This article was downloaded by: [New York University] On: 21 July 2015, At: 08:39 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: 5 Howick Place, London, SW1P 1WG
Aging, Neuropsychology, and Cognition: A Journal on Normal and Dysfunctional Development Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/nanc20
Cognitive function in older adults according to current socioeconomic status a
ab
c
Michael Zhang , Shawn D. Gale , Lance D. Erickson , Bruce L. a
b
ab
Brown , Parker Woody & Dawson W. Hedges a
Department of Psychology, Brigham Young University, Provo, UT, USA
Click for updates
b
The Neuroscience Center, Brigham Young University, Provo, UT, USA c
Department of Sociology, Brigham Young University, Provo, UT, USA Published online: 07 Jan 2015.
To cite this article: Michael Zhang, Shawn D. Gale, Lance D. Erickson, Bruce L. Brown, Parker Woody & Dawson W. Hedges (2015) Cognitive function in older adults according to current socioeconomic status, Aging, Neuropsychology, and Cognition: A Journal on Normal and Dysfunctional Development, 22:5, 534-543, DOI: 10.1080/13825585.2014.997663 To link to this article: http://dx.doi.org/10.1080/13825585.2014.997663
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.
Downloaded by [New York University] at 08:39 21 July 2015
This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions
Aging, Neuropsychology, and Cognition, 2015 Vol. 22, No. 5, 534–543, http://dx.doi.org/10.1080/13825585.2014.997663
Cognitive function in older adults according to current socioeconomic status Michael Zhanga, Shawn D. Galea,b, Lance D. Ericksonc, Bruce L. Browna, Parker Woodyb and Dawson W. Hedgesa,b*
Downloaded by [New York University] at 08:39 21 July 2015
a Department of Psychology, Brigham Young University, Provo, UT, USA; bThe Neuroscience Center, Brigham Young University, Provo, UT, USA; cDepartment of Sociology, Brigham Young University, Provo, UT, USA
(Received 6 May 2014; accepted 4 December 2014) Cognitive function may be influenced by education, socioeconomic status, sex, and health status. Furthermore, aging interacts with these factors to influence cognition and dementia risk in late life. Factors that may increase or decrease successful cognitive aging are of critical importance, particularly if they are modifiable. The purpose of this study was to determine if economic status in late life is associated with cognition independent of socioeconomic status in early life. Cross-sectional demographic, socioeconomic, and cognitive function data were obtained in 2592 older adults (average age 71.6 years) from the Center for Disease Control’s National Health and Nutrition Examination Survey (NHANES) and analyzed with linear regression modeling. Cognitive function, as measured with a test of processing speed, was significantly associated with poverty index scores after adjusting for educational attainment as an estimate of childhood socioeconomic status, ethnic background, age, health status, and sex (P < 0.001). Our findings suggest that current economic status is independently associated with cognitive function in adults over age 60 years. Keywords: cognitive function; dementia; older adults; poverty index; socioeconomic standing
Cognitive deficits and dementia are of considerable personal and public health importance (Kukull, 2006). After peaking at approximately age 40 years, cognitive function tends to decline (Anderson, Anderson, Northam, Jacobs, & Catroppa, 2001; Singh-Manoux et al., 2012) and can deteriorate to dementia. Although the three most common types of dementia – Alzheimer’s disease, cerebrovascular disease, and Lewy-body disease – are associated with a considerable amount of cognitive decline, they do not explain a majority of cognitive decline, and other causes of cognitive decline require consideration (Boyle et al. 2013). Among other factors most commonly associated with cognitive function in older adults are socioeconomic factors. These factors, including childhood socioeconomic conditions, education, income, occupation, and wealth, have been studied independently of and in relation to one another, with several studies suggesting an aggregate or cumulative effect of socioeconomic risks on cognitive impairment (Lee, Back, Kim, & Byeon, 2010; Long, Ickovics, Gill, & Horowitz, 2001; Zhang, Gu, & Hayward, 2008). Long et al. (2001), for example, examined whether the effects of low education, low *Corresponding author. Email: [email protected] © 2015 Taylor & Francis
Downloaded by [New York University] at 08:39 21 July 2015
Aging, Neuropsychology, and Cognition
535
income, and nonskilled work are cumulative as social class markers of adversity. Participants with the highest adversity scores performed worst on the cognitive assessment used in their study. Moreover, Lee et al. (2010) examined multiple socioeconomic indicators simultaneously on the premise that those of low socioeconomic status tend to be particularly vulnerable to health risks throughout their lifetimes. These researchers found that the combined effect of education, income, wealth, and occupation on cognition was greater than the individual effects of these socioeconomic indicators. Indeed, socioeconomic advantage across the life span is associated with cognition (Zhang et al., 2008). Socioeconomic status is a life-course phenomenon, with different socioeconomic indicators influencing cognitive health differently at different stages (Fors, Lennartsson, & Lundberg, 2009; González, Tarraf, Bowen, Johnson-Jennings, & Fisher, 2013; Lee et al., 2010). Early-life risks such as education and late-life risks such as income might influence cognitive health trajectories through different pathways (Lee et al., 2010), and a life-course perspective on socioeconomic status would allow the fuller context of socioeconomic risk factors for cognitive decline or impairment to be understood. Some recent studies taking a life-course perspective suggest the primacy of childhood socioeconomic conditions in influencing late-life cognitive well-being (Everson-Rose, Mendes De Leon, Bienias, Wilson, & Evans, 2003; Fors et al., 2009; Kaplan et al., 2001; Karlamangla et al., 2009; Zhang et al., 2008). Kaplan et al. (2001), for example, examined whether childhood socioeconomic environment has an effect on cognitive functioning in middle age in a population-based study of Finnish men and found an association between parental socioeconomic position and cognitive function. Participants with more socioeconomically disadvantaged childhood performed worst on the cognitive tests, and even after controlling for their own education, parental socioeconomic position still had a significant effect on cognitive functioning. Controlling for adult socioeconomic status, Zhang et al. (2008) found that early-life socioeconomic adversity was associated with cognition. Other studies have found that adult socioeconomic status mediates the relationship between childhood conditions and late-life cognitive function. The pathway or “indirect effects” model was consistent with the findings of Singh-Manoux, Richards, and Marmot (2005), which suggested that childhood socioeconomic position has an indirect effect on adult cognition through the pathways of education and adult socioeconomic position. Similarly, González et al. (2013) found that adult socioeconomic achievement predicted global cognitive functioning more than did childhood socioeconomic conditions in a longitudinal study of older adults, arguing that childhood conditions are associated with educational and adult socioeconomic opportunities. Furthermore, these researchers found that adult socioeconomic achievement may reverse adverse effects of childhood socioeconomic conditions and thus afford cognitive protection. Such a perspective raises questions about the influence of social mobility on late-life cognitive function and risk for impairment. Luo and Waite (2005) found that the negative impact of low childhood socioeconomic status is partly ameliorated by upward mobility to higher adult socioeconomic status, suggesting that instances of social mobility must also be considered. Because cumulative advantage and disadvantage – with early advantages or disadvantages compounding throughout the life span – have been observed, the effects of later-life disadvantages (or advantages) have not been assumed to operate independently. Since many studies have adopted the conventional wisdom of these linear lifecourse developmental trajectories, few studies have attempted to investigate whether laterlife socioeconomic circumstances influence cognitive function independent of early-life
Downloaded by [New York University] at 08:39 21 July 2015
536
M. Zhang et al.
socioeconomic circumstances. However, some recent studies have begun to consider these influences in the context of social mobility (Marengoni, Fratiglioni, Bandinelli, & Ferrucci, 2011; Nguyen, Couture, Alvarado, & Zunzunegui, 2008; Zeki Al Hazzouri et al., 2011). Zeki Al Hazzouri et al. (2011), for instance, found an association between socioeconomic position across life span and cognition in older Mexican-Americans. Furthermore, these authors also reported an association between cumulative socioeconomic position and cognition. These findings suggest that childhood socioeconomic conditions may not be the only determinant of cognitive functioning in later life and that adult and life-course socioeconomic conditions may play an important role in late-life cognitive ability. Similarly, Nguyen et al. (2008) found socioeconomic position across the life span and current economic resources to be significant predictors of cognitive impairment. Specifically, in the Latin-American context of their study, they found that early-life exposure to rural environments typically lead to low educational attainment and lifelong working farm labor. Occupation, in this regard, is a particularly strong risk factor. For example, the farming occupation is associated with childhood rural living, that is, poor socioeconomic conditions and low education levels. The authors found a unique effect where the association between farming and cognitive impairment existed even when controlling for rural childhood socioeconomic conditions and education attainment. The authors therefore show that there is an independent association between current socioeconomic status and cognitive function in later life, not to mention a socioeconomic disadvantage gradient with accumulating disadvantages reflecting an increased risk of cognitive impairment. In a third different national and ethnic context, Marengoni et al. (2011) studied a population-based sample of older Italian people residing in semi-rural areas and found that prevalent and incident cognitive impairment but without dementia varied in relation to life span socioeconomic status. They found that low education in early life and high physical job demand in adulthood were independently associated with prevalent cognitive impairment without dementia and that low educational attainment, manual occupation, and high physical job demands were associated with incident cognitive impairment without dementia. Interestingly, the effect of manual occupation on cognitive impairment without dementia was only significant in highly educated participants, suggesting that older adults experiencing different socioeconomic conditions during their lifetimes might decline differentially in cognitive health. Furthermore, Leist, Hessel, and Avendano (2014) studied the influence of economic recessions on late-life cognitive function and found that, for men, each additional recession between ages 45 and 49 years was associated with worse cognitive functioning in older age and that the same effect was present in women, with each additional recession experienced at ages 25–44 predicting worse late-life cognitive function. Recessions are typically associated with downward social ability and socioeconomic hardships independent of earlier-life socioeconomic conditions. Given the associations between socioeconomic factors and cognitive function, the objective of this study was to further examine the association between current socioeconomic factors and cognitive function in older adults while controlling for earlier life socioeconomic status estimated by educational attainment. We hypothesized that current socioeconomic status in older people would be independently associated with cognitive function in late life.
Aging, Neuropsychology, and Cognition
537
Downloaded by [New York University] at 08:39 21 July 2015
Method Subjects We obtained data for this study from the National Health and Nutrition Examination Survey (NHANES). We obtained demographic, socioeconomic, and cognitive function data in older adults by combining the NHANES 1999–2000 and 2001–2002 data sets. The NHANES is a publically available cross-sectional survey consisting of interviews and examinations related to a wide variety of health-related, demographic, and socioeconomic information. The NHANES uses a complex, multistage, probabilistic sampling design requiring proper weighting techniques to obtain representative estimates for noninstitutionalized residents of the United States (Centers for Disease Control and Prevention; CDC, 2013). From the 1999–2000 and 2001–2002 NHANES data sets, we included all female and male adults age 60 years or older for whom the relevant variables were available. Complete data for cognitive functioning, poverty-to-income ratio, and controls were available for 2,592 subjects. Socioeconomic status We estimated current socioeconomic status from a poverty index that assesses selfreported family income relative to the poverty threshold (U.S. Census Bureau, 2013). Low ratios indicate more impoverishment with an index below 1.0 indicating income below the poverty threshold. To control for socioeconomic status earlier in life, we used the level of educational attainment as a proxy for earlier socioeconomic status as it may reflect childhood socioeconomic positions (Bobak, Hertzman, Skodova, & Marmot, 2000; Davey Smith et al., 1998). Unfortunately, this NHANES survey did not ask participants to report more direct estimates of childhood socioeconomic status, such as parental education or occupation when the NHANES subjects were children. Cognitive assessment The Digit Symbol-Coding subtest of the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III; Wechsler, 1997) is a measure of cognitive processing speed that is sensitive to overall brain functioning and dementia (Lezak, Howieson, & Loring, 2004). The Digit Symbol-Coding test requires subjects to copy symbols that are matched with digits as quickly as possible. The subject has 120 seconds to complete as many of the 133 items as possible. The Digit Symbol-Coding subtest in the NHANES 1999–2000 and 2001–2002 data sets used in our study is referred to as the “Digit-Symbol Substitution Test,” likely because a previous NHANES survey (1988–1994; i.e., “NHANES III”) used a computerbased facsimile of Digit Symbol-Coding but named it “Digit Symbol Substitution” (Baker et al., 1985). In the current study, we refer to this test by its proper name, the Digit Symbol-Coding subtest of the WAIS-III. Although other NHANES surveys have included multiple tests of cognitive functioning, the only measure of cognitive function included in the data sets analyzed in the present study was Digit Symbol-Coding. Statistical analysis We used linear-regression modeling to examine the association between current poverty index and cognitive status estimated by performance on the Digit Symbol-Coding test while controlling for socioeconomic status earlier in life estimated by educational
538
M. Zhang et al.
Downloaded by [New York University] at 08:39 21 July 2015
attainment and also controlling for ethnic background, sex, age, and self-reported health based on the participant’s response to a 5-point health scale ranging from 1 (poor) to 5 (excellent). Stata 13.1 (StataCorp, College Station, TX) was used to carry out all statistical analyses. We included the sampling design to adjust the parameter estimates to be representative of noninstitutionalized residents of the United States ranging from 60 to 85 years of age.
Results Descriptive statistics (mean, standard deviation, minimum, and maximum) are presented in Table 1. The mean age of this sample was 71.6 years (SD 7.9), 50.8% were female, and the mean poverty index was 2.5 (SD 1.5). A correlation matrix of all study variables is presented in Table 2. The regression model used to estimate the relationship between the poverty-to-income ratio and cognitive functioning was as follows: Digit Symbol-Coding ¼ α þ β1 pir þ βx þ e where α is the intercept, β1 is the coefficient for poverty-to-income ratio, pir is poverty-toincome ratio, βx is a matrix of coefficients and control variables that include educational attainment, sex, age, race-ethnicity, and self-rated health, and e is an error term that is normally distributed with a mean of 0. Results for this regression model are presented in Table 3. The regression coefficient estimate for poverty index on the Digit SymbolCoding score was 2.40 (95% confidence interval (CI): 1.87–2.93, P < .001), indicating that Digit Symbol-Coding scores significantly increased with higher poverty index scores. Age was also significantly associated with lower Digit Symbol-Coding scores (regression estimate = −0.79, 95% CI: −0.87 to −0.72, P < .001), and men had significantly lower Digit Symbol-Coding scores than did women (regression estimate = −5.32, 95% CI: −6.56 to −4.09, P < .001). Overall, educational attainment was associated with higher Digit
Table 1.
Subject characteristics, cognitive performance, and poverty index.
Total subject number (N) Mean age
2,592 71.6 years (SD = 7.9, range: 60.0–85.0)
Sex
Female Male Less than high school High school or equivalent More than high school Mexican-American Other-Hispanic Non-Hispanic White Non-Hispanic Black Other 40.8 (SD = 18.5) 2.5 (SD = 1.5) 2.8 (SD = 1.07)
Education level Ethnic background
Digit Symbol-Coding test Poverty-to-income ratio Self-rated health
Sources: NHANES 1999–2000 and 2001–2002.
Number (%) 1,317 (50.8%) 1,277 (49.2%) 1,034 (39.9%) 641 (24.7%) 919 (35.4%) 479 (18.5%) 94 (3.62%) 1596 (61.5%) 367 (14.2%) 58 (2.2%)
Aging, Neuropsychology, and Cognition Table 2.
Correlation matrix of study variables. 1
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539
1. Digit Symbol-Coding Test 2. Poverty-to-income ratio 3. Male 4. Age 5. Non-Hispanic White 6. Non-Hispanic Black 7. Hispanic 8. Other race-ethnicity 9. No HS diploma 10. HS diploma 11. More than HS diploma 12. Self-rated health
2
3
4
5
6
7
1.00 −.02 .01 −.01 −.00 −.01 .01 −.06 .05
1.00 .30 −.15 −.21 −.04 −.01 .02 −.00
1.00 −.52 −.68 −.17 −.36 .20 .19
1.00 −.22 −.06 .12 −.06 −.07
−.31 −.31 −.03 −.01 −.21
.07
8
9
10
11
12
1.00 .47 1.00 −.09 −.27 .32 −.19 −.23 .03 −.50 .12 .40
.10 −.12 .24 −.08 −.22 .01 −.42 −.01 .44
1.00 −.07 1.00 .34 −.03 1.00 −.18 −.02 −.47 1.00 −.18 .04 −.60 −.42 1.00 .19
.01
.30 −.05 −.26 1.00
Notes: HS, high school. N = 2,592. Sources: NHANES 1999–2000 and 2001–2002.
Table 3. Predicting number correct on digit symbol-coding test: u/nstandardized coefficients, 95% CIs, and P-values from ordinary least squares regression. Coefficient Poverty-to-income ratio Sex Female Male Age Race-ethnicity Non-Hispanic White Non-Hispanic Black Hispanic Other Educational attainment No HS diploma HS diploma More than HS diploma Self-rated health Constant N R2
95% CI
P
2.40
1.87, 2.93
Aging, Neuropsychology, and Cognition: A Journal on Normal and Dysfunctional Development Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/nanc20
Cognitive function in older adults according to current socioeconomic status a
ab
c
Michael Zhang , Shawn D. Gale , Lance D. Erickson , Bruce L. a
b
ab
Brown , Parker Woody & Dawson W. Hedges a
Department of Psychology, Brigham Young University, Provo, UT, USA
Click for updates
b
The Neuroscience Center, Brigham Young University, Provo, UT, USA c
Department of Sociology, Brigham Young University, Provo, UT, USA Published online: 07 Jan 2015.
To cite this article: Michael Zhang, Shawn D. Gale, Lance D. Erickson, Bruce L. Brown, Parker Woody & Dawson W. Hedges (2015) Cognitive function in older adults according to current socioeconomic status, Aging, Neuropsychology, and Cognition: A Journal on Normal and Dysfunctional Development, 22:5, 534-543, DOI: 10.1080/13825585.2014.997663 To link to this article: http://dx.doi.org/10.1080/13825585.2014.997663
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.
Downloaded by [New York University] at 08:39 21 July 2015
This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions
Aging, Neuropsychology, and Cognition, 2015 Vol. 22, No. 5, 534–543, http://dx.doi.org/10.1080/13825585.2014.997663
Cognitive function in older adults according to current socioeconomic status Michael Zhanga, Shawn D. Galea,b, Lance D. Ericksonc, Bruce L. Browna, Parker Woodyb and Dawson W. Hedgesa,b*
Downloaded by [New York University] at 08:39 21 July 2015
a Department of Psychology, Brigham Young University, Provo, UT, USA; bThe Neuroscience Center, Brigham Young University, Provo, UT, USA; cDepartment of Sociology, Brigham Young University, Provo, UT, USA
(Received 6 May 2014; accepted 4 December 2014) Cognitive function may be influenced by education, socioeconomic status, sex, and health status. Furthermore, aging interacts with these factors to influence cognition and dementia risk in late life. Factors that may increase or decrease successful cognitive aging are of critical importance, particularly if they are modifiable. The purpose of this study was to determine if economic status in late life is associated with cognition independent of socioeconomic status in early life. Cross-sectional demographic, socioeconomic, and cognitive function data were obtained in 2592 older adults (average age 71.6 years) from the Center for Disease Control’s National Health and Nutrition Examination Survey (NHANES) and analyzed with linear regression modeling. Cognitive function, as measured with a test of processing speed, was significantly associated with poverty index scores after adjusting for educational attainment as an estimate of childhood socioeconomic status, ethnic background, age, health status, and sex (P < 0.001). Our findings suggest that current economic status is independently associated with cognitive function in adults over age 60 years. Keywords: cognitive function; dementia; older adults; poverty index; socioeconomic standing
Cognitive deficits and dementia are of considerable personal and public health importance (Kukull, 2006). After peaking at approximately age 40 years, cognitive function tends to decline (Anderson, Anderson, Northam, Jacobs, & Catroppa, 2001; Singh-Manoux et al., 2012) and can deteriorate to dementia. Although the three most common types of dementia – Alzheimer’s disease, cerebrovascular disease, and Lewy-body disease – are associated with a considerable amount of cognitive decline, they do not explain a majority of cognitive decline, and other causes of cognitive decline require consideration (Boyle et al. 2013). Among other factors most commonly associated with cognitive function in older adults are socioeconomic factors. These factors, including childhood socioeconomic conditions, education, income, occupation, and wealth, have been studied independently of and in relation to one another, with several studies suggesting an aggregate or cumulative effect of socioeconomic risks on cognitive impairment (Lee, Back, Kim, & Byeon, 2010; Long, Ickovics, Gill, & Horowitz, 2001; Zhang, Gu, & Hayward, 2008). Long et al. (2001), for example, examined whether the effects of low education, low *Corresponding author. Email: [email protected] © 2015 Taylor & Francis
Downloaded by [New York University] at 08:39 21 July 2015
Aging, Neuropsychology, and Cognition
535
income, and nonskilled work are cumulative as social class markers of adversity. Participants with the highest adversity scores performed worst on the cognitive assessment used in their study. Moreover, Lee et al. (2010) examined multiple socioeconomic indicators simultaneously on the premise that those of low socioeconomic status tend to be particularly vulnerable to health risks throughout their lifetimes. These researchers found that the combined effect of education, income, wealth, and occupation on cognition was greater than the individual effects of these socioeconomic indicators. Indeed, socioeconomic advantage across the life span is associated with cognition (Zhang et al., 2008). Socioeconomic status is a life-course phenomenon, with different socioeconomic indicators influencing cognitive health differently at different stages (Fors, Lennartsson, & Lundberg, 2009; González, Tarraf, Bowen, Johnson-Jennings, & Fisher, 2013; Lee et al., 2010). Early-life risks such as education and late-life risks such as income might influence cognitive health trajectories through different pathways (Lee et al., 2010), and a life-course perspective on socioeconomic status would allow the fuller context of socioeconomic risk factors for cognitive decline or impairment to be understood. Some recent studies taking a life-course perspective suggest the primacy of childhood socioeconomic conditions in influencing late-life cognitive well-being (Everson-Rose, Mendes De Leon, Bienias, Wilson, & Evans, 2003; Fors et al., 2009; Kaplan et al., 2001; Karlamangla et al., 2009; Zhang et al., 2008). Kaplan et al. (2001), for example, examined whether childhood socioeconomic environment has an effect on cognitive functioning in middle age in a population-based study of Finnish men and found an association between parental socioeconomic position and cognitive function. Participants with more socioeconomically disadvantaged childhood performed worst on the cognitive tests, and even after controlling for their own education, parental socioeconomic position still had a significant effect on cognitive functioning. Controlling for adult socioeconomic status, Zhang et al. (2008) found that early-life socioeconomic adversity was associated with cognition. Other studies have found that adult socioeconomic status mediates the relationship between childhood conditions and late-life cognitive function. The pathway or “indirect effects” model was consistent with the findings of Singh-Manoux, Richards, and Marmot (2005), which suggested that childhood socioeconomic position has an indirect effect on adult cognition through the pathways of education and adult socioeconomic position. Similarly, González et al. (2013) found that adult socioeconomic achievement predicted global cognitive functioning more than did childhood socioeconomic conditions in a longitudinal study of older adults, arguing that childhood conditions are associated with educational and adult socioeconomic opportunities. Furthermore, these researchers found that adult socioeconomic achievement may reverse adverse effects of childhood socioeconomic conditions and thus afford cognitive protection. Such a perspective raises questions about the influence of social mobility on late-life cognitive function and risk for impairment. Luo and Waite (2005) found that the negative impact of low childhood socioeconomic status is partly ameliorated by upward mobility to higher adult socioeconomic status, suggesting that instances of social mobility must also be considered. Because cumulative advantage and disadvantage – with early advantages or disadvantages compounding throughout the life span – have been observed, the effects of later-life disadvantages (or advantages) have not been assumed to operate independently. Since many studies have adopted the conventional wisdom of these linear lifecourse developmental trajectories, few studies have attempted to investigate whether laterlife socioeconomic circumstances influence cognitive function independent of early-life
Downloaded by [New York University] at 08:39 21 July 2015
536
M. Zhang et al.
socioeconomic circumstances. However, some recent studies have begun to consider these influences in the context of social mobility (Marengoni, Fratiglioni, Bandinelli, & Ferrucci, 2011; Nguyen, Couture, Alvarado, & Zunzunegui, 2008; Zeki Al Hazzouri et al., 2011). Zeki Al Hazzouri et al. (2011), for instance, found an association between socioeconomic position across life span and cognition in older Mexican-Americans. Furthermore, these authors also reported an association between cumulative socioeconomic position and cognition. These findings suggest that childhood socioeconomic conditions may not be the only determinant of cognitive functioning in later life and that adult and life-course socioeconomic conditions may play an important role in late-life cognitive ability. Similarly, Nguyen et al. (2008) found socioeconomic position across the life span and current economic resources to be significant predictors of cognitive impairment. Specifically, in the Latin-American context of their study, they found that early-life exposure to rural environments typically lead to low educational attainment and lifelong working farm labor. Occupation, in this regard, is a particularly strong risk factor. For example, the farming occupation is associated with childhood rural living, that is, poor socioeconomic conditions and low education levels. The authors found a unique effect where the association between farming and cognitive impairment existed even when controlling for rural childhood socioeconomic conditions and education attainment. The authors therefore show that there is an independent association between current socioeconomic status and cognitive function in later life, not to mention a socioeconomic disadvantage gradient with accumulating disadvantages reflecting an increased risk of cognitive impairment. In a third different national and ethnic context, Marengoni et al. (2011) studied a population-based sample of older Italian people residing in semi-rural areas and found that prevalent and incident cognitive impairment but without dementia varied in relation to life span socioeconomic status. They found that low education in early life and high physical job demand in adulthood were independently associated with prevalent cognitive impairment without dementia and that low educational attainment, manual occupation, and high physical job demands were associated with incident cognitive impairment without dementia. Interestingly, the effect of manual occupation on cognitive impairment without dementia was only significant in highly educated participants, suggesting that older adults experiencing different socioeconomic conditions during their lifetimes might decline differentially in cognitive health. Furthermore, Leist, Hessel, and Avendano (2014) studied the influence of economic recessions on late-life cognitive function and found that, for men, each additional recession between ages 45 and 49 years was associated with worse cognitive functioning in older age and that the same effect was present in women, with each additional recession experienced at ages 25–44 predicting worse late-life cognitive function. Recessions are typically associated with downward social ability and socioeconomic hardships independent of earlier-life socioeconomic conditions. Given the associations between socioeconomic factors and cognitive function, the objective of this study was to further examine the association between current socioeconomic factors and cognitive function in older adults while controlling for earlier life socioeconomic status estimated by educational attainment. We hypothesized that current socioeconomic status in older people would be independently associated with cognitive function in late life.
Aging, Neuropsychology, and Cognition
537
Downloaded by [New York University] at 08:39 21 July 2015
Method Subjects We obtained data for this study from the National Health and Nutrition Examination Survey (NHANES). We obtained demographic, socioeconomic, and cognitive function data in older adults by combining the NHANES 1999–2000 and 2001–2002 data sets. The NHANES is a publically available cross-sectional survey consisting of interviews and examinations related to a wide variety of health-related, demographic, and socioeconomic information. The NHANES uses a complex, multistage, probabilistic sampling design requiring proper weighting techniques to obtain representative estimates for noninstitutionalized residents of the United States (Centers for Disease Control and Prevention; CDC, 2013). From the 1999–2000 and 2001–2002 NHANES data sets, we included all female and male adults age 60 years or older for whom the relevant variables were available. Complete data for cognitive functioning, poverty-to-income ratio, and controls were available for 2,592 subjects. Socioeconomic status We estimated current socioeconomic status from a poverty index that assesses selfreported family income relative to the poverty threshold (U.S. Census Bureau, 2013). Low ratios indicate more impoverishment with an index below 1.0 indicating income below the poverty threshold. To control for socioeconomic status earlier in life, we used the level of educational attainment as a proxy for earlier socioeconomic status as it may reflect childhood socioeconomic positions (Bobak, Hertzman, Skodova, & Marmot, 2000; Davey Smith et al., 1998). Unfortunately, this NHANES survey did not ask participants to report more direct estimates of childhood socioeconomic status, such as parental education or occupation when the NHANES subjects were children. Cognitive assessment The Digit Symbol-Coding subtest of the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III; Wechsler, 1997) is a measure of cognitive processing speed that is sensitive to overall brain functioning and dementia (Lezak, Howieson, & Loring, 2004). The Digit Symbol-Coding test requires subjects to copy symbols that are matched with digits as quickly as possible. The subject has 120 seconds to complete as many of the 133 items as possible. The Digit Symbol-Coding subtest in the NHANES 1999–2000 and 2001–2002 data sets used in our study is referred to as the “Digit-Symbol Substitution Test,” likely because a previous NHANES survey (1988–1994; i.e., “NHANES III”) used a computerbased facsimile of Digit Symbol-Coding but named it “Digit Symbol Substitution” (Baker et al., 1985). In the current study, we refer to this test by its proper name, the Digit Symbol-Coding subtest of the WAIS-III. Although other NHANES surveys have included multiple tests of cognitive functioning, the only measure of cognitive function included in the data sets analyzed in the present study was Digit Symbol-Coding. Statistical analysis We used linear-regression modeling to examine the association between current poverty index and cognitive status estimated by performance on the Digit Symbol-Coding test while controlling for socioeconomic status earlier in life estimated by educational
538
M. Zhang et al.
Downloaded by [New York University] at 08:39 21 July 2015
attainment and also controlling for ethnic background, sex, age, and self-reported health based on the participant’s response to a 5-point health scale ranging from 1 (poor) to 5 (excellent). Stata 13.1 (StataCorp, College Station, TX) was used to carry out all statistical analyses. We included the sampling design to adjust the parameter estimates to be representative of noninstitutionalized residents of the United States ranging from 60 to 85 years of age.
Results Descriptive statistics (mean, standard deviation, minimum, and maximum) are presented in Table 1. The mean age of this sample was 71.6 years (SD 7.9), 50.8% were female, and the mean poverty index was 2.5 (SD 1.5). A correlation matrix of all study variables is presented in Table 2. The regression model used to estimate the relationship between the poverty-to-income ratio and cognitive functioning was as follows: Digit Symbol-Coding ¼ α þ β1 pir þ βx þ e where α is the intercept, β1 is the coefficient for poverty-to-income ratio, pir is poverty-toincome ratio, βx is a matrix of coefficients and control variables that include educational attainment, sex, age, race-ethnicity, and self-rated health, and e is an error term that is normally distributed with a mean of 0. Results for this regression model are presented in Table 3. The regression coefficient estimate for poverty index on the Digit SymbolCoding score was 2.40 (95% confidence interval (CI): 1.87–2.93, P < .001), indicating that Digit Symbol-Coding scores significantly increased with higher poverty index scores. Age was also significantly associated with lower Digit Symbol-Coding scores (regression estimate = −0.79, 95% CI: −0.87 to −0.72, P < .001), and men had significantly lower Digit Symbol-Coding scores than did women (regression estimate = −5.32, 95% CI: −6.56 to −4.09, P < .001). Overall, educational attainment was associated with higher Digit
Table 1.
Subject characteristics, cognitive performance, and poverty index.
Total subject number (N) Mean age
2,592 71.6 years (SD = 7.9, range: 60.0–85.0)
Sex
Female Male Less than high school High school or equivalent More than high school Mexican-American Other-Hispanic Non-Hispanic White Non-Hispanic Black Other 40.8 (SD = 18.5) 2.5 (SD = 1.5) 2.8 (SD = 1.07)
Education level Ethnic background
Digit Symbol-Coding test Poverty-to-income ratio Self-rated health
Sources: NHANES 1999–2000 and 2001–2002.
Number (%) 1,317 (50.8%) 1,277 (49.2%) 1,034 (39.9%) 641 (24.7%) 919 (35.4%) 479 (18.5%) 94 (3.62%) 1596 (61.5%) 367 (14.2%) 58 (2.2%)
Aging, Neuropsychology, and Cognition Table 2.
Correlation matrix of study variables. 1
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539
1. Digit Symbol-Coding Test 2. Poverty-to-income ratio 3. Male 4. Age 5. Non-Hispanic White 6. Non-Hispanic Black 7. Hispanic 8. Other race-ethnicity 9. No HS diploma 10. HS diploma 11. More than HS diploma 12. Self-rated health
2
3
4
5
6
7
1.00 −.02 .01 −.01 −.00 −.01 .01 −.06 .05
1.00 .30 −.15 −.21 −.04 −.01 .02 −.00
1.00 −.52 −.68 −.17 −.36 .20 .19
1.00 −.22 −.06 .12 −.06 −.07
−.31 −.31 −.03 −.01 −.21
.07
8
9
10
11
12
1.00 .47 1.00 −.09 −.27 .32 −.19 −.23 .03 −.50 .12 .40
.10 −.12 .24 −.08 −.22 .01 −.42 −.01 .44
1.00 −.07 1.00 .34 −.03 1.00 −.18 −.02 −.47 1.00 −.18 .04 −.60 −.42 1.00 .19
.01
.30 −.05 −.26 1.00
Notes: HS, high school. N = 2,592. Sources: NHANES 1999–2000 and 2001–2002.
Table 3. Predicting number correct on digit symbol-coding test: u/nstandardized coefficients, 95% CIs, and P-values from ordinary least squares regression. Coefficient Poverty-to-income ratio Sex Female Male Age Race-ethnicity Non-Hispanic White Non-Hispanic Black Hispanic Other Educational attainment No HS diploma HS diploma More than HS diploma Self-rated health Constant N R2
95% CI
P
2.40
1.87, 2.93
