Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2024 Dec 1.
Published in final edited form as: J Fam Psychol. 2023 Oct 5;37(8):1137–1147. doi: 10.1037/fam0001155

The Intergenerational Transmission of Economic Adversity, BMI, and Emotional Distress from Adolescence to Middle Adulthood

Tricia K Neppl 1, Jeenkyoung Lee 2, Olivia N Diggs 3, Brenda J Lohman 4, Daniel Russell 5
PMCID: PMC10872786  NIHMSID: NIHMS1933245  PMID: 37796604

Abstract

The current study examined the intergenerational transmission of economic adversity, as well as physical and mental health across generations. Specifically, we examined the effects of parental economic adversity, BMI, and emotional distress during the child’s adolescence on their economic adversity, BMI, and emotional distress in middle adulthood. The study included 366 generation one (G1) mothers and fathers and their adolescent (generation two; G2) in middle adulthood. G1 behavior was examined when G2 was 16 years old and G2 behavior was assessed at age 42. In line with aspects of the Family Stress Model, economic hardship was related to economic pressure, which in turn was related to emotional distress for both G1 and G2. For each generation, economic pressure was also associated with BMI. There was also evidence of the intergenerational transmission of economic hardship, BMI, and emotional distress from G1 to G2. Finally, the intergenerational transmission of economic adversity in the family of origin to adult health outcomes was explained by these same health behaviors of the first generation. Results suggest that economic adversity and parental health behaviors as experienced in adolescence has long-term economic and health consequences into middle adulthood.

Keywords: economic adversity, BMI, emotional distress, intergenerational transmission

There is evidence that early life experiences influence health and behavior during adulthood (Luo & Waite, 2005; Park, 2020), and such behavior may persist across generations (Neppl et al., 2009; Schoon & Melis, 2019). For example, research shows socioeconomic status (SES) and other adverse childhood experiences in the family of origin are associated with adult physical health and emotional wellbeing (Park, 2020; Pavela, 2017). Moreover, economic circumstance and other heath related behaviors are transmitted from one generation to the next (Jeon & Neppl, 2016; Thornberry et al., 2003). Despite this evidence, less is known about mechanisms that may help explain the intergenerational association between economic adversity and both physical and mental health, especially from adolescence into middle adulthood (Everson et al., 2002; Luo & Waite, 2005). This may be important as Evans and Cassells (2014) found that risk factors experienced in adolescence mediated the association between economic adversity in early childhood and mental health in adulthood. In addition, studies that examine both early and current economic conditions on adult health outcomes are limited and findings are inconsistent (see Luo & Waite, 2005). Some studies find adult health stems from early and current economic circumstances (Luo & Waite, 2005), while others do not find an association between early economic conditions on adult health once current economic circumstances are taken into account (Chapman, et al., 2009).

Thus, more prospective longitudinal research is needed to help understand how economic adversity as experienced in the family of origin, as well as current economic circumstances may relate to health outcomes in middle adulthood. There is research that examines early economic adversity and health in childhood and adolescence (i.e., Kim et al., 2020), but fewer studies examine these associations into middle adulthood, while taking earlier generation one (G1) health behavior and generation two (G2) adult economic adversity into account (Luo & Waite, 2005). Assessing middle adulthood is important as evidence suggests it may be a time when both physical and mental health problems increase (Wickrama et al., 2010). Indeed, obesity in adulthood is a risk factor for various chronic health conditions such as cardiovascular and heart disease, diabetes, stroke, cancer, and arthritis (Centers for Disease Control and Prevention [CDC], 2016). This may be especially evident for those residing in rural areas. Obesity rates are significantly higher for adults living in rural compared to urban areas, as there may be less access to healthy foods and fewer opportunities for physical activity (CDC, 2016).

In addition, during middle adulthood there is evidence that for some, it may be a time where stress increases as family and occupational roles stabilize. This may create financial and emotional difficulties (Graham & Pozuelo, 2016), especially for those who are already experiencing financial strain (Lang et al., 2011). Ultimately, these stressors can lead to health problems that extend into later adulthood (Neppl et al., 2021). Thus, understanding pathways to health-related behavior such as obesity and emotional wellbeing is critical for the implementation of successful prevention and intervention programs. One potential pathway may be through economic pressures experienced within the family (Jeon & Neppl, 2016). With these ideas in mind, the present investigation extends earlier research by examining the intergenerational transmission of economic adversity, obesity, and emotional wellbeing prospectively over 26 years across two generations. We examined G1 economic hardship, economic pressure, BMI, and emotional distress when G2 was in adolescence, and G2 economic hardship, economic pressure, BMI, and emotional distress during middle adulthood. This sample is unique as G2 adults were around the same age as G1 when G2 was in adolescence. We examined direct and indirect pathways from G1 economic adversity, BMI, and emotional distress to G2 economic adversity, BMI, and emotional distress in adulthood, separately by gender.

Economic Adversity and Emotional Distress

According to the Family Stress Model (FSM), economic hardship such as low income, adverse financial events, and high debt to asset ratio leads to felt economic pressure. Economic pressure includes unmet material needs such as insufficient food or clothing, the inability to pay bills or make ends meet, and having to cut back on expenses such as health insurance and medical care. Such pressures place individuals at an increased risk for emotional distress (i.e., hostility, depression, or anxiety) which are associated with disrupted family processes and individual adjustment. Pathways of the FSM originated using the same sample as in the present investigation and has been replicated with several study populations both within and outside the U.S. (see Conger et al., 2010; Neppl et al., 2016, for reviews). However, fewer studies have examined FSM pathways to consider how experiences in the family of origin influence these same behaviors in middle adulthood (see Neppl et al., 2021).

Several studies have examined the effects of economic adversity on child and adolescent wellbeing (see Neppl et al., 2016). For example, a meta-analysis showed that lower SES was related to depression for children and adolescents (Letourneau et al., 2013), and East et al. (2020) found that socioeconomic hardship in childhood related to anxiety and depression in both adolescence and young adulthood. Studies also show women may be more likely to experience depressive symptoms than men (e.g., Cohen et al., 2006). Fewer studies examine the consequence of experiencing family of origin economic adversity on mental health outcomes in middle adulthood, while taking adult economic circumstance into account (Park, 2020). One study found that both childhood and adult economic status were associated with depressive symptoms in adulthood (Park, 2020). Luo and Waite (2005) found that childhood SES was associated with adult health, including depressive symptoms, through educational attainment and household income in adulthood. However, both studies were retrospective and neither included other possible pathways between child economic circumstances and adult health, other than through adult economic status (Luo & Waite, 2005; Park, 2020). Thus, the current study examines family stress processes in adolescence to predict health behaviors in the middle adult years. We extend previous research by prospectively utilizing aspects of the FSM (economic hardship, economic pressure, and emotional distress) to examine the influence of economic adversity, and mental and physical health, both within and across generations.

Indeed, there is evidence that family adversity is transmitted across generations (Neppl et al., 2020; Schoon & Melis, 2019). For example, Thornberry et al. (2003) found G1 family poverty during G2 early adolescence was related to G2 financial stress in adulthood and Thaning (2021) showed family economic disadvantage was related to offspring economic status. Similarly, in the same longitudinal study used for the present analyses, Conger et al. (2012) found that G1 economic hardship was directly associated with G2 economic hardship in young adulthood. Jeon and Neppl (2016) found the intergenerational transmission of G1 to G2 economic hardship, positivity, and positive parenting. We extend this work by examining G1 to G2 economic adversity, emotional distress, and BMI in middle adulthood. The intergenerational transmission of emotional distress is also well-documented (i.e., Goodman, 2020). Studies have shown parental emotional distress which may include depressive symptoms increases risk for distress in adolescence (i.e., Garber & Cole, 2010) and adulthood (Jones et al., 2016). Utilizing the same longitudinal study as used in the present investigation, Neppl et al. (2020) found the continuity of G1 emotional distress when G2 was in adolescence to G2 distress in adulthood when their child was in the preschool years.

In addition, Kavanaugh et al. (2018) showed G1 economic pressure was associated with G1 maternal depressive symptoms when G2 was in early adolescence. G1 maternal depressive symptoms were associated with family stress processes when G2 was in middle to late adolescence, which was then related to G2 depressive symptoms in adulthood. This was true even after earlier adolescent depressive symptoms were considered. Thus, the continuity of emotional distress may be explained by family stress processes that link G1 parent distress to the distress of their G2 children into adulthood. These findings could also be explained by heritability where individuals with a genetic predisposition for emotional distress may be more negatively impacted by negative circumstances (Lau & Eley, 2008).

Economic Adversity and Obesity

Obesity is a public health concern in the U.S. Currently, four in ten or 41.9% of adults are obese, which increases risk for many leading causes of mortality (Hales et al., 2020; NHANES, 2021). According to a report from Trust for America’s Health (2022), obesity rates continue to increase nationwide with rural areas having higher rates than urban or suburban areas. Moreover, some studies show that economic adversity is associated with higher rates of obesity for women (Chapman et al., 2009; Khlat et al., 2009; Lee & Park, 2020), while others demonstrate differences for males only (Daundasekara et al., 2020). Thus, it is important to understand how these associations may contribute to obesity rates separately across gender.

There is evidence early life economic disadvantage is associated with being overweight or obese in adulthood, even after taking current economic circumstances into account. For example, controlling for current socio-demographic characteristics, Khlat et al. (2009) found that experiencing economic hardship in childhood was related to obesity for females in adulthood. Similarly, after controlling for SES during midlife, Lee and Park (2020) found that low childhood SES was related to having a higher BMI in midlife which extended into older adulthood. Other studies find the association between childhood economic adversity and obesity in adulthood is small once adult economic circumstances are included in the model. Pavela (2017) found that childhood SES was not associated with adult BMI after adjusting for SES in adulthood, suggesting that the direct association between SES in childhood and obesity in adulthood is small. Similarly, Chapman et al. (2009) demonstrated that childhood SES and obesity in adulthood was accounted for by SES in adulthood. Thus, some studies find economic circumstance in adulthood helps to explain the association between childhood economic adversity and obesity in adulthood, while others find an influence of SES in childhood even after adult SES is taken in account (see Chapman et al., 2009). The current investigation extends retrospective (Pavela, 2017) and cross sectional (Chapman et al., 2009) studies by prospectively examining the effects of economic pressure as experienced in adolescence on both economic pressure and BMI in adulthood. We also included G1 BMI as a pathway from early childhood economic adversity to adult BMI, as it is often missing in prior studies (Lee & Park, 2020).

Finally, evidence shows intergenerational continuity of BMI from parent to child. Hemmingsson et al. (2022) found that social adversity such as financial insecurity, as well as genetic predisposition were associated with the transmission of obesity. This was found both within a generation and across two generations. Similarly, Cuevas et al. (2022) found childhood adversity and polygenetic risk were associated with waist circumference in adulthood. Thus, while the intergenerational transmission of BMI could be explained by heritability, which could account for some of the association across generations, non-genetic components may play a part as well. Despite this evidence, few studies examine parental health behaviors that may help explain social adversity such as economic hardship and obesity (Daundasekara et al., 2020). Therefore, it is important to examine parental health factors such as BMI and emotional distress that may help explain the association between economic adversity experienced in the family of origin, and physical and mental health in adulthood. Indeed, there is evidence that BMI and emotional distress are linked (Luppino et al., 2010), and while they are related within the same generation during adulthood, less is known about how they may be transferred across generations. That is, fewer studies examine G2 economic adversity and both physical and mental health in adulthood, while taking these same health behaviors of G1 into account while G2 was in adolescence (Lee & Park, 2020).

The Present Investigation

The present study tested pathways in line with the FSM to understand how G1 economic adversity during G2 adolescence is associated with these same behaviors for G2 during middle adulthood, as well as how BMI and emotional distress may help explain these associations (see Figure 1). We used data from a nearly three-decade longitudinal study of G2s and their families followed from adolescence to adulthood. We measured G1 economic hardship, economic pressure, BMI, and emotional distress during G2’s adolescence (Time 1), as well as G2 economic hardship, economic pressure, BMI, and emotional distress during adulthood (Time 2, 26 years later). We expected continuity of economic hardship, economic pressure, BMI, and emotional distress from G1 to G2. We expected that economic hardship would predict economic pressure, and that economic pressure would be associated with both BMI and emotional distress within each generation. It was also expected that G1 economic pressure would be associated with G2 economic pressures through G1 BMI and emotional distress. Moreover, these pathways would be related to G2 BMI and emotional distress through G2 economic pressure.

Figure 1.

Figure 1

Open in a new tab

Conceptual Model

Finally, based on literature suggesting that depressive symptoms may be greater for women than men (Cohen et al., 2006) and mixed evidence in terms of gender differences in the association between economic adversity and obesity, all pathways were tested for G2 gender differences in the model. In addition to testing gender differences, we controlled for G2 nutritional practices and exercise. Previous research shows that dietary intake and physical activity level may help explain obesity rates for those in rural areas (Euler, et al., 2019). We also controlled for G1 age as, unlike the G2s, G1s were not all the same age when the study began. Age may be related to experiencing economic pressure (Graham & Pozuelo, 2016) and BMI increases with age (Meeuwsen et al., 2010).

Method

Participants

Data come from the Family Transitions Project (FTP, Conger & Conger, 2002), a longitudinal study of 559 rural youth and their families. The FTP includes participants from two original studies: Iowa Youth and Families Project (IYFP) and Iowa Single Parents Project (ISPP). In 1989, 451 families participated in the IYFP and were recruited from public and private schools in eight rural counties in Iowa. The data collection included reports from seventh-grade target adolescents (M age = 12.7; 236 females, 215 males), two biological parents, and a sibling within 4 years of the target adolescent. Among eligible families, 78% participated in the study. All participants were Caucasian due to the population distribution of the rural Midwest at the time of data collection. Among participants, 34% resided on farms, 12% lived in nonfarm rural areas, and 54% lived in towns with fewer than 6,500 people. The median family income was $33,700 (approximately $83,000 in 2023), and the parents’ average years of education was 13 years. The average age of fathers was 40 years, and it was 38 years for mothers.

In 1991, the ISSP (N = 108) included target adolescents who were in ninth grade and the same age as the IYFP targets who had been participating for the previous two years (M age = 14.8 years). Reports were collected from the target adolescents, their biological mothers, close-aged sibling, and non-residential fathers when applicable. Mothers of ISPP had experienced divorce within 2 years prior to the data collection. Only three families did not participate in the study. Like IYFP, participants of ISPP were Caucasian, mainly lower middle to middle class. Research procedures and measures for the ISPP parallel IYFP. In 1994, participants from these two separate studies were combined to create the FTP when adolescents were in 12th grade. In the first year of the FTP, target youth participated in the study with their parents as they had during earlier years of adolescence. Starting in 1995, the target adolescents (one year after completing high school for most) participated in the study with their own families.

The present study included 366 G2 adults who participated from adolescence through middle adulthood. It also included G2’s mother and father (when applicable). The data were analyzed using two developmental time periods. Time 1 examined G1 economic hardship, economic pressure, BMI, and emotional distress during G2’s adolescence (age 16; 1992). Time 2 included G2 economic hardship, economic pressure, BMI, and emotional distress during middle adulthood (age 42; 2018). G2s were 9.2% overweight and 13.6% obese during adolescence (CDC, 2023a), and the percentage in those categories increased during adulthood by 31.8% and 42.3%, respectively. G2s were also more obese than G1s (see Table 1).

Table 1.

Descriptive Statistics

Variable M SD Min Max n
G1
  Age 41.69 4.40 33.00 58 329
  Economic hardship 4.83 2.33 1 12 335
  Economic pressure
    Material Needs 15.59 5.01 6.00 30 337
    Ends Meet .01 1.72 −3.64 3.76 337
    Financial Cutbacks 6.26 4.36 0 19 332
  BMI 26.89 4.77 17.85 48.91 329
  Emotional distress
    Depression 1.44 .42 1 3.46 329
    Anxiety 1.28 .35 1 3.35 329
    Hostility 1.26 .28 1 2.83 329
G2
  Gender 1.58 .50 1 2 358
  Economic hardship 9.75 2.78 1 12 362
  Economic pressure
    Material Needs 10.77 5.16 5 30 362
    Ends Meet 1.92 .87 1 4.5 363
    Financial Cutbacks 4.11 4.97 0 23 363
  BMI 30.04 7.21 18.88 56.28 359
  Emotional distress
    Depression 1.50 .59 1 4.31 361
    Anxiety 1.27 .47 1 4.2 361
    Hostility 1.31 .38 1 3.67 361
  Nutritional Practices 1.84 .65 1 4 361
  Exercise 2.75 .78 1 4 361
Open in a new tab

Note. G1 = Generation 1; G2 = Generation 2.

Procedures

At Time 1 when G2 was in adolescence, a trained interviewer conducted home visits each year with families of origin. The visit took approximately 2 hours and each family member completed questionnaires regarding individual and family characteristics. For ISPP families, non-residential father responses to some of the same items as ISPP mothers were collected with a short telephone survey. At Time 2, when G2s participated in home visits with their own families as adults, G2 adults completed questionnaires reporting their individual characteristics. Informed consent was obtained from G1 and G2 at each time point. This study is not preregistered. The FTP has been approved by the Institutional Review Board at Iowa State University (#06-086).

Measures

Descriptive statistics of study variables, including means, standard deviations, minimum and maximum scores, and sample sizes are provided in Table 1 (using SPSS 26). All measures were reported by G1 at Time 1 and G2 at Time 2. We report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study. Data that support findings of this study are available from the corresponding author upon reasonable request.

Economic hardship.

Consistent with the Family Stress Model, two constructs were used as indicators of economic adversity: economic hardship and economic pressure. First, G1 and G2 income were used as a measure for economic hardship. At Time 2, G2 reported the household’s total income during the past twelve months on a 12-point Likert scale. They were asked to address their income from all sources, including wages and salary, benefits, compensations, interest payments, dividends, alimony, and government assistance. At Time 1, G1 reported their income from multiple sources in dollar amounts. We summed these values to create total household income and categorized the sum into the same Likert scale used for G2 income to match the scope of measures across the two generations. As a result, the response categories for G1 and G2 both ranged from 1 (under $5,000) to 12 (more than $104,999).

Economic pressure.

G1 and G2 economic pressure was assessed using three subscales. First, making ends meet consisted of two items. Participants reported their difficulty in paying bills in the past year by answering the question “How much difficulty have you had paying your bills?” ranging from 1 (a great deal of difficulty) to 5 (no difficulty at all). Responses were reverse-coded to indicate great difficulty with higher scores. The other question was, “Generally at the end of each month, how much money did you end up with?” It was responded to using a four-point scale ranging from 1 (more than enough money left over) to 4 (not enough to make ends meet). Responses to the two items (G1, r = .70; G2, r = .72, p < .01) were standardized and averaged to create the overall score. The second scale was material needs, with six items asking whether they had enough money to afford their home, clothing, furniture, car, food, and medical expenses. Response categories ranged from 1 (strongly agree) to 5 (strongly disagree), and the sum of the six items (G1, α = .93; G2, α = .96) was used as the measure of meeting material needs. The last scale, financial cutbacks, contained items asking whether they had made a significant adjustment in spending due to financial needs in the past 12 months such as “taking extra jobs”, “using savings to meet daily living expenses”, and “taking bankruptcy.” Each item was a dichotomous measure (1 = yes; 0 = no); G1 answered 27 items (α = .88), whereas G2 answered 32 items (α = .90). The summary score was an indicator of financial cutbacks. For these three scales, G1 father (when applicable) and mother reports were averaged together. Scores from these three scales were used for the latent construct of economic pressure.

Body Mass Index (BMI).

G1 and G2 BMI were calculated with self-reported height and weight using the Centers for Disease Control and Prevention formula: weight(lb)/[height(in)]2 x 703 (CDC, 2023b). Obesity is defined as a body mass index (BMI) of 30 or higher, severe obesity as a BMI of 40 or higher (CDC, 2023b). For G1 BMI, IYFP fathers and mothers were averaged while ISPP mother BMI was used (BMI for non-residential fathers was not collected)1. Prior to inclusion in this analysis, height and weight values for each individual were examined over the multiple time points for biologically implausible values over time or input errors.

Emotional Distress.

G1 and G2 emotional distress was assessed using the Symptom Checklist-90-Revised (SCL-90-R; Derogatis, 1994). G1s from IYFP and all G2s completed the full version, and G1s from ISPP completed the short version of the scale. Participants reported how distressed they felt during the past week, ranging from 1 (not at all) to 5 (extremely) on three subscales: depression, anxiety, and hostility. The depression subscale included items such as cried easily or felt worthless. IYFP G1s (α = .92) and G2s (α = .93) answered 13 items, and ISPP G1s answered seven items from the short version (α = .86). The anxiety subscale consisted of questions asking if the participant experienced feelings of tension, nervousness, or shakiness inside. IYFP G1s (α = .88) and G2s (α = .91) answered 10 items, and ISPP G1s answered three items (α = .70). The hostility subscale asked questions about behaviors such as having arguments, shouting, or throwing things. IYFP G1s (α = .63) and G2s (α = .75) answered 6 items, and ISPP G1s answered 4 items (α = .61). Average scores of each subscale for G1 and G2 were used as overall scores of depression, anxiety, and hostility and used as indicators for the latent construct of G1 and G2 emotional distress.

Control Variables.

G2 nutritional practices, G2 exercise, G2 gender, and G1 age were used as control variables. Three items were used to measure G2 nutritional practice: eat fruits and vegetables each day; eat three balanced meals a day; limit intake of foods high in sugar or fat (α = .65). G2 reported how often they followed these practices ranging from 1 (regularly, or most of the time) to 4 (never). For G2 exercise, they answered two items: (1) How often do you get physical exercise on your job; and (2) How often do you get physical exercise that is not related to your job (r = .13, p < .05). Responses ranged from 1 (regularly, or most of the time) to 4 (never). Both measures were reverse-coded, and scores were averaged to create nutritional practice and exercise parcels. Next, G2 gender (0 = female; 1 = male) was used as a control variable in the path analyses but later used as a grouping variable to test gender differences. G1 age in years was reported at Time 1 and included as a continuous variable.

Analytic Plan

To investigate associations among G1 and G2 economic adversity, BMI, and emotional distress, we employed Structural Equation Modeling (SEM) using Mplus Version 8 (Muthén & Muthén, 2017). As a first step, a measurement model was examined to obtain factor loadings for the latent constructs. We also examined bivariate zero-order correlations for all study variables at a significance level of 0.05 (see Table 2). Next, hypothesized pathways were tested using full information maximum likelihood (FIML) estimation to account for missing data. FIML is a procedure recommended to effectively handle missing data in longitudinal analyses, minimizing the loss of the sample and limiting the bias of estimates (Graham & Coffman, 2012; Newsom, 2015). Multiple fit indices were used to assess the model fit, including the chi-square estimate and significance value, comparative fit index (CFI, Bentler, 1990), and root mean square error of approximation (RMSEA, Browne & Cudeck, 1993). CFI value greater than .95 and RMSEA value less than .06 indicate a good model fit (Hu & Bentler, 1999).

Table 2.

Zero Order Correlations of Study Variables (N = 366)

Variable 1 2 3 4 5 6 7 8
1. G1 Economic hardship -
2. G1 Economic pressure .51*** -
3. G1 BMI .05 .16** -
4. G1 Emotional distress .24*** .32*** .04 -
5. G2 Economic hardship .21*** .18** .13* .11 -
6. G2 Economic pressure .23*** .23*** .23*** .22*** .59*** -
7. G2 BMI .15** .16** .40*** .01 .19*** .33*** -
8. G2 Emotional distress .07 .13* .19*** .22*** .30*** .49*** .28*** -
Open in a new tab

Note. Standardized correlation coefficients are shown.

*

p < .05.

**

p < .01.

***

p < .001.

We performed multiple group analysis using Mplus to test for gender differences. The hypothesized pathways including G2 variables (i.e., G2 economic pressure, BMI, emotional distress) were constrained to be identical across G2 gender in the fixed model. In the following free model, six paths that included G2 variables (three for women and three for men) were unconstrained and allowed to vary across G2 gender. Chi-square values between fixed and free models were compared to determine the significance of the gender differences in the paths. We controlled for G1 age on G1 economic hardship, economic pressure, BMI, and emotional distress. We also included G2 gender as a control variable on G2 economic hardship, economic pressure, BMI and emotional distress, and G2 nutritional practices and exercise as controls for G2 BMI. Moreover, BMI and emotional distress were allowed to be correlated for each generation. The variance of the measured exogenous variables, which were the control variables and G1 economic hardship, were set as free parameters to handle the missing data for these variables. BMI and emotional distress of G1 and G2 were both correlated with each other.

In addition to testing direct pathways, we examined indirect pathways using the bootstrap sampling method. The bootstrap approach assumes non-normality of study variables, allowing researchers to effectively evaluate the statistical significance of indirect effects (Shrout & Bolger, 2002). We tested all possible indirect pathways from G1 economic pressure to G2 BMI and G2 emotional distress with 95% bias-corrected confidence intervals and 1,000 bootstrap samples to estimate the indirect effects accurately (MacKinnon et al., 2004). For information regarding data and study materials, please contact the lead author of the current study.

Results

The measurement model provided a good fit to the data (X2 = 293.98, df = 148, p < .001, CFI = .95, RMSEA = .05). Results from the measurement model showed the factor loadings for all four latent variables, G1 and G2 economic pressure and emotional distress, were statistically significant. Specifically, standardized factor loadings ranged from .75 to .88 for G1 economic pressure; .78 to .89 for G1 emotional distress; .80 to .87 for G2 economic pressure; and .78 to .88 for G2 emotional distress. For economic pressure and emotional distress, we verified that all correlated error terms across the two generations were not statistically significant. Table 2 displays zero-order correlations among the variables. G1 economic hardship was significantly correlated with G1 economic pressure (r = .51, p < .001). G1 economic pressure was significantly correlated with both G1 BMI (r = .16, p < .01) and G1 emotional distress (r = .32, p < .001). The correlation between G1 BMI and G1 emotional distress (r = 04, p = .45) was not statistically significant. Like G1, G2 economic hardship was significantly correlated with G2 economic pressure (r = .59, p < .001). G2 economic pressure was significantly correlated with both G2 BMI (r = .33, p < .001) and G2 emotional distress (r = .49, p < .001). G2 BMI and G2 emotional distress were significantly correlated (r = .28, p < .001). Finally, economic hardship (r = .21, p < .001), economic pressure (r = .23, p < .001), BMI (r = 40, p < .001), and emotional distress (r = .22, p < .001) from G1 to G2 were all significantly correlated.

Figure 2 presents results from the SEM model showing standardized path coefficients. G1 economic hardship and G1 economic pressure represent exogenous variables, whereas G1 BMI, G1 emotional distress, G2 economic hardship, G2 economic pressure, G2 BMI, and G2 emotional distress represent endogenous variables in the model. The model fit the data well (X2 = 294.38, df = 148, p < .001, CFI = .95, RMSEA = .05). Both BMI (β = .32, SE = .05, p < .001) and emotional distress (β = .12, SE = .06, p < .05) were stable across two generations. Economic hardship across the two generations was statistically significantly associated (β = .22, SE = 05, p < .001). Although the zero-order correlation between G1 to G2 economic pressure was significant, the path was not significant in the SEM model (β = .08, SE = .06, p = .18).

Figure 2.

Figure 2

Open in a new tab

Statistical Model (N = 366)

Note. Standardized path coefficients are shown. Dashed lines indicate non-significant paths. Model fit: χ2 = 293.98, df = 148, p < .001, CFI = .95, RMSEA = .05. *p < .05. **p < .01. ***p < .001.

G1 economic hardship was positively associated with G1 economic pressure (β = .52, SE = 05, p < .001). G1 economic pressure was significantly associated with both G1 BMI (β = .17, SE = 06, p < .01) and G1 emotional distress (β = .32, SE = 06, p < .001). G1 age was significantly associated with G1 economic hardship (β = −.16, SE = .05, p < .01) and G1 BMI (β = .14, SE = 05, p < .01), but was not significantly associated with G1 emotional distress (β = −.01, SE = 06, p = .89). Similarly, G2 economic hardship was positively associated with G2 economic pressure (β = .56, SE = 04, p < .001). G2 economic pressure was also significantly associated with G2 BMI (β = .22, SE = 05, p < .001) and G2 emotional distress (β = .46, SE = .05, p < .001). G2 gender was significantly associated with G2 economic hardship (β = .12, SE = .05, p < .05) and G2 emotional distress (β = .11, SE = .05, p < .05), but was not significantly associated with G2 BMI (β = −.05, SE = .05, p = .26). Both G2 nutritional practices (β = .11, SE = 05, p < .05) and exercise (β = −.16, SE = .05, p < .01) were significantly associated with G2 BMI. The association between BMI and emotional distress was not statistically significant for both generations (G1, r = −.01, p = .91; G2, r = .11, p = .06). G1 BMI and G1 emotional distress were significantly related to G2 economic pressure (β = .14, SE = .05, p < .01; β = .13, SE = .05, p < .05, respectively). Additionally, we included the paths from G1 economic pressure to G2 BMI and G2 emotional distress to our hypothesized model to test the direct effects of G1 economic pressure, and neither path was statistically significant. Finally, we tested the model with the inclusion of G2 BMI at time 1. All significant pathways remained the same after controlling for G2 BMI during adolescence on G2 BMI in adulthood.

Gender Differences.

To test for gender differences, we conducted a multiple group analysis (women = 209; men = 152). The chi-square difference test between the fixed and free model was statistically significant revealing statistically significant gender differences in the associations (ΔX2 = 15.921, Δdf = 5, p < .01). The free model provided a good fit to the data (X2 = 506.043, df 2 303, p < .001, CFI = .93, RMSEA = .06). Six pathways including G2 variables were all unconstrained first, but the path from G1 BMI to G2 BMI did not differ by gender. Therefore, five pathways were allowed to differ in the final model. Specifically, G1 economic pressure was significantly associated with both G1 BMI and G1 emotional distress (G1 BMI, b = .21, SE = .07, p < .01; G1 emotional distress, b = .03, SE = .01, p < .001). Second, for G2 men but not women, their economic pressure was affected by G1 BMI (men, b = .15, SE = .06, p < .01; women, b = .09, SE = .06, p = .10) and G1 emotional distress (men, b = 2.58, SE = .80, p < .001; women, b = .64, SE = .77, p = .41). Third, G2 economic pressure was significantly associated with emotional distress for both men and women, but it was more strongly associated among women (women, b = .06, SE = .01, p < .001; men, b = .04, SE = .01, p < .01). G2 economic pressure was only associated with G2 female BMI (b = .50, SE = 12, p < .001) but not for males (b = .17, SE = .13, p = .19). Finally, G1 to G2 emotional distress was only significant for men (men, (b = .43, SE = 13, p < .001; women, b = .01, SE = 10, p = .93). For all pathways, unstandardized path coefficients are discussed.

Indirect Effects.

We tested indirect pathways from G1 economic pressure to G2 BMI and G2 emotional distress (see Table 3). All possible indirect paths were examined, but only significant pathways are shown in Table 3. For effects on G2 BMI, there were three significant indirect pathways. First, G1 economic pressure was associated with G2 BMI through G1 BMI and G2 economic pressure (β = .01, 95% CI = .001 to .037). Second, G1 economic pressure was associated with G2 BMI through G1 BMI (β = .06, 95% CI = .024 to .214). Third, G1 economic pressure was associated with G2 BMI through G1 emotional distress and G2 economic pressure (β = .01, 95% CI = .002 to .043). For effects on G2 emotional distress, there were two significant indirect pathways. First, G1 economic pressure was associated with G2 emotional distress through G1 emotional distress and G2 economic pressure (β = .02, 95% CI = .000 to .006). Second, G1 economic pressure was associated with G2 emotional distress through G1 BMI and G2 economic pressure (β = .01, 95% CI = .000 to .005). When the indirect pathways were estimated, the direct path from G1 BMI to G2 BMI remained significant (β = .32, SE = .06, p < .001), but the direct path from G1 emotional distress to G2 emotional distress became non-significant (β = .12, SE = .08, p = .17).

Table 3.

Indirect Pathways (N = 366)

Model Pathway 95% CI
β Lower Upper
Effects on G2 BMI
  G1 EP → G1 BMI → G2 EP → G2 BMI .01 .001 .037
  G1 EP → G1 BMI → G2 BMI .06 .024 .214
  G1 EP → G1 ED → G2 EP → G2 BMI .01 .002 .043
Effects on G2 emotional distress
  G1 EP → G1 ED → G2 EP → G2 ED .02 .000 .006
  G1 EP → G1 BMI → G2 EP → G2 ED .01 .000 .005
Open in a new tab

Note. G1 = Generation 1; G2 = Generation 2; EP = economic pressure; BMI = body mass index; ED = emotional distress. CI = bias-corrected confidence interval. Only significant indirect pathways are shown.

Discussion

The present investigation examined the role of G1 economic adversity, BMI, and emotional distress during G2 adolescence on G2 economic adversity, BMI, and emotional distress in middle adulthood. This study adds to the literature examining mechanisms that might help explain associations between G1 to G2 economic adversity and health. Results were consistent with aspects of the Family Stress Model (Conger et al., 2010) where economic hardship was related to economic pressure, which in turn was related to emotional distress for both G1 and G2. Similarly, for each generation, economic pressure was also associated with BMI which is consistent with previous studies (Everson et al., 2002). For G1, the association between BMI and emotional distress was not significant, but was significant for G2. Indeed, research on the relation between emotional distress and BMI is mixed. Some studies show an association between obesity and depression, while others report no association (see Dragan & Akhtar-Danesh, 2007). The results from the current study may, in part, be due to G2s being more obese than G1 (see Table 1), and some studies show a higher obesity rate may be a risk factor for depression (Dong et al., 2004).

There was also evidence of the intergenerational transmission of G1 to G2 economic hardship, BMI, and emotional distress. This is consistent with earlier studies demonstrating continuity of such behavior across generations (Hemmingsson et al., 2022; Jeon & Neppl, 2018). Moreover, although the zero-order correlation between G1 to G2 economic pressure was significant, in the prospective model this path was not significant, but showed G1 economic pressure was associated with G2 economic pressure via G1 BMI and emotional distress. This result supports previous research suggesting adverse childhood experiences in the family of origin are associated with poor outcomes in adulthood (Park, 2020; Pavela, 2017). We extend this work by prospectively examining G1 behaviors that may help explain the association between economic circumstance as experienced in adolescence and adult health, as well as assessing G1 and G2 physical and mental health in the same model (Park, 2020).

Results for G2 BMI show that G1 economic pressure was related to G2 BMI through G1 BMI and G2 economic pressure, as well as through G1 emotional distress and G2 economic pressure. Similarly, results for G2 emotional distress found that G1 economic pressure was related to G2 emotional distress via G1 emotional distress and G2 economic pressure, as well as via G1 BMI and G2 economic pressure. It is important to note the direct path from G1 economic pressure to G2 BMI and from G1 economic pressure to G2 emotional distress were not significant when G2 economic pressure was included in the model. This is consistent with studies that show economic circumstances in the family of origin are associated with both adult mental and physical health through economic circumstances in adulthood (Chapman et al., 2009; Luo & Waite, 2005). This suggests economic adversity influences physical and mental health within generations and that G1 health during G2 adolescence plays an important role in the felt economic pressures and subsequent health of the next generation in adulthood.

Results also showed pathways in the model differed by gender. For example, the influence of G1 health behavior to G2 was significant for males. That is, G1 to G2 emotional distress, as well as G1 BMI and G1 emotional distress to G2 economic pressure was stronger for males compared to females. This is inconsistent with research showing social adversity in the family of origin is more pronounced for females (Chapman et al., 2009; Lee & Park, 2019). However, most studies examine SES or other adverse childhood experiences on adult economic circumstance and health. Fewer studies also examine G1 health behaviors such as BMI or emotional wellbeing in these associations. Results also demonstrated within generation differences between G2 males and females. G2 economic pressure to G2 BMI and G2 emotional distress was significantly higher for females compared to males. This is consistent with research that shows obesity rates and depressive symptoms are stronger for females (Cohen et al., 2006; Kim et al., 2020). Taken together, it could be that females are more influenced by recent stressors, while males may be more impacted by elevated exposure to stress (Turner & Avison, 2003), such as those perhaps experienced in the family of origin. From the current results, it appears that childhood stressors lead to economic pressure more so for males, and the consequences of recent economic pressure may be higher for females. More research is needed to help disentangle these differential effects.

There are limitations of this study worth noting. The sample was primarily white and came from the rural Midwest which could limit generalizability of the findings. However, Luo and Waite (2005) found that effects of childhood adversity on adult outcomes were similar for White and non-White samples. In addition, studies utilizing aspects of the Family Stress Model using more diverse samples show similar results (see Neppl et al., 2016). These earlier findings help increase the confidence in the current results. Further research should compare rural and urban samples over a similar time period. It could also be that shared genetic factors passed directly from parent to child help explain some of the observed associations. For example, there could be a genetic explanation for the transmission of BMI across generations. Moreover, there may be genetically influenced differences contributing to the emotional distress of G1 parents experiencing economic difficulties. Nevertheless, it is important to investigate environmental factors, above and beyond controlling for both G1 and G2 BMI in the model (potentially a proxy for genetics), to more clearly understand how these processes contribute to economic adversity and negative health behaviors that extend into adulthood.

In addition, the emotional distress measure reflects functioning during a 1 week period of time. However, there is evidence for the stability of such behavior over time (Hovenkamp-Hermelink et al., 2019). Indeed, using a similar measure of emotional distress as the current study, Neppl et al. (2023) found adolescent emotional distress at age 16 related to emotional distress at age 18, which was associated with emotional distress at age 25. Next, the current study tested pathways consistent with the FSM, thus did not include other G2 economic measures such as educational attainment or household income in the model. Finally, the current study included self-reported height and weight, where individuals may underestimate their weight and overestimate height (Khlat et al., 2009). Despite this, Lipsky et al. (2019) found self-reported BMI to provide a reliable and valid estimate of measured BMI.

In closing, the current study used a prospective, 26-year longitudinal design with a rural sample to examine the association between economic adversity in the family of origin and both physical and mental health in adulthood. We extended the literature by examining G1 physical and mental health when G2 was in adolescence, as well as G2 economic adversity in adulthood. Results suggest that economic adversity and both physical and mental health may be transmitted across generations. Moreover, the intergenerational transmission of economic adversity in the family of origin to adult health outcomes may be explained by these same health behaviors of the first generation. More broadly, results contribute to the literature examining the influence of childhood economic circumstances on adult economic circumstances and health by examining the role of G1 health in these pathways. These findings can help inform clinicians about the long-term implications of economic adversity that start a cascade of negative consequences not only for the first generation, but also to the next generation into middle adulthood. Thus, it is important to intervene with those who are experiencing economic adversity as it may help prevent long-term economic and health consequences for their children in adulthood.

Funding

This research is currently supported by a grant from the National Institute on Aging (R15AG059286) and Iowa Agriculture Home Economics Experiment Station. Support for earlier years of the study also came from multiple sources, including the College of Human Sciences, National Institute on Aging (AG043599), Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD064687), National Institute of Mental Health (MH00567, MH19734, MH43270, MH59355, MH62989, MH48165, MH051361), the National Institute on Drug Abuse (DA05347), the National Institute of Child Health and Human Development (HD027724, HD051746, HD047573), the Bureau of Maternal and Child Health (MCJ-109572), and the MacArthur Foundation Research Network on Successful Adolescent Development Among Youth in High-Risk Settings. This content is solely the responsibility of the authors and does not necessarily represent the official views of the funding sources. An earlier version of this article was presented at the biennial meeting of the Life History Research Society, Oxford, England in July 2022. The data that support the findings of this study are available from the corresponding author upon reasonable request.

Footnotes

1

We used a multiple group approach to handle this missingness as not at random (MNAR, Ender, 2001). The chi-square difference test between the fixed model and the model with the freed BMI paths was not statistically significant (ΔX2= 10.39, Δdf = 5, p = .06).

Contributor Information

Tricia K. Neppl, Professor, Department of Human Development and Family Studies, Iowa State University, 2361C Palmer, Ames, IA 50011

Jeenkyoung Lee, Doctoral Student, Department of Human Development and Family Studies, Iowa State University, 2325 N Loop Drive, Ames, Iowa 50010.

Olivia N. Diggs, Research Scientist, Department of Human Development and Family Studies, Iowa State University, 2325 N Loop Drive, Ames, Iowa 50010

Brenda J. Lohman, Professor, Department of Human Development and Family Science, University of Missouri, 103 Gwynn Hall, Columbia, MO 65211

Daniel Russell, Professor Emeritus, Department of Human Development and Family Studies, Iowa State University, 2352 Palmer, Ames, IA 50011.

References

  1. Browne MW, & Cudeck R (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. 10.1177/0049124192021002005 [DOI] [Google Scholar]
  2. Centers for Disease Control and Prevention (2016). Behavioral Risk Factor Surveillance Survey. https://www.cdc.gov/media/releases/2018/s0614-obesity-rates.html
  3. Centers for Disease Control and Prevention (2023a). About child & teen BMI. https://www.cdc.gov/healthyweight/assessing/bmi/childrens_bmi/about_childrens_bmi.html
  4. Centers for Disease Control and Prevention (2023b). About adult BMI. https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html
  5. Chapman BP, Fiscella K, Duberstein P, Coletta M, & Kawachi I (2009). Can the influence of childhood socioeconomic status on men’s and women’s adult body mass be explained by adult socioeconomic status or personality? Findings from a national sample. Health Psychology, 28(4), 419. 10.1037/a0015212 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cohen LS, Soares CN, Vitonis AF, Otto MW, & Harlow B (2006). Risk for new onset of depression during the menopausal transition. The Harvard Study of Moods and Cycles. Archives of General Psychiatry 63(4), 385–390. 10.1001/archpsyc.63A385. [DOI] [PubMed] [Google Scholar]
  7. Conger RD, & Conger KJ (2002). Resilience in Midwestern families: Selected findings from the first decade of a prospective, longitudinal study. Journal of Marriage and Family, 64(2), 361–373. 10.1111/j.1741-3737.2002.00361.x [DOI] [Google Scholar]
  8. Conger KJ, Martin M, Ben TR, Little WM, Craine Selvin J, Shebloski B, & Conger RD (2012). Economic hardship and its consequences across generations. In Maholmes V & King R (Eds.), Oxford handbook of child development and poverty (pp. 37–53). Oxford University Press. [Google Scholar]
  9. Conger RD, Conger KJ, Martin MJ (2010). Socioeconomic status, family processes, and Individual development. Journal of Marriage and Family, 72(3), 685–704. 10.1111/j.1741-3737.2010.00725.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cuevas AG, Mann FD, & Krueger RF (2022). The weight of childhood adversity: evidence that childhood adversity moderates the impact of genetic risk on waist circumference in adulthood. International Journal of Obesity, 46(10), 1875–1882. 10.1038/s41366-022-01191-3 [DOI] [PubMed] [Google Scholar]
  11. Daundasekara SS, Schuler BR, & Hernandez DC (2020). Stability and change in early life economic hardship trajectories and the role of sex in predicting adolescent overweight/obesity. Journal of Youth and Adolescence, 49(8), 1645–1662. 10.1007/s10964-020-01249-3 [DOI] [PubMed] [Google Scholar]
  12. Derogatis LR (1994). Symptom checklist-90-R: Administration, scoring & procedure manual for the revised version of the SCL-90. Minneapolis, MN: National Computer Systems. [Google Scholar]
  13. Dong C, Sanchez LE, & Price RA (2004). Relationship of obesity to depression: A family-based study. International Journal of Obesity, 28(6), 790–795. 10.1038/sj.ijo.0802626 [DOI] [PubMed] [Google Scholar]
  14. Dragan A, & Akhtar-Danesh N (2007). Relation between body mass index and depression: a structural equation modeling approach. BMC Medical Research Methodology, 7, 1–8. 10.1186/1471-2288-7-17 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. East P, Doom J, Delker E, Blanco E, Burrows R, Correa-Burrows P, … & Gahagan S (2020). Childhood socioeconomic hardship, family conflict, and young adult hypertension: The Santiago Longitudinal Study. Social Science & Medicine, 253, 112962. 10.1016/j.socscimed.2020.112962 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Enders CK (2001). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling, 8(1), 128–141. 10.1207/S15328007SEM0801_7 [DOI] [Google Scholar]
  17. Evans GW, & Cassells RC (2014). Childhood Poverty, Cumulative Risk Exposure, and Mental Health in Emerging Adults. Clinical Psychological Science, 2(3), 287–296. 10.1177/2167702613501496 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Everson SA, Maty SC, Lynch JW, & Kaplan GA (2002). Epidemiologic evidence for the relation between socioeconomic status and depression, obesity, and diabetes. Journal of Psychosomatic Research, 53(4), 891–895. 10.1016/S0022-3999(02)00303-3 [DOI] [PubMed] [Google Scholar]
  19. Garber J, & Cole DA (2010). Intergenerational transmission of depression: A launch and grow model of change across adolescence. Development and Psychopathology, 22(4), 819–830. 10.1017/S0954579410000489 [DOI] [PubMed] [Google Scholar]
  20. Goodman SH (2020). Intergenerational transmission of depression. Annual Review of Clinical Psychology, 16, 213–238. 10.1146/annurev-clinpsy-071519-113915 [DOI] [PubMed] [Google Scholar]
  21. Graham JW, & Coffman DL (2012). Structural equation modeling with missing data. In Hoyle RH (Ed.), Handbook of structural equation modeling (pp. 277–295). The Guilford Press. [Google Scholar]
  22. Graham C & Pozuelo JR (2017). Happiness, stress, and age: How the U curve varies across people and places. Journal of Population Economics, 30(1), 225–264. 10.1007/s00148-016-0611-2 [DOI] [Google Scholar]
  23. Hales CM, Carroll MD, Fryar CD, & Ogden CL (2020). Prevalence of obesity and severe obesity among adults: United States, 2017–2018 (Data Brief No 360). National Center for Health Statistics. https://www.cdc.gov/nchs/products/databriefs/db360.htm [PubMed] [Google Scholar]
  24. Hemmingsson E, Nowicka P, Ulijaszek S, & Sørensen TI (2023). The social origins of obesity within and across generations. Obesity Reviews, 24(1), e13514. 10.1111/obr.13514 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hovenkamp-Hermelink JH, Jeronimus BF, Spinhoven P, Penninx BW, Schoevers RA, & Riese H (2019). Differential associations of locus of control with anxiety, depression and life-events: A five-wave, nine-year study to test stability and change. Journal of Affective Disorders, 253, 26–34. 10.1016/j.jad.2019.04.005 [DOI] [PubMed] [Google Scholar]
  26. Hu LT, & Bentler PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. 10.1080/10705519909540118 [DOI] [Google Scholar]
  27. Jeon S & Neppl TK (2016). Intergenerational continuity in economic hardship, parental positivity, and positive parenting: The association with child behavior. Journal of Family Psychology, 30, 22–32. 10.1037/fam0000151 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Jones TM, Hill KG, Epstein M, Olivia Lee J, Hawkins JD, & Catalano RF (2016). Understanding the interplay of individual and social–developmental factors in the progression of substance use and mental health from childhood to adulthood. Development and Psychopathology, 28, 721–741. 10.1017/S0954579416000274 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Khlat M, Jusot F, & Ville I (2009). Social origins, early hardship and obesity: a strong association in women, but not in men? Social Science & Medicine, 68(9), 1692–1699. 10.1016/j.socscimed.2009.02.024 [DOI] [PubMed] [Google Scholar]
  30. Kim Y, Lee H, & Park A (2020). Adverse childhood experiences, economic hardship, and obesity: Differences by gender. Children and Youth Services Review, 116, 105214. 10.1016/j.childyouth.2020.105214 [DOI] [Google Scholar]
  31. Lang IA, Llewellyn DJ, Hubbard RE, Langa KM, & Melzer D (2011). Income and the midlife peak in common mental disorder prevalence. Psychological Medicine, 41(7), 1365–1372. 10.1017/S0033291710002060 [DOI] [PubMed] [Google Scholar]
  32. Lau JY, & Eley TC (2008). Disentangling gene-environment correlations and interactions on adolescent depressive symptoms. Journal of Child Psychology and Psychiatry, 49(2), 142–150. 10.1111/j.1469-7610.2007.01803.x [DOI] [PubMed] [Google Scholar]
  33. Lee C, & Park S (2020). Examining cumulative inequality in the association between childhood socioeconomic status and body mass index from midlife to old age. The Journals of Gerontology: Series B, 75(6), 1264–1274. 10.1093/geronb/gbz081 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Letourneau NL, Duffett-Leger L, Levac L, Watson B, & Young-Morris C (2013). Socioeconomic Status and Child Development: A Meta-Analysis. Journal of Emotional and Behavioral Disorders, 21(3), 211–224. 10.1177/1063426611421007 [DOI] [Google Scholar]
  35. Lipsky LM, Haynie DL, Hill C, Nansel TR, Li K, Liu D, … & Simons-Morton B (2019). Accuracy of self-reported height, weight, and BMI over time in emerging adults. American Journal of Preventive Medicine, 56(6), 860–868. 10.1016/j.amepre.2019.01.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Luo Y, & Waite LJ (2005). The impact of childhood and adult SES on physical, mental, and cognitive well-being in later life. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 60(2), S93–S101. 10.1093/geronb/60.2.S93 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Luppino FS, de Wit LM, Bouvy PF, Stijnen T, Cuijpers P, Penninx BW, & Zitman FG (2010). Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Archives of General Psychiatry, 67(3), 220–229. 10.1001/archgenpsychiatry.2010.2 [DOI] [PubMed] [Google Scholar]
  38. MacKinnon DP, Lockwood CM, & Williams J (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39(1), 99–128. 10.1207/s15327906mbr3901_4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Meeuwsen S, Horgan GW, & Elia M (2010). The relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex. Clinical Nutrition, 29(5), 560–566. 10.1016/j.clnu.2009.12.011 [DOI] [PubMed] [Google Scholar]
  40. Muthén LK & Muthén BO (2017). Mplus User’s Guide. Eighth Edition. Muthén & Muthén. [Google Scholar]
  41. National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Data. U.S. Department of Health and Human Services, https://www.cdc.gov/obesity/data/adult.html [Google Scholar]
  42. Neppl TK, Diggs ON, & Cleveland MJ (2020). The intergenerational transmission of harsh parenting, substance use, and emotional distress: Impact on the third-generation child. Psychology of addictive behaviors, 34(8), 852. 10.1037/adb0000551 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Neppl TK, Diggs ON, Neppl AK, & Denburg NL (2023). Adolescent predictors of psychiatric disorders in adulthood: The role of emotional distress and problem drinking in emerging adulthood. Development and Psychopathology, 1–11. doi: 10.1017/S0954579423000081 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Neppl TK, Conger RD, Scaramella LV, & Ontai LL (2009). Intergenerational continuity in parenting behavior: mediating pathways and child effects. Developmental Psychology, 45(5), 1241. 10.1037/a0014850 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Neppl TK, Diggs ON, Wickrama KAS, & O’Neal CW (2021). Pathways of the family stress model in midlife on physical health in later adulthood. Journal of Family Psychology, 35(1). 22–32. 10.1037/fam0000714 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Neppl TK, Senia JM, & Donnellan MB (2016). Effects of economic hardship: Testing the family stress model over time. Journal of Family Psychology, 30(1), 12. 10.1037/fam0000168 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Newsom JT (2015). Longitudinal structural equation modeling: A comprehensive introduction. Routledge. [Google Scholar]
  48. Park A. (2020). The impact of childhood and adult educational attainment and economic status on later depressive symptoms and its intergenerational effect. International Journal of Environmental Research and Public Health, 17(23), 8970. 10.3390/ijerph17238970 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Pavela G. (2017). Is childhood socioeconomic status independently associated with adult BMI after accounting for adult and neighborhood socioeconomic status? PLoS One, 12(1), e0168481. 10.1371/journal.pone.0168481 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Schoon I, & Melis G (2019). Intergenerational transmission of family adversity: Examining constellations of risk factors. PLoS One, 14(4), e0214801. 10.1371/journal.pone.0214801 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Shrout PE, & Bolger N (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422–445. 10.1037/1082-989X.7A422 [DOI] [PubMed] [Google Scholar]
  52. Thaning M. (2021). Resource specificity in intergenerational inequality: The case of education, occupation, and income. Research in Social Stratification and Mobility, 75, 100644. 10.1016/j.rssm.2021.100644 [DOI] [Google Scholar]
  53. Thornberry TP, Freeman-Gallant A, Lizotte AJ, Krohn MD, & Smith CA (2003). Linked lives: The intergenerational transmission of antisocial behavior. Journal of Abnormal Child Psychology, 31, 171–184. 10.1023/A:1022574208366 [DOI] [PubMed] [Google Scholar]
  54. Trust for America’s Health (2022). https://www.tfah.org/wp-content/uploads/2022/09/2022ObesityReport_FINAL3923.pdf
  55. Turner RJ, & Avison WR (2003). Status variations in stress exposure: Implications for the interpretation of research on race, socioeconomic status, and gender. Journal of Health and Social Behavior, 488–505. 10.2307/1519795 [DOI] [PubMed] [Google Scholar]
  56. Wickrama KAS, Hwa Kwag K, Lorenz FO, Conger RD, & Surjadi FF (2010). Dynamics of family economic hardship and the progression of health problems of husbands and wives during the middle years: A perspective from rural Mid-West. Journal of Aging and Health, 22(8), 1132–1157. 10.1177/0898264310377353 [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES