Literacy Mediates Racial Differences in Financial and Healthcare Decision Making in Older Adults

S Duke Han; Lisa L Barnes; Sue Leurgans; Lei Yu; David A Bennett; Patricia A Boyle

doi:10.1111/jgs.16381

. Author manuscript; available in PMC: 2021 Jul 30.

Published in final edited form as: J Am Geriatr Soc. 2020 Feb 24;68(6):1279–1285. doi: 10.1111/jgs.16381

Literacy Mediates Racial Differences in Financial and Healthcare Decision Making in Older Adults

S Duke Han ^1,^2,^3,^4,^5,^6,⁷, Lisa L Barnes ^5,^6,⁷, Sue Leurgans ^6,⁷, Lei Yu ^6,⁷, David A Bennett ^6,⁷, Patricia A Boyle ^5,⁶

PMCID: PMC8324307 NIHMSID: NIHMS1720598 PMID: 32092157

Abstract

Background/Objectives:

Decision making in financial and healthcare matters is of critical importance for well-being in old age. Preliminary work suggests racial differences in decision making; however, the factors that drive racial differences in decision making remain unclear. We hypothesized literacy, particularly financial and health literacy, mediates racial differences in decision making.

Design:

Community-based epidemiologic cohort study.

Setting:

Communities in Northeastern Illinois.

Participants:

Non-demented Black participants (N=138) of the Rush Alzheimer’s Disease Center (RADC) Minority Aging Research Study (MARS) and the Rush Memory and Aging Project (MAP) who completed decision making and literacy measures were matched to White participants (N=138) according to age, education, sex, and global cognition using Mahalanobis distance (Total N=276).

Measurements:

All participants completed clinical assessments, a decision making measure that resembles real-world materials relevant to finance and healthcare, and a financial and health literacy measure. Regression models were used to examine racial differences in decision making and test the hypothesis that literacy mediates this association. In secondary analyses, we examined the impact of literacy in specific domains of decision making (financial and healthcare).

Results:

In models adjusted for age, education, sex, and global cognition, older Black adults performed lower than older White adults on literacy (Beta=−8.20, SE=1.34, 95% CI=[−10.82, −5.57], p<0.01) and separately on decision making (Beta=−0.80, SE=0.23, 95% CI=[−1.25, −0.34], p<0.01). However, when decision making was regressed on both race and literacy the association of race was attenuated and became non-significant (Beta=−0.45, SE=0.24, 95% CI=[−0.93, 0.02], p=0.06), but literacy remained significantly associated with decision making (Beta=0.04, SE=0.01, 95% CI=[0.02, 0.06], p<0.01). In secondary models, a similar pattern was observed for both financial and healthcare decision making.

Conclusions:

Racial differences in decision making are largely mediated by literacy. These findings suggest that efforts to improve literacy may help reduce racial differences in decision making and improve health and wellbeing for diverse populations.

Keywords: decision making, literacy, race, disparities, financial, health

INTRODUCTION

Decision making involves the consideration of multiple options and the selection of an optimal choice. In aging, decision making is of crucial importance, since older adults make highly consequential choices about financial and healthcare matters central to maintaining independence and well-being in old age. Although decision making in aging is a growing research area,^1,2 most studies have been conducted with primarily White participants, and very little is known about racial differences in financial and healthcare decision making. Evidence from economics studies suggests that older Black adults hold more conservative views regarding financial investments than White adults,^3,4 and some, but not all studies, suggest they may be more risk averse in their choice of assets.⁵ In the healthcare domain, there are reports of racial differences in preferences for healthcare procedures and end-of-life care.⁶ For example, Black adults tend to choose less invasive cardiac procedures than White adults,^7,8 but are more likely to prefer the use of life support.^9,10 Together, this work suggests racial differences in decision making may exist for financial and healthcare matters, and a greater understanding of these differences is critical for promoting the wellbeing and independence of a broad representation of older adults.

Importantly, social and environmental experiences over the life course vary by race due to historically documented institutionalized racism and unequal access to supportive resources,^11–14 and these differences may account for racial differences in decision making. For example, general verbal literacy level, a proxy for access to educational resources in earlier life, has been associated with differences in cognition and cognitive decline among older Black and White adults.^15,16 It is plausible therefore that differences in acquired financial and health literacy, a proxy for access to financial and health-related resources throughout the lifespan, may explain racial differences in financial and healthcare decision making in older adults,¹⁷ though this is currently unknown.

In the present study, we examined racial differences in financial and healthcare decision making in a well-characterized group of non-demented older Black adults matched to older White adults from the Rush Alzheimer’s Disease Center (RADC) cohort studies of aging according to age, education, sex, and global cognition using a Mahalanobis distance methodology. Further, we tested the hypothesis that literacy, particularly financial and health literacy, mediates racial differences in decision making. To our knowledge, this is the largest study of racial differences using a well-established measure of financial and healthcare decision making in older persons and the first to examine the role of literacy as a potential mediator of these differences.

METHODS

Participants

Older Black adult participants of the Rush Alzheimer’s Disease Center (RADC) Minority Aging Research Study (MARS)¹⁸ and the Rush Memory and Aging Project (MAP)¹⁹ completed a decision making substudy. The RADC decision making substudy started collecting data in MAP participants in 2010. Decision making data collection was expanded to the MARS cohort in 2017. Among older Black RADC participants, 150 completed decision making measures and a complete clinical evaluation. Three were excluded due to a dementia diagnosis (n=3), leaving 147 Black participants. Of the 147 Black participants, 9 had missing data on variables of interest, leaving 138 Black participants (80 participants from MARS, 58 participants from MAP). Next, we identified White participants with decision making data. There were 1118 White participants who could serve as potential matches for Black participants. Among those, 49 had dementia, leaving 1069 potential White participant matches. Of the 1069 Whites (all from MAP), 96 had missing data on variables of interest, leaving 973 potential White participant matches. Mahalanobis Distance was used to match an equal number of White participants (N=138) to Black participants (N=138) according to the pre-selected variables of age, education, sex, and global cognition for this study. Age (calculated from birthdate to date of decision making assessment), sex (male coded as 1 and female coded as 0), and education (self-reported number of years completed), were included as matching variables since these have previously demonstrated associations with decision making.²⁰ Global cognition was included as a matching variable (in addition to demographics) as global cognition has been shown to be associated with decision making,^21–23 and because there are well documented racial differences in cognitive performance in old age,²⁴ including for Blacks and Whites in the current cohorts.²⁵

Mahalanobis Distance Matching

Mahalanobis Distance matching is a technique that considers the correlation structure of the matching variables (in this case, age, education, sex, and global cognition) in order to create a multivariate distance by which matches with shortest distance to each case are selected for matching. For a randomly selected Black participant, we first identified a list of White participants with matching sex, age, education, and cognition. Briefly, sex was matched exactly at the individual level. Age was matched according to four categories; greater than or equal to 60 years old to less than 70 years old, greater than or equal to 70 years old to less than 80 years old, greater than or equal to 80 years old to 90 years old, and greater than or equal to 90 years old to less than 100 years old. Education was matched according three categories; from 0 to 12 years of education, 13 to 16 years of education, and greater than 16 years of education. Global cognition was matched within a range of +/− 0.25 z-score at the individual level. Employing these categories ensured that no match is overly distant. Next, we ranked the list based on Mahalanobis distance and selected the White participant with the shortest distance. We repeated the process for all Black participants, and during the process we ensured that no White participant was selected more than once as a match. This process resulted in 138 matched pairs. Mahalanobis Distance matching is similar to propensity score matching and yields similar results in simulations. A potential drawback to propensity score matching is that there is a possibility of a trade off among matching variables to make a propensity match. For example, someone high in age and low in education may be matched with someone low in age and high in education. For this reason, we have elected to use Mahalanobis Distance matching for our study.

Race

Race was determined by self-report in response to the question, “With which group do you most closely identify yourself?” Participants could respond according to 1990 U.S. Census race categories, which included categories of “White” and “Black or African American.”

Decision Making

We used a modified performance-based measure of financial and healthcare decision making that assesses decisions relevant to living independently. This measure includes a total of 12 items and is described in previous work.^{20–22,26–29} Of the total of 12 items, 6 items are specific to financial decision making and involve making accurate or optimal choices among mutual funds with varying levels of difficulty. The other 6 items are specific to healthcare decision making and involve choosing among different HMO healthcare plans with varying levels of difficulty. This measure yields a total decision making score of 0 to 12, as well as subscale scores for financial decision making and healthcare decision making of 0 to 6 each.

Literacy

Financial and health literacy were measured using 32 questions that assess knowledge of financial and health concepts.^26,30–32 Of these 32 questions, 23 assessed financial literacy, and many of these were adapted from the Health and Retirement Study (Lusardi and Mitchell, 2007).³³ Examples of topics measured were the ability to do calculations (numeracy), as well as semantic knowledge of financial concepts such as compound interest, stocks, and bonds. Of the 32 total literacy questions, 9 assessed health literacy. Health questions assessed health-related topics such as knowledge about Medicare and Medicare Part D, best practices for following prescription instructions, causes of mortality in old age, and understanding drug risks. The formats of the responses were multiple choice, true/false, and open-ended; and each item was scored as correct or incorrect. Because of differences in number of items across the domains of literacy, scores were recorded as the percent correct (from 0 to 100) out of total items within each domain. Total literacy score was the average of the two domain percentages. We have previously shown that this measure of literacy is related to engagement in health promoting behaviors, functional status, aspects of physical and mental health, financial and healthcare decision making, mild cognitive impairment, and cognitive decline.^26,30–32

Cognition

An established cognitive battery including 18 measures was utilized for assessment of global cognition.^19,34,35 Measures in the battery assessed a wide array of cognitive abilities. The battery included 2 semantic memory tests (Verbal Fluency and Boston Naming), 2 visuospatial ability tests (Judgment of Line Orientation and Standard Progressive Matrices), 4 perceptual speed tests (oral version of the Symbol Digit Modalities Test, Number Comparison, Stroop Color Naming, and Stroop Word Reading), 7 episodic memory tests (Word List Memory, Word List Recall and Word List Recognition from the procedures established by the CERAD; immediate and delayed recall of Logical Memory Story A; and immediate and delayed recall of the East Boston Story), and 3 working memory tests (Digit Span subtests forward and backward of the Wechsler Memory Scale-Revised and Digit Ordering). Performance scores on each of the individual measures were transformed into z-scores according to the mean and standard deviation of the baseline cognitive assessment of the entire sample of the parent study.³⁴ Global cognition was calculated by averaging the z-scores across all tests. Dementia diagnosis was determined according to standardized criteria.³⁶

Statistical Analysis

Descriptive and bivariate statistics characterized non-demented older Black and older White adults. Chi-square tests were used for categorical variables, t-tests were used for continuous variables, and non-parametric Wilcoxon Rank Sum tests were reported if distributions were not normal. Linear regression models were then performed to examine the associations between race (Black=1, White=0), literacy and decision making (total, financial, and health). Mediation was examined in accordance with Baron and Kenny (1986).³⁷ First, we examined the racial differences in literacy and decision making; second, we confirmed the association of literacy with decision making; finally, we regressed decision making on both race and literacy. Mediation would be evident if association of race with decision making is attenuated while literacy remains associated with decision making. The attenuation of race association is supported by the reduction in regression coefficient for race as well as its corresponding p-value. Although groups were rigorously matched using Mahalanobis Distance according to age, education, sex, and global cognition, individual pairwise cases were not matched exactly according to covariates due to the variation of age, education, and global cognition within the categorical ranges as described above. Hence, all models included terms to control for the effects of age, education, sex, and global cognition. Analyses were conducted in SAS version 9.3 software.

RESULTS

Descriptive Data

As a result of Mahalanobis matching, there were no racial differences according to age, education, sex, and global cognition (Table 1). Mean age was within the mid-70s, mean level of education was about 15 years, and the sample was predominantly female. Racial differences were observed for total decision making (Z=−2.993, p=0.003) as well as the subscales of financial decision making (Z=−2.534, p=0.011) and healthcare decision making (Z=−2.450, p=0.014) such that older Black adults performed lower than White participants. A similar race difference was observed for total literacy such that older Black adults performed lower.

Table 1. Demographic, cognitive, and other descriptive data.

Age and education are presented in years. Global cognition is a mean of z-scores. Literacy is percent correct, and decision making is total score correct. For age, global cognition, and literacy, t-values are reported. For sex, X² is reported. For education and decision making, Wilcoxon Z-values are reported.

	Black (N=138)		White (N=138)
	Mean	SD	Mean	SD	t, Z, X²	p-value
Age	76.85	6.07	77.30	6.38	0.60	0.55
Education	14.91	3.12	14.99	2.98	-0.32	0.75
Sex (M/F)	27/111		27/111		0	1
Global Cognition	0.11	0.53	0.15	0.50	0.59	0.56
Total Decision Making	6.89	2.52	7.75	2.58	-2.99	<0.01
Financial Decision Making	3.20	1.23	3.58	1.38	-2.53	0.01
Healthcare Decision Making	3.70	1.67	4.17	1.51	-2.45	0.01
Total Literacy	60.37	13.52	68.97	14.01	5.18	<0.01

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Decision Making

In linear regression models adjusted for age, education, sex, and global cognition, older Black adults performed lower than White adults on the total literacy score (Table 2, Model 1). Separately, older Black adults performed lower than White adults on the decision making total score (Table 3, Model 1). Literacy was also associated with total decision making (Table 3, Model 2); however, when included in the model with race, the race term was attenuated and no longer significant but the association of literacy persisted (Table 3, Model 3). This finding suggests that literacy largely accounts for racial differences in decision making (Figure 1).

Table 2. Associations of Race with Literacy.

The dependent variable is literacy percent correct of 32 total questions. Age and education are presented in years. Global cognition is a mean of z-scores of 18 cognitive tests. Literacy is the average of the two domain (financial and health) percentages correct. For sex, male is coded as 1 and female is coded as 0. For race, Black is coded as 1 and White is coded as 0.

	Model 1
Adjusted R-squared	0.4050
	Estimate (Standard Error, [95% Confidence Interval], p-value)
Age	-0.28 (0.11, [−0.50, −0.05], 0.02)
Education	1.05 (0.24, [0.59, 1.52], <0.01)
Sex (Male=1, Female=0)	5.48 (1.71, [2.12, 8.83], <0.01)
Global Cognition	11.84 (1.48, [8.94, 14.74], <0.01)
Race (Black=1, White=0)	-8.20 (1.34, [−10.82, −5.57], <0.01)

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Table 3. Associations of Race and Literacy with Total Decision Making.

The dependent variable is financial and healthcare decision making total score. Age and education are presented in years. Global cognition is a mean of z-scores of 18 cognitive tests. Literacy is the average of the two domain (financial and health) percentages correct. For sex, male is coded as 1 and female is coded as 0. For race, Black is coded as 1 and White is coded as 0.

	Model 1	Model 2	Model 3
Adjusted R-squared	0.4409	0.4666	0.4715
	Estimate (Standard Error, [95% Confidence Interval], p-value)
Age	-0.08 (0.02, [−0.12, −0.04], <0.01)	-0.06 (0.02, [−0.10, −0.03], <0.01)	-0.07 (0.02, [−0.10, −0.03], <0.01)
Education	0.21 (0.04, [0.13, 0.29], <0.01)	0.16 (0.04, [0.08, 0.24], <0.01)	0.12 (0.04, [0.09, 0.25], <0.01)
Sex (Male=1, Female=0)	1.16 (0.30, [0.58, 1.74], <0.01)	0.89 (0.29, [0.32, 1.47], <0.01)	0.93 (0.29, [0.35, 1.50], <0.01)
Global Cognition	2.29 (0.26, [1.78, 2.79], <0.01)	1.73 (0.28, [1.19, 2.27], <0.01)	1.79 (0.28, [1.25, 2.33], <0.01)
Race (Black=1, White=0)	-0.80 (0.23, [−1.25, −0.34], <0.01)		-0.45 (0.24, [−0.93, 0.02], 0.06)
Literacy		0.05 (0.01, [0.03, 0.07], <0.01)	0.04 (0.01, [0.02, 0.06], <0.01)

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Figure 1. — Standardized regression coefficients for the association between race and literacy (−8.20) and literacy and total decision making (0.04), after multiplication, estimate the indirect effect of race on total decision making through literacy. The direct effect of race on total decision making is estimated by standardized regression coefficient for the association between race and total decision making after controlling for literacy (−0.45). Regression models were adjusted for age, education, sex, and global cognition. *p<0.05

We further investigated the impact of literacy on the separate subscales (financial and healthcare) of the decision making measure. In models adjusting for age, education, sex, and global cognition, a racial difference was observed such that older Black adults performed lower on the financial decision making measure (Supplementary Table S1, Model 1). Total literacy was associated with financial decision making (Supplementary Table S1, Model 2), and again, when introduced into the model with race, the race term was attenuated and no longer significant (Supplementary Table S1, Model 3). Again, this suggests that literacy accounts for the racial difference in financial decision making (Supplementary Figure S1).

Finally, in regression models adjusting for age, education, sex, and global cognition, a racial difference was observed in healthcare decision making such that older Black adults performed lower than older White adults (Supplementary Table S2, Model 1). Total literacy was associated with healthcare decision making (Supplementary Table S2, Model 2), and again, when introduced into the model together with race, the race term became attenuated and nonsignificant (Supplementary Table S2, Model 3), suggesting that literacy accounts for racial differences in healthcare decision making (Supplementary Figure S2).

DISCUSSION

In 276 non-demented older Black and White adults matched on several important variables, we examined racial differences in financial and healthcare decision and tested the hypothesis that literacy mediates these racial differences. We observed a significant racial difference in decision making such that older Black adults performed lower than older White adults on the total decision making measure, as well as on specific domains of financial and healthcare decision making when considered separately. Importantly, financial and health literacy largely accounted for racial differences in total decision making, and this observation held for both financial and healthcare decision making when considered separately. To our knowledge, this is the first study to examine racial differences using a well-established measure of financial and healthcare decision making in older adults and to identify a contextual factor that accounts for racial differences in decision making performance.

These findings make an important contribution to the growing body of knowledge on decision making. Prior to this study, evidence from other financial and health preference survey studies suggested that racial differences in financial and healthcare decision making might exist among older adults.^3–5,7–10 Our study is unique in its examination of older Blacks and Whites robustly-matched using Mahalanobis distance according to important demographic and cognitive considerations and in its use of an established measure of decision making with real-world financial and healthcare implications. This study further extends the literature by providing support for a contextual factor as a likely mediator for racial differences in decision making. This finding has important public health implications, as contextual factors are inherently modifiable, and as such, the identification of a contextual factor as a mediator points to a feasible avenue for intervention to mitigate differences in decision making in old age.

Our results concerning financial and health literacy are important to consider. Previous work from our group has shown financial and health literacy to be strongly associated with financial and healthcare decision making.^26,38 In the broader health disparities research framework, social determinants due to historically institutionalized structural racism resulting in unequal access to supportive and beneficial resources are viewed as central mechanisms underlying racial differences^39,40 as opposed to inherent biological determinants. Older Black adults over the age of 65 in the U.S. have undoubtedly lived through social disadvantage and lack of equitable access to supportive resources due to historical racism and social injustice.^41–44 Older Black adults showed lower financial and health literacy than White adults in our study. Since this type of literacy has been viewed as a socially-influenced and community-dependent construct,^45–48 this difference can be ascribed to social disadvantage and lack of access to equal resources at the community level experienced by Blacks historically and currently. The present study underscores the critical importance of providing equal opportunities to develop financial and health literacy to persons of all backgrounds in order to support optimal decision making. Since decision making has been shown to be associated with severe adverse outcomes (e.g., mortality) in older persons, the equitable provision of financial and health literacy opportunities to adults of all ages should be considered an important public health priority.

Strengths of the current study include the utilization of a large, well-characterized sample of participants, the use of a robust statistical approach for group matching (Mahalanobis Distance), measures of decision making and literacy with significant financial and health implications, and consideration of covariates known to impact decision making. Weaknesses include the inability to make causal inferences due to the cross-sectional design (though longitudinal collection of these data is currently ongoing). The data are also from a selected, relatively small group of primarily English-speaking people who may differ from the general population of diverse older adults in education and other social factors. Furthermore, other racial groups are not specifically addressed in the present study. It will therefore be important to replicate these findings in more representative and other diverse samples. We acknowledge our decision making measure may not capture all types of decisions important for wellbeing in financial and healthcare matters, and we also acknowledge that financial and healthcare considerations may not be independent considerations in the present measure. Findings from the present study highlight the importance of financial and health literacy as a modifiable target for interventions to improve financial and healthcare decision making and reduce differences among demographically diverse older age groups.

Supplementary Material

supinfo

Supplementary Figure S1: Mediation of Race Association with Financial Decision Making by Literacy.

Supplementary Figure S2: Mediation of Race Association with Healthcare Decision Making by Literacy.

Supplementary Table S1: Associations of Race and Literacy with Financial Decision Making.

Supplementary Table S2: Associations of Race and Literacy with Healthcare Decision Making.

Supplementary Table S3: Mahalanobis Distance Matching 2:1 (N=414).

Supplementary Table S4: Propensity Score Weighting (Difference Threshold=0.01).

Supplementary Table S5: Propensity Score Weighting (Difference Threshold=0.05).

Supplementary Table S6: Associations of Education with Literacy in the Whole Sample (N=276).

Supplementary Table S7: Associations of Education with Literacy in Blacks (N=138).

Supplementary Table S8: Associations of Education with Literacy in Whites (N=138).

Supplementary Table S9: Associations of Race and Income at Age 40 with Total Decision Making (Education Included As Covariate).

Supplementary Table S10: Associations of Race and Income at Age 40 with Total Decision Making (Education Not Included as Covariate).

NIHMS1720598-supplement-supinfo.pdf^{(412.2KB, pdf)}

ACKNOWLEDGMENT

This work was supported by the National Institute on Aging at the National Institutes of Health grants R01AG055430 to SDH, RF1AG022018 to LLB, R01AG017917 to DAB, and R01AG033678 and R01AG060376 to PAB. The authors gratefully thank the Rush Minority Aging Research Study and Memory and Aging Project staff and participants.

Sponsor’s Role: None.

Footnotes

Conflict of Interest Disclosures: No conflicts of interest

SUPPORTING INFORMATION

Additional Supporting Information may be found on a document in the online version of this article. This document includes the following:

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