Intersectionality and Cognitive Weathering: a Growth Curve Analysis of Mid- to Later-Life

Intersectionality and Cognitive Weathering: A Growth Curve Analysis of Mid- to Later-Life

Cognitive Decline

Jo Mhairi Hale

Max Planck Institute for Demographic Research

Abstract

Alzheimer‘s disease (AD) is the sixth leading cause of death in the United States, afflicting 8.8% of Whites, but a fifth of Latinx and almost a quarter of Black elders (Alzheimer‘s

Association 2010). Drawing from intersectionality theory and the weathering hypothesis, I hypothesize that multiple social disadvantages intersect to cause these AD disparities. Using the

Health and Retirement Study (1992-2014), I estimate growth curve models of cognitive function for elders aged 50-90 (n=30,224). Life-course socioeconomic status (SES), health behaviors, and comorbidities only partially explain racial/ethnic and sex cognitive disparities. Multiple social disadvantages accumulate to negatively affect later-life cognition. White women‘s, Blacks‘,

Latinas‘, and US-born Latinos‘ cognitive function declines significantly faster that White males‘, net of controls. Life-course SES interacts with race/ethnicity and sex to affect cognitive trajectories in complicated ways, such that the applicability of theories of cumulative disadvantage, persistent inequality, or age-as-leveler depends on the subpopulation.

KEY WORDS: Cognitive Function; Alzheimer‘s; Life Course; Weathering; Race/Ethnicity;

Health Disparities; United States

Intersectionality and Cognitive Weathering: A Growth Curve Analysis of Mid- to Later-Life

Cognitive Decline

Populating aging has provoked a spate of research on cognitive decline because of the growing share of the population at risk of late-life cognitive impairment, which is most commonly caused by late-onset Alzheimer‘s disease (AD) (Alzheimer‘s Association 2016). AD has significant racial/ethnic, sex, and socioeconomic status (SES) disparities. In the U.S., AD afflicts 5.4 million people (Alzheimer‘s Association 2016). Whereas it is 8.8% of White elders, it is a fifth of Latinx and almost a quarter of Black elders (Alzheimer‘s Association 2010). Though sex disparities are often attributed to female‘s longer life expectancy, research cannot yet explain why at younger ages females‘ prevalence is 16% to males‘ 11% (Fargo, Bleiler, and Mebane-

Sims 2009). In terms of SES, individuals with less than a high school education have significantly higher prevalence and incidence than those with at least a high school diploma, and those with lower occupational attainment have over twice the risk of those with high occupational attainment (Meng and D‘Arcy 2012; Stern 2012). However, challenges inherent in life course research (Kuh et al. 2003) mean gaps remain in understanding social predictors of

AD.

Little research has addressed cognition from an intersectional perspective (Hulko 2004 for an exception). Just as intersecting ascribed (e.g., race/ethnicity, sex) and achieved (e.g., education) characteristics predict a range of life chances, I propose positionality on multiple axes of social stratification affects risk of cognitive decline.1 Social positionality affects health outcomes through structuring access to resources (Phelan, Link, and Tehranifar 2010). But, racism and sexism shape lives beyond that, including subjecting People of Color (POC) and women to chronic stressors (Collins 2015; Das 2013; Phelan and Link 2015; Williams 2012).

Geronimus‘s weathering hypothesis proposes that socially-disadvantaged individuals experience accelerated aging due to carrying higher allostatic load (Geronimus et al. 2015; Geronimus and

Korenman 1992). ―Allostatic load‖ is the level of physiological dysregulation believed to be caused by chronic or recurring stressors (Juster, McEwen, and Lupien 2010; Seeman et al. 2010).

I extrapolate that those who occupy multiple disadvantageous positions will experience faster cognitive decline, which I term ―cognitive weathering.‖

I derive hypotheses from intersectionality theory and the weathering hypothesis to evaluate how social factors, including race/ethnicity, sex, life-course SES, behavior, and health factors, intersect to affect later-life cognitive function and rate of cognitive decline. In other words, to what degree are these racial/ethnic and sex disparities explained by socially-patterned exposures, such as the unequal distribution of education and poverty (Stern 2012; Zhang,

Hayward, and Yu 2016)? I posit that the residual disparities may be related to POC‘s and women‘s higher allostatic load. Using the Health and Retirement Study (1992-2014), I estimate growth curve models of cognitive function for elders aged 50-90 (n=30,224), specifically considering the cumulative disadvantage/inequality, persistent inequality, and age-as-leveler hypotheses. My findings provide clear evidence for the importance of intersectional approaches.

Background

Alzheimer’s prevalence by sex, race/ethnicity, and SES

Current research shows associations between AD and sex, race/ethnicity, and some SES, behavioral, and health factors. Two-thirds of those with an Alzheimer‘s diagnosis are females, partially because they have longer life expectancy than males, i.e. males die before they are diagnosed and/or longer-living males possess protective factors (Resnick and Driscoll 2007).

Yet, taken at the same age, females‘ prevalence is still 5% higher (Alzheimer‘s Association

2015). Recent research suggests female carriers of a genetic risk factor (ApoE-ɛ4) may be at

higher risk of AD, as well as experiencing greater symptom severity (Mazure and Swendsen

2016). Although medical researchers continue to search for biological determinants for these disparities (Altmann et al. 2014; Zhao et al. 2016), cognitive differences are likely also rooted in social structures. For example, women over age 65 average less education (negatively associated with AD) than men (Siegel 2011; U.S. Census Bureau 2016). I argue that, in addition to these socially-patterned disparities, the stress of living in a patriarchal, sexist society may cause cognitive weathering (Collins 2015). Among other stressors, women are more likely to be survivors of domestic and sexual violence, and they are more likely than their same- race/ethnicity male counterparts to face employment discrimination (England 2010; Krieger

2014; Pedulla and Thebaud 2015; Read and Gorman 2010).

Similarly, despite evidence that POC are underdiagnosed for memory diseases, U.S.

Blacks and Latinx still have higher rates of Alzheimer‘s diagnosis than Whites (1.5 to 2 times and 1.5 times, respectively) (Manton, Stallard, and Corder 1997; Steenland et al. 2015). Blacks and Latinx transition to cognitive impairment earlier than Whites and live with cognitive impairment longer (Reuser, Willekens, and Bonneux 2011). Researchers have published conflicting results on how much SES factors, health behaviors, and chronic illnesses mediate

Black/White cognitive disparities (Zhang et al. 2016). But, most prevalence studies do not take life-course SES into consideration, thus it is unclear to what extent these disparities are driven by racially-patterned socioeconomic factors (Fargo et al. 2009; Reskin 2012). However, many other health disparities exist even net of SES (Hayward et al. 2000), and I suggest the stress of living in a White supremacist society may contribute to cognitive disparities beyond socioeconomic inequalities.

Related to other social disadvantages, researchers find significant independent effects on cognitive function of educational attainment and occupation (Richards and Sacker 2003). There are mixed results as to whether early-life SES affects later-life cognitive function (Turrell et al.

2002). Other significant early socioeconomic indicators include number of siblings and childhood area of residence (suburban/rural/urban) (Moceri et al. 2000). Education is negatively associated with AD risk and appears to delay age of clinical manifestation (cf. Siegel 2011). In the U.S., those with less than twelve years of education had a 35% greater chance of developing dementia than those with more than fifteen years of education, and those with less than eight years of formal schooling had over twice the risk of those with higher education (Kukull et al.

2002; Stern 2012). Those with higher occupational attainment had about 44% lower risk of cognitive impairment (Valenzuela and Sachdev 2006). Lower-SES is also associated with higher allostatic load (Seeman et al. 2010).

Most of the above research on race/ethnicity, sex, and SES focuses on one factor, not controlling for each other or confounding variables such as health behaviors or chronic illnesses

(cf. Zhang et al. 2016 for an exception). The evidence is therefore inconclusive as to how life- course social factors mediate racial/ethnic/sex disparities in cognition. These gaps in the literature motivate my first hypothesis.

Hypothesis 1: Life-course socioeconomic status, behavioral factors, and chronic illnesses

partially mediate racial/ethnic/sex disparities in mid- to later-life cognitive function.

As intersectionality theory indicates, however, analyzing race/ethnicity, sex, and SES separately paints an incomplete picture. Social positions on multiple axes interact to influence outcomes.

Intersectionality theory

Intersectionality theory asserts that ascribed and achieved characteristics intersect to influence opportunity structures through exposure to a particular balance of benefit and risk related to human and cultural capital in the form of ―material, relational, and lifestyle‖ factors

(Warner and Brown 2011:1238). These characteristics operate in complicated ways,

―produc[ing] something unique and distinct from any one form of discrimination standing alone‖

(Eaton 1993:229). As such, research loses explanatory power if it focuses exclusively on one status characteristic (Collins 2015; Weber and Parra-Medina 2003), as is common in the

Alzheimer‘s research.

Investigating the ways in which sex, race/ethnicity, and SES interact to affect health outcomes is thus key to a comprehensive analysis of health disparities (Diez Roux 2012). For instance, studying the health effects of being classified as ―Black‖ in a racist society without regard to how race intersects with other status hierarchies, e.g., gender or SES, limits our understanding of how social formations influence health outcomes. Applications of intersectionality theory to large, quantitative datasets have been critiqued for being reductionist

(Bonilla-Silva 2015; Weber and Parra-Medina 2003). However, Crenshaw (2011), who coined the term, encourages researchers to explore intersectionality-driven hypotheses across diverse outcomes and methods.

Social stratification and health research suggest that racial/ethnic disparities are compounded by other disadvantages. Of Blacks and Whites with equivalent income, the former are less likely to have accumulated wealth or to have wealthy social networks on which to rely should they experience economic hardship (Conley 1999; Neckerman and Torche 2007). Poor

POC are more likely to live in higher-crime, chronically-poor neighborhoods than their equally

poor White counterparts (Sharkey 2013). Another example of how intersecting characteristics may increase stress exposure is that Women of Color, especially Blacks, Latinx, and Indigenous women, are at significantly higher risk of sexual assault (Center for Disease Control and

Prevention 2012). I hypothesize these intersecting stressors partially explain why, in the U.S., it is Latinas and Black women who are at the highest risk of developing AD (Reuser et al. 2011).

Contingent upon living to age 55, over half of Black women and almost two-thirds of Latinas will develop cognitive impairment in their lifetimes (Reuser et al. 2011).

Per intersectionality theory, I posit there is a ―cumulative disadvantage‖ not just over time (cf. Luo and Waite 2005; O‘Rand and Hamil-Luker 2005; Willson et al. 2007), but to occupying multiple disadvantageous positions, resulting in a multiplicative negative effect on cognitive function.

Hypothesis 2: Those who occupy multiple socially-disadvantaged positions (race/ethnicity/sex/life-course SES) will have multiplicatively lower later-life cognition than their more privileged counterparts, net of behavioral and health factors.

Whereas intersectionality theory does not explicitly predict how social statuses interact to affect rate of cognitive decline, the weathering hypothesis offers a clear prediction for how social location affects the aging process.

Allostatic Load and Weathering

There is a growing body of research on how exposure to race, sex, or SES-related stress may have long-term health consequences (Pearlin et al. 2005). To show how the concept of allostatic load and weathering are related to cumulative disadvantage, I offer a brief summary of allostatic load. Allostasis encompasses the processes through which bodies respond to stress to maintain homeostasis (physiological stability). If the body is subjected to chronic or repeated stressors, systems must work harder for homeostasis, carving out potentially pathological

response patterns and leading, in part, to functional and structural brain changes (Aggarwal et al.

2014; Juster et al. 2010). A body‘s level of dysregulation is measured as ―allostatic load.‖ In essence, allostatic load is the ―multisystem biological consequences of repeated attempts to adapt to stressors‖ (Diez Roux 2012). This stress-triggered cumulative wear-and-tear causes accelerated aging—―weathering‖ (McEwen 1998; McEwen and Gianaros 2010).

Geronimus (1996) developed the weathering hypothesis to explain her findings that

Black women, especially low-SES Black women, were more likely than White women or wealthier Black women to birth children of low birthweight (LBW). She hypothesized poor

Black women‘s LBW rates were more comparable to their older (by birth), more privileged counterparts‘ LBW rates because poor Black women were aging faster, that they were

―biologically‖ older, or ―weathered.‖ She concluded that allostatic load is the mechanism behind weathering (Geronimus 1996). Weathering, thus, can be understood as the accumulating

―biological manifestation of inequality‖ because stress-exposure is related to positionality on status hierarchies (Geronimus et al. 2015). For example, a recent study of the Chicago Health and Aging Project finds perceived stress is associated with cognitive function and rate of decline, net of demographics, smoking, and other health factors (Aggarwal et al. 2014). Weathering‘s underlying logic is clearly grounded in both intersectionality theory and cumulative disadvantage. This research motivates Hypothesis 3.

Hypothesis 3a: People of Color and White women will experience a faster rate of cognitive decline than White men, controlling for life-course SES, behaviors, and comorbidities.

From its conception, the weathering hypothesis understood the toll of allostatic load as a function of how intersecting characteristics (SES, race/ethnicity, and sex) differentially expose

individuals to stressors and protective factors that affect biological aging (Geronimus et al.

2015).

Hypothesis 3b: Race/ethnicity, sex, and life-course SES will intersect to affect cognitive trajectories such that those multiply disadvantaged will experience a faster rate of cognitive decline, net of behaviors and comorbidities.

Age-as-leveler, Persistent Inequality, and Cumulative Disadvantage

Three theories are often invoked to explain how disparities change across age trajectories.

The weathering hypothesis is consistent with the theory of ―cumulative disadvantage/inequality,‖ which holds that disadvantage accumulates over the life course, leading to greater disparities in later-life (Ferraro and Shippee 2009; O‘Rand and Hamil-Luker 2005). In contrast, the ―age-as- leveler‖ hypothesis suggests health disparities diminish as subpopulations experience similar senescence - age-related deterioration - that levels the playing field, so to speak (Brown et al.

2016). Research that finds a more consistent gap is evidence for ―persistent inequality‖ (Ferraro and Farmer 1996; Kelley-Moore and Ferraro 2004). For example, Karlamangla and colleagues

(2009) conclude Blacks reach a low-cognition threshold at earlier ages than Whites because they start declining from a lower cognitive score, not because they decline faster. Researchers have found evidence for all three of these hypotheses. Dupre (2007) argues that evidence for age-as- leveler is merely an artifact of selective mortality in aggregate data and concludes cumulative disadvantage operates at the individual level. Contradictory findings may be rooted in subgroup differences and variation among health outcomes, motivating the need for intersectional analyses that focus on specific illnesses (Brown, O‘Rand, and Adkins 2012).

In sum, I explore three primary questions: (1) To what degree do socially-patterned exposures (e.g., education, occupation, and health behaviors) mediate racial/ethnic/sex differences in elders‘ cognition? (2) How do positions within race/ethnicity, sex, and SES

hierarchies intersect to affect later-life cognitive function? (3) How does the intersection of race/ethnicity and sex affect rate of cognitive decline, and does SES affect subpopulations differently?

Methods

Data and measures

Data are from all twelve waves (1992 to 2014) of the U.S. Health and Retirement Study

(HRS), which is a biennial, nationally-representative, population-based panel survey. The HRS surveys the age 50 plus population and their spouses through face-to-face and telephone interviews, including questions on early childhood, educational attainment, occupation, lifestyles, wealth, and health factors. The HRS oversamples Blacks and Latinx. Although I use demographic and health data from the earlier waves of the survey, HRS consistently administered selected tests of cognitive function only from 1998-2014 (details below). Because of small sample sizes and selectivity at the youngest and oldest ages, I restrict analyses to respondents and their spouses aged 50 to 90 (n=30,224), and in sensitivity analyses, 50-80 (n= n=25,022) and 50-75 (n=23,775).

Fluid Cognition

I use the HRS‘s version of the Telephone Interview for Cognitive Status (TICS), which was tailored to detect AD (Fong et al. 2009). TICS is approximately normally distributed, has high test, re-test reliability, is relatively insulated against ceiling and floor effects, and has high sensitivity (94%) and specificity (100%) in identifying AD (Crimmins et al. 2011; Fong et al.

2009; Karlamangla et al. 2009). It is similarly effective at detecting early decline across clinicians and in phone versus in-person interviews (Karlamangla et al. 2009). I use the

University of Michigan Survey Research Center‘s imputed values (detailed in Fisher et al. 2017).

From the TICS, I extract a subset of questions that represent ―fluid intelligence‖ (Horn

1982). Fluid intelligence reflects neurophysiological health (e.g., short-term acquisition and retrieval, problem-solving skills) and is less correlated with education and cultural factors than

―crystallized intelligence‖ (Akshoomoff et al. 2013; Ghisletta et al. 2012). Additionally, dimensions of cognition decline at different rates (Early et al. 2013), so using a composite value of all the cognitive function questions from the TICS likely misestimates true cognitive functionality. For example, crystallized cognition (CC e.g., vocabulary) does not decline as early or as quickly as fluid cognition (FC) (Ghisletta et al. 2012), possibly misleading researchers to conclude that the more highly educated are not experiencing cognitive decline. From a practical perspective, impaired problem-solving skills (FC) will inhibit independent living before vocabulary deficiencies (CC).

HRS administers the TICS biennially unless a proxy is required. I generated standardized scores2 for immediate and delayed word recall, ―serial 7s‖ (counting backward from 100 by sevens), and counting backward from twenty (correct first time, correct second time, or incorrect). Principal components analysis validated the scores‘ combination into a single variable. I average the four scores to generate Fluid Cognition (FC), which is approximately normally distributed (-0.01, 0.7).

Independent Variables

Controls. Because cognitive function is curvilinear over the life-course (Fig. 6), I measure age as years since birth, centered at age 60, and include a quadratic age term. For sensitivity analyses, I generate binary indicators Proxy Attrition and Death Attrition to indicate whether a respondent ever needed a proxy or died over the course of the HRS from 1992-2014.

EthSex. Sex is male/female. Ethnicity/Race is Non-Hispanic White, African

American/Black Hispanic (to reflect research showing Black Hispanics have more similar health outcomes to Blacks than to non-Black Hispanics) (Elo, Mehta, and Huang 2008), Non-Black

Hispanic, and ―Other.‖3 Most ―Hispanics‖ in the U.S. are from Mexico, Central, or South

America. Henceforth, I simplify to ―Latinx‖ (Latino or cLatina) and ―Black.‖ I test race/ethnicity and sex together by generating an interaction term EthSex.

Early-life factors. Early-SES is generated using principal components analysis4 including: whether father contributed economically (Father Unemployed/Absent/Dead = 1), whether family ever had to move due to financial difficulty or needed financial help, and self-reported Family-

SES (poor, average, or wealthy).5 Rural childhood is binary (Glymour et al. 2008). Childhood

Health is poor/fair, good, very good, excellent. Parent’s Education is a categorical variable (<1,

1-8, 8.5-12, more than HS) for father‘s education, and if father‘s is missing,6 mother‘s education.

Mid- to Later-life SES. Educational Attainment is less than high school, high school/general educational development (GED), some college, and at least a Bachelor‘s to account for the non-linear association between education and mortality across race, gender, and age (Montez, Hummer, and Hayward 2012). I collapsed RAND‘s time-invariant Longest

Occupation categories into: Office - office, professional, and government work; Manual - construction, extraction, and manufacturing jobs; Service; and Farming, Forestry, and Fishing.

The HRS has comprehensive measures for wealth, from stocks, bonds, and trusts to mortgages and student loans. To capture average Later-Life Wealth, it is the absolute value of the respondent‘s average over survey period of income and assets, minus their debt ($0-49k, $50-

199k, $200-499k, $500-999k, $1 million+).7 Life-course SES is generated using the same method

as Early-SES, but includes respondents‘ educational degree, longest occupation, and average later-life wealth (Eigenvalue = 3; slightly left-skewed (0,1)).

Behavioral and health factors. I control for Partnership Status (never married, widow(er)ed, separated/divorced/spouse absent, married/partnered), frequency of Socializing with Neighbors (daily/weekly, biweekly/monthly, yearly/never), and binary indicators for

Exercising (≤ 1/week, > 1/week) and Smoker (never, former, current). Alcohol Use is average number of drinks per week in the last three months (abstinent/rare, 1 drink/day, 2/day, 3+/day).

Health factors include respondent‘s average body mass index (BMI) over the study period

(Center for Disease Control and Prevention‘s categories) and score on the Center for

Epidemiologic Studies Depression Scale (CESD) (0, 1, 2-4, 5-8, higher scores=more depressed).

The Comorbidity Index covers self-report of doctor diagnosis (stroke, diabetes/high blood sugar, heart condition, or high blood pressure/hypertension). Behavioral and health factors are all time- variant and lagged one year, except smoker and BMI.

Parents‘ education (5%), childhood rurality (8%), and occupation8 account for most missingness. Missingness on covariates is correlated with both SES and cognition (e.g., those with less education are more likely to have lower or missing FC), thus estimates of associations between low SES and cognition may be downwardly biased. I do not impute missing data because it is not missing at random (MAR). For categorical variables, I generate a category for missing. My final analytic sample is 30,224 individuals and 162,700 person-observations.

Models with Life-course SES have 26,013 individuals and 143,357 person-observations because the SES variable cannot include missing categories.

Analytic Strategy

Donohue et al. (2011) find that using a Cox proportional hazards model on dichotomous or ordinal variables (non-demented/mild cognitive impairment-MCI/demented) was advantageous only if there was a low threshold and high event rate, not the case for MCI or AD.

When the threshold is high and the event uncommon, the quadratic linear mixed model had consistently more analytical power, was more robust and efficient, and compensates for bias caused by violations of the proportional hazards assumption (Donohue et al. 2011). Mixed effects models are also ideal for analyzing unbalanced longitudinal data, while accounting for endogenous selection. Therefore, I use growth curve models that are two-level hierarchical models with individuals at Level 2 and an individual‘s cognitive test scores from each wave at

Level 1. I allow an individual‘s intercept and rate of change to vary (Pais 2014; Rabe-Hesketh and Skrondal 2012).

For Hypothesis 1, I construct a set of nested models to analyze the mediation of the association between race/ethnicity/sex and cognition across subsequent models.

[Eq. 1]

∑ ∑

In Equation 1, Cognition is modeled for individual i at age t as a function of the intercept (α),

the individual‘s trajectory of decline by age and the quadratic age term (β1, β2), a vector of time- invariant (TIV) predictors j for person i, a vector of time-variant predictors k for person i at age t, the random intercept and random linear slope for person i (ζ1i and ζ2i), and the residual (εit).

Hypotheses 2 and 3 have the same basic structure as Eq. 1, except to evaluate Hypothesis

2, I interact race/ethnicity/sex and the composite variable Life-course SES. To test Hypothesis 3a that race/ethnicity/sex subgroups experience different cognitive trajectories, I include interaction

terms for race/ethnicity/sex, age, and age squared, controlling for life-course SES, behavior, and health factors. For Hypothesis 3b, I include the interaction of race/ethnicity/sex and life-course

SES with the linear and quadratic age. I estimate all models with an unstructured covariance matrix, as the data are unbalanced, and the assumption is violated that the random intercept, ζ1i, and error term, εit, are uncorrelated (Rabe-Hesketh and Skrondal 2012).

Results

I lay out my results in several steps, first briefly describing the sample. I use a staged life- course approach to evaluate Hypothesis 1 and present predicted values. Because the 3-way and

4-way interactions required to test Hypothesis 2 and 3 make the coefficients less immediately interpretable, I exclusively use average marginal effects and predicted values of Fluid Cognition to depict these results.

Descriptive Results

Blacks, Latinx, and Others have statistically significantly lower Fluid Cognition (FC taken at last measure) than Whites, even though the mean age for Whites is slightly older.

Trajectory of cognitive decline also appears to vary by race/ethnic/sex groups ([Fig. 1). In general, Whites have higher SES on all measures than Blacks, Latinx, and Others. This is especially dramatic in later-life wealth. In comparison to POC, Whites had much higher average wealth, with about half holding over $200,000 versus only 14% of Blacks, whereas over 50% of

Blacks and Latinx had assets below $50,000 (19% of Whites). Additional descriptives are in

Appendix

APPENDIX B: Descriptive Statistics and Regression Tables

Table 1.

[Fig. 1 Fluid Cognition across age by sex and race/ethnicity (HRS 1998-2014)]

Multivariate Results

H1. To what extent do life-course social factors mediate the association between race/ethnicity/sex and cognitive function?

Social factors over different stages of the life course partially mediate cognitive disparities (Appendix Table 2). In all models, age and the age quadratic are negative and significant, showing the curvilinear decline of FC. In the Base Model, all POC have statistically significantly lower FC than White males and White females. Black males and females (b = –

0.54, –0.44) and Latinx (b = –0.42, –0.45) have the lowest predicted scores.

However, in the Early-SES Model, we can see that per Hypothesis 1, early-SES mediates the race/ethnicity/sex coefficient by approximately 12-15% for Blacks and 29-33% for Latinx.

The Education Model shows education is strongly associated with FC, even controlling for early-

SES. Respondents with less than high school perform particularly poorly (b = –0.6, p < .001) compared to those with college or more. In support of Hypothesis 1, education further mediates the FC disparity for Blacks and Latinx, reducing the coefficients by over a third for Latinx compared to the Early-SES model. Occupation and wealth also partially mediate most associations including all race/ethnicity/sex coefficients (Wealth, Table 2).

Although behavioral factors have significant independent effects, they only marginally attenuate the association between race/ethnicity/sex and FC (Behavioral, Table 2). The Full

model includes BMI, CESD, the Comorbidity Index, and all previous factors. Including BMI in a model without comorbidities does not influence the other associations (not pictured). Stroke, individually, is the most strongly associated with FC (not pictured). The health variables do not offer much more explanatory power than the previous models (Full, Table 2). Life-course factors significantly mediate the association between race/ethnicity/sex and FC for all groups except

White women, cutting the coefficient by approximately 42% to 75% for Blacks and Latinx, respectively. And yet, even in the Full Model, race/ethnicity/sex remains a statistically significant predictor.

To examine the substantive significance, I calculate predicted cognitive function scores at age 70 for each subpopulation for the Base, Wealth, and Full Models. [Fig. 2 shows the attenuation of the association between cognition and race/ethnicity/sex for Blacks and Latinx in the Full Model9 (represented by the black bars) compared to the Base Model (dark grey bars). I include the Wealth model (light grey) as an intermediate model to demonstrate that, once accounting for the earlier factors, behavioral and health factors do not significantly mediate the association. In sum, some, but not all of the racial/ethnic disparities in cognition stem from socioeconomic and health disparities.

[Fig. 2 Racial/ethnic and sex disparities in Fluid Cognition at age 70 in Base, Wealth, and Full Models (standard deviation=0.7)]

Studying how life-course social factors mediate racial/ethnic and sex cognitive disparities is integral to understanding predictors of cognitive function. Yet, intersectionality theory implies that race/ethnicity, sex, and SES may interact in non-straightforward ways. To this I now turn.

H2. How do race/ethnicity, sex, and SES intersect to affect cognition?

I interact Life-course SES with race/ethnicity/sex, while controlling other factors. This provides a broader picture of how locations on these three stratification axes intersect to influence cognitive function. To help visualize these disparities, I calculate average marginal effects of SES and predicted cognitive function scores by race/ethnicity/sex across SES.

[Fig. 3 Average marginal effects on Fluid Cognition of being in the 1st, 2nd, or 3rd Quartile of Life-course SES compared to the highest quartile, by race/ethnicity/sex]

Being in the lower quartiles of Life-course SES compared to the highest quartile appears more influential for Blacks, Latinas, and Others than Whites ([Fig. 3 and Appendix Table 3). This supports Hypothesis 2 that there is a multiplicative effect of occupying multiple disadvantaged positions on social hierarchies, with the exception of Latinos and White women.

[Fig. 4 Predicted cognition at age 80 by Life-course SES, race/ethnicity, and sex]

[Fig. 4 shows that the lower the SES, the more differentiated the subpopulations (compare the differences within the black bars and the dark grey bars). Consistent with a resilience perspective, I interpret this to mean higher life-course SES may be more protective for POC than

Whites.

H3. How do social factors intersect to affect cognitive trajectories?

The previous analyses provide an average disparity across ages 50-90 or at a selected age

(e.g., age 80 in Fig. 4). To examine if cognitive trajectories are associated with race/ethnicity and sex, I interact race/ethnicity/sex with the age terms, net of other factors. Holding other variables at their means, White females and all POC (except Latinos and Others) decline statistically significantly faster (two-tail, 95% CI, p<.01 or <.001) than White males either via

the linear age term (Black men and Latinas), the quadratic age term (linear age is negative, but non-significant for White women), or both (Black women). Most dramatically, between ages 50 and 80, Black women‘s FC declined 40% more than White men‘s, and Black men‘s by 33% more. All women except ―Other‖ also decline faster than their same-race/ethnicity male counterparts. The sex disparities are particularly strong for Latinos and Latinas. Latinas declined

24% more than White males, (95%, CI: -0.008, -0.002, p<.01), but Latinos only 10% (95%, CI: -

0.006, 0.001). Interestingly however, Black men have a significant positive term for the age quadratic (Latinx and Other women also do, though it is non-significant), meaning that the curve of the decline is steeper for White men than Black men. Concretely, the cognitive disparity between Black and White men at age 80 is a quarter of a standard deviation, but by age 90 the disparity is about a tenth of a standard deviation. In general, these findings support Hypothesis

3a that POC and White women experience cognitive weathering.

To test Hypothesis 3b, I interact Life-course SES with race/ethnicity/sex, age, and quadratic age to determine if the intersection of multiple axes of stratification is associated with rate of decline. The main effects of race/ethnicity/sex and Life-course SES are consistent with the previous models. Some interaction effects are also significant. [Fig. 5 shows cognitive trajectories for subpopulations in the lowest quartile, at the mean, and in the highest quartile of

SES, controlling for all covariates. Sample sizes are small for POC in the highest SES quartile in the older age categories (85+), but estimates for the overall trajectory should be robust.

Coefficients are also consistent in sensitivity analyses on a subsample aged 50-80 or 50-75.

[Fig. 5 Predicted cognition across age by the intersection of race/ethnicity/sex and life-course SES]

In support of Hypothesis 3b, Blacks and Latinas in the lowest Life-course SES quartile decline faster than White high-SES men ([Fig. 5). For example, by about age 75, White women,

Latinas, and Blacks of lower SES have declined by slightly less than two-thirds of a standard deviation, three-quarters, and almost one, respectively. In contrast, their White male counterparts decline by about half a standard deviation (0.144 to –0.225). Low-SES Latinos do not experience significantly different decline.

At mean SES, Black women appear to decline faster than White men. As with low-SES,

White women and men show the same convergence pattern. Black males in the 2nd and 3rd SES quartiles have a significant negative linear slope, but positive quadratic term, as in Hypothesis

3a. Latinx in the 2nd and 3rd SES quartile have a positive linear term, but negative quadratic term, which is significant for Latinas (Ethsex*SES*age b=.007 Ethsex*SES*age2 b=-0.001, p<.01).

High-SES Black women and White women appear to decline faster than White males.

High-SES Black men show an unusual pattern, but coefficients are non-significant, more reflective of persistent inequality. Latinx in the highest SES quartile have significantly lower FC at age 50 than White men, but the disparity narrows as they age. High-SES Latinas and White men converge around age 85 and Latinos by age 90. The findings for Hypothesis 3b, therefore, are mixed.

Sensitivity Analyses

All the following sensitivity analyses are consistent with the presented models, suggesting the models are robust to alternative specifications. I estimate models with a categorical variable for age and on a subsample of those aged 50-80 and 50-75 to mitigate selectivity and smaller cell sizes at the oldest ages. Despite decreasing the sample, age

restrictions have a negligible effect on any coefficients. In short, the selectivity of the oldest old does not appear to be driving the results.

Practice effects (improvement in scores due to previous test-taking) are not significant in the TICS, and I use an age quadratic (Plassman et al. 1994). However to confirm practice effects do not bias my estimates, I include a dummy for First Test (per Vivot et al. 2016). I do not use

Childhood Region (North/East/Midwest, South, West, and non-U.S.) in the main models because race/ethnicity is highly correlated. Blacks are significantly more likely to be Southern; Latinx and Others are more likely to be raised outside the U.S. However, social factors (e.g., schooling quality) do vary regionally, so it is worth exploring (Glymour et al. 2008). Region‘s inclusion does not significantly impact the race/ethnicity/sex associations despite having significant effects

(Southerners and Westerners have lower cognition than North/East/Midwesterners). I use a cohort-standardized measure of years of education because education quality and its association with SES shifted across the range of cohorts in my sample (Hendi 2015). Because there are few individuals with wealth over one million dollars, I also cut wealth at $500k. Again, there are no significant differences.

Longitudinal analysis often suffers from attrition, and the HRS‘s aged sample exacerbates this problem. Though the HRS‘s rolling enrollment can be considered MAR, cognition is statistically significantly higher for those who have complete data. 8% of respondents are missing the outcome variable despite participating in the wave. That is partially by design, which meets the requirements for MAR. But also, almost 20% of the sample has a proxy at some point over the course of the survey. Cognition predicts age of death. Withdrawal due to morbidity or mortality violate the MAR assumption. To partially address this bias, I control for whether the

respondent died and/or ever had a proxy over the course of the survey. Proxy and mortality attrition are significantly associated with lower cognitive function, but results are consistent.

Discussion

Intersectionality theory provides a framework for understanding social risk factors for later-life cognitive decline from a life-course perspective. I extrapolate from Geronimus‘s weathering hypothesis that socially-disadvantaged groups experience accelerated aging (as a result of the accumulation of allostatic load) (Geronimus et al. 2015; Seeman et al. 2010) to the outcome of cognitive function. To that end, I use growth curve models on the HRS data to analyze how intersections of race/ethnicity, sex, and life-course SES affect Fluid Cognition.

My continuous measure of FC contrasts to much research on later-life cognition, wherein researchers study doctor diagnoses of AD or dementia or set a post hoc threshold that indicates, at most, non-demented/MCI/impaired. The threshold approach often means restricting analyses to the ―oldest old‖ because in mid-life there are very few individuals below the threshold, exacerbating endogenous sample selection due to survival bias (or institutionalization) (Elwert and Winship 2014). Furthermore, imposing a threshold point truncates valuable information on either side of the threshold about the progression of the illness. Employing a continuous measure for cognitive function and a wider age range (50-90) means I can track cognitive trajectories well-before a threshold, institutionalization, or mortality attrition. Importantly, FC measures the latent construct of brain health, indicative of ability to function in everyday contexts. Significant declines would manifest in reduced ability to live independently.

As predicted, life-course SES, behavior, and chronic illnesses only partially mediate the association between race/ethnicity/sex and FC (H1). Disparities remain. Early-life socioeconomic status and education account for most of that mediation, with wealth, health, and

behavioral factors not adding significant explanatory power ([Fig. 2). There is a multiplicatively negative effect of exposure to low life-course SES for POC (except Latinos) in comparison to

Whites ([Fig. 3), which is evidence for cumulative disadvantage (H2).

Net of SES, behavioral, and health factors, all groups including White women (except

Latino men and ―Others‖), decline significantly faster than White men, evidence for cognitive weathering and the theory of cumulative disadvantage (H3a). In attempting to understand why

Latinos do not experience the same accelerated decline as other socially-disadvantaged groups, I considered only U.S.-born Latinos. U.S.-born Latinos do decline significantly faster than White men, whereas those born outside the U.S. do not. This fits with other research on the ―Hispanic

Paradox‖ (Markides and Eschbach 2005). Though it is unclear why Latinas would follow a different pattern, there is evidence foreign-born Latinas do have some worse health outcomes than their White counterparts (Williams and Sternthal 2010).

The relationships among life-course SES, race/ethnicity, and sex are more complex than predicted (H3b). Black women of all SES quartiles, Black men in the lowest three quartiles, and

Latinas in the lowest quartile have steeper trajectories of cognitive decline. White women have the same pattern across all SES groups in which they have significantly higher FC at age 50 than

White men, but converge over time. That being part of multiple disadvantaged groups has a multiplicatively negative effect on cognitive health is further evidence for cumulative disadvantage. Yet, low-SES Latinos, mean-SES Latinx, and high-SES Black men may provide evidence for the persistent inequality hypothesis. The cognitive disparities between them and

Whites remain relatively consistent from age 50 to 90.

In contrast, higher-SES Latinx seem to demonstrate the age-as-leveler hypothesis; the gap in cognition between them and Whites shrinks with age. It is possible some portion of this is related to selectivity in the oldest ages if people with lower cognition either die younger or reverse migrate (Palloni and Arias 2004), in which case those who have better cognition remain to “pull up” the curve. That said, the bias would have to be large to invalidate these results ([Fig. 5). There is lower likelihood of significant bias because 1) estimates are consistent even for people younger than 76, and 2) as above noted, foreign-born Latinx have longer life expectancy than Whites. In this sample, most Latinx are several years younger than Whites, have a mortality rate about half Whites’, and over half are foreign- born (APPENDIX B: Descriptive Statistics and Regression Tables

Table 1). Based on H3 findings, I propose one reason the literature contains contradictory evidence for age-as-leveler, persistent inequality, and cumulative disadvantage is that most researchers do not take an intersectional approach.

Limitations

Quantitative analyses that take intersectionality seriously continue to face limitations related to data availability (Springer, Hankivsky, and Bates 2012). Race/ethnicity and sex/gender are oversimplified in large datasets. I am not aware of any nationally-representative dataset of elders that accounts for the gender/sex continuum or the social construction of race, including the prevalence of mixed-race individuals or the importance of ―perceived race‖ (James 2001;

Saperstein and Penner 2012). Nevertheless, in the U.S.‘s ascriptive stratification system, race/ethnicity and sex/gender are such dominant features in structuring interpersonal and institutional interactions that despite this oversimplification, racial/ethnic and sex self- identification remain predictive of discrimination and life chances (Krieger 2014; Ridgeway

2013).

Social selection may be a confounder of racial/ethnic or sex disparities. People from socially-disadvantaged groups may, for example, attain less education, select into more hazardous occupations, and have more health insults, obscuring to what extent health disparities stem from ―ism-related‖ (e.g., racism) stressors versus channeling over the life course (Adler,

Bush, and Pantell 2012; Pais 2014). Whereas much cognition research is limited by smaller, regional samples (Zhang et al. 2016), my HRS analytical sample has approximately 30,000 individuals with diverse paths, providing enough power to disentangle these life-course factors, to a certain extent.

Health disparities researchers are often faced with the challenge of identifying whether disparities precede or are caused or exacerbated by lack of health care, i.e., reverse causality.

These confounders are due to disparities in insurance coverage and ability to afford health care

(Beckfield, Olafsdottir, and Sosnaud 2013), but also gender differences in likelihood of seeking treatment (Read and Gorman 2010) and racial/ethnic differences in care-provision (Malat 2006).

In contrast, cognitive decline is primarily non-responsive to medical interventions, minimizing concerns of reverse causality.

I employ robustness checks such as including a binary indicator for proxy and mortality attrition, minimizing bias for those who have at least one value of the outcome. Nevertheless, premature mortality may have selected out the most socially-disadvantaged, thus estimates for the SES-FC association may be downwardly biased. Growth curve modeling should minimize some of this bias, as each person-observation contributes to estimated trajectories regardless of attrition (Harvey, Beckett, and Mungas 2003).

Conclusion

Life-course SES, behavior, and comorbidities only partially explain racial/ethnic and sex cognitive disparities across ages 50-90. White females‘, Blacks‘, Latinas‘, and U.S.-born

Latinos‘ cognition declines faster than White men‘s, net of other factors, evidence for ―cognitive weathering.‖ Higher life-course SES appears more beneficial to POC‘s cognitive function than

Whites‘. Interactions among age, race/ethnicity, sex, and SES show that the applicability of

cumulative disadvantage, persistent inequality, or age-as-leveler may depend on the subpopulation.

Although some POC subgroups decline at the same rate as White men, because POC average lower FC at age 50, most subgroups are predicted to hit an impairment threshold years before Whites (as in Karlamangla et al. 2009). For example, if the threshold below which individuals cannot live independently is one standard deviation below the mean, Latinx and

Blacks are predicted to cross that threshold about five to eight years prior to Whites, even if they had the same SES and education. Of course, because a disproportionate share of Blacks and

Latinx in the U.S. are poor (see APPENDIX B: Descriptive Statistics and Regression Tables

Table 1), they would actually reach an entire standard deviation below the mean 10-15 years before average White men. In addition to the obvious human costs, dementia care is so expensive (Hurd et al. 2013) this puts POC, who already average fewer financial resources, at severe disadvantage.

These interactions have key implications. First, beneficial social factors (e.g., educational attainment, frequent socialization, and greater wealth) may be especially protective for POC.

Second, Alzheimer‘s research, which often relies on education as a proxy for cognitive reserve, should be amended to encompass other aspects of SES, including early-life factors, wealth, behavioral factors, and chronic illnesses. Third, the oft-reported link between Alzheimer‘s disease and being Black or Latinx appears only partially driven by racially-patterned mediators.

That said, this analysis is limited to a specific set of SES measures available in the HRS.

In reality, differential accumulation of wealth and residential segregation mean POC are more likely to live in economic precarity and reside in areas considered ―disadvantaged‖ than their same-income White counterparts (Conley 1999; Sharkey 2013). While this could be interpreted

as ―measurement error‖ on the wealth variable, it seems probable allostatic load is still the mechanism connecting economic precarity or neighborhood with FC.

In sum, being disadvantaged across multiple axes in the U.S. social stratification system negatively affects baseline cognition and cognitive trajectories. I hypothesize the mechanism that explains racial/ethnic disparities beyond SES is exposure to ―socially structured stressors,‖ such as racism and sexism (Geronimus et al. 2015). I thus propose the tangible (but important) goal of reducing socioeconomic inequality will not eliminate cognitive weathering (cf. Phelan and Link

2015). If these explanations are accurate, then anti-discrimination and housing integration policies, as well as anti-racism and anti-sexism programs may be protective (eventually).

In the interim, the disadvantaged will likely bear the brunt of Alzheimer‘s disease, just as other illnesses. The lifetime cost of Alzheimer‘s outpaces most illnesses because of its incapacitation of elders and its lengthy duration. Policy makers should, thus, consider plans to handle the inevitable strain on Medicare and Medicaid as the population ages. The estimated projections of 135 million persons afflicted with dementia globally demand a closer analysis of the social causes of memory diseases (World Health Organization 2016).

Notes

1. Although AD is the most common cause of late-life cognitive impairment (Alzheimer‘s

Association 2016), in analyzing survey data, studying cognitive decline as a continuous

measure is preferable to imposing a post hoc diagnosis threshold (methods section for

details).

2. I exclude Waves 1 and 2 because of inconsistencies in administration and restrict the

standardization to those age 40-90.

3. Unfortunately, ―Other‖ cannot be decomposed into subgroups and includes people who

identify multiple races.

4. Stata‘s ―polychoric‖ command is consistent, asymptotically normal and asymptotically

efficient for analysis of ordinal variables (Kolenikov and Angeles 2004).

5. We maintain father‘s (or mother‘s) education and early-life health separate on theoretical

and statistical grounds (the factor loadings were low and neither contributed significantly

to the latent factor).

6. Father is reported as unemployed/absent/deceased in 43% of the cases where education is

missing,. In Wave 3, HRS only asks whether parents have at least 8 years of education.

Respondents are given 7.5 if less than 8 and 8.5 if more than 8. This adds some

unavoidable error to this measure.

7. In sensitivity analyses I also test the upper limit as $500k+.

8. Women account for 75% of the missingness on longest occupation (5% men vs. 20%

women), which likely reflects the lack of women‘s labor force participation in these

cohorts.

9. Holding life-course factors at their means likely downwardly biases the race/ethnicity/sex

coefficient because, for example, Blacks and Latino/as are less likely to have the mean

level of education. Hypotheses 2 and 3address this bias.

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Figures

Mean Fluid Cognition across age by sex, ethnicity/race Females Males

.5 .5

0 0

-.5 -.5

-1 -1

-1.5 -1.5 50 60 70 80 90 50 60 70 80 90 Age Age

White Black Latinx Other

Fig. 6 Fluid Cognition across age by sex and race/ethnicity (HRS 1998-2014)

0.2

0.1

-0.1

-0.2 Base Wealth -0.3 Full -0.4 Predicted Cognition Fluid -0.5

-0.6 Male Female Male Female Male Female Male Female White Black Latinx Other

Fig. 7 Racial/ethnic and sex disparities in Fluid Cognition at age 70 in Base, Wealth, and Full

Models (standard deviation=0.7)

Average Marginal Effects of Life-Course SES

-.2

-.4

-.6

Latino Latina

White Male Black Male Other Male White Female Black Female Other Female Ethnicity*Sex

Lowest Quartile 2nd Quartile 3rd Quartile

Fig. 8 Average marginal effects on Fluid Cognition of being in the 1st, 2nd, or 3rd Quartile of Life-

course SES compared to the highest quartile, by race/ethnicity/sex

Predicted cognition at age 80 by life-course SES, race/ethnicity, and sex Female Male

-.2

-.4

-.6 White Black Latinx Other White Black Latinx Other SES Quartiles Lowest 2nd 3rd Highest

Fig. 9 Predicted cognition at age 80 by Life-course SES, race/ethnicity, and sex

Whites and Blacks

Low SES Mean SES High SES

.5 .5 .5

0 0 0

-.5 -.5 -.5

-1 -1 -1

-1.5 -1.5 -1.5 50 60 70 80 90 50 60 70 80 90 50 60 70 80 90 Age Age Age

White Male White Female Black Male Black Female

White Men and Latinx

Low SES Mean SES High SES

.5 .5 .5

0 0 0

-.5 -.5 -.5

-1 -1 -1

-1.5 -1.5 -1.5 50 60 70 80 90 50 60 70 80 90 50 60 70 80 90 Age Age Age

White Male Latino Latina

Fig. 10 Predicted cognition across age by the intersection of race/ethnicity/sex and life-course

SES

APPENDIX A: Additional Figure

Polynomial fit by Ethnicity/Race White Black

.5 .5

0 Observed 0 Observed Linear Linear

-.5 Quadratic -.5 Quadratic Cubic Cubic

-1 -1

-1.5 -1.5 50 60 70 80 90 50 60 70 80 90

Latinx Other

.5 .5

0 Observed 0 Observed Linear Linear

-.5 Quadratic -.5 Quadratic Cubic Cubic

-1 -1

-1.5 -1.5 50 60 70 80 90 50 60 70 80 90

Fig. 6 Polynomial Fit of Fluid Cognition by Race/Ethnicity*Sex

APPENDIX B: Descriptive Statistics and Regression Tables

Table 1 Descriptive statistics by race/ethnicity

Non-Hispanic Black/Black Non-Black "Other" White Latinx Latinx Variable Mean/% SD Mean/% SD Mean/% SD Mean/% SD Ethnoracial Distribution 68.39 17.79 10.99 2.83 Cognitive Function Mean Minimum* -0.44 0.71 -0.89 0.82 -0.79 0.78 -0.58 0.76 Mean # of Cognitive Test Scores* 5.72 2.99 4.69 2.80 4.62 2.77 4.49 2.82 Died 1992-2014* 34.89 0.48 28.16 0.45 18.94 0.39 17.52 0.38 Proxy 1992-2014* 16.82 0.37 15.99 0.36 17.64 0.38 14.95 0.36 Demographic characteristics Age* 72.17 10.9 67.17 10.4 66.15 10.3 65.41 10.4 Female* 56.24 60.52 56.88 54.21 Early-Life Socioeconomic Status Early-SES* Lowest 35.85 45.25 41.01 35.98 Middle 57.39 50.03 52.82 51.99 Highest 6.76 4.72 6.17 12.03 Childhood Rural 47.95 51.85 43.48 51.41 Childhood Health* Poor/Fair 5.86 7.64 11.05 8.53 Good 15.5 18.91 25.26 15.65 Very Good 25.72 24.94 21.92 21.14 Excellent 52.7 48.15 41.19 53.74 Parental Education* <1 1.09 3.83 21.14 6.66 1–8 years 35.42 41.18 45.92 30.49 8.5-12 43.53 35.63 16.95 34.23 More than High School 14.73 6.56 6.05 20.33 Childhood Region* North/East/Midwest 56.75 24.75 7.95 16.94 South 29.38 61.6 23.28 29.67 West 10.22 2.77 15.27 12.62 Non-US 3.11 6.55 52.3 39.49 Educational Attainment* Less than High School 16.93 32.86 52.66 21.73 GED/H.S. Grad 55.18 48.8 36.16 41.12 Some College 4.98 5.65 3.4 5.49 College + 22.9 12.68 7.74 31.66 Longest Occupation* Office 55.3 32.82 25.44 47.78 Manual 21.1 27.93 34.75 23.25 Service 8.67 23.51 16.68 11.57 Farm/Forest/Fish 1.81 1.66 4.55 2.92 Missing 13.13 14.08 18.58 14.49 Wealth Average* $0-49K 18.97 55.03 51.25 33.41 $50-199K 29.42 31.13 30.17 28.27

$200-499K 26.23 10.17 13.04 21.14 $500-999K 15.04 2.73 3.67 10.05 $1mil+ 10.34 0.93 1.87 7.13 Life-course SES* Lowest Quartile 20.08 38.25 41.53 23.09 2nd Quartile 21.66 29.44 30.47 24.18 3rd Quartile 27.29 20.97 18.16 23.63 Highest Quartile 30.98 11.34 9.84 29.1 Behavioral Factors Relationship Status* Never Married 3.71 11.54 5.9 5.65 Widowed 27.94 26.39 17.8 17.31 Sep/Divorced/Absent 13.12 22.79 17.04 15.19 Married/Partnered 55.24 39.29 59.26 61.84 Socializing with Neighbors* Daily/Monthly 37.58 36.67 28.68 36.88 Exercise 1x/week or less* 85.73 86.35 84.11 79.98 Alcohol (Moderate/Heavy)* 7.05 7.92 11.58 7.18 Abstinent/Rare 45.99 58.8 55.51 62.54 1 drink/day 20.08 11.93 10.93 14.37 2 drinks/day 18.05 12.51 10.74 9.42 3+ drinks/day 15.88 16.75 22.82 13.66 Smoker* Never 40.94 40.17 47.79 48.83 Former 36.02 28.73 29.78 25.35 Current 23.03 31.1 22.43 25.82 Health CESD* 0 15.37 10.59 13.03 14.17 1 21.47 19.33 16.27 20.19 2-4 37.95 37.88 31.11 38.84 5-8 25.21 32.2 39.59 26.8 BMI* Underweight 1.48 1.00 0.63 0.82 Normal 35.83 22.37 22.37 37.85 Over 38.31 35.82 41.13 36.8 Obese 24.27 40.54 33.33 23.95 Comorbidity Index* None 25.48 16.94 28.67 29.12 One 33.82 34.89 31.89 32.16 Two 25.93 29.48 26.65 24.68 Three 12.09 14.51 10.18 11.23 Four 2.68 4.18 2.62 2.81 * Differences between racial/ethnic groups are statistically significant at p<0.001

Table 2 Nested growth curve models estimating Fluid Cognition Base Early-SES Education Wealth Behavioral Full b SE b SE b SE b SE b SE b SE Age -0.014*** (0.000) -0.012*** (0.000) -0.010*** (0.000) -0.011*** (0.000) -0.011*** (0.000) -0.011*** (0.000) Age Quadratic -0.001*** (0.000) -0.001*** (0.000) -0.001*** (0.000) -0.001*** (0.000) -0.001*** (0.000) -0.001*** (0.000) Race/Ethnicity White Female 0.107*** (0.007) 0.110*** (0.007) 0.131*** (0.007) 0.124*** (0.007) 0.125*** (0.007) 0.122*** (0.007) Black Male -0.544*** (0.013) -0.479*** (0.012) -0.407*** (0.011) -0.327*** (0.011) -0.321*** (0.011) -0.316*** (0.011) Black Female -0.435*** (0.011) -0.367*** (0.010) -0.311*** (0.010) -0.232*** (0.010) -0.225*** (0.010) -0.223*** (0.010) Latino -0.420*** (0.015) -0.277*** (0.015) -0.167*** (0.014) -0.108*** (0.014) -0.104*** (0.014) -0.103*** (0.013) Latina -0.450*** (0.013) -0.317*** (0.013) -0.189*** (0.012) -0.136*** (0.012) -0.130*** (0.013) -0.126*** (0.012) Other Male -0.255*** (0.027) -0.220*** (0.026) -0.217*** (0.024) -0.177*** (0.024) -0.171*** (0.024) -0.163*** (0.023) Other Female -0.267*** (0.025) -0.230*** (0.024) -0.210*** (0.022) -0.173*** (0.022) -0.166*** (0.022) -0.160*** (0.022) Early SES Early-SES (ref. highest) Lowest -0.023+ (0.013) 0.028* (0.012) 0.035** (0.011) 0.035** (0.011) 0.033** (0.011) Middle -0.005 (0.012) 0.029* (0.011) 0.028* (0.011) 0.028* (0.011) 0.024* (0.011) Childhood Rural -0.124*** (0.006) -0.074*** (0.006) -0.069*** (0.006) -0.069*** (0.006) -0.067*** (0.005) Childhood Health (ref. Good) Poor/Fair -0.187*** (0.012) -0.133*** (0.011) -0.104*** (0.011) -0.103*** (0.011) -0.090*** (0.011) Very good -0.119*** (0.008) -0.071*** (0.008) -0.053*** (0.007) -0.053*** (0.007) -0.048*** (0.007) Excellent -0.041*** (0.007) -0.022*** (0.007) -0.014* (0.006) -0.014* (0.006) -0.012+ (0.006) Parents' Mean Education (ref.MTHS) <1 -0.430*** (0.018) -0.191*** (0.017) -0.151*** (0.016) -0.149*** (0.016) -0.146*** (0.016) 1-8 years -0.156*** (0.010) -0.021* (0.009) -0.004 (0.009) -0.007 (0.009) -0.010 (0.009) 8-12 -0.087*** (0.009) -0.007 (0.009) 0.002 (0.009) -0.002 (0.009) -0.004 (0.009) Educational Attainment (ref. College+) Less than High School -0.595*** (0.010) -0.430*** (0.010) -0.430*** (0.010) -0.423*** (0.010) HS/GED -0.239*** (0.007) -0.152*** (0.008) -0.150*** (0.008) -0.148*** (0.008) Some College -0.163*** (0.013) -0.105*** (0.013) -0.102*** (0.013) -0.100*** (0.013) Adulthood SES Longest Occupation (ref. Office/Professional) Manual -0.128*** (0.008) -0.127*** (0.008) -0.126*** (0.007) Service -0.136*** (0.009) -0.135*** (0.009) -0.134*** (0.009)

Farming/Forestry/Fishing -0.167*** (0.019) -0.171*** (0.019) -0.174*** (0.019) Average Wealth (ref. $50k-199k) 0-$49k -0.147*** (0.007) -0.142*** (0.007) -0.133*** (0.007) $200-499k 0.057*** (0.007) 0.053*** (0.007) 0.048*** (0.007) $500-999k 0.078*** (0.009) 0.073*** (0.009) 0.066*** (0.009) $1million+ 0.107*** (0.011) 0.100*** (0.011) 0.094*** (0.011) Behaviors & Health Relationship Status (ref. Married/Partnered) Never Married -0.032** (0.011) -0.028* (0.011) Widowed -0.006 (0.005) 0.001 (0.005) Seperated/Divorced/Absent 0.002 (0.006) 0.008 (0.006) Socializing with Neighbors (ref. Annually/Never) Bi-Weekly or Monthly 0.010* (0.004) 0.008* (0.004) Daily or Weekly 0.006+ (0.003) 0.004 (0.003) Exercise ≤1x/week -0.021*** (0.003) -0.015*** (0.003) Alcohol Use (ref. 1 drink/day) Abstinent/Rare -0.019*** (0.004) -0.014** (0.004) 2 drinks/day -0.008 (0.006) -0.009 (0.006) 3+ drinks/day -0.011 (0.007) -0.011 (0.007) Smoker (ref. Never) Former -0.004 (0.006) -0.001 (0.006) Current -0.015* (0.007) -0.012+ (0.007) BMI (ref. "Normal" 18.5-24.9) Underweight≤18.5 -0.047+ (0.025) Over=25-29 0.016* (0.006) Obese=30-39 0.018* (0.007) CESD (ref. 1) None 0.011** (0.003) 2-4 -0.022*** (0.004) 5-8 -0.055*** (0.005) Comorbidity Index (ref. 2) None 0.058*** (0.005) 1 0.035*** (0.005) 3 -0.053*** (0.008) Stroke, High BP, Heart Disease, and Diabetes -0.185*** (0.018)

Constant 0.255*** (0.006) 0.478*** (0.013) 0.514*** (0.013) 0.467*** (0.014) 0.488*** (0.015) 0.437*** (0.016) Individuals 30,224 30,224 30,224 30,224 30,224 30,224 Person-Observations 162,700 162,700 162,700 162,700 162,700 162,700 AIC 243303 240629 236976 235337 235115 234500 Chi2 22378 26099 31708 34494 34971 36185 Standard errors in parentheses; 2-tail tests; CI 95%; *** p<0.001, ** p<0.01, * p<0.05

Table 3 Hypothesis 2: Selected coefficients of associations between Fluid Cognition and intersections of race/ethnicity, sex, and Life-course SES

Life-course SES (ref. Highest) Lowest Quartile 2nd Quartile 3rd Quartile EthSex*Life-course SES b SE b SE b SE White Female 0.037+ (0.020) 0.057** (0.019) 0.045* (0.018) Black Male -0.165*** (0.042) -0.123** (0.043) -0.104* (0.045) Black Female -0.197*** (0.031) -0.136*** (0.032) -0.076* (0.033) Latino 0.004 (0.054) 0.028 (0.055) 0.049 (0.059) Latina -0.081* (0.041) -0.094* (0.043) -0.039 (0.045) Other Male -0.207** (0.069) 0.001 (0.069) -0.026 (0.074) Other Female -0.186** (0.067) -0.220** (0.067) -0.059 (0.063) Main Effects Race/Ethnicity White Female 0.080*** (0.012) Black Male -0.279*** (0.037) Black Female -0.150*** (0.026) Latino -0.251*** (0.049) Latina -0.161*** (0.035) Other Male -0.122* (0.047) Other Female -0.060 (0.044) Life-course SES (ref. Highest Quartile) Lowest Quartile -0.234*** (0.015) 2nd Quartile -0.207*** (0.014) 3rd Quartile -0.112*** (0.013) Constant 0.512*** (0.015) Number of individuals 26,013 Person-Observations 143,357 AIC 204947 Chi2 24729 Adjusted for Age, Age2, Rural, Childhood Health, Parent‘s Education, Behaviors, and Health Standard errors in parentheses; 2-tail tests; CI 95%; *** p<0.001, ** p<0.01, * p<0.05