Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Mar 1.
Published in final edited form as: J Aging Health. 2020 Nov 23;33(3-4):217–226. doi: 10.1177/0898264320972547

Are Positive Childhood Experiences Linked to Better Cognitive Functioning in Later Life?: Examining the Role of Life Course Pathways

Haena Lee 1, Markus Schafer 2
PMCID: PMC7906946  NIHMSID: NIHMS1660386  PMID: 33228449

Abstract

Objectives:

We examine whether childhood family well-being is associated with cognitive functioning and to what extent the association between the family context and cognitive functioning is explained by adulthood resources.

Methods:

Data are drawn from the National Social Life, Health, and Aging Project Wave 3 (2015/2016; N = 3361). We measured cognitive functioning using the Montreal Cognitive Assessment. Childhood family factors included family-life happiness, family structure, and family socioeconomic status. Education, social connectedness, self-mastery, and self-rated health were assessed as adulthood resources.

Results:

Respondents who grew up in a happy family had significantly higher levels of cognitive functioning. The formal mediation test suggests that a happy family life during childhood has a positive association with later cognition, in part, by enhancing self-mastery in adulthood.

Discussion:

Our findings provide evidence that positive childhood experiences are linked to later life cognition. The sense of control people have over their life circumstances is one potential pathway explaining this association.

Keywords: cumulative advantage, childhood happiness, cognitive function, self-mastery, National Social Life, Health and Aging Project

Introduction

Positive family environments have lifelong implications for people’s well-being. Considerable work has documented that parental support, intact family structure, and financial stability during childhood predict better health in adulthood (Gilman et al., 2003; Larson & Halfon, 2013; Meadows et al., 2006). In recent years, researchers have documented that this protective effect lasts long into adulthood. For example, adults growing up with close family relationships and in two-parent families tend to have better physical health, lower depressive symptoms, and fewer chronic conditions than those who did not (Andersson, 2016; Chen & Harris, 2019; Chopik & Edelstein, 2019; Ferraro et al., 2016; O’Rand & Hamil-Luker, 2005). While the role of positive childhood experiences on people’s overall health is well documented, much less is known about their impact on cognitive health at older ages. Here, we build on prior work to examine how positive childhood experiences protect later cognitive functioning.

Research on late life cognitive function has expanded in recent years, as more individuals face a higher lifetime risk of poor cognitive function and neurodegenerative diseases with greater longevity. Alzheimer’s disease, for example, is already the sixth leading cause of death in the United States (Alzheimer’s Association, 2016), and the social and economic burden of dementia has increased from $18 billion in 1993 to $200 billion in 2010 (Hurd et al., 2013). Prior work has identified adult socioeconomic status, social relationships, and health conditions as key risk factors for cognitive function in later life (Dale et al., 2018). An increasing number of studies have also linked early life factors to later cognition, but they have primarily focused on childhood SES and health as key indicators (Luo & Waite, 2005; Lyu & Burr, 2016; Marden et al., 2017; Zhang et al., 2016). The current study extends prior work to investigate the extent to which childhood family social environment, together with childhood socioeconomic status (SES) and health, contribute to healthy cognitive function in later life.

Background

Childhood Experiences, Adult Resources, and Cognitive Health

Cognitive function rapidly develops in the early years, rises to a peak at maturity, and gradually declines with age (Kuh, 2007). Given this age-related pattern of brain growth and development, researchers and policy makers have applied the life course perspective to inform intervention efforts. Life course theory posits that early life experiences may contribute to cognitive health through direct and indirect processes.

Childhood experiences and exposures may directly influence later cognitive function, as early childhood circumstances become “embodied” to affect later health (Hertzman, 1999). Most research linking early life experiences to later cognition has focused on how experiences of low financial well-being during childhood lower later cognitive function. Using the Health and Retirement Study, Luo and Waite (2005) showed that older adults who grew up with parents with less than 8 years of school have lower levels of cognitive function than those who did not. Nutritional deprivation and poverty, likewise, appear to have negative long-term effects (Barnes et al., 2012; Hale, 2017). Research also shows that early experiences shape the progression of cognitive decline; cognitive function across multiple domains drops most precipitously over a 12-year period for those growing up in disadvantaged homes (Lyu & Burr, 2016). The effect of childhood SES in these studies often remained significant irrespective of adult experiences, suggesting that growing up in a socioeconomically unstable family may exert long-term influences on cognitive health in later adulthood.

Although parental education and family economic resources are important inputs to health in adulthood, other features of the childhood family, overlapping with, yet distinct from household income and education, may play an important role. We are interested in two family factors, childhood family-life happiness and family structure, as indicators of early life family social well-being. As shown in Figure 1, we conceptualize that cognitive function at older ages is affected by childhood experiences, characterized by childhood family social and financial well-being (i.e., family-life happiness, family structure, and family SES) through adulthood resources.

Figure 1.

Figure 1.

Open in a new tab

Conceptual Framework: Childhood Experiences, Adulthood Resources, and Cognitive Function in Older Adults.

Several pathways link childhood family to late life cognitive function. Though these developmental pathways cannot be explicitly tested in this study, they form the basis of our initial hypothesis. First, research in neuroscience suggests that exposure to positive emotions such as happiness may increase the brain activity in the prefrontal cortex, the brain region responsible for memory function during childhood (Schwartz & Davidson, 1997). Early brain activity and structure are found to carry over into adult cognitive function (Wang et al., 2013), providing a potential mechanism through which family social well-being may have enduring effects on cognitive health. Second, family social well-being may influence brain and cognitive development by promoting ongoing opportunities for learning and social interactions (Rogoff, 1990). For example, responsive parenting, verbal openness, and emotional support in two-parent families promote mental stimulation and related process that are ideal for cognitive development (Carlson & Corcoran, 2001). Exposure to adult language and conversation with multiple parents can also benefit cognitive maturity through language and reasoning development (Lewis & Lamb, 2003; Romeo et al., 2018). Other research has shown that children who are raised with lack of parental care tend to have enlarged amygdale in adulthood (Lyons-Ruth et al., 2016). Despite ready evidence of family social well-being on early brain development, it is unknown whether such benefits last into later adulthood. Given the extensive line of literature on positive family experiences and cognitive function, we expect to find the following:

Hypothesis 1: Individuals who grew up with higher levels of family social and financial well-being during childhood will have better cognitive function than those who did not.

Early life experiences and exposures can also indirectly impact cognitive health through adult resource and opportunity pathways (Ferraro & Schafer, 2017; Ferraro & Shippee, 2009). Ferraro et al. (2016) argue that “early disadvantage increases the likelihood of exposure to later risks—but resources help individuals respond to those negative exposures” (p. 109). Education is one such adult resource with important implications for lifelong cognitive health. Indeed, the cognitive reserve’s perspective (Stern, 2009) suggests that education builds more efficient brain networks and helps people adapt to brain pathology and retain cognitive function. Childhood conditions correlate with education, and individuals from high SES and stable families (two-parent families) tend to have higher adult education than those from disadvantaged backgrounds (Luo & Waite, 2005; Monserud & Elder, 2011). Previous research also suggests that supportive parenting and a nurturing family environment promote educational attainment (Melby et al., 2008; Robertson & Reynolds, 2010). Education provides access to mentally demanding occupations, and this stimulation can promote neuronal resources that preserve cognitive function (Fisher et al., 2014).

Early life experiences may also contribute to the development of multiple resources throughout the life course, especially psychosocial assets that promote cognitive health. Growing up in a nurturing, supportive family environment provides strong social attachments and fosters emotional competencies (Batcho et al., 2011; Kaufman, 2019), the factors which can help become and remain socially connected as they age. Social connectedness is a critical resource for cognitive aging because relationships impose cognitive demands and promote mental stimulation. Indeed, older adults with large networks, diverse social ties, and greater social contacts and participation have better cognitive function than those who are socially disconnected (Kelly et al., 2017; Lee & Ang, 2019). Positive childhood experiences may also promote sense of meaning in their lives and strengthen the ability to manage goals, behaviors, and problems (Biglan et al., 2012). Sense of control (self-mastery), the belief that one can master, control, and shape their own life, for example, is found to confer cognitive benefits by helping individuals interfere with reaching goals and cope with stress and strain (Soederberg Miller & Lachman, 2000). Good physical health is yet another adult resource that protects cognitive function (Dale et al., 2018), and early life experiences are known to influence health through a variety of mechanisms, including educational attainment, social connectedness, and self-mastery (Ferraro et al., 2016; Luo & Waite, 2005).

Many studies on this topic examine education, but very little research examines the possibility that social connectedness and sense of control, together with physical health status, mediate the relationship between childhood experiences and later cognitive health. To the extent that positive early life conditions increase the acquisition of key resources across the life course, we hypothesize the following:

Hypothesis 2: Positive family experiences during childhood will promote older adults’ cognitive function in part through their impact on adult education, social connectedness, sense of control, and physical health status.

Data and Methods

We used data from Wave 3 of the National Social Life, Health, and Aging Project (NSHAP; 2015/2016), a nationally representative longitudinal survey of US older adults. Initial face-to-face interviews occurred in 2005/2006 (including 3005 community-dwelling individuals aged 57–85 years), with subsequent waves fielded every 5 years (O’Muircheartaigh et al., 2014). NSHAP is, to our knowledge, one of the only population-based studies that include information on childhood and family social environment, social network characteristics, and cognitive functioning in older age. NSHAP first asked about childhood background in Wave 2’s leave-behind questionnaire (LBQ) and continued to collect this information when a refresher sample of baby boomers (born 1948–1965) entered the survey in 2015/2016. On average, 85% of respondents in Wave 2 and Wave 3 returned their LBQ. We used Wave 3 because it is the most recent round of data collection with the largest sample. This maximizes statistical power and minimizes the potential attrition problems for individuals with cognitive impairment at earlier waves. Our analytic sample was restricted to age-eligible Wave 3 respondents (aged 50 or older) who completed the NSHAP-LBQ and cognitive functioning test and was further reduced due to item-response missing data ranging from .25% (race/ethnicity) to 20.36% (childhood living arrangement), yielding an analytic sample of 3361.

Measures

Dependent variable.

We measured cognitive function using an adapted Montreal Cognitive Assessment (MoCA). The MoCA was developed as a cognitive screening tool for use in clinical settings (Nasreddine et al., 2005). After extensive pilot testing, the NSHAP team developed an 18-item MoCA-SA for use by field workers during in-home, face-to-face interviews. The 18 items of MoCA-SA are as follows: (1) Orientation: date and month (2 points total); (2) Executive function: abstraction—similarity of watch and ruler (1 point)—modified Trails-b (1 point); (3) Visuospatial skills: clock—contour, numbers, and hands (3 points total); (4) Memory: 5-word delayed recall (5 points); (5) Attention: forward digits (1 point), backward digits (1 point), subtract 7 s (3 points); and (6) Language: naming rhinoceros (1 point), phonemic fluency—words with the letter “F” (1 point for >10 words in 60 seconds)—and sentence repetition (1 point) (e.g., see Kotwal et al., 2015; Shega et al., 2014 for more detailed information on this measure). Since NSHAP is a probability-based nonclinical research sample, MoCA-SA scores were then transformed into full MoCA scores using the following validated equation: MoCA = (1.14*MoCA-SA) + 6.83 (Lindau et al., 2018; Shega et al., 2014). MoCA scores range from 0–30, with higher scores indicating better cognition.

Childhood experiences.

Childhood measures included childhood family-life happiness, family structure, and family SES. First, following the previous work, (Batcho et al., 2011; Bellis et al., 2013), we assessed childhood family-life happiness by using an item in NSHAP-LBQ that asked respondents to report how much they agreed with the statement: “when I was growing up, my family life was always happy” with a 6-point scale ranging from 1 (disagree very much) to 6 (agree very much). Respondents who reported that their family life was always “very much happy” and “pretty much happy” were compared to those whose family life was “a little” happy or those who disagreed to the statement about their childhood family being happy. Preliminary analyses with the original coding of childhood family-life happiness did not alter our overall findings.

The childhood family structure (corresponding to age 6–16 years) was grouped into two categories: an intact family with both parents and a nonintact family (reference). We assessed childhood family SES using respondents’ report on both parents’ educational attainment and financial well-being of their childhood family. Father’s and mother ‘s education were asked in a question, “what is the highest grade of school your father/mother completed?” Possible answers included 1 (no formal education), 2 (1–11 grades), 3 (high school graduate), 4 (13–15 some college), 5 (college graduate), and 6 (postcollege). Following the previous work (Andersson, 2016), we took the maximum value of parents’ level of education when both were reported and used either value when one was missing. NSHAP assessed respondents’ financial well-being during childhood in a question, “during the time from about age 6 to 16 years, would you say your family was very well-off financially, fairly well-off, about average, not so well-off, or not well-off at all?” with a 5-point scale ranging from 1 (very well-off financially) to 5 (not well-off at all). We reverse-coded the answers so that higher scores indicate better financial well-being. To create continuity among the three family SES items, we standardized and averaged the responses (mean = 0, SD =1).

Adult resources.

Four types of adult resources were measured: education, social connectedness, self-mastery, and self-rated health. We measured education by assessing respondents’ educational attainment with four categories: less than high school (reference), high school diploma, some college, and college or more.

NSHAP assessed respondents’ social networks using a name generator technique in the network roster. Respondents were asked to list the people with whom they discussed important matters (up to five people) and, if a spouse or partner did not appear on this initial list, they were asked about their partnership status. Spouses or partners were then added to the network roster. Following the previous work (Cornwell & Waite, 2009; Schafer, 2018), we created a Social Connectedness Index by standardizing and averaging the following items (alpha = .67): (1) core network size; (2) network range, which indicates the extent to which the respondent is connected to a distinctive type of individual (e.g., spouse, friend, and co-worker); (3) proportion of core network members who live in the household (reverse-scored); (4) frequency of contact with network members which indicates an individual’s exposure to his network members; (5) number of friends; (6) frequency of volunteering; (7) frequency of attending meetings of an organized group; and (8) frequency of interacting with friends and family. This measure of social connectedness has been validated in previous work (Cornwell & Waite, 2009; Schafer, 2018).

The self-mastery scale used in this study is a modified version of Cohen’s 4-item Perceived Stress Scale designed to capture individual appraisals of life events as controllable and overwhelming. Following the previous work (Karraker, 2014; Shiovitz-Ezra et al., 2009), self-mastery was measured using four items that asked respondents about, in the past week, (1) whether they were unable to control important things, (2) whether they felt difficulties are piling up so they could not overcome them, (3) whether they felt confident about their ability to handle personal problems, and (4) whether they felt things were going their way, using a 4-point Likert scale which ranged from 1 (rarely) to 4 (most of the time). The responses were reverse-coded and averaged (alpha = .60). This measure of self-mastery has been validated in previous work (Karraker, 2014; Shiovitz-Ezra et al., 2009).

Last, self-rated physical health was assessed using the question of “would you say your health is excellent, very good, good, fair, or poor?” Respondents who reported that their health is “excellent,” “very good,” and “good” were (=1) compared to those who reported “fair” or “poor” health (=0).

Covariates.

All models included demographic covariates including age in years, gender (1 = female), and race/ethnicity in four categories, non-Hispanic white (reference), non-Hispanic Black, Hispanic, and other. Following the previous work (Lee, 2019; Luo & Waite, 2005; Zhang et al., 2016), we also controlled for childhood health, as it is closely related to later cognition, using the question: “from age 6 to 16 years, would you say that your health during that time was excellent, very good, good, fair, or poor?” Respondents who reported that they had “fair” or “poor” health were compared to those whose health was “excellent,” “very good,” or “good.”

Analytic Strategy

We fitted OLS linear regression models for MoCA and carried out the analyses in two steps. First, we conducted a series of nested linear regression models to examine the role of childhood experiences and adult resources in explaining cognitive functioning at older ages (Table 2). Model 1 in Table 2 includes childhood experiences and covariates. Model 2 included educational attainment to assess the extent to which the association between childhood experiences and cognitive functioning observed in Model 1 is attenuated once education in included. In Model 3, we added psychosocial resources into the model to test whether the association between childhood experiences and cognitive functioning is further reduced by adult social connectedness and self-mastery net of education and other covariates. Last, in Model 4, we added self-rated health into the model to test whether the relationship between childhood experiences and cognitive function persists net of adult education, psychosocial resources, and health.

Table 2.

Regression Estimates of the Associations of Childhood Experiences, Adult Education and Psychosocial Resources, and Cognitive Function, NSHAP, 2015/2016 (N = 3361).

Model 1 Model 2 Model 3 Model 4
Childhood experiences
Family-life happiness .29 (.12)* .25 (.11)* .09 (.11) .07 (.11)
Family structure (two-parent household) .30 (.18)† .13 (.17) .14 (.17) .13 (.17)
Family SES 2.56 (.38)*** .13 (.39) .09 (.39) .05 (.40)
Adulthood resources
Educational attainment (less than high school=ref.)
 High school diploma 2.49 (.28)*** 2.33 (.28)*** 2.23 (.29)***
 Some college 3.57 (.28)*** 3.33 (.28)*** 3.20 (.28)***
 College or more 4.85 (.29)*** 4.45 (.29)*** 4.28 (.29)***
Social connectedness .57 (.14)*** .48 (.14)***
Self-mastery .70 (.10)*** .63 (.10)***
Self-rated physical health −.73 (.14)***
Covariates
Age −.10 (.01)*** −.10 (.01)*** −.10 (.01)*** −.10 (.01)***
Female .18 (.13) .27 (.12)* .20 (.12) .19 (.12)
Race/ethnicity (NH-white = ref.)
 NH-Black −2.76 (.20)*** −2.63 (.15)*** −2.59 (.14)*** −2.55 (.14)***
 Hispanic −3.20 (.39)*** −2.33 (.29)*** −2.20 (.28)*** −2.15 (.28)***
 Other −1.77 (.38)*** −1.93 (.34)*** −1.74 (.31)*** −1.80 (.31)***
Poor childhood health −1.49 (.35)*** −1.12 (.31)*** −.94 (.31)** −.85 (.30)**
Constant 29.53 (.55)*** 27.21 (.47)*** 25.26 (.60)*** 25.82 (.59)***
N 3361 3361 3361 3361
R-squared .21 .32 .34 .34
Open in a new tab

Note. NH = non-Hispanic, ref. = reference.

†

p < .10;

*

p < .05;

**

p < .01;

***

p < .001.

Values in parentheses are standard errors.

Preliminary analyses included employment statuses (e.g., working, retired, and unemployed) as part of adult achievements, but it did not affect the overall results nor explained the association between childhood experiences and cognition (results from the mediation analysis are not shown, available upon request), so we excluded it from the final model. In addition, evidence indicates that the effects of childhood experiences on cognitive function may differ by race (Zhang et al., 2016), but preliminary analyses found no significant interaction terms between childhood experience variables and race/ethnicity, so we did not stratify the final models by race/ethnicity.

Next, we conducted formal mediation tests using the Karlson-Holm-Breen (KHB) method (Breen et al., 2013; Kohler et al., 2011). These results are presented in Table 3. Assessing the relative importance of the direct and indirect pathways from childhood experiences to later life cognitive functioning requires an appropriate decomposition method. The KHB method estimates the percentage of the indirect effect that is explained by each of the possible mediating variables, allowing us to determine the relative magnitude of the proposed pathways (Kohler et al., 2011). The KHB method has been widely used in previous studies of mediation analysis of cognitive functioning (Saenz et al., 2018; Saenz & Wong, 2016). All models were adjusted using survey weights in Stata 16.

Table 3.

Decomposition of the Effect of Childhood Family-life happiness on Cognitive Function, NSHAP, 2015/2016 (N = 3366).

OR p %
Decomposition of total effect of childhood family-life happiness on cognitive function
 Total effect .29 *
 Direct effect .07 — 24.21
 Indirect effect .22 *** 75.79
Decomposition of indirect effect by educational, social, and psychological pathways
 Education pathway — — 13.56
 Social connectedness pathway — — 22.89
 Self-mastery pathway — — 43.68
 Self-rated health pathway — — 19.86
Open in a new tab

Note. p = p value.

*

p < .05;

**

p < .01;

***

p < .001.

Results

Table 1 presents sample characteristics of key variables. The average score of MoCA for cognitive functioning was 24.25 (SD = 3.72) on a 30-point scale. A sizable amount of respondents reported they had a very happy or happy family life during childhood (62.68%). The majority of respondents grew up with two parents (83.78%). Almost 70% of respondents attended some college or more. Sense of control was positively skewed, with the vast majority of respondents indicating that they had high mastery over their circumstances (mean = 3.33, SD = .65, range = 1–4). Additional analyses show that respondents who reported they had a very happy or happy family tend to report they had higher SES and lived with two parents during childhood. Childhood family SES was also related to family structure; intact families had a .39 mean value for childhood SES, while nonintact families had a .32 mean value for childhood SES. Respondents who reported a very happy or happy family during childhood also tend to report higher educational attainment, social connectedness, self-mastery, and better health than those who grew up in less happy homes. Demographically, most respondents were white (78.88%) and 54% were women.

Table 1.

Weighted Sample Characteristics, National Social Life, Health and Aging Project, 2015/2016 (N = 3361).

Variable Mean/proportion
Cognitive function 24.26 (3.72)
Childhood family experiences
 Family-life happiness (growing up in a happy family = 1) 62.79
 Family structure (two-parent household = 1) 83.71
 Family SES .39 (.17)
Adulthood resources
 Educational attainment
  Less than high school 9.25
  High school diploma 22.58
  Some college 36.87
  College or more 31.30
 Social connectedness .01 (.47)
 Self-mastery 3.33 (.65)
 Self-rated physical health (average, very good, or excellent = 1) 78.82
Covariates
 Age 63.83 (9.60)
 Female 54.18
 Race/ethnicity (%)
  Non-Hispanic white 78.81
  Non-Hispanic Black 9.68
  Hispanic 7.14
  Other 4.37
Poor childhood health 5.46
Open in a new tab

Note. Values in parentheses are standard deviations of means.

Next, we turn to results from a series of nested models that test our hypotheses. Consistent with Hypothesis 1, we found in Model 1 that high levels of family social and financial well-being were positively and significantly associated with cognitive functioning. Specifically, those who grew up in a happy family had cognitive scores .29 units higher than those in less happy homes (p < .05). Similarly, those from high SES families had cognitive scores 2.56 units higher than those from low SES families (p < .001). This corresponds to about 9% change in the outcome variable. We also found that, though marginally significant, growing up in an intact family with two parents is also positively associated with better cognitive functioning (b = .30, p < .10).

When we introduced educational attainment in Model 2, we found the association between childhood family-life happiness and cognitive functioning, which was attenuated by 13% but remained statistically significant (b = .25, p < .05). However, the association of childhood SES with cognitive functioning became statistically insignificant after adding education in Model 2, suggesting, as others have found, that accumulation of socioeconomic resources in adulthood may compensate for the early financial disadvantage. Consistent with previous research, education was a strong predictor of later cognition, with increases in educational attainment associated with better cognitive functioning.

Finally, the coefficient for childhood family-life happiness was substantially attenuated and became insignificant when we added social connectedness and self-mastery (Model 3). Both social connectedness and self-mastery were strongly and positively associated with cognitive functioning (b = .57 and .70, respectively, both p < .001). When self-rated health was introduced into Model 4, the coefficient for childhood family-life happiness barely changed, from .09 to .07 (n.s.).

Overall, Models 2–4 suggest that the quality of family life during childhood may be an important predictor for cognitive functioning in old age, but works in large part through its influence on the achievement of adulthood resources, especially psychosocial resources. In Table 3, we conducted a formal mediation test to decompose the relative importance of the direct and indirect pathways between childhood family-life happiness and cognitive functioning (upper panel) and to estimate which pathways (adult education, social connectedness, self-mastery, and self-rated health) play the greatest role in mediating the association (lower panel). Results from the upper panel show that the majority of the total effect is, in fact, explained by the indirect, rather than direct, effect of childhood family-life happiness on cognitive functioning. Almost 76% of the association is explained by adult resources. And the lower panel confirms that, of those indirect effects, 44% were explained by the self-mastery pathway. The social connectedness pathway explains 23% of the indirect effect, while the rest is explained by the educational and health pathways.

Discussion

The current study had two goals. One was to examine whether childhood family social environment is, along with childhood SES, associated with later cognitive function. The second was to investigate how adult education, psychosocial resources, and physical health explain the association between childhood experiences and later cognition. We hypothesized that growing up in a socially and financially enriched family environment is positively associated with cognitive health in later life and that these childhood advantages work, in part, through their effects on adult resources.

We found partial evidence supporting our hypotheses. First, childhood family-life happiness (i.e., growing up in a happy family) and SES significantly predicted older adult cognitive functioning. Second, part of the effect of childhood family SES on cognitive functioning occurred through education. Third, the relationship between childhood family-life happiness and cognitive functioning was explained, in large part, by adult psychosocial resource in the form of self-mastery net of demographic covariates, education, and adult health.

Our results suggest that growing up in a nurturing, cherished family environment has the potential to cultivate a sense of control over life and build resilience which could provide an important pathway to successful cognitive aging. This is yet another reminder that family is one of the most important social contexts that affect lifelong health (McEwen & McEwen, 2017). The family environment reflects not only material but also emotional and psychosocial resources of caregivers and the quality of one’s learning environment. Although parental education and family income are closely related to adult health, our findings reveal that other features of the childhood family may also play an important role in shaping later cognitive function. The finding that childhood family-life happiness associated with cognitive function net of childhood SES and adult education suggests that differences in later cognitive function are not merely the product of socioeconomic origins.

To our understanding, the current study is one of the first to examine the potential mechanisms through which positive childhood experiences and growing up in a happy family, in particular, are associated with late life cognitive function in a nationally representative sample. Mediation analysis suggests that growing up in a nurturing family environment has the potential to cultivate a sense of control over life and build resilience which could provide an important pathway to successful cognitive aging. Although prior studies find that childhood consequences for cognitive function operate through adult pathways, existing research focuses on adult socioeconomic position and health conditions as potential mechanisms (Luo & Waite, 2005; Lyu & Burr, 2016; Zhang et al., 2016). Studies investigating the mediating role of psychosocial resources are limited mainly to physical health outcomes (Ferraro et al., 2016; Morton et al., 2012). Our findings provide novel insights into how family social environment improves and boosts resilience that may well protect cognitive function later.

The present study has several strengths, including the assessment of both family social and financial well-being as early life determinants of cognitive functioning, the exploration of adult resources as life course pathways to differences in cognitive performance in later life, and the application of a formal mediation test to determine which pathways play the greatest role in explaining the long-term effect of childhood family conditions on later cognition.

Despite these strengths, the study has several limitations. First, the measures of childhood circumstances are retrospective and, therefore, may be subject to recall bias. Such bias could conceivably lead to under or overestimates in our regression analysis. Should people with memory problems impute undue negativity to their childhood home, the association between family-life happiness and cognition would be upwardly biased. On the other hand, if impaired memory tends to produce excessively rosy recollections, associations would be underestimated. Ultimately, it is unknown how poor cognitive health affects people of how they remember their childhood home, so the direction of the bias is uncertain. Fortunately, Leyhe et al. (2009) found that older adults in population-based social survey seem to recall childhood circumstances quite well and propose that early life autobiographical memories may not be susceptible to declines in recall. Still, the possibility of recall bias in retrospective variables suggests that caution is needed when interpreting results. In addition, the generalizability of our findings may be limited to those cognitively intact who still live independently in their communities. NSHAP did not assess cognitive function for those who are institutionalized at the time of survey and who had proxy-interviewed, and their cognitive function may be at greater risk of being poor cognitive function. It is also possible that those who were not healthy did not survive to Wave 3. Respondents of Waves 1and 2 who survived to the Wave 3 interview may, therefore, be healthier and more cognitively intact than the average older adult. This selection bias might have resulted in a relative underrepresentation of respondents who developed poor cognitive functioning, potentially leading to overly conservative estimates. Again, careful interpretation of results is warranted.

Furthermore, this study was cross-sectional and, therefore, unable to explore whether childhood family-life happiness contributes to changes in cognitive function over time. Some studies have found positive parent-child relationships benefit health over time (Andersson, 2016; Chen & Harris, 2019; Chopik & Edelstein, 2019). However, the underlying mechanisms that produce cumulative advantage processes for cognitive health during late adulthood are not yet well understood. We call for research testing causal pathways through which positive childhood experiences contribute to the progression of cognitive decline.

Last, the childhood family-life happiness measure used in this study focuses primarily on whether respondents had a happy family life and may not identify all key characteristics of a happy childhood. Positive childhood memories can occur across multiple contexts of individuals’ lives, including home, extended family, school, and community. Warm and cohesive relationships with grandparents, peers, and teachers and a sense of belonging to community could all positively contribute to childhood memories and experiences. In addition, interactions with older siblings may promote language and cognitive development for younger children and help create a cognitively stimulating home environment (Brody, 2004). However, information about birth order, number of siblings, and relationships with extended family members and other people in schools and neighborhoods is not available in NSHAP. Future work should determine if adult health is linked to these other dimensions of happy childhoods.

This study highlights the importance of accounting for childhood family life when examining how early life experiences predict late life cognitive health. We provide evidence that growing up in a socially enriched, nurturing family environment has a positive association with cognitive function, even decades later, in part, through developing mastery, a sense of control over goals, behaviors, and problems in one’s life. Research on resilience also suggests that mastery could be an important resource for those growing up with challenging circumstances by helping them recover, sustain a sense of purpose, and thrive (Infurna et al., 2015; Masten et al., 1990). Policies and programs encouraging the development of mastery during childhood and beyond may help individuals overcome early adversity and maintain healthy cognition over the life course.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Multidisciplinary Research Training in Gerontology at the University of Southern California [T32AG000037] and from the NSHAP Fellows Program at NORC at the University of Chicago. The National Social Life, Health, and Aging Project is supported by the National Institute on Aging and the National Institutes of Health [R01AG043538; R01AG048511]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Author’s Note

An earlier version of this study was presented at The 2019 Gerontological Society of America Annual Scientific Meeting in Austin, Texas.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

  1. Alzheimer’s Association. (2016). 2016 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 12(4), 459–509. [DOI] [PubMed] [Google Scholar]
  2. Andersson MA (2016). Chronic disease at midlife: Do parent-child bonds modify the effect of childhood SES? Journal of Health and Social Behavior, 57(3), 373–389. doi: 10.1177/0022146516661596 [DOI] [PubMed] [Google Scholar]
  3. Barnes LL, Wilson RS, Everson-Rose SA, Hayward MD, Evans DA, & Mendes de Leon CF (2012). Effects of early-life adversity on cognitive decline in older African Americans and whites. Neurology, 79(24), 2321–2327. doi: 10.1212/WNL.0b013e318278b607 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Batcho KI, Nave AM, & DaRin ML (2011). A retrospective survey of childhood experiences. Journal of Happiness Studies, 12(4), 531–545. doi: 10.1007/s10902-010-9213-y [DOI] [Google Scholar]
  5. Bellis MA, Hughes K, Jones A, Perkins C, & McHale P (2013). Childhood happiness and violence: A retrospective study of their impacts on adult well-being. BMJ Open, 3(9), Article e003427. doi: 10.1136/bmjopen-2013-003427 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Biglan A, Flay BR, Embry DD, & Sandler IN (2012). The critical role of nurturing environments for promoting human wellbeing. American Psychologist, 67(4), 257–271. doi: 10.1037/a0026796 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Breen R, Karlson KB, & Holm A (2013). Total, direct, and indirect effects in logit and probit models. Sociological Methods & Research, 42(2), 164–191. doi: 10.1177/0049124113494572 [DOI] [Google Scholar]
  8. Brody GH (2004). Siblings’ direct and indirect contributions to child development. Current Directions in Psychological Science, 13(3), 124–126. doi: 10.1111/j.0963-7214.2004.00289.x [DOI] [Google Scholar]
  9. Carlson MJ, & Corcoran ME (2001). Family structure and children’s behavioral and cognitive outcomes. Journal of Marriage and Family, 63(3), 779–792. doi: 10.1111/j.1741-3737.2001.00779.x [DOI] [Google Scholar]
  10. Chen P, & Harris KM (2019). Association of positive family relationships with mental health trajectories from adolescence to midlife. JAMA Pediatrics, 173(12), e193336. doi: 10.1001/jamapediatrics.2019.3336 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chopik WJ, & Edelstein RS (2019). Retrospective memories of parental care and health from mid- to late life. Health Psychology, 38(1), 84–93. doi: 10.1037/hea0000694 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cornwell EY, & Waite LJ (2009). Social disconnectedness, perceived isolation, and health among older adults. Journal of Health and Social Behavior, 50(1), 31–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dale W, Kotwal AA, Shega JW, Schumm LP, Kern DW, Pinto JM, Pudelek KM, Waite LJ, & McClintock MK (2018). Cognitive function and its risk factors among older US adults living at home. Alzheimer Disease and Associated Disorders, 32(3), 207. doi: 10.1097/WAD.0000000000000241 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Ferraro KF, & Schafer MH (2017). Visions of the life course: Risks, resources, and vulnerability. Research in Human Development, 14(1), 88–93. doi: 10.1080/15427609.2016.1268895 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Ferraro KF, Schafer MH, & Wilkinson LR (2016). Childhood disadvantage and health problems in middle and later life: Early imprints on physical health? American Sociological Review, 81(1), 107–133. doi: 10.1177/0003122415619617 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Ferraro KF, & Shippee TP (2009). Aging and cumulative inequality: How does inequality get under the skin? The Gerontologist, 49(3), 333–343. doi: 10.1093/geront/gnp034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fisher GG, Stachowski A, Infurna FJ, Faul JD, Grosch J, & Tetrick LE (2014). Mental work demands, retirement, and longitudinal trajectories of cognitive functioning. Journal of Occupational Health Psychology, 19(2), 231–242. doi: 10.1037/a0035724 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gilman SE, Kawachi I, Fitzmaurice GM, & Buka SL (2003). Socio-economic status, family disruption and residential stability in childhood: Relation to onset, recurrence and remission of major depression. Psychological Medicine, 33(8), 1341–1355. doi: 10.1017/S0033291703008377 [DOI] [PubMed] [Google Scholar]
  19. Hale JM (2017). Cognitive disparities: The impact of the great depression and cumulative inequality on later-life cognitive function. Demography 54(6), 2125–2158. doi: 10.1007/s13524-017-0629-4 [DOI] [PubMed] [Google Scholar]
  20. Hertzman C (1999). The biological embedding of early experience and its effects on health in adulthood. Annals of the New York Academy of Sciences, 896(1), 85–95. [DOI] [PubMed] [Google Scholar]
  21. Hurd MD, Martorell P, Delavande A, Mullen KJ, & Langa KM (2013). Monetary costs of dementia in the United States. New England Journal of Medicine, 368(14), 1326–1334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Infurna FJ, Rivers CT, Reich J, & Zautra AJ (2015). Childhood trauma and personal mastery: Their influence on emotional reactivity to everyday events in a community sample of middle-aged adults. Plos One, 10(4), Article e0121840. doi: 10.1371/journal.pone.0121840 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kaufman M (2019). Redefining success in America: A new theory of happiness and human development. University of Chicago Press. [Google Scholar]
  24. Karraker A (2014). “Feeling poor” perceived economic position and environmental mastery among older Americans. Journal of Aging and Health, 26(3), 474–494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kelly ME, Duff H, Kelly S, McHugh Power JE, Brennan S, Lawlor BA, & Loughrey DG (2017). The impact of social activities, social networks, social support and social relationships on the cognitive functioning of healthy older adults: A systematic review. Systematic Reviews, 6, 259. doi: 10.1186/s13643-017-0632-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kohler U, Karlson KB, & Holm A (2011). Comparing coefficients of nested nonlinear probability models. The Stata Journal: Promoting Communications on Statistics and Stata, 11 (3), 420–438. doi: 10.1177/1536867X1101100306 [DOI] [Google Scholar]
  27. Kotwal AA, Schumm P, Kern DW, McClintock MK, Waite LJ, Shega JW, Huisingh-Scheetz MJ, & Dale W (2015). Evaluation of a brief survey instrument for assessing subtle differences in cognitive function among older adults. Alzheimer Disease and Associated Disorders, 29(4), 317–324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kuh D (2007). A life course approach to healthy aging, frailty, and capability. The Journals of Gerontology: Series A, 62(7), 717–721. doi: 10.1093/gerona/62.7.717 [DOI] [PubMed] [Google Scholar]
  29. Larson K, & Halfon N (2013). Parental divorce and adult longevity. International Journal of Public Health, 58(1), 89–97. doi: 10.1007/s00038-012-0373-x [DOI] [PubMed] [Google Scholar]
  30. Lee H (2019). A life course approach to total tooth loss: Testing the sensitive period, accumulation, and social mobility models in the health and retirement study. Community Dentistry and Oral Epidemiology, 47(4), 333–339. doi: 10.1111/cdoe.12463 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lee H, & Ang S (2019). Productive activities and risk of cognitive impairment and depression: Does the association vary by gender? Sociological Perspectives, 63, 608–629. doi: 10.1177/0731121419892622 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Lewis C, & Lamb ME (2003). Fathers’ influences on children’s development: The evidence from two-parent families. European Journal of Psychology of Education, 18(2), 211–228. doi: 10.1007/BF03173485 [DOI] [Google Scholar]
  33. Leyhe T, Müller S, Milian M, Eschweiler GW, & Saur R (2009). Impairment of episodic and semantic autobiographical memory in patients with mild cognitive impairment and early Alzheimer’s disease. Neuropsychologia, 47(12), 2464–2469. doi: 10.1016/j.neuropsychologia.2009.04.018 [DOI] [PubMed] [Google Scholar]
  34. Lindau ST, Dale W, Feldmeth G, Gavrilova N, Langa KM, Makelarski JA, & Wroblewski K (2018). Sexuality and cognitive status: A U.S. nationally representative study of home-dwelling older adults. Journal of the American Geriatrics Society, 66(10), 1902–1910. doi: 10.1111/jgs.15511 [DOI] [PubMed] [Google Scholar]
  35. 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, 60(2), S93–S101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lyons-Ruth K, Pechtel P, Yoon SA, Anderson CM, & Teicher MH (2016). Disorganized attachment in infancy predicts greater amygdala volume in adulthood. Behavioural Brain Research, 308, 83–93. doi: 10.1016/j.bbr.2016.03.050 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lyu J, & Burr JA (2016). Socioeconomic status across the life course and cognitive function among older adults: An examination of the latency, pathways, and accumulation hypotheses. Journal of Aging and Health, 28(1), 40–67. doi: 10.1177/0898264315585504 [DOI] [PubMed] [Google Scholar]
  38. Marden JR, Tchetgen Tchetgen EJ, Kawachi I, & Glymour MM (2017). Contribution of socioeconomic status at 3 life-course periods to late-life memory function and decline: Early and late predictors of dementia risk. American Journal of Epidemiology, 186(7), 805–814. doi: 10.1093/aje/kwx155 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Masten AS, Best KM, & Garmezy N (1990). Resilience and development: Contributions from the study of children who overcome adversity. Development and Psychopathology, 2(4), 425–444. doi: 10.1017/S0954579400005812 [DOI] [Google Scholar]
  40. McEwen CA, & McEwen BS (2017). Social structure, adversity, toxic stress, and intergenerational poverty: An early childhood model. Annual Review of Sociology, 43(1), 445–472. doi: 10.1146/annurev-soc-060116-053252 [DOI] [Google Scholar]
  41. Meadows SO, Brown JS, & Elder GH (2006). Depressive symptoms, stress, and support: Gendered trajectories from adolescence to young adulthood. Journal of Youth and Adolescence, 35(1), 89–99. doi: 10.1007/s10964-005-9021-6 [DOI] [Google Scholar]
  42. Melby JN, Conger RD, Fang S-A, Wickrama KAS, & Conger KJ (2008). Adolescent family experiences and educational attainment during early adulthood. Developmental Psychology, 44(6), 1519–1536. doi: 10.1037/a0013352 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Monserud MA, & Elder GH (2011). Household structure and children’s educational attainment: A perspective on coresidence with grandparents. Journal of Marriage and Family, 73(5), 981–1000. doi: 10.1111/j.1741-3737.2011.00858.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Morton PM, Schafer MH, & Ferraro KF (2012). Does childhood misfortune increase cancer risk in adulthood? Journal of Aging and Health, 24(6), 948–984. doi: 10.1177/0898264312449184 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Nasreddine ZS, Phillips NA, Bédirian V. r., Charbonneau S, Whitehead V, Collin I, Cummings JL, & Chertkow H (2005). The montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53(4), 695–699. [DOI] [PubMed] [Google Scholar]
  46. O’Muircheartaigh C, English N, Pedlow S, & Kwok PK (2014). Sample design, sample augmentation, and estimation for Wave 2 of the NSHAP. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 69(Suppl 2), S15–S26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. O’Rand AM, & Hamil-Luker J (2005). Processes of cumulative adversity: Childhood disadvantage and increased risk of heart attack across the life course. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 60(Special Issue 2), S117–S124. doi: 10.1093/geronb/60.Special_Issue_2.S117 [DOI] [PubMed] [Google Scholar]
  48. Robertson DL, & Reynolds AJ (2010). Family profiles and educational attainment. Children and Youth Services Review, 32(8), 1077–1085. doi: 10.1016/j.childyouth.2009.10.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Rogoff B (1990). Apprenticeship in thinking: Cognitive development in social context. Oxford University Press. [Google Scholar]
  50. Romeo RR, Leonard JA, Robinson ST, West MR, Mackey AP, Rowe ML, & Gabrieli JDE (2018). Beyond the 30-million-word gap: Children’s conversational exposure is associated with language-related brain function. Psychological Science, 29(5), 700–710. doi: 10.1177/0956797617742725 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Saenz JL, Downer B, Garcia MA, & Wong R (2018). Cognition and context: Rural-urban differences in cognitive aging among older mexican adults. Journal of Aging and Health, 30(6), 965–986. doi: 10.1177/0898264317703560 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Saenz JL, & Wong R (2016). Educational gradients and pathways of disability onset among older Mexicans. Research on Aging, 38(3), 299–321. doi: 10.1177/0164027515620243 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Schafer MH (2018). (Where) is functional decline isolating? Disordered environments and the onset of disability. Journal of Health and Social Behavior, 59(1), 38–55. doi: 10.1177/0022146517748411 [DOI] [PubMed] [Google Scholar]
  54. Schwartz GE, & Davidson RJ (1997). Neuroanatomical correlates of happiness, sadness, and disgust. The American Journal of Psychiatry, 154(7), 926–933. [DOI] [PubMed] [Google Scholar]
  55. Shega JW, Sunkara PD, Kotwal A, Kern DWW, Henning SL, McClintock MK, Schumm P, Waite LJ, & Dale W (2014). Measuring cognition: The Chicago cognitive function measure in the national social life, health and aging project, wave 2. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 69(Suppl 2), S166–S176. doi: 10.1093/geronb/gbu106 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Shiovitz-Ezra S, Leitsch S, Graber J, & Karraker A (2009). Quality of life and psychological health indicators in the national social life, health, and aging project. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 64B(suppl 1), i30–i37. doi: 10.1093/geronb/gbn020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Soederberg Miller LM, & Lachman ME (2000). Cognitive performance and the role of control beliefs in midlife. Aging, Neuropsychology, and Cognition, 7(2), 69–85. doi: 10.1076/1382-5585(200006)7:2;1-U;FT069 [DOI] [Google Scholar]
  58. Stern Y (2009). Cognitive reserve. Neuropsychologia, 47(10), 2015–2028. doi: 10.1016/j.neuropsychologia.2009.03.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Wang L, Paul N, Stanton SJ, Greeson JM, & Smoski M (2013). Loss of sustained activity in the ventromedial prefrontal cortex in response to repeated stress in individuals with early-life emotional abuse: Implications for depression vulnerability. Frontiers in Psychology, 4, 320. doi: 10.3389/fpsyg.2013.00320 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Zhang Z, Hayward MD, & Yu Y-L (2016). Life course pathways to racial disparities in cognitive impairment among older Americans. Journal of Health and Social Behavior, 57(2), 184–199. doi: 10.1177/0022146516645925 [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES