescence (ages 13–16)  Poor parent–child communication 52.4 (6.9) 38–72  Low parental supervision 5.9 (1.2) 4–10  Low parental expectations 27.9 (6.4) 17–53  Smoking: never or infrequent 95.4%   Daily or near daily 4.6%  Alcohol: never 34.7%   ≤10 Drinks/week 43.2%   >10 Drinks/week 22.1% Adulthood (mean age 32; range 30–34)  Adult SES 32.2 (15.3) 6–66  Life engagement 19.5 (3.0) 10–24  Optimism 17.1 (3.2) 7–24  Self-mastery 26.9 (4.6) 9–35  Self-esteem 22.7 (5.2) 0–30  Alcohol use (drinks/week) 5.3 (7.5) 0–39  Smoking: never 42.3%   ≤10 Cigarettes/day 38.8%   >10 Cigarettes/day 18.9%  Physical activity 1,470 (1,919) 0–14,716  Fruit/vegetable consumption 16.9 (12.8) 0–85 SES is rated on the two-factor Hollingshead scale (72). View Large Table 1 Descriptive statistics of study variables Total (N = 312) M (SD) or % Range Childhood (ages 7–9)  Family SES 36.8 (10.8) 6–66 Adolescence (ages 13–16)  Poor parent–child communication 52.4 (6.9) 38–72  Low parental supervision 5.9 (1.2) 4–10  Low parental expectations 27.9 (6.4) 17–53  Smoking: never or infrequent 95.4%   Daily or near daily 4.6%  Alcohol: never 34.7%   ≤10 Drinks/week 43.2%   >10 Drinks/week 22.1% Adulthood (mean age 32; range 30–34)  Adult SES 32.2 (15.3) 6–66  Life engagement 19.5 (3.0) 10–24  Optimism 17.1 (3.2) 7–24  Self-mastery 26.9 (4.6) 9–35  Self-esteem 22.7 (5.2) 0–30  Alcohol use (drinks/week) 5.3 (7.5) 0–39  Smoking: never 42.3%   ≤10 Cigarettes/day 38.8%   >10 Cigarettes/day 18.9%  Physical activity 1,470 (1,919) 0–14,716  Fruit/vegetable consumption 16.9 (12.8) 0–85 Total (N = 312) M (SD) or % Range Childhood (ages 7–9)  Family SES 36.8 (10.8) 6–66 Adolescence (ages 13–16)  Poor parent–child communication 52.4 (6.9) 38–72  Low parental supervision 5.9 (1.2) 4–10  Low parental expectations 27.9 (6.4) 17–53  Smoking: never or infrequent 95.4%   Daily or near daily 4.6%  Alcohol: never 34.7%   ≤10 Drinks/week 43.2%   >10 Drinks/week 22.1% Adulthood (mean age 32; range 30–34)  Adult SES 32.2 (15.3) 6–66  Life engagement 19.5 (3.0) 10–24  Optimism 17.1 (3.2) 7–24  Self-mastery 26.9 (4.6) 9–35  Self-esteem 22.7 (5.2) 0–30  Alcohol use (drinks/week) 5.3 (7.5) 0–39  Smoking: never 42.3%   ≤10 Cigarettes/day 38.8%   >10 Cigarettes/day 18.9%  Physical activity 1,470 (1,919) 0–14,716  Fruit/vegetable consumption 16.9 (12.8) 0–85 SES is rated on the two-factor Hollingshead scale (72). View Large Table 2 Bivariate correlations among study variables 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 1. Childhood SES – 2. Poor parent–child communication −.24* – 3. Low parental supervision −.28* .64* – 4. Low parental expectations −.16* .46* .42* – 5. Adult SES .36* −.26* −.33* −.26* – 6. Life engagement .09 −.27* −.22* −.23* .20* – 7. Optimism .11 −.26* −.20* −.27* .20* .65* – 8. Self-mastery .004 −.33* −.23* −.17* .20* .63* .63* – 9. Self-esteem .01 −.21* −.23* −.20* .23* .69* .59* .63* – 10. Alcohol use .02 .02 .06 .11 −.01 .02 −.05 .03 .001 – 11. Cigarette smoking (y/n > 10/day) −.06 .12* .10 .08 −.20* −.14* −.12* −.13* −.15* −.003 – 12. Physical activity .15* −.09 −.09 −.08 .24* .22* .16* .19* .17* .05 −.08 – 13. Fruit/vegetable consumption .05 −.18* −.11* −.15* .26* .24* .25* .25* .22* −.09 −.11 .16* 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 1. Childhood SES – 2. Poor parent–child communication −.24* – 3. Low parental supervision −.28* .64* – 4. Low parental expectations −.16* .46* .42* – 5. Adult SES .36* −.26* −.33* −.26* – 6. Life engagement .09 −.27* −.22* −.23* .20* – 7. Optimism .11 −.26* −.20* −.27* .20* .65* – 8. Self-mastery .004 −.33* −.23* −.17* .20* .63* .63* – 9. Self-esteem .01 −.21* −.23* −.20* .23* .69* .59* .63* – 10. Alcohol use .02 .02 .06 .11 −.01 .02 −.05 .03 .001 – 11. Cigarette smoking (y/n > 10/day) −.06 .12* .10 .08 −.20* −.14* −.12* −.13* −.15* −.003 – 12. Physical activity .15* −.09 −.09 −.08 .24* .22* .16* .19* .17* .05 −.08 – 13. Fruit/vegetable consumption .05 −.18* −.11* −.15* .26* .24* .25* .25* .22* −.09 −.11 .16* Alcohol use and physical activity were natural log transformed and fruit/vegetable consumption (times/week) was square-root transformed prior to calculating bivariate correlations to achieve normal distributions. Cigarette smoking is treated as a binary variable reflected y/n smoking greater than or equal to 10 cigarettes per day. SES socioeconomic status. *p ≤ .05. View Large Table 2 Bivariate correlations among study variables 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 1. Childhood SES – 2. Poor parent–child communication −.24* – 3. Low parental supervision −.28* .64* – 4. Low parental expectations −.16* .46* .42* – 5. Adult SES .36* −.26* −.33* −.26* – 6. Life engagement .09 −.27* −.22* −.23* .20* – 7. Optimism .11 −.26* −.20* −.27* .20* .65* – 8. Self-mastery .004 −.33* −.23* −.17* .20* .63* .63* – 9. Self-esteem .01 −.21* −.23* −.20* .23* .69* .59* .63* – 10. Alcohol use .02 .02 .06 .11 −.01 .02 −.05 .03 .001 – 11. Cigarette smoking (y/n > 10/day) −.06 .12* .10 .08 −.20* −.14* −.12* −.13* −.15* −.003 – 12. Physical activity .15* −.09 −.09 −.08 .24* .22* .16* .19* .17* .05 −.08 – 13. Fruit/vegetable consumption .05 −.18* −.11* −.15* .26* .24* .25* .25* .22* −.09 −.11 .16* 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 1. Childhood SES – 2. Poor parent–child communication −.24* – 3. Low parental supervision −.28* .64* – 4. Low parental expectations −.16* .46* .42* – 5. Adult SES .36* −.26* −.33* −.26* – 6. Life engagement .09 −.27* −.22* −.23* .20* – 7. Optimism .11 −.26* −.20* −.27* .20* .65* – 8. Self-mastery .004 −.33* −.23* −.17* .20* .63* .63* – 9. Self-esteem .01 −.21* −.23* −.20* .23* .69* .59* .63* – 10. Alcohol use .02 .02 .06 .11 −.01 .02 −.05 .03 .001 – 11. Cigarette smoking (y/n > 10/day) −.06 .12* .10 .08 −.20* −.14* −.12* −.13* −.15* −.003 – 12. Physical activity .15* −.09 −.09 −.08 .24* .22* .16* .19* .17* .05 −.08 – 13. Fruit/vegetable consumption .05 −.18* −.11* −.15* .26* .24* .25* .25* .22* −.09 −.11 .16* Alcohol use and physical activity were natural log transformed and fruit/vegetable consumption (times/week) was square-root transformed prior to calculating bivariate correlations to achieve normal distributions. Cigarette smoking is treated as a binary variable reflected y/n smoking greater than or equal to 10 cigarettes per day. SES socioeconomic status. *p ≤ .05. View Large The structural equation model with direct and indirect effects from childhood SES to parenting in adolescence and adult SES and to health behaviors and psychological resources in adulthood, controlling for race and adolescent smoking and alcohol, is presented in Fig. 1. Only significant standardized path coefficients are displayed, although all unstandardized path coefficients are provided in Table 3. The model demonstrated good fit with the data (χ2(53) = 82.4, p = .01, RMSEA = .043, CFI = .971, SRMR = .029). Table 3 Model results with unstandardized path coefficients and latent factor loadings B (SE) p Path model Family SES →   Positive parenting .02(.01) <.001   Adult SES .30(.09) <.001   Physical activity .01(.01) .17   Alcohol use .003(.01) .62   Cigarette smokinga −.01(.01) .18   Fruit/vegetable consumption −.01(.01) .41   Psychological resources −.01(.01) .21  Positive parenting →   Adult SES 6.06(1.51) <.001   Physical activity .06(.11) .57   Alcohol use −.06(.13) .65   Cigarette smokinga −.20(.12) .10   Fruit/vegetable consumption .23(.12) .062   Psychological resources .45(.09) <.001  Adult SES →   Physical activity .02(.004) <.001   Alcohol use .002(.004) .59   Cigarette smokinga −.02(.004) <.001   Fruit/vegetable consumption .02(.004) <.001   Psychological resources .01(.004) .052 Latent factors  Positive parenting   Parent–child communication 1.0   Supervision 1.03 (.14) <.001   Expectations for future 0.82 (.10) <.001  Psychological resources   Self-esteem 1.0   Optimism 0.99 (.10) <.001   Life engagement 1.04 (.09) <.001   Self-mastery 1.02 (.09) <.001 B (SE) p Path model Family SES →   Positive parenting .02(.01) <.001   Adult SES .30(.09) <.001   Physical activity .01(.01) .17   Alcohol use .003(.01) .62   Cigarette smokinga −.01(.01) .18   Fruit/vegetable consumption −.01(.01) .41   Psychological resources −.01(.01) .21  Positive parenting →   Adult SES 6.06(1.51) <.001   Physical activity .06(.11) .57   Alcohol use −.06(.13) .65   Cigarette smokinga −.20(.12) .10   Fruit/vegetable consumption .23(.12) .062   Psychological resources .45(.09) <.001  Adult SES →   Physical activity .02(.004) <.001   Alcohol use .002(.004) .59   Cigarette smokinga −.02(.004) <.001   Fruit/vegetable consumption .02(.004) <.001   Psychological resources .01(.004) .052 Latent factors  Positive parenting   Parent–child communication 1.0   Supervision 1.03 (.14) <.001   Expectations for future 0.82 (.10) <.001  Psychological resources   Self-esteem 1.0   Optimism 0.99 (.10) <.001   Life engagement 1.04 (.09) <.001   Self-mastery 1.02 (.09) <.001 Model fit indices were as follows, χ2(df) = 82.4(53), p =.01, CFI = .971, RMSEA [90% CI] = .043 [.023, .060], SRMR = .029. CFI comparative fit index; CI confidence interval; RMSEA root mean square error of approximation; SE standard error; SES socioeconomic status; SRMR standardized root mean square residual. aCigarette smoking was run in a separate model using WLSMV estimation. This model is displayed in Fig. 1. All error variances were statistically significant. The paths from positive parenting to parent–child communication and from psychological resources to self-esteem were fixed to 1 for all analyses. Coefficients for family SES, parenting in adolescence, adult health behaviors, and psychological resources regressed on race and adolescent alcohol and cigarette use, respectively, are not displayed as these variables were included as covariates. View Large Table 3 Model results with unstandardized path coefficients and latent factor loadings B (SE) p Path model Family SES →   Positive parenting .02(.01) <.001   Adult SES .30(.09) <.001   Physical activity .01(.01) .17   Alcohol use .003(.01) .62   Cigarette smokinga −.01(.01) .18   Fruit/vegetable consumption −.01(.01) .41   Psychological resources −.01(.01) .21  Positive parenting →   Adult SES 6.06(1.51) <.001   Physical activity .06(.11) .57   Alcohol use −.06(.13) .65   Cigarette smokinga −.20(.12) .10   Fruit/vegetable consumption .23(.12) .062   Psychological resources .45(.09) <.001  Adult SES →   Physical activity .02(.004) <.001   Alcohol use .002(.004) .59   Cigarette smokinga −.02(.004) <.001   Fruit/vegetable consumption .02(.004) <.001   Psychological resources .01(.004) .052 Latent factors  Positive parenting   Parent–child communication 1.0   Supervision 1.03 (.14) <.001   Expectations for future 0.82 (.10) <.001  Psychological resources   Self-esteem 1.0   Optimism 0.99 (.10) <.001   Life engagement 1.04 (.09) <.001   Self-mastery 1.02 (.09) <.001 B (SE) p Path model Family SES →   Positive parenting .02(.01) <.001   Adult SES .30(.09) <.001   Physical activity .01(.01) .17   Alcohol use .003(.01) .62   Cigarette smokinga −.01(.01) .18   Fruit/vegetable consumption −.01(.01) .41   Psychological resources −.01(.01) .21  Positive parenting →   Adult SES 6.06(1.51) <.001   Physical activity .06(.11) .57   Alcohol use −.06(.13) .65   Cigarette smokinga −.20(.12) .10   Fruit/vegetable consumption .23(.12) .062   Psychological resources .45(.09) <.001  Adult SES →   Physical activity .02(.004) <.001   Alcohol use .002(.004) .59   Cigarette smokinga −.02(.004) <.001   Fruit/vegetable consumption .02(.004) <.001   Psychological resources .01(.004) .052 Latent factors  Positive parenting   Parent–child communication 1.0   Supervision 1.03 (.14) <.001   Expectations for future 0.82 (.10) <.001  Psychological resources   Self-esteem 1.0   Optimism 0.99 (.10) <.001   Life engagement 1.04 (.09) <.001   Self-mastery 1.02 (.09) <.001 Model fit indices were as follows, χ2(df) = 82.4(53), p =.01, CFI = .971, RMSEA [90% CI] = .043 [.023, .060], SRMR = .029. CFI comparative fit index; CI confidence interval; RMSEA root mean square error of approximation; SE standard error; SES socioeconomic status; SRMR standardized root mean square residual. aCigarette smoking was run in a separate model using WLSMV estimation. This model is displayed in Fig. 1. All error variances were statistically significant. The paths from positive parenting to parent–child communication and from psychological resources to self-esteem were fixed to 1 for all analyses. Coefficients for family SES, parenting in adolescence, adult health behaviors, and psychological resources regressed on race and adolescent alcohol and cigarette use, respectively, are not displayed as these variables were included as covariates. View Large Fig. 1 View largeDownload slide Standardized path coefficients and factor loadings shown. Only significant paths are depicted. Health behaviors and psychological resources were allowed to correlate. The psychological resources factor was significantly correlated with physical activity (r = .167, p = .019) and fruit/vegetable consumption (r = .209, p = .001). SES socioeconomic status. Fig. 1 View largeDownload slide Standardized path coefficients and factor loadings shown. Only significant paths are depicted. Health behaviors and psychological resources were allowed to correlate. The psychological resources factor was significantly correlated with physical activity (r = .167, p = .019) and fruit/vegetable consumption (r = .209, p = .001). SES socioeconomic status. Family SES in childhood was associated with positive parenting, such that families with higher SES in childhood reported more positive parenting during adolescence, independent of race, adolescent cigarette use, and adolescent alcohol use. Direct effects showed that higher family SES in childhood was also associated with higher adult SES, independent of race, and adolescent cigarette and alcohol use. There were no significant direct effects between family SES in childhood and physical activity, cigarette smoking, alcohol use, fruit and vegetable consumption, or psychological resources in adulthood. Direct effects showed significant associations between positive parenting in adolescence and higher adult SES and greater psychological resources in adulthood. Finally, adult SES was associated with greater physical activity, less cigarette smoking, more fruit and vegetable consumption, and greater psychological resources in adulthood (Table 3). Tests of indirect effects from childhood SES to positive parenting and adult SES and to health behaviors and psychological resources in adulthood are presented in Table 4. Associations between childhood SES and adult health behaviors and psychological resources (hereafter “outcomes”) may exist via three pathways, (i) childhood SES to positive parenting to the outcomes, (ii) childhood SES to adult SES to the outcomes, and (iii) childhood SES to positive parenting to adult SES to the outcomes. There was a significant indirect effect of childhood SES on psychological resources and adult SES, but not on health behaviors, through positive parenting alone (pathway 1). There were also significant indirect effects of childhood SES on adult physical activity, cigarette smoking, and fruit/vegetable consumption, but not alcohol use or psychological resources, through adult SES alone (pathway 2). Finally, there were significant indirect effects of childhood SES on adult physical activity, cigarette smoking, and fruit/vegetable consumption, but not alcohol use or psychological resources, through positive parenting in adolescence and adult SES (pathway 3). Additional models tested whether parenting moderated the associations between childhood SES and adult health behaviors in linear regression models. All interactions between childhood SES and parenting predicting health behaviors were not significant, ps > .27. Table 4 Unstandardized indirect effects from bootstrapped analysis (5,000 resamples) Estimate [95% CI] Childhood SES → positive parenting →  Adult SES .113 [.054, .205]*  Physical activity .001 [−.003, .005]  Alcohol use −.001 [−.006, .003]  Cigarette smokinga −.004 [−.009, .000]  Fruit/vegetable consumption .004 [.000, .010]  Psychological resources .008 [.005, .015]* Childhood SES → adult SES →  Physical activity .005 [.002, .009]*  Alcohol use .001 [−.002, .004]  Cigarette smokinga −.006 [−.012, −.002]*  Fruit/vegetable consumption .005 [.002, .009]*  Psychological resources .002 [.000, .006] Childhood SES → positive parenting → adult SES →  Physical activity .002 [.001, .004]*  Alcohol use .000 [−.001, .001]  Cigarette smokinga −.003 [−.005, −.001]*  Fruit/vegetable consumption .002 [.001, .004]*  Psychological resources .001 [.000, .002] Estimate [95% CI] Childhood SES → positive parenting →  Adult SES .113 [.054, .205]*  Physical activity .001 [−.003, .005]  Alcohol use −.001 [−.006, .003]  Cigarette smokinga −.004 [−.009, .000]  Fruit/vegetable consumption .004 [.000, .010]  Psychological resources .008 [.005, .015]* Childhood SES → adult SES →  Physical activity .005 [.002, .009]*  Alcohol use .001 [−.002, .004]  Cigarette smokinga −.006 [−.012, −.002]*  Fruit/vegetable consumption .005 [.002, .009]*  Psychological resources .002 [.000, .006] Childhood SES → positive parenting → adult SES →  Physical activity .002 [.001, .004]*  Alcohol use .000 [−.001, .001]  Cigarette smokinga −.003 [−.005, −.001]*  Fruit/vegetable consumption .002 [.001, .004]*  Psychological resources .001 [.000, .002] SES socioeconomic status. aCigarette smoking was run in a separate model using WLSMV estimation. *p < .05. View Large Table 4 Unstandardized indirect effects from bootstrapped analysis (5,000 resamples) Estimate [95% CI] Childhood SES → positive parenting →  Adult SES .113 [.054, .205]*  Physical activity .001 [−.003, .005]  Alcohol use −.001 [−.006, .003]  Cigarette smokinga −.004 [−.009, .000]  Fruit/vegetable consumption .004 [.000, .010]  Psychological resources .008 [.005, .015]* Childhood SES → adult SES →  Physical activity .005 [.002, .009]*  Alcohol use .001 [−.002, .004]  Cigarette smokinga −.006 [−.012, −.002]*  Fruit/vegetable consumption .005 [.002, .009]*  Psychological resources .002 [.000, .006] Childhood SES → positive parenting → adult SES →  Physical activity .002 [.001, .004]*  Alcohol use .000 [−.001, .001]  Cigarette smokinga −.003 [−.005, −.001]*  Fruit/vegetable consumption .002 [.001, .004]*  Psychological resources .001 [.000, .002] Estimate [95% CI] Childhood SES → positive parenting →  Adult SES .113 [.054, .205]*  Physical activity .001 [−.003, .005]  Alcohol use −.001 [−.006, .003]  Cigarette smokinga −.004 [−.009, .000]  Fruit/vegetable consumption .004 [.000, .010]  Psychological resources .008 [.005, .015]* Childhood SES → adult SES →  Physical activity .005 [.002, .009]*  Alcohol use .001 [−.002, .004]  Cigarette smokinga −.006 [−.012, −.002]*  Fruit/vegetable consumption .005 [.002, .009]*  Psychological resources .002 [.000, .006] Childhood SES → positive parenting → adult SES →  Physical activity .002 [.001, .004]*  Alcohol use .000 [−.001, .001]  Cigarette smokinga −.003 [−.005, −.001]*  Fruit/vegetable consumption .002 [.001, .004]*  Psychological resources .001 [.000, .002] SES socioeconomic status. aCigarette smoking was run in a separate model using WLSMV estimation. *p < .05. View Large Discussion The aim of the study was to examine whether parenting practices and adult SES help to explain prospective associations between childhood SES and adult health behaviors and psychological resources in Black and White men. As expected, higher SES in childhood was associated with more positive parenting in adolescence and higher adult SES. Positive parenting in adolescence also predicted higher adult SES, and the indirect effect from childhood SES to positive parenting to adult SES was significant. Higher SES in childhood was indirectly associated with greater psychological resources in adulthood via positive parenting in adolescence. Higher childhood SES was also linked with greater physical activity, less cigarette smoking, and greater fruit and vegetable consumption in adulthood via two separate indirect paths, one through adult SES and a second through both positive parenting and adult SES. All effects were independent of cigarette and alcohol use in adolescence. Despite significant race differences in levels of several factors in the path model, race differences in the latent variable factor structure and path loadings were not evident. This suggests that the structure of the model linking SES in childhood to parenting styles in adolescence and adult SES and to adult health behaviors and psychological resources was equivalent across race in this urban sample of Black and White men. Childhood SES was prospectively associated with psychological resources, but not health behaviors, via positive parenting. These results are consistent with prior research showing that parenting during adolescence has important implications for the development and maintenance of positive psychological attributes into adulthood (e.g., [89, 90]). For example, a decline in the quality of the parent–child relationship accounts for differences in psychological well-being in adulthood between those whose parents divorced in childhood or adolescence and those whose parents stayed continuously married [91]. In contrast, all significant indirect effects between childhood SES and adult health behaviors involved adult SES, reflecting the importance of concurrent opportunities and attitudes for the engagement in health behaviors. Participation in health behaviors is socially stratified due to a variety of reasons, including differences in structural constraints (i.e., access, opportunities), differences in norms regarding the importance of health behaviors, and differences in beliefs about the beneficial aspects of health behaviors [7, 92]. This study provides empirical support for the Family Stress Model and the Risky Families Model [63, 64], as in both models positive parenting strategies link lower SES in childhood with SES in adulthood, poorer health behaviors, and lower psychological resources. Direct effects between childhood SES and health behaviors or psychological resources in adulthood were not significant in the full model (Fig. 1). Rather, the evidence supported indirect associations of childhood SES with health behaviors and psychological resources through positive parenting and adult SES. The findings are bolstered by the prospective design of the current study. These results add to growing evidence that positive parenting in adolescence contributes to health outcomes in adulthood. For example, teachers’ reports of parental support (versus parental neglect) when children were 9–10 years old were predictive of lower obesity rates up to 10 years later [93]. There are important structural factors that may underlie the association between childhood SES and parenting. That is, socioeconomic factors may influence the availability and flexibility of time spent with family, parental stress, residential density, financial strain, and accessibility and willingness to adopt “expert” parenting advice, which may contribute to the differences we noted in positive parenting [8, 94]. It is critical to note that heterogeneous parenting practices are evident both within and between socioeconomic strata. One of the key strengths of the Risky Families Model is that it defines psychosocial characteristics that confer risk to downstream health outcomes rather than assume these characteristics are congruent with economic status [64]. Indeed, the lack of direct effects from childhood SES to health behaviors and psychological resources supports the primary role for parenting in downstream health outcomes. It is important in future research to consider broader factors in the childhood environment that affect health risks into adulthood and may shape socioeconomic and parenting opportunities [8, 95]. We note, however, that conclusions drawn from the results presented in this paper were identical when a composite measure of childhood socioeconomic disadvantage (i.e., sum of the presence of parental occupational occupation of semi-skilled worker or less, parental unemployment for at least 4 months, highest parental education below high school completion, receipt of public assistance, and single parent household) was utilized instead of childhood SES (i.e., Hollingshead index). This study extends the literature by including psychological resources as an additional outcome, which has independent, salubrious effects on myriad of health outcomes (e.g., 14–16). We chose to model psychological resources as an independent, yet correlated, outcome alongside the health behaviors given that these factors were measured simultaneously and given our interest in providing a holistic picture of health and well-being among relatively young adults. Conceptually, however, psychological resources may be thought of as a mediator of the associations between child SES, positive parenting, and health behaviors. Prior evidence suggests that psychological resources and health behaviors, especially physical activity, have a reciprocal relationship, wherein those with greater resources are more likely to participate in healthy behaviors, and participating in healthy behaviors leads to greater resources (e.g., [96, 97]). Conversely, psychological resources may also function to moderate the associations between childhood SES and health outcomes. That is, SES gradients in health outcomes are attenuated among individuals with high well-being, including purpose in life [19–22]. However, we did not see evidence of psychological resources moderating the associations between childhood SES and health behaviors in the current sample (data not shown). Causality cannot be determined from the current study design. However, if the association between childhood SES and adult health behaviors via parenting in adolescence were shown to be casual in nature, the parent–child relationship may be a viable target for intervention. The current results suggest that efforts to improve the family environment and parent–child relationship may have lasting effects on SES later in life as well as on behavioral and psychological outcomes in adulthood. This is consistent with prior work demonstrating improved physical and mental health outcomes for several decades following family-based interventions. For example, the Family Check-Up randomized trial, which focused on improving aspects of parenting such as monitoring, communication, and involvement, was associated with enhanced nutritional quality of family meals, better health behaviors, and lower depression in adolescents and less risk for obesity in early adulthood [54, 56]. Other efforts to increase parental responsiveness and control among low-income, minority families was effective at reducing weight gain in children [55], and a separate intervention among rural, African American families (i.e., the Strong African American Families program) led to fewer increases in risky behavior in adolescence and emerging adulthood [58, 59] as well as lower inflammatory markers in emerging adulthood [57]. Though not explicitly tested, these efforts may additionally have intergenerational effects, creating a positive feedback loop wherein future generations benefit from positive changes in the relationship between caregiver and child [98]. The findings from the present study should be considered in light of several limitations. First, only men from a geographically restricted region (Pittsburgh, PA) were included, making it impossible to determine whether the findings would extend to women or to a more geographically representative sample of men. The original sample was recruited to over represent children with early conduct problems (in first grade) and there was some attrition across the nearly 30-year measurement window, which may also limit generalizability. While there were differences in two of the parenting measures among the analytic sample and those with data in adolescence who did not participate in the most recent follow-up, we note that the analytic sample did not differ significantly on many key variables from the rest of the men in the initial PYS sample and these parenting differences were nonsignificant when excluding those incarcerated at follow-up. Second, the relevance of the present findings to clinical outcomes, such as morbidity, remains unclear given that the outcomes in the current study were self-reported health behaviors and psychological resources. Third, parenting was measured via self-report, potentially introducing social desirability bias. However, the means and ranges on the parenting measures indicated considerable variability in these measures and not only in a socially desirable manner. While observational reports offer more objective insights into the parent–child relationship, we viewed the use of multiple informants as a key strength of the study. Finally, these data are observational in nature, making causal claims about parenting untenable; we note that the present findings are in line with randomized control trials that have successfully manipulated the parent–child relationship and demonstrated health benefits. In light of these limitations, the prospective, longitudinal design, use of multiple informants, rich assessments of the parent–child relationship, and multiple health-relevant outcomes strengthen confidence in the current findings. Overall, results suggest that positive parent–child relationships are important for understanding how SES experienced in early life has lasting effects on SES, health behaviors, and psychological resources decades later, and these patterns were consistent in both Black and White men. Given the increasing interest in the impact of early life experiences on long-term health outcomes, but the lack of prospective data and reliance on primarily White samples, these results are especially informative for ongoing work in this area. Acknowledgments Special thanks to the study originators Drs. Rolf Loeber and Magda Stouthamer-Loeber. Source of Funding This research was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health (R01HL111802, 5T32HL007560-32). Data collection for the Pittsburgh Youth Study has been funded by the National Institute on Drug Abuse (DA411018), National Institute on Mental Health (MH48890, MH50778), Pew Charitable Trusts, and the Office of Juvenile Justice and Delinquency Prevention (96-MU-FX-0012). Compliance with Ethical Standards Informed Consent The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Conflict of interest All authors declare no conflicts of interest and have fully complied with the ethical standards of this journal. Author Contributions J. M. Boylan conceptualized the research, analyzed and interpreted the data, and drafted the original manuscript. K. P. Jakubowski contributed to the conceptualization of the research, acquisition, analysis, and interpretation of data, and provided critical revisions to the manuscript. J. M. Cundiff contributed to the conceptualization of the research, data analysis and interpretation, and provided critical revisions to the manuscript. D. A. Pardini and K. A. Matthews contributed to the conceptualization of the research, data acquisition, data analysis and interpretation, and provided critical revisions to the manuscript. Ethical Approval This study was approved by the Institutional Review Board at the University of Pittsburgh, and all men provided written, informed consent. References 1. Cohen S , Janicki-Deverts D , Chen E , Matthews KA . Childhood socioeconomic status and adult health . Ann N Y Acad Sci . 2010 ; 1186 : 37 – 55 . Google Scholar CrossRef Search ADS PubMed 2. Galobardes B , Lynch JW , Davey Smith G . Childhood socioeconomic circumstances and cause-specific mortality in adulthood: systematic review and interpretation . Epidemiol Rev . 2004 ; 26 : 7 – 21 . Google Scholar CrossRef Search ADS PubMed 3. Galobardes B , Smith GD , Lynch JW . Systematic review of the influence of childhood socioeconomic circumstances on risk for cardiovascular disease in adulthood . Ann Epidemiol . 2006 ; 16 ( 2 ): 91 – 104 . Google Scholar CrossRef Search ADS PubMed 4. Poulton R , Caspi A , Milne BJ et al. Association between children’s experience of socioeconomic disadvantage and adult health: a life-course study . Lancet . 2002 ; 360 ( 9346 ): 1640 – 1645 . Google Scholar CrossRef Search ADS PubMed 5. Melchior M , Moffitt TE , Milne BJ , Poulton R , Caspi A . Why do children from socioeconomically disadvantaged families suffer from poor health when they reach adulthood? A life-course study . Am J Epidemiol . 2007 ; 166 ( 8 ): 966 – 974 . Google Scholar CrossRef Search ADS PubMed 6. Blackwell DL , Hayward MD , Crimmins EM . Does childhood health affect chronic morbidity in later life ? Soc Sci Med . 2001 ; 52 ( 8 ): 1269 – 1284 . Google Scholar CrossRef Search ADS PubMed 7. Pampel FC , Krueger PM , Denney JT . Socioeconomic disparities in health behaviors . Annu Rev Sociol . 2010 ; 36 : 349 – 370 . Google Scholar CrossRef Search ADS PubMed 8. Schreier HM , Chen E . Socioeconomic status and the health of youth: a multilevel, multidomain approach to conceptualizing pathways . Psychol Bull . 2013 ; 139 ( 3 ): 606 – 654 . Google Scholar CrossRef Search ADS PubMed 9. Lee JO , Hill KG , Hartigan LA et al. Unemployment and substance use problems among young adults: does childhood low socioeconomic status exacerbate the effect ? Soc Sci Med . 2015 ; 143 : 36 – 44 . Google Scholar CrossRef Search ADS PubMed 10. Barbeau EM , Krieger N , Soobader MJ . Working class matters: socioeconomic disadvantage, race/ethnicity, gender, and smoking in NHIS 2000 . Am J Public Health . 2004 ; 94 ( 2 ): 269 – 278 . Google Scholar CrossRef Search ADS PubMed 11. Hanson MD , Chen E . Socioeconomic status and health behaviors in adolescence: a review of the literature . J Behav Med . 2007 ; 30 ( 3 ): 263 – 285 . Google Scholar CrossRef Search ADS PubMed 12. Matthews KA , Kelsey SF , Meilahn EN , Kuller LH , Wing RR . Educational attainment and behavioral and biologic risk factors for coronary heart disease in middle-aged women . Am J Epidemiol . 1989 ; 129 ( 6 ): 1132 – 1144 . Google Scholar CrossRef Search ADS PubMed 13. NHLBI . Managing Overweight and Obesity in Adults: Systematic Evidence Review From the Obesity Expert Panel ; 2013 . https://www.nhlbi.nih.gov/health-topics/managing-overweight-obesity-in-adults Accessibility verified April 15, 2016. 14. Keyes CL . The mental health continuum: from languishing to flourishing in life . J Health Soc Behav . 2002 ; 43 ( 2 ): 207 – 222 . Google Scholar CrossRef Search ADS PubMed 15. Gallo LC , Matthews KA . Understanding the association between socioeconomic status and physical health: do negative emotions play a role ? Psychol Bull . 2003 ; 129 ( 1 ): 10 – 51 . Google Scholar CrossRef Search ADS PubMed 16. Ryff CD . Psychological well-being revisited: advances in the science and practice of eudaimonia . Psychother Psychosom . 2014 ; 83 ( 1 ): 10 – 28 . Google Scholar CrossRef Search ADS PubMed 17. Boehm JK , Kubzansky LD . The heart’s content: the association between positive psychological well-being and cardiovascular health . Psychol Bull . 2012 ; 138 ( 4 ): 655 – 691 . Google Scholar CrossRef Search ADS PubMed 18. Chida Y , Steptoe A . Positive psychological well-being and mortality: a quantitative review of prospective observational studies . Psychosom Med . 2008 ; 70 ( 7 ): 741 – 756 . Google Scholar CrossRef Search ADS PubMed 19. Cohen R , Bavishi C , Rozanski A . Purpose in life and its relationship to all-cause mortality and cardiovascular events: a meta-analysis . Psychosom Med . 2016 ; 78 ( 2 ): 122 – 133 . Google Scholar CrossRef Search ADS PubMed 20. Pressman SD , Cohen S . Does positive affect influence health ? Psychol Bull . 2005 ; 131 ( 6 ): 925 – 971 . Google Scholar CrossRef Search ADS PubMed 21. Morozink JA , Friedman EM , Coe CL , Ryff CD . Socioeconomic and psychosocial predictors of interleukin-6 in the MIDUS national sample . Health Psychol . 2010 ; 29 ( 6 ): 626 – 635 . Google Scholar CrossRef Search ADS PubMed 22. Turiano NA , Chapman BP , Agrigoroaei S , Infurna FJ , Lachman M . Perceived control reduces mortality risk at low, not high, education levels . Health Psychol . 2014 ; 33 ( 8 ): 883 – 890 . Google Scholar CrossRef Search ADS PubMed 23. Lachman ME , Weaver SL . The sense of control as a moderator of social class differences in health and well-being . J Pers Soc Psychol . 1998 ; 74 ( 3 ): 763 – 773 . Google Scholar CrossRef Search ADS PubMed 24. Chen E , Miller GE . “Shift-and-Persist” strategies: why low socioeconomic status isn’t always bad for health . Perspect Psychol Sci . 2012 ; 7 ( 2 ): 135 – 158 . Google Scholar CrossRef Search ADS PubMed 25. Fava GA , Ruini C , Rafanelli C et al. Well-being therapy of generalized anxiety disorder . Psychother Psychosom . 2005 ; 74 ( 1 ): 26 – 30 . Google Scholar CrossRef Search ADS PubMed 26. Fava GA , Ruini C , Rafanelli C , Finos L , Conti S , Grandi S . Six-year outcome of cognitive behavior therapy for prevention of recurrent depression . Am J Psychiatry . 2004 ; 161 ( 10 ): 1872 – 1876 . Google Scholar CrossRef Search ADS PubMed 27. King LA . The health benefits of writing about life goals . Pers Soc Psychol Bull . 2001 ; 27 : 798 – 807 . Google Scholar CrossRef Search ADS 28. van der Spek N , Vos J , van Uden-Kraan CF et al. Efficacy of meaning-centered group psychotherapy for cancer survivors: a randomized controlled trial . Psychol Med . 2017 ; 47 ( 11 ): 1990 – 2001 . Google Scholar CrossRef Search ADS PubMed 29. Breitbart W , Poppito S , Rosenfeld B et al. Pilot randomized controlled trial of individual meaning-centered psychotherapy for patients with advanced cancer . J Clin Oncol . 2012 ; 30 ( 12 ): 1304 – 1309 . Google Scholar CrossRef Search ADS PubMed 30. Gebreab SY , Diez Roux AV , Brenner AB et al. The impact of lifecourse socioeconomic position on cardiovascular disease events in African Americans: the Jackson Heart Study . J Am Heart Assoc . 2015 ; 4 ( 6 ): e001553 . Google Scholar CrossRef Search ADS PubMed 31. Lynch JW , Kaplan GA , Salonen JT . Why do poor people behave poorly? Variation in adult health behaviours and psychosocial characteristics by stages of the socioeconomic lifecourse . Soc Sci Med . 1997 ; 44 ( 6 ): 809 – 819 . Google Scholar CrossRef Search ADS PubMed 32. Gilman SE , Abrams DB , Buka SL . Socioeconomic status over the life course and stages of cigarette use: initiation, regular use, and cessation . J Epidemiol Community Health . 2003 ; 57 ( 10 ): 802 – 808 . Google Scholar CrossRef Search ADS PubMed 33. van de Mheen H , Stronks K , Looman CW , Mackenbach JP . Does childhood socioeconomic status influence adult health through behavioural factors ? Int J Epidemiol . 1998 ; 27 : 431 – 437 . Google Scholar CrossRef Search ADS PubMed 34. Harper S , Lynch J , Hsu WL et al. Life course socioeconomic conditions and adult psychosocial functioning . Int J Epidemiol . 2002 ; 31 ( 2 ): 395 – 403 . Google Scholar CrossRef Search ADS PubMed 35. Adler NE , Stewart J . Health disparities across the lifespan: meaning, methods, and mechanisms . Ann N Y Acad Sci . 2010 ; 1186 : 5 – 23 . Google Scholar CrossRef Search ADS PubMed 36. Lutfey K , Freese J . Toward some fundamentals of fundamental causality: socioeconomic status and health in the routine clinic visit for diabetes . Am J Sociol . 2005 ; 110 : 1326 – 1372 . Google Scholar CrossRef Search ADS 37. Pearlin LI . The sociological study of stress . J Health Soc Behav . 1989 ; 30 ( 3 ): 241 – 256 . Google Scholar CrossRef Search ADS PubMed 38. Wardle J , Steptoe A . Socioeconomic differences in attitudes and beliefs about healthy lifestyles . J Epidemiol Community Health . 2003 ; 57 ( 6 ): 440 – 443 . Google Scholar CrossRef Search ADS PubMed 39. Smith KP , Christakis NA . Social networks and health . Ann Rev Sociol . 2008 ; 34 : 405 – 429 . Google Scholar CrossRef Search ADS 40. Looker ED . Accuracy of proxy reports of parental status characteristics . Sociol Educ . 1988 ; 62 : 257 – 276 . Google Scholar CrossRef Search ADS 41. Wagmiller R , Adelman R. Childhood and Intergenerational Poverty: The Long-Term Consequences of Growing Up Poor . New York: National Center on Child Poverty Reports ; 2009 . 42. Brody GH , Yu T , Beach SR , Kogan SM , Windle M , Philibert RA . Harsh parenting and adolescent health: a longitudinal analysis with genetic moderation . Health Psychol . 2014 ; 33 ( 5 ): 401 – 409 . Google Scholar CrossRef Search ADS PubMed 43. Taylor SE , Way BM , Seeman TE . Early adversity and adult health outcomes . Dev Psychopathol . 2011 ; 23 ( 3 ): 939 – 954 . Google Scholar CrossRef Search ADS PubMed 44. Moretti MM , Peled M . Adolescent-parent attachment: bonds that support healthy development . Paediatr Child Health . 2004 ; 9 ( 8 ): 551 – 555 . Google Scholar CrossRef Search ADS PubMed 45. Viner RM , Ross D , Hardy R et al. Life course epidemiology: recognising the importance of adolescence . J Epidemiol Community Health . 2015 ; 69 ( 8 ): 719 – 720 . Google Scholar CrossRef Search ADS PubMed 46. Hess RD , Holloway SD . Family and school as educational institutions . In: Parke R , ed. Review of Child Development Research . Vol. 7 . Chicago : University of Chicago Press ; 1984 : 179 – 222 . 47. Resnick MD , Bearman PS , Blum RW et al. Protecting adolescents from harm. Findings from the National Longitudinal Study on Adolescent Health . JAMA . 1997 ; 278 ( 10 ): 823 – 832 . Google Scholar CrossRef Search ADS PubMed 48. Davis-Kean PE . The influence of parent education and family income on child achievement: the indirect role of parental expectations and the home environment . J Fam Psychol . 2005 ; 19 ( 2 ): 294 – 304 . Google Scholar CrossRef Search ADS PubMed 49. Nash SG , McQueen A , Bray JH . Pathways to adolescent alcohol use: family environment, peer influence, and parental expectations . J Adolesc Health . 2005 ; 37 ( 1 ): 19 – 28 . Google Scholar CrossRef Search ADS PubMed 50. Simons-Morton BG . The protective effect of parental expectations against early adolescent smoking initiation . Health Educ Res . 2004 ; 19 ( 5 ): 561 – 569 . Google Scholar CrossRef Search ADS PubMed 51. McLoyd VC . The impact of economic hardship on black families and children: psychological distress, parenting, and socioemotional development . Child Dev . 1990 ; 61 ( 2 ): 311 – 346 . Google Scholar CrossRef Search ADS PubMed 52. McLoyd VC . Socioeconomic disadvantage and child development . Am Psychol . 1998 ; 53 ( 2 ): 185 – 204 . Google Scholar CrossRef Search ADS PubMed 53. Murry VM , Berkel C , Simons RL , Simons LG , Gibbons FX . A twelve-year longitudinal analysis of positive youth development among rural African American males . J Res Adolescence . 2014 ; 24 : 512 – 25 . Google Scholar CrossRef Search ADS 54. Van Ryzin MJ , Nowicka P . Direct and indirect effects of a family-based intervention in early adolescence on parent-youth relationship quality, late adolescent health, and early adult obesity . J Fam Psychol . 2013 ; 27 ( 1 ): 106 – 116 . Google Scholar CrossRef Search ADS PubMed 55. Brotman LM , Dawson-McClure S , Huang KY et al. Early childhood family intervention and long-term obesity prevention among high-risk minority youth . Pediatrics . 2012 ; 129 ( 3 ): e621 – e628 . Google Scholar CrossRef Search ADS PubMed 56. Smith JD , Montaño Z , Dishion TJ , Shaw DS , Wilson MN . Preventing weight gain and obesity: indirect effects of the family check-up in early childhood . Prev Sci . 2015 ; 16 ( 3 ): 408 – 419 . Google Scholar CrossRef Search ADS PubMed 57. Miller GE , Brody GH , Yu T , Chen E . A family-oriented psychosocial intervention reduces inflammation in low-SES African American youth . Proc Natl Acad Sci USA . 2014 ; 111 ( 31 ): 11287 – 11292 . Google Scholar CrossRef Search ADS PubMed 58. Brody GH , Chen YF , Kogan SM , Murry VM , Brown AC . Long-term effects of the strong African American families program on youths’ alcohol use . J Consult Clin Psychol . 2010 ; 78 ( 2 ): 281 – 285 . Google Scholar CrossRef Search ADS PubMed 59. Brody GH , Chen YF , Kogan SM , Smith K , Brown AC . Buffering effects of a family-based intervention for African American emerging adults . J Marriage Fam . 2010 ; 72 ( 5 ): 1426 – 1435 . Google Scholar CrossRef Search ADS PubMed 60. Newland RP , Crnic KA , Cox MJ , Mills-Koonce WR ; Family Life Project Key Investigators . The family model stress and maternal psychological symptoms: mediated pathways from economic hardship to parenting . J Fam Psychol . 2013 ; 27 ( 1 ): 96 – 105 . Google Scholar CrossRef Search ADS PubMed 61. Conger RD , Donnellan MB . An interactionist perspective on the socioeconomic context of human development . Annu Rev Psychol . 2007 ; 58 : 175 – 199 . Google Scholar CrossRef Search ADS PubMed 62. Conger RD , Elder GH. Families in Troubled Times: Adapting to Change in Rural America . Hawthorne, NY: Aldine de Gruyter ; 1994 . 63. Conger KJ , Rueter MA , Conger RD . The role of economic pressure in the lives of parents and their adolescents: the family stress model . In: Crockett LJ , Silberstein RK , eds. Negotiating Adolescence in Times of Social Change . New York : Cambridge University Press ; 2000 . Google Scholar CrossRef Search ADS 64. Repetti RL , Taylor SE , Seeman TE . Risky families: family social environments and the mental and physical health of offspring . Psychol Bull . 2002 ; 128 ( 2 ): 330 – 366 . Google Scholar CrossRef Search ADS PubMed 65. Troxel WM , Matthews KA . What are the costs of marital conflict and dissolution to children’s physical health ? Clin Child Fam Psychol Rev . 2004 ; 7 ( 1 ): 29 – 57 . Google Scholar CrossRef Search ADS PubMed 66. Barboza Solís C , Kelly-Irving M , Fantin R et al. Adverse childhood experiences and physiological wear-and-tear in midlife: findings from the 1958 British birth cohort . Proc Natl Acad Sci USA . 2015 ; 112 ( 7 ): E738 – E746 . Google Scholar CrossRef Search ADS PubMed 67. Non AL , Rewak M , Kawachi I et al. Childhood social disadvantage, cardiometabolic risk, and chronic disease in adulthood . Am J Epidemiol . 2014 ; 180 ( 3 ): 263 – 271 . Google Scholar CrossRef Search ADS PubMed 68. Poulton R , Moffitt TE , Silva PA . The Dunedin Multidisciplinary Health and Development Study: overview of the first 40 years, with an eye to the future . Soc Psychiatry Psychiatr Epidemiol . 2015 ; 50 ( 5 ): 679 – 693 . Google Scholar CrossRef Search ADS PubMed 69. Pulkki-Råback L , Elovainio M , Hakulinen C et al. Cumulative effect of psychosocial factors in youth on ideal cardiovascular health in adulthood: the Cardiovascular Risk in Young Finns Study . Circulation . 2015 ; 131 ( 3 ): 245 – 253 . Google Scholar CrossRef Search ADS PubMed 70. Loeber R , Farrington D , Stouthamer-Loeber M , White H. Violence and Serious Theft: Development and Prediction From Childhood to Adulthood . New York : Taylor & Francis Group ; 2008 . 71. Cundiff JM , Boylan JM , Pardini DA , Matthews KA . Moving up matters: socioeconomic mobility prospectively predicts better physical health . Health Psychol . 2017 ; 36 ( 6 ): 609 – 617 . Google Scholar CrossRef Search ADS PubMed 72. Hollingshead AB. Four Factor Index of Social Status . 1975 . Unpublished manuscript. New Haven, CT : Yale University . 73. Loeber R , Farrington D , Stouthamer-Loeber M , van Kammen W. Antisocial Behavior and Mental Health Problems: Risk Factors in Childhood and Adolescence . Mahwah, NJ : Erlbaum ; 1998 . 74. Moos RH , Moos BS . Evaluating correctional and community settings . In: Moos RH , ed. Families . New York : Wiley ; 1975 : 263 – 86 . 75. Skinner HA , Steinhauer PD , Santa-Barbara J . The family assessment measure . Can J Commun Ment Health . 1983 ; 2 : 91 – 103 . Google Scholar CrossRef Search ADS 76. De Los Reyes A , Augenstein TM , Wang M et al. The validity of the multi-informant approach to assessing child and adolescent mental health . Psychol Bull . 2015 ; 141 ( 4 ): 858 – 900 . Google Scholar CrossRef Search ADS PubMed 77. Centers for Disease Control and Prevention (CDC) . Behavioral Risk Factor Surveillance System Survey Questionnaire . Atlanta, Georgia : U.S. Department of Health and Human Services, Centers for Disease Control and Prevention ; 2011 . PubMed PubMed 78. Paffenbarger RS Jr , Wing AL , Hyde RT . Physical activity as an index of heart attack risk in college alumni . Am J Epidemiol . 1978 ; 108 ( 3 ): 161 – 175 . Google Scholar CrossRef Search ADS PubMed 79. Scheier MF , Carver CS , Bridges MW . Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the Life Orientation Test . J Pers Soc Psychol . 1994 ; 67 ( 6 ): 1063 – 1078 . Google Scholar CrossRef Search ADS PubMed 80. Scheier MF , Wrosch C , Baum A et al. The Life Engagement Test: assessing purpose in life . J Behav Med . 2006 ; 29 ( 3 ): 291 – 298 . Google Scholar CrossRef Search ADS PubMed 81. Pearlin LI , Lieberman MA , Menaghan EG , Mullan JT . The stress process . J Health Soc Behav . 1981 ; 22 ( 4 ): 337 – 356 . Google Scholar CrossRef Search ADS PubMed 82. Rosenberg M. Society and the Adolescent Self-Image . Princeton : Princeton University Press ; 1965 . Google Scholar CrossRef Search ADS 83. MacKinnon DP , Fairchild AJ , Fritz MS . Mediation analysis . Annu Rev Psychol . 2007 ; 58 : 593 – 614 . Google Scholar CrossRef Search ADS PubMed 84. MacKinnon DP , Lockwood CM , Hoffman JM , West SG , Sheets V . A comparison of methods to test mediation and other intervening variable effects . Psychol Methods . 2002 ; 7 ( 1 ): 83 – 104 . Google Scholar CrossRef Search ADS PubMed 85. Hu L , Bentler PM . Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives . Struct Equ Modeling . 1999 ; 6 : 1 – 55 . Google Scholar CrossRef Search ADS 86. McDonald RP , Ho MH . Principles and practice in reporting structural equation analyses . Psychol Methods . 2002 ; 7 ( 1 ): 64 – 82 . Google Scholar CrossRef Search ADS PubMed 87. Preacher KJ , Hayes AF . Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models . Behav Res Methods . 2008 ; 40 ( 3 ): 879 – 891 . Google Scholar CrossRef Search ADS PubMed 88. Muthén LK , Muthén BO. Mplus User’s Guide . 7th ed . Los Angeles, CA : Muthén & Muthén ; 2012 . 89. Granic I , Dishion TJ , Hollenstein T . The family ecology of adolescence: a dynamic systems perspective on normative development . In: Adams GR , Berzonsky MD , eds. Blackwell Handbook of Adolescence . Oxford, UK : Blackwell Publishing Ltd ; 2003 : 60 – 91 . Google Scholar CrossRef Search ADS 90. Holmbeck GN , Paikoff RL , Brooks-Gunn J . Parenting adolescents . In: Borenstein M , ed. Handbook of parenting: Vol. 1. Children and parenting . Mahwah, NJ : Erlbaum ; 1995 : 91 – 118 . 91. Amato PR , Sobolewski JM . The effects of divorce and marital discord on adult children’s psychological well-being . Am Sociol Rev . 2001 ; 66 : 900 – 921 . Google Scholar CrossRef Search ADS 92. Oyserman D , Smith GC , Elmore K. Identity-based motivation: implications for health and health disparities . J Socl Issues . 2014 ; 70 : 206 – 225 . Google Scholar CrossRef Search ADS 93. Lissau I , Sørensen TI . Parental neglect during childhood and increased risk of obesity in young adulthood . Lancet . 1994 ; 343 ( 8893 ): 324 – 327 . Google Scholar CrossRef Search ADS PubMed 94. Hoff E , Laursen B , Tardif T . Socioeconomic status and parenting . In: Borenstein M , ed. Handbook of Parenting: Vol. 2. Biology and Ecology of Parenting . Mahwah, NJ : Erlbaum ; 2002 : 231 – 52 . 95. Non AL , Román JC , Gross CL et al. Early childhood social disadvantage is associated with poor health behaviours in adulthood . Ann Hum Biol . 2016 ; 43 ( 2 ): 144 – 153 . Google Scholar CrossRef Search ADS PubMed 96. Ruuskanen JM , Ruoppila I . Physical activity and psychological well-being among people aged 65 to 84 years . Age Ageing . 1995 ; 24 ( 4 ): 292 – 296 . Google Scholar CrossRef Search ADS PubMed 97. McAuley E , Blissmer B . Self-efficacy determinants and consequences of physical activity . Exerc Sport Sci Rev . 2000 ; 28 ( 2 ): 85 – 88 . Google Scholar PubMed 98. Conger RD , Belsky J , Capaldi DM . The intergenerational transmission of parenting: closing comments for the special section . Dev Psychol . 2009 ; 45 ( 5 ): 1276 – 1283 . Google Scholar CrossRef Search ADS PubMed © Society of Behavioral Medicine 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Behavioral Medicine Oxford University Press

Pathways Linking Childhood SES and Adult Health Behaviors and Psychological Resources in Black and White Men

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10.1093/abm/kay006
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Abstract

Abstract Background Exposure to low socioeconomic status (SES) in childhood predicts increased morbidity and mortality. However, little prospective evidence is available to test pathways linking low childhood SES to adult health. Purpose In the current study, indirect effects through positive parenting in adolescence and adult SES were tested in the association between childhood SES and adult health behaviors and psychological resources. Methods Men (n = 305; 53% Black) were followed longitudinally from ages 7 to 32. SES was measured annually in childhood (ages 7–9) and again in adulthood (age 32) using the Hollingshead index. Parenting was assessed annually (ages 13–16) using caregivers’ and boys’ self-report of supervision, communication, and expectations for their son’s future. Health behaviors (cigarette and alcohol use, fruit and vegetable consumption, and physical activity) and psychological resources (optimism, purpose in life, self-mastery, and self-esteem) were assessed in adulthood (age 32). Results Structural equation modeling showed that higher childhood SES was associated with more positive parenting in adolescence and higher adult SES. Higher childhood SES was indirectly associated with healthier behaviors and higher psychological resources in adulthood through pathways involving positive parenting during adolescence and SES in adulthood. Findings were consistent in both racial groups. Conclusions Positive parenting in adolescence was an important pathway in understanding associations among childhood SES and health behaviors and psychological resources in adulthood. Low childhood SES was prospectively associated with healthier behaviors and greater psychological resources in part through more positive parenting in adolescence. Socioeconomic status, Childhood, Parenting, Health behaviors, Psychological resources Introduction The socioeconomic environment in early life can have lasting influences on morbidity and mortality decades later [1–3]. Individuals from families of lower socioeconomic status (SES) have higher rates of cardiovascular disease, diabetes, cancer, and asthma, among other adverse health outcomes as compared to individuals raised in more advantaged environments [4–6]. Understanding the pathways from SES in childhood to health in adulthood is complicated, given a multitude of psychosocial, behavioral, and biological processes that underlie associations and unfold over several decades (reviewed in [1]). In the current study, we consider both the parent–child relationship in adolescence and SES in adulthood as mediators of links between childhood SES and adult health behaviors and psychological resources, respectively, using data from a prospective, longitudinal study of Black and White men followed from ages 7 to 32. Behavioral factors and psychological resources are considered outcomes in the present research. Health behaviors, such as cigarette and alcohol use, fruit and vegetable consumption, and physical activity, are important mediators of the association between childhood SES and morbidity and mortality [7, 8]. Adults from lower SES backgrounds report more smoking, more sedentary behavior and less physical activity, poorer dietary habits, and more drug use, especially in the context of current unemployment [5, 9–12]. These health behaviors can affect physical health directly via physiological pathways, and they may also promote obesity, which has independent, negative health consequences [13]. Psychological resources, here defined to include purpose in life, mastery, self-esteem, and optimism, are important components of mental health, reflecting the presence of positive attributes and not simply the absence of depression and anxiety [14]. In the Reserve Capacity Model [15], such resources mitigate against stressful events and help explain how lower SES may lead to increased morbidity and mortality. The psychological resource composite utilized in the current study focuses on intrapersonal resources thought to develop in childhood and also is akin to eudaimonic well-being, which is multidimensional in nature and captures psychological flourishing and self-realization with important linkages to health (e.g., [16]). Such resources are uniquely predictive of physical health, including better self-rated health, lower rates of disease and increased longevity as well as more favorable profiles of biological risk factors [16–20]. Associations between resources and health may be stronger among lower, as compared to higher, SES individuals [21–24]. Intervention efforts focused on enhancing psychological well-being in primarily clinical populations have demonstrated concomitant improvements in self-reported mental and physical health outcomes [25–29]. Including both behavioral and psychological factors as outcomes offers a holistic window into health and well-being among a diverse sample of relatively young adults. Childhood SES and Adult Health Behaviors and Psychological Resources The socioeconomic environment in childhood predicts adult health behaviors, such as smoking, alcohol consumption, and physical activity [4, 30–33]. For example, childhood socioeconomic position was associated with smoking, physical activity, and body mass index, but not diet in adulthood among African Americans in the Jackson Heart Study [30]. Developmental data support the idea that psychological factors in adulthood also vary as a function of childhood SES, such that men with lower childhood SES had higher scores on measures of hostility, hopelessness, and depressive symptoms [31, 34]. Relatively less attention has been paid to associations between childhood SES and positive measures of psychological resources in adulthood. Explanations for the associations between low SES in early life and unhealthy behaviors involve more than personal lifestyle choices, and include social, structural, and economic circumstances that differ systematically by SES. For example, lower SES environments have easier access to tobacco, alcohol, and fast food and less availability of affordable fresh food and safe places for physical activity (e.g., green space, sidewalks; [35]). Further, unhealthy behaviors are often sources of stress-relief and mood regulation, which may offset the burden of chronic stress disproportionately affecting socioeconomically disadvantaged individuals [7, 36, 37]. There are important socioeconomic differences in attitudes and beliefs about healthy behaviors as well, such that lower SES individuals are more fatalistic about their ability to reduce health risks and have stronger beliefs in the influence of chance factors affecting health [7, 38]. Family and peers are also influential in the adoption and maintenance of health behaviors [39]. Such factors contribute to the aforementioned differences in health behavior profiles and psychological dispositions as a function of childhood SES. A key limitation in the literature on childhood SES and adult health behaviors and psychological factors, however, is that childhood SES is often assessed retrospectively, which is subject to recall bias and measurement error [40]. Associations between childhood SES and adult health are typically stronger in prospective studies that do not rely on adult recall of childhood SES [3]. Further, childhood and adult SES are correlated [41], and childhood SES may be associated with adult health outcomes at least in part because it is highly correlated with adult SES. Thus, longitudinal data, such as those utilized in the present study, are necessary to discern the shared and independent associations of SES and health behaviors at different points across the life course. There is also a notable lack of diversity in sample populations of the extant literature on childhood SES, and health behaviors and psychological factors, primarily relying on White Americans and Western European samples (cf., [30, 42]). This study seeks to address this shortcoming by including both White and Black men. Parenting and Adult Health Behaviors and Psychological Resources The family context is critical for understanding how childhood SES affects adult health behaviors and psychological dispositions. Parents typically provide the most proximal social environment for children and can shape adult psychological and behavioral factors [43]. During adolescence, there are notable changes in the parent–child relationships, including parents granting greater autonomy and adolescents beginning to shape their own social environment. However, parents remain a key influence for adolescent development, and the establishment of lasting health behaviors typically occurs during this developmental period as well [1, 44, 45]. In the current study, multiple aspects of parenting during adolescence are highlighted, including communication styles marked by warmth, consistency of supervision, and positive expectations for the future. Factors such as parental expectations for achievement and discipline strategies stand out as important parental and home factors that link socioeconomic factors to achievement in school [46]. In particular, parenting that involves close supervision, consistency, future orientation, and warmth is especially important among socioeconomically disadvantaged children living in urban environments [47–53]. In a 12-year longitudinal study of positive youth development, factors that distinguished among rural African American males classified as high versus low risk for substance use and sexual risk behaviors included exposure to harsh and inconsistent parenting, lower future orientation, and more deviant peers [53]. Further, randomized trials designed to improve parental monitoring and communication with adolescents have led to reductions in childhood and adolescent obesity through improvements in health-related behaviors and decreases in depressive symptoms [54–56]; reduced inflammation, particularly in adolescents from low-SES families [57]; and fewer increases in risky behaviors (e.g., marijuana and alcohol use, sexual risk behaviors) in emerging adults, especially among those reporting high stress [58, 59]. In each of these trials, effects on health were at least partially mediated by changes in parenting and in the parent–adolescent relationship following the intervention. Several theoretical models inform the ways in which parenting practices may differ by early life SES and affect health and well-being in childhood, adolescence, and into adulthood. The Family Stress Model emphasizes that socioeconomic disadvantage and accompanying economic stress contribute to family distress and dysfunction, which are linked to parental depressive symptoms [60], hostility among family members, and maladaptive parenting practices. The key parental practices associated with more socioeconomic disadvantage are insufficient supervision, a harsher, more authoritarian style, a lack of warmth and support, and inconsistency [61, 62]. These parenting practices in turn affect child and adolescent adjustment [63]. Similarly, the Risky Families Model posits that a negative family social environment, including dimensions of conflict, neglect, and a lack of warmth and support, can affect emotional, behavioral, and biological processes throughout childhood and adolescence, contributing to mental and physical health problems in adulthood [64]. Parenting practices of monitoring, modeling, discipline, communication, and affection have further been identified as key pathways linking marital conflict and dissolution to emotional dysregulation, emotional insecurity, and ultimately physical health problems [65]. While these respective models have garnered considerable empirical support, linkages to adult outcomes are rare, given the paucity of relevant prospective, longitudinal data over several decades (cf., [66–69]). Study Aim and Hypotheses Using a prospective, longitudinal design of Black and White men starting at age 7 and into their early 30s, the current study addresses several shortcomings of prior research by testing a model with direct and indirect effects from SES in childhood to parenting factors in adolescence to adult SES and to health behaviors and psychological resources in adulthood. We examined the following hypotheses: 1. Positive health behaviors and psychological resources in adulthood are predicted by higher childhood SES, more positive parenting in adolescence, and higher adult SES. 2. Childhood SES is linked with adult health behaviors and psychological resources in part through its association with positive parenting in adolescence. 3. Childhood SES is linked with adult health behaviors and psychological resources in part through its association with adult SES. Methods Sample Data for the current study came from the youngest cohort of the Pittsburgh Youth Study (PYS), a longitudinal study of boys initially recruited from a pool of first graders enrolled in the Pittsburgh Public Schools in 1987–1988 (N = 503). The sample was recruited from an original pool of 1,165 boys registered to attend the first grade. From that pool, 849 were randomly chosen to undergo a multi-informant (i.e., parent, teacher, child report) screening that assessed early conduct problems (e.g., fighting, stealing). Boys identified at the top 30% on the screening risk measure (N = 256), and a roughly equal number of boys randomly selected from the remainder (N = 247), were selected for longitudinal follow-up (total N = 503). At screening, the mean age was 6.2 years, and the sample was predominately White (40.6%) and Black (55.7%). The PYS sample was followed at least annually until age 19 with reports from the boys, their primary caretaker, and teachers. Further detail about the PYS is available elsewhere [70]. Men in the PYS were contacted to participate in the current study focused on cardiovascular health and risk for cardiovascular disease in adulthood (mean age = 32 years; range 30–34 years). At the time of the current study, 18 men were deceased, 44 had previously dropped out of the PYS, 4 were severely mentally disabled, and 42 were incarcerated. Of the remaining 395 men, 312 participated (79%). Among those eligible but who did not participate, 27% (n = 22) declined participation, 23% (n = 19) failed to respond to contact or missed appointments, and 51% (n = 42) could not be located. The current sample did not differ from the initial PYS sample on race, risk for conduct problems, reported overweight, childhood SES, or number of health problems in childhood, ps > .05 [71]. However, comparing the analytic sample to those with adolescent data who did not participate in the most recent follow-up (n = 169), revealed less supervision and lower expectations for the future among those who did not participate in adulthood as compared to those in the analytic sample, but no differences in parent–child communication. It is worth noting that these differences in parenting were nonsignificant when we compared the analytic sample to those with adolescent data who did not participate in follow-up in adulthood, excluding those incarcerated at follow-up. This study was approved by the Institutional Review Board at the University of Pittsburgh, and all men provided written, informed consent. Measures Socioeconomic status SES was measured annually via the two-factor Hollingshead index [72], which incorporates parental educational attainment and occupational status as reported by the boy’s primary caretaker (childhood SES) or by the participant (adult SES). For childhood SES, the higher of the two parents was used for two-parent families. The mean Hollingshead SES across six occasions between ages 7 and 9 was used as the overall index of childhood SES. Adult SES was only measured on one occasion. Positive parenting Positive parenting in adolescence was assessed as a latent variable with scales of parental supervision, expectations for the future, and parent–child communication as the indicator variables [70]. Dimensions of positive parenting were self-reported annually by the boy’s primary caretaker (86.3% were biological mother) when participants were between the ages of 13 and 16. These scales were initially developed based on pilot research conducted at the Oregon Social Learning Center [73], including literature reviews of the impact of parenting on childhood outcomes, items from existing scales adapted for an urban sample with a substantial minority membership and a range of SES (e.g., Family Environment Scale; [74]), and the Family Assessment Measure [75], followed by detailed psychometric analyses. Parental supervision and involvement was assessed with four items (e.g., “Do you know who your son’s companions are when he is not at home?”) on a 3-point scale (1 = almost never, 2 = sometimes, 3 = almost always). Internal consistency for the supervision and involvement scale ranged from .63 to .74 (mean = .70) across three annual assessments (supervision and involvement was not collected at one of the annual waves). Expectations for the future were assessed by primary caretaker’s indicating how likely it was that their son would achieve 17 different goals or activities related to money, hard work, family life, and legal issues (e.g., “have a well-paying job,” “have a happy family life”) on a 4-point scale (1 = very likely, 4 = not likely at all). Internal consistency for expectations for the future was .64 at both of the two annual assessments (expectations for the future was not collected at two of the annual waves). Parent–child communication was measured with 37 items regarding the parent–child relationship (e.g., “Do you openly show affection to your son?”, “Do you think that your son feels close to you?”) on a 3-point scale (1 = almost never, 2 = sometimes, 3 = almost always). Internal consistency for the communication scale ranged from .58 to .68 (mean = .62) across the four annual assessments and across informants. For parent–child communication, the primary caretaker and the participant both completed the instrument, and these ratings were averaged on an annual basis, which is a commonly accepted method for combining multiple-informant ratings in the developmental literature [76]. The correlation between parent-reported communication and child-reported communication was .34. An overall score was calculated for each dimension by averaging each year’s assessment within the 3-year window. Item scores were re-coded so that higher scale scores reflected more supervision and communication, and higher expectations, respectively. An initial principal components analysis was conducted on the three parenting scales. Using an eigen value greater than one criterion and via inspection of the scree plot, one factor was extracted, explaining 66.3% of the variance. Factor loadings for the three parenting scales ranged from .80 to .84 (median factor loading = .81), indicating that each individual scale was highly correlated with the extracted factor. Health behaviors Health behaviors in adulthood were self-reported by the men and included assessments of cigarette smoking, fruit and vegetable intake, alcohol consumption, and physical activity. Cigarette smoking was treated as a three-level ordinal variable (never smoker or not in past year, less than or equal to 10 cigarettes/day in past year, and greater than 10 cigarettes/day in past year). Fruit and vegetable intake was measured as the average weekly intake of fruits and vegetables. Data on fruit and vegetable consumption were collected using six items from the Behavioral Risk Factor Surveillance System fruit and vegetable module [77]. Participants indicated how often they consumed fruit, fruit juice, lettuce salad, fried potatoes, other kinds of potatoes, and vegetables other than lettuce salads and potatoes on a daily, weekly, or monthly basis. Alcohol consumption was scored as total drinks consumed in an average week. Physical activity was measured using the Paffenbarger Physical Activity Questionnaire and treated as a continuous variable in analyses [78]. Four items assessed the general levels of exercise in terms of kilocalories expended weekly. Participants reported on their regular activity, the amount of walking and stairs they climb each day, and any sports or recreational activities they participated in during the past week and how much time they spent in each activity. Psychological resources Psychological resources in adulthood were assessed with a latent variable with scales of optimism, purpose in life, mastery, and self-esteem as the indicator variables. Optimism was evaluated via the Life Orientation Test-Revised [79]. This scale has six items (e.g., “In uncertain times, I usually expect the best”), and ratings are made on a 4-point scale (1 = strongly disagree, 4 = strongly agree; alpha = .80). Purpose in life was measured with the 6-item Life Engagement Test [80]; participants rate the extent of their agreement to statements such as, “I have lots of reasons for living,” on a 4-point scale (1 = strongly disagree, 4 = strongly agree; alpha = .82). The Pearlin Mastery Scale assessed the extent to which individuals perceive they are in control of forces that significantly affect their lives [81]. The Pearlin Mastery Scale has seven items (e.g., “I can do just about anything I really set my mind to do”), and participants rate the extent to which they agree with the items on a 5-point scale (1 = strongly disagree, 5 = strongly agree; alpha = .80). Finally, self-esteem was evaluated using the Rosenberg Self-Esteem Scale [82]. Participants rated the extent to which they agreed with 10 items (e.g., “I feel that I have a number of good qualities”) on a 4-point scale (1 = strongly disagree, 4 = strongly agree; alpha = .85). For all measures, higher scores reflect greater psychological resources. An initial principal components analysis was conducted on the four psychological resources scales. Using an eigen value greater than one criterion and via inspection of the scree plot, one factor was extracted, explaining 72.7% of the variance. Factor loadings for the four psychological resources scales ranged from .84 to .87 (median factor loading = .85), indicating that each individual scale was highly correlated with the extracted factor. Covariates Race (0 = Black, 1 = White) was self-reported at age 32 and was included as a covariate in all analyses. Cigarette and alcohol use in adolescence (ages 13–16) were also included as covariates in order to address whether these factors may confound any observed associations between parenting in adolescence and health behaviors in adulthood. Cigarette use in adolescence was treated as a binary variable reflecting daily or near daily smoking (smoking on 312 days per year or more) versus not. Alcohol use in adolescence was characterized using two dummy coded variables to reflect never drinking as compared to light users (less than or equal to 10 drinks/week) and never drinking as compared to heavy users (greater than 10 drinks/week). Statistical Analysis Structural equation modeling was used to examine hypothesized associations among childhood SES, positive parenting in adolescence, adult SES, and health behaviors and psychological resources in adulthood. Indirect effects between childhood SES and the outcomes were tested via: (i) positive parenting, (ii) adult SES, or (iii) positive parenting to adult SES [83, 84]. All outcome variables were allowed to correlate with each other, with the exception of smoking as it was run in a separate model (see the following paragraphs). Model fit was assessed using χ2 tests, the root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), and the comparative fit index (CFI). Good fit is indicated by χ2 tests with p > .05, RMSEA less than .05, SRMR less than .08, and CFI greater than .95 [85, 86]. Maximum likelihood estimation was used to produce model parameters for continuous outcomes (all outcomes except smoking), whereas robust weighted least squares (WLSMV) estimation was used to produce model parameters for the smoking outcome. Maximum likelihood estimation is not appropriate for mediation modeling with an ordinal dependent variable (i.e., smoking in adulthood, categorized as never smoker or not in the past year; ≤10 cigarettes/day in past year, >10 cigarettes/day in past year). In both ML and WLSMV models, indirect effects were estimated using bootstrapping procedures with 5,000 resamples [87]. In order to achieve normal distributions, alcohol consumption and physical activity were natural log transformed, and fruit and vegetable consumption was square-root transformed prior to analyses. All parenting and psychological resource variables were standardized (z-scored) prior to analyses. Preliminary analyses determined metric invariance in both latent factors (positive parenting and psychological resources) across race (following procedures outlined in [88]). Further, there was no evidence of moderation by race as model fit was not significantly improved when factor loadings and path coefficients were freed (v. fixed) across race. Therefore, all participants were examined in the same model. Race and adolescent cigarette and alcohol were included as covariates in all models. Analyses were performed with Mplus, version 7.3. Results Table 1 presents descriptive information on all study variables in childhood, adolescence, and adulthood for the full sample. Table 2 presents bivariate correlations among study variables. Lower childhood SES was significantly correlated with poorer parent–child communication, less supervision, and lower parental expectations in adolescence, as well as lower SES and less physical activity in adulthood. Table 1 Descriptive statistics of study variables Total (N = 312) M (SD) or % Range Childhood (ages 7–9)  Family SES 36.8 (10.8) 6–66 Adolescence (ages 13–16)  Poor parent–child communication 52.4 (6.9) 38–72  Low parental supervision 5.9 (1.2) 4–10  Low parental expectations 27.9 (6.4) 17–53  Smoking: never or infrequent 95.4%   Daily or near daily 4.6%  Alcohol: never 34.7%   ≤10 Drinks/week 43.2%   >10 Drinks/week 22.1% Adulthood (mean age 32; range 30–34)  Adult SES 32.2 (15.3) 6–66  Life engagement 19.5 (3.0) 10–24  Optimism 17.1 (3.2) 7–24  Self-mastery 26.9 (4.6) 9–35  Self-esteem 22.7 (5.2) 0–30  Alcohol use (drinks/week) 5.3 (7.5) 0–39  Smoking: never 42.3%   ≤10 Cigarettes/day 38.8%   >10 Cigarettes/day 18.9%  Physical activity 1,470 (1,919) 0–14,716  Fruit/vegetable consumption 16.9 (12.8) 0–85 Total (N = 312) M (SD) or % Range Childhood (ages 7–9)  Family SES 36.8 (10.8) 6–66 Adolescence (ages 13–16)  Poor parent–child communication 52.4 (6.9) 38–72  Low parental supervision 5.9 (1.2) 4–10  Low parental expectations 27.9 (6.4) 17–53  Smoking: never or infrequent 95.4%   Daily or near daily 4.6%  Alcohol: never 34.7%   ≤10 Drinks/week 43.2%   >10 Drinks/week 22.1% Adulthood (mean age 32; range 30–34)  Adult SES 32.2 (15.3) 6–66  Life engagement 19.5 (3.0) 10–24  Optimism 17.1 (3.2) 7–24  Self-mastery 26.9 (4.6) 9–35  Self-esteem 22.7 (5.2) 0–30  Alcohol use (drinks/week) 5.3 (7.5) 0–39  Smoking: never 42.3%   ≤10 Cigarettes/day 38.8%   >10 Cigarettes/day 18.9%  Physical activity 1,470 (1,919) 0–14,716  Fruit/vegetable consumption 16.9 (12.8) 0–85 SES is rated on the two-factor Hollingshead scale (72). View Large Table 1 Descriptive statistics of study variables Total (N = 312) M (SD) or % Range Childhood (ages 7–9)  Family SES 36.8 (10.8) 6–66 Adolescence (ages 13–16)  Poor parent–child communication 52.4 (6.9) 38–72  Low parental supervision 5.9 (1.2) 4–10  Low parental expectations 27.9 (6.4) 17–53  Smoking: never or infrequent 95.4%   Daily or near daily 4.6%  Alcohol: never 34.7%   ≤10 Drinks/week 43.2%   >10 Drinks/week 22.1% Adulthood (mean age 32; range 30–34)  Adult SES 32.2 (15.3) 6–66  Life engagement 19.5 (3.0) 10–24  Optimism 17.1 (3.2) 7–24  Self-mastery 26.9 (4.6) 9–35  Self-esteem 22.7 (5.2) 0–30  Alcohol use (drinks/week) 5.3 (7.5) 0–39  Smoking: never 42.3%   ≤10 Cigarettes/day 38.8%   >10 Cigarettes/day 18.9%  Physical activity 1,470 (1,919) 0–14,716  Fruit/vegetable consumption 16.9 (12.8) 0–85 Total (N = 312) M (SD) or % Range Childhood (ages 7–9)  Family SES 36.8 (10.8) 6–66 Adolescence (ages 13–16)  Poor parent–child communication 52.4 (6.9) 38–72  Low parental supervision 5.9 (1.2) 4–10  Low parental expectations 27.9 (6.4) 17–53  Smoking: never or infrequent 95.4%   Daily or near daily 4.6%  Alcohol: never 34.7%   ≤10 Drinks/week 43.2%   >10 Drinks/week 22.1% Adulthood (mean age 32; range 30–34)  Adult SES 32.2 (15.3) 6–66  Life engagement 19.5 (3.0) 10–24  Optimism 17.1 (3.2) 7–24  Self-mastery 26.9 (4.6) 9–35  Self-esteem 22.7 (5.2) 0–30  Alcohol use (drinks/week) 5.3 (7.5) 0–39  Smoking: never 42.3%   ≤10 Cigarettes/day 38.8%   >10 Cigarettes/day 18.9%  Physical activity 1,470 (1,919) 0–14,716  Fruit/vegetable consumption 16.9 (12.8) 0–85 SES is rated on the two-factor Hollingshead scale (72). View Large Table 2 Bivariate correlations among study variables 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 1. Childhood SES – 2. Poor parent–child communication −.24* – 3. Low parental supervision −.28* .64* – 4. Low parental expectations −.16* .46* .42* – 5. Adult SES .36* −.26* −.33* −.26* – 6. Life engagement .09 −.27* −.22* −.23* .20* – 7. Optimism .11 −.26* −.20* −.27* .20* .65* – 8. Self-mastery .004 −.33* −.23* −.17* .20* .63* .63* – 9. Self-esteem .01 −.21* −.23* −.20* .23* .69* .59* .63* – 10. Alcohol use .02 .02 .06 .11 −.01 .02 −.05 .03 .001 – 11. Cigarette smoking (y/n > 10/day) −.06 .12* .10 .08 −.20* −.14* −.12* −.13* −.15* −.003 – 12. Physical activity .15* −.09 −.09 −.08 .24* .22* .16* .19* .17* .05 −.08 – 13. Fruit/vegetable consumption .05 −.18* −.11* −.15* .26* .24* .25* .25* .22* −.09 −.11 .16* 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 1. Childhood SES – 2. Poor parent–child communication −.24* – 3. Low parental supervision −.28* .64* – 4. Low parental expectations −.16* .46* .42* – 5. Adult SES .36* −.26* −.33* −.26* – 6. Life engagement .09 −.27* −.22* −.23* .20* – 7. Optimism .11 −.26* −.20* −.27* .20* .65* – 8. Self-mastery .004 −.33* −.23* −.17* .20* .63* .63* – 9. Self-esteem .01 −.21* −.23* −.20* .23* .69* .59* .63* – 10. Alcohol use .02 .02 .06 .11 −.01 .02 −.05 .03 .001 – 11. Cigarette smoking (y/n > 10/day) −.06 .12* .10 .08 −.20* −.14* −.12* −.13* −.15* −.003 – 12. Physical activity .15* −.09 −.09 −.08 .24* .22* .16* .19* .17* .05 −.08 – 13. Fruit/vegetable consumption .05 −.18* −.11* −.15* .26* .24* .25* .25* .22* −.09 −.11 .16* Alcohol use and physical activity were natural log transformed and fruit/vegetable consumption (times/week) was square-root transformed prior to calculating bivariate correlations to achieve normal distributions. Cigarette smoking is treated as a binary variable reflected y/n smoking greater than or equal to 10 cigarettes per day. SES socioeconomic status. *p ≤ .05. View Large Table 2 Bivariate correlations among study variables 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 1. Childhood SES – 2. Poor parent–child communication −.24* – 3. Low parental supervision −.28* .64* – 4. Low parental expectations −.16* .46* .42* – 5. Adult SES .36* −.26* −.33* −.26* – 6. Life engagement .09 −.27* −.22* −.23* .20* – 7. Optimism .11 −.26* −.20* −.27* .20* .65* – 8. Self-mastery .004 −.33* −.23* −.17* .20* .63* .63* – 9. Self-esteem .01 −.21* −.23* −.20* .23* .69* .59* .63* – 10. Alcohol use .02 .02 .06 .11 −.01 .02 −.05 .03 .001 – 11. Cigarette smoking (y/n > 10/day) −.06 .12* .10 .08 −.20* −.14* −.12* −.13* −.15* −.003 – 12. Physical activity .15* −.09 −.09 −.08 .24* .22* .16* .19* .17* .05 −.08 – 13. Fruit/vegetable consumption .05 −.18* −.11* −.15* .26* .24* .25* .25* .22* −.09 −.11 .16* 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 1. Childhood SES – 2. Poor parent–child communication −.24* – 3. Low parental supervision −.28* .64* – 4. Low parental expectations −.16* .46* .42* – 5. Adult SES .36* −.26* −.33* −.26* – 6. Life engagement .09 −.27* −.22* −.23* .20* – 7. Optimism .11 −.26* −.20* −.27* .20* .65* – 8. Self-mastery .004 −.33* −.23* −.17* .20* .63* .63* – 9. Self-esteem .01 −.21* −.23* −.20* .23* .69* .59* .63* – 10. Alcohol use .02 .02 .06 .11 −.01 .02 −.05 .03 .001 – 11. Cigarette smoking (y/n > 10/day) −.06 .12* .10 .08 −.20* −.14* −.12* −.13* −.15* −.003 – 12. Physical activity .15* −.09 −.09 −.08 .24* .22* .16* .19* .17* .05 −.08 – 13. Fruit/vegetable consumption .05 −.18* −.11* −.15* .26* .24* .25* .25* .22* −.09 −.11 .16* Alcohol use and physical activity were natural log transformed and fruit/vegetable consumption (times/week) was square-root transformed prior to calculating bivariate correlations to achieve normal distributions. Cigarette smoking is treated as a binary variable reflected y/n smoking greater than or equal to 10 cigarettes per day. SES socioeconomic status. *p ≤ .05. View Large The structural equation model with direct and indirect effects from childhood SES to parenting in adolescence and adult SES and to health behaviors and psychological resources in adulthood, controlling for race and adolescent smoking and alcohol, is presented in Fig. 1. Only significant standardized path coefficients are displayed, although all unstandardized path coefficients are provided in Table 3. The model demonstrated good fit with the data (χ2(53) = 82.4, p = .01, RMSEA = .043, CFI = .971, SRMR = .029). Table 3 Model results with unstandardized path coefficients and latent factor loadings B (SE) p Path model Family SES →   Positive parenting .02(.01) <.001   Adult SES .30(.09) <.001   Physical activity .01(.01) .17   Alcohol use .003(.01) .62   Cigarette smokinga −.01(.01) .18   Fruit/vegetable consumption −.01(.01) .41   Psychological resources −.01(.01) .21  Positive parenting →   Adult SES 6.06(1.51) <.001   Physical activity .06(.11) .57   Alcohol use −.06(.13) .65   Cigarette smokinga −.20(.12) .10   Fruit/vegetable consumption .23(.12) .062   Psychological resources .45(.09) <.001  Adult SES →   Physical activity .02(.004) <.001   Alcohol use .002(.004) .59   Cigarette smokinga −.02(.004) <.001   Fruit/vegetable consumption .02(.004) <.001   Psychological resources .01(.004) .052 Latent factors  Positive parenting   Parent–child communication 1.0   Supervision 1.03 (.14) <.001   Expectations for future 0.82 (.10) <.001  Psychological resources   Self-esteem 1.0   Optimism 0.99 (.10) <.001   Life engagement 1.04 (.09) <.001   Self-mastery 1.02 (.09) <.001 B (SE) p Path model Family SES →   Positive parenting .02(.01) <.001   Adult SES .30(.09) <.001   Physical activity .01(.01) .17   Alcohol use .003(.01) .62   Cigarette smokinga −.01(.01) .18   Fruit/vegetable consumption −.01(.01) .41   Psychological resources −.01(.01) .21  Positive parenting →   Adult SES 6.06(1.51) <.001   Physical activity .06(.11) .57   Alcohol use −.06(.13) .65   Cigarette smokinga −.20(.12) .10   Fruit/vegetable consumption .23(.12) .062   Psychological resources .45(.09) <.001  Adult SES →   Physical activity .02(.004) <.001   Alcohol use .002(.004) .59   Cigarette smokinga −.02(.004) <.001   Fruit/vegetable consumption .02(.004) <.001   Psychological resources .01(.004) .052 Latent factors  Positive parenting   Parent–child communication 1.0   Supervision 1.03 (.14) <.001   Expectations for future 0.82 (.10) <.001  Psychological resources   Self-esteem 1.0   Optimism 0.99 (.10) <.001   Life engagement 1.04 (.09) <.001   Self-mastery 1.02 (.09) <.001 Model fit indices were as follows, χ2(df) = 82.4(53), p =.01, CFI = .971, RMSEA [90% CI] = .043 [.023, .060], SRMR = .029. CFI comparative fit index; CI confidence interval; RMSEA root mean square error of approximation; SE standard error; SES socioeconomic status; SRMR standardized root mean square residual. aCigarette smoking was run in a separate model using WLSMV estimation. This model is displayed in Fig. 1. All error variances were statistically significant. The paths from positive parenting to parent–child communication and from psychological resources to self-esteem were fixed to 1 for all analyses. Coefficients for family SES, parenting in adolescence, adult health behaviors, and psychological resources regressed on race and adolescent alcohol and cigarette use, respectively, are not displayed as these variables were included as covariates. View Large Table 3 Model results with unstandardized path coefficients and latent factor loadings B (SE) p Path model Family SES →   Positive parenting .02(.01) <.001   Adult SES .30(.09) <.001   Physical activity .01(.01) .17   Alcohol use .003(.01) .62   Cigarette smokinga −.01(.01) .18   Fruit/vegetable consumption −.01(.01) .41   Psychological resources −.01(.01) .21  Positive parenting →   Adult SES 6.06(1.51) <.001   Physical activity .06(.11) .57   Alcohol use −.06(.13) .65   Cigarette smokinga −.20(.12) .10   Fruit/vegetable consumption .23(.12) .062   Psychological resources .45(.09) <.001  Adult SES →   Physical activity .02(.004) <.001   Alcohol use .002(.004) .59   Cigarette smokinga −.02(.004) <.001   Fruit/vegetable consumption .02(.004) <.001   Psychological resources .01(.004) .052 Latent factors  Positive parenting   Parent–child communication 1.0   Supervision 1.03 (.14) <.001   Expectations for future 0.82 (.10) <.001  Psychological resources   Self-esteem 1.0   Optimism 0.99 (.10) <.001   Life engagement 1.04 (.09) <.001   Self-mastery 1.02 (.09) <.001 B (SE) p Path model Family SES →   Positive parenting .02(.01) <.001   Adult SES .30(.09) <.001   Physical activity .01(.01) .17   Alcohol use .003(.01) .62   Cigarette smokinga −.01(.01) .18   Fruit/vegetable consumption −.01(.01) .41   Psychological resources −.01(.01) .21  Positive parenting →   Adult SES 6.06(1.51) <.001   Physical activity .06(.11) .57   Alcohol use −.06(.13) .65   Cigarette smokinga −.20(.12) .10   Fruit/vegetable consumption .23(.12) .062   Psychological resources .45(.09) <.001  Adult SES →   Physical activity .02(.004) <.001   Alcohol use .002(.004) .59   Cigarette smokinga −.02(.004) <.001   Fruit/vegetable consumption .02(.004) <.001   Psychological resources .01(.004) .052 Latent factors  Positive parenting   Parent–child communication 1.0   Supervision 1.03 (.14) <.001   Expectations for future 0.82 (.10) <.001  Psychological resources   Self-esteem 1.0   Optimism 0.99 (.10) <.001   Life engagement 1.04 (.09) <.001   Self-mastery 1.02 (.09) <.001 Model fit indices were as follows, χ2(df) = 82.4(53), p =.01, CFI = .971, RMSEA [90% CI] = .043 [.023, .060], SRMR = .029. CFI comparative fit index; CI confidence interval; RMSEA root mean square error of approximation; SE standard error; SES socioeconomic status; SRMR standardized root mean square residual. aCigarette smoking was run in a separate model using WLSMV estimation. This model is displayed in Fig. 1. All error variances were statistically significant. The paths from positive parenting to parent–child communication and from psychological resources to self-esteem were fixed to 1 for all analyses. Coefficients for family SES, parenting in adolescence, adult health behaviors, and psychological resources regressed on race and adolescent alcohol and cigarette use, respectively, are not displayed as these variables were included as covariates. View Large Fig. 1 View largeDownload slide Standardized path coefficients and factor loadings shown. Only significant paths are depicted. Health behaviors and psychological resources were allowed to correlate. The psychological resources factor was significantly correlated with physical activity (r = .167, p = .019) and fruit/vegetable consumption (r = .209, p = .001). SES socioeconomic status. Fig. 1 View largeDownload slide Standardized path coefficients and factor loadings shown. Only significant paths are depicted. Health behaviors and psychological resources were allowed to correlate. The psychological resources factor was significantly correlated with physical activity (r = .167, p = .019) and fruit/vegetable consumption (r = .209, p = .001). SES socioeconomic status. Family SES in childhood was associated with positive parenting, such that families with higher SES in childhood reported more positive parenting during adolescence, independent of race, adolescent cigarette use, and adolescent alcohol use. Direct effects showed that higher family SES in childhood was also associated with higher adult SES, independent of race, and adolescent cigarette and alcohol use. There were no significant direct effects between family SES in childhood and physical activity, cigarette smoking, alcohol use, fruit and vegetable consumption, or psychological resources in adulthood. Direct effects showed significant associations between positive parenting in adolescence and higher adult SES and greater psychological resources in adulthood. Finally, adult SES was associated with greater physical activity, less cigarette smoking, more fruit and vegetable consumption, and greater psychological resources in adulthood (Table 3). Tests of indirect effects from childhood SES to positive parenting and adult SES and to health behaviors and psychological resources in adulthood are presented in Table 4. Associations between childhood SES and adult health behaviors and psychological resources (hereafter “outcomes”) may exist via three pathways, (i) childhood SES to positive parenting to the outcomes, (ii) childhood SES to adult SES to the outcomes, and (iii) childhood SES to positive parenting to adult SES to the outcomes. There was a significant indirect effect of childhood SES on psychological resources and adult SES, but not on health behaviors, through positive parenting alone (pathway 1). There were also significant indirect effects of childhood SES on adult physical activity, cigarette smoking, and fruit/vegetable consumption, but not alcohol use or psychological resources, through adult SES alone (pathway 2). Finally, there were significant indirect effects of childhood SES on adult physical activity, cigarette smoking, and fruit/vegetable consumption, but not alcohol use or psychological resources, through positive parenting in adolescence and adult SES (pathway 3). Additional models tested whether parenting moderated the associations between childhood SES and adult health behaviors in linear regression models. All interactions between childhood SES and parenting predicting health behaviors were not significant, ps > .27. Table 4 Unstandardized indirect effects from bootstrapped analysis (5,000 resamples) Estimate [95% CI] Childhood SES → positive parenting →  Adult SES .113 [.054, .205]*  Physical activity .001 [−.003, .005]  Alcohol use −.001 [−.006, .003]  Cigarette smokinga −.004 [−.009, .000]  Fruit/vegetable consumption .004 [.000, .010]  Psychological resources .008 [.005, .015]* Childhood SES → adult SES →  Physical activity .005 [.002, .009]*  Alcohol use .001 [−.002, .004]  Cigarette smokinga −.006 [−.012, −.002]*  Fruit/vegetable consumption .005 [.002, .009]*  Psychological resources .002 [.000, .006] Childhood SES → positive parenting → adult SES →  Physical activity .002 [.001, .004]*  Alcohol use .000 [−.001, .001]  Cigarette smokinga −.003 [−.005, −.001]*  Fruit/vegetable consumption .002 [.001, .004]*  Psychological resources .001 [.000, .002] Estimate [95% CI] Childhood SES → positive parenting →  Adult SES .113 [.054, .205]*  Physical activity .001 [−.003, .005]  Alcohol use −.001 [−.006, .003]  Cigarette smokinga −.004 [−.009, .000]  Fruit/vegetable consumption .004 [.000, .010]  Psychological resources .008 [.005, .015]* Childhood SES → adult SES →  Physical activity .005 [.002, .009]*  Alcohol use .001 [−.002, .004]  Cigarette smokinga −.006 [−.012, −.002]*  Fruit/vegetable consumption .005 [.002, .009]*  Psychological resources .002 [.000, .006] Childhood SES → positive parenting → adult SES →  Physical activity .002 [.001, .004]*  Alcohol use .000 [−.001, .001]  Cigarette smokinga −.003 [−.005, −.001]*  Fruit/vegetable consumption .002 [.001, .004]*  Psychological resources .001 [.000, .002] SES socioeconomic status. aCigarette smoking was run in a separate model using WLSMV estimation. *p < .05. View Large Table 4 Unstandardized indirect effects from bootstrapped analysis (5,000 resamples) Estimate [95% CI] Childhood SES → positive parenting →  Adult SES .113 [.054, .205]*  Physical activity .001 [−.003, .005]  Alcohol use −.001 [−.006, .003]  Cigarette smokinga −.004 [−.009, .000]  Fruit/vegetable consumption .004 [.000, .010]  Psychological resources .008 [.005, .015]* Childhood SES → adult SES →  Physical activity .005 [.002, .009]*  Alcohol use .001 [−.002, .004]  Cigarette smokinga −.006 [−.012, −.002]*  Fruit/vegetable consumption .005 [.002, .009]*  Psychological resources .002 [.000, .006] Childhood SES → positive parenting → adult SES →  Physical activity .002 [.001, .004]*  Alcohol use .000 [−.001, .001]  Cigarette smokinga −.003 [−.005, −.001]*  Fruit/vegetable consumption .002 [.001, .004]*  Psychological resources .001 [.000, .002] Estimate [95% CI] Childhood SES → positive parenting →  Adult SES .113 [.054, .205]*  Physical activity .001 [−.003, .005]  Alcohol use −.001 [−.006, .003]  Cigarette smokinga −.004 [−.009, .000]  Fruit/vegetable consumption .004 [.000, .010]  Psychological resources .008 [.005, .015]* Childhood SES → adult SES →  Physical activity .005 [.002, .009]*  Alcohol use .001 [−.002, .004]  Cigarette smokinga −.006 [−.012, −.002]*  Fruit/vegetable consumption .005 [.002, .009]*  Psychological resources .002 [.000, .006] Childhood SES → positive parenting → adult SES →  Physical activity .002 [.001, .004]*  Alcohol use .000 [−.001, .001]  Cigarette smokinga −.003 [−.005, −.001]*  Fruit/vegetable consumption .002 [.001, .004]*  Psychological resources .001 [.000, .002] SES socioeconomic status. aCigarette smoking was run in a separate model using WLSMV estimation. *p < .05. View Large Discussion The aim of the study was to examine whether parenting practices and adult SES help to explain prospective associations between childhood SES and adult health behaviors and psychological resources in Black and White men. As expected, higher SES in childhood was associated with more positive parenting in adolescence and higher adult SES. Positive parenting in adolescence also predicted higher adult SES, and the indirect effect from childhood SES to positive parenting to adult SES was significant. Higher SES in childhood was indirectly associated with greater psychological resources in adulthood via positive parenting in adolescence. Higher childhood SES was also linked with greater physical activity, less cigarette smoking, and greater fruit and vegetable consumption in adulthood via two separate indirect paths, one through adult SES and a second through both positive parenting and adult SES. All effects were independent of cigarette and alcohol use in adolescence. Despite significant race differences in levels of several factors in the path model, race differences in the latent variable factor structure and path loadings were not evident. This suggests that the structure of the model linking SES in childhood to parenting styles in adolescence and adult SES and to adult health behaviors and psychological resources was equivalent across race in this urban sample of Black and White men. Childhood SES was prospectively associated with psychological resources, but not health behaviors, via positive parenting. These results are consistent with prior research showing that parenting during adolescence has important implications for the development and maintenance of positive psychological attributes into adulthood (e.g., [89, 90]). For example, a decline in the quality of the parent–child relationship accounts for differences in psychological well-being in adulthood between those whose parents divorced in childhood or adolescence and those whose parents stayed continuously married [91]. In contrast, all significant indirect effects between childhood SES and adult health behaviors involved adult SES, reflecting the importance of concurrent opportunities and attitudes for the engagement in health behaviors. Participation in health behaviors is socially stratified due to a variety of reasons, including differences in structural constraints (i.e., access, opportunities), differences in norms regarding the importance of health behaviors, and differences in beliefs about the beneficial aspects of health behaviors [7, 92]. This study provides empirical support for the Family Stress Model and the Risky Families Model [63, 64], as in both models positive parenting strategies link lower SES in childhood with SES in adulthood, poorer health behaviors, and lower psychological resources. Direct effects between childhood SES and health behaviors or psychological resources in adulthood were not significant in the full model (Fig. 1). Rather, the evidence supported indirect associations of childhood SES with health behaviors and psychological resources through positive parenting and adult SES. The findings are bolstered by the prospective design of the current study. These results add to growing evidence that positive parenting in adolescence contributes to health outcomes in adulthood. For example, teachers’ reports of parental support (versus parental neglect) when children were 9–10 years old were predictive of lower obesity rates up to 10 years later [93]. There are important structural factors that may underlie the association between childhood SES and parenting. That is, socioeconomic factors may influence the availability and flexibility of time spent with family, parental stress, residential density, financial strain, and accessibility and willingness to adopt “expert” parenting advice, which may contribute to the differences we noted in positive parenting [8, 94]. It is critical to note that heterogeneous parenting practices are evident both within and between socioeconomic strata. One of the key strengths of the Risky Families Model is that it defines psychosocial characteristics that confer risk to downstream health outcomes rather than assume these characteristics are congruent with economic status [64]. Indeed, the lack of direct effects from childhood SES to health behaviors and psychological resources supports the primary role for parenting in downstream health outcomes. It is important in future research to consider broader factors in the childhood environment that affect health risks into adulthood and may shape socioeconomic and parenting opportunities [8, 95]. We note, however, that conclusions drawn from the results presented in this paper were identical when a composite measure of childhood socioeconomic disadvantage (i.e., sum of the presence of parental occupational occupation of semi-skilled worker or less, parental unemployment for at least 4 months, highest parental education below high school completion, receipt of public assistance, and single parent household) was utilized instead of childhood SES (i.e., Hollingshead index). This study extends the literature by including psychological resources as an additional outcome, which has independent, salubrious effects on myriad of health outcomes (e.g., 14–16). We chose to model psychological resources as an independent, yet correlated, outcome alongside the health behaviors given that these factors were measured simultaneously and given our interest in providing a holistic picture of health and well-being among relatively young adults. Conceptually, however, psychological resources may be thought of as a mediator of the associations between child SES, positive parenting, and health behaviors. Prior evidence suggests that psychological resources and health behaviors, especially physical activity, have a reciprocal relationship, wherein those with greater resources are more likely to participate in healthy behaviors, and participating in healthy behaviors leads to greater resources (e.g., [96, 97]). Conversely, psychological resources may also function to moderate the associations between childhood SES and health outcomes. That is, SES gradients in health outcomes are attenuated among individuals with high well-being, including purpose in life [19–22]. However, we did not see evidence of psychological resources moderating the associations between childhood SES and health behaviors in the current sample (data not shown). Causality cannot be determined from the current study design. However, if the association between childhood SES and adult health behaviors via parenting in adolescence were shown to be casual in nature, the parent–child relationship may be a viable target for intervention. The current results suggest that efforts to improve the family environment and parent–child relationship may have lasting effects on SES later in life as well as on behavioral and psychological outcomes in adulthood. This is consistent with prior work demonstrating improved physical and mental health outcomes for several decades following family-based interventions. For example, the Family Check-Up randomized trial, which focused on improving aspects of parenting such as monitoring, communication, and involvement, was associated with enhanced nutritional quality of family meals, better health behaviors, and lower depression in adolescents and less risk for obesity in early adulthood [54, 56]. Other efforts to increase parental responsiveness and control among low-income, minority families was effective at reducing weight gain in children [55], and a separate intervention among rural, African American families (i.e., the Strong African American Families program) led to fewer increases in risky behavior in adolescence and emerging adulthood [58, 59] as well as lower inflammatory markers in emerging adulthood [57]. Though not explicitly tested, these efforts may additionally have intergenerational effects, creating a positive feedback loop wherein future generations benefit from positive changes in the relationship between caregiver and child [98]. The findings from the present study should be considered in light of several limitations. First, only men from a geographically restricted region (Pittsburgh, PA) were included, making it impossible to determine whether the findings would extend to women or to a more geographically representative sample of men. The original sample was recruited to over represent children with early conduct problems (in first grade) and there was some attrition across the nearly 30-year measurement window, which may also limit generalizability. While there were differences in two of the parenting measures among the analytic sample and those with data in adolescence who did not participate in the most recent follow-up, we note that the analytic sample did not differ significantly on many key variables from the rest of the men in the initial PYS sample and these parenting differences were nonsignificant when excluding those incarcerated at follow-up. Second, the relevance of the present findings to clinical outcomes, such as morbidity, remains unclear given that the outcomes in the current study were self-reported health behaviors and psychological resources. Third, parenting was measured via self-report, potentially introducing social desirability bias. However, the means and ranges on the parenting measures indicated considerable variability in these measures and not only in a socially desirable manner. While observational reports offer more objective insights into the parent–child relationship, we viewed the use of multiple informants as a key strength of the study. Finally, these data are observational in nature, making causal claims about parenting untenable; we note that the present findings are in line with randomized control trials that have successfully manipulated the parent–child relationship and demonstrated health benefits. In light of these limitations, the prospective, longitudinal design, use of multiple informants, rich assessments of the parent–child relationship, and multiple health-relevant outcomes strengthen confidence in the current findings. Overall, results suggest that positive parent–child relationships are important for understanding how SES experienced in early life has lasting effects on SES, health behaviors, and psychological resources decades later, and these patterns were consistent in both Black and White men. Given the increasing interest in the impact of early life experiences on long-term health outcomes, but the lack of prospective data and reliance on primarily White samples, these results are especially informative for ongoing work in this area. Acknowledgments Special thanks to the study originators Drs. Rolf Loeber and Magda Stouthamer-Loeber. Source of Funding This research was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health (R01HL111802, 5T32HL007560-32). Data collection for the Pittsburgh Youth Study has been funded by the National Institute on Drug Abuse (DA411018), National Institute on Mental Health (MH48890, MH50778), Pew Charitable Trusts, and the Office of Juvenile Justice and Delinquency Prevention (96-MU-FX-0012). Compliance with Ethical Standards Informed Consent The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Conflict of interest All authors declare no conflicts of interest and have fully complied with the ethical standards of this journal. Author Contributions J. M. Boylan conceptualized the research, analyzed and interpreted the data, and drafted the original manuscript. K. P. Jakubowski contributed to the conceptualization of the research, acquisition, analysis, and interpretation of data, and provided critical revisions to the manuscript. J. M. Cundiff contributed to the conceptualization of the research, data analysis and interpretation, and provided critical revisions to the manuscript. D. A. Pardini and K. A. Matthews contributed to the conceptualization of the research, data acquisition, data analysis and interpretation, and provided critical revisions to the manuscript. Ethical Approval This study was approved by the Institutional Review Board at the University of Pittsburgh, and all men provided written, informed consent. References 1. Cohen S , Janicki-Deverts D , Chen E , Matthews KA . Childhood socioeconomic status and adult health . Ann N Y Acad Sci . 2010 ; 1186 : 37 – 55 . Google Scholar CrossRef Search ADS PubMed 2. Galobardes B , Lynch JW , Davey Smith G . Childhood socioeconomic circumstances and cause-specific mortality in adulthood: systematic review and interpretation . Epidemiol Rev . 2004 ; 26 : 7 – 21 . Google Scholar CrossRef Search ADS PubMed 3. Galobardes B , Smith GD , Lynch JW . Systematic review of the influence of childhood socioeconomic circumstances on risk for cardiovascular disease in adulthood . Ann Epidemiol . 2006 ; 16 ( 2 ): 91 – 104 . Google Scholar CrossRef Search ADS PubMed 4. Poulton R , Caspi A , Milne BJ et al. Association between children’s experience of socioeconomic disadvantage and adult health: a life-course study . Lancet . 2002 ; 360 ( 9346 ): 1640 – 1645 . Google Scholar CrossRef Search ADS PubMed 5. Melchior M , Moffitt TE , Milne BJ , Poulton R , Caspi A . Why do children from socioeconomically disadvantaged families suffer from poor health when they reach adulthood? A life-course study . Am J Epidemiol . 2007 ; 166 ( 8 ): 966 – 974 . Google Scholar CrossRef Search ADS PubMed 6. Blackwell DL , Hayward MD , Crimmins EM . Does childhood health affect chronic morbidity in later life ? Soc Sci Med . 2001 ; 52 ( 8 ): 1269 – 1284 . Google Scholar CrossRef Search ADS PubMed 7. Pampel FC , Krueger PM , Denney JT . Socioeconomic disparities in health behaviors . Annu Rev Sociol . 2010 ; 36 : 349 – 370 . Google Scholar CrossRef Search ADS PubMed 8. Schreier HM , Chen E . Socioeconomic status and the health of youth: a multilevel, multidomain approach to conceptualizing pathways . Psychol Bull . 2013 ; 139 ( 3 ): 606 – 654 . Google Scholar CrossRef Search ADS PubMed 9. Lee JO , Hill KG , Hartigan LA et al. Unemployment and substance use problems among young adults: does childhood low socioeconomic status exacerbate the effect ? Soc Sci Med . 2015 ; 143 : 36 – 44 . Google Scholar CrossRef Search ADS PubMed 10. Barbeau EM , Krieger N , Soobader MJ . Working class matters: socioeconomic disadvantage, race/ethnicity, gender, and smoking in NHIS 2000 . Am J Public Health . 2004 ; 94 ( 2 ): 269 – 278 . Google Scholar CrossRef Search ADS PubMed 11. Hanson MD , Chen E . Socioeconomic status and health behaviors in adolescence: a review of the literature . J Behav Med . 2007 ; 30 ( 3 ): 263 – 285 . Google Scholar CrossRef Search ADS PubMed 12. Matthews KA , Kelsey SF , Meilahn EN , Kuller LH , Wing RR . Educational attainment and behavioral and biologic risk factors for coronary heart disease in middle-aged women . Am J Epidemiol . 1989 ; 129 ( 6 ): 1132 – 1144 . Google Scholar CrossRef Search ADS PubMed 13. NHLBI . Managing Overweight and Obesity in Adults: Systematic Evidence Review From the Obesity Expert Panel ; 2013 . https://www.nhlbi.nih.gov/health-topics/managing-overweight-obesity-in-adults Accessibility verified April 15, 2016. 14. Keyes CL . The mental health continuum: from languishing to flourishing in life . J Health Soc Behav . 2002 ; 43 ( 2 ): 207 – 222 . Google Scholar CrossRef Search ADS PubMed 15. Gallo LC , Matthews KA . Understanding the association between socioeconomic status and physical health: do negative emotions play a role ? Psychol Bull . 2003 ; 129 ( 1 ): 10 – 51 . Google Scholar CrossRef Search ADS PubMed 16. Ryff CD . Psychological well-being revisited: advances in the science and practice of eudaimonia . Psychother Psychosom . 2014 ; 83 ( 1 ): 10 – 28 . Google Scholar CrossRef Search ADS PubMed 17. Boehm JK , Kubzansky LD . The heart’s content: the association between positive psychological well-being and cardiovascular health . Psychol Bull . 2012 ; 138 ( 4 ): 655 – 691 . Google Scholar CrossRef Search ADS PubMed 18. Chida Y , Steptoe A . Positive psychological well-being and mortality: a quantitative review of prospective observational studies . Psychosom Med . 2008 ; 70 ( 7 ): 741 – 756 . Google Scholar CrossRef Search ADS PubMed 19. Cohen R , Bavishi C , Rozanski A . Purpose in life and its relationship to all-cause mortality and cardiovascular events: a meta-analysis . Psychosom Med . 2016 ; 78 ( 2 ): 122 – 133 . Google Scholar CrossRef Search ADS PubMed 20. Pressman SD , Cohen S . Does positive affect influence health ? Psychol Bull . 2005 ; 131 ( 6 ): 925 – 971 . Google Scholar CrossRef Search ADS PubMed 21. Morozink JA , Friedman EM , Coe CL , Ryff CD . Socioeconomic and psychosocial predictors of interleukin-6 in the MIDUS national sample . Health Psychol . 2010 ; 29 ( 6 ): 626 – 635 . Google Scholar CrossRef Search ADS PubMed 22. Turiano NA , Chapman BP , Agrigoroaei S , Infurna FJ , Lachman M . Perceived control reduces mortality risk at low, not high, education levels . Health Psychol . 2014 ; 33 ( 8 ): 883 – 890 . Google Scholar CrossRef Search ADS PubMed 23. Lachman ME , Weaver SL . The sense of control as a moderator of social class differences in health and well-being . J Pers Soc Psychol . 1998 ; 74 ( 3 ): 763 – 773 . Google Scholar CrossRef Search ADS PubMed 24. Chen E , Miller GE . “Shift-and-Persist” strategies: why low socioeconomic status isn’t always bad for health . Perspect Psychol Sci . 2012 ; 7 ( 2 ): 135 – 158 . Google Scholar CrossRef Search ADS PubMed 25. Fava GA , Ruini C , Rafanelli C et al. Well-being therapy of generalized anxiety disorder . Psychother Psychosom . 2005 ; 74 ( 1 ): 26 – 30 . Google Scholar CrossRef Search ADS PubMed 26. Fava GA , Ruini C , Rafanelli C , Finos L , Conti S , Grandi S . Six-year outcome of cognitive behavior therapy for prevention of recurrent depression . Am J Psychiatry . 2004 ; 161 ( 10 ): 1872 – 1876 . Google Scholar CrossRef Search ADS PubMed 27. King LA . The health benefits of writing about life goals . Pers Soc Psychol Bull . 2001 ; 27 : 798 – 807 . Google Scholar CrossRef Search ADS 28. van der Spek N , Vos J , van Uden-Kraan CF et al. Efficacy of meaning-centered group psychotherapy for cancer survivors: a randomized controlled trial . Psychol Med . 2017 ; 47 ( 11 ): 1990 – 2001 . Google Scholar CrossRef Search ADS PubMed 29. Breitbart W , Poppito S , Rosenfeld B et al. Pilot randomized controlled trial of individual meaning-centered psychotherapy for patients with advanced cancer . J Clin Oncol . 2012 ; 30 ( 12 ): 1304 – 1309 . Google Scholar CrossRef Search ADS PubMed 30. Gebreab SY , Diez Roux AV , Brenner AB et al. The impact of lifecourse socioeconomic position on cardiovascular disease events in African Americans: the Jackson Heart Study . J Am Heart Assoc . 2015 ; 4 ( 6 ): e001553 . Google Scholar CrossRef Search ADS PubMed 31. Lynch JW , Kaplan GA , Salonen JT . Why do poor people behave poorly? Variation in adult health behaviours and psychosocial characteristics by stages of the socioeconomic lifecourse . Soc Sci Med . 1997 ; 44 ( 6 ): 809 – 819 . Google Scholar CrossRef Search ADS PubMed 32. Gilman SE , Abrams DB , Buka SL . Socioeconomic status over the life course and stages of cigarette use: initiation, regular use, and cessation . J Epidemiol Community Health . 2003 ; 57 ( 10 ): 802 – 808 . Google Scholar CrossRef Search ADS PubMed 33. van de Mheen H , Stronks K , Looman CW , Mackenbach JP . Does childhood socioeconomic status influence adult health through behavioural factors ? Int J Epidemiol . 1998 ; 27 : 431 – 437 . Google Scholar CrossRef Search ADS PubMed 34. Harper S , Lynch J , Hsu WL et al. Life course socioeconomic conditions and adult psychosocial functioning . Int J Epidemiol . 2002 ; 31 ( 2 ): 395 – 403 . Google Scholar CrossRef Search ADS PubMed 35. Adler NE , Stewart J . Health disparities across the lifespan: meaning, methods, and mechanisms . Ann N Y Acad Sci . 2010 ; 1186 : 5 – 23 . Google Scholar CrossRef Search ADS PubMed 36. Lutfey K , Freese J . Toward some fundamentals of fundamental causality: socioeconomic status and health in the routine clinic visit for diabetes . Am J Sociol . 2005 ; 110 : 1326 – 1372 . Google Scholar CrossRef Search ADS 37. Pearlin LI . The sociological study of stress . J Health Soc Behav . 1989 ; 30 ( 3 ): 241 – 256 . Google Scholar CrossRef Search ADS PubMed 38. Wardle J , Steptoe A . Socioeconomic differences in attitudes and beliefs about healthy lifestyles . J Epidemiol Community Health . 2003 ; 57 ( 6 ): 440 – 443 . Google Scholar CrossRef Search ADS PubMed 39. Smith KP , Christakis NA . Social networks and health . Ann Rev Sociol . 2008 ; 34 : 405 – 429 . Google Scholar CrossRef Search ADS 40. Looker ED . Accuracy of proxy reports of parental status characteristics . Sociol Educ . 1988 ; 62 : 257 – 276 . Google Scholar CrossRef Search ADS 41. Wagmiller R , Adelman R. Childhood and Intergenerational Poverty: The Long-Term Consequences of Growing Up Poor . New York: National Center on Child Poverty Reports ; 2009 . 42. Brody GH , Yu T , Beach SR , Kogan SM , Windle M , Philibert RA . Harsh parenting and adolescent health: a longitudinal analysis with genetic moderation . Health Psychol . 2014 ; 33 ( 5 ): 401 – 409 . Google Scholar CrossRef Search ADS PubMed 43. Taylor SE , Way BM , Seeman TE . Early adversity and adult health outcomes . Dev Psychopathol . 2011 ; 23 ( 3 ): 939 – 954 . Google Scholar CrossRef Search ADS PubMed 44. Moretti MM , Peled M . Adolescent-parent attachment: bonds that support healthy development . Paediatr Child Health . 2004 ; 9 ( 8 ): 551 – 555 . Google Scholar CrossRef Search ADS PubMed 45. Viner RM , Ross D , Hardy R et al. Life course epidemiology: recognising the importance of adolescence . J Epidemiol Community Health . 2015 ; 69 ( 8 ): 719 – 720 . Google Scholar CrossRef Search ADS PubMed 46. Hess RD , Holloway SD . Family and school as educational institutions . In: Parke R , ed. Review of Child Development Research . Vol. 7 . Chicago : University of Chicago Press ; 1984 : 179 – 222 . 47. Resnick MD , Bearman PS , Blum RW et al. Protecting adolescents from harm. Findings from the National Longitudinal Study on Adolescent Health . JAMA . 1997 ; 278 ( 10 ): 823 – 832 . Google Scholar CrossRef Search ADS PubMed 48. Davis-Kean PE . The influence of parent education and family income on child achievement: the indirect role of parental expectations and the home environment . J Fam Psychol . 2005 ; 19 ( 2 ): 294 – 304 . Google Scholar CrossRef Search ADS PubMed 49. Nash SG , McQueen A , Bray JH . Pathways to adolescent alcohol use: family environment, peer influence, and parental expectations . J Adolesc Health . 2005 ; 37 ( 1 ): 19 – 28 . Google Scholar CrossRef Search ADS PubMed 50. Simons-Morton BG . The protective effect of parental expectations against early adolescent smoking initiation . Health Educ Res . 2004 ; 19 ( 5 ): 561 – 569 . Google Scholar CrossRef Search ADS PubMed 51. McLoyd VC . The impact of economic hardship on black families and children: psychological distress, parenting, and socioemotional development . Child Dev . 1990 ; 61 ( 2 ): 311 – 346 . Google Scholar CrossRef Search ADS PubMed 52. McLoyd VC . Socioeconomic disadvantage and child development . Am Psychol . 1998 ; 53 ( 2 ): 185 – 204 . Google Scholar CrossRef Search ADS PubMed 53. Murry VM , Berkel C , Simons RL , Simons LG , Gibbons FX . A twelve-year longitudinal analysis of positive youth development among rural African American males . J Res Adolescence . 2014 ; 24 : 512 – 25 . Google Scholar CrossRef Search ADS 54. Van Ryzin MJ , Nowicka P . Direct and indirect effects of a family-based intervention in early adolescence on parent-youth relationship quality, late adolescent health, and early adult obesity . J Fam Psychol . 2013 ; 27 ( 1 ): 106 – 116 . Google Scholar CrossRef Search ADS PubMed 55. Brotman LM , Dawson-McClure S , Huang KY et al. Early childhood family intervention and long-term obesity prevention among high-risk minority youth . Pediatrics . 2012 ; 129 ( 3 ): e621 – e628 . Google Scholar CrossRef Search ADS PubMed 56. Smith JD , Montaño Z , Dishion TJ , Shaw DS , Wilson MN . Preventing weight gain and obesity: indirect effects of the family check-up in early childhood . Prev Sci . 2015 ; 16 ( 3 ): 408 – 419 . Google Scholar CrossRef Search ADS PubMed 57. Miller GE , Brody GH , Yu T , Chen E . A family-oriented psychosocial intervention reduces inflammation in low-SES African American youth . Proc Natl Acad Sci USA . 2014 ; 111 ( 31 ): 11287 – 11292 . Google Scholar CrossRef Search ADS PubMed 58. Brody GH , Chen YF , Kogan SM , Murry VM , Brown AC . Long-term effects of the strong African American families program on youths’ alcohol use . J Consult Clin Psychol . 2010 ; 78 ( 2 ): 281 – 285 . Google Scholar CrossRef Search ADS PubMed 59. Brody GH , Chen YF , Kogan SM , Smith K , Brown AC . Buffering effects of a family-based intervention for African American emerging adults . J Marriage Fam . 2010 ; 72 ( 5 ): 1426 – 1435 . Google Scholar CrossRef Search ADS PubMed 60. Newland RP , Crnic KA , Cox MJ , Mills-Koonce WR ; Family Life Project Key Investigators . The family model stress and maternal psychological symptoms: mediated pathways from economic hardship to parenting . J Fam Psychol . 2013 ; 27 ( 1 ): 96 – 105 . Google Scholar CrossRef Search ADS PubMed 61. Conger RD , Donnellan MB . An interactionist perspective on the socioeconomic context of human development . Annu Rev Psychol . 2007 ; 58 : 175 – 199 . Google Scholar CrossRef Search ADS PubMed 62. Conger RD , Elder GH. Families in Troubled Times: Adapting to Change in Rural America . Hawthorne, NY: Aldine de Gruyter ; 1994 . 63. Conger KJ , Rueter MA , Conger RD . The role of economic pressure in the lives of parents and their adolescents: the family stress model . In: Crockett LJ , Silberstein RK , eds. Negotiating Adolescence in Times of Social Change . New York : Cambridge University Press ; 2000 . Google Scholar CrossRef Search ADS 64. Repetti RL , Taylor SE , Seeman TE . Risky families: family social environments and the mental and physical health of offspring . Psychol Bull . 2002 ; 128 ( 2 ): 330 – 366 . Google Scholar CrossRef Search ADS PubMed 65. Troxel WM , Matthews KA . What are the costs of marital conflict and dissolution to children’s physical health ? Clin Child Fam Psychol Rev . 2004 ; 7 ( 1 ): 29 – 57 . Google Scholar CrossRef Search ADS PubMed 66. Barboza Solís C , Kelly-Irving M , Fantin R et al. Adverse childhood experiences and physiological wear-and-tear in midlife: findings from the 1958 British birth cohort . Proc Natl Acad Sci USA . 2015 ; 112 ( 7 ): E738 – E746 . Google Scholar CrossRef Search ADS PubMed 67. Non AL , Rewak M , Kawachi I et al. Childhood social disadvantage, cardiometabolic risk, and chronic disease in adulthood . Am J Epidemiol . 2014 ; 180 ( 3 ): 263 – 271 . Google Scholar CrossRef Search ADS PubMed 68. Poulton R , Moffitt TE , Silva PA . The Dunedin Multidisciplinary Health and Development Study: overview of the first 40 years, with an eye to the future . Soc Psychiatry Psychiatr Epidemiol . 2015 ; 50 ( 5 ): 679 – 693 . Google Scholar CrossRef Search ADS PubMed 69. Pulkki-Råback L , Elovainio M , Hakulinen C et al. Cumulative effect of psychosocial factors in youth on ideal cardiovascular health in adulthood: the Cardiovascular Risk in Young Finns Study . Circulation . 2015 ; 131 ( 3 ): 245 – 253 . Google Scholar CrossRef Search ADS PubMed 70. Loeber R , Farrington D , Stouthamer-Loeber M , White H. Violence and Serious Theft: Development and Prediction From Childhood to Adulthood . New York : Taylor & Francis Group ; 2008 . 71. Cundiff JM , Boylan JM , Pardini DA , Matthews KA . Moving up matters: socioeconomic mobility prospectively predicts better physical health . Health Psychol . 2017 ; 36 ( 6 ): 609 – 617 . Google Scholar CrossRef Search ADS PubMed 72. Hollingshead AB. Four Factor Index of Social Status . 1975 . Unpublished manuscript. New Haven, CT : Yale University . 73. Loeber R , Farrington D , Stouthamer-Loeber M , van Kammen W. Antisocial Behavior and Mental Health Problems: Risk Factors in Childhood and Adolescence . Mahwah, NJ : Erlbaum ; 1998 . 74. Moos RH , Moos BS . Evaluating correctional and community settings . In: Moos RH , ed. Families . New York : Wiley ; 1975 : 263 – 86 . 75. Skinner HA , Steinhauer PD , Santa-Barbara J . The family assessment measure . Can J Commun Ment Health . 1983 ; 2 : 91 – 103 . Google Scholar CrossRef Search ADS 76. De Los Reyes A , Augenstein TM , Wang M et al. The validity of the multi-informant approach to assessing child and adolescent mental health . Psychol Bull . 2015 ; 141 ( 4 ): 858 – 900 . Google Scholar CrossRef Search ADS PubMed 77. Centers for Disease Control and Prevention (CDC) . Behavioral Risk Factor Surveillance System Survey Questionnaire . Atlanta, Georgia : U.S. Department of Health and Human Services, Centers for Disease Control and Prevention ; 2011 . PubMed PubMed 78. Paffenbarger RS Jr , Wing AL , Hyde RT . Physical activity as an index of heart attack risk in college alumni . Am J Epidemiol . 1978 ; 108 ( 3 ): 161 – 175 . Google Scholar CrossRef Search ADS PubMed 79. Scheier MF , Carver CS , Bridges MW . Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the Life Orientation Test . J Pers Soc Psychol . 1994 ; 67 ( 6 ): 1063 – 1078 . Google Scholar CrossRef Search ADS PubMed 80. Scheier MF , Wrosch C , Baum A et al. The Life Engagement Test: assessing purpose in life . J Behav Med . 2006 ; 29 ( 3 ): 291 – 298 . Google Scholar CrossRef Search ADS PubMed 81. Pearlin LI , Lieberman MA , Menaghan EG , Mullan JT . The stress process . J Health Soc Behav . 1981 ; 22 ( 4 ): 337 – 356 . Google Scholar CrossRef Search ADS PubMed 82. Rosenberg M. Society and the Adolescent Self-Image . Princeton : Princeton University Press ; 1965 . Google Scholar CrossRef Search ADS 83. MacKinnon DP , Fairchild AJ , Fritz MS . Mediation analysis . Annu Rev Psychol . 2007 ; 58 : 593 – 614 . Google Scholar CrossRef Search ADS PubMed 84. MacKinnon DP , Lockwood CM , Hoffman JM , West SG , Sheets V . A comparison of methods to test mediation and other intervening variable effects . Psychol Methods . 2002 ; 7 ( 1 ): 83 – 104 . Google Scholar CrossRef Search ADS PubMed 85. Hu L , Bentler PM . Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives . Struct Equ Modeling . 1999 ; 6 : 1 – 55 . Google Scholar CrossRef Search ADS 86. McDonald RP , Ho MH . Principles and practice in reporting structural equation analyses . Psychol Methods . 2002 ; 7 ( 1 ): 64 – 82 . Google Scholar CrossRef Search ADS PubMed 87. Preacher KJ , Hayes AF . Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models . Behav Res Methods . 2008 ; 40 ( 3 ): 879 – 891 . Google Scholar CrossRef Search ADS PubMed 88. Muthén LK , Muthén BO. Mplus User’s Guide . 7th ed . Los Angeles, CA : Muthén & Muthén ; 2012 . 89. Granic I , Dishion TJ , Hollenstein T . The family ecology of adolescence: a dynamic systems perspective on normative development . In: Adams GR , Berzonsky MD , eds. Blackwell Handbook of Adolescence . Oxford, UK : Blackwell Publishing Ltd ; 2003 : 60 – 91 . Google Scholar CrossRef Search ADS 90. Holmbeck GN , Paikoff RL , Brooks-Gunn J . Parenting adolescents . In: Borenstein M , ed. Handbook of parenting: Vol. 1. Children and parenting . Mahwah, NJ : Erlbaum ; 1995 : 91 – 118 . 91. Amato PR , Sobolewski JM . The effects of divorce and marital discord on adult children’s psychological well-being . Am Sociol Rev . 2001 ; 66 : 900 – 921 . Google Scholar CrossRef Search ADS 92. Oyserman D , Smith GC , Elmore K. Identity-based motivation: implications for health and health disparities . J Socl Issues . 2014 ; 70 : 206 – 225 . Google Scholar CrossRef Search ADS 93. Lissau I , Sørensen TI . Parental neglect during childhood and increased risk of obesity in young adulthood . Lancet . 1994 ; 343 ( 8893 ): 324 – 327 . Google Scholar CrossRef Search ADS PubMed 94. Hoff E , Laursen B , Tardif T . Socioeconomic status and parenting . In: Borenstein M , ed. Handbook of Parenting: Vol. 2. Biology and Ecology of Parenting . Mahwah, NJ : Erlbaum ; 2002 : 231 – 52 . 95. Non AL , Román JC , Gross CL et al. Early childhood social disadvantage is associated with poor health behaviours in adulthood . Ann Hum Biol . 2016 ; 43 ( 2 ): 144 – 153 . Google Scholar CrossRef Search ADS PubMed 96. Ruuskanen JM , Ruoppila I . Physical activity and psychological well-being among people aged 65 to 84 years . Age Ageing . 1995 ; 24 ( 4 ): 292 – 296 . Google Scholar CrossRef Search ADS PubMed 97. McAuley E , Blissmer B . Self-efficacy determinants and consequences of physical activity . Exerc Sport Sci Rev . 2000 ; 28 ( 2 ): 85 – 88 . Google Scholar PubMed 98. Conger RD , Belsky J , Capaldi DM . The intergenerational transmission of parenting: closing comments for the special section . Dev Psychol . 2009 ; 45 ( 5 ): 1276 – 1283 . Google Scholar CrossRef Search ADS PubMed © Society of Behavioral Medicine 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Journal

Annals of Behavioral MedicineOxford University Press

Published: Mar 13, 2018

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