Rural-Urban Differences in Cognition: Findings From the Advanced Cognitive Training for Independent and Vital Elderly Trial

Abstract

Objectives

We examined associations between three geographic areas (urban, suburban, rural) and cognition (memory, reasoning, processing speed) over a 10-year period. Methods: Data were obtained from 2539 participants in the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) trial. Multilevel, mixed-effects linear regression was used to estimate cognitive trajectories by geographical areas over 10 years, after adjusting for social determinants of health. Results: Compared to urban and suburban participants, rural participants fared worse on all cognitive measures—memory (B = −1.17 (0.17)), reasoning (B = −1.55 (0.19)), and processing speed (B = 0.76 (0.19)) across the 10-year trajectory. Across geographic areas, greater economic stability, health care access and quality, and neighborhood resources were associated with better cognition over time. Discussion: Findings highlight the importance of geographical location when examining cognition later in life. More research examining place-based life experiences is needed to make the greatest impact on geographically diverse communities.

Keywords

geographic region cognition social determinants of health aging

Recent interest has been placed on the geographic region in which one resides and subsequent cognitive decline. Over the past several decades, there has been continued growth of older adults residing in rural-dwelling communities (Smith & Trevelyan, 2019), as well as growing diversity of the population in rural areas (James & Cossman, 2017; James et al., 2017; Jensen et al., 2020). In fact, rural communities have a greater proportion of older adults than do urban areas and are typically of poorer health (Cohen et al., 2017; West et al., 2014). However, the structure of many rural communities (e.g., population sparsity, distance from key resources, lack of public transportation systems) may limit opportunities and experiences for rural older adults compared to those who live in resource-rich, urban contexts. In turn, such place-based characteristics may contribute to disparities in cognitive health later in life.

Aging, Cognition, and Rurality

To date, there is limited research examining differences in cognitive aging across distinct geographic areas and what these patterns and potential causes look like in those respective communities. Collectively, studies suggest that rural-dwelling older adults have worse performance on assessments of cognitive functioning (Cassarino et al., 2018; Lorenzo-Lopez et al., 2017; Saenz et al., 2018; Weden et al., 2018; Wu et al., 2017; Xu et al., 2017), as well as have a higher prevalence of cognitive impairment and dementia (Hendrie et al., 2018; Mattos et al., 2017; Meyer et al., 2017; Robbins et al., 2019; Russ et al., 2012; Weden et al., 2018) compared to urban-dwelling older adults. Internationally, a cognitive disadvantage for rural-dwelling older adults has been found in several countries, including Ireland (Cassarino et al., 2016), South Africa (Peltzer et al., 2019), Portugal (Nunes et al., 2010), and Japan (Nakamura et al., 2016). In Mexico, Saenz and colleagues (2018) examined differences in cognitive functioning across rural and urban areas among older adults using data from the 2012 Mexican Health and Aging study. The most rural-dwelling older adults had lower performance across five cognitive domains (verbal learning, verbal memory, verbal fluency, orientation, and attention); however, most of these differences could be explained, in part, by lower educational attainment, greater chronic conditions, and lack of health insurance among rural older adults.

Although the exact reasons are still unclear, there are several plausible reasons for why rural older adults have greater cognitive decline and increased risk of dementia later in life. For instance, rural residents have higher rates of chronic conditions and morbidities, namely, hypertension (Kumar et al., 2001), diabetes (Kumar et al., 2001; O’Connor & Wellenius, 2012), obesity (Cohen et al., 2017), and a sedentary lifestyle (Weden et al., 2018), all of which are precursors of cognitive impairment. Rural communities also face unique challenges and have different needs than their urban or suburban counterparts, including differences in educational attainment, occupational hazards, community resources, and access to healthcare (Saint Onge & Smith, 2020), which have also been linked to cognition in later life.

Gaining a better understanding of the role of geographic region in cognition can offer the potential for older adults to live better, more independent lives, while also offering other social and economic impacts (Langa et al., 2017; Robbins et al., 2019); however, only a handful of studies have examined specific place-based characteristics contributing to differences in late-life cognition across geographic regions (Robbins et al., 2019; Weden et al., 2018).

Social Determinants of Health and Rurality

The current study expands on prior work by examining cognitive trajectories through the lens of social determinants of health (SDoH), which can reveal mechanisms that promote health disparities that impact cognition in later life (Cohen et al., 2017; Eberhardt & Pamuk, 2004). Healthy People 2030, which sets national standards for improving the health and well-being of the US population, suggest five main SDoH categories for achieving healthier individuals and communities: economic stability, health care access and quality, education access and quality, neighborhood and built environment, and social and community context (Office of Disease Prevention and Health Promotion [ODPHP], n.d.).

Economic stability refers to an individual’s socioeconomic status or position. Rural communities have experienced both structural and economic challenges that have led to increasing poverty and inequality–as poverty rates have declined across other parts of the United States (Saint Onge & Smith, 2020). Specifically, rural regions across the United States including Alabama, Georgia, Kentucky, Mississippi, North Carolina, and Ohio account for over a third of the non-urban poverty (Pacas & Rothwell, 2020). The overlooked gaps in income and resources have affected the quality of life and, in turn, health of individuals residing in rural communities. Researchers have found that socioeconomic status influences health across various pathways, including differential exposure to more intermediary factors like health-related behaviors (Haq & Penning, 2020). Negative behaviors such as alcohol consumption, smoking, and lower physical activity are more prevalent in rural populations (Matthews et al., 2017) and adversely affect one’s cognitive health (Haq & Penning, 2020). This is further supported by less access to resources (e.g., gym facilities, community centers) necessary to maintain good health and prevent the adoption of harmful health behaviors within rural communities (Haq & Penning, 2020; Hiscock et al., 2012; Smith & Goldman, 2007; Wister, 1996).

Another important aspect influencing cognition in later life is health care access and quality. Many rural communities have poorer quality care and reduced access to public health preventive health infrastructures (Saint Onge & Smith, 2020; Weden et al., 2018), thus contributing to higher rates of chronic conditions in rural communities (Cohen et al., 2017; West et al., 2014). Even if healthcare facilities are accessible, many rural employees do not have health insurance through their employer and may have more structural barriers to enrolling in government programs (Saint Onge & Smith, 2020).

Educational access and quality are also very influential in predicting late-life cognition. One’s own educational attainment, as well as the educational level of a close family member or spouse can impact cognitive outcomes in later life (Saenz et al., 2020; Xu, 2020); with higher levels of educational attainment serving as a key promoter of lifetime cognitive health (Weden et al., 2018). However, compared to urban areas, rural communities continue to struggle in educational quality and attainment rates of college and advanced degrees despite increases over the last few decades (Sisco et al., 2015; Weden et al., 2018). This divide in educational attainment by geographic status is associated with major impacts on cognitive health and resiliency throughout the life course (Weden et al., 2018). Further, to the extent education is tied to occupation, cognitively stimulating occupations are strongly associated with positive late-life cognitive outcomes (Andel et al., 2007; Bosma et al., 2003; Potter et al., 2006; Schooler et al., 1999). Many rural Americans hold occupations that are more repetitive and less cognitively stimulating daily such as laborers, farmers, and miners. Occupational differences between rural and urban individuals potentially contribute to the divide in cognitive outcomes across geographic regions (Saint Onge & Smith, 2020).

Lastly, neighborhood and built environment, and other social and community contextual factors have also been explored to offer a greater understanding of their role related to cognition. As highlighted earlier, rural communities face increasing poverty and economic gaps (Pacas & Rothwell, 2020; Saint Onge & Smith, 2020). Findings in the literature suggest that lower community SES and lower education levels of the community are associated with worse cognition (Wight et al., 2006; Wu et al., 2015). Further, communities with lower SES tend to have fewer resources, which may limit opportunities for cognitive, physical, and social engagement across the life course, contributing to cognitive impairments later in life (Crowe et al., 2003; Friedland et al., 2001; Kåreholt et al., 2011). Opportunities for community engagement may be further restricted by changes in mobility or health limitations (Levasseur et al., 2015), especially in rural communities where access to transportation may be more limited. Moreover, many of the socioeconomic limitations that link the neighborhood environment to physical health outcomes, depression, and quality of life (Koohsari et al., 2015; Li et al., 2009; Mair, Diez, & Galea, 2008; Renalds et al., 2010; Sallis et al., 2009) may also explain associations between the neighborhood context and cognition in later life (Besser et al., 2017; Meyer et al., 2017; Sisco, 2012; Sisco & Marsiske, 2012). This is supported by other findings linking poor health and rurality to worsening cognition later in life among rural dwellers (Deckers et al., 2015; Eberhardt et al., 2001; Harris et al., 2016; Saenz et al., 2018; Weden et al., 2018; Wen et al., 2018).

Present Study

This study builds on work from the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study, a randomized, controlled clinical trial examining the effects of three cognitive interventions on community-dwelling older adults (Jobe et al., 2001). Regarding social determinants of health, previous work in ACTIVE has examined the impact of neighborhood and community-level factors on cognitive decline and risk of dementia (Meyer et al., 2017; Sisco, 2012; Sisco & Marsiske, 2012), but no work to date has explored the association between geographic region and cognitive performance among ACTIVE participants. Examining the influence of geographic region on cognition in the ACTIVE cohort will offer a greater understanding of the impact of place-based characteristics on cognitive performance over a 10-year period among urban, suburban, and rural adults.

Specifically, the main goal of the current study is to examine differences in memory, reasoning, and speed of processing composite scores over a 10-year period by urban, suburban, and rural status, after adjusting for intervention status and SDoH variables. We hypothesize that urban and suburban residents will have higher scores for each cognitive outcome over the 10-year period, compared to those who were classified as rural residents.

Methods

Sample

Data were collected as part of the ACTIVE study (see Ball et al., 2002 and Jobe et al., 2001 for full details of recruitment procedures, eligibility criteria, and study design). Briefly, individuals were recruited from six field sites located across multiple geographic areas in the United States. Potential participants were eligible for inclusion if they were at least 65 years old, had no serious cognitive deficits (Mini-Mental Status Examination (MMSE) score ≥23; Folstein et al., 1975), and did not self-report functional declines or medical conditions that would prohibit participation (e.g., diagnosis of Alzheimer’s disease; stroke in previous 12 months; cancer with limited life expectancy; current chemotherapy or radiation treatment; communication problems). Eligible individuals (N = 2802) were randomized to one of three cognitive training interventions: memory (n = 703), reasoning (n = 699), or speed of processing (n = 702), or to a no-contact control group (n = 698).

For the present study, we used the full ACTIVE analytical sample, N = 2802. Based on prior work in ACTIVE (Sisco, 2012; Sisco & Marsiske, 2012), we created categories to approximate three geographic regions (e.g., urban, suburban, rural) based upon their zip-codes at baseline and the corresponding census block data’s rural-urban spectrum (Ingram & Franco, 2014). Specifically, classification of geographic region was achieved by linking participants’ addresses with their associated 2000 U.S. Census tract numbers, which allowed for the tract information to be appended to the individual-level ACTIVE data (Sisco, 2012). Geographic regions for categorizing participants were defined as follows: (1) Urban-- approximated by census tracts that are 50% or more Urban Area (UA), or a core census block with more than 1000 people per square mile, and within a city with population >250,000; (2) Suburban—approximated by census tracts with majority UA (>50%) located in a city with population <250,00; 3) Rural—areas approximated by census tracts with less than a majority UA (<50%) and any percent Urban Cluster (UC), areas of surrounding census blocks that have at least 500 people per square mile, or any rural areas, which are territory not classified as UA or UC (Sisco, 2012).

Addresses with invalid house numbers, street names, and ZIP codes were unable to be geocoded (Sisco, 2012). In addition, participants receiving mail by post-office box were unable to be geocoded because addresses were not able to be verified to represent the participant’s actual place of residence. Overall, 93% of ACTIVE participant’s addresses were geocoded (Sisco, 2012). Thus, the final sample size was n = 2503 and individuals were categorized as Urban (Urbanized Area in a Major City), n = 913; Suburban (Urbanized Area not in a Major City), n = 1147; and Rural (Urban Cluster/Rural), n = 443.

Measures

Cognitive measures

Memory

The memory unit-weighted composite was constructed using immediate recall from three verbal episodic memory tests: Hopkins Verbal Learning Test (HVLT; Brandt, 1991), a 12-word list memory task assessing immediate and delayed recall over three learning trials; Rey Auditory Verbal Learning Test (AVLT; Rey, 1964), a 15-word list memory task assessing immediate and delayed recall over five learning trials; and a paragraph recall subtest from the Rivermead Behavioral Memory Test (RBMT; Wilson et al., 1985), assessing immediate verbal episodic memory.

Reasoning

The reasoning unit-weighted composite was represented by the Letter Series, Letter Sets, and Word Series tasks (Ekstrom et al., 1976; Gonda & Schaie, 1985; Thurstone & Thurstone, 1949; Willis, 1996). The Letter Series task requires accurate identification of the next logical letter group in a series of letters (Thurstone & Thurstone, 1949). Letter Sets task is an inductive reasoning task requiring participants to identify which of four sets of letters is unrelated to the others (Ekstrom et al., 1976), and the Word Series task requires accurate identification of the next logical word in a series (Gonda & Schaie, 1985).

Speed of processing

For the speed of processing unit-weighted composite, the last three subscales from the Useful Field of View Task (Owsley et al., 2002), a computer-administered task measuring visual sustained, selective, and divided attention through four subtasks was used.

For each cognitive domain, scores for individual assessments were first standardized to their baseline mean and standard deviation using Blom transformation procedures (Blom, 1958). These standardized scores were averaged to create unit-weighted composite measures (memory, reasoning, speed of processing) to be used for analysis. Because alternate versions of the memory assessments were administered across study time points, the composite memory measure was further adjusted using an equipercentile equating procedure to account for differences in test difficulty, thus allowing for valid within-person comparisons over time (Gross et al., 2012). Higher scores on memory and reasoning composites reflect better cognitive performance, whereas lower scores on the speed of processing composite are representative of better cognitive performance.

Covariates

Age (centered at 70 years), sex, testing site, intervention group, number of chronic conditions (osteoporosis, asthma, cataracts, glaucoma, macular degeneration, diabetic retinopathy, heart disease, congestive heart failure, stroke, hypertension, high cholesterol), and replicate code were included in the final adjusted models. Additionally, social determinants of health (SDoH) factor score measures for economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context measures (Clay et al., under review) were also included in the final adjusted models. Specifically, the SDoH factor scores were defined as follows: Economic Stability (occupational complexity, median home value, median rent, percent with college degree), Education Access and Quality (self-reported years of education, vocabulary test scores as a proxy for literacy/educational quality, neighborhood education level), Health Care Access and Quality (access to pharmacies and drug stores, physicians’ offices, services for the elderly, supermarkets and other grocery), Neighborhood and Built Environment (owner occupancy, single family dwellings), and Social and Community Context (marital status, number of co-residents, neighborhood segregation) (Clay et al., under review).

Analytic Strategy

Multilevel, mixed-effects linear regression was used to estimate longitudinal trajectories of the memory, reasoning, and speed of processing composite measures by urban, suburban, and rural status through the 10-year follow-up period in the ACTIVE study. Urban status was the reference group for all models. For each cognitive outcome composite, we examined an unadjusted model and three adjusted models (Model 1, Model 2, Model 3). Model 1 was the unadjusted model plus baseline covariates. Model 2 included the baseline covariates and the SDoH composites. Model 3 included baseline covariates, SDoH composites, as well as assessment period (immediate post-test and at 1-, 2 -,3 -,5-, and 10-year post-baseline assessment) and interaction terms for assessment period and geographic area (i.e., Suburban and Rural) to examine change in each cognitive composite score by geographic area compared to Urban area (reference group) across the 10-year follow-up period. In all models, random effects for both the participant and visit number were estimated using an unstructured covariance matrix. All analyses were conducted using StataSE 16 (StataCorp, 2019).

Results

Sample Characteristics

Overall, the sample included 2503 participants, whose addresses were able to be geocoded and verified. Across groups, participants were 76% female, 72% white, and had on average 14 years of education (Table 1). Further baseline characteristics of the sample by geographic region (i.e., Urban, Suburban, Rural) and any differences among them can also be found in Table 1.

Table 1.

Baseline Descriptive Statistics.

	Total	Urban	Suburban	Rural	p-value
	N = 2503	n = 913	n = 1147	n = 443
	M (SD)	M (SD)	M (SD)	M (SD)
Demographics
Age at baseline (in years)	74 (6)	73 (6)	74 (6)	73 (6)	<0.001
Female (%)	76%	80%	72%	78%	<0.001
Race (%)					<0.001
White	72%	34%	93%	97%
Black/African American	28%	66%	7%	3%
Years of education	14 (3)	13 (3)	14 (3)	13 (2)	<0.001
SF-36 general health	69 (19)	69 (19)	70 (19)	68 (20)	0.14
Site (%)					<0.001
UAB	17%	0%	33%	13%
IU	18%	46%	2%	1%
HSL	13%	3%	27%	<1%
JHU	17%	18%	1%	54%
PSU	17%	<1%	24%	31%
WSU	18%	33%	14%	<1%
Total chronic conditions (%)					0.60
0	11%	10%	11%	14%
1	21%	21%	22%	21%
2	30%	29%	31%	28%
3	20%	22%	20%	19%
4	10%	12%	9%	10%
5	5%	5%	5%	6%
6	1%	2%	1%	2%
7	1%	0%	1%	0%
Social determinants of health factor scores
Economic stability	−0.0022 (1.00)	−0.2786 (0.76)	0.4435 (1.12)	−0.5846 (0.39)	<0.001
Access to health care	0.0005 (1.00)	0.0765 (1.03)	0.0081 (0.99)	−0.1755 (0.94)	<0.001
Education access and quality	−0.0017 (1.00)	−0.2910 (0.65)	−0.0953 (0.89)	0.8367 (1.35)	<0.001
Neighborhood and built environment	−0.0000 (1.00)	−0.1290 (1.00)	−0.0511 (1.06)	0.3979 (0.69)	<0.001
Social and community context	0.0003 (1.00)	−0.4603 (0.88)	0.2231 (1.05)	0.3738 (0.70)	<0.001
Cognitive composite scores
Memory	−0.01 (2.46)	0.23 (2.30)	−0.10 (2.49)	−0.27 (2.63)	<0.001
Reasoning	−0.00 (2.34)	−0.47 (2.28)	0.31 (2.38)	0.14 (2.20)	<0.001
Speed of processing	−0.02 (2.67)	−0.70 (2.66)	0.46 (2.64)	0.12 (2.47)	<0.001

Note. SF-36 general health scores range from 0 to 100. UAB = University of Alabama at Birmingham, IU = Indiana University School of Medicine, HSL = Hebrew Senior-Life, Boston, JHU = Johns Hopkins University, PSU = Pennsylvania State University, WSU = Wayne State University. X² tests were used for categorical variables and ANOVA was used for continuous variables.

Mixed-Effects Models

Presented below are results from our fully adjusted models (Model 3) (Table 2). The pattern of findings regarding the association between rurality and cognition did not differ substantially across models. Results from Model 1 (with baseline covariates) and Model 2 (with baseline covariates and SDoH variables) can be found in Supplemental Table S1 and S2, respectively.

Table 2.

Regression Estimates of Fully Adjusted Model (Model 3) for Memory, Reasoning, and Speed of Processing from Mixed Effects Regression.

	Memory		Reasoning		Speed of processing
Variable	Estimate	SE	Estimate	SE	Estimate	SE
Urban	Ref	(.)	Ref	(.)	Ref	(.)
Suburban	0	0.15	−0.07	0.17	0.05	0.17
Rural	−1.17***	0.17	−1.55***	0.19	0.76***	0.19
Age (centered)	−0.15***	0.01	−0.16***	0.01	0.21***	0.01
Female	0.99***	0.09	0.15	0.11	0.05	0.10
Site
UAB	Ref	(.)	Ref	(.)	Ref	(.)
IU	0.78***	0.19	1.16***	0.22	−0.61**	0.20
HSL	0.1	0.16	0.57**	0.19	−0.17	0.18
JHU	0.45*	0.18	0.93***	0.21	−0.69***	0.20
PSU	0.21	0.15	0.77***	0.18	−0.39*	0.17
WSU	0.67***	0.16	1.08***	0.19	−0.68***	0.17
Intervention Arm
Memory	Ref	(.)	Ref	(.)	Ref	(.)
Reasoning	−0.34**	0.11	0.54***	0.12	0.09	0.12
Speed	−0.35***	0.11	−0.08	0.12	−1.71***	0.12
Control	−0.33**	0.11	−0.17	0.12	0.16	0.12
Total chronic conditions	−0.16***	0.01	−0.35***	0.01	0.57***	0.02
Social determinants of health factor scores
EAQ	−0.10*	0.05	−0.07	0.06	0.07	0.05
ES	0.37***	0.05	0.60***	0.06	−0.23***	0.06
AHC	0.59***	0.04	0.76***	0.05	−0.29***	0.04
NBE	0.17***	0.04	0.16**	0.05	−0.13**	0.05
SCC	−0.09*	0.05	−0.09	0.05	0.09	0.05
Assessment period	−0.28***	0.02	−0.10***	0.01	0.15***	0.02
Suburban ⅹ assessment period	−0.05**	0.02	−0.03*	0.02	0.06**	0.02
Rural ⅹ assessment period	−0.03	0.02	−0.01	0.02	0.07**	0.02

Note. Higher scores on memory and reasoning represent better performance, whereas lower scores on speed of processing represent better performance. UAB = University of Alabama at Birmingham, IU = Indiana University School of Medicine, HSL = Hebrew Senior-Life, Boston, JHU = Johns Hopkins University, PSU = Pennsylvania State University, WSU = Wayne State University. EAQ = Education Access and Quality, ES = Economic Stability, AHC = Access to Health Care, NBE = Neighborhood and Built Environment, SCC = Social and Community Context. *p < .05. **p < .01. ***p < .001.

Memory

In our fully adjusted model (Model 3), rural participants performed worse than urban participants on the memory composite measure at baseline (B = −1.17, SE = .17, p < .001); there was no difference in memory performance between suburban and urban participants. At baseline, older participants demonstrated worse performance on memory tasks compared to younger participants (B = −.15, SE = .01, p < .001), while females performed better than their male counterparts (B = .99, SE = .09, p < .001). Increasing numbers of chronic conditions at baseline was also negatively associated with memory (B = −.16, SE = .01, p < .001).

All five social determinants of health composites assessed at baseline were significantly associated with memory performance over time (10-year period): Education Access and Quality (B = −.10, SE = .05, p < .05), Economic Stability (B = .37, SE = .05, p < .001), Health Care Access and Quality (B = .59., SE = .04, p < .001), Neighborhood and Built Environment (B = .17, SE = .04, p < .001), and Social and Community Context (B = −.09, SE = .05, p < .05). Over the 10-year study period, individuals performed worse on memory tasks at each subsequent assessment period (B = −.28, SE = .02, p < .001). The interaction term between geographic area and assessment period was significant for suburban status (B = −.05, SE = .02, p < .01) across the 10-year period, with individuals residing in suburban areas having lower performance on the memory composite, compared to those residing in urban areas over time.

Reasoning

For Reasoning, rural participants scored worse than urban participants on the reasoning composite measure at baseline (B = −1.55, SE = .19, p < .001); there was no significant difference in reasoning scores between suburban and urban participants (Model 3). At baseline, older participants demonstrated worse performance on reasoning tasks compared to younger participants (B = −.16, SE = .01, p < .001). Increasing numbers of chronic conditions was also negatively associated with reasoning (B = −.35, SE = .01, p < .001).

Over the 10-year study period, three social determinants of health composites measured at baseline were significantly associated with reasoning performance: Economic Stability (B = .60, SE = .06, p < .001), Health Care Access and Quality (B = .76., SE = .05, p < .001), and Neighborhood and Built Environment (B = .16, SE = .05, p < .01). Similar to the pattern of findings for the memory composite, individuals demonstrated lower performance on reasoning tasks at each subsequent assessment period (B = −.10, SE = .01, p < .001). The interaction term between geographic area and assessment period was significant for suburban status (B = −.03, SE = .02, p < .05), but not for rural status (B = −.01, SE = .02, p =.63) across the 10-year period. As such, individuals in suburban areas demonstrated lower performance on the reasoning composite, compared to those residing in urban areas over time.

Speed of Processing

For Speed of Processing, the same trends held, with rural participants scoring worse on the speed of processing composite measure compared to urban participants (B = .76, SE = .19, p < .001) at baseline (Model 3): there was no significant difference in speed of processing scores between suburban and urban participants (Model 3). Older individuals performed worse on baseline measures of processing speed (B = .21, SE = .01, p < .001). Increasing numbers of chronic conditions at baseline was also associated with slower processing speed over time (B = .57, SE = .2, p < .001).

Three social determinants of health composites were significantly associated with speed of processing over time: Economic Stability (B = −.23, SE = .06, p < .001), Health Care Access and Quality (B = −.29, SE = .04, p < .001), and Neighborhood and Built Environment (B = −.13, SE = .05, p < .01). Over time, individuals performed worse on processing speed tasks at each subsequent assessment period (B = .15, SE = .02, p < .001). The interaction term between geographic area and assessment period was significant for both suburban status (B = .06, SE = .02, p < .01) and for rural status (B = .07, SE = .02, p < .01) across the 10-year period; with both individuals residing in rural and suburban areas demonstrating slower performance on processing speed tasks over time.

Discussion

To our knowledge, this study is the first to evaluate cognitive performance across urban, suburban, and rural older adults over an extended time frame (10-year period). Our findings are consistent with prior cross-sectional studies suggesting poorer cognitive performance of rural older adults when compared to urban older adults (Cassarino et al., 2018; Lorenzo-Lopez et al., 2017; Saenz et al., 2018; Weden et al., 2018; Wu et al., 2017; Xu et al., 2017); with rural participants faring worse across all baseline cognitive measures—memory, reasoning, and speed of processing composite scores—compared to urban participants in the ACTIVE trial.

More interestingly, however, is that when differences in cognitive trajectories over a 10-year period across geographic regions were examined, the rate of change between rural and urban populations differed only for speed of processing, after adjusting for intervention status. As such, those living in rural settings had greater decline on speed of processing than those in urban settings. In addition, the rate of change significantly differed between suburban and urban populations for all three cognitive outcome measures (memory, reasoning, and speed of processing), with suburban residents demonstrating lower performance than urban residents over time. While there is far less research comparing physical and cognitive health between suburban and urban communities (Finlay et al., 2020; Lorenzo-Lopez et al., 2017; Sturm & Cohen, 2004; Wells, 2010), prior work by Cassarino and colleagues (2018) may shed some light on our findings. After controlling for demographic, health, and lifestyle factors among older adult participants, they found individuals living in medium-to-very high urban areas (similar to major cities) had better cognitive functioning than those living in very low urban areas (similar to suburban communities) (Cassarino et al., 2018). It may be that urban areas afford more opportunities for cognitive stimulation and social interaction as compared to the suburban areas, as well as have fewer environmental barriers (e.g., public transportation) for remaining engaged later in life—a protective factor for cognitive health. However, more research is needed to determine if the trends hold true in other studies or settings.

Overall, our findings are largely consistent with both cross-sectional (Lorenzo-Lopez et al., 2017; Saenz et al., 2018; Wu et al., 2017) and longitudinal (Cassarino et al., 2018; Hendrie et al., 2018; Weden et al., 2018; Xu et al., 2017) findings, suggesting differences in cognitive performance between rural and urban participants. However, it is important to note, that there is a great deal of heterogeneity among rural populations, especially in terms of resources, infrastructure, and other social and societal conditions. This may be highlighted by our SDoH factor score results, particularly education access and quality and social and community context, as these factors have multiple components linked to the heterogeneity of a community that cannot be easily teased apart from the combined effects of the factor score.

Although ACTIVE rural participants were disadvantaged in terms of economic stability and access to health care, they were advantaged on other social determinants of health indicators at baseline, including education access and quality, neighborhood and built environment, and social and community context. As ACTIVE was a multisite trial, it may be that our rural sites and residents were more resource-rich than other rural populations studied in the literature. Some of the rural sites were in close proximity to the academic institution conducting the ACTIVE trial, which may have contributed to the neighborhood and community context, as well as afforded other educational resources and opportunities for social and community engagement. Moreover, in ACTIVE, the rural participants were compared to mostly minority participants in low-SES urban communities like Detroit, Baltimore, and Indianapolis, which may have narrowed previously found disparities between rural and urban populations. As most prior studies have examined rural-urban difference in cognition in other countries (e.g., Mexico, Ireland, Portugal), more research is needed that includes diverse rural and urban communities within the United States.

Regardless of geographic area, several SDoH composites (economic stability, health care access and quality, and neighborhood and built environment) were significantly associated with better cognitive performance on measures of memory, reasoning, and speed of processing over the 10-year period. Other studies have also suggested that poor socioeconomic status of the community (Wight et al., 2006; Wu et al., 2015), limited access to healthcare (Saint Onge & Smith, 2020), and fewer neighborhood resources (e.g., Meyer et al., 2017; Sisco & Marsiske, 2012S. M. Sisco & Marsiske, 2012) can negatively impact cognition in later life. Although lower educational attainment and quality is consistently associated with worse cognition later in life (Helmes & Van Gerven, 2017; Sisco et al., 2015; Weden et al., 2018), surprisingly, we found greater factor scores for education access and quality were associated with worse memory performance. This discrepancy may be due, in part, to how educational attainment and quality was defined in our study. The composite measure included both individual education level (self-reported years of education, vocabulary test scores as a proxy for literacy/educational quality) and neighborhood education level, which most studies do not examine in combination.

Together, our findings suggest that environment and contextual factors matter for the cognitive aging process, with resource-rich environments more positively influencing cognition in later life. There have been several other studies suggesting that environment can shape brain structure and function (Leon & Woo, 2018; Sampedro-Piquero & Begega, 2017), and that enriched environments are linked to cognitive reserve and resilience (Petrosini et al., 2009). Moreover, when controlling for social determinants of health, we found a persistent rural disadvantage across all three cognitive measures at baseline, as well as evidence that participants in rural and suburban areas had lower cognitive performance than participants in urban over the 10-year period. However, there were several other environmental, social, and community factors that we did not examine, individually or in combination, that may also highlight the societal and social conditions that put rural or suburban older adults at a cognitive disadvantage. For instance, factors such as social isolation and worse mental health along with poor health care access may contrast with more positive, protective factors like community support, involvement, and resources. For suburban adults, the focus may be more environmental concerns relating to the level of urbanicity of the area, or community support and access related to suburban sprawl that may create barriers for older adults to get around, be engaged, and be healthy and active.

As ACTIVE was conducted between 1998 and 2008, it should also be noted that the composition and infrastructure of geographic regions may change over time, with some rural areas flourishing economically, socially, and societally. This may be especially true with the growth of technologies for communication, ride-sharing services (e.g., Uber, Lyft), and e-commerce. More research is needed examining the dynamic interplay of environment and cognition over time. As such, gaining a better understanding of cognitive aging across geographic regions, as well as the impact of structural determinants of health on cognitive performance, is critical for decisions regarding the allocation of resources and how to best target and tailor public health interventions in diverse communities.

Strengths and Limitations

To the best of our knowledge, this is the first study considering cognition in older adults across rural, suburban, and urban geographic regions. The ACTIVE study offers a strong longitudinal design assessing cognitive performance over a 10-year period from multiple sites in different geographical regions of the United States. Although our findings underscore the importance of considering geographic region in cognitive aging research, this study is not without limitations. Our study considered an individual’s residential status by their location at baseline, once they were well into adulthood. As individuals may move during their lifetime, the geographical region assigned for study purposes may not reflect earlier life experiences and exposures. Further, this does not capture whether they relocated to a different geographic area during ACTIVE follow-up. Mobility of participants was not well-captured during or after the study and could not be included in our analyses. Additionally, the zip code tabulation and the census tracts used in the current study are widely accepted, but do not always capture cohesive areas and even lessen the areas’ diversity, whether that be socioeconomically or by race and ethnicity (Bennett et al., 2019). As such, there may be potential biases related to the determination of rurality, since there are several different schemes that determine rurality in the United States (Bennett et al., 2019).

It is also important to note that there is a great deal of heterogeneity in the demographic, economic, and social characteristics within each of our defined geographic areas-rural, suburban, and urban. As such, the social determinants of health variables may have different context or impacts by rural-urban status that we may not fully understand. However, by including social determinants of health, we may better start to identify how underlying place-based characteristics contribute to health disparities in cognition. Lastly, there was differential loss-to-follow-up by geographic region, which may confound the relationship between performance over time and geographic region (urban: 32.3% (n = 295); suburban: 20.3% (n = 233); and rural: 22.1% (n = 98)). These differences were supported by a Χ² test (p = .01) and may have contributed to the performance over time across groups, particularly urban, which had the highest attrition.

Conclusion

With continued growth of rural-dwelling older adult communities (Smith & Trevelyan, 2019), focusing on the differences between rural, urban, and suburban communities continues to be an important area of study for cognitive health. Moving forward, there should be replication of this comparison of cognition across the full rural-urban spectrum in later life in large, nationally representative studies with considerations for changing of one’s location over time, as well as closer examination of SDoH factors as moderators of cognitive performance over time. There are clear distinctions between the urban, suburban, and rural groups in their cognitive performance over the 10-year period in the ACTIVE trial, but many questions remain about the nuances in a person’s exposures and experiences throughout their life in these places and how they may impact cognition. Continued work to understand the role of geographic region and social determinants of health influences on cognition can offer the potential for improvements in older adults’ health and well-being as they continue to age.

Supplemental Material

Supplemental Material - Rural-Urban Differences in Cognition: Findings From the Advanced Cognitive Training for Independent and Vital Elderly Trial

Supplemental Material for Rural-Urban Differences in Cognition: Findings From the Advanced Cognitive Training for Independent and Vital Elderly Trial by Nessa Steinberg, Jeanine M. Parisi, Danielle M. Feger, Olivio J. Clay, Sherry L. Willis, Karlene K. Ball, Michael Marsiske, Erin R. Harrell, Shannon M. Sisco, and George W. Rebok in Journal of Aging and Health

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The current study is supported by NIA R01 AG056486. The ACTIVE Cognitive Training Trial was supported by grants from the National Institutes of Health to six field sites and the coordinating center, including: Hebrew Senior-Life, Boston (NR04507), Indiana University School of Medicine (NR04508), Johns Hopkins University (AG014260), New England Research Institutes (AG014282), Pennsylvania State University (AG14263), University of Alabama at Birmingham (AG14289), and University of Florida (AG014276). The opinions here are those of the authors and do not necessarily reflect those of the funding agencies, academic, research, governmental institutions, or corporations involved.

ORCID iDs

Jeanine M. Parisi

Danielle M. Feger

Olivio J. Clay

Erin R. Harrell

George W. Rebok

Supplemental material

Supplemental material for this article is available online.

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