Childhood Adversity and Adult Health: A Dyadic Analysis of Black American Couples

Abstract

Objective:

The association between childhood adversity and adulthood health is well-established, but few studies have examined potential effects of childhood adversity on partner health in couples. This study examines the long-term health impact of childhood adversity on individuals as well as their significant others.

Methods:

The participants were 163 distinguishable dyads from the Family and Community Health Study (FACHS). Health outcomes included both self-reported chronic illness and a messenger RNA (mRNA) index of accelerated aging. The actor-partner interdependence model (APIM) with structural equation methods was used to test the hypothesis.

Results:

Replicating prior research, childhood adversity was associated with more chronic illness and an accelerated speed of aging. Further, participants’ health in adulthood was affected by both own and partner experiences of childhood adversity. There were no gender differences.

Conclusion:

Our findings replicate and extend prior research on the long-term impact of exposure to childhood adversity, suggesting that adverse childhood experiences are also harmful to romantic partners. Further studies are required to examine potential mechanisms.

Exposure to adverse childhood experiences has been found to predict adult accelerated aging and onset and progression of chronic illnesses such as cardiovascular disease, metabolic syndrome, premature mortality, and type II diabetes (Suglia et al, 2018). This enduring effect of childhood on adult health has been referred as “the long arm of childhood” (Hayward & Gorman, 2004), and it has been shown to hold for both White and Black American individuals. Given that Black American children are at greater risk for exposure to contextual and neighborhood disadvantage across the lifespan, it seems likely that the long arm of childhood contributes to the health disparities experienced by Black Americans. In particular, because of its wide range of effects, childhood adversity may contribute to greater multi-morbidity, leading to an accumulation of chronic health conditions across adulthood and resulting in decreased quality of life and increased mortality (Nunes, et al., 2016). In addition to the effects of one’s own childhood events, a systematic review by Meyler and colleagues (2007) suggested the likely importance of partner experiences. They found that dating or married couples often have concordant or similar health behaviors or health problems, suggesting that couples’ health is mutually determined.

The present study investigated whether the established impact of individual early-life adversity on adult health should be expanded to include effects of the partner’s childhood experiences. Specifically, this study examined whether a romantic partner’s childhood experiences of adversity may exert an influence on health beyond that posed by one’s own history of adversity. There is a reason to believe that this is plausible; with past research including a recent meta-analysis (Cao et al. 2020), suggesting that a history of childhood disadvantage not only threatens a person’s personal health, but also has a corrosive impact on the quality of their romantic relationships. The latter effect is likely a consequence of distrust, low self-esteem, hostility, and depression that often develop in response to difficult childhood experiences (Sun et al., 2021). Importantly, there is an extensive literature showing that the quality of romantic relationships impacts the health of the partners; positive relationships promote health whereas troubled relationships increase the chances of illness (Song et al. 2021). The foregoing review suggests the hypothesis that the health of partners in a romantic relationship may be influenced by both their own and their partner’s history of adversity.

Despite the numerous studies on the negative impacts of childhood adversity on individuals, few studies have focused on how this effect may operate in romantic partnerships. To study interdependent individuals with distinguishable characteristics, this study employs the actor-partner interdependence model (APIM) with structural equation methods where the individuals not only influence themselves (actor effect), but also each other (partner effect). These effects were tested using both self-reports of diagnosed chronic illness (e.g., diabetes, high blood pressure, coronary heart disease) as well as a messenger RNA (mRNA) measure of accelerated aging (Peters et al., 2015) that reflects the disparity between an individual’s biological and chronological age (Simons et al., 2019). Given that the link between childhood adversity and health status may differ among males and females, the current study also hypothesizes that there would be gender differences in the actor and partner effects observed.

Methods

Sample and Procedure

The present study uses data from the Family and Community Health Study (FACHS). The first wave of FACHS contained 889 Black American 5th graders (targets), their primary caregivers, and other family members when present in the home. At each wave, both targets and primary caregivers completed self-report questionnaires, describing family processes, community characteristics, and current health. Written consent was obtained from all participants. All procedures were approved by the University of Georgia Institutional Review Board (see Simons and colleagues (2011) for more details). At wave 7, 186 target youth indicated that they had a committed relationship with a primary romantic partner, and these romantic partners were recruited as well, with both providing a blood sample. The current investigation focused on heterosexual couples, allowing us to test models using distinguishable dyads to test gender effects. Individuals who did not have completed data on accelerated aging or self-report (n=23) were dropped. After listwise deletion, the final analytic sample comprises a subsample of 163 distinguishable dyads, a total of 326 individuals from Wave 7 (2015–16). Of these, 45 were married and 118 were not married.

Measures

Accelerated mRNA age.

A mRNA measure of aging was recently developed by Peters et al. (2015). This index consists of 1,497 transcripts for which level of gene expression (amount of mRNA) is highly correlated with age. A measure of accelerated aging was formulated using the unstandardized residual scores from the regression of mRNA age on chronological age. The differences between an individual’s predicted and chronological age indicates, in years, the extent to which they are experiencing accelerated or decelerated aging. Detail regarding the preparation of mRNA data is described in the online supplement (see Beach et al. (2018) and Simons et al. (2019) for more details).

Chronic Illness was measured at Wave 7. Respondents were asked if they had even been diagnosed with nine illnesses: cardiovascular problems, peptic ulcer, high blood pressure, thyroid problems, liver disease, diabetes, kidney problems, depression, and cancer. These diseases were selected because these all have been related to childhood experiences in prior literature. For each illness, “no” was coded as 0 and “yes” was coded as 1. Items were summed to provide an index that ranged from 0 to 9 with higher scores indicating greater multi-morbidity. Such measures are common assessing chronic illness (Beach et al., 2021).

Childhood Adversity was assessed retrospectively by a 23-item questionnaire developed for FACHS (see Lei, Beach, & Simons (2018) for additional information). The instrument asks respondents to report (1 = yes, 0 = no) whether they experienced specific adverse events prior to 10 years of age, including family financial hardship, physical abuse, disorganized neighborhood, and racial discrimination (see online supplement table S2 for all items). Items were summed to form an index of childhood adversity that ranged from 0 to 23. Coefficient α for this scale was .78. All items and item-total correlations are listed in the online-only supplemental materials.

Analytic Strategy

This study employs the actor-partner interdependence model (APIM) to estimate the relation of a pair of outcomes (each partner’s health) to a pair of independent variables (each partner’s childhood adversity). The actor-partner interdependence model (APIM) estimates the intrapersonal effect (actor effect; Figure 1, Path b) and interpersonal effect (partner effect; Figure 1, Path a) from each dyad member (Iida, Seidman, & Shrout, 2018). The APIM was fitted using structural equation methods (Mplus version 7.4) with maximum likelihood estimates of the parameters. Structural equation modeling is useful for testing complex relationships simultaneously in a model to estimate a conservative p-value. A series of three models for each outcome were conducted to examine the two outcomes: chronic illness and accelerated aging. Model 1 were the unconstrained model showing the differences in partner and actor effects. To examine whether partner effects were significantly different from each other for males and females, partner effects were constrained to be equal in Model 2 and compared its fit to Model 1, yielding a chi-square change with 2 degrees of freedom. Assuming no significant deterioration in model fit, actor effects were then constrained to be equal in the third model and compared its fit to Model 2, yielding a chi-square change with 2 degrees of freedom. Non-significant chi-square differences between the models were taken to indicate no gender differences for partner or actor effects. Model fit statistics including Comparative Fit Index (CFI) and Tucker–Lewis Index (TLF) were reported.

Figure 1.

a. Effects of childhood adversity on chronic illness.

b. Effects of childhood adversity on mRNA aging.

Results

Descriptive Statistics

On average, the males in this sample were 30.62 years old and the females were 29.17 years old. Among the male and female respondents, the mean of mRNA aging was 29.56 (SD=.25) and 29.43 (SD=.25), respectively. The couples accelerated aging scores were not statistically different from each other, and were related (r=.21, p=.008), indicating that the couples accelerated aging scores were generally close. The means for chronic illness for males and females were .313 (SD=.54) and .48 (SD=.78), with roughly 28% of the males and 36% of the females reporting that they had at least one diagnosed chronic disease. The small but significant difference between the means indicates that females report more diagnosed chronic illness than males. Number of chronic illnesses for males and females were significantly correlated (r=.48, p<.01). The means for childhood adversity for males and females were 4.03 (SD=3.71) and 3.96 (SD=3.69), respectively. The numbers of experiences of childhood adversity for males and females were significantly associated (r=.17, p=.032). There was no significant difference on the experiences of childhood adversity. Overall, the results suggest that couples shared similar adverse childhood experiences and their health outcomes were correlated.

APIM Analysis of Childhood Adversity on Chronic Illness

The nested models 1 to 3 in Table 1 test the conceptual model in Figure 1a, showing the results for the APIM model applied to chronic illness. Beginning with an unconstrained model as model 1, Model 2 constrained the partner-effect coefficients to be equal between males and females. The chi-squared change test indicated that there was no significant deterioration in fit for Model 2 relative to Model 1 (Δχ²₍₁₎ =1.94, p = 1.6) when partner effects were constrained to be equal (b_male = b_female = .02, SE = .01). Finally, actor-effects were constrained to be equal between males and females. Again, there was no significant deterioration in fit for Model 3 relative to Model 2 (Δχ²₍₁₎ =.07, p = .80). Thus, the final model indicates that individuals who had more adverse childhood experiences had more chronic illness as adults and that their adverse experiences affected their own as well as their partner’s chronic illness, with no significant gender differences in the partner or actor effects.

Table 1.

APIM results relating childhood adversity to health (N = 163 dyads)

	Chronic Illness						Aging
	Model 1		Model 2		Model 3		Model 4		Model 5		Model 6
	Estimate	SE	Estimate	SE	Estimate	SE	Estimate	SE	Estimate	SE	Estimate	SE
Actor effects
Child adversitymale → Healthmale	.018	.012	.025*	.011	.024*b	.010	.080	.064	.089	.062	.108*b	.044
Child adversityfemale → Healthfemale	.030	.022	.021	.018	.024*b	.010	.136*	.064	.127*	.062	.108*b	.044
Partner effects
Child adversitymale → Healthfemale	.004	.017	.023*a	.009	.023*a	.009	.073	.063	.101*a	.044	.101*a	.044
Child adversityfemale → Healthmale	.032*	.013	.023*a	.009	.023*a	.009	.129*	.064	.101*a	.044	.101*a	.044
Child adversitymale Child adversityfemale	2.283**	.895	2.283*	.895	2.283*	.895	2.283*	1.08	2.283* 1.080		2.283*	1.080
Healthmale Healthfemale	.184**	.045	.187**	.045	.186**	.045	1.457*	.691	1.463*	.693	1.475*	.693
Model fit
χ2	0		1.944		2.009		0		.374		.566
df	0		1		1		0		1		1
Δχ2			1.944		.065		0		.374		.192
p-value for Δχ2			.163		.799				.541		.661
CFI	1.000		.981		1.000				1.000		1.000
TLI	1.000		.907		1.000				1.000		1.000

Note.

^abThese coefficients were constrained to be equal.

⁺p < 0.1,

^*p < 0.05,

^**p < 0.01

APIM Analysis of Childhood Adversity on mRNA Aging

The nested models 4 to 6 in Table 1 and Figure 1b show the results for the APIM analysis using accelerated mRNA aging as the outcome. Again, the analysis began with an unconstrained model in Model 4, and partner effects were constrained to be the same in Model 5. The chi-squared test indicated that there was no significant deterioration in fit for Model 5 relative to Model 4 (Δχ²₍₁₎ =.37, p = .54), indicating that partner effects could be estimated to be the same for males and females (b_male = b_female = .10, SE = .04). In Model 6, actor effects were constrained to be equal for males and females, and there was no significant deterioration in fit for Model 6 relative to model 5 (Δχ²₍₁₎ =.19, p = .66), suggesting no significant differences between male and female actor effects on accelerated mRNA aging. More specifically, the final model indicated that childhood adversity and accelerated mRNA aging were significantly associated; individuals with more adverse childhood experiences aged faster. Additionally, childhood experiences not only impacted the individual’s own speed of aging, but also their spouse’s speed of aging. Furthermore, there was no gender difference between males and females in terms of either partner or actor effects. For every adverse childhood experience, there was an .11 years increase in aging scores for themselves, and a .10 years increase for their spouses.

Discussion

Despite a robust literature on the impact of childhood adversity on adulthood health, little is known about how this relationship applies to romantic partners. Using a distinguishable dyadic sample of Black-Americans, the current study indicated that (a) childhood adversity was positively associated with adult chronic illness as well as accelerated aging; (b) in addition to this actor effect, one’s childhood adversity was positively associated with the romantic partner’s chronic illness as well as accelerated aging; (c) there were no gender differences in either partner or actor effects.

Our findings support and expand the “long arm of childhood adversity” literature, indicating that childhood experiences not only influence individuals as adults but also impact their romantic partners’ health status. Although alarming in some respects, our result is encouraging in suggesting that some effects of childhood adversity may influenced by the partner. Childhood adversity has been connected to the quality of close personal relationships (Umberson et al., 2014), suggesting potential social mechanisms that could be examined in future studies, such as relationship status and quality, potentially explaining partner effects and identifying a target of preventive intervention. Additionally, the results indicate that couples’ childhood adversity experiences and health outcomes are both correlated, supporting the prior studies on the concordance of experiences between couples (Meyler et al., 2007). Future research could also look at couple processes promoting resilience —such as dyadic communication styles and health behaviors that could offset accelerated aging processes.

Although one of the contributions of the present study was to use of two indices of youth adult health, it was surprising that chronic illness and biological aging, were not correlated. One possible explanation is that the average age of the studied sample is relatively young and so there is still limited variability in numbers of chronic illness. Conversely, speed of aging has greater variability, is also more sensitive to chronic stress, and may precede onset of chronic illness, making it a better indicator of health-related outcomes for this young sample. Second, a strength of the sample is its homogeneity, allowing stronger conclusions about relevance of dyadic effects for the health of Black-Americans, examination in other samples representing other racial and ethnic groups would be useful to examine the generalizability of these effects to other. Furthermore, gender differences of the effects should be further tested in different age groups. Lastly, this study was exploratory in many aspects; future research should examine both mechanisms, such as racial discrimination, and additional specific outcomes, such as official diagnosis of chronic illness, to better guide the development of preventive interventions.

Despite these limitations, our study extends our understanding of the long-term impact of childhood adversity on adult health among romantic partners. The findings support the effect of childhood adversity on young adults’ individual health for both males and females, as well as for their romantic partners’ health. Identifying such partner effects contributes to the developmental and family processes literature and underscores the importance of interventions concerned with addressing childhood adversity.