Abstract
Background:
Cognitive decline is a disproportionately prevalent condition among older Mexican Americans, highlighting the need to understand the biopsychosocial factors contributing to cognitive health in this population. The study aims to investigate the longitudinal association between functional difficulty and cognitive decline and to determine the moderating effect of neighborhood disadvantage among older Mexican Americans.
Methods:
We used four waves of data from the Hispanic Established Population for the Epidemiologic Study of the Elderly (2004–2005 to 2012–2013) linked to the 2010 National Neighborhood Data Archive. We selected older Mexican Americans who scored ≥ 21 on the Mini-Mental State Examination (MMSE) at baseline (n = 1,189). Cognitive function was measured by the MMSE. Neighborhood disadvantage was assessed based on five census-tract indicators. Functional difficulty was indicated by limitations in activities of daily living (ADL) and instrumental ADL (IADL). Multilevel mixed-effects regression models were performed to estimate cognitive change over time.
Results:
There was an average decline of 0.70 in the MMSE score as a function of ADL (p < 0.001), but no significant changes were observed for IADL. Significant interactions were observed between study wave and ADL/IADL (both p < 0.001) in predicting MMSE score. Participants with ADL/IADL difficulty experienced faster cognitive decline. Neighborhood disadvantage was associated with cognitive declines among participants with IADL difficulty, but not among those without.
Conclusion:
Neighborhood disadvantage may represent a socioeconomic stressor that can negatively impact cognitive health among older Mexican Americans with functional difficulty. Culturally responsive neighborhood-level efforts may contribute to reducing the risk of cognitive decline among older Mexican Americans.
Keywords: cognitive decline, neighborhood, functional difficulty, socioeconomic status, Mexican Americans
Introduction
Cognitive decline is disproportionately prevalent among some socio-demographic groups. For example, the risk of cognitive decline increases with advanced age [1, 2]. Cognitive decline is associated with a wide range of negative health consequences, including increased mortality and caregiving burden [3, 4]. Findings based on national surveys show that subjective cognitive impairment affects 9.9% of Hispanic adults [5, 6], and incident dementia is highest among older Hispanic adults aged 65 or older [7]. Additionally, it is projected that Mexican Americans aged 65 and older will increase from 54 million in 2019 to 94.7 million in 2060 [8]. With the rapidly growing aging population, older Mexican Americans face substantial challenges in cognitive decline and associated socioeconomic burdens, health complications, and cultural barriers to cognitive health promotion.
Functional Difficulty and Cognitive Decline
Functional difficulty has been identified as a potential risk factor for cognitive decline. Defined as difficulty in performing basic and instrumental activities that are essential to daily living, functional difficulty plays an important role in older adults’ quality of life [9]. Research on functional difficulty has relied on self-reported limitations in basic activities of daily living (ADL) that constitute self-care activities such as eating and bathing, and instrumental activities of daily living (IADL) that include more complex household tasks such as financial management and grocery shopping [10]. Performing ADL and IADL is cognitively demanding, requiring the engagement of higher-order neurocognitive functioning. As such, performing essential daily activities can be highly sensitive to declines in cognitive functioning. ADL and IADL difficulty can considerably erode older adults’ maintenance of independent living. Functional difficulty can potentially signal the presence of cognitive decline that was not previously detected, which may progress to dementia [11]. A recent scoping review has identified multiple studies suggesting that IADL is a stronger predictor of subjective cognitive decline than ADL [12], suggesting that the performance of IADL is more cognitively demanding than basic daily activities, and thus more sensitive to changes in cognitive functioning.
Older Mexican Americans also have a greater risk of having functional difficulty compared to non-Hispanic White adults [13, 14]. Among adults aged 75 and older, Mexican Americans are at nearly three times at risk of concurrent declines in functional and cognitive health compared to non-Hispanic White adults [15]. The differences in functional and cognitive outcomes between older Mexican Americans and non-Hispanic White adults can be due to multiple psychosocial factors. For example, compared to older White adults, older Mexican Americans are at heightened risk of functional and cognitive decline, partly due to limited access to healthcare and to cultural and language barriers to social participation [6].
However, focusing on older Mexican Americans specifically, the majority of research so far has focused on how cognitive decline is linked to subsequent ADL and IADL difficulty [14, 16, 17], whereas less is known about how functional difficulty is linked to subsequent cognitive decline among this group. Given that evidence from studies on older adults in general suggest that functional difficulty may be linked to subsequent cognitive declines [15, 18], it is possible that older Mexican Americans experiencing functional difficulty are at increased risk of cognitive decline. A more nuanced understanding of whether functional difficulty is linked to subsequent cognitive declines specifically among older Mexican Americans is important as it can help clarify potential mechanisms underlying cognitive aging in this group. In the existing literature, racial and ethnic comparisons in functional and cognitive health can conceal the heterogeneity among Mexican Americans, and it remains unclear as to whether such findings apply to older Mexican Americans specifically. Therefore, it is important to examine within-group differences to understand how functional difficulty influences subsequent cognitive decline.
Conceptual Framework
It is important to consider functional difficulty and cognitive disabilities within the neighborhood context [19, 20]. According to the Ecological Theory of Aging, older adults’ adaptations to environmental stressors play an important role in optimizing person-in-environment fit and overall well-being [21]. Neighborhood environment, as an upstream social determinant of health, can influence the onset and development of cognitive decline [22, 23]. Specifically, neighborhood disadvantage, characterized by neighborhood-level socioeconomic status such as percents of households below poverty level, has been commonly studied in relation to late-life health. Socioeconomic disadvantages in the neighborhood may signal collectively reduced adaptations to environmental stressors and diminished capacity to health promotion [22]. Focusing on older Mexican Americans, prior evidence shows that individuals living in socioeconomically disadvantaged neighborhoods experience significantly faster rates of cognitive decline than those in more affluent neighborhoods [24]. However, no study so far has examined the association between functional difficulty and cognitive health within the context of neighborhood socioeconomic disadvantage focusing on older Mexican Americans. It remains understudied as to whether neighborhood disadvantages affect older adults with functional difficulty to a greater extent compared to those without. Emerging evidence shows that neighborhood disadvantages are linked to both functional difficulty and cognitive declines [22, 25], suggesting that the association between neighborhood disadvantage and cognitive decline may differ between older adults with and without functional difficulty. A further understanding of neighborhood-cognition association stratified by functional status may have implications for community-level efforts to promote successful aging in place. For example, identifying whether older adults with functional difficulty are more vulnerable to the adverse effects of neighborhood disadvantage can inform the place-based interventions that specifically target populations at heightened risk, which may have the potential to reduce health disparities in cognitive declines among older Mexican Americans in disadvantaged neighborhoods.
Focusing on neighborhood factors of cognitive health, the purpose of the study is to understand how functional difficulty is longitudinally associated with cognitive decline and how neighborhood socioeconomic disadvantages influence this association. To bridge the research gaps discussed above, the present study aims to address the following two research questions: 1) Is difficulty in ADL and IADL longitudinally associated with cognitive decline over time among older Mexican Americans? 2) Is difficulty in ADL and IADL associated with faster cognitive decline over time among older Mexican Americans? 3) Is baseline neighborhood disadvantage associated with cognitive decline among older Mexican Americans with and without ADL/IADL difficulty? We hypothesize that 1) ADL/IADL difficulty is longitudinally associated with cognitive decline among older Mexican Americans, 2) ADL/IADL difficulty is associated with faster cognitive decline, and 3) Living in disadvantaged neighborhoods is more likely to be associated with cognitive decline among participants with ADL/IADL difficulty compared to those without ADL/IADL difficulty.
Methods
Data
We utilized four waves of data from the Hispanic Established Population for the Epidemiological Study of the Elderly (HEPESE) linked to the National Neighborhood Data Archive (NaNDA). The HEPESE is a national population-based study of older Mexican Americans aged 65 and older from five Southwestern states in the US (Texas, California, New Mexico, Colorado, and Arizona). The HEPESE data collection began in 1993–1994, and respondents were re-interviewed every two or three years. NaNDA is a publicly available neighborhood data archive containing measures on physical, socioeconomic, demographic, and social environments at multiple spatial levels such as census tracts [26, 27]. Data archived in NaNDA were based on government sources such as the US Census and American Community Survey. Data on neighborhood socioeconomic status came from NaNDA.
The present study used wave 5 (2004–2005) to wave 8 (2012–2013) of HEPESE due to its temporal alignment with tract-level data from NaNDA based on Census 2010. Also, HEPESE replenished its sample in wave 5, and using it as the baseline wave allowed for the inclusion of a newer cohort. The initial baseline sample size was n = 1,951 participants with an average follow-up time of 2.4 waves or approximately 6 years. To reduce the effects of baseline cognitive impairment on cognitive changes in subsequent waves [16], we excluded 762 participants at baseline who had cognitive impairment, defined as the Mini-Mental State Examination score lower than 21 [28], resulting in a baseline sample of n = 1,189. We used a cut-off score of ≥ 21 for MMSE to indicate moderate-to-high cognitive functioning at baseline. The cut-off score of 21 is considered optimal in previous research for specificity and sensitivity in detecting cognitive impairment [29, 30]. Additionally, the cut-off score of 21 is extensively utilized specifically among older Mexican Americans [31, 32]. The purpose of using the cut-off score is to obtain a sample with moderate to high cognitive function at baseline to establish the temporal sequence between functional difficulty and subsequent cognitive decline.
Measures
Cognitive Decline.
Cognitive decline was assessed by the English and Spanish versions of the Mini-Mental State Examination (MMSE) using in-person interviews. The MMSE was adopted from the Diagnostic Interview Schedule based on prior community surveys [33]. The MMSE has been commonly used as a brief instrument to measure cognitive health among older adults [29]. The MMSE utilized 30 items to evaluate seven domains of cognitive functioning, including orientation to time (5 points), orientation to place (5 points), 3-word registration (3 points), calculation (5 points), 3-word recall (3 points), language (8 points), and the ability to draw pentagon (1 point). The total score ranged from 0 to 30, with a lower score indicating more severe cognitive impairment. The MMSE has been validated and shown reliability among both English and Spanish individuals [29, 34]. The Spanish version of the MMSE has met the standard criteria for translated instruments and implemented in surveys among older Mexican Americans [35]. Cognitive decline is a continuous time-varying outcome variable.
Functional Difficulty.
Functional difficulty was measured in terms of difficulty in performing ADLs and IADLs. Respondents were asked whether they can perform the activities without help and the response categories were Yes or No. ADLs were assessed in seven activities, including walking across a room, bathing, personal grooming, dressing, eating, getting from a bed or chair, and using the toilet. The number of activities that older adults have difficulty performing was summed up, with a final score of ADL ranging from 0 to 7. Similarly, IADLs were assessed in ten activities, including using a telephone, vehicle traveling, grocery shopping, meal preparation, light household, taking medicine, banking, heavy household work, climbing stairs, and walking half a mile [16]. A final score of IADL ranging from 0 to 10 was created. Both ADL and IADL were then dichotomized to indicate whether older adults experienced any difficulty in ADL and IADL, respectively.
Neighborhood Socioeconomic Disadvantages.
Neighborhood socioeconomic disadvantages was based on census tract data and created using the 11-digit census code linked to NaNDA. We used five census tract-level indicators of neighborhood socioeconomic status to measure neighborhood socioeconomic disadvantages, which were percentages of the population within a census tract that were under 18, unemployed, below the poverty level, receiving social assistance, and female-headed households. Consistent with previous measures on neighborhood disadvantage [36], we created Z scores for each indicator and averaged across the five indicators to create a score indicating the level of neighborhood disadvantage, with a higher score indicating higher levels of disadvantage.
Control Variables.
Relevant socio-demographic covariates were controlled due to their potential confounding effects on cognitive status. We controlled for age (in years), sex (male or female), and years of education. Socioeconomic indicators included financial strain, which was measured using the question, “how much difficulty do you have in meeting monthly payments on your bills?” The response categories ranged from 0 (none) to 3 (a great deal). Language used for the survey was documented as English or Spanish. Body mass index (BMI) was calculated as weight in kilograms divided by height in square meters, and was categorized as underweight or normal, overweight, or obese. Self-reported medical conditions included hypertension, diabetes, and stroke (yes or no). Depressive symptoms was measured with the 20-item Center for Epidemiological Studies Depression (CES-D) scale. Census tract-level control was Hispanic density indicated by percentage of individuals who were Hispanic. We computed Z score for Hispanic density. All variables were treated as time-varying except sex, nativity, years of education, language of interview, neighborhood disadvantage, and Hispanic density.
Data Analysis
We calculated means for continuous variables and frequency for categorical variables to capture the sample characteristics. Multilevel mixed-effects linear models were performed to test the two research questions. Four models were conducted to examine the first research question on the longitudinal effects of functional difficulty on cognitive declines. In Model 1, we entered ADL and IADL difficulty to examine their association with cognitive decline. In Model 2, we added individual-level and census tract-level control variables. In Model 3 and Model 4, we added interaction terms, “ADL × survey wave” and “IADL × survey wave” respectively, to test whether baseline functional difficulty influences cognitive changes at a different rate by functional status. Significant interactions were plotted to facilitate the interpretation.
For the second research question, we performed stratified analysis to test whether neighborhood disadvantages were associated with cognitive decline among participants with and without functional difficulty. We entered neighborhood disadvantages as a predicting variable stratified by ADL or IADL status, respectively. For models stratified by ADL status, we controlled for IADL status as a covariate. For models stratified by IADL, we controlled ADL status as a covariate. ADL and IADL dummy variables were included in respective models to isolate variance specifically associated with the functional difficulty of interest.
As the predicting variables included census tract-level variables and time-varying variables, we accounted for the within-individual correlations over time and between-individual correlations clustered by census tract to handle the correlated data. For example, individuals living in the same census tract may be influenced by the same set of neighborhood social and structural factors. Thus, each model incorporated three levels, which were level 1 (within-person differences over survey wave), level 2 (between-person differences within census tract), and level 3 (between-census tract differences).
Results
Table 1 presents descriptive statistics on baseline sample characteristics. The average age was 81, with the majority being women (62%), unmarried (54.9%), US-born (61%), and Spanish-speaking (78.1%). On average, participants received 6 years of education and moderate financial strain. Over one-third of participants were overweight (37%) and about a quarter had obesity (26%). The majority reported having hypertension (65%), 33% reported having diabetes and about 11% reported having a stroke. The average score of CES-D was 8, indicating low levels of depressive symptoms. The study participants at baseline had moderate to high cognitive function, with an average MMSE score of 25 (range: 21–30). For census tract-level variables, the average density of Hispanic residents among census tracts was high at 80%. The neighborhood disadvantage items with the highest average percentage were households below poverty and with individuals under 18, both at 30%. The lowest disadvantage item was unemployed households at 10%.
Table 1.
Sample Characteristics at Baseline (n = 1,189).
| n (%) | mean (SD) | range | |
|---|---|---|---|
| Individual-level variables | |||
| Age | 80.8 (4.3) | 74 – 109 | |
| Gender | |||
| Male | 452 (38.0) | ||
| Female | 737 (62.0) | ||
| Years of education | 6.0 (4.0) | 0 – 17 | |
| Financial strain | 1.5 (1.0) | 0 – 3 | |
| Marital status | |||
| Married | 534 (45.1) | ||
| Unmarried | 651 (54.9) | ||
| Language | |||
| English | 260 (21.9) | ||
| Spanish | 929 (78.1) | ||
| Nativity | |||
| US-born | 722 (60.7) | ||
| Mexico-born | 467 (39.3) | ||
| BMI categories | |||
| Underweight or normal | 338 (28.4) | ||
| Overweight | 441 (37.1) | ||
| Obesity | 311 (26.2) | ||
| Having hypertension | 711 (65.2) | ||
| Having diabetes | 395 (33.3) | ||
| Having stroke | 128 (10.8) | ||
| Depressive symptoms (CESD) | 8.0 (7.9) | 0 – 49 | |
| Cognitive status (MMSE) | 25.2 (3.1) | 21 – 30 | |
| Census-tract level variables | |||
| % Hispanic density | 0.8 (0.2) | 0.1 – 1.0 | |
| Neighborhood disadvantage (z-score) | 0 (0.8) | −2.0 – 3.2 | |
| % under 18 | 0.3 (0.1) | 0.02 – 0.4 | |
| % below poverty | 0.3 (0.1) | 0.04 – 0.7 | |
| % receiving social assistance | 0.2 (0.1) | 0.01 – 0.4 | |
| % unemployed | 0.1 (0.04) | 0.02 – 0.2 | |
| % female-headed households | 0.2 (0.1) | 0.03 – 0.5 |
Note. ADL=activities of daily living; IADL=instrumental activities of daily living; SD=standard deviation. Neighborhood disadvantage was created by averaging across the Z scores of each of the five neighborhood socioeconomic indicators.
Table 2 presents the difficulty in individual activity for ADL and IADL. While less than a quarter of participants had any ADL difficulty (23%), nearly two-thirds reported difficulty in any IADL (62.2%). The most frequently reported ADL difficulty was walking across the room (16%) and least often ADL difficulty was getting from a bed or chair (3.7%). For IADL, the most reported difficulty was in heavy housework at 50%, whereas the least reported difficulty was in using the telephone (4%).
Table 2.
Frequency of Difficulty in Individual ADL and IADL activity (n = 1,189).
| N (%) | |
|---|---|
| Having any ADL difficulty | 276 (23.2) |
| Walking across the room | 193 (16.2) |
| Bathing | 155 (13.0) |
| Personal grooming | 71 (6.0) |
| Dressing | 94 (7.9) |
| Eating | 169 (8.2) |
| Getting from a bed or chair | 44 (3.7) |
| Using the toilet | 195 (16.4) |
| Having any IADL difficulty | 740 (62.24) |
| Using telephone | 49 (4.1) |
| Vehicle travelling | 303 (32.2) |
| Grocery shopping | 267 (22.5) |
| Meal preparation | 134 (11.3) |
| Light household | 153 (12.9) |
| Taking medicine | 68 (5.8) |
| Banking | 139 (11.7) |
| Heavy housework | 600 (50.6) |
| Walking on stairs | 424 (35.8) |
| Walking half a mile | 438 (37.5) |
Note. ADL=activities of daily living; IADL=instrumental activities of daily living
Table 3 presents results from multi-level mixed-effects linear regression models of cognitive changes as a function of ADL/IADL difficulty. In Model 1, the unadjusted baseline model shows an association of ADL/IADL difficulty with cognitive decline. Having any difficulty in ADL and IADL was associated with 1.48 (p < 0.001) and 0.83 (p < 0.001) points decrease in MMSE score, respectively. In Model 2 adjusted for socio-demographic and health characteristics, ADL difficulty remained significantly related to cognitive decline (B = −0.7, P < 0.001), whereas IADL difficulty became non-significant. Model 3 and Model 4 show the interaction terms between ADL/IADL difficulty and study wave. The interactions of “ADL × wave” (p < 0.001) and “IADL × wave” (p < 0.001) are depicted in Figure 1 and Figure 2, respectively. Both figures show that all participants experienced cognitive decline over time. However, those with any ADL and IADL difficulty demonstrated a faster decline compared to those without. Supplementary analyses controlling for baseline MMSE score and using continuous measures of ADL/IADL (i.e., the number of activity limitations) are presented in Supplementary Table 1 and Table 2, respectively. Findings largely conformed to our main analyses.
Table 3.
Results From Multi-Level Mixed-Effects Linear Regression Models of Functional Difficulty Predicting Longitudinal Cognitive Changes (n = 1,189).
| Model 1 | Model 2 | Model 3 | Model 4 | |
|---|---|---|---|---|
| Variables | B (95% CI) | B (95% CI) | B (95% CI) | B (95% CI) |
| ADL difficulty (ref=no) | −1.48 (−1.86, −1.10)*** | −0.70 (−1.03, −0.37)*** | 1.10 (0.46, 1.74)** | −0.67 (−0.99, −0.34)*** |
| IADL difficulty (ref=no) | −0.83 (−1.20, −0.46)*** | −0.30 (−0.64, 0.02) | −0.42 (−0.75, −0.09)* | 0.94 (0.32, 1.56)** |
| ADL difficulty × wave | - | - | −0.91 (−1.19, −0.63)*** | - |
| IADL difficulty × wave | - | - | - | −0.74 (−1.06, −0.43)*** |
| Neighborhood disadvantage | - | −0.40 (−0.75, −0.06)* | −0.37 (−0.72, −0.03)* | −0.43 (−0.77, −0.08)* |
| Hispanic density | - | 0.20 (−0.06, 0.46) | 0.17 (−0.09, 0.43) | 0.22 (−0.04, 0.48) |
| Age | - | −0.09 (−0.13, −0.05)*** | −0.09 (−0.13, −0.05)*** | −0.10 (−0.14, −0.05)*** |
| Female (ref=male) | - | 0.84 (0.45, 1.23)*** | 0.80 (0.42, 1.19)*** | 0.79 (0.40, 1.18)*** |
| US-born (ref=Mexico-born) | - | 0.02 (−0.38, 0.41) | 0.01 (−0.38, 0.40) | 0.03 (−0.37, 0.42) |
| Years of education | - | 0.27 (0.22, 0.32)*** | 0.27 (0.22, 0.32)*** | 0.28 (0.23, 0.32)*** |
| Married (ref=not married) | - | 0.13 (−0.22, 0.48) | 0.13 (−0.22, 0.48) | 0.14 (−0.22, 0.49) |
| Financial strain | - | −0.07 (−0.22, 0.08) | −0.09 (−0.24, 0.05) | −0.08 (−0.22, 0.07) |
| Spanish (Ref=English) | - | 0.48 (−0.01, 0.97) | 0.46 (−0.03, 0.94) | 0.49 (0.00, 0.97)* |
| BMI (ref=normal) | ||||
| Overweight | - | −0.04 (−0.39, 0.32) | −0.07 (−0.42, 0.28) | −0.05 (−0.40, 0.30) |
| Obese | - | −0.10 (−0.52, 0.32) | −0.16 (−0.58, 0.26) | −0.11 (−0.53, 0.30) |
| Hypertension (ref=no) | - | 0.26 (−0.06, 0.59) | 0.28 (−0.05, 0.60) | 0.26 (−0.07, 0.58) |
| Diabetes (ref=0) | - | −0.14 (−0.50, 0.21) | −0.16 (−0.51, 0.19) | −0.19 (−0.54, 0.16) |
| Stroke (ref=0) | - | −0.37 (−0.88, 0.14) | −0.34 (−0.85, 0.16) | −0.40 (−0.91, 0.10) |
| Depressive symptoms (CESD) | - | −0.05 (−0.07, −0.04)*** | −0.06 (−0.07, −0.04)*** | −0.05 (−0.07, −0.04)*** |
| Wave | −1.32 (−1.51, −1.12)*** | −0.69 (−0.88, −0.51)*** | −0.32 (−0.54, 0.11)** | −0.12 (−0.42, 0.18) |
| Model Specifics | ||||
| χ2 (df) | 400.37 (3)*** | 673.71 (19)*** | 725.24 (20)*** | 701.16 (20)*** |
| # of persons | 1,189 | 1,177 | 1,177 | 1,177 |
| # of census tracts | 329 | 328 | 328 | 328 |
| # of person-years | 3,207 | 3,051 | 3,051 | 3,051 |
Note. ADL=activities of daily living; IADL=instrumental activities of daily living; CI=confidence interval. Ref=reference group
p<0.05
p<0.01
p<0.001.
Figure 1.
Significant interaction between ADL difficulty and survey wave based on results in Table 3 Model 3. Older Mexican Participants all showed decline in cognitive functioning, but those with ADL difficulty had a faster decline than those without.
Figure 2.
Significant interaction between IADL difficulty and survey wave based on results in Table 3 Model 4. Older Mexican Participants all showed decline in cognitive functioning, but those with IADL difficulty had a faster decline than those without.
Table 4 shows associations of neighborhood disadvantages and cognitive decline between participants with and without ADL/IADL difficulty. Among participants without any ADL difficulty, a one-unit increase in neighborhood disadvantage was associated with 0.42-point decrease in MMSE score (p < 0.05) over time (Model 1a), but this association was not significant among participants with any ADL difficulty (Model 1b). On the contrary, among participants without any IADL difficulty, there was no significant association between neighborhood disadvantage and cognitive decline (Model 2a). However, a one-unit increase in neighborhood disadvantage was significantly related to 0.47-point decrease in MMSE score among those with any IADL difficulty (Model 2b).
Table 4.
Results From Multi-Level Mixed-Effects Linear Regression Models of Neighborhood Disadvantage Predicting Cognitive Decline by Functional Difficulty Status
| Model 1 | Model 2 | |||
|---|---|---|---|---|
| Variables | (a) No ADL difficulty | (b) Having ADL difficulty | (a) No IADL difficulty | (b) Having IADL difficulty |
| B (95% CI) | B (95% CI) | B (95% CI) | B (95% CI) | |
| Neighborhood disadvantage | −0.42 (−0.78, −0.06)* | −0.13 (−0.76, 0.51) | −0.42 (−0.88, 0.04) | −0.47 (−0.89, −0.06)* |
| ADL difficulty (ref=no) | - | - | 0.52 (−1.26, 0.22) | −0.81 (−1.19, −0.43)*** |
| IADL difficulty (ref=no) | −0.52 (−0.84, −0.19)** | −0.31 (−1.34, 0.72) | - | - |
| Hispanic density | 0.19 (−0.08, 0.46) | 0.08 (−0.40, 0.56) | 0.17 (−0.18, 0.53) | 0.28 (−0.03, 0.60) |
| Age | −0.06 (−0.11, −0.02) | −0.16 (−0.23, −0.08)*** | −0.09 (−0.16, −0.02)** | −0.09 (−0.14, −0.04)*** |
| Female (ref=male) | 0.58 (0.20, 0.97)** | 1.60 (0.84, 2.36)*** | 0.63 (0.11, 1.15)* | 0.97 (0.48, 1.46)*** |
| US-born (ref=non-US born) | −0.13 (−0.53, 0.28) | 0.44 (−0.28, 1.15) | −0.19 (−0.76, 0.38) | 0.11 (−0.37, 0.59) |
| Years of education | 0.27 (0.22, 0.32)*** | 0.25 (0.16, 0.34)*** | 0.25 (0.18, 0.31)*** | 0.29 (0.23, 0.35)*** |
| Married (ref=not married) | 0.20 (−0.17, 0.57) | −0.09 (−0.79, 0.61) | −0.04 (−0.55, 0.48) | 0.14 (−0.31, 0.58) |
| Financial strain | −0.11 (−0.27, 0.05) | −0.18 (−0.48, 0.11) | −0.24 (−0.46, −0.01)* | −0.04 (−0.22, 0.14) |
| Spanish (Ref=English) | 0.56 (0.07, 1.04)* | −0.04 (−0.98, 0.88) | 0.50 (−0.20, 1.21) | 0.52 (−0.07, 1.11) |
| BMI (ref=normal) | ||||
| Overweight | −0.15 (−0.52, 0.23) | 0.12 (−0.64, 0.89) | −0.03 (−0.56, 0.49) | 0.01 (−0.45, 0.46) |
| Obese | −0.14 (−2.20, −0.49) | 0.14 (−0.71, 1.00) | −0.38 (−0.02, 0.27) | 0.19 (−0.32, 0.72) |
| Hypertension (ref=no) | 0.29 (−0.06, 0.64) | 0.22 (−0.46, 0.90) | 0.53 (0.04, 1.01)* | 0.14 (−0.28, 0.56) |
| Diabetes (ref=0) | −0.11 (−0.48, 0.26) | −0.28 (−0.96, 0.39) | −0.27 (−0.81, 0.26) | −0.18 (−0.61, 0.25) |
| Stroke (ref=0) | −0.32 (−0.91, 0.28) | −0.48 (−1.39, 0.44) | −0.98 (−1.95, −0.02)* | −0.30 (−0.90, 0.30) |
| Depressive symptoms (CESD) | −0.03 (−0.05, −0.01)** | −0.10 (−0.13, −0.06)*** | −0.06 (−0.10, −0.03)*** | −0.05 (−0.08, −0.03)*** |
| Wave | −0.31 (−0.51, −0.11)** | −1.10 (−1.42, −0.76)*** | −0.08 (−0.39, 0.22) | −0.88 (−1.10, −0.67)*** |
| Model Specifics | ||||
| χ2 (df) | 262.43 (18)*** | 327.80 (18)*** | 140.84 (18)*** | 495.05 (18)*** |
| # of persons | 971 | 637 | 546 | 1,002 |
| # of census tracts | 304 | 241 | 226 | 308 |
| # of person-years | 1,970 | 1,081 | 935 | 2,116 |
Note. ADL=activities of daily living; IADL=instrumental activities of daily living; CI=confidence interval
p<0.05
p<0.01
p<0.001.
Discussion
This study seeks to understand the longitudinal associations between functional difficulty and cognitive decline among older Mexican Americans in the context of neighborhood disadvantages. Using a national sample of older Mexican Americans, two major findings emerged from our analyses. First, difficulty in ADL, but not IADL, is independently associated with cognitive decline among older Mexican Americans. Among individuals with any ADL/IADL difficulty, the rate of cognitive decline is faster compared to those without any difficulty. Second, neighborhood disadvantages are related to cognitive decline among those without ADL difficulty and those with IADL difficulty. This study adds to the limited evidence on how functional difficulty influences cognitive decline over time among older Mexican Americans and explores how neighborhood factors differently affect those with and without ADL/IADL difficulty.
Our findings partially confirm our first hypothesis. In the unadjusted models, both ADL and IADL difficulty are associated with cognitive declines among older Mexican Americans, but only ADL difficulty retains statistical significance in the adjusted model. The significant association between ADL difficulty and cognitive decline among older Mexican Americans is in alignment with previous findings that include broader samples of older Americans [15, 37]. Performance of ADL, such as eating and bathing, constitutes tasks at a fine psychomotor level and usually takes place in small spaces [38], and may be highly sensitive to cognitive changes. On the other hand, our finding differs from some prior evidence that IADL difficulty independently contributes to the onset of cognitive decline [12, 37]. IADL concerns more complex cognitive processes and social functioning (e.g., using a telephone). Given the relatively cognitively normal sample in the study, the relationship between IADL difficulty and cognitive decline over time may be less pronounced among older Mexican Americans. In addition, measures on IADL among older Mexican Americans, such as grocery shopping, require consideration of cultural and linguistic factors [39], which may also influence cognitive decline. Furthermore, recent evidence suggests that chronic conditions, such as heart problems and diabetes, are linked to higher risk of developing ADL difficulty compared to IADL difficulty [40]. Therefore, it is possible that ADL difficulty among older Mexican Americans reflect more severe physical functioning that further influence cognitive decline over time [41].
The study findings confirm our second hypothesis. Significant interaction between time (i.e., study wave) and ADL/IADL difficulty suggests that both ADL and IADL difficulties are independently associated with a faster cognitive decline among older Mexican Americans compared to those without functional difficulty. This finding is similar to previous evidence based on a sample of Hispanic older adults in Sacramento Area, CA that functional impairment is linked to more rapid decline in cognitive functioning [42]. However, our findings also differ from other evidence based on a national sample of older Americans, which finds that while ADL and IADL difficulty is related to cognitive decline cross-sectionally, they have no association with the rate of decline [37]. Importantly, this evidence based on a national sample of older Americans may have concealed the within-group heterogeneity among older Mexican Americans. By focusing on older Mexican Americans only, we utilize a within-group design that considers cultural factors such as language (i.e., English or Spanish-speaking) and Hispanic density in the neighborhood that may constitute distinct social experiences of this ethnic group.
Regarding our third hypothesis, neighborhood socioeconomic disadvantage was associated with cognitive decline over time among older Mexican Americans without ADL difficulty and those with IADL difficulty. We surmise that people without any difficulty in ADL are more likely to engage in neighborhood activities and thus are more likely to be exposed to the adverse effects of neighborhood disadvantages. On the other hand, those with IADL difficulty may be facing challenges in managing complex daily tasks, such as grocery shopping, making them more dependent on neighborhood resources and support. Given that the performance of IADL tasks requires higher neuropsychological functioning [43], the presence of IADL difficulty may indicate a predisposition to cognitive decline. A disadvantaged neighborhood environment may discourage older adults from engaging in neighborhood activities and perform IADL activities [25], increasing their risk of IADL difficulty and subsequent cognitive decline.
Our findings also align with the Ecological Theory of Aging, which emphasizes the dynamic interplay between older adults and their environmental factors [21]. Neighborhood disadvantage may represent a structural barrier to health promotion among older Mexican Americans and may also reflects large psychosocial stressors that influence physical and cognitive functioning among this group. In this context, neighborhood socioeconomic disadvantage may limit older Mexican Americans’ access to resources such as healthcare, social support, and safe space for physical activity. Additionally, neighborhood disadvantage may contribute to heightened exposure to psychosocial stressors, such as perceived social disorder that increase the risk of functional difficulty and thus subsequent cognitive declines [25]. Taken together, our findings suggest that considering broader environmental and structural factors is essential for understanding cognitive health among older Mexican Americans. Reducing neighborhood disadvantages may represent a promising avenue for lowering the risk of cognitive decline in this population. Further research is needed to identify the specific components of neighborhood disadvantage that are most strongly associated with cognitive decline, which can inform community-level interventions aimed at promoting cognitive health among older Mexican Americans.
Several limitations should be noted in interpreting the study findings. First, we focused on a sample of older Mexican Americans with moderate-to-high cognitive functioning at baseline. Therefore, the study findings are not generalizable to Mexican Americans who are cognitively impaired. While participants at baseline had moderate to high cognitive function at baseline wave, cognitive declines in subsequent study waves may influence functional difficulty, and we cannot determine the cause of functional difficulty. Given the complex interplay between functional and cognitive difficulty, it is possible that cognitive impairment and functional difficulty can concurrently influence each other in subsequent waves. A related limitation is that we were not able to account for acculturation factors (i.e., duration in the US) and it is important to consider the differences between US-born and foreign-born Mexican Americans. In addition, although we used MMSE scores to select participants with moderate to high cognitive functioning at baseline, some individuals may nevertheless have been experiencing early cognitive decline. Also, the MMSE does not assess executive functioning, which may be more closely tied to IADL difficulties, further limiting our assessment of cognitive status. Second, our measures relied on subjective self-reported measures on key health indicators such as functional difficulty, which may involve recall bias and social desirability. Future research needs to consider performance-based measures on physical functions in addition to self-reported ADL/IADL difficulty. Also, the measures on ADL and IADL did not capture whether participants never performed an activity. For example, it is possible that a participant never managed their banking. If this is the case, requiring help with banking may not indicate a functional difficulty. Third, as our sample is older with an average age of 81, the study findings may not be generalizable to young-old adults. The rate of cognitive decline is generally faster with older age [44], and our finding may not apply to young-old adults. There are additional confounders that were not available in our analysis. Larger socio-structural factors, such as racism and cultural barriers, may inflate or obscure neighborhood effects.
Conclusions
Using longitudinal data from a population study of older Mexican Americans, our study contributed to a better understanding of the longitudinal association between functional difficulty and cognitive decline among older Mexican Americans within the context of neighborhood disadvantage. In particular, ADL and IADL difficulty are related to both cognitive decline and faster rate of decline among older Mexican Americans. Neighborhood socioeconomic disadvantage may be a social stressor that is differentially associated with cognitive outcomes among those with and without functional difficulty. Overall, our study provides evidence of neighborhood-level factors that may inform community efforts to prevent cognitive declines among older Mexican Americans with functional difficulty, and insights into the effects of neighborhood disadvantages on cognitive health.
Supplementary Material
Funding
The preparation of this article was supported in part by the National Institute on Aging of the National Institutes of Health under the award number T32AG000221, P30AG017266 and R01AG010939. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The preliminary findings of the study were presented in an oral symposium session at the Gerontological Society of America Annual Scientific Meeting on November 11, 2024, in Seattle, WA.
Footnotes
Conflicts of interest
The authors declare that there is no conflict of interest.
Ethics Approval
This is secondary research based on public-use data and is exempt from IRB review.
Contributor Information
Weidi Qin, Sandra Rosenbaum School of Social Work, University of Wisconsin–Madison, Madison, WI.
Lucia Cavanagh, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA.
Joshua Owens, Department of Public Health and Health Professions, University of Florida, Gainesville, Florida, USA.
Soham Al Snih, Department of Population Health and Health Disparities, The University of Texas Medical Branch, Galveston, TX, USA.
Irving E. Vega, Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI, USA.
Availability of data and material
The data and documents from the Hispanic Established Population for the Epidemiological Study of the Elderly (HEPESE) can be accessed under the Terms of Use with ICPSR at https://www.icpsr.umich.edu/web/NACDA/series/546
Code Availability
Codes are available upon request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data and documents from the Hispanic Established Population for the Epidemiological Study of the Elderly (HEPESE) can be accessed under the Terms of Use with ICPSR at https://www.icpsr.umich.edu/web/NACDA/series/546
Codes are available upon request.