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. Author manuscript; available in PMC: 2022 Sep 1.
Published in final edited form as: Aging Ment Health. 2020 Apr 30;25(9):1666–1675. doi: 10.1080/13607863.2020.1758917

Couple Processes of Family Economic Hardship, Depressive Symptoms, and Later-life Subjective Memory Impairment: Moderating Role of Relationship Quality

Kandauda A S Wickrama 1, Catherine Walker O’Neal 2
PMCID: PMC7643052  NIHMSID: NIHMS1637312  PMID: 32349526

Abstract

Objectives:

To examine a) processes through which family economic hardship (FEH) contributes to spouses’ mental health and subsequent subjective memory impairment (SMI) in later years and b) the moderating effect of overall relationship quality on these associations.

Methods:

With prospective data over 27 years from a sample of 224 husbands and wives in enduring marriages, the present study utilized latent growth curves to identify how FEH trajectories are associated with both spouses’ depressive symptoms trajectories across their mid-later years (average age 40-65 years) and subsequent SMI in later life (> 67 years). The moderating role of relationship quality between depressive symptoms and SMI was also examined.

Results:

FEH experiences across the mid-later years (1991-2015) explained variation in husbands’ and wives’ depressive symptoms trajectories (1994-2015). Change in depressive symptoms was associated with husbands’ and wives’ SMI in later life (2017) after taking the level of depressive symptoms into account. Spousal dependencies, including partner effects, existed among husbands’ and wives’ depressive symptoms trajectories and SMI outcomes. Some of these dependencies were moderated by couples’ overall relationship quality.

Conclusion:

FEH has a persistent influence on husbands’ and wives’ SMI in later years. Depressive symptoms mediated the influence of FEH on later wellbeing. The findings are discussed as they relate to family systems and life course stress process theories. Implications are addressed at multiple levels including national- and state-policies and clinical interventions.

Keywords: depression, marriage, mental health, midlife, financial stress

Consistent with the life course stress process perspective (Elder, 1998; Pearlin et al., 2005), previous research has shown that exposure to stressful experiences can have mental and physical health consequences over the life course, but less is known about long-term, life course processes responsible for this connection. Depression is one of the most obvious reflections of stress because stressful experiences, such as family economic hardship (FEH), often foster a sense of hopelessness and helplessness (Pearlin, 1989). Family research has documented that elevated levels of depressive symptoms for husbands and wives are, to some extent, a result of long-term stressful FEH (Conger et al., 2010; Mossakowski, 2003; Wickrama et al., 2010).

The life course stress process perspective (Elder, 1998; Pearlin et al., 2005) also posits that stressors proliferate over the life course within the same life domain and even across life domains. Parallel with this life course notion, we posit that the consequences of stress also proliferate over the life course and across indicators of wellbeing, which is consistent with the life course perspective’s cumulative risk (Kuh et al., 2003) contention that risks stemming from stress exposure sequentially accumulate over the life course. These notions are supported by previous studies focusing specifically on older adults, which have shown that depressive symptoms often contribute subsequent cognitive decline over time (e.g., Chow et al., 2007; Gerstorf et al., 2009; Hülür et al., 2015). In particular, in older adults, subjective memory impairment (SMI) (as reflected by complaints of everyday memory failure) has been shown to be a central marker of quality of life because it influences individuals’ ability to function and live independently (von Gunten et al., 2005) and, more broadly, adapt and age successfully (Ryff & Singer, 1998). Moreover, subjective memory impairment is an early indicator of more severe memory and cognitive declines later in life (e.g., dementia and Alzheimer’s disease) (Hülür et al., 2015; Rönnlund et al., 2015). Accordingly, we expect that trajectories of depressive symptoms in mid-later years influenced by FEH may contribute to additional health risks, such as SMI, with advancing age (Cook et al., 2017; Seo et al., 2017).

Furthermore, according to the “linked life” notion of the life course perspective (Elder, 1998), we expect dyadic associations involving husbands’ and wives’ depressive symptoms and SMI. Investigations of husbands and wives together allow for analyses assessing partner influences that can reveal dynamic dyadic processes. Previous dyadic studies have shown that mental health outcomes of husbands and wives are significantly associated (Wickrama, O’Neal, & Lorenz, 2018).

In addition, consistent with the family systems theory (Fingerman & Bermann, 2000), there are predictable patterns of spousal interactions that emerge within the family system. For example, these patterns may include their expressions of warmth and hostility, shared or collective activities, and conflict resolution styles. These characteristics of the couple likely reflect their overall relationship quality. Because husbands’ and wives’ actor and partner effects involving depressive symptoms trajectories and later-life SMI operate within the enduring relationship context, these effects may vary depending on their overall relationship quality (e.g., the association between depressive symptoms and SMI may be moderated by marital quality).

Yet, the design of most previous studies has not allowed for an investigation of spouses’ life experiences prospectively over an extended period of time, nor has it been possible for studies to comprehensively assess life course “accumulation of risk” process involving FEH, depressive symptoms trajectories, and later-life SMI within the context of their couple relationship. Analytically, more comprehensive dyadic analyses of these processes are important because separate analyses of husbands and wives fail to account for dependencies between spouses, which increases the likelihood of biased parameter estimates (Kenny et al., 2006) necessitates.

Thus, as shown in Figure 1, using prospective data over 27 years from a sample of 224 husbands and wives in enduring marriages (consistently married over 42 years), the present study investigates a dyadic life course model that examines how couple FEH trajectories (from 1991-2015) influence husbands’ and wives’ depressive symptoms trajectories across their mid-later years (average age 40-65 years from 1994-2015) and how these symptom trajectories are associated with spouses’ SMI in later years (> 67 years, 2017). Furthermore, as depicted by the vertical, upwards arrow, the present study investigates whether marital quality over the mid-later years moderates the influence of spouses’ depressive symptoms on their own SMI (actor effect) and their partner’s SMI (partner effects). These hypothesized associations are further discussed in the paragraphs that follow.

Figure 1.

Figure 1.

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The Theoretical Framework

Family Economic Hardship and Depressive Symptoms

Stressful experiences, including FEH, may contribute to depressive symptoms as a direct reflection of stress as well as through adverse physiological functioning. Research has shown that chronic stressful conditions adversely influence physiological functioning (e.g., cardiovascular, neurological, hormonal, and immunological functioning; Lovallo, 2005; McEwen & McEwen, 2015), which, in turn, can serve to further elevate depressive symptoms (Lewinsohn et al., 1996; Yang et al., 2015). FEH may also influence husbands’ and wives’ depressive symptoms due to material deprivation, including a shortage of healthy food options, lack of leisure availability, and poor housing conditions (e.g., Evans et al., 2003; Heflin & Iceland, 2009). Research notes that chronic stressors, such as sustained economic hardship, are more powerful predictors of depressive symptoms than acute stressful experiences (Hammen et al., 2009; Mossakowski, 2003).

In the present study, consistent with the life course and stress process perspectives (Elder & Giele, 2009; Pearlin et al., 2005), we take a long view in examining adults’ mental health as a dynamic developmental process captured by trajectories of husbands’ and wives’ depressive symptoms. Thus, we expect changes in couple FEH prompt parallel changes in both spouses’ depressive symptoms over the mid-later years (i.e., parallel trajectories of couple FEH and spouses’ depressive symptoms).

Depressive Symptoms Trajectories and Subjective Memory Impairment (SMI)

Depressed mood has been shown to contemporaneously influence both retrieval and encoding cognitive processes resulting in memory problems (Lewis & Critchley, 2003). Other studies have shown that depression causes cognitive decline, including impaired memory functioning (Cook et al., 2017). Using data spanning over 10 years, Gerstorf et al. (2009) showed that depressive symptoms preceded a decline in episodic memory for married older women, with no evidence of an effect in the other direction (i.e., from memory to depressive symptoms). However, other research has documented that memory impairment may also influence depressive symptoms over time (Aichele et al., 2018). Still, other studies suggest that the association between memory impairment and depressive symptoms is spurious due to the effects of an underlying third variable influencing both variables, including stress exposure such as FEH (Verhaegen et al., 2003). Consistent with the life course cumulative risk notion (Kuh et al., 2003), we expect that long-term, escalating depressive symptoms over the mid-later years may contribute to adverse cognitive processes, such as deterioration of retrieval and encoding capabilities processes, which are at least partly reflected by older adults’ subjective memory complaints (i.e., SMI) (Hülür et al., 2015; Seo et al., 2017). Several elements of the current study in combination provide an appropriate test of this hypothesis. More specifically, the present study (a) incorporates FEH, depressive symptoms, and SMI in a single analytical framework, (b) uses the proper temporal ordering of variables to strengthen the hypothesized causal argument, and (c) tests the influence of FEH on both depressive symptoms and SMI outcomes to minimize the possibility of spurious associations between depressive symptoms and SMI.

Dyadic Associations Between Spouses in Enduring Marriages

As indicated previously, spouses’ daily life activities are closely connected, and there can be various crossover, or partner, effects between spouses. Regarding depressive symptoms, a spouse’s stress, mood, and feelings can be transmitted to the other (Kiecolt-Glaser & Wilson, 2017). Crossover effects are also plausible between a spouse’s depressive symptoms and his/her partner’s SMI (Wickrama, O’Neal, Klopack, & Neppl, 2018). These dependencies may be particularly strong for older spouses in enduring marriages because emotional investment in the marriage relationship and partner often increase over time (i.e., “linked lives”) (Elder & Geile, 2009; Meegan & Berg, 2002). These partner effects are also consistent with research noting, with advancing age, that the health congruence increases between partners (i.e., “love sick”; Kiecolt-Glaser & Wilson, 2017).

The Moderating Role of Couples’ Relationship Quality

We expect the influence of depressive symptoms over the mid-later years on later-life SMI will vary depending on the marital context. Specifically, we posit that for couples in high-quality relationships there will be increased reactivity to both partners’ levels and changes in depressive symptoms. For couples with high relationship quality, the strong salience of the marriage role identity and the high levels of marital commitment may cause older adults’ memory impairment to be more affected by depressive symptoms, in part because depressive symptoms may disrupt their marital functioning. This is also consistent with identity theory, which contends that the consequences of identity disruptions are stronger when the salience of the identity is high (Marcussen et al., 2004). In contrast, we expect depressive symptoms will be relatively greater for those in the low relationship quality group (compared to the high relationship quality group) resulting in less involvement in day-to-day or joint activities and, consequently, fewer opportunities for memory loss and memory complaints. Therefore, the association between depressive symptoms and SMI may be weaker for couples in the low relationship quality group even though their levels of depressive symptoms and SMI may generally be high. This moderation may partly be attributed to the relatively low occurrence of depressive symptoms and SMI for those in high-quality relationships, resulting in room for subsequent increases in SMI. In contrast, depressive symptoms and SMI are both generally higher in the context of low marital quality, and, thus, there are fewer chances for further increases in SMI (i.e., a ceiling effect).

Consistent with Figure 1, specific study hypotheses are:

  1. Across the mid-later years, the level and rate of change in couple FEH trajectories (average age of 40-65 years from 1991-2015) will influence husbands’ and wives’ levels and rates of change in depressive symptoms trajectories (average age of 43-65 years from 1994-2015) (parallel trajectories).

  2. Individuals’ level of depressive symptoms (1994) will influence their partners’ change in depressive symptoms over their mid-later years (1994-2015) (partner effects).

  3. Changes in husbands’ and wives’ depressive symptoms trajectories over their mid-later years (1994-2015) will influence husbands’ and wives’ SMI in later years (2017, average age of 67 years) after controlling for the level of depressive symptoms and age.

  4. Both the level and change in individuals’ depressive symptoms over their mid-later years (1994-2015) will influence their partners’ SMI in later years (2017) (partner effects).

  5. The presence of a high-quality marital relationship will amplify actor and partner effects involving spouses’ depressive symptoms and their SMI.

Methods

Participants and Procedures

The data used to evaluate these hypotheses are from the Iowa Youth and Family Project (IYFP, 1989-1994), which was later continued as two panel studies: the Midlife Transitions Project (MTP) (2001) and the Later Adulthood Study (LAS) (2015-2017). Together, these projects provide data over 27 years on rural families from a cluster of eight counties in north-central Iowa that closely mirror the economic diversity of the rural Midwest. The IYFP began in 1989 as a study of rural couples with children, at least one of whom was a seventh-grader in 1989 (Conger & Elder, 1994). We limited our sample for the present study to husbands and wives who were consistently married from 1991 to 2017 (N=224) and participated in 1991, 1994, 2001, 2015, and 2017 data collections. Data collected in 1991, rather than 1989, were used as the first time point of the present study due to the availability of study variables.

The attrition rate was 31% from 1991 to 2017. An attrition analysis compared demographic characteristics (i.e., age, education level, economic hardship measured by counts of economic cutbacks, and divorce proneness (Booth et al., 1983)) and study variables (e.g., depressive symptoms) in 1991 between the current analytic sample of consistently married couples and couples who were excluded from the current analyses due to divorce and study attrition. The only significant difference noted was for divorce proneness in 1991, with higher scores reported for couples who were excluded from the current analysis.

In 1991, spouses were in their early middle years. The average ages of husbands and wives were 42 and 40 years, respectively, and their ages ranged from 33 to 59 for husbands and 31 to 55 for wives. On average, the couples had been married for 19 years and had three children. The median age of the youngest child was 12. In 1989, the average number of years of education for husbands and wives was 13.68 and 13.54 years, respectively. Because there are very few minorities in the rural area studied, all participating families were White.

Measures

Family economic hardship

The index of economic problems was adapted from Dohrenwend et al. (1978) to capture families’ economic circumstances. Both husbands and wives completed the measure in 1991, 1994, 2001, and 2015. Separately for each time point, “yes” responses to each of the 27 items were summed to indicate economic problems experienced during the past year by the family (1=yes, 0=no). The list of economic problems included items such as “borrowed money to help pay bills” and “sold possessions or cashed in life insurance.” Means and standard deviations of all study variables are provided in Table 1.

Table 1.

Descriptive statistics of study variables.

Full
Sample
M (SD) 		High Marital Quality
Group (n=XX)
M (SD) 		Low Marital Quality
Group (n= XX)
M (SD)
Family Economic Hardship
 W. 1991 5.64 (5.01) 6.01 (5.15) 6.61 (5.25)
 W. 1994 5.45 (5.20) 4.86 (5.05) 6.51 (5.50)*
 W. 2001 4.65 (4.33) 4.01 (4.36) 5.84 (4.56)
 W. 2015 3.70 (4.10) 4.40 (4.10) 3.27 (3.80)
 H. 1991 4.63 (4.95) 4.01 (4.39) 5.50 (5.62)
 H. 1994 4.87 (5.15) 4.14 (4.51) 5.75 (5.81)*
 H. 2001 3.88 (4.23) 4.96 (4.62) 3.39 (3.53)
 H. 2015 3.18 (3.19) 3.13 (3.51) 3.38 (3.60)
Depressive Symptoms
 W. 1994 18.72 (7.22) 16.94 (4.52) 21.91 (8.49)*
 W. 2001 19.41 (6.39) 18.28 (6.34) 21.67 (7.10)*
 W. 2015 19.14 (6.51) 17.99 (5.10) 21.03 (6.41)*
 H. 1994 16.84 (5.49) 16.38 (4.81) 17.70 (5.42)*
 H. 2001 17.98 (5.55) 17.23 (4.50) 19.11 (6.51)
 H. 2015 18.56 (5.63) 17.81 (5.55) 19.70 (6.00)
Subjective Memory Impairment
 W. 2017 1.59 (.54) 1.56 (.65) 1.69 (.70)*
 H. 2017 1.66 (.67) 1.58 (.41) 1.78 (.42)*
Overall Marital Quality (1994-2015) 4.07 (.45) 4.34 (.25) 3.59 (.30)
Demographic Variables
W. Age (1991) 40.03 (4.11) 40.17(3.80) 40.05(3.51)
H. Age (1991) 42.01 (4.67) 42.12(4.51) 42.01(4.49)
W. Education (1991) 13.63 (2.98) 13.53 (1.70) 13.98 (2.00)
H. Education (1991) 13.78 (2.18) 13.70 (2.10) 12.81 (1.80)
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Notes. W. = Wives. H. = Husbands.

*

denotes statistically significant difference between the high and low marital quality groups at p < .05.

Depressive symptoms

Thirteen items from the Symptom Checklist (SCL-90-R; Derogatis & Melisaratos, 1983) captured self-report ratings of depressive symptoms from the previous week for husbands and wives in 1994, 2001, and 2015. Sample items include, “feelings of worthlessness” and “feeling hopeless about the future.” These items were scored on a 5-point Likert-type scale (1=Not at all, 5=Extremely). A sum score was computed with higher scores indicating more depressive symptoms. The internal consistencies were greater than .90 for husbands and wives across measurement occasions.

Subjective memory impairment

SMI was measured in 2017 using the revised, shortened Everyday Memory Questionnaire (R-EMQ), which captures three aspects of memory performance, including “retrieval,” “attentional tracking,” and “visual reconstruction” (Royle & Lincoln, 2008). Thirteen items indicated SMI in the previous month for both husbands and wives. This revised version of the original 28 item-EMQ has been shown to be a reliable and valid measure of SMI with good face validity (Royle & Lincoln, 2008). Sample items include, “having to check whether you had done something that you should have done” and “forgetting when it was that something happened; e.g., “was it yesterday or last week” (1=Once or less in the last month, 5=Once or more in a day). A mean score was computed with higher scores indicating more SMI. The internal consistencies were .94 and .93 for husbands and wives, respectively.

Couple relationship quality

Couple relationship quality in 1994, 2001, and 2015 was assessed by a composite measure averaging measures of three dimensions of marital quality: spousal support, constructive conflict resolution, and behavioral integration (see below). First, separately for each dimension and each measurement occasion, husbands and wives scores were averaged to create couple scores. Then, the scores were averaged across the dimensions to provide an average marital quality score for each measurement occasion (1994: M=4.77, SD=.68; 2001: M=4.04, SD=.51; 2015: M=4.04, SD=.52). Last, an overall score of couple relationship quality from 1994 to 2015 was computed by averaging the scores across measurement occasions. These indicators were significantly correlated across time points (r ranged from .60 to .78). A mean split of this overall relationship quality average capturing 1994, 2001, and 2015 identified couples with relatively “low” and “high” marital quality.

Couple’s spousal support.

Ten items were used to capture spousal reports of expressions of warmth and support from their partners during interactions in the year preceding survey completion. Sample items include how often their spouse: “let you know she/he really cares about you” and “acted loving and affectionate toward you.” The items were rated using a 7-point Likert scale (7=always, 1=never). Mean scores were computed for husbands and wives separately for each data collection point. The internal consistencies ranged from were.80 and .84. Husbands’ and wives’ scores were highly correlated (ranged from .42 to .51, p<.01).

Couple’s constructive conflict resolution.

Participants reported their spouse’s constructive conflict resolution behaviors. These measures were created for the IYFP (Matthews et al., 1996). Respondents indicated how frequently their spouse exhibited specific behaviors when they had a problem to solve together. Eight items assessed constructive problem solving (e.g., “has good ideas about how to solve the problem” and “compromises or changes their point of view to help solve the problem”) with responses ranging from 1=never to 7=always. Mean scores were computed. The internal consistencies ranged from .85 to .89. Husbands’ and wives’ scores were significantly correlated (ranged from .38 to .40, p<.01).

Couple’s behavioral integration.

In line with previous research, behavioral integration reflects the amount of time and frequency couples spend together engaging in joint or collective activities (Wickrama, O’Neal, & Neppl, 2018). The respondents answered nine questions (1=never, 4=often) about their joint participation in pleasurable activities. These questions tap into activities that couples often do together, including hobbies, socialization with friends, community, school, and church activities, going out, and overnight trips. To be consistent with other response categories assessing components of marital quality, these response categories were recoded as 1=1, 2=3.5, 3=5.5, and 4=7. The recoded variable was highly correlated with the original variable (r > .80 across time points and respondents). The internal consistencies ranged from .63 to .70. Items were averaged. Husbands’ and wives’ scores were significantly correlated (r ranged from .40 to .60, p<.01).

Statistical Analyses

Within a single, comprehensive dyadic model, we examined (1) parallel trajectories between FEH and depressive symptoms over husbands’ and wives’ mid-later years, specifically the influence of the level and rate of change of couple FEH (1991-2015) on the level and rate of change in husbands’ and wives’ depressive symptoms (1994-2015) and (2) the influence of change in both spouses’ depressive symptoms on their subsequent SMI in later adulthood (2017) after controlling for the influence of the level of depressive symptoms and age on SMI. We also tested a fully recursive alternative model incorporating direct paths between FEH and SMI. All models were tested using Mplus, version 8 (Muthen & Muthen, 1998-2017). The moderation effect of overall relationship quality (averaging responses from 1994, 2001, and 2015) was tested by comparing the model for two groups (i.e., a low relationship quality group and high relationship quality group).

Of 224 couples, variables for some respondents were unavailable at a specific wave of data collection (nearly 9% of the data). Full information maximum likelihood (FIML) was utilized to test the hypotheses with all available data. We used a range of indices to evaluate model fit including the chi-square statistic, the comparative fit index (CFI), and the root mean square error of approximation (RMSEA). Bentler (1990) reported that a CFI value greater than >.95 indicates a respectable model fit. MacCallum et al. (1996) report that a RMSEA nearing .08 indicates reasonably good model fit.

Results

Estimating Growth Curves of FEH and Depressive Symptoms

First, we estimated a second-order growth curve for couples’ FEH (known as a curve-of-factors model; Wickrama et al., 2016) using latent constructs of couple FEH as repeated measures (see Figure 2). The initial level and slope constructs of this second-order couple FEH growth curve were 3.80 and −.69, respectively. The negative slope indicates that, across the sample, there was a general decrease in FEH from 1991 to 2015. However, there was significant variability in the initial level and rate of change in FEH (8.42 and 1.12, respectively, p<.01), indicating sufficient variability for estimating our conceptual model.

Figure 2.

Figure 2.

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A second-order growth curve (i.e., curve-of-factors model) of couple family economic hardship.

Notes. FEH=Family economic hardship. Unstandardized coefficients. CFI=.99. RMSEA=.06. χ2(df)=21.66(13), p=.06. (measurement errors are correlated, not shown)

Second, growth curves were estimated separately for husbands’ and wives’ depressive symptoms using their reports in 1994, 2001, and 2015. On average, depressive symptoms increased from 1994 to 2015 (mean slope of .12 for both husbands and wives, p<.05). There was significant variability in the slope of husbands’ symptoms (2.11, p<.01) but not wives’ symptoms.

Testing the Hypothesized Model

Regarding hypothesis 1, the initial level of couple FEH in 1991 was related to the severity (i.e., level) of husbands’ and wives’ depressive symptoms (b=.63 and .73, respectively, p<.001); such that, in families with greater economic hardship, both spouses generally reported more depressive symptoms compared to families with less economic hardship (see Figure 3). For both husbands and wives, the rate of change (i.e., slope) in couple FEH from 1991 to 2015 also explained variation in the trajectories of depressive symptoms (b=.44 and .75, respectively, p<.01). Thus, in families who reported an increase in economic hardship over time, there was a trend of increasing depressive symptoms for both husbands and wives.

Figure 3.

Figure 3.

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Results from a model examining family economic hardship trajectories, depressive symptoms, and the level of SMI.

Notes. H-DS=Husbands’ depressive symptoms. W-DS=Wives’ depressive symptoms. Unstandardized coefficients, except for correlations, with standard errors in parentheses. FEH was modeled as a second-order growth curve (i.e., a curve-of-factors model). The correlation between the level and slope across spouses was not significant and is not shown). CFI=.92. RMSEA=.06. χ2(df)=230(120)***p<.001. **p<.01 *p<.05.

Regarding hypothesis 2 for the partner effect involving depressive symptoms, the level of husbands’ depressive symptoms influenced subsequent change in wives’ depressive symptoms and the level of wives’ depressive symptoms influenced subsequent changes in husbands’ depressive symptoms (b=.11, p<.05 and .12, respectively, p<.01), suggesting interlocking trajectories of spouses’ depressive symptoms with crossover influences. The rate of change for husbands’ depressive symptoms was not significantly correlated with the rate of change for wives’ depressive symptoms. Also, husbands’ and wives’ levels of depressive symptoms in 1994 were not correlated (not shown in Figure 3).

Supporting the hypothesis 3, for both husbands and wives, their rate of change in depressive symptoms from 1994 to 2015 was associated with their SMI (b=.11, and .18, respectively, p<.05) after controlling for the influence of the level of depressive symptoms (1994) (b=.06 and .05 for husbands and wives, p<.01) and age. However, regarding hypothesis 4, there were no significant partner effects involving depressive symptoms and SMI. Consequently, for enhanced model parsimony, these paths were removed from the final model. Spouses’ reports of SMI in 2017 were significantly correlated (r=.06, p<.01), suggesting a similar rank order between spouses’ reports of SMI. Overall, the model shown in Figure 3 fit the data reasonably well (CFI=.92, RMSEA=.06, and χ2 (df)=230(120). The model explained 18% and 27% of the variance in husbands’ and wives’ SMI, respectively.

In testing the fully recursive alternative model, the direct effects between the levels and slopes of couple FEH trajectories and husbands’ and wives’ SMI were not statistically significant. More importantly, the significant associations between spouses’ depressive symptoms growth factors and their own SMI remained significant suggesting that those associations are not spurious due to the common influence of couple FEH. The change in chi-square between these two nested models (with and without the direct paths) was 5.00 for 4 degrees of freedom, suggesting the reduced model was a better fit with the data.

Testing Relationship Quality as a Moderator

Analyses testing hypothesis 5, examining moderation of the associations between depressive symptoms and SMI by couples’ overall relationship quality, indicated that none of the effects (both actor and partner) from depressive symptoms growth factors to SMI were statistically significant for couples in the low relationship quality group (see Figure 4). The results showed that, for those in the high relationship quality group, all of the actor effects were significant except for the effect of wives’ change in depressive symptoms, which demonstrates gender asymmetry. For couples in this group, several significant partner effects emerged. Wives’ level of depressive symptoms influenced husbands’ SMI (b=.12, p<.05.), and both the level and change in husbands’ depressive symptoms influenced wives’ SMI (b=.05 and .12, respectively, p<.05). Equality constraint tests comparing coefficients for the high and low marital quality groups showed that the four paths from spouses’ growth factors of depressive symptoms to wives’ SMI were significantly different (change in chi-square for 4 df = 9.69, p<.05). A similar equality constraint test assessing the statistical significance of the depressive symptoms’ growth factors on husbands’ SMI approached significance (change in chi-square for 4 df = 7.89, p<.10).

Figure 4.

Figure 4.

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Examining relationship quality as a moderator of the associations between husbands’ and wives’ depressive symptoms and their subjective memory impairment.

Notes. RH-DS=Husbands’ depressive symptoms. W-DS=Wives’ depressive symptoms. Unstandardized coefficients, except for correlations, with standard errors in parentheses. For the overall model: CFI=.96. RMSEA=.01. χ2(df)=52.95(38)***p<.001. **p<.01 *p<.05.

Discussion

Research suggests that despite advances in the medical field and extended life expectancy, older adults today experience poorer health and wellbeing compared to previous generations (King et al., 2013). Because most of the baby boom generation has now reached later adulthood (65 years of age and older), these health concerns affect a significant portion of the United States’ population (Pruchno, 2012). Poor mental health and everyday memory problems also play an important role in their wellbeing (Mogle et al., 2017). However, less is known about how cumulative stressful life experiences in adulthood, such as FEH, may contribute to the wellbeing of older married adults. This is a particularly important question because marital partners navigate stressful experiences together and are influential for health and wellbeing outcomes in later life stages.

Thus, the present study utilized the life course stress process perspective (Elder, 1998; Pearlin et al., 2005) and previous research (e.g., Wickrama, O’Neal, & Lorenz, 2018) to inform the hypothesized comprehensive life course model elucidating a dyadic health process for husbands and wives stemming from their FEH in mid-later years. Trajectories of FEH and depressive symptoms over 25 years capture long-term, intra-individual stability and change, which is consistent with the “long-view” focus of life course perspective (Elder, 1998). The results supported the hypothesized model; FEH trajectories influenced husbands’ and wives’ trajectories of depressive symptoms over the mid-later years, and these depressive symptoms’ trajectories were associated with their SMI in later years.

Consistent with the life course perspective (Elder, 1998), it appears that not only the severity (the level) of a stressful family context, but also adverse changes in family context, may drive both spouses’ depressive symptoms trajectories. These parallel trajectories are consistent with both emotional response (Pearlin, 1989) and material deprivation mechanisms (e.g., Evans et al., 2003), explicating how FEH is consequential for depressive symptoms. The present study investigated life course trajectories of FEH and depressive symptoms using repeated measures over 25 years. Although these long-term trajectories capture life course trends in FEH and depressive symptoms and their associations, they do not reveal short-term, or acute, changes in FEH and depressive symptoms. The results showed that FEH is largely shared between spouses, and it may drive adverse psychological and cognitive processes for both husbands and wives as a common fate factor.

Consistent with the “linked life” notion of the life course perspective (Elder, 1998), the dyadic process of depressive symptoms appears to be inter-locking and self-perpetuating. That is, the results showed that spouses’ levels of depressive symptoms are implicated in the subsequent changes in partners’ depressive symptoms. This process generates a within-couple, self-perpetuating adverse mental health process noting how individuals’ development both influences, and is influenced by, their partner. Such processes may be particularly strong in enduring marriages that are characterized by high levels of relationship salience, closeness, and shared activities (Gerstorf et al., 2009; Meegan & Berg, 2002).

Importantly, both the level and change in depressive symptoms were associated with husbands’ and wives’ SMI. It appears that change in depressive symptoms over 25 years contributed to later-life SMI after taking the level of depressive symptoms into account. Controlling for the level of depressive symptoms may have mitigated the possibility of reverse causation. It is possible that long-term increases in depressive symptoms cumulatively influenced SMI over time through the adverse impact of depressive symptoms on brain regions and their connections (Cook et al., 2017).

Although the temporal ordering of variables somewhat mitigated the possibility of reversed causation from SMI to depressive symptoms, a bi-directional effect between SMI and depressive symptoms is still possible. The present study was not able to investigate the bi-directional association between depressive symptoms and SMI or auto-regressive models of SMI because SMI was captured only in later life at a single occasion. Future studies should incorporate such bi-directional associations in life course dyadic investigations.

The multiple-group comparison of couples showed that depressive symptoms are more influential for older adults’ SMI in the context of a high-quality marital relationship. Similarly, regarding partner effects, it appears that a positive couple relationship context facilitates crossover influences between husbands and wives. These crossover influences were not found for couples with lower relationship quality. The presence of crossover influences only in a positive marital context may be attributed to the fact that, in later life, spouses’ everyday activities are concentrated around their relationship. Thus, despite the broad reputation that positive relationships have as protective agents that reduce individuals’ health risks; in the case of depressive symptoms and SMI, the findings provide evidence to the contrary, as the mental health symptoms of one partner were detrimental for their spouse’s SMI when partners were more closely connected. As previously noted, this observed moderation may also be attributed to a possible “ceiling effect” in low relationship quality group.

However, it is important to note that the partner effect between the change in depressive symptoms and SMI was significant for husbands’ depressive symptoms but not wives. This finding is consistent with the notion of gender asymmetry, which posits that when examining inter-individual influences, husbands are more influential than wives, and wives have more permeable boundaries given that they appear more responsive to husbands’ wellbeing than vice versa (Gerstorf et al., 2009).

There are limitations to the present study that should be noted. First, memory problems were assessed using subjective reports of everyday memory failures in later years. Although SMI is an important outcome for older adults, it is also important to examine objective memory decline as a long-term consequence of adult FEH. The second limitation relates to the temporal order of measurements. Because of data availability at specific measurement occasions, FEH and depressive symptoms were measured at overlapping time points (i.e., parallel trajectories). Thus, reverse causation or bidirectional effects are a possibility. Although FEH items capture concrete behaviors (e.g., borrowed money to pay bills), individuals with a high initial level of depressive symptoms may have reported more FEH due to their tendency to view the world more negatively. The third limitation relates to the generalizability of the results. The sample was comprised only of European-American individuals that lived in rural Iowa during the farm crisis of the 1980s. While the farm crisis provided the opportunity to research relatively widespread FEH, future studies testing similar models with a more diverse population are needed. For instance, future samples should include multiple ethnicities, greater variation in the length of marriage, and other geographic locations. Finally, the present study limited its investigation to FEH, but other life course stressors warrant investigation, such as stressful family events and marital strain, as they may also have long-term cognitive effects.

These findings provide support for the value and necessity of national- and state-level policies aimed at improving families’ economic conditions. These results are also important for consideration by mental health professionals and counselors as future interventions should not overlook FEH as a potential cause or correlate of memory problems. Clinical implications also include the prevention of depressive symptoms and improving interpersonal processes as a way to protect from the negative consequences of FEH. Finally, the amplifying effect of relationship quality on the association between depressive symptoms and SMI is important to consider in such interventions.

Acknowledgments

Funding:

This research is currently supported by a grant from the National Institute on Aging (AG043599, Kandauda A. S. Wickrama, PI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. Support for earlier years of the study also came from multiple sources, including the 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, HD064687), 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.

Contributor Information

Kandauda A. S. Wickrama, Department of Human Development and Family Science, The University of Georgia, 107 Family Science Center I (House A), Athens, GA 30602

Catherine Walker O’Neal, Department of Human Development and Family Science, The University of Georgia, 107 Family Science Center II (House D), Athens, GA 30602

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